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Thermosteric sea-level rise patterns #4047
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Should the title be sterodynamic sea level patterns? Or thermosteric and sterodynamic sea level change? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Would a figure like Figure 2 in Palmer et al., (2020) be possible? https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019EF001413 There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Add something on applying the methods of Palmer et al., (2020) which are rooted in AR5 methods. Could add something about Hermans et al., (2021) also using CMIP6 models with these models to project global mean sea level change and refer to that for the reader to explore the differences? Although, Hermans use a least squares quadratic fit for drift correction - same as Palmer et al. (2020)? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Could add what scenarios are available? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I've updated the title and added the available scenarios. In theory a figure like Fig. 2 of Palmer et al., (2020) would be possible but I'd need to get historical data from all the models to calculate the 1986-2005 baseline. This would add quite a lot of extra processing and time for this particular PR. However it could definitely be added in the future if that's something you'd like to do :) I'll get back to you on the references tomorrow :) There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. @mo-jenniferweeks I've added a note on Palmer et al., (2020) but I'll leave it to you to add in any other references and context you think might be relevant in another PR which should be easy to do! If you'd like to send me anything offline I can add it in here as well of course. |
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| .. _recipes_steric_patterns: | ||
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| Calculate sterodynamic sea-level patterns from CMIP6 models | ||
| =========================================================== | ||
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| Overview | ||
| -------- | ||
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| The recipe recipe_steric_patterns calculates sterodynamic sea-level change | ||
| patterns from CMIP6 model datasets. Patterns are calculated for the SSP2-4.5, | ||
| SSP3-7.0 and SSP5-8.5 scenarios. | ||
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| These patterns are useful because we can emulate spatially-resolved | ||
| sterodynamic sea-level rise from global thermal expansion at a fraction of the | ||
| computational cost of running a fully-coupled Earth system model or ocean model. | ||
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| The methods for this recipe are derived from those in Palmer et al. (2020) which | ||
| themselves are rooted in the IPCC AR5 report. | ||
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| .. note:: | ||
| The regrid setting in the recipe is set to a (180, 360) grid to put | ||
| all models on the same grid. | ||
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| Available recipes and diagnostics | ||
| --------------------------------- | ||
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| Recipes are stored in esmvaltool/recipes/ | ||
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| * recipe_steric_patterns.yml | ||
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| Diagnostics are stored in esmvaltool/diag_scripts/steric_patterns/ | ||
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| * steric_patterns.py: generates sterodynamic patterns from input datasets | ||
| * sub_funcs.py: set of sub functions to assist with the main script | ||
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| User settings in recipe | ||
| ----------------------- | ||
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| #. Script steric_patterns.py | ||
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| *Required settings for script* | ||
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| None | ||
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| *Optional settings for script* | ||
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| None | ||
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| *Required settings for variables* | ||
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| * short_name | ||
| * additional_datasets | ||
| * exp | ||
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| *Optional settings for variables* | ||
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| None | ||
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| *Required settings for preprocessor* | ||
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| * regular_grid: regrids data to a (180, 360) grid | ||
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| *Optional settings for preprocessor* | ||
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| None | ||
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| Variables | ||
| --------- | ||
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| #. Script steric_patterns.py | ||
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| * zostoga (ocean, monthly, time) | ||
| * zos (ocean, monthly, longitude, latitude, time) | ||
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| Observations and reformat scripts | ||
| --------------------------------- | ||
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| None | ||
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| References | ||
| ---------- | ||
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| * Palmer, M. D., Gregory, J. M., Bagge, M., Calvert, D., Hagedoorn, J. M., | ||
| & Howard, T., et al. (2020). Exploring the drivers of global and local | ||
| sea-level change over the 21st century and beyond. Earth's Future, 8, | ||
| e2019EF001413. https://doi.org/10.1029/2019EF001413 | ||
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| * Perks, R., & Weeks, J. (2023). MetOffice/ProFSea-tool: v1.0.0 (v1.0.0). | ||
| Zenodo. https://doi.org/10.5281/zenodo.10255468 | ||
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| Example plots | ||
| ------------- | ||
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| .. _fig_steric_patterns_1: | ||
| .. figure:: /recipes/figures/steric_patterns/detrended.png | ||
| :align: center | ||
| :width: 80% | ||
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| Detrended zostoga, correcting for model drift using the pre-industrial | ||
| (PiControl) experiment following Palmer et al., (2020). This is done for | ||
| each model and scenario. | ||
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| .. _fig_steric_patterns_2: | ||
| .. figure:: /recipes/figures/steric_patterns/regressions.png | ||
| :align: center | ||
| :width: 80% | ||
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| Example of the regressions between the global thermal expansion (zostoga) and | ||
| local dynamic sea-level height (zos) for three random grid-cells. The coordinate | ||
| for each of the grid-cells is shown in the top-right corner of each panel. | ||
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| .. _fig_steric_patterns_3: | ||
| .. figure:: /recipes/figures/steric_patterns/predictions.png | ||
| :align: center | ||
| :width: 80% | ||
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| Example end-of-century predictions from the UKESM1-0-LL model patterns | ||
| for each SSP, as well as a timeseries of globally-averaged mean-squared | ||
| error. Two polar artifacts can be seen in each map panel, seemingly | ||
| occuring due to ESMValTool's regridding functionality. This seems to occur | ||
| more often on low-resolution models as opposed to high-res. |
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