An Overview of CMIP5 and CMIP6 Simulated Cloud Ice, Radiation Fields, Surface Wind Stress, Sea Surface Temperatures, and Precipitation Over Tropical and Subtropical Oceans

Abstract

The potential links between ice water path (IWP), radiation, circulation, sea surface temperature (SST), and precipitation over the Pacific and Atlantic Oceans resulting from the falling ice radiative effects (FIREs) are examined from Coupled Model Intercomparison Project phase 5 (CMIP5) and phase 6 (CMIP6) historical simulations. The latter is divided into two subsets with (SON6) and without FIREs (NOS6) in CMIP6. Improvement in nonfalling cloud ice (~20 g m−2) is noticeable over convective regions in CMIP6 relative to CMIP5. The inclusion of FIREs in SON6 subset may contribute to reduce biases of overestimated outgoing longwave radiation and downward surface shortwave and underestimated reflected shortwave at the top of the atmosphere (TOA) by magnitudes of ~8 W m−2 over convective regions against CERES, compared to NOS6 subset. The reduced biases in radiative fluxes in convective regions stabilize the atmosphere and lead to circulation, SST, cloud, and precipitation changes over the trade wind regions, as seen from improved radiative fluxes (~15 W m−2), surface wind stress biases, SST (~0.8 K), and precipitation (1 mm day−1) biases. The significant improvement from NOS6 to SON6 leads to improved multimodel means for CMIP6 relative to CMIP5 for radiation fields over the trade wind regions but the degradation over convective zones is attributed to NOS6 subset. The results suggest that other sources of uncertainty and deficiencies in climate models may play significant roles for reducing discrepancies although FIREs, via radiation‐circulation coupling, may be one of the factors that help to reduce regional biases.

Discrepancies of Upper Troposphere Summer Thermal Contrast Between Tibetan Plateau and Tropical Indian Ocean in Multiple Data

“…The TP has a stronger warming trend in the upper troposphere than the TIO area for the radiosonde and reanalysis data (except for NCEP/DOE), while the model results generally display a significant warming, which is consistent with Figure 4C. The clear warm bias in CMIP6 in the troposphere has received a lot of attention (Li et al, 2020;McKitrick and Christy, 2020;Mitchell et al, 2020). Figure 7 also shows a warm bias in the upper troposphere.…”

Changes of South‐Central Pacific Large‐Scale Environment Associated With Hydrometeors‐Radiation‐Circulation Interactions in a Coupled GCM

“…The improvements have been made in its newer version CESM2‐CAM6 participating in CMIP6, which includes prognostic FIREs and other important upgrades in cloud‐related physical parameterizations compared to CESM1‐CAM5. For example, CESM2‐CAM6 has improved tropical hydrometeor‐radiation interactions (Li, Xu, Jiang, et al., 2020) and precipitation simulation (Li, Xu, Richardson, et al., 2020; Woelfle et al., 2019).…”

“…The too‐high SSTs biases are reduced in SON (Figures 1e and 1f) in the trade‐wind regions (Li et al., 2013; Li, Lee, et al., 2014). These biases are also found in most of CMIP5 and some CMIP6 models that do not consider the radiative impacts of precipitating ice, producing biases in surface wind stress over the tropical Pacific Ocean against observations (Li, Xu, Jiang, et al., 2020).…”

“…A number of studies has revealed the potential importance of falling ice radiative effects (FIREs) over tropical and subtropical oceans (Gettelman & Morrison, 2015; Li et al., 2012; Li, Lee, et al., 2014; Li, Xu, Jiang, et al., 2020; Li, Xu, Richardson, et al., 2020; Michibata et al., 2019; Waliser et al., 2009, 2011). Li et al.…”

Snow-induced buffering in aerosol–cloud interactions

“…This over-weights autoconversion relative to accretion to produce precipitation (Posselt and Lohmann, 2008), which results in the pronounced sensitivity of cloud water to aerosols because autoconversion is the only process that directly depends on aerosols (Gettelman et al, 2013). Snow also has significant effects on collection processes among other hydrometeors (Sant et al, 2015), as well as on atmospheric circulation (Li et al, 2014). However, snow-induced impacts on ERF aci are much less understood (Waliser et al, 2011) be-cause extremely limited GCMs incorporate prognostic precipitation with the radiative effects of falling hydrometeors (see discussion in Michibata et al, 2019).…”