Atmospheric Model Intercomparison Project Simulations Research Articles

The Summer North Atlantic Oscillation, Arctic sea ice, and Arctic jet Rossby wave forcing.

We use Coupled Model Intercomparison Project Phase 6 (CMIP6) coupled and Atmospheric Model Intercomparison Project (AMIP) climate models, dynamical analyses, and observations to investigate interactions between summer Arctic sea ice concentration (SIC) variations and the Summer North Atlantic Oscillation (SNAO). Observations suggest that SIC-SNAO relationships mainly come from the East Siberian to Arctic Canada (ESAC) region where a weak atmospheric jet stream exists in summer. Twelve CMIP6 models with the most realistic atmospheric climatologies over the North Atlantic and Europe agree well with reanalyses on relationships between SIC and Northern Hemisphere atmospheric circulation. CMIP6 model data indicate that ESAC SIC influences the SNAO with a lead time of several weeks. However, AMIP simulations do not reproduce the observed atmospheric circulation when observed sea ice is prescribed. Rossby wave analyses do though support observed ESAC SIC influences on the SNAO. We conclude that ESAC Arctic SIC modestly influences the SNAO, and such investigations require the use of coupled models.

Impacts of the new UM convection scheme, CoMorph-A, over the Indo-Pacific and Australian regions

A new convection scheme, ‘CoMorph-A’, has been introduced into the latest UK Met Office coupled (GC4) and atmosphere-only (GA8) models. In this study, the impact of CoMorph-A is assessed in atmosphere-only Atmospheric Model Intercomparison Project simulations, as well as in sets of initialised 28-day forecasts with both the coupled and uncoupled models. Initial results show improvements over the Indo-Pacific and northern Australian regions, as well as improvements in the rainfall bias, Madden–Julian Oscillation simulation and prediction, tropical cyclone forecasts and the diurnal cycle of rainfall over the Maritime Continent. The improvements are mostly consistent across the initialised forecasts and the climate simulations, indicating the effectiveness of the new scheme across applications. The use of this new convection scheme is promising for future model configurations, and for improving the simulation and prediction of Australian weather and climate. The UK Met Office is continuing to develop CoMorph and will soon release version B.

Modulation of the Pacific Meridional Mode on the Dipole Pattern of the CONUS Summertime Precipitation

AbstractHistorically, the precipitation trend over the past few decades in the Contiguous United States (CONUS) exhibits a “Dry‐West Wet‐East” pattern; this is manifested by recent droughts/floods in the western/eastern US. However, it remains elusive what atmospheric phenomenon has potentially driven such a remarkable, and impactful precipitation pattern. Here we found that a coupled climate mode—the Pacific Meridional Mode (PMM) exerted strong impacts on the precipitation pattern over the CONUS during the summer season. We discovered a significant association between the PMM index and precipitation across the majority of the CONUS; this was manifested as a zonal dipole pattern—negative correlations in the western U.S. along with positive correlations in the eastern and central U.S. Overall, the physical mechanisms based on observations were supported by using Atmospheric Model Intercomparison Project simulations available from the Coupled Model Intercomparison Project Phase 6.

A New GFSv15 With FV3 Dynamical Core Based Climate Model Large Ensemble and Its Application to Understanding Climate Variability, and Predictability

AbstractNOAA Climate Prediction Center (CPC) has generated a 100‐member ensemble of atmospheric model simulations from 1979 to present using the Global Forecast System version 15 (GFSv15) with FV3 dynamical core. The intent of this study is to document a development in an infrastructure capability with a focus to demonstrate the quality of these new simulations is on par with the previous GFSv2 Atmospheric Model Intercomparison Project simulations. These simulations are part of CPC's efforts to attribute observed seasonal climate variability to sea surface temperature (SST) forcings and get updated once a month by available observed SST. The performance of these simulations in replicating observed climate variability and trends, together with an assessment of climate predictability and the attribution of some climate events is documented. A particular focus of the analysis is on the US climate trend, Northern Hemisphere winter height variability, US climate response to three strong El Niño events, the analysis of signal to noise ratio, the anomaly correlation for seasonal climate anomalies, and the South Asian flooding of 2022 summer, and thereby samples wide aspects that are important for attributing climate variability. Results indicate that the new model can realistically reproduce observed climate variability, trends, and extreme events, better capturing the US climate response to extreme El Niño events and the 2022 summer South Asian record‐breaking flooding than GFSv2. The new model also shows an improvement in the wintertime simulation fidelity of US surface climate, mainly confined in the Northern and Southeastern US for precipitation and in the east for temperature.

Hotspots of Monthly Land Precipitation Variations Affected by SST Anomalies

Abstract Climate predictability at seasonal to interannual time scales is mainly associated with sea surface temperature anomalies (SSTAs). How to quantitatively assess the impact of SSTAs on climate variability and predictability is an unresolved topic. Using a novel metric [bulk connectivity (BC)], the integrated influences of global SSTAs on precipitation anomalies over land are examined in observations and compared with Atmospheric Model Intercomparison Project (AMIP) simulations in 1957–2018. The hotspots of the land precipitation variation affected by global SSTA are identified, and the seasonality is evaluated. Such hotspots indicate the regions of land precipitation predictability caused by SSTAs. The hotspots are observed in the Sahel region in September–March, in the Indochina Peninsula in April and May, and in southwestern United States in December–March, which are mostly linked to the influence of El Niño–Southern Oscillation (ENSO). The overall impact of SSTAs on land precipitation is larger in the Southern Hemisphere than in the Northern Hemisphere. The spatial variations of BC and hotspots in the observations are partially reproduced in the AMIP simulations. However, an individual run in the AMIP simulations underestimates the integrated influence of global SSTA on land precipitation anomalies, while the ensemble mean amplifies the integrated influence, and both show a challenge in capturing the seasonality of the SST influence, particularly the time of the strongest impact. The results of the BC metric can serve as a benchmark to evaluate climate models and to identify the predictability sources.

Biases of the Barotropic Atmospheric Circulation Variability in CMIP6 Models

Abstract Two ensembles of Atmospheric Model Intercomparison Project (AMIP) simulations, in the scope of the current Coupled Model Intercomparison Project (CMIP6), are compared with their fully coupled counterparts. The atmospheric models simulate less barotropic atmospheric circulation variability over the North Atlantic and more barotropic atmospheric circulation variability over the North Pacific when compared with reanalysis variability, at intraseasonal and interannual scales. The coupled climate simulations have smaller global barotropic variability than the corresponding AMIP simulations. The smaller variability of the coupled simulations results in no mean overestimation of the subtropical jet variability in the North Pacific, but further underestimation of the jet stream variability in the North Atlantic. The results suggest that the reduction of the biases, in the North Pacific barotropic atmospheric variability, of coupled climate simulations is achieved through compensating biases in the mean sea surface temperatures (SSTs). Moreover, the reduction of the positive biases in the North Pacific seems to be associated with a reduction of the excitation of the most unstable barotropic mode of the atmospheric circulation, which contributes also to a reduction of the barotropic atmospheric variability in the North Atlantic.

Evaluation of the seasonality and spatial aspects of the Southern Annular Mode in CMIP6 models

AbstractThe simulation skills of the Southern Annular Mode (SAM) pattern for the CMIP6 models are evaluated in this study. Both the Atmospheric Model Intercomparison Project (AMIP) and historical experiments are utilized to understand the effect of SST forcing and air–sea interaction. The spatial correlation coefficient, standard deviation, and root‐mean‐square error (RMSE) are used as the objective metrics. The results suggest that the multimodel mean (MME) for both AMIP and historical data could capture the basic spatial patterns of the SAM for all seasons, featuring the opposite sign between the middle and high latitudes with a zonal three‐wave structure in the middle latitudes (positive phase). However, the overall skill of the AMIP simulation is higher than that of the historical simulation, which could be attributed to realistic SST forcing being applied. The simulation skill varies for different seasons: the skill for boreal spring (March–May [MAM]) is the lowest, while it is highest in boreal winter (December–February [DJF]). Further analysis shows that the simulations of the SAM asymmetric part are the key component for the simulation of the spatial pattern of the SAM, especially sensitive in MAM. The wave–current interaction simulation at mid–high latitudes is suggested to be improved for the advancement of SAM simulations in future model development.

Tropical Cyclone Characteristics in the MERRA‐2 Reanalysis and AMIP Simulations

AbstractThis study evaluates the tropical cyclone (TC) activity in two high‐resolution data sets—MERRA‐2 Reanalysis (Modern‐Era Retrospective Analysis for Research and Applications, Version 2) and MERRA‐2 AMIP (Atmospheric Model Intercomparison Project). These data sets use the same atmospheric model, the Goddard Earth Observing System Model, Version 5 (GEOS‐5) during the same period. However, while MERRA‐2 AMIP is a free‐running atmospheric simulation forced only with sea surface temperature (SST), MERRA‐2 Reanalysis uses an advanced data assimilation system to include a large variety of data sets. Thus, we analyze (1) the sensitivity of TC activity to the model forcing, (2) how well the TCs in both data sets replicate observed TC characteristics, (3) the sensitivity of these results to tracking schemes and thresholds. Standard diagnostics such as the number of tropical cyclones and their intensity distribution are very similar in the AMIP model and the reanalysis. TCs in both data sets are weaker than observed, as is typical for the spatial resolution of these global models. Overall, the use of data assimilation in the MERRA‐2 Reanalysis does not lead to a significantly better TC climatology than in AMIP. Furthermore, comparison of the MERRA‐2 Reanalysis to two other reanalysis data sets shows that MERRA‐2 generates fewer, but more intense TCs, than those reanalysis products.

Intermodel Diversity of Simulated Long-term Changes in the Austral Winter Southern Annular Mode: Role of the Southern Ocean Dipole

The Southern Annular Mode (SAM) plays an important role in regulating Southern Hemisphere extratropical circulation. State-of-the-art models exhibit intermodel spread in simulating long-term changes in the SAM. Results from Atmospheric Model Intercomparison Project (AMIP) experiments from 28 models archived in CMIP5 show that the intermodel spread in the linear trend in the austral winter (June−July−August) SAM is significant, with an intermodel standard deviation of 0.28 (10 yr)−1, larger than the multimodel ensemble mean of 0.18 (10 yr)−1. This study explores potential factors underlying the model difference from the aspect of extratropical sea surface temperature (SST). Extratropical SST anomalies related to the SAM exhibit a dipole-like structure between middle and high latitudes, referred to as the Southern Ocean Dipole (SOD). The role of SOD-like SST anomalies in influencing the SAM is found in the AMIP simulations. Model performance in simulating the SAM trend is linked with model skill in reflecting the SOD−SAM relationship. Models with stronger linkage between the SOD and the SAM tend to simulate a stronger SAM trend. The explained variance is about 40% in the AMIP runs. These results suggest improved simulation of the SOD−SAM relationship may help reproduce long-term changes in the SAM.

Origins of the IOD-like Biases in CMIP Multimodel Ensembles: The Atmospheric Component and Ocean–Atmosphere Coupling

AbstractPrevious studies reveal that the last generation of coupled general circulation models (CGCMs) commonly suffer from the so-called Indian Ocean dipole (IOD)-like biases, lowering the models’ ability in climate prediction and projection. The present study shows that such IOD-like biases are reduced insignificantly or even worsen in CGCMs from phase 5 to phase 6 of the Coupled Model Intercomparison Project (CMIP). The origins of the IOD-like biases in CGCMs are further investigated by comparing model outputs from CMIP and the Atmospheric Model Intercomparison Project (AMIP). The CGCMs’ errors are divided into the biases from the AMIP simulation (AMIP biases) and ocean–atmosphere coupling (coupling biases). For the multimodel ensemble mean, the AMIP (coupling) biases account for about two-thirds (one-third) of the IOD-like CMIP biases. In AMIP simulations, the South Asian summer monsoon (SASM) is overly strong; therefore, it could advect overly large easterly momentum from the south Indian Ocean (IO) to the equator. The resultant equatorial easterly wind bias would initiate the convection–circulation feedback and develop large IOD-like AMIP biases. In contrast, the coupling biases weaken the SASM and hence generate warm SST error over the western IO during boreal summer. Such SST error persists to boreal autumn and triggers the Bjerknes feedback, developing the IOD-like coupling biases. Furthermore, the intermodel spread in the IOD-like CMIP biases is largely explained by the intermodel differences in the coupling biases rather than the AMIP biases. The results imply that substantial efforts should be respectively made on reducing the atmospheric models’ intrinsic monsoon biases as well as advancing the simulations of ocean–atmosphere coupling processes.

Effects of Horizontal Resolution on Hourly Precipitation in AGCM Simulations

AbstractTo analyze the effects of horizontal resolution on hourly precipitation, four Atmospheric Model Intercomparison Project simulations are carried out using the Chinese Academy of Sciences Earth System Model (CAS-ESM) and the Community Earth System Model (CESM) during 1998–2016. They include CAS-ESM at resolutions of 1.4° latitude × 1.4° longitude (CAS-ESM L) and 0.5° × 0.5° (CAS-ESM H), and CESM at resolutions of 1.9° latitude × 2.5° longitude (CESM L) and 0.47° × 0.63° (CESM H), respectively. We focus on the simulated hourly precipitation frequency and assess the frequency with respect to high-resolution satellite observations and reanalysis. The high-resolution experiments show some improvements of measurable precipitation (>0.02 mm h−1) frequency. Noticeable improvement of heavy rainfall (>2 mm h−1) frequency is demonstrated at the high resolutions. The zonal mean, seasonal mean, and area-weighted average frequency support the above results. The high-resolution experiments outperform the low-resolution experiments in reproducing hourly precipitation intensity and amount. The added value is apparent in heavy precipitation intensity from CAS-ESM H and CESM H. Over the monsoon regions and tropical convergence zones, the patterns of probability density functions for precipitation from high-resolution experiments are closer to the observations and reanalysis than those from the low-resolution simulations. The improvement of measurable precipitation frequency is mainly caused by the reductions of the convective rainfall occurrence at high resolutions. The increasing large-scale precipitation and reasonable integrated water vapor flux contribute to the improvements in measurable rainfall intensity and heavy precipitation characteristics. The results of this study support the concept that high-resolution global simulations could produce improved hourly precipitation capabilities, especially for heavy rainfall.

Atmospheric Diffusivity: A New Energetic Framework for Understanding the Midlatitude Circulation Response to Climate Change

AbstractThe midlatitude circulation response to climate change is not well understood. Within an energetic framework the circulation is connected to the atmospheric diffusivity. Here we define a novel diagnostic diffusivity framework as a tool for interpreting changes in the midlatitude atmospheric circulation. The diffusivity is computed by dividing the zonally and vertically integrated transient eddy moist static energy (MSE) transport by the zonal‐mean 925 hPa MSE gradient, and we investigate the diffusivity response to global‐scale sea surface temperature (SST) perturbations in aquaplanet and Atmospheric Model Intercomparison Project (AMIP) general circulation model simulations. The midlatitude diffusivity maximum shifts poleward with increasing SST in both the aquaplanet simulations and Southern Hemisphere winter of the AMIP simulations. Our framework indicates that this diffusivity shift is associated with Ferrel circulation and 925 hPa MSE gradient changes, rather than with total MSE transport changes. The MSE gradient‐related shift cannot be understood purely in terms of Clausius‐Clapeyron changes in specific humidity following the global‐mean temperature response, because atmospheric temperature gradients change significantly even when SST gradients are held fixed. The intensity of the diffusivity maximum also increases with SST for the aquaplanet configuration but is relatively insensitive to SST for AMIP Southern Hemisphere winter. Additional energy balance model experiments suggest that while diffusivity changes caused by SST perturbations can be large, their effect on zonal‐mean temperature is minor. Finally, the latitudes and intensities of the diffusivity maxima are strongly correlated with the same properties of the zonal‐mean eddy kinetic energy maxima. However, existing theories cannot adequately explain these linear relationships.

Evaluation of CMIP6 AMIP climate simulations with the ACCESS-AM2 model

The most recent version of the ACCESS-AM2 atmosphere-only climate model is introduced with results from the CMIP6 Atmospheric Model Intercomparison Project (AMIP) experiments configured with two land-surface models: CABLE and JULES. AMIP simulations are required as part of the CMIP6 core experiments. They are forced by prescribed time-varying observed sea surface temperature and sea-ice variations as well as variations in natural and anthropogenic external forcings. We evaluate the performance of the two configurations using three historical realisations for each. Model biases are estimated both globally and for the Australian region. The model shows close agreement with observed interannual variations of global-mean temperature across the latitude range 65°N–65°S. This is also true for the land-only temperature for 65°N–65°S, and a more stringent test of the model is driven by specified observed sea surface temperatures. Patterns of mean precipitation are simulated reasonably well, although there are biases in the amount and distribution of precipitation, typical of longstanding problems in representing this aspect of the climate. Selected features of the atmospheric circulation are discussed, including air temperatures and wind speeds. For the Australian region, in addition to examining the climatological patterns of temperature and precipitation, important drivers of climate variability are reviewed: El Niño-Southern Oscillation, the Indian Ocean Dipole and the Southern Annular Mode. In general, the correlation patterns for precipitation simulated by ACCESS-AM2 are somewhat weaker than in observations, although the ensemble means show better agreement than individual ensemble members. Overall, the two different land-surface schemes perform similarly. ACCESS-AM2 has reduced root mean square errors for both temperature and precipitation of around 15–20% at the global scale compared to the older CMIP5 versions of the model: ACCESS 1.0 and ACCESS 1.3.

How much of monthly mean precipitation variability over global land is associated with SST anomalies?

The role of sea surface temperature (SST) in determining the predictability of monthly mean precipitation over the global land is assessed by analyzing the Atmospheric Model Intercomparison Project (AMIP)-like simulations forced by observed SST, which provides a benchmark for the impact of SST on the precipitation. The correlations of monthly mean precipitation anomalies between the ensemble mean of the AMIP simulations and observations are dominated by positive values with maxima around 0.3–0.4 in the tropical North Africa along 15° N and northeastern Brazil. The SST forcing for the precipitation variability is mainly associated with the El Nino-Southern Oscillation (ENSO) and in the tropical Indian Ocean. Statistically, positive and negative SST anomalies associated with an ENSO cycle have a comparable influence on precipitation variability over the land. In addition to the spatial variations, the precipitation responses to SST also vary with season and decade. Pattern correlations are larger in boreal winter than in boreal summer in the Northern Hemisphere, and relatively larger in April-June and September–November in the Southern Hemisphere. The global average of correlation is lower during 1957–1980 and 2000–2018, and higher in between. The interdecadal fluctuation of the pattern correlations is coherent with the interdecadal variation of the amplitude of ENSO.

Sahelian Precipitation Change Induced by SST Increase: The Contrasting Roles of Regional and Larger‐Scale Drivers

AbstractRepresenting the West African Monsoon is a major challenge in climate modeling because of complex mechanisms of interaction across scales. We study the monsoon precipitation response to a 4K increase in sea surface temperatures using an idealized meridional‐vertical (2D) model, which allows separating regional‐ and larger‐scale influences. The 2D simulations reproduce key features of Atmospheric Model Intercomparison Project simulations and indicate that, in response to sea surface temperature increase, large‐scale changes induce a decrease of Sahel precipitation, while regional‐scale mechanisms generate a southward shift of the rainband. Large‐scale changes moisten and warm the free troposphere, while regional circulation changes increase low‐level moisture. Precipitation in the control simulations accounts for much of the variance in precipitation change for both Atmospheric Model Intercomparison Project and 2D simulations. This response is strongly connected to the large‐scale drivers of temperature and moisture, with an additional spread associated with the formulation of the convective parameterization.

Evaluation of Surface Radiative Fluxes over the Tropical Oceans in AMIP Simulations

The performance of 20 models from the Atmospheric Model Intercomparison Project (AMIP) was evaluated concerning surface radiation over the tropical oceans (30° S–30° N) from 1979 to 2000. The model ensemble mean of the net surface shortwave radiation (QSW) was underestimated compared to the International Satellite Cloud Climatology Project (ISCCP) data by 4 W m−2. On the other hand, net longwave radiation (QLW) was overestimated by 4 W m−2, leading to an underestimation of the net surface radiation (Qrad) by 8 W m−2. The most prominent bias in the Qrad appears to be over regions of low-level clouds in the off-equatorial eastern Pacific, eastern Atlantic, and the south-eastern Indian Ocean. The root means squared error of QLW was larger than that of QSW in 17 out of 20 AMIP models. Overestimation of the total cloud cover and atmospheric humidity contributed to the underestimation of Qrad. In general, models with higher horizontal resolutions performed slightly better than those with coarser horizontal resolutions, although some systematic bias persists in all models and in all seasons, in particular, in regions of low-level clouds for QLW, and high-level clouds for QSW. The ensemble mean performed better than most models, but two high-resolution models (GFDL-HIRAM-C180 and GFDL-HIRAM-C360) outperform the model ensemble.

The Summertime Precipitation Bias in E3SM Atmosphere Model Version 1 over the Central United States

AbstractThis study analyzes the summertime precipitation bias over the Central United States and its relationship to the simulated large‐scale environment and the convection scheme in the Energy Exascale Earth System Model Atmosphere Model version 1. This relationship is mainly examined in a set of short‐term hindcasts initialized with realistic large‐scale conditions for the summer of 2011. Besides the uniform 1° model resolution, we adopt Regionally Refined Meshes to increase the model resolution to 0.25° over the contiguous United States. Additional five‐year Atmospheric Model Intercomparison Project simulations are conducted to confirm that the results from the hindcasts are consistent with the climate runs. We find that the summertime dry precipitation bias over the Great Plains and the wet bias over the Rockies cannot be reduced simultaneously by changing resolution or tuning parameters. As for the diurnal cycle, Energy Exascale Earth System Model Atmosphere Model version 1 captures the general diurnal variation of the large‐scale moisture transport and the large‐scale upward motion over the Central United States. However, the diurnal cycle of precipitation over the Great Plains is out of phase with the diurnal variation of the large‐scale environment because the convective precipitation dominates the total precipitation and its diurnal cycle, and it does not directly respond to the local moisture convergence and the large‐scale upward motion. These results reemphasize the importance of improving the coupling of the convection to the large‐scale environment in reducing the summer precipitation bias over the Central United States in climate models with the resolution of ~0.25°.

Reduction in Northern Midlatitude 2-m Temperature Variability due to Arctic Sea Ice Loss

Abstract In this study, we investigate links between Arctic sea ice loss and the variability of 2-m temperatures over a 6-month period (November–April) over two domains centered over northern Eurasia and northern North America. Based on data from the Climate Forecast System Reanalysis (CFSR), there has been an increase (a decrease) in recent seasonal temperature variability over Eurasia (North America), which can be attributed to cooling (warming) during the winter months. Decreases in the intraseasonal variability of temperature anomalies, however, are noted in both regions for the November–April period. This study investigates the role of different forcings on the changes seen in the reanalysis product using Atmospheric Model Intercomparison Project simulations forced with repeating sea surface temperature, sea ice, and carbon dioxide concentration relative to climatologies from two different base periods, 1981–90 and 2005–14. The seasonal temperature and intraseasonal anomaly variabilities are examined, and we find that only the simulations with reduction in sea ice (2005–14 base-period sea ice concentration) produce significant decreases in intraseasonal temperature anomaly variability over these regions, agreeing with the CFSR analysis. Runs that reduce sea ice also result in a significant decrease in the frequency and magnitude of extreme warm and cold temperature anomalies. It is proposed that the weakened latitudinal temperature gradient, resulting from decreased sea ice, leads to reduced meridional temperature advection variability, which in turn contributes to the reduction in the variability of temperature anomalies.

CAS FGOALS-f3-L Model Datasets for CMIP6 Historical Atmospheric Model Intercomparison Project Simulation

The outputs of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic, Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project (CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total, there are three ensemble experiments over the period 1979–2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden-Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP.

CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site

AbstractAll the weather and climate models participating in the Clouds Above the United States and Errors at the Surface project show a summertime surface air temperature (T2 m) warm bias in the region of the central United States. To understand the warm bias in long‐term climate simulations, we assess the Atmospheric Model Intercomparison Project simulations from the Coupled Model Intercomparison Project Phase 5, with long‐term observations mainly from the Atmospheric Radiation Measurement program Southern Great Plains site. Quantities related to the surface energy and water budget, and large‐scale circulation are analyzed to identify possible factors and plausible links involved in the warm bias. The systematic warm season bias is characterized by an overestimation of T2 m and underestimation of surface humidity, precipitation, and precipitable water. Accompanying the warm bias is an overestimation of absorbed solar radiation at the surface, which is due to a combination of insufficient cloud reflection and clear‐sky shortwave absorption by water vapor and an underestimation in surface albedo. The bias in cloud is shown to contribute most to the radiation bias. The surface layer soil moisture impacts T2 m through its control on evaporative fraction. The error in evaporative fraction is another important contributor to T2 m. Similar sources of error are found in hindcast from other Clouds Above the United States and Errors at the Surface studies. In Atmospheric Model Intercomparison Project simulations, biases in meridional wind velocity associated with the low‐level jet and the 500 hPa vertical velocity may also relate to T2 m bias through their control on the surface energy and water budget.