Characterizing temperature and precipitation multi‐variate biases in 12 and 2.2 km UK Climate Projections
Many impactful weather and climate events include two or more variables (like temperature or precipitation) having high or low values (e.g., hot dry summers). Understanding biases in the relationship between modelled variables is important for characterizing uncertainties in the risks associated wit...
| Published in: | International Journal of Climatology |
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| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/1983/ff7030cb-acdd-45a0-b85d-eec7571a32ec https://research-information.bris.ac.uk/en/publications/ff7030cb-acdd-45a0-b85d-eec7571a32ec https://doi.org/10.1002/joc.8006 |
| Summary: | Many impactful weather and climate events include two or more variables (like temperature or precipitation) having high or low values (e.g., hot dry summers). Understanding biases in the relationship between modelled variables is important for characterizing uncertainties in the risks associated with compound events. We present a framework for evaluating the relationships between different variables (multi-variate bias). We illustrate our approach with UK temperature and precipitation, using HadUK-Grid observations and two model ensembles (12 and 2.2 km horizontal resolution) of the HadGEM3 regional model used in UK Climate Projections both forced with the same driving conditions. There are distinct regional patterns in the biases of both the Pearson correlation coefficients and coefficients of linear regression between temperature and precipitation in both resolutions, for example, large areas of positive biases in the Pearson correlation coefficients across the United Kingdom in winter, and negative biases across most of England in summer. We combine the Pearson correlation coefficients and bias in the coefficient of linear regression into a combined metric and consider regions where either the bias in the coefficient of linear regression or the bias in Pearson correlation coefficient is significantly dominant over the other. By considering only days with similar North Atlantic driving conditions using Met Office Weather Patterns we can identify regions with significant differences between the two model resolutions that are attributable to the difference in model resolution and structural design. The root mean square error (RMSE) of correlation bias across the United Kingdom is reduced in the 2.2 km compared to the 12 km model data in each season except summer where it is broadly similar. For Weather Pattern 2 (North Atlantic Oscillation positive) days the RMSE for correlation coefficient and the coefficient of linear regression is twice as large than for all conditions. |
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