Spatial and temporal dependence in distribution‐based evaluation of CMIP6 daily maximum temperatures
Abstract Climate models are increasingly used to derive localised, specific information to guide adaptation to climate change. Model projections of future scenarios are conferred credibility by evaluating model skill in reproducing large‐scale properties of the observed climate system. Model evaluat...
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | Atmospheric Science Letters |
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| Online Access: | https://doi.org/10.1002/asl.1290 |
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| author | Mala Virdee Ieva Kazlauskaite Emma J. D. Boland Emily Shuckburgh Alison Ming |
| author_facet | Mala Virdee Ieva Kazlauskaite Emma J. D. Boland Emily Shuckburgh Alison Ming |
| author_sort | Mala Virdee |
| collection | DOAJ |
| description | Abstract Climate models are increasingly used to derive localised, specific information to guide adaptation to climate change. Model projections of future scenarios are conferred credibility by evaluating model skill in reproducing large‐scale properties of the observed climate system. Model evaluation at fine spatial and temporal scales and for rare extreme events is critical for provision of reliable adaptation‐relevant information, but may be challenging given significant internal variability and limited observed data in this setting. Comparing distributions of physical variables from historical simulations of Coupled Model Intercomparison Project models against observed distributions provides a comprehensive, concise and physically‐justified skill measure. Calculating divergence between distributions requires aggregation of data spatially or temporally. The spatial and temporal scales at which a divergence measure converges to a consistent value can indicate the scales at which a well‐defined climate signal emerges from internal variability. Below this threshold, there may be insufficient data for robust evaluation, particularly for rare extremes. Here, the behaviour of several divergence measures in response to spatial and temporal aggregation is analysed empirically to give a novel evaluation of CMIP6 daily maximum temperature simulations against reanalysis. Some key insights presented here can inform methodological choices made when deriving adaptation‐relevant information. Convergence varies according to model, geographic region and divergence measure; selection of the level of precision at which models can provide reliable information therefore requires a context‐specific understanding. For this purpose, an interactive tool provided alongside this study demonstrates scale‐dependent evaluation across several geographic regions. Commonly applied measures are found to be only weakly sensitive to discrepancies in the tails of distributions. |
| format | Article |
| id | doaj-art-986aedca6ad2460e962bf33bfa41a400 |
| institution | DOAJ |
| issn | 1530-261X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Atmospheric Science Letters |
| spelling | doaj-art-986aedca6ad2460e962bf33bfa41a4002025-08-20T03:11:24ZengWileyAtmospheric Science Letters1530-261X2025-02-01262n/an/a10.1002/asl.1290Spatial and temporal dependence in distribution‐based evaluation of CMIP6 daily maximum temperaturesMala Virdee0Ieva Kazlauskaite1Emma J. D. Boland2Emily Shuckburgh3Alison Ming4Department of Computer Science and Technology University of Cambridge Cambridge UKDepartment of Engineering University of Cambridge Cambridge UKBritish Antarctic Survey Cambridge UKDepartment of Computer Science and Technology University of Cambridge Cambridge UKDepartment of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge UKAbstract Climate models are increasingly used to derive localised, specific information to guide adaptation to climate change. Model projections of future scenarios are conferred credibility by evaluating model skill in reproducing large‐scale properties of the observed climate system. Model evaluation at fine spatial and temporal scales and for rare extreme events is critical for provision of reliable adaptation‐relevant information, but may be challenging given significant internal variability and limited observed data in this setting. Comparing distributions of physical variables from historical simulations of Coupled Model Intercomparison Project models against observed distributions provides a comprehensive, concise and physically‐justified skill measure. Calculating divergence between distributions requires aggregation of data spatially or temporally. The spatial and temporal scales at which a divergence measure converges to a consistent value can indicate the scales at which a well‐defined climate signal emerges from internal variability. Below this threshold, there may be insufficient data for robust evaluation, particularly for rare extremes. Here, the behaviour of several divergence measures in response to spatial and temporal aggregation is analysed empirically to give a novel evaluation of CMIP6 daily maximum temperature simulations against reanalysis. Some key insights presented here can inform methodological choices made when deriving adaptation‐relevant information. Convergence varies according to model, geographic region and divergence measure; selection of the level of precision at which models can provide reliable information therefore requires a context‐specific understanding. For this purpose, an interactive tool provided alongside this study demonstrates scale‐dependent evaluation across several geographic regions. Commonly applied measures are found to be only weakly sensitive to discrepancies in the tails of distributions.https://doi.org/10.1002/asl.1290climate modelsCMIP6model evaluationtemperature extremes |
| spellingShingle | Mala Virdee Ieva Kazlauskaite Emma J. D. Boland Emily Shuckburgh Alison Ming Spatial and temporal dependence in distribution‐based evaluation of CMIP6 daily maximum temperatures Atmospheric Science Letters climate models CMIP6 model evaluation temperature extremes |
| title | Spatial and temporal dependence in distribution‐based evaluation of CMIP6 daily maximum temperatures |
| title_full | Spatial and temporal dependence in distribution‐based evaluation of CMIP6 daily maximum temperatures |
| title_fullStr | Spatial and temporal dependence in distribution‐based evaluation of CMIP6 daily maximum temperatures |
| title_full_unstemmed | Spatial and temporal dependence in distribution‐based evaluation of CMIP6 daily maximum temperatures |
| title_short | Spatial and temporal dependence in distribution‐based evaluation of CMIP6 daily maximum temperatures |
| title_sort | spatial and temporal dependence in distribution based evaluation of cmip6 daily maximum temperatures |
| topic | climate models CMIP6 model evaluation temperature extremes |
| url | https://doi.org/10.1002/asl.1290 |
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