Evaluation of tropical water vapour from CMIP6 global climate models using the ESA CCI Water Vapour climate data records

The tropospheric water vapour data record generated within the ESA Climate Change Initiative Water Vapour project (ESA TCWV-COMBI) is used to evaluate the interannual variability of global climate models (CMIP6 framework under AMIP scenarios) and reanalysis (ECMWF ERA5). The study focuses on the tro...

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Veröffentlicht in:Atmospheric chemistry and physics 2022-09, Vol.22 (18), p.12591-12606
Hauptverfasser: He, Jia, Brogniez, Helene, Picon, Laurence
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Sprache:eng
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Zusammenfassung:The tropospheric water vapour data record generated within the ESA Climate Change Initiative Water Vapour project (ESA TCWV-COMBI) is used to evaluate the interannual variability of global climate models (CMIP6 framework under AMIP scenarios) and reanalysis (ECMWF ERA5). The study focuses on the tropical belt, with a separation of oceanic and continental situations. The intercomparison is performed according to the probability density function (PDF) of the total column water vapour (TCWV) defined yearly from the daily scale, as well as its evolution with respect to large-scale overturning circulation. The observational diagnostic relies on the decomposition of the tropical atmosphere into percentile of the PDF and into dynamical regimes defined from the atmospheric vertical velocity. Large variations are observed in the patterns among the data records over tropical land, while oceanic situations show more similarities in both interannual variations and percentile extremes. The signatures of El Niño and La Niña events, driven by sea surface temperatures, are obvious over the oceans. Differences also occur over land for both trends (a strong moistening is observed in the ESA TCWV-COMBI data record, which is absent in CMIP6 models and ERA5) and extreme years. The discrepancies are probably associated with the scene selection applied in the data process. Since the results are sensitive to the scene selection applied in the data process, discrepancies are observed among the datasets. Therefore, the normalization process is employed to analyse the time evolution with respect to the mean state. Other sources of differences, linked to the models and their parametrizations, are highlighted.
ISSN:1680-7324
1680-7316
1680-7324
DOI:10.5194/acp-22-12591-2022