2026-06-27T15:19:47Zhttps://docta.ucm.es/rest/oai/request
oai:docta.ucm.es:20.500.14352/49062025-02-14T14:52:07Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense author Vicente Serrano, S.M. author García Herrera, Ricardo Francisco author Peña Angulo, D. author Tomas‑Burguera, M. author Domínguez Castro, F. author Noguera, I. author Calvo Fernández, Natalia author Murphy, C. author Nieto, R. author Gimeno, L. author Gutiérrez, J.M. author Azorín Molina, César author El Kenawy, A. 2023-06-16T14:23:24Z 2023-06-16T14:23:24Z 2021-11-06 0930-7575 10.1007/s00382-021-06034-x https://hdl.handle.net/20.500.14352/4906 http://dx.doi.org/10.1007/s00382-021-06034-x https://link.springer.com/ 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. eng Atribución 3.0 España Do CMIP models capture long-term observed annual precipitation trends? journal article URL https://docta.ucm.es/bitstreams/fd9968a5-3b2b-4917-bcfc-38cb1971bf61/download File MD5 af9ec4f94549f38ae8b57ad288473c6c 7641610 application/pdf calvofernandez45libre+CC.pdf URL https://docta.ucm.es/bitstreams/ac8ed103-baa7-4e83-804e-bfc914efc3d6/download File MD5 7e7699b9adf0836678e2b5b727c39fb0 72714 text/plain calvofernandez45libre+CC.pdf.txt