Drivers of the enhanced decline of land near-surface relative humidity to abrupt 4xCO2 in CNRM-CM6-1

Abstract

Projected changes in near-surface relative humidity (RH) remain highly model-dependent over land and may have been underestimated by the former generation global climate models. Here the focus in on the recent CNRM-CM6-1 model, which shows an enhanced land surface drying in response to quadrupled atmospheric CO2 compared to its CNRM-CM5 predecessor. Atmosphere-only experiments with prescribed sea surface temperature (SST) are used to decompose the simulated RH changes into separate responses to uniform SST warming, pattern of SST anomalies, changes in seaice concentration, as well as direct radiative and physiological CO2 effects. Results show that the strong drying simulated by CNRM-CM6-1 is due to both fast CO2 effects and a SST-mediated response. The enhanced drying compared to CNRM-CM5 is partly due to the introduction of a physiological CO2 effect that was not accounted for in CNRM-CM5. The global ocean warming also contributes to the RH decline over land, in reasonable agreement with the moisture advection mechanism proposed by earlier studies which however does not fully capture the contrasted RH response between the two CNRM models. The SST anomaly pattern is a significant driver of changes in RH humidity at the regional scale, which are partly explained by changes in atmospheric circulation. The improved land surface model may also contribute to a stronger soil moisture feedback in CNRM-CM6-1, which can amplify the surface aridity induced by global warming and, thereby, lead to a non-linear response of RH.

A drier than expected future, supported by near-surface relative humidity observations

“…Although the coverage of land areas is not complete even at a 5° resolution (e.g., some limited gaps in the tropics), we consider that the whole time series are representative of the global land domain. This is consistent with our previous finding ( 26 ) that applying a HadISDH mask to the ERA5 reanalysis does not alter the annual mean time series compared to the full-coverage ERA5 global land values (cf. their figure 1).…”

“…3A) suggests a key role for land surface processes. This hypothesis is consistent with a detailed analysis of the RH response to an abrupt quadrupling of atmospheric CO 2 in the standard configuration of the CMIP6 global climate model from the Centre National de Recherches Météorologiques (CNRM-CM6-1), where a direct CO 2 effect on plant transpiration was identified as a key factor for understanding a stronger continental drying than in the previous-generation CNRM-CM5 model (26). However, a key question that could not be investigated in this study based on atmosphere-only simulations was the extent to which the land surface processes can influence the projected global warming.…”

Projected changes in synoptic circulations over Europe and their implications for summer precipitation: A CMIP6 perspective

“…Still, most of the expected changes will result from WC variations. The dominant drivers for within‐class variations can arise from (a) smaller‐scale phenomena, like orographically forced rain or mesoscale storms (Beck et al, 2007), (b) modification in precipitation‐generating processes and/or model inability to accurately simulate these processes (Santos et al, 2016), (c) an increased convective inhibition due to a projected decrease in near‐surface relative humidity (Chen et al, 2020; Douville et al, 2020) or (d) from SST influence on horizontal moisture transport (Douville et al, 2020).…”

Underestimated increase and intensification of humid-heat extremes across southeast China due to humidity data inhomogeneity

“…Large interannual to interdecadal variability could be found in the domain-average RH series in homogenized observations (black curve, OBS-1, Figure 2A), experiencing a relatively wet regime before 2000 followed by a shift to a dry episode. This might be explained via a moist static energy balance perspective in the context of equal fractional changes in specific humidity over land and neighboring oceans (Byrne and O'Gorman, 2018) through remote moisture transport and local evapotranspiration processes (Byrne and O'Gorman, 2016;Douville et al, 2020). Though the regional variability is generally captured by other datasets, the RH drop around 2000, when coincidentally the observational network across China shifted from manual to automatic practices (2000)(2001)(2002)(2003)(2004)(2005), is exaggerated in non-homogenized datasets (except for CRA-40).…”