Publication: Do CMIP models capture long-term observed annual precipitation trends?
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2021-11-06
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Springer
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Abstract
This study provides a long-term (1891-2014) global assessment of precipitation trends using data from two station-based gridded datasets and climate model outputs evolved through the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively). Our analysis employs a variety of modeling groups that incorporate low- and high-top level members, with the aim of assessing the possible effects of including a well-resolved stratosphere on the model's ability to reproduce long-term observed annual precipitation trends. Results demonstrate that only a few regions show statistically significant differences in precipitation trends between observations and models. Nevertheless, this pattern is mostly caused by the strong interannual variability of precipitation in most of the world regions. Thus, statistically significant model-observation differences on trends (1891-2014) are found at the zonal mean scale. The different model groups clearly fail to reproduce the spatial patterns of annual precipitation trends and the regions where stronger increases or decreases are recorded. This study also stresses that there are no significant differences between low- and high-top models in capturing observed precipitation trends, indicating that having a well-resolved stratosphere has a low impact on the accuracy of precipitation projections.
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This work was supported by the research projects CGL2017-82216-R, PID2019-108589RA-I00 and PCI2019-103631 financed by the Spanish Commission of Science and Technology and FEDER and CROSSDRO project financed by the AXIS (Assessment of Cross(X)-sectoral climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission. This study is also supported by "Unidad Asociada CSIC-Universidad de Vigo: Grupo de Física de la Atmósfera y del Oceano". CM acknowledges funding from the Irish Environmental Protection Agency (2019-CCRP-MS.60). LG and RN received partial support from the Xunta de Galicia under the Project ED431C 2017/64-GRC Programa de Consolidación e Estructuración de Unidades de Investigación Competitivas (Grupos de Referencia Competitiva) and Consellería de Educación e Ordenación Universitaria. Previous funders received cofounding from the ERDF, in the agenda of the Operational Program Galicia 2014-2020."