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Add functions to partition uncertainties according to method from Haw…
…kins & Sutton (2009) (#1262) <!--Please ensure the PR fulfills the following requirements! --> <!-- If this is your first PR, make sure to add your details to the AUTHORS.rst! --> ### Pull Request Checklist: - [x] This PR addresses an already opened issue (for bug fixes / features) - This PR fixes #771 - [x] Tests for the changes have been added (for bug fixes / features) - [ ] (If applicable) Documentation has been added / updated (for bug fixes / features) - [x] HISTORY.rst has been updated (with summary of main changes) - [x] Link to issue (:issue:`number`) and pull request (:pull:`number`) has been added ### What kind of change does this PR introduce? * Add functions to compute variance using method from Hawkins & Sutton (2009). ### Does this PR introduce a breaking change? No ### Other information: Only tested qualitatively so far. Looks ok but I'd like a more solid validation. See related PRs: - Ouranosinc/xclim-testdata#23 - Ouranosinc/figanos#9
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| Original file line number | Diff line number | Diff line change |
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| from __future__ import annotations | ||
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| import numpy as np | ||
| import xarray as xr | ||
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| from xclim.ensembles import hawkins_sutton | ||
| from xclim.ensembles._filters import _concat_hist, _model_in_all_scens, _single_member | ||
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| def test_hawkins_sutton_smoke(open_dataset): | ||
| """Just a smoke test.""" | ||
| dims = {"run": "member", "scen": "scenario"} | ||
| da = ( | ||
| open_dataset( | ||
| "uncertainty_partitioning/cmip5_pr_global_mon.nc", branch="hawkins_sutton" | ||
| ) | ||
| .pr.sel(time=slice("1950", None)) | ||
| .rename(dims) | ||
| ) | ||
| da1 = _model_in_all_scens(da) | ||
| dac = _concat_hist(da1, scenario="historical") | ||
| das = _single_member(dac) | ||
| hawkins_sutton(das) | ||
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| def test_hawkins_sutton_synthetic(): | ||
| """Test logic of Hawkins-Sutton's implementation using synthetic data.""" | ||
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| # Time, scenario, model | ||
| # Here the scenarios don't change over time, so there should be no model variability (since it's relative to the | ||
| # reference period. | ||
| sm = np.arange(10, 41, 10) # Scenario mean | ||
| mm = np.arange(-6, 7, 1) # Model mean | ||
| mean = mm[np.newaxis, :] + sm[:, np.newaxis] | ||
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| # Natural variability | ||
| r = np.random.randn(4, 13, 60) | ||
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| x = r + mean[:, :, np.newaxis] | ||
| time = xr.date_range("1970-01-01", periods=60, freq="Y") | ||
| da = xr.DataArray(x, dims=("scenario", "model", "time"), coords={"time": time}) | ||
| m, v = hawkins_sutton(da) | ||
| # Mean uncertainty over time | ||
| vm = v.mean(dim="time") | ||
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| # Check that the mean relative to the baseline is zero | ||
| np.testing.assert_array_almost_equal(m.mean(dim="time"), 0, decimal=1) | ||
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| # Check that the scenario uncertainty is zero | ||
| np.testing.assert_array_almost_equal(vm.sel(uncertainty="scenario"), 0, decimal=1) | ||
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| # Check that model uncertainty > variability | ||
| assert vm.sel(uncertainty="model") > vm.sel(uncertainty="variability") | ||
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| # Smoke test with polynomial of order 2 | ||
| fit = da.polyfit(dim="time", deg=2, skipna=True) | ||
| sm = xr.polyval(coord=da.time, coeffs=fit.polyfit_coefficients).where(da.notnull()) | ||
| hawkins_sutton(da, sm=sm) | ||
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| # Test with a multiplicative variable and time evolving scenarios | ||
| r = np.random.randn(4, 13, 60) + np.arange(60) | ||
| x = r + mean[:, :, np.newaxis] | ||
| da = xr.DataArray(x, dims=("scenario", "model", "time"), coords={"time": time}) | ||
| m, v = hawkins_sutton(da, kind="*") | ||
| su = v.sel(uncertainty="scenario") | ||
| # We expect the scenario uncertainty to grow over time | ||
| # The scenarios all have the same absolute slope, but since their reference mean is different, the relative increase | ||
| # is not the same and this creates a spread over time across "relative" scenarios. | ||
| assert ( | ||
| su.sel(time=slice("2020", None)).mean() | ||
| > su.sel(time=slice("2000", "2010")).mean() | ||
| ) |
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