Speaker
Description
Water vapor and clouds are among the fundamental atmospheric components, and as such, are part of the Essential Climate Variable (ECV) monitored by the Global Climate Observing System (GCOS). In this work, the global water vapor Climate Data Record (CDR) generated within the ESA Water Vapor climate change initiative project (WV_cci) is used as reference (daily, 0.1°, 2003-2014) to evaluate a sample of the Coupled Model Intercomparison Project phase 6 (CMIP6) as well as the fifth generation ECMWF reanalysis (ERA5), with a focus on temporal signal decomposition. This temporal decomposition is performed using multi-resolution analysis (MRA). MRA is a mathematical tool which consists of decomposing a signal into its subcomponents on different time scales. Using this tool, the representation of the total column water vapor (tcwv) and total cloud coverage (tcc) over the tropics in the CMIP6 models and ERA5 can be assessed separately from daily to annual and decadal time scales, including monthly and seasonal time scales. Hence, at the global-tropical scale, CMIP6 models produce reliable evolution of water vapour and cloud coverage at seasonal (2 - 8 months) and interannual (1 - 1.4 year) time scales. However, most of the CMIP6 models underestimate the trend observed in the evolution of water vapour over the ocean. While over the land, ESA shows an increase of ~2 kg/m²/dec which is very high and probably due to the instrumentation used. Using a linear regression, we attempt to reconstruct the WV_cci signal using the CMIP6 models as explanatory variables based on the correlation found between the models and WV_cci at each level of decomposition. Such reconstruction highlights the scales of variability that are closest to the observed one. The reconstructed signal of (water vapour / cloud coverage) shows higher correlation with respect to ESA over the ocean (0.7/0.55) than land (0.63/0.28). This can be explained by the use of AMIP simulations which are forced by the Sea Surface Temperatures (SST) leading to good correlation over ocean. The representation of cloud coverage in CMIP6 remains challenging except for the IPSL model over the ocean.
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