The Taiwan Earth System Model (TaiESM) version 1 is developed based on Community Earth System Model version 1.2.2 of National Center for Atmospheric Research. Several innovative physical and chemical parameterizations, including trigger functions for deep convection, cloud macrophysics, aerosol, and three-dimensional radiation–topography interaction, as well as a one-dimensional mixed-layer model optional for the atmosphere component, are incorporated. The precipitation variability, such as diurnal cycle and propagation of convection systems, is improved in TaiESM. TaiESM demonstrates good model stability in the 500-year preindustrial simulation in terms of the net flux at the top of the model, surface temperatures, and sea ice concentration. In the historical simulation, although the warming before 1935 is weak, TaiESM captures the increasing trend of temperature after 1950 well. The current climatology of TaiESM during 1979–2005 is evaluated by observational and reanalysis datasets. Cloud amounts are too large in TaiESM, but their cloud forcing is only slightly weaker than observational data. The mean bias of the sea surface temperature is almost 0, whereas the surface air temperatures over land and sea ice regions exhibit cold biases. The overall performance of TaiESM is above average among models in Coupled Model Intercomparison Project phase 5, particularly in that the bias of precipitation is smallest. However, several common discrepancies shared by most models still exist, such as the double Intertropical Convergence Zone bias in precipitation and warm bias over the Southern Ocean.

This study evaluates the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the observed climate variability in the historical simulation of the Coupled Model Intercomparison Phase 6 (CMIP6). TaiESM1 is developed on the basis of the Community Earth System Model version 1.2.2, with the inclusion of several new physical schemes and improvements in the atmosphere model. The new additions include an improved triggering function in the cumulus convection scheme, a revised distribution‐based formula in the cloud fraction scheme, a new aerosol scheme, and a unique scheme for three‐dimensional surface absorption of shortwave radiation that accounts for the influence of complex terrains. In contrast to the majority of model evaluation processes, which focus mainly on the climatological mean, this evaluation focuses on climate variability parameters, including the diurnal rainfall cycle, precipitation extremes, synoptic eddy activity, intraseasonal fluctuation, monsoon evolution, and interannual and multidecadal atmospheric and oceanic teleconnection patterns. A series of intercomparisons between the simulations of TaiESM1 and CMIP6 models and observations indicate that TaiESM1, a participating model in CMIP6, can realistically simulate the observed climate variability at various time scales and are among the leading CMIP6 models in terms of many key climate features.

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.

Topography exerts significant influences on the incoming solar radiation at the land surface. A few stand-alone regional and global atmospheric models have included parameterizations for sub-grid topographic effects on solar radiation. However, nearly all Earth system models (ESMs) that participated in the Coupled Model Intercomparison Project (CMIP6) use a plane-parallel (PP) radiative transfer scheme that assumes that the terrain is flat. In this study, we incorporated a well-validated sub-grid topographic (TOP) parameterization in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) version 1.0 to quantify the effects of sub-grid topography on solar radiation flux, including the shadow effects and multi-scattering between adjacent terrain. We studied the role of sub-grid topography by performing ELM simulations with the PP and TOP schemes over the Tibetan Plateau (TP). Additional ELM simulations were performed at multiple spatial resolutions to investigate the role of spatial scale on sub-grid topographic effects on solar radiation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to compare with the ELM simulations. The results show that topography has non-negligible effects on surface energy budget, snow cover, snow depth, and surface temperature over the TP. The absolute differences in surface energy fluxes for net solar radiation, latent heat flux, and sensible heat flux between TOP and PP exceed 20, 10, and 5 W m⁻², respectively. The differences in land surface albedo, snow cover fraction, snow depth, and surface temperature between TOP and PP exceed 0.1, 0.1, 10 cm, and 1 K, respectively. The magnitude of the sub-grid topographic effects is dependent on seasons and elevations and is also sensitive to the spatial scales. Although the sub-grid topographic effects on solar radiation are larger with more spatial details at finer spatial scales, they cannot be simply neglected at coarse spatial scales. When compared to MODIS data, incorporating the sub-grid topographic effects overall reduces the biases of ELM in simulating surface energy balance, snow cover, and surface temperature, especially in the high-elevation and snow-covered regions over the TP. The inclusion of sub-grid topographic effects on solar radiation parameterization in ELM will contribute to advancing our understanding of the role of the surface topography on terrestrial processes over complex terrain.

Sub‐grid topographic heterogeneity has large impacts on surface energy balance and land‐atmosphere interactions. However, the impacts of representing sub‐grid topographic effects in land surface models (LSMs) on surface energy balance and boundary conditions remain unclear. This study analyzed and evaluated the impacts of sub‐grid topographic representations on surface energy balance, turbulent heat flux, and scalar (co‐)variances in the Energy Exascale Earth System Model (E3SM) land model (ELM). Three sub‐grid topographic representations in ELM were compared: (a) the default sub‐grid structure (D), (b) the recently developed sub‐grid topographic structure (T), and (c) high spatial resolution (1KM). Additionally, two different solar radiation schemes in ELM were compared: (a) the default plane‐parallel radiative transfer scheme (PP) and (b) the parameterization scheme (TOP) that accounts for sub‐grid topographic effects on solar radiation. A series of offline simulations with the three grid discretization structures (D, T, and 1KM) and two schemes of solar radiation (TOP and PP) were carried out using the Sierra Nevada, California. 1KM simulations with TOP well capture the spatial heterogeneity of surface fluxes compared to Moderate Resolution Imaging Spectroradiometer remote sensing data. There are significant differences between TOP and PP in the 1‐km simulated surface energy balance, but the differences in mean values and standard deviations become small when aggregated to the grid scale (i.e., 0.5°). The T configuration better mimics the 1KM simulations with TOP than the D configuration and better captures the sub‐grid topographic effects on surface energy balance and boundary conditions. These results underline the importance of representing sub‐grid topographic heterogeneities in LSMs and motivate future research to understand the sub‐grid topographic effects on land‐atmosphere interactions over mountainous areas.