NCAR CESM1 Research Articles

Proposal and application of a convective wind gustiness parameterization based on convective available potential energy

Wind gustiness is an effective approach for addressing mesoscale enhancement in estimating air-sea interface fluxes. In this study, an improved convective gustiness parameterization scheme based on convective available potential energy (CAPE) is formulated utilizing reanalysis data and wind observations from moored buoys. This new parameterization was examined by implementing it in the air-sea flux transfer scheme within the National Center for Atmospheric Research Community Earth System Model (NCAR CESM) version 2.1.3 and contrasting it with a previously proposed scheme based on convective precipitation rate. The results indicate a significant reduction in negative biases of surface winds and latent heat fluxes over the tropical Indian Ocean and western Pacific during summer with the implementation of either gustiness scheme, resulting in an average increase in latent heat flux of approximately 9 W·m− 2. Specifically, the CAPE-based scheme shows a more favorable impact in the Indo-Pacific Warm Pool region, whereas the precipitation-based scheme exhibits inferior performance in the western Pacific Intertropical Convergence Zone. Further analysis reveals that the inclusion of the gustiness scheme distinctly alters surface evaporation and latent heat flux, influencing vertical motion in the area with active convective activity and effectively improving precipitation simulations by up to 18%. Moreover, the CAPE-based scheme reduces the bias in summer precipitation simulations by approximately 5% more than the precipitation-based scheme.

Retaining Short‐Term Variability Reduces Mean State Biases in Wind Stress Overriding Simulations

AbstractPositive feedbacks in climate processes can make it difficult to identify the primary drivers of climate phenomena. Some recent global climate model (GCM) studies address this issue by controlling the wind stress felt by the surface ocean such that the atmosphere and ocean become mechanically decoupled. Most mechanical decoupling studies have chosen to override wind stress with an annual climatology. In this study we introduce an alternative method of interannually varying overriding which maintains higher frequency momentum forcing of the surface ocean. Using a GCM (NCAR CESM1), we then assess the size of the biases associated with these two methods of overriding by comparing with a freely evolving control integration. We find that overriding with a climatology creates sea surface temperature (SST) biases throughout the global oceans on the order of ±1°C. This is substantially larger than the biases introduced by interannually varying overriding, especially in the tropical Pacific. We attribute the climatological overriding SST biases to a lack of synoptic and subseasonal variability, which causes the mixed layer to be too shallow throughout the global surface ocean. This shoaling of the mixed layer reduces the effective heat capacity of the surface ocean such that SST biases excite atmospheric feedbacks. These results have implications for the reinterpretation of past climatological wind stress overriding studies: past climate signals attributed to momentum coupling may in fact be spurious responses to SST biases.

How do the characteristics of monsoon low pressure systems over India change under a warming climate? A modeling study using the NCAR CESM

How do the characteristics of monsoon low pressure systems over India change under a warming climate? A modeling study using the NCAR CESM

The dependence of aerosols' global and local precipitation impacts on the emitting region

Abstract. The influence of the geographic distribution of aerosol emissions on the magnitude and spatial pattern of their precipitation impacts remains poorly understood. In this study, the global climate model NCAR CESM1 (National Center for Atmospheric Research Community Earth System Model version 1.2) is used in coupled atmosphere–slab ocean mode to simulate the global hydrological-cycle response to a fixed amount and composition of aerosol emitted from eight key source regions. The results indicate that the location of aerosol emissions is a strong determinant of both the magnitude and spatial distribution of the hydrological response. The global-mean precipitation response to aerosol emissions is found to vary over a 6-fold range depending solely on source location. Mid-latitude sources generate larger global-mean precipitation responses than do tropical and sub-tropical sources, driven largely by the former's stronger global-mean temperature influence. However, the spatial distribution of precipitation responses to some (largely tropical and sub-tropical) regional emissions is almost entirely localized within the source region, while responses to other (primarily mid-latitude) regional emissions are almost entirely remote. It is proposed that this diversity arises from the differing strength with which each region's emissions generate fast precipitation responses that remain largely localized. The findings highlight that tropical regions are particularly susceptible to hydrological-cycle change from either local or remote aerosol emissions, encourage greater investigation of the processes controlling localization of the precipitation response to regional aerosols, and demonstrate that the geographic distribution of anthropogenic aerosol emissions must be considered when estimating their hydrological impacts.

Changes in the characteristics of monsoon low pressure systems over India under a warming climate: A modeling study using the NCAR CESM

Changes in the characteristics of monsoon low pressure systems over India under a warming climate: A modeling study using the NCAR CESM

The relationship between PM<sub>2.5</sub> and anticyclonic wave activity during summer over the United States

Abstract. To better understand the role of atmospheric dynamics in modulating surface concentrations of fine particulate matter (PM2.5), we relate the anticyclonic wave activity (AWA) metric and PM2.5 data from the Interagency Monitoring of Protected Visual Environment (IMPROVE) data for the period of 1988–2014 over the US. The observational results are compared with hindcast simulations over the past 2 decades using the National Center for Atmospheric Research–Community Earth System Model (NCAR CESM). We find that PM2.5 is positively correlated (up to R=0.65) with AWA changes close to the observing sites using regression analysis. The composite AWA for high-aerosol days (all daily PM2.5 above the 90th percentile) shows a similarly strong correlation between PM2.5 and AWA. The most prominent correlation occurs in the Midwestern US. Furthermore, the higher quantiles of PM2.5 levels are more sensitive to the changes in AWA. For example, we find that the averaged sensitivity of the 90th-percentile PM2.5 to changes in AWA is approximately 3 times as strong as the sensitivity of 10th-percentile PM2.5 at one site (Arendtsville, Pennsylvania; 39.92∘ N, 77.31∘ W). The higher values of the 90th percentile compared to the 50th percentile in quantile regression slopes are most prominent over the northeastern US. In addition, future changes in US PM2.5 based only on changes in climate are estimated to increase PM2.5 concentrations due to increased AWA in summer over areas where PM2.5 variations are dominated by meteorological changes, especially over the western US. Changes between current and future climates in AWA can explain up to 75 % of PM2.5 variability using a linear regression model. Our analysis indicates that higher PM2.5 concentrations occur when a positive AWA anomaly is prominent, which could be critical for understanding how pollutants respond to changing atmospheric circulation as well as for developing robust pollution projections.

Effects of Improved Simulation of Precipitation on Evapotranspiration and Its Partitioning Over Land

AbstractEvapotranspiration (ET) is a key component of the global hydrological cycle, which is strongly modulated by the occurrence of different rainfall intensities. Global climate models (GCMs) commonly suffer from “too much light rain” and a negative bias in the ratio of transpiration (T) to ET (T/ET). It is unclear whether these biases are related. Here we show that with the improved simulation of probability density functions of rainfall intensity by suppressing light‐rain occurrence using a stochastic convection parameterization in the NCAR CESM1.2, the canopy T increases in tropical forests while evaporation from canopy interception and bare soil decreases. The simulated T/ET is increased by 2.5% globally and up to 8% regionally, primarily attributable to reduced fraction of wet leaves due to less frequent light rain despite its weak intensity. These results imply that excessive light rain is an important cause of the negative T/ET bias in GCMs.

Validation and Projections of Climate Characteristics in the Saginaw Bay Watershed, MI, for Hydrologic Modeling Applications

Fundamental differences in the nature of climate and hydrologic models make coupling of future climate projections to models of watershed hydrology challenging. This study uses the NCAR Weather Research and Forecast model (WRF) to dynamically downscale climate simulations over the Saginaw Bay Watershed, MI and prepare the results for input into semi-distributed hydrologic models. One realization of the bias-corrected NCAR CESM1 model's RCP 8.5 climate scenario is dynamically downscaled at a spatial resolution of 3 km by 3 km for the end of the twenty-first century and validated based on a downscaled run for the end of the twentieth century in comparison to ASOS and NWS COOP stations. Bias-correction is conducted using Quantile Mapping to correct daily maximum and minimum temperature, precipitation, and relative humidity for use in future hydrologic model experiments. In the Saginaw Bay Watershed the end of the twenty-first century is projected to see maximum and minimum average daily temperatures warming by 5.7 and 6.3°C respectively. Precipitation characteristics over the watershed show an increase in mean annual precipitation (average of +14.3 mm over the watershed), mainly due to increases in precipitation intensity (average of +0.3 mm per precipitation day) despite a decrease in frequency of −10.7 days per year. The projected changes have substantial implications for watershed processes including flood prediction, erosion, mobilization of non-point source and legacy contaminants, and evapotranspirative demand, among others. We present these results in the context of usefulness of the downscaled and bias corrected data for semi-distributed hydrologic modeling.

A Global Flood Risk Modeling Framework Built With Climate Models and Machine Learning

AbstractLarge scale flood risk analyses are fundamental to many applications requiring national or international overviews of flood risk. While large‐scale climate patterns such as teleconnections and climate change become important at this scale, it remains a challenge to represent the local hydrological cycle over various watersheds in a manner that is physically consistent with climate. As a result, global models tend to suffer from a lack of available scenarios and flexibility that are key for planners, relief organizations, regulators, and the financial services industry to analyze the socioeconomic, demographic, and climatic factors affecting exposure. Here we introduce a data‐driven, global, fast, flexible, and climate‐consistent flood risk modeling framework for applications that do not necessarily require high‐resolution flood mapping. We use statistical and machine learning methods to examine the relationship between historical flood occurrence and impact from the Dartmouth Flood Observatory (1985–2017), and climatic, watershed, and socioeconomic factors for 4,734 HydroSHEDS watersheds globally. Using bias‐corrected output from the NCAR CESM Large Ensemble (1980–2020), and the fitted statistical relationships, we simulate 1 million years of events worldwide along with the population displaced in each event. We discuss potential applications of the model and present global flood hazard and risk maps. The main value of this global flood model lies in its ability to quickly simulate realistic flood events at a resolution that is useful for large‐scale socioeconomic and financial planning, yet we expect it to be useful to climate and natural hazard scientists who are interested in socioeconomic impacts of climate.

Summertime low clouds mediate the impact of the large-scale circulation on Arctic sea ice

The rapid Arctic sea ice retreat in the early 21st century is believed to be driven by several dynamic and thermodynamic feedbacks, such as ice-albedo feedback and water vapor feedback. However, the role of clouds in these feedbacks remains unclear since the causality between clouds and these processes is complex. Here, we use NASA CERES satellite products and NCAR CESM model simulations to suggest that summertime low clouds have played an important role in driving sea ice melt by amplifying the adiabatic warming induced by a stronger anticyclonic circulation aloft. The upper-level high pressure regulates low clouds through stronger downward motion and increasing lower troposphere relative humidity. The increased low clouds favor more sea ice melt via emitting stronger longwave radiation. Then decreased surface albedo triggers a positive ice-albedo feedback, which further enhances sea ice melt. Considering the importance of summertime low clouds, accurate simulation of this process is a prerequisite for climate models to produce reliable future projections of Arctic sea ice.

Climate response to introduction of the ESA CCI land cover data to the NCAR CESM

The European Space Agency Climate Change Initiative Land Cover data (ESA CCI-LC, from 1992 to 2015) is introduced to the National Center for Atmospheric Research Community Earth System Model version 1.2.1 (NCAR CESM1.2.1). In comparison with the original land surface data in the Community Land Model version 4 (ORG), the new data features notable land use and land cover change (LULCC) with increased forests over northeastern Asia and Alaska by decreasing shrublands and grasslands. Overestimated bare land cover over the Tibetan Plateau (TP) and the Rocky Mountains in the ORG are corrected with the replacements by grasslands and shrublands respectively in the new data. The model simulation results show that with the introduction of the ESA CCI-LC, the simulated surface albedo, surface net radiation flux, sensible and latent heat fluxes are relatively improved over the regions where significant LULCC exists, such as northeastern Asia, Alaska, the TP, and Australia. Surface air temperature, precipitation, and atmospheric circulation are improved in boreal winter but degraded in summer. The winter warming over northeastern Asia results from increased longwave downwelling flux and adiabatic heating while the notable winter cooling over Alaska is attributed to strong cold advection followed by reduced longwave downwelling flux. LULCC alters precipitation by influencing water vapor content. In winter, LULCC affects atmospheric circulation via modulating baroclinicity while in summer, it influences land-sea thermal contrast, thus affecting the intensity of East Asian summer monsoon. LULCC also alters the simulated dust burden.

Estimating Submicron Aerosol Mixing State at the Global Scale With Machine Learning and Earth System Modeling

AbstractThis study integrates machine learning and particle‐resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model (ESM) simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the ESM, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. We used PartMC‐MOSAIC as the particle‐resolved model and NCAR's CESM as the ESM. We trained emulators for three different mixing state indices for submicron aerosol in terms of chemical species abundance (χa), the mixing of optically absorbing and nonabsorbing species (χo), and the mixing of hygroscopic and nonhygroscopic species (χh). Our global mixing state maps show considerable spatial and seasonal variability unique to each mixing state index. Seasonal averages varied spatially between 13% and 94% for χa, between 38% and 94% for χo, and between 20% and 87% for χh with global annual averages of 67%, 68%, and 56%, respectively. High values in one index can be consistent with low values in another index depending on the grouping of species and their relative abundance, meaning that each mixing state index captures different aspects of the population mixing state. Although a direct validation with observational data has not been possible yet, our results are consistent with mixing state index values derived from ambient observations. This work is a prototypical example of using machine learning emulators to add information to ESM simulations.

Emerging Anthropogenic Influences on the Southcentral Alaska Temperature and Precipitation Extremes and Related Fires in 2019

The late-season extreme fire activity in Southcentral Alaska during 2019 was highly unusual and consequential. Firefighting operations had to be extended by a month in 2019 due to the extreme conditions of hot summer temperature and prolonged drought. The ongoing fires created poor air quality in the region containing most of Alaska’s population, leading to substantial impacts to public health. Suppression costs totaled over $70 million for Southcentral Alaska. This study’s main goals are to place the 2019 season into historical context, provide an attribution analysis, and assess future changes in wildfire risk in the region. The primary tools are meteorological observations and climate model simulations from the NCAR CESM Large Ensemble (LENS). The 2019 fire season in Southcentral Alaska included the hottest and driest June–August season over the 1979–2019 period. The LENS simulation analysis suggests that the anthropogenic signal of increased fire risk had not yet emerged in 2019 because of the CESM’s internal variability, but that the anthropogenic signal will emerge by the 2040–2080 period. The effect of warming temperatures dominates the effect of enhanced precipitation in the trend towards increased fire risk.

Role of Equatorial Cold Tongue in Central Pacific Double-ITCZ Bias in the NCAR CESM1.2

AbstractWarm SST bias underlying the spurious southern ITCZ has long been recognized as one of the main causes for double-ITCZ bias in coupled GCMs in the central Pacific. This study demonstrates that the NCAR CESM1.2 can still simulate significant double-ITCZ bias even with cold SST bias in the southern ITCZ region, indicating that warm SST bias is not a necessary condition for double-ITCZ bias in the central Pacific. Further analyses suggest that the equatorial cold tongue (ECT) biases play important roles in the formation of double-ITCZ bias in the central Pacific. The severe cold SST biases in the ECT region in the central Pacific may enhance the SST gradient between the ECT and southern ITCZ region, strengthening the lower-troposphere dynamical convergence and hence convection in the southern ITCZ region. The formation mechanism of excessive ECT bias is further investigated. It is shown that the cold SST biases in the ECT region can be largely attributed to the anomalous cooling tendency produced by the upper-ocean zonal advection due to overly strong zonal currents. In the ECT region, the westward ocean surface zonal current is driven by the equatorial easterly surface winds. It is shown that convection bias simulated by the atmospheric model in the equatorial Amazon region may lead to easterly wind bias in the downwind side (west) of convection region. The mean Walker circulation transports these easterly wind momentum anomalies downward and westward to the surface, resulting in the overly strong surface easterly wind in the central equatorial Pacific.

Local and remote mean and extreme temperature response to regional aerosol emissions reductions

Abstract. The climatic implications of regional aerosol and precursor emissions reductions implemented to protect human health are poorly understood. We investigate the mean and extreme temperature response to regional changes in aerosol emissions using three coupled chemistry–climate models: NOAA GFDL CM3, NCAR CESM1, and NASA GISS-E2. Our approach contrasts a long present-day control simulation from each model (up to 400 years with perpetual year 2000 or 2005 emissions) with 14 individual aerosol emissions perturbation simulations (160–240 years each). We perturb emissions of sulfur dioxide (SO2) and/or carbonaceous aerosol within six world regions and assess the statistical significance of mean and extreme temperature responses relative to internal variability determined by the control simulation and across the models. In all models, the global mean surface temperature response (perturbation minus control) to SO2 and/or carbonaceous aerosol is mostly positive (warming) and statistically significant and ranges from +0.17 K (Europe SO2) to −0.06 K (US BC). The warming response to SO2 reductions is strongest in the US and Europe perturbation simulations, both globally and regionally, with Arctic warming up to 1 K due to a removal of European anthropogenic SO2 emissions alone; however, even emissions from regions remote to the Arctic, such as SO2 from India, significantly warm the Arctic by up to 0.5 K. Arctic warming is the most robust response across each model and several aerosol emissions perturbations. The temperature response in the Northern Hemisphere midlatitudes is most sensitive to emissions perturbations within that region. In the tropics, however, the temperature response to emissions perturbations is roughly the same in magnitude as emissions perturbations either within or outside of the tropics. We find that climate sensitivity to regional aerosol perturbations ranges from 0.5 to 1.0 K (W m−2)−1 depending on the region and aerosol composition and is larger than the climate sensitivity to a doubling of CO2 in two of three models. We update previous estimates of regional temperature potential (RTP), a metric for estimating the regional temperature responses to a regional emissions perturbation that can facilitate assessment of climate impacts with integrated assessment models without requiring computationally demanding coupled climate model simulations. These calculations indicate a robust regional response to aerosol forcing within the Northern Hemisphere midlatitudes, regardless of where the aerosol forcing is located longitudinally. We show that regional aerosol perturbations can significantly increase extreme temperatures on the regional scale. Except in the Arctic in the summer, extreme temperature responses largely mirror mean temperature responses to regional aerosol perturbations through a shift of the temperature distributions and are mostly dominated by local rather than remote aerosol forcing.

Global Climate and Human Health Effects of the Gasoline and Diesel Vehicle Fleets.

The global gasoline and diesel fuel vehicle fleets impose substantial impacts on air quality, human health, and climate change. Here we quantify the global radiative forcing and human health impacts of the global gasoline and diesel sectors using the NCAR CESM global chemistry‐climate model for year 2015 emissions from the IIASA GAINS inventory. Net global radiative effects of short‐lived climate forcers (including aerosols, ozone, and methane) from the gasoline and diesel sectors are +13.6 and +9.4 mW m−2, respectively. The annual mean net aerosol contributions to the net radiative effects of gasoline and diesel are −9.6 ± 2.0 and +8.8 ± 5.8 mW m−2. Aerosol indirect effects for the gasoline and diesel road vehicle sectors are −16.6 ± 2.1 and −40.6 ± 4.0 mW m−2. The fractional contributions of short‐lived climate forcers to the total global climate impact including carbon dioxide on the 20‐year time scale are similar, 14.9% and 14.4% for gasoline and diesel, respectively. Global annual total PM2.5‐ and ozone‐induced premature deaths for gasoline and diesel sectors approach 115,000 (95% CI: 69,000–153,600) and 122,100 (95% CI: 78,500–157,500), with corresponding years of life lost of 2.10 (95% CI: 1.23–2.66) and 2.21 (95% CI: 1.47–2.85) million years. Substantial regional variability of premature death rates is found for the diesel sector when the regional health effects are normalized by the annual total regional vehicle distance traveled. Regional premature death rates for the gasoline and diesel sectors, respectively, vary by a factor of eight and two orders of magnitude, with India showing the highest for both gasoline and diesel sectors.

The Impacts of Horizontal Resolution on the Seasonally Dependent Biases of the Northeastern Pacific ITCZ in Coupled Climate Models

ABSTRACTThe double-ITCZ bias has puzzled the climate modeling community for more than two decades. Here we show that, over the northeastern Pacific Ocean, precipitation and sea surface temperature (SST) biases are seasonally dependent in the NCAR CESM1 and 37 CMIP5 models, with positive biases during boreal summer–autumn and negative biases during boreal winter–spring, although the easterly wind bias persists year round. This seasonally dependent bias is found to be caused by the model’s failure to reproduce the climatological seasonal wind reversal of the North American monsoon. During winter–spring, the observed easterly wind dominates, so the simulated stronger wind speed enhances surface evaporation and lowers SST. It is opposite when the observed wind turns to westerly during summer–autumn. An easterly wind bias, mainly evident in the lower troposphere, also occurs in the atmospheric model when the observed SST is prescribed, suggesting that it is of atmospheric origin. When the atmospheric model resolution is doubled in the CESM1, both SST and precipitation are improved in association with the reduced easterly wind bias. During boreal spring, when the double-ITCZ bias is most significant, the northern and southern ITCZ can be improved by 29.0% and 18.8%, respectively, by increasing the horizontal resolution in the CESM1. When dividing the 37 CMIP5 models into two groups on the basis of their horizontal resolutions, it is found that both the seasonally dependent biases over the northeastern Pacific and year-round biases over the southeastern Pacific are reduced substantially in the higher-resolution models, with improvement of ~30% in both regions during boreal spring. Close relationships between wind and precipitation biases over the northeastern and southeastern Pacific are also found among CMIP5 models.

Culprit of the Eastern Pacific Double-ITCZ Bias in the NCAR CESM1.2

Abstract The eastern Pacific double-ITCZ bias has long been attributed to the warm bias of SST in the southeastern Pacific and associated local air–sea interaction. In this study, we conducted two simulations using the NCAR CESM1.2.1 to demonstrate that significant double-ITCZ bias can still form in the eastern Pacific through air–sea coupled feedback even when there is cold SST bias in the southeastern Pacific, indicating that other nonlocal culprits and mechanisms should be responsible for the double-ITCZ bias in the eastern Pacific. Further analyses show that the oversimulated convection in the northern ITCZ region and Central America in boreal winter may result in biases in the surface wind fields in the tropical northeastern Pacific in the atmospheric model, which favor the cooling of the ocean mixed layer through enhancement of latent heat flux and Ekman upwelling. These biases are passed into the ocean model in coupled simulations and result in a severe cold bias of SST in the northern ITCZ region. The overly cold SST bias persists in the subsequent spring, leading to the suppression of convection in the northern ITCZ region. The enhanced low-level cross-equatorial northerly wind strengthens the wind convergence south of the equator and transports abundant water vapor to the convergence zone, strengthening the southern ITCZ convection. All these processes lead to the disappearance of the northern ITCZ and the enhancement of the southern ITCZ in boreal spring, forming a seasonally alternating double-ITCZ bias. This study suggests that convection biases in the northern ITCZ region and Central America in boreal winter may be a culprit for the double-ITCZ bias in the eastern Pacific.

Dynamical and Thermodynamical Influences of the Maritime Continent on ENSO Evolution

El Niño-Southern Oscillation (ENSO) exerts tremendous influences on the global climate. Through dynamic lifting and thermal forcing, the Maritime Continent (MC) plays an important role in affecting global atmospheric circulation. In spite of the extensive studies on ENSO mechanisms, the influence of MC on the characteristics of ENSO life cycle remains unclear. Our coupled model experiments reveal that the absence of the MC land contributes to a strong ENSO asymmetry and a weakened nonlinear atmospheric response to the combined seasonal and interannual SST variations (i.e. the combination mode) that prolongs the warm events, resulting in a reduction of ENSO frequency. On the other hand, our experiments suggest that the global climate model applied (NCAR CESM) overestimates the MC topographic uplifting effect on ENSO simulation. Overall, this study provides a new physical insight into the nature of the MC influence on ENSO evolution.

The Roles of Convection Parameterization in the Formation of Double ITCZ Syndrome in the NCAR CESM: I. Atmospheric Processes

AbstractSeveral improvements are implemented in the Zhang‐McFarlane (ZM) convection scheme to investigate the roles of convection parameterization in the formation of double intertropical convergence zone (ITCZ) bias in the NCAR CESM1.2.1. It is shown that the prominent double ITCZ biases of precipitation, sea surface temperature (SST), and wind stress in the standard CESM1.2.1 are largely eliminated in all seasons with the use of these improvements in convection scheme. This study for the first time demonstrates that the modifications of convection scheme can eliminate the double ITCZ biases in all seasons, including boreal winter and spring. Further analysis shows that the elimination of the double ITCZ bias is achieved not by improving other possible contributors, such as stratus cloud bias off the west coast of South America and cloud/radiation biases over the Southern Ocean, but by modifying the convection scheme itself. This study demonstrates that convection scheme is the primary contributor to the double ITCZ bias in the CESM1.2.1, and provides a possible solution to the long‐standing double ITCZ problem. The atmospheric model simulations forced by observed SST show that the original ZM convection scheme tends to produce double ITCZ bias in high SST scenario, while the modified convection scheme does not. The impact of changes in each core component of convection scheme on the double ITCZ bias in atmospheric model is identified and further investigated.