diff --git a/notebooks/enso-globus-flow.ipynb b/notebooks/enso-globus-flow.ipynb index c0d9860..d4db2da 100644 --- a/notebooks/enso-globus-flow.ipynb +++ b/notebooks/enso-globus-flow.ipynb @@ -793,12 +793,21 @@ "- All functions/libraries/packages need to be imported and defined within the function to execute\n", "- The output from the function needs to serializable (ex. xarray.Dataset, numpy.array)\n", "\n", - "Using these constraints, we setup the following function, with the key parameter being which model (`source_id`) to compare. Two examples here include The National Center for Atmospheric Research (NCAR) Model CESM2 and the Model for Interdisciplinary Research on Climate (MIROC) Model MIROC6." + "Using these constraints, we setup the following function, with the key parameter being which model (`source_id`) to compare. Two examples here include The National Center for Atmospheric Research (NCAR) Model CESM2 and the Model for Interdisciplinary Research on Climate (MIROC) Model MIROC6. Valid responses for this exercise include:\n", + "- `ACCESS-ESM1-5`\n", + "- `EC-Earth3-CC`\n", + "- `MPI-ESM1-2-LR`\n", + "- `CanESM5`\n", + "- `MIROC6`\n", + "- `EC-Earth3`\n", + "- `CESM2`\n", + "- `EC-Earth3-Veg`\n", + "- `NorCPM1`" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 102, "id": "2b74d939-f87d-4a44-9e4a-6643b7d04fe7", "metadata": { "tags": [] @@ -850,7 +859,7 @@ " # Determine the non-time dimensions and average using these\n", " non_time_dims = set(tos_nino34_anom.dims)\n", " non_time_dims.remove(ds.tos.cf[\"T\"].name)\n", - " weighted_average = tos_nino34_anom.weighted(ds[\"areacello\"]).mean(dim=list(non_time_dims))\n", + " weighted_average = tos_nino34_anom.weighted(ds[\"areacello\"].fillna(0)).mean(dim=list(non_time_dims))\n", "\n", " # Calculate the rolling average\n", " rolling_average = weighted_average.rolling(time=5, center=True).mean()\n", @@ -920,7 +929,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 106, "id": "fe8d9e8b-e38d-41a5-b5f6-df9916d69f83", "metadata": { "tags": [] @@ -942,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 107, "id": "0aa43e9e-6840-4b46-9a0c-ceeef8ca7e1e", "metadata": { "tags": [] @@ -954,7 +963,7 @@ "Executor" ] }, - "execution_count": 64, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -977,7 +986,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 108, "id": "664c9fd2-8822-4e34-9c2b-8558c489e487", "metadata": { "tags": [] @@ -1633,7 +1642,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 113, "id": "aa5d4f65-a8ef-4ed0-bd6e-f49efb4f23ab", "metadata": { "tags": [] @@ -1690,7 +1699,7 @@ " # Determine the non-time dimensions and average using these\n", " non_time_dims = set(tos_nino34_anom.dims)\n", " non_time_dims.remove(ds.tos.cf[\"T\"].name)\n", - " weighted_average = tos_nino34_anom.weighted(ds[\"areacello\"]).mean(dim=list(non_time_dims))\n", + " weighted_average = tos_nino34_anom.weighted(ds[\"areacello\"].fillna(0)).mean(dim=list(non_time_dims))\n", "\n", " # Calculate the rolling average\n", " rolling_average = weighted_average.rolling(time=5, center=True).mean()\n", @@ -1749,7 +1758,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 114, "id": "6ef8039b-b894-42b3-af19-dff99a6cff11", "metadata": { "tags": [] @@ -2274,12 +2283,12 @@ "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ - "
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