Abstract
Abstract The representation of heatwaves (HWs) in the Coupled Model Intercomparison Project phase 6 (CMIP6) models is analyzed. This study (a) evaluates the performance of CMIP6 simulations against global reanalysis and observations regarding time- and intensity-related criteria and (b) investigates how HWs are projected to change at different global warming levels (GWLs). During 1979?2014, the dispersion of the models is comparable to the observational uncertainty for the time indices (duration, frequency, number of events). It is of the order of one event per year, 1 day for the duration of the events and 2 days for the frequency, with tendencies for over- or underestimation, depending on the reference data set and the region considered. For the HW magnitude, the models' dispersion can reach 15°C for a given region and is significantly higher than the observational uncertainty. The mean intensity of HWs tends to be overestimated, which is partly attributed to overly pronounced drying of the soil during HW events. The contribution of the soil moisture anomaly to the temperature anomaly during recent specific HWs is shown to reach up to 30% of the signal. For a given GWL, intensification of HW occurrence, spatial extension, and duration is detected worldwide, but it is modulated at the regional scale and strongly model dependent. For time-related indices, tropical regions and the Arabian Peninsula will be most impacted, but the maximum temperature will strongly increase in mid-latitude regions. Time?space analyses of the evolution of HW properties for a given GWL are discussed.
| Original language | English |
|---|---|
| Article number | e2022EF003301 |
| Journal | Earth's Future |
| Volume | 11 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 15 Sep 2023 |
Bibliographical note
Funding Information:A.A. and Y.Z. acknowledge funding and support from the CMUG and ESA-CCI programs (https://climate.esa.int/en/esa-climate/esa-cci/). This study benefited from the ESPRI (Ensemble de Service Pour la Recherche l'IPSL) computing and data center (https://mesocentre.ipsl.fr) [Software], which is supported by CNRS, Sorbonne University, Ecole Polytechnique, CNES, and through national and international grants. W.T. acknowledges partial funding from the Belgian Science Policy Office (BELSPO) through the project “LAnd MAnagement for CLImate Mitigation and Adaptation” (LAMACLIMA) (Grant agreement no. 300478), which is part of ERA4CS, an ERA-NET initiated by JPI Climate. We acknowledge accessing the CMIP6 data (https://esgf-node.llnl.gov/projects/cmip6/) [Dataset], through ESGF and the National Computing Infrastructure ESGF in Australia (https://esgf.nci.org.au/projects/esgf-nci/) [Dataset], the BEST dataset at http://berkeleyearth.org/data/ [Dataset], the ERA5 and ERA-Interim reanalysis data at https://cds.climate.copernicus.eu/cdsapp#!/home [Dataset], the HadGHCND data at https://www.metoffice.gov.uk/hadobs/hadghcnd/ [Dataset], the GLEAM data at https://www.gleam.eu/ [Dataset], the SMOS-IC data at https://ib.remote-sensing.inrae.fr [Dataset], the ESA-CCI data at https://climate.esa.int/en/projects/soil-moisture/data/ [Dataset], and finally the GSWP3 data from http://hydro.iis.u-tokyo.ac.jp/GSWP3/ [Dataset].
Funding Information:
A.A. and Y.Z. acknowledge funding and support from the CMUG and ESA‐CCI programs ( https://climate.esa.int/en/esa-climate/esa-cci/ ). This study benefited from the ESPRI (Ensemble de Service Pour la Recherche l'IPSL) computing and data center ( https://mesocentre.ipsl.fr ) [Software], which is supported by CNRS, Sorbonne University, Ecole Polytechnique, CNES, and through national and international grants. W.T. acknowledges partial funding from the Belgian Science Policy Office (BELSPO) through the project “LAnd MAnagement for CLImate Mitigation and Adaptation” (LAMACLIMA) (Grant agreement no. 300478), which is part of ERA4CS, an ERA‐NET initiated by JPI Climate. We acknowledge accessing the CMIP6 data ( https://esgf-node.llnl.gov/projects/cmip6/ ) [Dataset], through ESGF and the National Computing Infrastructure ESGF in Australia ( https://esgf.nci.org.au/projects/esgf-nci/ ) [Dataset], the BEST dataset at http://berkeleyearth.org/data/ [Dataset], the ERA5 and ERA‐Interim reanalysis data at https://cds.climate.copernicus.eu/cdsapp#!/home [Dataset], the HadGHCND data at https://www.metoffice.gov.uk/hadobs/hadghcnd/ [Dataset], the GLEAM data at https://www.gleam.eu/ [Dataset], the SMOS‐IC data at https://ib.remote-sensing.inrae.fr [Dataset], the ESA‐CCI data at https://climate.esa.int/en/projects/soil-moisture/data/ [Dataset], and finally the GSWP3 data from http://hydro.iis.u-tokyo.ac.jp/GSWP3/ [Dataset].
Publisher Copyright:
© 2023 The Authors. Earth's Future published by Wiley Periodicals LLC on behalf of American Geophysical Union.