Understanding the Asian summer monsoon response to greenhouse warming: the relative roles of direct radiative forcing and sea surface temperature change

Abstract

Future hydroclimate projections from state-of-the-art climate models show large uncertainty and model spread, particularly in the tropics and over the monsoon regions. The precipitation and circulation responses to rising greenhouse gases involve a fast component associated with direct radiative forcing and a slow component associated with sea surface temperature (SST) warming; the relative importance of the two may contribute to model discrepancies. In this study, regional hydroclimate responses to greenhouse warming are assessed using output from coupled general circulation models in the Coupled Model Intercomparison Project-Phase 5 (CMIP5) and idealized atmospheric general circulation model experiments from the Atmosphere Model Intercomparison Project. The thermodynamic and dynamic mechanisms causing the rainfall changes are examined using moisture budget analysis. Results show that direct radiative forcing and SST change exert significantly different responses both over land and ocean. For most part of the Asian monsoon region, the summertime rainfall changes are dominated by the direct CO₂ radiative effect through enhanced monsoon circulation. The response to SST warming shows a larger model spread compared to direct radiative forcing, possibly due to the cancellation between the thermodynamical and dynamical components. While the thermodynamical response of the Asian monsoon is robust across the models, there is a lack of consensus for the dynamical response among the models and weak multi-model mean responses in the CMIP5 ensemble, which may be related to the multiple physical processes evolving on different time scales.

Appendix

1.1 Evaluation of Asian summer monsoon rainfall climatology in coupled and atmosphere-only models

In this section, we provide a brief evaluation of the coupled and atmosphere-only models available for this study. Figure 12 shows the Taylor diagram (Taylor 2001) of JJA rainfall climatology over the Asian monsoon region ($5 ^\circ N$–$55 ^\circ N$, $60 ^\circ E$–$150 ^\circ E$, land-only) in 35 CMIP5 coupled models (black) and the 11 AMIP models (red). We used monthly data from two gridded observational datasets for precipitation: the Climate Research Unit (CRU) at the University of East Anglia (UEA) version 3.2 (Harris et al. 2014), and the Global Precipitation Climatology Centre (GPCC) Full Data Product version 6 from the World Climate Research Programme (WCRP) Global Climate Observing System (GCOS) (Schneider et al. 2011). The spatial resolution is $0.5^\circ \times 0.5^\circ$ for both datasets. We interpolated all observed and modeled data into a $1^\circ \times 1^\circ$ spatial resolution for direct comparison. The time period for the climatology is 1976–2005 for CMIP5 models and the 30 years available for the AMIP models. CRU is chosen as the reference.

Fig. 12

Taylor diagram showing (blue dot-dashed lines) the spatial pattern correlation coefficient, (black dotted contours) standard deviation, (green dashed contours) the root-mean-square difference (RMSD) for JJA area averaged ($5 ^\circ N$–$55 ^\circ N$, $60 ^\circ E$–$150 ^\circ E$) land precipitation climatology in (black dots) 35 CMIP5 models (1976–2005) and (red dots) 11 AMIP models. Orange dots show observations (CRU, GPCC). CRU is used as the reference field. Rainfall is in ${\rm{mm}}\,{\rm{day}}^{-1}$

The spatial correlations range from 0.6 to 0.8 for most of the coupled models, with comparable standard deviations as observations. The coupled (black) and atmosphere-only (red) models do not exhibit significant differences, suggesting that ocean coupling does not have much effect in simulating monsoon climatology. Only one AGCM (FGOALS-g2) produces an unrealistically large standard deviation due to an overestimation of rainfall over India and Indochina (not shown), thus we eliminated this model from further analysis.