diff --git a/.coveragerc b/.coveragerc
index 74b29768..06eaf740 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -1,2 +1,2 @@
 [run]
-omit = aneris/tutorial.py
\ No newline at end of file
+omit = aneris/tutorial.py
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index edb8f708..11443450 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -16,7 +16,7 @@ jobs:
         python-version: [3.8, 3.11] # to minimise complexity we only test a min and a max version
         include:
           # on all platforms and versions do everything
-          - tox-envs: [docs, lint, build, test]
+          - tox-envs: [lint, test, docs, build]
 
     runs-on: ${{ matrix.platform }}
 
@@ -24,10 +24,17 @@ jobs:
     - name: Checkout
       uses: actions/checkout@v3
 
+    # pandoc is required by nbsphinx for building the notebook-based docs
+    - name: Setup pandoc for building docs
+      uses: r-lib/actions/setup-pandoc@v2
+      with:
+        pandoc-version: '2.19.2'
+
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python-version }}
+        cache: 'pip'
 
     - name: Install tox
       run: |
diff --git a/.gitignore b/.gitignore
index 39ead749..dcc20714 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,13 +1,72 @@
-#*
-aneris/_version.py
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
 
-*#
+# C extensions
+*.so
+
+# editors
+*.swp
 *~
-*.pyc
-build
-dist
-*.egg-info
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+_version.py
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
 .cache
-.*
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Sphinx documentation
+docs/_build/
+docs/html
+docs/latex
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
 
-venv
+# Editor settings
+.spyderproject
+.spyproject
+.ropeproject
+.vscode
\ No newline at end of file
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 00000000..16b0b2c8
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,61 @@
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.4.0
+  hooks:
+  - id: check-merge-conflict
+  - id: end-of-file-fixer
+  #- id: fix-encoding-pragma # ruff does not thing this makes sense
+  - id: mixed-line-ending
+  - id: trailing-whitespace
+  - id: check-added-large-files
+    args: ["--maxkb=2000"]
+
+# # Convert relative imports to absolute imports
+# - repo: https://github.com/MarcoGorelli/absolufy-imports
+#   rev: v0.3.1
+#   hooks:
+#   - id: absolufy-imports
+
+# Find common spelling mistakes in comments and docstrings
+- repo: https://github.com/codespell-project/codespell
+  rev: v2.2.2
+  hooks:
+  - id: codespell
+    args: ['--ignore-regex="(\b[A-Z]+\b)"', '--ignore-words-list=fom'] # Ignore capital case words, e.g. country codes
+    types_or: [python, rst, markdown]
+    files: ^(scripts|doc)/
+
+# Make docstrings PEP 257 compliant
+- repo: https://github.com/PyCQA/docformatter
+  rev: 06907d0  # Update to new version when https://github.com/PyCQA/docformatter/issues/293 is closed
+  hooks:
+  - id: docformatter
+    args: ["--in-place", "--make-summary-multi-line", "--pre-summary-newline"]
+
+- repo: https://github.com/keewis/blackdoc
+  rev: v0.3.8
+  hooks:
+  - id: blackdoc
+
+# Formatting with "black" coding style
+- repo: https://github.com/psf/black
+  rev: 23.1.0
+  hooks:
+      # Format Python files
+  - id: black
+      # Format Jupyter Python notebooks
+  - id: black-jupyter
+
+# Linting with ruff
+- repo: https://github.com/charliermarsh/ruff-pre-commit
+  # Ruff version.
+  rev: 'v0.0.245'
+  hooks:
+    - id: ruff
+      args: [--fix, --exit-non-zero-on-fix]
+
+# # Check for FSFE REUSE compliance (licensing)
+# - repo: https://github.com/fsfe/reuse-tool
+#   rev: v1.1.2
+#   hooks:
+#   - id: reuse
diff --git a/.readthedocs.yml b/.readthedocs.yml
index 030f9047..fd231945 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -13,4 +13,4 @@ python:
   install:
   - method: pip
     path: .
-    extra_requirements: [docs]
\ No newline at end of file
+    extra_requirements: [docs]
diff --git a/README.rst b/README.rst
index a5048700..bde470f8 100644
--- a/README.rst
+++ b/README.rst
@@ -3,7 +3,7 @@
 
 **To reproduce harmonization routines from [Gidden et al.
 (2019)](https://gmd.copernicus.org/articles/12/1443/2019/), use `v0.3.2` (or
-earlier). Subsequent versions introduce backwards incompatibilities.**    
+earlier). Subsequent versions introduce backwards incompatibilities.**
 
 Documentation
 -------------
diff --git a/ci/.coveragerc b/ci/.coveragerc
index 4c1f3474..42ced134 100644
--- a/ci/.coveragerc
+++ b/ci/.coveragerc
@@ -1,3 +1,3 @@
 [report]
 omit =
-    aneris/_version.py
\ No newline at end of file
+    aneris/_version.py
diff --git a/ci/py2/Dockerfile b/ci/py2/Dockerfile
index 550ab5d2..bdb8e558 100644
--- a/ci/py2/Dockerfile
+++ b/ci/py2/Dockerfile
@@ -2,4 +2,4 @@ FROM gidden/python2-viz
 
 COPY . /aneris
 WORKDIR /
-RUN cd /aneris && python2 setup.py install 
+RUN cd /aneris && python2 setup.py install
diff --git a/ci/py3/Dockerfile b/ci/py3/Dockerfile
index 8ac6ff3e..4d651a26 100644
--- a/ci/py3/Dockerfile
+++ b/ci/py3/Dockerfile
@@ -2,4 +2,4 @@ FROM gidden/python3-viz
 
 COPY . /aneris
 WORKDIR /
-RUN cd /aneris && python3 setup.py install 
+RUN cd /aneris && python3 setup.py install
diff --git a/ci/travis-install-miniconda.sh b/ci/travis-install-miniconda.sh
index 0c39107f..d14e5f85 100644
--- a/ci/travis-install-miniconda.sh
+++ b/ci/travis-install-miniconda.sh
@@ -19,4 +19,3 @@ fi
 
 # update conda
 conda update --yes conda
-
diff --git a/doc/.gh-config b/doc/.gh-config
index 88598c80..e31e921e 100644
--- a/doc/.gh-config
+++ b/doc/.gh-config
@@ -4,4 +4,4 @@ include:
     - _static
     - _modules
     - _templates
-    - _downloads
\ No newline at end of file
+    - _downloads
diff --git a/doc/source/_bib/index.bib b/doc/source/_bib/index.bib
index 8c27696a..f1cfc4d4 100644
--- a/doc/source/_bib/index.bib
+++ b/doc/source/_bib/index.bib
@@ -7,4 +7,4 @@ @article{Gidden:2019:aneris
 volume = {105},
 journal = {Environmental Modelling & Software},
 doi = {10.1016/j.envsoft.2018.04.002}
-}
\ No newline at end of file
+}
diff --git a/doc/source/_static/logo.svg b/doc/source/_static/logo.svg
index 2f0501fb..8773c55b 100644
--- a/doc/source/_static/logo.svg
+++ b/doc/source/_static/logo.svg
@@ -10,330 +10,330 @@
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diff --git a/doc/source/_themes/LICENSE b/doc/source/_themes/LICENSE
index 81f4d305..718c53a5 100644
--- a/doc/source/_themes/LICENSE
+++ b/doc/source/_themes/LICENSE
@@ -1,9 +1,9 @@
-Modifications: 
+Modifications:
 
 Copyright (c) 2010 Kenneth Reitz.
 
 
-Original Project: 
+Original Project:
 
 Copyright (c) 2010 by Armin Ronacher.
 
diff --git a/doc/source/_themes/README.rst b/doc/source/_themes/README.rst
index e8179f96..8d15beb9 100644
--- a/doc/source/_themes/README.rst
+++ b/doc/source/_themes/README.rst
@@ -22,4 +22,3 @@ The following themes exist:
 
 **kr_small**
 	small one-page theme.  Intended to be used by very small addon libraries.
-
diff --git a/doc/source/_themes/kr/static/flasky.css_t b/doc/source/_themes/kr/static/flasky.css_t
index 57743105..ac43777e 100644
--- a/doc/source/_themes/kr/static/flasky.css_t
+++ b/doc/source/_themes/kr/static/flasky.css_t
@@ -442,4 +442,4 @@ a:hover tt {
 
 .revsys-inline {
     display: none!important;
-}
\ No newline at end of file
+}
diff --git a/doc/source/_themes/kr/static/small_flask.css b/doc/source/_themes/kr/static/small_flask.css
index 8d55e95f..a0af646e 100644
--- a/doc/source/_themes/kr/static/small_flask.css
+++ b/doc/source/_themes/kr/static/small_flask.css
@@ -87,4 +87,4 @@ div.body {
 
 .github {
     display: none;
-}
\ No newline at end of file
+}
diff --git a/doc/source/_themes/kr/theme.conf b/doc/source/_themes/kr/theme.conf
index 307a1f0d..07698f6f 100644
--- a/doc/source/_themes/kr/theme.conf
+++ b/doc/source/_themes/kr/theme.conf
@@ -4,4 +4,4 @@ stylesheet = flasky.css
 pygments_style = flask_theme_support.FlaskyStyle
 
 [options]
-touch_icon = 
+touch_icon =
diff --git a/doc/source/_themes/kr_small/static/flasky.css_t b/doc/source/_themes/kr_small/static/flasky.css_t
index fe2141c5..71961a27 100644
--- a/doc/source/_themes/kr_small/static/flasky.css_t
+++ b/doc/source/_themes/kr_small/static/flasky.css_t
@@ -8,11 +8,11 @@
  * :license: BSD, see LICENSE for details.
  *
  */
- 
+
 @import url("basic.css");
- 
+
 /* -- page layout ----------------------------------------------------------- */
- 
+
 body {
     font-family: 'Georgia', serif;
     font-size: 17px;
@@ -35,7 +35,7 @@ div.bodywrapper {
 hr {
     border: 1px solid #B1B4B6;
 }
- 
+
 div.body {
     background-color: #ffffff;
     color: #3E4349;
@@ -46,7 +46,7 @@ img.floatingflask {
     padding: 0 0 10px 10px;
     float: right;
 }
- 
+
 div.footer {
     text-align: right;
     color: #888;
@@ -55,12 +55,12 @@ div.footer {
     width: 650px;
     margin: 0 auto 40px auto;
 }
- 
+
 div.footer a {
     color: #888;
     text-decoration: underline;
 }
- 
+
 div.related {
     line-height: 32px;
     color: #888;
@@ -69,18 +69,18 @@ div.related {
 div.related ul {
     padding: 0 0 0 10px;
 }
- 
+
 div.related a {
     color: #444;
 }
- 
+
 /* -- body styles ----------------------------------------------------------- */
- 
+
 a {
     color: #004B6B;
     text-decoration: underline;
 }
- 
+
 a:hover {
     color: #6D4100;
     text-decoration: underline;
@@ -89,7 +89,7 @@ a:hover {
 div.body {
     padding-bottom: 40px; /* saved for footer */
 }
- 
+
 div.body h1,
 div.body h2,
 div.body h3,
@@ -109,24 +109,24 @@ div.indexwrapper h1 {
     height: {{ theme_index_logo_height }};
 }
 {% endif %}
- 
+
 div.body h2 { font-size: 180%; }
 div.body h3 { font-size: 150%; }
 div.body h4 { font-size: 130%; }
 div.body h5 { font-size: 100%; }
 div.body h6 { font-size: 100%; }
- 
+
 a.headerlink {
     color: white;
     padding: 0 4px;
     text-decoration: none;
 }
- 
+
 a.headerlink:hover {
     color: #444;
     background: #eaeaea;
 }
- 
+
 div.body p, div.body dd, div.body li {
     line-height: 1.4em;
 }
@@ -164,25 +164,25 @@ div.note {
     background-color: #eee;
     border: 1px solid #ccc;
 }
- 
+
 div.seealso {
     background-color: #ffc;
     border: 1px solid #ff6;
 }
- 
+
 div.topic {
     background-color: #eee;
 }
- 
+
 div.warning {
     background-color: #ffe4e4;
     border: 1px solid #f66;
 }
- 
+
 p.admonition-title {
     display: inline;
 }
- 
+
 p.admonition-title:after {
     content: ":";
 }
@@ -254,7 +254,7 @@ dl {
 dl dd {
     margin-left: 30px;
 }
- 
+
 pre {
     padding: 0;
     margin: 15px -30px;
diff --git a/doc/source/api.rst b/doc/source/api.rst
index 8cab06e2..ed7dd9d9 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -32,7 +32,7 @@ Methods: :code:`aneris.methods`
 
 .. automodule:: aneris.methods
    :members:
-   
+
 
 Tools/Utilities: :code:`aneris.utils`
 -------------------------------------
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 78a27c77..9be3b208 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -43,6 +43,7 @@
     "nbsphinx",
     "sphinxcontrib.bibtex",
     "sphinxcontrib.programoutput",
+    "sphinxcontrib.exceltable",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -304,4 +305,4 @@
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {"https://docs.python.org/": None}
 
-bibtex_bibfiles = "./_bib/index.bib"
+bibtex_bibfiles = ["./_bib/index.bib"]
diff --git a/doc/source/config.rst b/doc/source/config.rst
index 9928d3cf..6a4d5975 100644
--- a/doc/source/config.rst
+++ b/doc/source/config.rst
@@ -10,4 +10,3 @@ shown below.
 .. program-output:: python -c 'import aneris; print(aneris.RC_DEFAULTS)'
 
 .. _yaml: http://www.yaml.org/
-
diff --git a/doc/source/contribute.rst b/doc/source/contribute.rst
index e441ddaa..a0580f9b 100644
--- a/doc/source/contribute.rst
+++ b/doc/source/contribute.rst
@@ -9,4 +9,4 @@ You can add your own using the tutorial below.
 .. todo::
 
   Write an example tutorial - maybe harmonization that returns straight
-  trajectories?
\ No newline at end of file
+  trajectories?
diff --git a/doc/source/design.rst b/doc/source/design.rst
index 84e2ebef..04e3e25d 100644
--- a/doc/source/design.rst
+++ b/doc/source/design.rst
@@ -31,7 +31,7 @@ The `Harmonization` module takes as input
    examples) (optional)
 
 It then harmonizes the IAM data to historical data based either on default logic
-or via user-provided logic. 
+or via user-provided logic.
 
 It provides as output
 
@@ -50,13 +50,13 @@ The module is described in more detail in the following sections
 
 .. todo::
 
-  Add documentaion for logic
+  Add documentation for logic
 
 Downscaling
 ~~~~~~~~~~~
 
 The `Downscaling` module implements different downscaling routines to enhance
-the spatial resolution of data. It reqiures 
+the spatial resolution of data. It reqiures
 
 1. IAM model data at a given region and variable (sector and gas by default)
    resolution - in a standard workflow, this would be the output of the
@@ -75,11 +75,11 @@ the spatial resolution of data. It reqiures
 It provides as output
 
 1. IAM data at a given variable (sector and gas by default) resolution and at
-   the *higher spatial resolution* of the historical data used 
+   the *higher spatial resolution* of the historical data used
 
 .. todo::
 
-  Add documentaion for logic
+  Add documentation for logic
 
 Gridding
 ~~~~~~~~
@@ -87,7 +87,7 @@ Gridding
 The `Gridding` module generates spatial grids of emissions data compliant with
 CMIP/ESGF dataformats
 
-It takes as input 
+It takes as input
 
 1. IAM data at the *country-level* defined by emissions species and sector -
    normally an output of the `Downscaling` module
@@ -101,7 +101,7 @@ It provides as output
 
 .. todo::
 
-  Add documentaion for installing pattern files
+  Add documentation for installing pattern files
 
 Climate
 ~~~~~~~
@@ -115,4 +115,4 @@ Workflow
 
 .. todo::
 
-  Write documentation once we have some example workflows
\ No newline at end of file
+  Write documentation once we have some example workflows
diff --git a/doc/source/index.rst b/doc/source/index.rst
index d533e3bf..138d73e7 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -1,5 +1,5 @@
 
-aneris: Harmonization for Integrated Assessment Models 
+aneris: Harmonization for Integrated Assessment Models
 ======================================================
 
 Release v\ |version|.
@@ -34,7 +34,7 @@ Release v\ |version|.
 
 .. |doi| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.802832.svg
    :target: https://doi.org/10.5281/zenodo.802832
-   
+
 The open-source Python package |aneris| :cite:`Gidden:2019:aneris` is a library
 and Command Line Interface (CLI) for harmonization of IAM results with
 historical data sources. Currently, emissions trajectories are supported.
diff --git a/doc/source/install.rst b/doc/source/install.rst
index d3af7a81..b7ba60f4 100644
--- a/doc/source/install.rst
+++ b/doc/source/install.rst
@@ -3,8 +3,8 @@
 Install
 *******
 
-Via Conda (installs depedencies for you)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Via Conda (installs dependencies for you)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code-block:: bash
 
@@ -24,9 +24,9 @@ From Source
 
     pip install git+https://github.com/iiasa/aneris.git
 
-Depedencies
-~~~~~~~~~~~
+Dependencies
+~~~~~~~~~~~~
 
-The depedencies for :code:`aneris` are:
+The dependencies for :code:`aneris` are:
 
   .. program-output:: python -c 'import sys, os; sys.path.append("../.."); import setup; print("\n".join([r for r in setup.REQUIREMENTS]))'
diff --git a/doc/source/theory.rst b/doc/source/theory.rst
index 42853ca5..b94e9dfa 100644
--- a/doc/source/theory.rst
+++ b/doc/source/theory.rst
@@ -11,7 +11,7 @@ All harmonization is based on the following equations.
 :math:`\beta`: the harmonization convergence parameter
 
 .. math::
-    
+
     \begin{equation}\label{eqs:factor}
       \beta(t, t_i, t_f) =
       \begin{cases}
@@ -31,7 +31,7 @@ All harmonization is based on the following equations.
 :math:`m^{off}`: offset-based harmoniation
 
 .. math::
-    
+
     \begin{equation}\label{eqs:offset}
       m^{off}(t, m, h, t_i, t_f) = \beta(t, t_i, t_f) (h(t_i) - m(t_i)) + m(t)
     \end{equation}
@@ -39,7 +39,7 @@ All harmonization is based on the following equations.
 :math:`m^{int}`: linear-interoplation-based harmoniation
 
 .. math::
-      
+
     \begin{equation}\label{eqs:interpolate}
       m^{int}(t, m, h, t_i, t_f) =
       \begin{cases}
@@ -54,7 +54,7 @@ selection. Available names are listed below:
 
 .. list-table:: All Harmonization Methods Provided in :code:`aneris`
    :header-rows: 1
-  
+
    * - Method Name
      - Harmonization Family
      - Convergence Year
@@ -73,13 +73,13 @@ selection. Available names are listed below:
    * - :code:`linear_inerpolate_<year>`
      - interpolation
      - :math:`t_f = \texttt{<year>}`
-     
+
 
 Default Decision Tree
 ~~~~~~~~~~~~~~~~~~~~~
 
 While any method can be used to harmonize a given trajectory, intelligent
-defaults are made available to the user. These default methods are deteremined
+defaults are made available to the user. These default methods are determined
 by use of a *decision tree*, which analyzes the historical trajectory, model
 trajectory, and relative difference between trajectories in the harmonization
 year. The decision tree as implemented is provided below:
diff --git a/notebooks/grid.ipynb b/notebooks/grid.ipynb
new file mode 100644
index 00000000..671a559c
--- /dev/null
+++ b/notebooks/grid.ipynb
@@ -0,0 +1,1826 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d11922d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pyam\n",
+    "\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "\n",
+    "from aneris.grid import grid\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8ae2da3",
+   "metadata": {},
+   "source": [
+    "# Data Set Up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8d2a7ee1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_path = Path(\n",
+    "    \"C:/Users/gidden/IIASA/RESCUE - Documents/WP 1/data/gridding_process_files\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9d3b2ee0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gidden\\Miniconda3\\envs\\aneris\\lib\\site-packages\\xarray\\backends\\plugins.py:71: RuntimeWarning: Engine 'cfgrib' loading failed:\n",
+      "Cannot find the ecCodes library\n",
+      "  warnings.warn(f\"Engine {name!r} loading failed:\\n{ex}\", RuntimeWarning)\n",
+      "pyam - INFO: Running in a notebook, setting up a basic logging at level INFO\n",
+      "pyam.core - INFO: Reading file C:\\Users\\gidden\\IIASA\\RESCUE - Documents\\WP 1\\data\\gridding_process_files\\..\\iam_files\\cmip6\\REMIND-MAGPIE_SSP5-34-OS\\B.REMIND-MAGPIE_Harmonized-DB_emissions_downscaled.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "idxr = xr.open_dataarray(base_path / \"iso_mask.nc\", chunks={\"iso\": 10})\n",
+    "proxy = xr.open_dataarray(\n",
+    "    base_path / \"proxy_rasters/anthro_CO2.nc\", chunks={\"year\": 1, \"sector\": 1}\n",
+    ")\n",
+    "df = pyam.IamDataFrame(\n",
+    "    base_path\n",
+    "    / \"../iam_files/cmip6/REMIND-MAGPIE_SSP5-34-OS/B.REMIND-MAGPIE_Harmonized-DB_emissions_downscaled.csv\",\n",
+    "    region=\"iso\",\n",
+    ").data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0fa05620",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sector_mapping = {\n",
+    "    \"Aircraft\": \"AIR\",\n",
+    "    \"International Shipping\": \"SHP\",\n",
+    "    \"Agricultural Waste Burning\": \"AWB\",\n",
+    "    \"Agriculture\": \"AGR\",\n",
+    "    \"Energy Sector\": \"ENE\",\n",
+    "    \"Forest Burning\": \"FRTB\",\n",
+    "    \"Grassland Burning\": \"GRSB\",\n",
+    "    \"Industrial Sector\": \"IND\",\n",
+    "    \"Peat Burning\": \"PEAT\",\n",
+    "    \"Residential Commercial Other\": \"RCO\",\n",
+    "    \"Solvents Production and Application\": \"SLV\",\n",
+    "    \"Transportation Sector\": \"TRA\",\n",
+    "    \"Waste\": \"WST\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2dd23f33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>scenario</th>\n",
+       "      <th>iso</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>unit</th>\n",
+       "      <th>year</th>\n",
+       "      <th>value</th>\n",
+       "      <th>gas</th>\n",
+       "      <th>sector</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>REMIND-MAGPIE</td>\n",
+       "      <td>SSP5-34-OS-V25</td>\n",
+       "      <td>abw</td>\n",
+       "      <td>CEDS+|9+ Sectors|Emissions|CO2|Agriculture|Har...</td>\n",
+       "      <td>Mt CO2/yr</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>AGR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>REMIND-MAGPIE</td>\n",
+       "      <td>SSP5-34-OS-V25</td>\n",
+       "      <td>abw</td>\n",
+       "      <td>CEDS+|9+ Sectors|Emissions|CO2|Agriculture|Har...</td>\n",
+       "      <td>Mt CO2/yr</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>AGR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>REMIND-MAGPIE</td>\n",
+       "      <td>SSP5-34-OS-V25</td>\n",
+       "      <td>abw</td>\n",
+       "      <td>CEDS+|9+ Sectors|Emissions|CO2|Agriculture|Har...</td>\n",
+       "      <td>Mt CO2/yr</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>AGR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>REMIND-MAGPIE</td>\n",
+       "      <td>SSP5-34-OS-V25</td>\n",
+       "      <td>abw</td>\n",
+       "      <td>CEDS+|9+ Sectors|Emissions|CO2|Agriculture|Har...</td>\n",
+       "      <td>Mt CO2/yr</td>\n",
+       "      <td>2040</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>AGR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>REMIND-MAGPIE</td>\n",
+       "      <td>SSP5-34-OS-V25</td>\n",
+       "      <td>abw</td>\n",
+       "      <td>CEDS+|9+ Sectors|Emissions|CO2|Agriculture|Har...</td>\n",
+       "      <td>Mt CO2/yr</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>AGR</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             model        scenario  iso  \\\n",
+       "410  REMIND-MAGPIE  SSP5-34-OS-V25  abw   \n",
+       "411  REMIND-MAGPIE  SSP5-34-OS-V25  abw   \n",
+       "412  REMIND-MAGPIE  SSP5-34-OS-V25  abw   \n",
+       "413  REMIND-MAGPIE  SSP5-34-OS-V25  abw   \n",
+       "414  REMIND-MAGPIE  SSP5-34-OS-V25  abw   \n",
+       "\n",
+       "                                              variable       unit  year  \\\n",
+       "410  CEDS+|9+ Sectors|Emissions|CO2|Agriculture|Har...  Mt CO2/yr  2015   \n",
+       "411  CEDS+|9+ Sectors|Emissions|CO2|Agriculture|Har...  Mt CO2/yr  2020   \n",
+       "412  CEDS+|9+ Sectors|Emissions|CO2|Agriculture|Har...  Mt CO2/yr  2030   \n",
+       "413  CEDS+|9+ Sectors|Emissions|CO2|Agriculture|Har...  Mt CO2/yr  2040   \n",
+       "414  CEDS+|9+ Sectors|Emissions|CO2|Agriculture|Har...  Mt CO2/yr  2050   \n",
+       "\n",
+       "     value  gas sector  \n",
+       "410    0.0  CO2    AGR  \n",
+       "411    0.0  CO2    AGR  \n",
+       "412    0.0  CO2    AGR  \n",
+       "413    0.0  CO2    AGR  \n",
+       "414    0.0  CO2    AGR  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"gas\"] = df.variable.apply(lambda x: x.split(\"|\")[3])\n",
+    "df[\"sector\"] = df.variable.apply(lambda x: x.split(\"|\")[4]).replace(sector_mapping)\n",
+    "data = df[\n",
+    "    (df.sector.isin(np.unique(proxy.sector))) & (df.gas.isin(np.unique(proxy.gas)))\n",
+    "]\n",
+    "data = data.rename(columns={\"region\": \"iso\"})\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85bf112a",
+   "metadata": {},
+   "source": [
+    "# Perform Calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "97b36793",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gidden\\Miniconda3\\envs\\aneris\\lib\\site-packages\\dask\\array\\core.py:4830: PerformanceWarning: Increasing number of chunks by factor of 24\n",
+      "  result = blockwise(\n",
+      "root - WARNING: Missing from x iso: ['Pitcairn', 'Northern Mariana Islands', 'Tuvalu', 'Mayotte', 'Jersey', 'Guernsey', 'Bonaire, Sint Eustatius and Saba', 'San Marino', 'Monaco', 'Norfolk Island', 'Saint Helena, Ascension and Tristan da Cunha', 'Svalbard and Jan Mayen', 'Andorra', 'Anguilla', 'Isle of Man', 'Nauru']\n",
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+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (year: 10, gas: 1, sector: 7, lat: 280, lon: 720, month: 12)&gt;\n",
+       "dask.array&lt;truediv, shape=(10, 1, 7, 280, 720, 12), dtype=float64, chunksize=(1, 1, 1, 280, 720, 12), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * year     (year) int64 2015 2020 2030 2040 2050 2060 2070 2080 2090 2100\n",
+       "  * gas      (gas) object &#x27;CO2&#x27;\n",
+       "  * sector   (sector) object &#x27;AGR&#x27; &#x27;ENE&#x27; &#x27;IND&#x27; &#x27;RCO&#x27; &#x27;SLV&#x27; &#x27;TRA&#x27; &#x27;WST&#x27;\n",
+       "  * lat      (lat) float64 -55.75 -55.25 -54.75 -54.25 ... 82.75 83.25 83.75\n",
+       "  * lon      (lon) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
+       "  * month    (month) int32 1 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 10</li><li><span class='xr-has-index'>gas</span>: 1</li><li><span class='xr-has-index'>sector</span>: 7</li><li><span class='xr-has-index'>lat</span>: 280</li><li><span class='xr-has-index'>lon</span>: 720</li><li><span class='xr-has-index'>month</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0aa2695b-fc8b-45f5-a85d-3c3404d74ce5' class='xr-array-in' type='checkbox' checked><label for='section-0aa2695b-fc8b-45f5-a85d-3c3404d74ce5' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 1, 280, 720, 12), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.26 GiB </td>\n",
+       "                        <td> 18.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (10, 1, 7, 280, 720, 12) </td>\n",
+       "                        <td> (1, 1, 1, 280, 720, 12) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 70 chunks in 43 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"347\" height=\"197\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "  <text x=\"196.473918\" y=\"87.450980\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,196.473918,87.450980)\">720</text>\n",
+       "  <text x=\"124.725490\" y=\"153.725490\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,124.725490,153.725490)\">280</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-5c4b9c05-b71a-4df9-a623-ac5ef8d63421' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c4b9c05-b71a-4df9-a623-ac5ef8d63421' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2015 2020 2030 ... 2080 2090 2100</div><input id='attrs-c43d3d26-f357-4c5e-8e96-1386cfd8237e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c43d3d26-f357-4c5e-8e96-1386cfd8237e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-179e0fcb-e2e8-4357-a9af-76ae0d42bb0d' class='xr-var-data-in' type='checkbox'><label for='data-179e0fcb-e2e8-4357-a9af-76ae0d42bb0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2015, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gas</span></div><div class='xr-var-dims'>(gas)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;CO2&#x27;</div><input id='attrs-ccc0a711-e2dc-4dbd-9af7-824b6b1cedbc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ccc0a711-e2dc-4dbd-9af7-824b6b1cedbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aeddcd29-d154-4cc9-bcd7-b68226e30b14' class='xr-var-data-in' type='checkbox'><label for='data-aeddcd29-d154-4cc9-bcd7-b68226e30b14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;CO2&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sector</span></div><div class='xr-var-dims'>(sector)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;AGR&#x27; &#x27;ENE&#x27; &#x27;IND&#x27; ... &#x27;TRA&#x27; &#x27;WST&#x27;</div><input id='attrs-faba6d11-8d1f-4257-a4f0-40eaff45b3ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-faba6d11-8d1f-4257-a4f0-40eaff45b3ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9ca2ee6-5d78-47a9-9410-f1b265c07de6' class='xr-var-data-in' type='checkbox'><label for='data-a9ca2ee6-5d78-47a9-9410-f1b265c07de6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AGR&#x27;, &#x27;ENE&#x27;, &#x27;IND&#x27;, &#x27;RCO&#x27;, &#x27;SLV&#x27;, &#x27;TRA&#x27;, &#x27;WST&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-55.75 -55.25 ... 83.25 83.75</div><input id='attrs-b82be242-d6f7-48da-9995-44763daf1a38' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b82be242-d6f7-48da-9995-44763daf1a38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e12c934-ec68-4580-b633-07ebd9ce06aa' class='xr-var-data-in' type='checkbox'><label for='data-9e12c934-ec68-4580-b633-07ebd9ce06aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-55.75, -55.25, -54.75, ...,  82.75,  83.25,  83.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-34f1e56d-e27f-4de3-9e1d-34dc16d0208c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34f1e56d-e27f-4de3-9e1d-34dc16d0208c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-510a5aa4-841b-47c1-9c8e-8f3830b2bfa7' class='xr-var-data-in' type='checkbox'><label for='data-510a5aa4-841b-47c1-9c8e-8f3830b2bfa7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>modulo :</span></dt><dd>360.0</dd><dt><span>realtopology :</span></dt><dd>circular</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>topology :</span></dt><dd>circular</dd></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-6027a9d1-d581-4982-bb77-a428e22aacbd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6027a9d1-d581-4982-bb77-a428e22aacbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59d52961-0b32-46c4-b68e-afdcbcf673b5' class='xr-var-data-in' type='checkbox'><label for='data-59d52961-0b32-46c4-b68e-afdcbcf673b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e59a398e-be28-4f58-b561-5bd320949268' class='xr-section-summary-in' type='checkbox'  ><label for='section-e59a398e-be28-4f58-b561-5bd320949268' class='xr-section-summary' >Indexes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e6fe7155-eb5a-4b63-a418-e40449aa2b20' class='xr-index-data-in' type='checkbox'/><label for='index-e6fe7155-eb5a-4b63-a418-e40449aa2b20' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([2015, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100], dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gas</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aa3c2831-8791-471c-b950-5a16b473bf5f' class='xr-index-data-in' type='checkbox'/><label for='index-aa3c2831-8791-471c-b950-5a16b473bf5f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;CO2&#x27;], dtype=&#x27;object&#x27;, name=&#x27;gas&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sector</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-de683770-d5c4-4369-8b13-eaa8eac53140' class='xr-index-data-in' type='checkbox'/><label for='index-de683770-d5c4-4369-8b13-eaa8eac53140' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;AGR&#x27;, &#x27;ENE&#x27;, &#x27;IND&#x27;, &#x27;RCO&#x27;, &#x27;SLV&#x27;, &#x27;TRA&#x27;, &#x27;WST&#x27;], dtype=&#x27;object&#x27;, name=&#x27;sector&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-837a7694-3a0f-436b-bda6-7c9e0d200707' class='xr-index-data-in' type='checkbox'/><label for='index-837a7694-3a0f-436b-bda6-7c9e0d200707' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-55.75, -55.25, -54.75, -54.25, -53.75, -53.25, -52.75, -52.25,\n",
+       "              -51.75, -51.25,\n",
+       "              ...\n",
+       "               79.25,  79.75,  80.25,  80.75,  81.25,  81.75,  82.25,  82.75,\n",
+       "               83.25,  83.75],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=280))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b6d7145-1edd-4218-ad5a-f8894f5de767' class='xr-index-data-in' type='checkbox'/><label for='index-9b6d7145-1edd-4218-ad5a-f8894f5de767' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-179.75, -179.25, -178.75, -178.25, -177.75, -177.25, -176.75,\n",
+       "              -176.25, -175.75, -175.25,\n",
+       "              ...\n",
+       "               175.25,  175.75,  176.25,  176.75,  177.25,  177.75,  178.25,\n",
+       "               178.75,  179.25,  179.75],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>month</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b83e19da-750a-4c67-881b-296b60e4c4bf' class='xr-index-data-in' type='checkbox'/><label for='index-b83e19da-750a-4c67-881b-296b60e4c4bf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int64&#x27;, name=&#x27;month&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e490ce80-a406-4f6b-9a02-9a388bfe687c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e490ce80-a406-4f6b-9a02-9a388bfe687c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (year: 10, gas: 1, sector: 7, lat: 280, lon: 720, month: 12)>\n",
+       "dask.array<truediv, shape=(10, 1, 7, 280, 720, 12), dtype=float64, chunksize=(1, 1, 1, 280, 720, 12), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * year     (year) int64 2015 2020 2030 2040 2050 2060 2070 2080 2090 2100\n",
+       "  * gas      (gas) object 'CO2'\n",
+       "  * sector   (sector) object 'AGR' 'ENE' 'IND' 'RCO' 'SLV' 'TRA' 'WST'\n",
+       "  * lat      (lat) float64 -55.75 -55.25 -54.75 -54.25 ... 82.75 83.25 83.75\n",
+       "  * lon      (lon) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
+       "  * month    (month) int32 1 2 3 4 5 6 7 8 9 10 11 12"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kg_per_mt = 1e9\n",
+    "s_per_yr = 365 * 24 * 60 * 60\n",
+    "\n",
+    "ds = grid(data, proxy, idxr, as_flux=True) * kg_per_mt / s_per_yr\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "16b52425",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gidden\\Miniconda3\\envs\\aneris\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: invalid value encountered in divide\n",
+      "  return func(*(_execute_task(a, cache) for a in args))\n"
+     ]
+    }
+   ],
+   "source": [
+    "da = ds.sel(year=2015, sector=\"ENE\").mean(dim=\"month\").compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "25d27ed7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x242d1471880>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xr.where(da > 0, da, np.nan).plot(vmax=1e-9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b2b9a40",
+   "metadata": {},
+   "source": [
+    "# Check against previous data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "47fefd84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;CO2-em-anthro&#x27; (time: 120, sector: 7, lat: 360, lon: 720)&gt;\n",
+       "dask.array&lt;getitem, shape=(120, 7, 360, 720), dtype=float32, chunksize=(12, 1, 360, 720), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * lon      (lon) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
+       "  * lat      (lat) float64 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75\n",
+       "  * sector   (sector) &lt;U3 &#x27;AGR&#x27; &#x27;ENE&#x27; &#x27;IND&#x27; &#x27;RCO&#x27; &#x27;SLV&#x27; &#x27;TRA&#x27; &#x27;WST&#x27;\n",
+       "  * time     (time) object 2015-01-16 00:00:00 ... 2100-12-16 00:00:00\n",
+       "Attributes:\n",
+       "    units:         kg m-2 s-1\n",
+       "    cell_methods:  time: mean\n",
+       "    long_name:     CO2-em-anthro</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'CO2-em-anthro'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 120</li><li><span class='xr-has-index'>sector</span>: 7</li><li><span class='xr-has-index'>lat</span>: 360</li><li><span class='xr-has-index'>lon</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-07a6a0c3-b267-4b3d-a0f0-6f7a139605c9' class='xr-array-in' type='checkbox' checked><label for='section-07a6a0c3-b267-4b3d-a0f0-6f7a139605c9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(12, 1, 360, 720), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 830.57 MiB </td>\n",
+       "                        <td> 11.87 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (12, 1, 360, 720) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 70 chunks in 3 graph layers </td>\n",
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+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "  <line x1=\"245\" y1=\"14\" x2=\"245\" y2=\"74\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"125.9485979497544,14.948597949754403 245.9485979497544,14.948597949754403 245.9485979497544,74.9485979497544 125.9485979497544,74.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"185.948598\" y=\"94.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >720</text>\n",
+       "  <text x=\"265.948598\" y=\"44.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,265.948598,44.948598)\">360</text>\n",
+       "  <text x=\"108.474299\" y=\"87.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,108.474299,87.474299)\">7</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-dd27f3c0-0f91-4dae-8bd6-673b8a18ec83' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd27f3c0-0f91-4dae-8bd6-673b8a18ec83' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-948a129c-e127-4363-9d9d-d134d7c2e57d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-948a129c-e127-4363-9d9d-d134d7c2e57d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7448493a-e15c-4e94-abd6-f0c6e7d256fe' class='xr-var-data-in' type='checkbox'><label for='data-7448493a-e15c-4e94-abd6-f0c6e7d256fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>modulo :</span></dt><dd>360.0</dd><dt><span>realtopology :</span></dt><dd>circular</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>topology :</span></dt><dd>circular</dd></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.75 -89.25 ... 89.25 89.75</div><input id='attrs-5a28bed9-c5b3-4571-be80-44edb520c737' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a28bed9-c5b3-4571-be80-44edb520c737' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-600f148a-3606-4a56-b52b-93f535345c37' class='xr-var-data-in' type='checkbox'><label for='data-600f148a-3606-4a56-b52b-93f535345c37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>realtopology :</span></dt><dd>linear</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sector</span></div><div class='xr-var-dims'>(sector)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;AGR&#x27; &#x27;ENE&#x27; &#x27;IND&#x27; ... &#x27;TRA&#x27; &#x27;WST&#x27;</div><input id='attrs-a350c037-2079-4afd-92cb-3a3075285b3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a350c037-2079-4afd-92cb-3a3075285b3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41b4203f-b91f-490c-a64b-98372afb7217' class='xr-var-data-in' type='checkbox'><label for='data-41b4203f-b91f-490c-a64b-98372afb7217' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AGR&#x27;, &#x27;ENE&#x27;, &#x27;IND&#x27;, &#x27;RCO&#x27;, &#x27;SLV&#x27;, &#x27;TRA&#x27;, &#x27;WST&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2015-01-16 00:00:00 ... 2100-12-...</div><input id='attrs-dda1e0ac-29e0-4595-a57f-7d06f8f1480e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dda1e0ac-29e0-4595-a57f-7d06f8f1480e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-622b681e-2afd-4c85-b7de-079ab6502dfe' class='xr-var-data-in' type='checkbox'><label for='data-622b681e-2afd-4c85-b7de-079ab6502dfe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>realtopology :</span></dt><dd>linear</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2015, 1, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 3, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 5, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 7, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 8, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 10, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2015, 12, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2020, 1, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2020, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2020, 3, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2020, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2020, 5, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2020, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2020, 7, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2020, 8, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2020, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2020, 10, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2020, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2020, 12, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2030, 1, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2030, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2030, 3, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2030, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2030, 5, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2030, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2030, 7, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2030, 8, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2030, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2030, 10, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2030, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2030, 12, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2040, 1, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2040, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2040, 3, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2040, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2040, 5, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2040, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2040, 7, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2040, 8, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2040, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2040, 10, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2040, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2040, 12, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2050, 1, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2050, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2050, 3, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2050, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2050, 5, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2050, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2050, 7, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2050, 8, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2050, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2050, 10, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2050, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2050, 12, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2060, 1, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2060, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2060, 3, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2060, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2060, 5, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2060, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2060, 7, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2060, 8, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2060, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2060, 10, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2060, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2060, 12, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2070, 1, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2070, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2070, 3, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2070, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2070, 5, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2070, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2070, 7, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2070, 8, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2070, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2070, 10, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2070, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2070, 12, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2080, 1, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2080, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2080, 3, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2080, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2080, 5, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2080, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2080, 7, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2080, 8, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2080, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2080, 10, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2080, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2080, 12, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2090, 1, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2090, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2090, 3, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2090, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2090, 5, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2090, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2090, 7, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2090, 8, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2090, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2090, 10, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2090, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2090, 12, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 1, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 3, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 5, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 7, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 8, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 10, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2100, 12, 16, 0, 0, 0, 0, has_year_zero=True)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e7e955f-5652-4446-a12a-14739cb37aac' class='xr-section-summary-in' type='checkbox'  ><label for='section-9e7e955f-5652-4446-a12a-14739cb37aac' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-180c78fe-6863-4c3a-8370-f7bec362d09c' class='xr-index-data-in' type='checkbox'/><label for='index-180c78fe-6863-4c3a-8370-f7bec362d09c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-179.75, -179.25, -178.75, -178.25, -177.75, -177.25, -176.75,\n",
+       "              -176.25, -175.75, -175.25,\n",
+       "              ...\n",
+       "               175.25,  175.75,  176.25,  176.75,  177.25,  177.75,  178.25,\n",
+       "               178.75,  179.25,  179.75],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b716bbcb-0a05-40bf-9785-9487a1468954' class='xr-index-data-in' type='checkbox'/><label for='index-b716bbcb-0a05-40bf-9785-9487a1468954' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-89.75, -89.25, -88.75, -88.25, -87.75, -87.25, -86.75, -86.25,\n",
+       "              -85.75, -85.25,\n",
+       "              ...\n",
+       "               85.25,  85.75,  86.25,  86.75,  87.25,  87.75,  88.25,  88.75,\n",
+       "               89.25,  89.75],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sector</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9232355f-39b7-4585-bf37-db5cddcc4b5e' class='xr-index-data-in' type='checkbox'/><label for='index-9232355f-39b7-4585-bf37-db5cddcc4b5e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;AGR&#x27;, &#x27;ENE&#x27;, &#x27;IND&#x27;, &#x27;RCO&#x27;, &#x27;SLV&#x27;, &#x27;TRA&#x27;, &#x27;WST&#x27;], dtype=&#x27;object&#x27;, name=&#x27;sector&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d71f30c2-dd04-4dcb-b678-d73b163e3f78' class='xr-index-data-in' type='checkbox'/><label for='index-d71f30c2-dd04-4dcb-b678-d73b163e3f78' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2015-01-16 00:00:00, 2015-02-15 00:00:00, 2015-03-16 00:00:00,\n",
+       "             2015-04-16 00:00:00, 2015-05-16 00:00:00, 2015-06-16 00:00:00,\n",
+       "             2015-07-16 00:00:00, 2015-08-16 00:00:00, 2015-09-16 00:00:00,\n",
+       "             2015-10-16 00:00:00,\n",
+       "             ...\n",
+       "             2100-03-16 00:00:00, 2100-04-16 00:00:00, 2100-05-16 00:00:00,\n",
+       "             2100-06-16 00:00:00, 2100-07-16 00:00:00, 2100-08-16 00:00:00,\n",
+       "             2100-09-16 00:00:00, 2100-10-16 00:00:00, 2100-11-16 00:00:00,\n",
+       "             2100-12-16 00:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=120, calendar=&#x27;noleap&#x27;, freq=&#x27;None&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-51bff22c-d650-4e8a-ab99-f1b845ed7071' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51bff22c-d650-4e8a-ab99-f1b845ed7071' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>CO2-em-anthro</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'CO2-em-anthro' (time: 120, sector: 7, lat: 360, lon: 720)>\n",
+       "dask.array<getitem, shape=(120, 7, 360, 720), dtype=float32, chunksize=(12, 1, 360, 720), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * lon      (lon) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
+       "  * lat      (lat) float64 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75\n",
+       "  * sector   (sector) <U3 'AGR' 'ENE' 'IND' 'RCO' 'SLV' 'TRA' 'WST'\n",
+       "  * time     (time) object 2015-01-16 00:00:00 ... 2100-12-16 00:00:00\n",
+       "Attributes:\n",
+       "    units:         kg m-2 s-1\n",
+       "    cell_methods:  time: mean\n",
+       "    long_name:     CO2-em-anthro"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exp_raster = xr.open_dataset(\n",
+    "    base_path\n",
+    "    / \"../iam_files/cmip6/REMIND-MAGPIE_SSP5-34-OS/CO2-em-anthro_input4MIPs_emissions_CMIP_REMIND-MAGPIE-SSP5-34-OS-V1_gn_201501-210012.nc\",\n",
+    "    chunks={\"time\": 12, \"sector\": 1},\n",
+    ")[\"CO2-em-anthro\"]\n",
+    "exp_raster = exp_raster.sel(sector=[0, 1, 2, 3, 5, 6, 7])\n",
+    "exp_raster[\"sector\"] = [\"AGR\", \"ENE\", \"IND\", \"RCO\", \"SLV\", \"TRA\", \"WST\"]\n",
+    "exp_raster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "9ab3e473",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x242d1398280>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da_exp = exp_raster.isel(time=slice(0, 12)).sel(sector=\"ENE\").mean(dim=\"time\").compute()\n",
+    "xr.where(da_exp > 0, da_exp, np.nan).plot(vmax=1e-9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ffd2046a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def grid_diff(exp_raster, obs_raster, sector, relative=False):\n",
+    "    exp = exp_raster.isel(time=slice(0, 12)).sel(sector=sector).mean(dim=\"time\")\n",
+    "    obs = obs_raster.sel(year=2015, gas=\"CO2\", sector=sector).mean(dim=\"month\")\n",
+    "    if relative:\n",
+    "        return ((exp - obs) / exp).compute()\n",
+    "    else:\n",
+    "        return (exp - obs).compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ac7b5682",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gidden\\Miniconda3\\envs\\aneris\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: invalid value encountered in divide\n",
+      "  return func(*(_execute_task(a, cache) for a in args))\n"
+     ]
+    }
+   ],
+   "source": [
+    "gdiff = grid_diff(exp_raster, ds, sector=\"TRA\", relative=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "046d623c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x242d10d3a00>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gdiff.plot(vmin=-1e-9, vmax=1e-9, cmap=\"RdBu_r\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4b4e073e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checks first year values across all sectors\n",
+    "def check_values(exp_raster, obs_raster, sum_dim=None):\n",
+    "    exp = (\n",
+    "        (exp_raster.isel(time=slice(0, 12)).mean(dim=\"time\")).sum(dim=sum_dim).compute()\n",
+    "    )\n",
+    "    obs = (\n",
+    "        (obs_raster.sel(year=2015, gas=\"CO2\").mean(dim=\"month\")).sum(sum_dim).compute()\n",
+    "    )\n",
+    "    return exp, obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "90bac319",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gidden\\Miniconda3\\envs\\aneris\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: invalid value encountered in divide\n",
+      "  return func(*(_execute_task(a, cache) for a in args))\n"
+     ]
+    }
+   ],
+   "source": [
+    "s_exp, s_obs_us = check_values(exp_raster, ds, sum_dim=[\"lat\", \"lon\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "98f1ee02",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>gas</th>\n",
+       "      <th>rel_diff</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sector</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AGR</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENE</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>-0.047221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IND</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>0.068426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RCO</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>0.047668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SLV</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>-0.023627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TRA</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>-0.060756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WST</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>0.109542</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        year  gas  rel_diff\n",
+       "sector                     \n",
+       "AGR     2015  CO2       NaN\n",
+       "ENE     2015  CO2 -0.047221\n",
+       "IND     2015  CO2  0.068426\n",
+       "RCO     2015  CO2  0.047668\n",
+       "SLV     2015  CO2 -0.023627\n",
+       "TRA     2015  CO2 -0.060756\n",
+       "WST     2015  CO2  0.109542"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(100 * (s_exp - s_obs_us) / s_exp).to_dataframe(name=\"rel_diff\")  # units: %"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62a4e223",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyproject.toml b/pyproject.toml
index c7d2275d..cfb79ed5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,8 @@ dependencies = [
   "openpyxl",
   "matplotlib",
   "pyomo>=5",
-  "pandas-indexing",
+  "pandas-indexing>=0.4.0",
+  "pycountry",
 ]
 dynamic = ["version"]
 
@@ -44,23 +45,28 @@ docs = [
         "sphinx",
         "sphinxcontrib-bibtex",
         "sphinxcontrib-programoutput",
+        "sphinxcontrib-exceltable",
         "sphinx-gallery",
         "nbsphinx",
         "numpydoc",
         "nbformat",
-        "ipython",
-        "jupyter",
-        "jupyter_contrib_nbextensions",
         "pillow",
     ]
 lint = [
     "black",
     "ruff"
 ]
+geo = [
+  "ptolemy-iamc @ git+https://github.com/gidden/ptolemy.git",
+  "pycountry",
+  "xarray",
+  "dask",
+]
 
 [project.scripts]
 aneris = "aneris.cli:main"
 
+
 [tool.setuptools_scm]
 fallback_version = "999"
 
@@ -81,6 +87,8 @@ exclude = [
     "doc",
     "_typed_ops.pyi",
 ]
+
+[tool.ruff.lint]
 # E402: module level import not at top of file
 # E501: line too long - let black worry about that
 # E731: do not assign a lambda expression, use a def
@@ -103,11 +111,11 @@ select = [
     "UP",
 ]
 
-[tool.ruff.per-file-ignores]
+[tool.ruff.lint.per-file-ignores]
 # F401: imported but unsued
 "__init__.py" = ["F401"]
 
-[tool.ruff.isort]
+[tool.ruff.lint.isort]
 lines-after-imports = 2
 known-first-party = ["aneris"]
 
diff --git a/src/aneris/_io.py b/src/aneris/_io.py
index 0134922c..66149967 100644
--- a/src/aneris/_io.py
+++ b/src/aneris/_io.py
@@ -3,6 +3,7 @@
 
 The default configuration values are provided in aneris.RC_DEFAULTS.
 """
+
 import os
 from collections import abc
 
@@ -89,7 +90,7 @@ def read_excel(f):
 
     # a single row of nans implies only configs provided,
     # if so, only return the empty df
-    if len(overrides) == 1 and overrides.isnull().values.all():
+    if len(overrides) == 1 and overrides.isnull().all(axis=None):
         overrides = pd.DataFrame([], columns=iamc_idx + ["Unit"])
 
     return model, overrides, config
diff --git a/src/aneris/cli.py b/src/aneris/cli.py
index 13e964a4..855b9ac6 100644
--- a/src/aneris/cli.py
+++ b/src/aneris/cli.py
@@ -1,6 +1,7 @@
 """
 Harmonization CLI for aneris.
 """
+
 import argparse
 import os
 
diff --git a/src/aneris/cmip6/cmip6_utils.py b/src/aneris/cmip6/cmip6_utils.py
index 39ef36d6..2c3d8e1d 100644
--- a/src/aneris/cmip6/cmip6_utils.py
+++ b/src/aneris/cmip6/cmip6_utils.py
@@ -160,9 +160,8 @@ def pd_write(df, f, *args, **kwargs):
     if f.endswith("csv"):
         df.to_csv(f, index=index, *args, **kwargs)
     else:
-        writer = pd.ExcelWriter(f)
-        df.to_excel(writer, index=index, *args, **kwargs)
-        writer.save()
+        with pd.ExcelWriter(f) as writer:
+            df.to_excel(writer, index=index, *args, **kwargs)
 
 
 def recalculated_row_idx(df, prefix="", suffix=""):
diff --git a/src/aneris/cmip6/driver.py b/src/aneris/cmip6/driver.py
index 9f75e5a6..da801e3e 100644
--- a/src/aneris/cmip6/driver.py
+++ b/src/aneris/cmip6/driver.py
@@ -1,10 +1,11 @@
 import numpy as np
 import pandas as pd
+from pandas_indexing import assignlevel, isin
 
 import aneris.cmip6.cmip6_utils as cmip6_utils
 import aneris.utils as utils
 from aneris.harmonize import Harmonizer, _log, _warn
-from aneris.utils import isin, pd_read
+from aneris.utils import pd_read
 
 
 class _TrajectoryPreprocessor:
@@ -276,10 +277,7 @@ def harmonize(self, scenario, diagnostic_config=None):
         )
 
         # collect metadata
-        self._meta = self._meta.reset_index()
-        self._meta["model"] = self.model_name
-        self._meta["scenario"] = scenario
-        self._meta = self._meta.set_index(["model", "scenario"])
+        self._meta = assignlevel(self._meta, model=self.model_name, scenario=scenario)
         self._postprocess_trajectories(scenario)
 
         # store results
@@ -423,7 +421,7 @@ def _harmonize_regions(
             model, mapping=mapping, rfrom="Native Region Code", rto="5_region"
         )
         model = pd.concat([model, aggdf])
-        assert not model.isnull().values.any()
+        assert not model.isnull().any(axis=None)
 
     # duplicates come in from World and World being translated
     duplicates = model.index.duplicated(keep="first")
diff --git a/src/aneris/convenience.py b/src/aneris/convenience.py
index 64825ce2..6f851a21 100644
--- a/src/aneris/convenience.py
+++ b/src/aneris/convenience.py
@@ -1,7 +1,7 @@
 import pandas as pd
 import pyam
 from openscm_units import unit_registry
-from pandas_indexing import isin, semijoin
+from pandas_indexing import isin, projectlevel, semijoin
 
 from .errors import (
     AmbiguousHarmonisationMethod,
@@ -26,7 +26,7 @@ def xform(x):
     units = xform(to).join(xform(fr), how="left", lsuffix="_to", rsuffix="_fr")
     # can get duplicates if multiple regions with same conversion
     units = units[~units.index.duplicated(keep="first")]
-    assert not units.isnull().values.any()
+    assert not units.isnull().any(axis=None)
     # downselect to non-comparable
     units = units[units.unit_to != units.unit_fr]
     # combine units that don't need changing with those that do
@@ -61,7 +61,7 @@ def _knead_overrides(overrides, scen, harm_idx):
     # check if no index and single value - this should be the override for everything
     if overrides.index.names == [None] and len(overrides["method"]) == 1:
         _overrides = pd.Series(
-            overrides["method"].values[0],
+            overrides["method"].iloc[0],
             index=pd.Index(scen.region, name=harm_idx[-1]),  # only need to match 1 dim
             name="method",
         )
@@ -78,12 +78,12 @@ def _knead_overrides(overrides, scen, harm_idx):
         _overrides = overrides
 
     # do checks
-    if _overrides.isnull().values.any():
-        missing = _overrides[_overrides.isnull().any(axis=1)]
+    if isinstance(_overrides, pd.DataFrame) and _overrides.isnull().any(axis=None):
+        missing = _overrides.loc[_overrides.isnull().any(axis=1)]
         raise AmbiguousHarmonisationMethod(
             f"Overrides are missing for provided data:\n" f"{missing}"
         )
-    if _overrides.index.to_frame().isnull().values.any():
+    if _overrides.index.to_frame().isnull().any(axis=None):
         missing = _overrides[_overrides.index.to_frame().isnull().any(axis=1)]
         raise AmbiguousHarmonisationMethod(
             f"Defined overrides are missing data:\n" f"{missing}"
@@ -100,9 +100,8 @@ def _knead_overrides(overrides, scen, harm_idx):
 def _check_data(hist, scen, year):
     check = ["region", "variable"]
 
-    # @coroa - this may be a very slow way to do this check..
     def downselect(df):
-        return df.filter(year=year)._data.reset_index().set_index(check).index.unique()
+        return projectlevel(df._data.index[isin(df._data, year=year)], check)
 
     s = downselect(scen)
     h = downselect(hist)
diff --git a/src/aneris/downscaling/__init__.py b/src/aneris/downscaling/__init__.py
new file mode 100644
index 00000000..1f262c3a
--- /dev/null
+++ b/src/aneris/downscaling/__init__.py
@@ -0,0 +1 @@
+from aneris.downscaling.core import Downscaler  # noqa: F401
diff --git a/src/aneris/downscaling/core.py b/src/aneris/downscaling/core.py
new file mode 100644
index 00000000..ff7c32a8
--- /dev/null
+++ b/src/aneris/downscaling/core.py
@@ -0,0 +1,299 @@
+from functools import partial
+from typing import Optional, Sequence, Union
+
+from pandas import DataFrame, MultiIndex, Series
+from pandas_indexing import concat, semijoin
+
+from ..errors import MissingHistoricalError, MissingProxyError
+from ..methods import default_methods
+from ..utils import logger
+from .data import DownscalingContext
+from .methods import (
+    base_year_pattern,
+    default_method_choice,
+    growth_rate,
+    intensity_convergence,
+    simple_proxy,
+)
+
+
+DEFAULT_INDEX = ("sector", "gas")
+
+
+class Downscaler:
+    _methods = {
+        "ipat_2100_gdp": partial(
+            intensity_convergence, convergence_year=2100, proxy_name="gdp"
+        ),
+        "ipat_2150_pop": partial(
+            intensity_convergence, convergence_year=2150, proxy_name="pop"
+        ),
+        "base_year_pattern": base_year_pattern,
+        "growth_rate": growth_rate,
+        "proxy_gdp": partial(simple_proxy, proxy_name="gdp"),
+        "proxy_pop": partial(simple_proxy, proxy_name="pop"),
+    }
+
+    def add_method(self, name, method):
+        self._methods = self._methods | {name: method}
+
+    def __init__(
+        self,
+        model: DataFrame,
+        hist: DataFrame,
+        year: int,
+        region_mapping: Union[Series, MultiIndex],
+        luc_sectors: Sequence[str] = [],
+        index: Sequence[str] = DEFAULT_INDEX,
+        method_choice: Optional[callable] = None,
+        return_type=DataFrame,
+        **additional_data: DataFrame,
+    ):
+        self.model = model
+        self.hist = hist
+        self.return_type = return_type
+        regionmap = DownscalingContext.to_regionmap(region_mapping)
+        self.context = DownscalingContext(
+            index,
+            year,
+            regionmap,
+            additional_data,
+        )
+
+        assert (
+            hist[self.year].groupby(list(index) + [self.country_level]).count() <= 1
+        ).all(), "Ambiguous history"
+
+        missing_hist = (
+            model.index.join(self.context.regionmap, how="left")
+            .pix.project(list(index) + [self.country_level])
+            .difference(hist.index.pix.project(list(index) + [self.country_level]))
+        )
+        if not missing_hist.empty:
+            raise MissingHistoricalError(
+                "History missing for variables/countries:\n"
+                + missing_hist.to_frame().to_string(index=False, max_rows=100)
+            )
+
+        # TODO Make configurable by re-using config just as in harmonizer
+        self.fallback_method = None
+        self.intensity_method = None
+        self.luc_method = None
+        self.method_choice = method_choice
+        self.luc_sectors = luc_sectors
+
+    @property
+    def index(self):
+        return self.context.index
+
+    @property
+    def year(self):
+        return self.context.year
+
+    @property
+    def region_mapping(self) -> MultiIndex:
+        return self.context.regionmap
+
+    @property
+    def additional_data(self):
+        return self.context.additional_data
+
+    @property
+    def country_level(self):
+        return self.context.country_level
+
+    @property
+    def region_level(self):
+        return self.context.region_level
+
+    def check_proxies(self, methods: Series) -> None:
+        """
+        Checks proxies required for chosen `methods`
+
+        Parameters
+        ----------
+        methods : Series
+            Methods to be used for each trajectory
+
+        Raises
+        ------
+        MissingProxyError
+            if a required proxy is missing or incomplete
+        """
+        for method in methods.unique():
+            proxy_name = getattr(self._methods[method], "keywords", {}).get(
+                "proxy_name"
+            )
+            if proxy_name is None:
+                continue
+
+            proxy = self.additional_data.get(proxy_name)
+            if proxy is None:
+                raise MissingProxyError(
+                    f"Downscaling method `{method}` requires the additional data"
+                    f" `{proxy_name}`"
+                )
+
+            trajectory_index = methods.index[methods == method]
+
+            # trajectory index typically has the levels model, scenario, region, sector,
+            # gas, while proxy data is expected on country level (and probably no model,
+            # scenario dependency, but potentially)
+            proxy = semijoin(proxy, self.context.regionmap, how="right")
+
+            common_levels = [
+                lvl for lvl in trajectory_index.names if lvl in proxy.index.names
+            ]
+            missing_proxy = (
+                trajectory_index.pix.project(common_levels)
+                .difference(proxy.index.pix.project(common_levels))
+                .unique()
+            )
+            if not missing_proxy.empty:
+                raise MissingProxyError(
+                    f"The proxy data `{proxy_name}` is missing for the following "
+                    "trajectories:\n"
+                    + missing_proxy.to_frame().to_string(index=False, max_rows=100)
+                )
+
+            if not isinstance(proxy, DataFrame):
+                return
+
+            missing_years = self.model.columns.difference(proxy.columns)
+            if not missing_years.empty:
+                raise MissingProxyError(
+                    f"The proxy data `{proxy_name}` is missing model year(s): "
+                    + ", ".join(missing_years.astype(str))
+                )
+
+    def downscale(
+        self, methods: Optional[Series] = None, check_result: bool = True
+    ) -> DataFrame:
+        """
+        Downscale aligned model data from historical data, and socio-economic
+        scenario.
+
+        Notes
+        -----
+        model.index contains at least the downscaling index levels, but also any other
+        levels.
+
+        hist.index contains at least the downscaling index levels other index levels are
+        allowed, but only one value per downscaling index value.
+
+        Parameters
+        ----------
+        methods : Series Methods to apply
+
+        check_result : bool, default True
+            Check whether the downscaled trajectories sum up to the regional totals
+        """
+
+        if methods is None:
+            methods = self.methods()
+
+        hist_ext = semijoin(self.hist, self.context.regionmap, how="right")
+        self.check_proxies(methods)
+
+        downscaled = []
+        method_groups = methods.index.groupby(methods)
+        for method, trajectory_index in method_groups.items():
+            hist = semijoin(hist_ext, trajectory_index, how="right")
+            model = semijoin(self.model, trajectory_index, how="right")
+
+            downscaled.append(self._methods[method](model, hist, self.context))
+
+        downscaled = concat(downscaled)
+        if check_result:
+            self.check_downscaled(downscaled)
+
+        return self.return_type(downscaled)
+
+    def check_downscaled(self, downscaled, rtol=1e-02, atol=1e-06):
+        def warn_if_differences(actual, should, message):
+            actual, should = actual.align(should, join="left")
+            diff = actual - should
+            diff_exceeded = abs(diff) > atol + rtol * abs(should)
+            if diff_exceeded.any():
+                logger().warning(
+                    "%s:\n%s",
+                    message,
+                    DataFrame(dict(actual=actual, should=should, diff=diff))
+                    .loc[diff_exceeded]
+                    .to_string(),
+                )
+
+        downscaled_region = (
+            downscaled.groupby(self.model.index.names, dropna=False)
+            .sum()
+            .rename_axis(columns="year")
+            .stack()
+        )
+        model = self.model.loc[:, self.year :].rename_axis(columns="year").stack()
+
+        warn_if_differences(
+            downscaled_region,
+            model,
+            "Downscaled trajectories do not sum up to regional totals",
+        )
+
+        hist = self.hist
+        if isinstance(hist, DataFrame):
+            hist = hist.loc[:, self.year]
+        hist = hist.pix.semijoin(downscaled.index, how="right")
+        non_zero_region = (
+            abs(hist)
+            .groupby(hist.index.names.difference(["region"]))
+            .max()
+            .loc[lambda s: s > 0]
+            .index
+        )
+        downscaled_start = downscaled.loc[:, self.year]
+
+        warn_if_differences(
+            downscaled_start.pix.semijoin(non_zero_region, how="right"),
+            hist.pix.semijoin(non_zero_region, how="right"),
+            "Downscaled trajectories do not start from history",
+        )
+
+    def methods(self, method_choice=None, overwrites=None):
+        if method_choice is None:
+            method_choice = self.method_choice
+
+        if method_choice is None:
+            method_choice = default_method_choice
+
+        kwargs = {
+            "method_choice": method_choice,
+            "fallback_method": self.fallback_method,
+            "intensity_method": self.intensity_method,
+            "luc_method": self.luc_method,
+            "luc_sectors": self.luc_sectors,
+        }
+
+        hist_agg = (
+            semijoin(self.hist, self.context.regionmap, how="right")
+            .groupby(list(self.index) + [self.region_level], dropna=False)
+            .sum()
+        )
+        methods, meta = default_methods(
+            semijoin(hist_agg, self.model.index, how="right").reorder_levels(
+                self.model.index.names
+            ),
+            self.model,
+            self.year,
+            **{k: v for k, v in kwargs.items() if v is not None},
+        )
+
+        if overwrites is None:
+            return methods
+        elif isinstance(overwrites, str):
+            return Series(overwrites, methods.index)
+        elif isinstance(overwrites, dict):
+            overwrites = Series(overwrites).rename_axis("sector")
+
+        return (
+            semijoin(overwrites, methods.index, how="right")
+            .combine_first(methods)
+            .rename("method")
+        )
diff --git a/src/aneris/downscaling/data.py b/src/aneris/downscaling/data.py
new file mode 100644
index 00000000..a4ff0f29
--- /dev/null
+++ b/src/aneris/downscaling/data.py
@@ -0,0 +1,58 @@
+from collections.abc import Mapping, Sequence
+from dataclasses import dataclass, field
+from typing import Union
+
+from pandas import DataFrame, MultiIndex, Series
+
+
+@dataclass
+class DownscalingContext:
+    """
+    Context in which downscaling needs to happen.
+
+    Attributes
+    ----------
+    index : sequence of str
+        index levels that differentiate trajectories
+    year : int
+        base year for downscaling
+    regionmap : MultiIndex
+        map from fine to coarse level
+        (there can be overlapping coarse levels)
+    additional_data : dict, default {}
+        named `DataFrame`s or `Series` the methods need as proxies
+
+    Derived attributes
+    -------------------
+    country_level : str, default "country"
+        name of the fine index level
+    region_level : str, default "region"
+        name of the coarse index level
+
+    Notes
+    -----
+    Passed as context argument to each downscaling method
+    """
+
+    index: Sequence[str]
+    year: int
+    regionmap: MultiIndex
+    additional_data: Mapping[str, Union[Series, DataFrame]] = field(default_factory=dict)
+
+    @property
+    def country_level(self) -> str:
+        return self.regionmap.names[0]
+
+    @property
+    def region_level(self) -> str:
+        return self.regionmap.names[1]
+
+    @staticmethod
+    def to_regionmap(region_mapping: Union[Series, MultiIndex]):
+        if isinstance(region_mapping, MultiIndex):
+            return region_mapping
+
+        return MultiIndex.from_arrays(
+            [region_mapping.index, region_mapping.values],
+            names=[region_mapping.index.name, region_mapping.name],
+        )
diff --git a/src/aneris/downscaling/intensity_convergence.py b/src/aneris/downscaling/intensity_convergence.py
new file mode 100644
index 00000000..2babd648
--- /dev/null
+++ b/src/aneris/downscaling/intensity_convergence.py
@@ -0,0 +1,482 @@
+import logging
+from typing import Any, Optional, Union
+
+import numpy as np
+import pandas_indexing.accessors  # noqa: F401
+from pandas import DataFrame, MultiIndex, Series, concat
+from pandas_indexing import isin, semijoin
+from scipy.interpolate import interp1d
+from scipy.optimize import root_scalar
+
+from ..utils import normalize, skipempty
+from .data import DownscalingContext
+
+
+logger = logging.getLogger(__name__)
+
+
+class ConvergenceError(RuntimeError):
+    pass
+
+
+def make_affine_transform(x1, x2, y1=0.0, y2=1.0):
+    """
+    Returns an affine transform that maps `x1` to `y1` and `x2` to `y2`
+    """
+
+    def f(x):
+        return (y2 - y1) * (x - x1) / (x2 - x1) + y1
+
+    return f
+
+
+def make_affine_transform_pair(x1, x2, y1, y2):
+    f = make_affine_transform(x1, x2, y1, y2)
+    inv_f = make_affine_transform(y1, y2, x1, x2)
+    return f, inv_f
+
+
+def compute_intensity(
+    model: DataFrame, reference: DataFrame, convergence_year: int
+) -> DataFrame:
+    intensity = model.pix.divide(reference, join="left")
+
+    model_years = model.columns
+    if convergence_year > model_years[-1]:
+        x2 = model_years[-1]
+        x1 = x2 - 10 if x2 - 10 in model_years else model_years[-2]
+
+        y1 = model[x1]
+        y2 = model[x2]
+        model_conv = (y2 * (y2 / y1) ** ((convergence_year - x2) / (x2 - x1))).where(
+            y2 > 0, y2 + (y2 - y1) * (convergence_year - x2) / (x2 - x1)
+        )
+
+        y1 = reference[x1]
+        y2 = reference[x2]
+        reference_conv = y2 * (y2 / y1) ** ((convergence_year - x2) / (x2 - x1))
+
+        intensity[convergence_year] = model_conv / reference_conv
+    else:
+        intensity = intensity.loc[:, :convergence_year]
+
+    return intensity
+
+
+def determine_scaling_parameter(
+    alpha: Series,
+    intensity_hist: Series,
+    intensity: Series,
+    reference: DataFrame,
+    intensity_projection_linear: DataFrame,
+    index: dict[str, Any],
+    context: DownscalingContext,
+) -> float:
+    """
+    Determine scaling parameter for negative exponential intensity model.
+
+    Gamma parameter for a single macro trajectory
+
+    Parameters
+    ----------
+    alpha : Series
+        Map from years to 0-1 range
+    intensity_hist : Series
+        Historic intensity of countries in base year
+    intensity : Series
+        Projected intensity of one worldregion/model
+    reference : DataFrame
+        Denominator of intensity
+    intensity_projection_linear : DataFrame
+        Per-country intensities previously determined by linear model
+    index : dict[str, Any]
+        Index levels of the full dataframe intensity
+    context : DownscalingContext
+
+    Returns
+    -------
+    gamma : float
+    """
+    negative_at_start = intensity.iloc[0] < 0
+    if negative_at_start:
+        raise ConvergenceError("Trajectory is fully negative")
+
+    selector = isin(**index, ignore_missing_levels=True)
+    reference = reference.loc[selector]
+    intensity_hist = intensity_hist.loc[selector]
+    intensity_projection_linear = intensity_projection_linear.loc[selector]
+
+    # determine alpha_star, where projected emissions become negative
+    res = root_scalar(
+        interp1d(alpha, intensity),
+        method="brentq",
+        bracket=[0, 1],
+    )
+    if not res.converged:
+        raise ConvergenceError(
+            "Could not find alpha_star for which emissions cross into zero"
+        )
+    alpha_star = res.root
+    year_star = make_affine_transform(0, 1, *intensity.index[[0, -1]])(alpha_star)
+
+    # reference at alpha_star
+    def at_alpha_star(df, alpha=alpha):
+        return df.apply(
+            (
+                lambda s: interp1d(alpha, s, kind="slinear", fill_value="extrapolate")(
+                    alpha_star
+                )
+            ),
+            axis=1,
+        )
+
+    ref = at_alpha_star(reference, alpha=alpha[: len(reference.columns)])
+
+    if not intensity_projection_linear.empty:
+        offset = (ref * at_alpha_star(intensity_projection_linear)).sum()
+    else:
+        offset = 0
+
+    # determine gamma scaling parameter with which the sum of the weights from
+    # the transformed model vanish at alpha_star
+    def sum_weight_at_alpha_star(gamma):
+        x0, x1 = intensity.iloc[[0, -1]]
+        f, inv_f = make_affine_transform_pair(x0, x1, gamma, 1.0)
+
+        return (
+            ref * inv_f((f(x1) / f(intensity_hist)) ** alpha_star * f(intensity_hist))
+        ).sum() + offset
+
+    # Widen gamma_max until finding a sign flip in sum_weight_at_alpha_star
+    gamma_min = 1.5
+    gamma_max = 10 * gamma_min
+    sum_weight_min = sum_weight_at_alpha_star(gamma_min)
+    while sum_weight_min * sum_weight_at_alpha_star(gamma_max) > 0:
+        if gamma_max >= 1e7:
+            raise ConvergenceError(
+                f"Exponential model does not converge to "
+                f"{intensity.iloc[-1]} at {intensity.index[-1]}, "
+                f"while guaranteeing zero emissions in {year_star}"
+            )
+        gamma_max *= 10
+
+    res = root_scalar(
+        sum_weight_at_alpha_star,
+        method="brentq",
+        bracket=[gamma_max / 10, gamma_max],
+    )
+    if not res.converged:
+        raise ConvergenceError(
+            "Could not determine scaling parameter gamma such that the weights"
+            "from the exponential model vanish exactly with intensity"
+        )
+
+    gamma = res.root
+
+    logger.debug(
+        "Determined year(alpha_star) = %.2f, and gamma = %.2f",
+        year_star,
+        gamma,
+    )
+
+    return gamma
+
+
+def negative_exponential_intensity_model(
+    alpha: Series,
+    intensity_hist: Series,
+    intensity: DataFrame,
+    reference: DataFrame,
+    intensity_projection_linear: DataFrame,
+    context: DownscalingContext,
+    allow_fallback_to_linear: bool = True,
+) -> DataFrame:
+    """
+    Create a per-country time-series of intensities w/ negative intensities.
+
+    Parameters
+    ----------
+    alpha : Series
+        Map from years to 0-1 range
+    intensity_hist : Series
+        Historic intensity of countries in base year
+    intensity : DataFrame
+        Projected intensity of worldregion
+    reference : DataFrame
+        Denominator of intensity
+    intensity_projection_linear : DataFrame
+        _description_
+    context : DownscalingContext
+
+    Returns
+    -------
+    DataFrame
+        _description_
+
+    Raises
+    ------
+    ConvergenceError
+        if it can not determine all scaling parameters
+    """
+
+    gammas = np.empty(len(intensity))
+
+    for i, (index, intensity_traj) in enumerate(intensity.iterrows()):
+        index = dict(zip(intensity.index.names, index))
+        try:
+            gammas[i] = determine_scaling_parameter(
+                alpha,
+                intensity_hist,
+                intensity_traj,
+                reference,
+                intensity_projection_linear,
+                index,
+                context,
+            )
+        except ConvergenceError:
+            if not allow_fallback_to_linear:
+                raise
+            gammas[i] = np.nan
+
+    gammas = Series(gammas, intensity.index)
+
+    intensity_conv, intensity_hist_conv = intensity.loc[gammas.notna()].align(
+        intensity_hist, join="left", axis=0, copy=False
+    )
+    gammas_conv = semijoin(gammas, intensity_conv.index, how="right")
+
+    def ts(s):
+        if isinstance(s, (DataFrame, Series)):
+            s = s.to_numpy()
+        return np.asarray(s)[:, np.newaxis]
+
+    f, inv_f = make_affine_transform_pair(
+        ts(intensity_conv.iloc[:, 0]),
+        ts(intensity_conv.iloc[:, -1]),
+        ts(gammas_conv),
+        1.0,
+    )
+    intensity_projection = DataFrame(
+        inv_f(
+            (
+                (f(ts(intensity_conv.to_numpy()[:, -1])) / f(ts(intensity_hist_conv)))
+                ** alpha.to_numpy()
+            )
+            * f(ts(intensity_hist_conv))
+        ),
+        index=intensity_hist_conv.index,
+        columns=intensity.columns,
+    )
+
+    return concat(
+        [
+            intensity_projection,
+            intensity_growth_rate_model(intensity.loc[gammas.isna()], intensity_hist),
+        ],
+        sort=False,
+    )
+
+
+def exponential_intensity_model(
+    alpha: Series, intensity_hist: Series, intensity: DataFrame
+) -> DataFrame:
+    positive_intensity = intensity.iloc[:, -1] > 0
+    if positive_intensity.all():
+        f = inv_f = lambda x: x
+    else:
+        f = lambda x: x.where(positive_intensity, x + 1)
+        inv_f = lambda x: x.where(positive_intensity, x - 1)
+
+    intensity_hist = semijoin(intensity_hist, intensity.index, how="right")
+
+    intensity_projection = inv_f(
+        DataFrame(
+            (f(intensity.iloc[:, -1]) / f(intensity_hist)).to_numpy()[:, np.newaxis]
+            ** alpha.to_numpy()
+            * f(intensity_hist).to_numpy()[:, np.newaxis],
+            index=intensity_hist.index,
+            columns=intensity.columns,
+        ),
+    )
+
+    return intensity_projection
+
+
+def linear_intensity_model(
+    alpha: Series, intensity_hist: Series, intensity: DataFrame
+) -> DataFrame:
+    intensity, intensity_hist = intensity.align(
+        intensity_hist, join="left", copy=False, axis=0
+    )
+    intensity_projection = DataFrame(
+        (1 - alpha).to_numpy() * (intensity_hist).to_numpy()[:, np.newaxis]
+        + alpha.to_numpy() * intensity.to_numpy()[:, -1:],
+        index=intensity.index,
+        columns=intensity.columns,
+    )
+
+    return intensity_projection
+
+
+@np.errstate(invalid="ignore")
+def intensity_growth_rate_model(
+    intensity: DataFrame, intensity_hist: Series
+) -> DataFrame:
+    intensity, intensity_hist = intensity.align(
+        intensity_hist, join="left", axis=0, copy=False
+    )
+
+    years_downscaling = intensity.columns
+    intensity_projection = DataFrame(
+        (
+            1
+            + (intensity.iloc[:, -1] / intensity_hist - 1)
+            / (years_downscaling[-1] - years_downscaling[0])
+        ).to_numpy()[:, np.newaxis]
+        ** np.arange(0, len(years_downscaling))
+        * intensity_hist.to_numpy()[:, np.newaxis],
+        index=intensity_hist.index,
+        columns=years_downscaling.rename("year"),
+    ).where(intensity_hist != 0, 0.0)
+    return intensity_projection
+
+
+def intensity_convergence(
+    model: DataFrame,
+    hist: Union[Series, DataFrame],
+    context: DownscalingContext,
+    proxy_name: str = "gdp",
+    convergence_year: Optional[int] = 2100,
+    allow_fallback_to_linear: bool = True,
+) -> DataFrame:
+    """
+    Downscales emission data using emission intensity convergence.
+
+    Parameters
+    ----------
+    model : DataFrame
+        model emissions for each world region and trajectory
+    historic : DataFrame or Series
+        historic emissions for each country and trajectory
+    context : DownscalingContext
+        settings for downscaling, like the regionmap, and
+        additional_data.
+    proxy_name : str, default "gdp"
+        name of the additional data used as a reference for intensity
+        (intensity = model/reference)
+    convergence_year : int, default 2100
+        year of emission intensity convergence
+
+    Returns
+    -------
+    DataFrame
+        downscaled emissions for countries
+
+    TODO
+    ----
+    We are assembling a dictionary, with intermediate results as `diagnostics`. Would be
+    nice to give the user intuitive access.
+
+    References
+    ----------
+    Gidden, M. et al. Global emissions pathways under different socioeconomic
+    scenarios for use in CMIP6: a dataset of harmonized emissions trajectories
+    through the end of the century. Geoscientific Model Development Discussions 12,
+    1443–1475 (2019).
+    """
+
+    model = model.loc[:, context.year :]
+    if isinstance(hist, DataFrame):
+        hist = hist.loc[:, context.year]
+
+    reference = semijoin(context.additional_data[proxy_name], context.regionmap)[
+        model.columns
+    ]
+    reference_region = reference.groupby(context.region_level).sum()
+    hist = semijoin(hist, context.regionmap)
+
+    intensity = compute_intensity(model, reference_region, convergence_year)
+    intensity_hist = hist / reference.iloc[:, 0]
+
+    alpha = make_affine_transform(intensity.columns[0], convergence_year)(
+        intensity.columns
+    )
+    intensity_countries, intensity_hist = intensity.align(
+        intensity_hist, join="left", axis=0
+    )
+    intensity_idx = intensity_countries.index
+
+    levels = list(model.index.names) + [context.country_level]
+    empty_intensity = DataFrame(
+        [],
+        index=MultiIndex.from_arrays([[] for _ in levels], names=levels),
+        columns=intensity.columns,
+    )
+
+    # use a linear model for countries with an intensity below the convergence intensity
+    low_intensity = intensity_hist <= intensity_countries.iloc[:, -1]
+
+    if low_intensity.any():
+        intensity_projection_linear = linear_intensity_model(
+            alpha,
+            intensity_hist.loc[low_intensity],
+            intensity_countries.loc[low_intensity],
+        )
+        logger.debug(
+            "Linear model was chosen for some trajectories:\n%s",
+            intensity_hist.index[low_intensity].to_frame().to_string(index=False),
+        )
+    else:
+        intensity_projection_linear = empty_intensity
+    del intensity_countries
+
+    negative_convergence = intensity.iloc[:, -1] < 0
+    if negative_convergence.any():
+        negative_convergence_i = negative_convergence.index[negative_convergence]
+        # sum does not work here. We need the individual per-country dimension
+        negative_intensity_projection = negative_exponential_intensity_model(
+            alpha,
+            intensity_hist.loc[~low_intensity],
+            intensity.loc[negative_convergence],
+            reference,
+            semijoin(intensity_projection_linear, negative_convergence_i, how="inner"),
+            context,
+        )
+
+    else:
+        negative_intensity_projection = empty_intensity
+
+    if not negative_convergence.all():
+        exponential_intensity_projection = exponential_intensity_model(
+            alpha,
+            intensity_hist.loc[~negative_convergence & ~low_intensity],
+            intensity.loc[~negative_convergence],
+        )
+    else:
+        exponential_intensity_projection = empty_intensity
+
+    intensity_projection = concat(
+        skipempty(
+            exponential_intensity_projection,
+            negative_intensity_projection,
+            intensity_projection_linear,
+        ),
+        sort=False,
+    ).reindex(index=intensity_idx)
+
+    # if convergence year is past model horizon, intensity_projection is longer
+    intensity_projection = intensity_projection.loc[:, : model.columns[-1]]
+
+    if model.columns[-1] > intensity_projection.columns[-1]:
+        # Extend modelled intensity projection beyond year_convergence
+        intensity_projection = intensity_projection.reindex(
+            columns=model.columns, method="ffill"
+        )
+
+    weights = (
+        intensity_projection.pix.multiply(reference, join="left")
+        .groupby(model.index.names, dropna=False)
+        .transform(normalize)
+    )
+    res = model.pix.multiply(weights, join="left")
+    return res.where(semijoin(model != 0, res.index, how="right"), 0)
diff --git a/src/aneris/downscaling/methods.py b/src/aneris/downscaling/methods.py
new file mode 100644
index 00000000..be36b7a2
--- /dev/null
+++ b/src/aneris/downscaling/methods.py
@@ -0,0 +1,184 @@
+import logging
+from typing import Union
+
+import pandas_indexing.accessors  # noqa: F401
+from pandas import DataFrame, Series
+from pandas_indexing import semijoin
+
+from ..utils import normalize
+from .data import DownscalingContext
+from .intensity_convergence import intensity_convergence  # noqa: F401
+
+
+logger = logging.getLogger(__name__)
+
+
+def base_year_pattern(
+    model: DataFrame, hist: Union[Series, DataFrame], context: DownscalingContext
+) -> DataFrame:
+    """
+    Downscales emission data using a base year pattern.
+
+    Parameters
+    ----------
+    model : DataFrame
+        model emissions for each world region and trajectory
+    historic : DataFrame or Series
+        historic emissions for each country and trajectory
+    context : DownscalingContext
+        settings for downscaling, like the regionmap
+
+    Returns
+    -------
+    DataFrame:
+        downscaled emissions for countries
+
+    Notes
+    -----
+    1. All trajectories in `model` exist in `hist`
+       a. `model` has the levels in `index` and "region"
+       b. `hist` has the levels in `index` and "country"
+    2. region mapping has two indices the first one is fine, the second coarse
+
+    See also
+    --------
+    DownscalingContext
+    """
+
+    model = model.loc[:, context.year :]
+    if isinstance(hist, DataFrame):
+        hist = hist.loc[:, context.year]
+
+    weights = (
+        semijoin(hist, context.regionmap, how="right")
+        .groupby(model.index.names, dropna=False)
+        .transform(normalize)
+    )
+
+    # the semijoin in where should not be necessary, but pandas fails without it
+    res = model.pix.multiply(weights, join="left")
+    return res.where(semijoin(model != 0, res.index, how="right"), 0)
+
+
+def simple_proxy(
+    model: DataFrame,
+    _hist: Union[Series, DataFrame],
+    context: DownscalingContext,
+    proxy_name: str,
+) -> DataFrame:
+    """
+    Downscales emission data using the shares in a proxy scenario.
+
+    Parameters
+    ----------
+    model : DataFrame
+        model emissions for each world region and trajectory
+    _hist : DataFrame or Series
+        historic emissions for each country and trajectory.
+        Unused for this method.
+    context : DownscalingContext
+        settings for downscaling, like the regionmap
+    proxy_name : str
+        name of the additional data to be used as proxy
+
+    Returns
+    -------
+    DataFrame:
+        downscaled emissions for countries
+
+    See also
+    --------
+    DownscalingContext
+    """
+
+    model = model.loc[:, context.year :]
+
+    proxy_data = context.additional_data[proxy_name]
+    common_levels = [lvl for lvl in model.index.names if lvl in proxy_data.index.names]
+    weights = (
+        semijoin(proxy_data, context.regionmap)[model.columns]
+        .groupby(common_levels + [context.region_level], dropna=False)
+        .transform(normalize)
+    )
+
+    # the semijoin in where should not be necessary, but pandas fails without it
+    res = model.pix.multiply(weights, join="left")
+    return res.where(semijoin(model != 0, res.index, how="right"), 0)
+
+
+def growth_rate(
+    model: DataFrame,
+    hist: Union[Series, DataFrame],
+    context: DownscalingContext,
+) -> DataFrame:
+    """
+    Downscales emission data using growth rates.
+
+    Assumes growth rates in all sub regions are the same as in the macro_region
+
+    Parameters
+    ----------
+    model : DataFrame
+        model emissions for each world region and trajectory
+    historic : DataFrame or Series
+        historic emissions for each country and trajectory
+    context : DownscalingContext
+        settings for downscaling, like the regionmap
+
+    Returns
+    -------
+    DataFrame:
+        downscaled emissions for countries
+
+    Notes
+    -----
+    1. All trajectories in `model` exist in `hist`
+       a. `model` has the levels in `index` and "region"
+       b. `hist` has the levels in `index` and "country"
+    2. region mapping has two indices the first one is fine, the second coarse
+    """
+
+    model = model.loc[:, context.year :]
+    if isinstance(hist, DataFrame):
+        hist = hist.loc[:, context.year]
+
+    cumulative_growth_rates = (model / model.shift(axis=1, fill_value=1)).cumprod(
+        axis=1
+    )
+
+    weights = (
+        cumulative_growth_rates.pix.multiply(
+            semijoin(hist, context.regionmap, how="right"),
+            join="left",
+        )
+        .groupby(model.index.names, dropna=False)
+        .transform(normalize)
+    )
+
+    # the semijoin in where should not be necessary, but pandas fails without it
+    res = model.pix.multiply(weights, join="left")
+    return res.where(semijoin(model != 0, res.index, how="right"), 0)
+
+
+def default_method_choice(
+    traj,
+    fallback_method="proxy_gdp",
+    intensity_method="ipat_2100_gdp",
+    luc_method="base_year_pattern",
+    luc_sectors=None,
+):
+    """
+    Default downscaling decision tree.
+    """
+    luc_sectors = luc_sectors or ("Agriculture", "LULUCF")
+
+    # special cases
+    if traj.h == 0:
+        return fallback_method
+    if traj.zero_m:
+        return fallback_method
+
+    if traj.get("sector", None) in luc_sectors:
+        return luc_method
+
+    return intensity_method
diff --git a/src/aneris/errors.py b/src/aneris/errors.py
index 583a7178..60d47e60 100644
--- a/src/aneris/errors.py
+++ b/src/aneris/errors.py
@@ -10,6 +10,12 @@ class MissingHistoricalError(ValueError):
     """
 
 
+class MissingProxyError(ValueError):
+    """
+    Error raised when required proxy data is missing.
+    """
+
+
 class MissingScenarioError(ValueError):
     """
     Error raised when scenario data is missing.
@@ -20,3 +26,21 @@ class MissingHarmonisationYear(ValueError):
     """
     Error raised when the harmonisation year is missing.
     """
+
+
+class MissingLevels(ValueError):
+    """
+    Error raised when an index level is expected but missing.
+    """
+
+
+class MissingDimension(ValueError):
+    """
+    Error raised when a spatial dimension is expected but missing.
+    """
+
+
+class MissingCoordinateValue(ValueError):
+    """
+    Error raised when a spatial dimension is expected but missing.
+    """
diff --git a/src/aneris/grid.py b/src/aneris/grid.py
new file mode 100644
index 00000000..65bf2d19
--- /dev/null
+++ b/src/aneris/grid.py
@@ -0,0 +1,280 @@
+from __future__ import annotations
+
+from functools import cached_property
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+import dask
+import pandas as pd
+import ptolemy as pt
+import xarray as xr
+from attrs import define, field
+from pandas_indexing import isin
+
+from .utils import Pathy, logger
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable, Sequence
+
+    from typing_extensions import Self
+
+
+DEFAULT_INDEX = ("sector", "gas")
+
+
+@dask.delayed
+def verify_global_values(
+    aggregated, tabular, output_variable, index, abstol=1e-8, reltol=1e-6
+) -> pd.DataFrame | None:
+    tab_df = tabular.groupby(level=index).sum().unstack("year")
+    grid_df = aggregated.to_series().groupby(level=index).sum().unstack("year")
+    grid_df, tab_df = grid_df.align(tab_df, join="inner")
+
+    absdiff = abs(grid_df - tab_df)
+    if (absdiff >= abstol + reltol * abs(tab_df)).any(axis=None):
+        reldiff = (absdiff / tab_df).where(abs(tab_df) > 0, 0)
+        logger().warning(
+            f"Yearly global totals relative values between grids and global data for ({output_variable}) not within {reltol}:\n"
+            f"{reldiff}"
+        )
+        return reldiff
+    else:
+        logger().info(
+            f"Yearly global totals relative values between grids and global data for ({output_variable}) within tolerance"
+        )
+        return
+
+
+@define
+class GlobalIndexraster:
+    """Pseudo indexraster mimicking ptolemy's indexraster for simple global aggregation"""
+
+    dim: str = "country"
+
+    @property
+    def index(self):
+        return ["World"]
+
+    def grid(self, data):
+        # Relies on xarray's broadcasting to spread out the data on the proxy file
+        return data
+
+    def aggregate(self, da):
+        return da.sum(["lat", "lon"])
+
+
+@define
+class GriddingContext:
+    indexraster_country: pt.IndexRaster
+    indexraster_region: pt.IndexRaster
+    cell_area: xr.DataArray
+    index: Sequence[str] = field(factory=lambda: list(DEFAULT_INDEX))
+    extra_spatial_dims: Sequence[str] = field(factory=lambda: ["level"])
+    mean_time_dims: Sequence[str] = field(factory=lambda: ["month"])
+    country_level: str = "country"
+    year_level: str = "year"
+
+    @property
+    def indexrasters(self):
+        return {
+            "country": self.indexraster_country,
+            "region": self.indexraster_region,
+            "global": GlobalIndexraster(self.country_level),
+        }
+
+    @property
+    def concat_dim(self):
+        return self.index[0]
+
+    @property
+    def index_year(self):
+        return [*self.index, self.year_level]
+
+    @property
+    def index_all(self):
+        return [*self.index, self.country_level, self.year_level]
+
+
+@define
+class Gridded:
+    data: xr.DataArray
+    downscaled: pd.DataFrame
+    proxy: Proxy
+    meta: dict[str, str] = field(factory=dict)
+
+    def verify(self, compute: bool = True):
+        return self.proxy.verify_gridded(self.data, self.downscaled, compute=compute)
+
+    def prepare_dataset(self, callback: Callable | None = None):
+        name = self.proxy.name
+        ds = self.data.to_dataset(name=name)
+
+        if callback is not None:
+            ds = callback(ds, name=name, **self.meta)
+
+        return ds
+
+    def fname(
+        self,
+        template_fn: str,
+        directory: Pathy | None = None,
+    ):
+        meta = self.meta | dict(name=self.proxy.name)
+        fn = template_fn.format(
+            **{k: v.replace("_", "-").replace(" ", "-") for k, v in meta.items()}
+        )
+        if directory is not None:
+            fn = Path(directory) / fn
+        return fn
+
+    def to_netcdf(
+        self,
+        template_fn: str,
+        callback: Callable | None = None,
+        encoding_kwargs: dict | None = None,
+        directory: Pathy | None = None,
+        compute: bool = True,
+    ):
+        ds = self.prepare_dataset(callback)
+        encoding_kwargs = (
+            ds[self.proxy.name].encoding
+            | {
+                "zlib": True,
+                "complevel": 2,
+            }
+            | (encoding_kwargs or {})
+        )
+        return ds.to_netcdf(
+            self.fname(template_fn, directory),
+            encoding={self.proxy.name: encoding_kwargs},
+            compute=compute,
+        )
+
+
+@define(slots=False)  # cached_property's need __dict__
+class Proxy:
+    # data is assumed to be given as a flux (beware: CEDS is in absolute terms)
+    data: xr.DataArray
+    levels: frozenset[str]
+    context: GriddingContext
+    name: str = "unnamed"
+
+    @classmethod
+    def from_files(
+        cls,
+        name: str,
+        paths: Sequence[Pathy],
+        levels: frozenset[str],
+        context: GriddingContext,
+        index_mappings: dict[str, dict[str, str]] | None = None,
+    ) -> Self:
+        if levels > (set(context.indexrasters) | {"global"}):
+            raise ValueError(
+                f"Variables need indexrasters for all levels: {', '.join(levels)}"
+            )
+
+        proxy = xr.concat(
+            [
+                xr.open_dataarray(path, chunks="auto", engine="h5netcdf").chunk(
+                    {"lat": -1, "lon": -1}
+                )
+                for path in paths
+            ],
+            dim=context.concat_dim,
+        )
+
+        for dim in context.index:
+            mapping = index_mappings.get(dim)
+            if mapping is not None:
+                proxy = (
+                    proxy.rename({dim: f"proxy_{dim}"})
+                    .sel({f"proxy_{dim}": xr.DataArray(mapping, dims=[dim])})
+                    .drop_vars(f"proxy_{dim}")
+                )
+
+        return cls(proxy, levels, context, name)
+
+    def reduce_dimensions(self, da):
+        da = da.mean(self.context.mean_time_dims)
+        spatial_dims = set(da.dims) & set(self.context.extra_spatial_dims)
+        if spatial_dims:
+            da = da.sum(spatial_dims)
+        return da * self.context.cell_area
+
+    @cached_property
+    def weight(self):
+        proxy_reduced = self.reduce_dimensions(self.data)
+
+        return {
+            level: self.context.indexrasters[level].aggregate(proxy_reduced).chunk(-1)
+            for level in self.levels
+        }
+
+    def assert_single_pathway(self, downscaled):
+        pathways = downscaled.pix.unique(
+            downscaled.index.names.difference(self.context.index_all)
+        )
+        assert (
+            len(pathways) == 1
+        ), "`downscaled` is needed as a single scenario, but there are: {pathways}"
+        return dict(zip(pathways.names, pathways[0]))
+
+    def prepare_downscaled(self, downscaled):
+        meta = self.assert_single_pathway(downscaled)
+        downscaled = (
+            downscaled.stack(self.context.year_level)
+            .pix.semijoin(
+                pd.MultiIndex.from_product(
+                    [self.data.indexes[d] for d in self.context.index_year]
+                ),
+                how="inner",
+            )
+            .pix.project(self.context.index_all)
+            .sort_index()
+            .astype(self.data.dtype, copy=False)
+        )
+        downscaled.attrs.update(meta)
+        return downscaled
+
+    def verify_gridded(self, gridded, downscaled, compute: bool = True):
+        scen = self.prepare_downscaled(downscaled)
+
+        global_gridded = self.reduce_dimensions(gridded).sum(["lat", "lon"])
+        diff = verify_global_values(
+            global_gridded, scen, self.name, self.context.index_year
+        )
+        return diff.compute() if compute else diff
+
+    def grid(self, downscaled: pd.DataFrame) -> Gridded:
+        scen = self.prepare_downscaled(downscaled)
+
+        def weighted(scen, weight):
+            indexers = {
+                dim: weight.indexes[dim].intersection(scen.pix.unique(dim))
+                for dim in self.context.index
+            }
+            scen = xr.DataArray.from_series(scen).reindex(indexers, fill_value=0)
+            weight = weight.reindex_like(scen)
+            return (scen / weight).where(weight, 0).chunk()
+
+        gridded = []
+        for level in self.levels:
+            indexraster = self.context.indexrasters[level]
+            weight = self.weight[level]
+            scen_ = scen.loc[isin(**{self.context.country_level: indexraster.index})]
+            gridded_ = indexraster.grid(weighted(scen_, weight)).drop_vars(
+                indexraster.dim
+            )
+
+            if gridded_.size > 0:
+                gridded.append(self.data * gridded_)
+
+        return Gridded(
+            xr.concat(gridded, dim=self.context.concat_dim).assign_attrs(
+                units=f"{scen.attrs['unit']} m-2"
+            ),
+            downscaled,
+            self,
+            scen.attrs,
+        )
diff --git a/src/aneris/harmonize.py b/src/aneris/harmonize.py
index 5e1e5dc4..04cc203d 100644
--- a/src/aneris/harmonize.py
+++ b/src/aneris/harmonize.py
@@ -2,7 +2,7 @@
 from itertools import chain
 
 import pandas as pd
-from pandas_indexing import projectlevel, semijoin
+from pandas_indexing import projectlevel, semijoin, uniquelevel
 
 from aneris import utils
 from aneris.errors import (
@@ -38,29 +38,25 @@ def _check_data(hist, scen, year, idx):
     if "unit" not in idx:
         idx += ["unit"]
 
-    # @coroa - this may be a very slow way to do this check..
-    def downselect(df):
-        return df[year].reset_index().set_index(idx).index.unique()
-
-    s = downselect(scen)
-    h = downselect(hist)
+    s = uniquelevel(scen, idx)
+    h = uniquelevel(hist, idx)
     if h.empty:
         raise MissingHarmonisationYear("No historical data in harmonization year")
 
     if not s.difference(h).empty:
         raise MissingHistoricalError(
             "Historical data does not match scenario data in harmonization "
-            f"year for\n {s.difference(h)}"
+            f"year for\n {s.difference(h).to_frame().to_string(index=False, max_rows=100)}"
         )
 
     if not h.difference(s).empty:
         raise MissingScenarioError(
             "Scenario data does not match historical data in harmonization "
-            f"year for\n {h.difference(s)}"
+            f"year for\n {h.difference(s).to_frame().to_string(index=False, max_rows=100)}"
         )
 
 
-def _check_overrides(overrides, idx):
+def _check_overrides(overrides, data_index):
     if overrides is None:
         return
 
@@ -70,8 +66,16 @@ def _check_overrides(overrides, idx):
     if not overrides.name == "method":
         raise ValueError("Overrides name must be method")
 
-    if not overrides.index.name != idx:
-        raise ValueError(f"Overrides must be indexed by {idx}")
+    # Check whether there exists an override for at least one data variable
+    _, lidx, _ = overrides.index.join(data_index, how="right", return_indexers=True)
+    if lidx is None:
+        return
+
+    if (lidx == -1).all():
+        raise ValueError(
+            "overrides must have at least one index dimension "
+            f"aligned with methods: {data_index.names}"
+        )
 
 
 class Harmonizer:
@@ -155,16 +159,18 @@ def check_idx(df, label):
         )
         self.method_choice = method_choice
 
-        # get default methods to use in decision tree
+        # set default methods to use in decision tree
         self.ratio_method = config.get("default_ratio_method")
         self.offset_method = config.get("default_offset_method")
         self.luc_method = config.get("default_luc_method")
         self.luc_cov_threshold = config.get("luc_cov_threshold")
 
-    def metadata(self):
+    def metadata(self, year=None):
         """
         Return pd.DataFrame of method choice metadata.
         """
+        base_year = year if year is not None else self.base_year or 2015
+
         methods = self.methods_used
         if isinstance(methods, pd.Series):  # only defaults used
             methods = methods.to_frame()
@@ -178,10 +184,10 @@ def metadata(self):
                 methods["override"],
                 self.offsets,
                 self.ratios,
-                self.history[self.base_year],
+                self.history[base_year],
                 self.history.apply(coeff_of_var, axis=1),
-                self.data[self.base_year],
-                self.model[self.base_year],
+                self.data[base_year],
+                self.model[base_year],
             ],
             axis=1,
         )
@@ -200,48 +206,50 @@ def metadata(self):
 
     def _default_methods(self, year):
         assert year is not None
+
+        kwargs = {
+            "method_choice": self.method_choice,
+            "ratio_method": self.ratio_method,
+            "offset_method": self.offset_method,
+            "luc_method": self.luc_method,
+            "luc_cov_threshold": self.luc_cov_threshold,
+        }
         methods, diagnostics = default_methods(
             self.history.droplevel(
                 list(set(self.history.index.names) - set(self.harm_idx))
             ),
             self.data.droplevel(list(set(self.data.index.names) - set(self.harm_idx))),
             year,
-            method_choice=self.method_choice,
-            ratio_method=self.ratio_method,
-            offset_method=self.offset_method,
-            luc_method=self.luc_method,
-            luc_cov_threshold=self.luc_cov_threshold,
+            **{k: v for k, v in kwargs.items() if v is not None},
         )
         return methods
 
     def _harmonize(self, method, idx, check_len, base_year):
         # get data
-        def downselect(df, idx, level="unit"):
-            return df.reset_index(level=level).loc[idx].set_index(level, append=True)
-
-        model = downselect(self.data, idx)
-        hist = downselect(self.history, idx)
-        offsets = downselect(self.offsets, idx)["offset"]
-        ratios = downselect(self.ratios, idx)["ratio"]
+        model = semijoin(self.data, idx, how="right")
+        hist = semijoin(self.history, idx, how="right")
+        offsets = semijoin(self.offsets, idx, how="right")
+        ratios = semijoin(self.ratios, idx, how="right")
 
         # get delta
         delta = hist if method == "budget" else ratios if "ratio" in method else offsets
 
         # checks
-        assert not model.isnull().values.any()
-        assert not hist.isnull().values.any()
-        assert not delta.isnull().values.any()
+        assert not model.isnull().any(axis=None)
+        assert not hist.isnull().any(axis=None)
+        assert not delta.isnull().any(axis=None)
         if check_len:
             assert (len(model) < len(self.data)) & (len(hist) < len(self.history))
 
         # harmonize
         model = Harmonizer._methods[method](model, delta, harmonize_year=base_year)
 
-        y = str(base_year)
-        if model.isnull().values.any():
+        if model.isnull().any(axis=None):
             msg = "{} method produced NaNs: {}, {}"
             where = model.isnull().any(axis=1)
-            raise ValueError(msg.format(method, model.loc[where, y], delta.loc[where]))
+            raise ValueError(
+                msg.format(method, model.loc[where, base_year], delta.loc[where])
+            )
 
         # construct the full df of history and future
         return model
@@ -254,17 +262,11 @@ def methods(self, year=None, overrides=None):
         pd.DataFrame of overrides.
         """
         # get method listing
-        base_year = year if year is not None else self.base_year or "2015"
-        _check_overrides(overrides, self.harm_idx)
+        base_year = year if year is not None else self.base_year or 2015
+        _check_overrides(overrides, self.data.index)
         methods = self._default_methods(year=base_year)
 
         if overrides is not None:
-            # overrides requires an index
-            if overrides.index.names == [None]:
-                raise ValueError(
-                    "overrides must have at least on index dimension "
-                    f"aligned with methods: {methods.index.names}"
-                )
             # expand overrides index to match methods and align indicies
             overrides = semijoin(overrides, methods.index, how="right").reorder_levels(
                 methods.index.names
@@ -277,10 +279,11 @@ def methods(self, year=None, overrides=None):
             overrides.name = methods.name
 
             # overwrite defaults with overrides
-            final_methods = overrides.combine_first(methods).to_frame()
-            final_methods["default"] = methods
-            final_methods["override"] = overrides
-            methods = final_methods
+            methods = (
+                overrides.combine_first(methods)
+                .to_frame()
+                .assign(default=methods, override=overrides)
+            )
 
         return methods
 
@@ -289,13 +292,9 @@ def harmonize(self, year=None, overrides=None):
         Return pd.DataFrame of harmonized trajectories given pd.DataFrame
         overrides.
         """
-        base_year = year if year is not None else self.base_year or "2015"
+        base_year = year if year is not None else self.base_year or 2015
         _check_data(self.history, self.data, base_year, self.harm_idx)
-        _check_overrides(overrides, self.harm_idx)
 
-        self.model = pd.Series(
-            index=self.data.index, name=base_year, dtype=float
-        ).to_frame()
         self.offsets, self.ratios = harmonize_factors(
             self.data, self.history, base_year
         )
@@ -307,20 +306,23 @@ def harmonize(self, year=None, overrides=None):
         if isinstance(methods, pd.DataFrame):
             methods = methods["method"]  # drop default and override info
         if (methods == "unicorn").any():
+            self.model = pd.Series(
+                index=self.data.index, name=base_year, dtype=float
+            ).to_frame()
             msg = """Values found where model has positive and negative values
             and is zero in base year. Unsure how to proceed:\n{}\n{}"""
             cols = ["history", "unharmonized"]
-            df1 = self.metadata().loc[methods == "unicorn", cols]
+            df1 = self.metadata(year=base_year).loc[methods == "unicorn", cols]
             df2 = self.data.loc[methods == "unicorn"]
             raise ValueError(msg.format(df1.reset_index(), df2.reset_index()))
 
         dfs = []
         y = base_year
+        check_len = len(methods.unique()) > 1
         for method in methods.unique():
             _log(f"Harmonizing with {method}")
             # get subset indicies
             idx = methods[methods == method].index
-            check_len = len(methods.unique()) > 1
             # harmonize
             df = self._harmonize(method, idx, check_len, base_year=base_year)
             if method not in ["model_zero", "hist_zero"]:
diff --git a/src/aneris/methods.py b/src/aneris/methods.py
index cb429947..ac58072a 100644
--- a/src/aneris/methods.py
+++ b/src/aneris/methods.py
@@ -12,7 +12,7 @@
 from aneris import utils
 
 
-def harmonize_factors(df, hist, harmonize_year="2015"):
+def harmonize_factors(df, hist, harmonize_year=2015):
     """
     Calculate offset and ratio values between data and history.
 
@@ -40,7 +40,7 @@ def harmonize_factors(df, hist, harmonize_year="2015"):
     return offset, ratios
 
 
-def constant_offset(df, offset, harmonize_year="2015"):
+def constant_offset(df, offset, harmonize_year=2015):
     """
     Calculate constant offset harmonized trajectory.
 
@@ -65,7 +65,7 @@ def constant_offset(df, offset, harmonize_year="2015"):
     return df
 
 
-def constant_ratio(df, ratios, harmonize_year="2015"):
+def constant_ratio(df, ratios, harmonize_year=2015):
     """
     Calculate constant ratio harmonized trajectory.
 
@@ -90,7 +90,7 @@ def constant_ratio(df, ratios, harmonize_year="2015"):
     return df
 
 
-def linear_interpolate(df, offset, final_year="2050", harmonize_year="2015"):
+def linear_interpolate(df, offset, final_year=2050, harmonize_year=2015):
     """
     Calculate linearly interpolated convergence harmonized trajectory.
 
@@ -122,7 +122,7 @@ def linear_interpolate(df, offset, final_year="2050", harmonize_year="2015"):
     return df
 
 
-def reduce_offset(df, offset, final_year="2050", harmonize_year="2015"):
+def reduce_offset(df, offset, final_year=2050, harmonize_year=2015):
     """
     Calculate offset convergence harmonized trajectory.
 
@@ -157,7 +157,7 @@ def reduce_offset(df, offset, final_year="2050", harmonize_year="2015"):
     return df
 
 
-def reduce_ratio(df, ratios, final_year="2050", harmonize_year="2015"):
+def reduce_ratio(df, ratios, final_year=2050, harmonize_year=2015):
     """
     Calculate ratio convergence harmonized trajectory.
 
@@ -197,7 +197,7 @@ def reduce_ratio(df, ratios, final_year="2050", harmonize_year="2015"):
     return df
 
 
-def budget(df, df_hist, harmonize_year="2015"):
+def budget(df, df_hist, harmonize_year=2015):
     r"""
     Calculate budget harmonized trajectory.
 
@@ -253,8 +253,8 @@ def budget(df, df_hist, harmonize_year="2015"):
 
     harmonize_year = int(harmonize_year)
 
-    df = df.set_axis(df.columns.astype(int), axis="columns")
-    df_hist = df_hist.set_axis(df_hist.columns.astype(int), axis="columns")
+    # df = df.set_axis(df.columns.astype(int), axis="columns")
+    # df_hist = df_hist.set_axis(df_hist.columns.astype(int), axis="columns")
 
     data_years = df.columns
     hist_years = df_hist.columns
@@ -344,13 +344,13 @@ def l2_norm():
     df_harm = pd.DataFrame(
         harmonized,
         index=df.index,
-        columns=years.astype(str),
+        columns=years,
     )
 
     return df_harm
 
 
-def model_zero(df, offset, harmonize_year="2015"):
+def model_zero(df, offset, harmonize_year=2015):
     """
     Returns result of aneris.methods.constant_offset()
     """
@@ -385,12 +385,17 @@ def coeff_of_var(s):
     c_v : float
         coefficient of variation
     """
-    x = np.diff(s.values)
-    return np.abs(np.std(x) / np.mean(x))
+    x = np.diff(s.to_numpy())
+    with np.errstate(invalid="ignore"):
+        return np.abs(np.std(x) / np.mean(x))
 
 
 def default_method_choice(
-    row, ratio_method, offset_method, luc_method, luc_cov_threshold
+    row,
+    ratio_method="reduce_ratio_2080",
+    offset_method="reduce_offset_2080",
+    luc_method="reduce_offset_2150_cov",
+    luc_cov_threshold=10,
 ):
     """
     Default decision tree as documented at.
@@ -439,7 +444,7 @@ def default_method_choice(
 
 def default_methods(hist, model, base_year, method_choice=None, **kwargs):
     """
-    Determine default harmonization methods to use.
+    Determine default harmonization or downscaling methods to use.
 
     See http://mattgidden.com/aneris/theory.html#default-decision-tree for a
     graphical description of the decision tree.
@@ -455,17 +460,24 @@ def default_methods(hist, model, base_year, method_choice=None, **kwargs):
     method_choice : function, optional
         codified decision tree, see `default_method_choice` function
     **kwargs :
-        Additional parameters passed on to the choice function:
+        Additional parameters passed on to the choice functions.
 
-        ratio_method : string, optional
+        Harmonization functions might depend on the following method names:
+        ratio_method : string
             method to use for ratio harmonization, default: reduce_ratio_2080
-        offset_method : string, optional
+        offset_method : string
             method to use for offset harmonization, default: reduce_offset_2080
-        luc_method : string, optional
+        luc_method : string
             method to use for high coefficient of variation, reduce_offset_2150_cov
         luc_cov_threshold : float
             cov threshold above which to use `luc_method`
 
+        Downscaling functions require the following choices:
+        intensity_method : string
+            method to use for intensity convergence, default ipat_gdp_2100
+        luc_method : string
+            method to use for agriculture and luc emissions, default base_year_pattern
+
     Returns
     -------
     methods : pd.Series
@@ -478,15 +490,6 @@ def default_methods(hist, model, base_year, method_choice=None, **kwargs):
     `default_method_choice`
     """
 
-    if kwargs.get("ratio_method") is None:
-        kwargs["ratio_method"] = "reduce_ratio_2080"
-    if kwargs.get("offset_method") is None:
-        kwargs["offset_method"] = "reduce_offset_2080"
-    if kwargs.get("luc_method") is None:
-        kwargs["luc_method"] = "reduce_offset_2150_cov"
-    if kwargs.get("luc_cov_threshold") is None:
-        kwargs["luc_cov_threshold"] = 10
-
     y = str(base_year)
     try:
         h = hist[base_year]
diff --git a/src/aneris/utils.py b/src/aneris/utils.py
index 17054a77..0f1ba4c8 100644
--- a/src/aneris/utils.py
+++ b/src/aneris/utils.py
@@ -1,12 +1,14 @@
 import logging
 import os
-from functools import reduce
-from operator import and_
+from pathlib import Path
+from typing import TypeAlias
 
-import numpy as np
 import pandas as pd
+import pycountry
 
 
+Pathy: TypeAlias = str | Path
+
 _logger = None
 
 # Index for iamc
@@ -42,24 +44,6 @@ def numcols(df):
     return [i for i in dtypes.index if dtypes.loc[i].name.startswith(("float", "int"))]
 
 
-def isin(df=None, **filters):
-    """
-    Constructs a MultiIndex selector.
-
-    Usage
-    -----
-    > df.loc[isin(region="World", gas=["CO2", "N2O"])]
-    or with explicit df to get boolean mask
-    > isin(df, region="World", gas=["CO2", "N2O"])
-    """
-
-    def tester(df):
-        tests = (df.index.isin(np.atleast_1d(v), level=k) for k, v in filters.items())
-        return reduce(and_, tests, next(tests))
-
-    return tester if df is None else tester(df)
-
-
 def isstr(x):
     """
     Returns True if x is a string.
@@ -119,6 +103,18 @@ def pd_write(df, f, *args, **kwargs):
     if f.endswith("csv"):
         df.to_csv(f, index=index, *args, **kwargs)
     else:
-        writer = pd.ExcelWriter(f)
-        df.to_excel(writer, index=index, *args, **kwargs)
-        writer.save()
+        with pd.ExcelWriter(f) as writer:
+            df.to_excel(writer, index=index, *args, **kwargs)
+
+
+def normalize(s):
+    return s / s.sum()
+
+
+def country_name(iso: str):
+    country_obj = pycountry.countries.get(alpha_3=iso)
+    return iso if country_obj is None else country_obj.name
+
+
+def skipempty(*dfs):
+    return [df for df in dfs if not df.empty]
diff --git a/tests/test_default_decision_tree.py b/tests/test_default_decision_tree.py
index 64b5d7b4..9946c4d2 100644
--- a/tests/test_default_decision_tree.py
+++ b/tests/test_default_decision_tree.py
@@ -127,7 +127,13 @@ def test_branch6(index1):
 
 
 def test_custom_method_choice(index1, index1_co2):
-    def method_choice(row, ratio_method, offset_method, luc_method, luc_cov_threshold):
+    def method_choice(
+        row,
+        ratio_method="reduce_ratio_2080",
+        offset_method=None,
+        luc_method=None,
+        luc_cov_threshold=None,
+    ):
         return "budget" if row.gas == "CO2" else ratio_method
 
     # CH4
diff --git a/tests/test_harmonize.py b/tests/test_harmonize.py
index d37153b2..5dd1c0f4 100644
--- a/tests/test_harmonize.py
+++ b/tests/test_harmonize.py
@@ -60,7 +60,9 @@
 def test_factors():
     df = _df.copy()
     hist = _hist.copy()
-    obsoffset, obsratio = harmonize.harmonize_factors(df.copy(), hist.copy())
+    obsoffset, obsratio = harmonize.harmonize_factors(
+        df.copy(), hist.copy(), harmonize_year="2015"
+    )
     # im lazy; test initially written when these were of length 2
     exp = np.array([0.01 - 3, -1.0])
     npt.assert_array_almost_equal(exp, obsoffset[-2:])
@@ -89,7 +91,9 @@ def test_harmonize_constant_offset():
 def test_no_model():
     df = pd.DataFrame({"2015": [0]})
     hist = pd.DataFrame({"2015": [1.5]})
-    obsoffset, obsratio = harmonize.harmonize_factors(df.copy(), hist.copy())
+    obsoffset, obsratio = harmonize.harmonize_factors(
+        df.copy(), hist.copy(), harmonize_year="2015"
+    )
     exp = np.array([1.5])
     npt.assert_array_almost_equal(exp, obsoffset)
     exp = np.array([0])
@@ -283,7 +287,7 @@ def test_harmonize_budget():
     def _carbon_budget(emissions):
         # trapezoid rule
         dyears = np.diff(emissions.columns.astype(int))
-        emissions = emissions.values
+        emissions = emissions.to_numpy()
         demissions = np.diff(emissions, axis=1)
 
         budget = (dyears * (emissions[:, :-1] + demissions / 2)).sum(axis=1)
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 6af5a32c..03492adc 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,6 +1,4 @@
 import pandas as pd
-import pandas.testing as pdt
-import pytest
 
 import aneris.utils as utils
 
@@ -23,28 +21,3 @@ def combine_rows_df():
         }
     ).set_index(utils.df_idx)
     return df
-
-
-def test_isin():
-    df = combine_rows_df()
-    exp = pd.DataFrame(
-        {
-            "sector": [
-                "sector1",
-                "sector2",
-                "sector1",
-            ],
-            "region": ["a", "a", "b"],
-            "2010": [1.0, 4.0, 2.0],
-            "foo": [-1.0, -4.0, 2.0],
-            "unit": ["Mt"] * 3,
-            "gas": ["BC"] * 3,
-        }
-    ).set_index(utils.df_idx)
-    obs = exp.loc[
-        utils.isin(sector=["sector1", "sector2"], region=["a", "b", "non-existent"])
-    ]
-    pdt.assert_frame_equal(obs, exp)
-
-    with pytest.raises(KeyError):
-        utils.isin(df, region="World", non_existing_level="foo")
diff --git a/tox.ini b/tox.ini
index b2c720fb..b3a71130 100644
--- a/tox.ini
+++ b/tox.ini
@@ -53,7 +53,7 @@ deps =
     ruff
 skip_install = true
 commands =
-    black --check --diff {posags:src tests}
+    black --check --diff {posargs:src tests}
     ruff --diff {posargs:src tests doc}
 
 # asserts package build integrity
@@ -84,10 +84,9 @@ commands =
 # if this fails, most likely RTD build will fail
 [testenv:docs]
 package = editable
-extras = doc
-# TODO: add docs back, currently fail saying can't find pandoc
-# commands =
-#     sphinx-build {posargs:-E} -b html docs docs/html
+extras = docs
+commands =
+    sphinx-build {posargs:-E} -b html doc/source doc/build/html
 
 # safety checks
 [testenv:safety]