Inter-model spread of atmospheric shortwave absorption in CMIP

Chapter 4. Dependency of tropical mean climate pattern on the water vapor shortwave

4.3. Results

4.3.1. Inter-model spread of atmospheric shortwave absorption in CMIP

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Table 4.1. Description of CMIP models used in this study. Models are sorted in the ascending order of global-mean SWA [W m^-2].

CMIP

Model name Institution

SWA AMIP

version value availability

CMIP5 IPSL-CM5A-LR IPSL, France 67.93 Yes

CMIP6 CNRM-CM6-1-HR CNRM and CERFACS, France 68.07 Yes

CMIP5 IPSL-CM5A-MR IPSL, France 68.22 Yes

CMIP6 FGOALS-g3 CAS, china 68.71 Yes

CMIP5 GISS-E2-R NASA/GISS, USA 68.79 Yes

CMIP5 GISS-E2-R-CC NASA/GISS, USA 68.80 No

CMIP5 GISS-E2-H-CC NASA/GISS, USA 68.81 No

CMIP5 GISS-E2-H NASA/GISS, USA 69.00 No

CMIP6 CNRM-ESM2-1 CNRM and CERFACS, France 69.21 Yes

CMIP6 CNRM-CM6-1 CNRM and CERFACS, France 69.23 Yes

CMIP6 IITM-ESM CCCR, India 69.45 No

CMIP5 IPSL-CM5B-LR IPSL, France 69.56 Yes

CMIP6 MIROC6 AORI, NIES, and JAMSTEC, Japan 71.11 Yes

CMIP6 MIROC-ES2L AORI, NIES, and JAMSTEC, Japan 71.28 Yes

CMIP5 FIO-ESM FIO, China 71.28 No

CMIP6 INM-CM5-0 INM, Russia 71.37 Yes

CMIP5 HadGEM2-CC MOHC, UK 71.47 No

CMIP5 ACCESS1-0 CSIRO and BOM, Australia 71.85 Yes

CMIP6 INM-CM4-8 INM, Russia 71.91 Yes

CMIP5 MRI-CGCM3 MRI, Japan 72.00 Yes

CMIP5 HadGEM2-ES MOHC, UK 72.02 No

CMIP5 ACCESS1-3 CSIRO and BOM, Australia 72.05 Yes

CMIP5 GFDL-CM3 GFDL, USA 72.33 Yes

CMIP6 HadGEM3-GC31-LL MOHC, UK 72.37 Yes

CMIP6 ACCESS-CM2 CSIRO and BOM, Australia 72.42 Yes

CMIP6 BCC-ESM1 BCC, china 72.50 Yes

CMIP6 HadGEM3-GC31-MM MOHC, UK 72.64 Yes

CMIP6 BCC-CSM2-MR BCC, china 72.76 Yes

CMIP5 GFDL-ESM2G GFDL, USA 72.83 No

CMIP6 IPSL-CM6A-LR IPSL, France 72.97 Yes

CMIP5 GFDL-ESM2M GFDL, USA 73.02 No

CMIP6 ACCESS-ESM1-5 CSIRO and BOM, Australia 73.20 Yes

CMIP6 GFDL-CM4 GFDL, USA 73.26 Yes

CMIP5 bcc-csm1-1 BCC, CMA, China 73.37 Yes

CMIP5 MIROC-ESM AORI, NIES, and JAMSTEC, Japan 73.54 Yes

CMIP5 bcc-csm1-1-m BCC, CMA, China 73.55 Yes

CMIP5 MIROC-ESM-CHEM AORI, NIES, and JAMSTEC, Japan 73.57 No

CMIP6 MPI-ESM-1-2-HAM MPI-M, Germany 73.86 No

CMIP6 NorCPM1 NCC, Norway 74.04 Yes

CMIP6 AWI-ESM-1-1-LR AWI, Germany 74.04 No

CMIP6 AWI-CM-1-1-MR AWI, Germany 74.09 No

CMIP6 GFDL-ESM4 GFDL, USA 74.10 No

CMIP6 MPI-ESM1-2-HR MPI-M, Germany 74.12 Yes

CMIP5 MIROC5 AORI, NIES, and JAMSTEC, Japan 74.13 Yes

CMIP5 MPI-ESM-LR MPI-M, Germany 74.15 Yes

CMIP5 MPI-ESM-P MPI-M, Germany 74.20 No

CMIP5 NorESM1-ME NCC, Norway 74.20 No

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CMIP5 MPI-ESM-MR MPI-M, Germany 74.27 Yes

CMIP5 NorESM1-M NCC, Norway 74.31 Yes

CMIP6 MPI-ESM1-2-LR MPI-M, Germany 74.35 No

CMIP6 GISS-E2-1-G NASA/GISS, USA 74.38 Yes

CMIP6 GISS-E2-1-H NASA/GISS, USA 74.59 No

CMIP6 SAM0-UNICON SNU, Republic of korea 74.78 Yes

CMIP5 BNU-ESM BNU, China 74.93 No

CMIP6 GISS-E2-1-G-CC NASA/GISS, USA 74.98 No

CMIP6 NorESM2-MM NCC, Norway 75.00 No

CMIP6 NESM3 NUIST, china 75.00 Yes

CMIP5 CCSM4 NCAR, USA 75.21 Yes

CMIP5 CESM1-BGC NCAR, USA 75.25 No

CMIP5 CESM1-FASTCHEM NCAR, USA 75.52 No

CMIP6 NorESM2-LM NCC, Norway 75.63 Yes

CMIP6 CanESM5 CCCma, Canada 75.73 Yes

CMIP5 CESM1-WACCM NCAR, USA 75.76 No

CMIP6 E3SM-1-1-ECA LLNL, USA 75.77 No

CMIP6 MRI-ESM2-0 MRI, Japan 75.82 Yes

CMIP6 CESM2-WACCM NCAR, USA 75.85 Yes

CMIP6 CESM2-FV2 NCAR, USA 75.88 Yes

CMIP6 E3SM-1-0 LLNL, USA 76.02 No

CMIP6 FGOALS-f3-L CAS, china 76.02 No

CMIP6 CESM2 NCAR, USA 76.04 Yes

CMIP6 E3SM-1-1 LLNL, USA 76.08 No

CMIP6 NorESM1-F NCC, Norway 76.12 No

CMIP5 CanESM2 CCCma, Canada 76.31 Yes

CMIP5 CESM1-CAM5 NCAR, USA 76.73 Yes

CMIP6 TaiESM1 RCEC, Taiwan 76.81 Yes

CMIP6 CESM2-WACCM-FV2 NCAR, USA 76.87 Yes

CMIP5 inmcm4 INM, Russia 78.50 Yes

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Figure 4.2. The attribution of the inter-model spread of global-mean SWA to the inter-model spread in the following radiative properties: SWA_𝑆^aprp, SWA^aprp_𝛼 , SWA^aprp_clt , SWA_𝜇^aprp_cld, SWA^aprp_𝛾cld , SWA^aprp_𝜇clr and SWA^aprp_𝛾clr , which respectively denote the SWA associated with the solar insolation (𝑆), surface albedo (𝛼), total-column cloud area fraction (clt), cloud absorptivity (𝜇cld), cloud reflectivity (𝛾cld), clear-sky absorptivity (𝜇clr) and clear-sky reflectivity (𝛾_clr). The CMIP models are colored according to the global-mean SWA (circles for CMIP5 and squares for CMIP6) and the CESM FOM (asterisk) and FSST (plus) results are juxtaposed on the right. The multi-model mean of CMIP5/6 is treated as the reference climatology when applying the APRP method. The multi-model mean of SWA is added to the analysis. The correlation coefficients of each component to the model SWA are inserted separately for CMIP5 and CMIP6 at the bottom of the figure, with the statistically significant values at the 95 % confidence level in bold.

The geographical distribution of the inter-model spread in SWA_𝜇^aprp_clr provides insight into the origin of the SWA inter-model spread (Figure 4.3a, b). The clear-sky SWA occurs through atmospheric gases including water vapor, which have a relatively zonally symmetric distribution over the globe, and aerosols, with localized peaks over the deserts in northern Africa and central Asia where the aerosol optical depth is high due to natural aerosol emissions (Fiedler et al., 2019). To explore the effect of the spatial pattern of SWA on the global-mean SWA, we calculate the inter-model correlation coefficients between SWA_𝜇^aprp_clr at each grid point and its global-mean (Figure 4.3b).

Equatorward of 60^o, where most of the global-mean SWA occurs (e.g., 92.7 % based on the multi- model mean of CMIP5/6 pre-industrial climate), ocean regions with lower aerosol optical depth exhibit high correlation coefficients, whereas the desert regions with high natural aerosol emissions show relatively low correlations. Since the gaseous absorption dominates over the ocean but coincides with the aerosol absorption over the deserts, high correlations over the ocean indicate that the uncertainty of gaseous absorption contributes most to the inter-model spread in global-mean SWA.

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Figure 4.3. (a) The inter-model standard deviation of the SWA associated with the clear-sky absorptivity (SWA_𝜇^aprp_clr). (b) The correlation coefficients between SWA^aprp_𝜇clr at each model grid point and the global-mean SWA_𝜇^aprp_clr. Both the CMIP5 and CMIP6 models are used in the analysis, but considering CMIP5/6 separately yields similar results.

The inter-model spread of gaseous SWA should largely originate from the differences in water vapor absorption, which constitutes almost 72 % of the total gaseous SWA (Kiehl and Trenberth 1997). The differences can be further ascribed to 1) the water vapor distribution – the amount and the spatial pattern – and 2) radiative parameterization of the water vapor SWA – how much SW is absorbed by given amount of water vapor. First, we estimate the effect of the water vapor distribution by comparing the global-mean SWA_𝜇^aprp_clr with the global-mean SWA^kernel_𝑞 using the same specific humidity radiative kernel for all models (Figure 4.4a). Adopting the same radiative kernel excludes the contribution from differences in parameterization among models. The inter-model variance in SWA^kernel_𝑞 is only 6.6 % of that in SWA_𝜇^aprp_clr , indicating that the uncertainty in parameterizations is mostly responsible for the inter-model spread of global-mean SWA.

To evaluate the uncertainty in parameterization of SWA by water vapor among models, the clear-sky absorptivity – clear-sky SWA divided by solar insolation – at each grid point over the tropical ocean (30^oS-30^oN) is averaged after sorting into corresponding bins of column integrated water vapor amount, providing the absorption curve for each model as in DeAngelis et al. (2015) (Figure 4.4b).

Differences in absorption curves mostly originate from a large range of y-intercept, rather than slope.

This indicates that a different degree of absorptivity for the given amount of water vapor across models contributes most to the inter-model spread of global-mean SWA climatology. The absorptivity for a given amount of water vapor is higher in CMIP6 than CMIP5 on average, which explains the partial increase in global-mean SWA from CMIP5 to CMIP6 (Figure 4.1). Note that differences in the slope of absorption curves – different degree of absorptivity enhancement per unit water vapor increase – are important for explaining the inter-model uncertainty in SWA enhancement with global

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warming (DeAngelis et al. 2015). This implies that different aspects of water vapor SWA parameterization matter for the uncertainty in future projections compared to the mean climate state.

This leads us to ask, what is the impact of the SWA inter-model spread on mean climate state? We address this question by altering the water vapor SW absorptivity in CESM, as described in section 2.1. The experiments capture the inter-model spread in the intercepts of the absorption curves while retaining a similar slope, roughly consistent with the behavior of the CMIP models (Figure 4.4b). We next examine the effects of varying water vapor SW absorptivity on a wide range of climate patterns, including the SST, precipitation, and large-scale atmospheric circulation.

Figure 4.4. (a) The scatter plot between the global-mean SWA estimated from the specific humidity radiative kernel (SWAkernel𝑞 ) and that associated with the clear-sky absorptivity (SWA^aprp_𝜇clr). Green (blue) circles denote the CMIP5 (CMIP6) pre-industrial simulations. Note that the multi-model mean of CMIP5/6 is treated as the reference climatology in the radiative kernel calculation. The multi-model mean of global-mean SWA is added in the analysis. (b) The clear-sky atmospheric SW absorptivity with respect to the column-integrated water vapor. Green (blue) solid lines indicate the CMIP5 (CMIP6) models, and the achromatic colored lines indicate the CESM experiments with FOM in solid and FSST in dashed.

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