Coupled General Circulation Model Research Articles

Coupled high-resolution GCM downscaling framework for projecting dynamics and drivers of ecosystem services in Pearl River Basin, China

The projection of future ecosystem service (ES) dynamics under SSP-RCP scenarios would be beneficial for the development of effective climate mitigation strategies for policymakers. However, current projection frameworks generally neglect the downscaling of high-resolution climate and land use data, leading to uncertainties in the simulation of ESs under the SSP-RCP scenarios. To address this issue, this study proposes an integrated analysis framework to downscale climate and land use data. We first identified areas of high risk in terms of inadequate water-related ESs in the Pearl River Basin (PRB) of China. Subsequently, we analyzed key socio-environmental factors driving the dynamics of water-related ESs under future SSP-RCP scenarios. The results indicate that 1) water-related ESs in the PRB are expected to show an overall increasing trend, 2) the sub-watersheds located in the downstream is at a higher risk of experiencing insufficient supply capacity for water-related ESs, and 3) under the coupling effects of climate change, land use change, and socio-economic development, drastic changes in future land use are expected to have a stronger impact on watershed ESs than the climate (i.e., global warming). Our work provides valuable insights for policymakers and managers regarding future water and land resource planning. The proposed framework can be integrated with other hydrological or land surface models to project the spatial–temporal evolution characteristics of future watershed ESs with finer temporal resolution.

Tropical Indian Ocean Mixed Layer Bias in CMIP6 CGCMs Primarily Attributed to the AGCM Surface Wind Bias

Abstract The relatively weak sea surface temperature bias in the tropical Indian Ocean (TIO) simulated in the coupled general circulation model (CGCM) from the recently released CMIP6 has been found to be important in model simulations of regional and global climate. However, the cause of the bias is debated because the bias is strongly model dependent and shows marked seasonality. In this study, we separate the bias in CGCMs into bias arising from oceanic GCMs (OGCMs) and bias that is independent of OGCMs using a set of CMIP6 and OMIP6 models. We found that OGCMs contribute little to mixed layer bias in the CGCMs. The OGCM-independent bias exhibits a large-scale cold mixed layer bias in the TIO throughout the year, with an unexpectedly high degree of model consistency. By conducting a set of OGCM experiments, we show that the OGCM-independent mixed layer bias is caused mainly by surface wind bias in the utilized CGCMs. About 89% of surface wind bias in the CGCMs is due to the inability of atmospheric GCMs (AGCMs), whereas atmosphere–ocean coupling in the CGCMs has only a minor influence on surface wind bias. The bias in surface wind is also found to be the cause of subsurface temperature bias besides the ocean dynamics such as vertical mixing and vertical shear in currents. Our results indicate that correcting TIO mixed layer bias in CGCMs requires improvement in the capability of AGCM in simulating the climatological surface winds. Significance Statement We aimed to discover the cause of temperature bias in the Indian Ocean in CMIP6 models. The bias was separated into oceanic model and ocean-model-independent bias to correspond exactly to bias caused by the oceanic model and by the atmospheric model and coupling, respectively. Oceanic bias contributes little to bias in CMIP6, but ocean-model-independent bias explains the CMIP6 bias throughout the year. We ran oceanic model experiments to show that surface wind bias causes ocean-model-independent temperature bias in the entire TIO and subsurface temperature bias in some areas of the Indian Ocean. We further found that 89% of surface wind bias originates from the atmospheric model. The results improve our understanding of the cause of the bias in the Indian Ocean and show that our method of bias separation is effective for attributing the source of bias to different proposed mechanisms.

Instability of Atlantic Meridional Overturning Circulation: Observations, Modelling and Relevance to Present and Future

The Atlantic Meridional Overturning Circulation (AMOC) has changed dramatically during the glacial–interglacial cycle. One leading hypothesis for these abrupt changes is thermohaline instability. Here, I review recent progress towards understanding thermohaline instability in both observations and modelling. Proxy records available seem to favor thermohaline instability as the cause of the abrupt climate changes during the glacial–deglacial period because the deep North Atlantic water mass and AMOC seemed to have changed before the North Atlantic climate. However, most fully Coupled General Circulation Models (CGCMs) so far seem to exhibit monostable AMOC, because (1) these models have failed to simulate abrupt AMOC changes unless they are forced by an abrupt change of external forcing and, (2) these models have shown opposite freshwater convergence from the current observations. This potential model bias in the AMOC stability leaves the model projection of the future AMOC change uncertain.

On Oceanic Initial State Errors in the Ensemble Data Assimilation for a Coupled General Circulation Model

AbstractIn the construction of an ensemble‐based data assimilation system for a complex fully coupled general circulation model (CGCM), the model state errors at initial time of assimilation have an important influence on assimilation quality. In this study, with the Community Earth System Model (CESM) and Data Assimilation Research Testbed (DART), we found that the influence of initial states errors persists throughout a vicious cycle and cannot be automatically remedied via consequent assimilations. As such, two strategies were applied to alleviate the initial state errors, and a reliable assimilation system was developed. Data assimilation experiments using oceanic observations were conducted over the period from 2005 to 2014 to investigate the impact of these different strategies. The evaluation revealed that the assimilation of observation‐derived climatological data is an effective approach to reduce initial state errors and preserve the balance between different variables to the largest extent, which significantly improved the performance of the assimilation system in the investigated time period. It was further found that the developed assimilation system can produce high‐quality oceanic analysis results comparable to the ECDA and GODAS, two widely used reanalysis products. Perspectives toward further improvement of coupled data assimilation are also outlined.

Representation of Atmospheric Water Budget and Uncertainty Quantification of Future Changes in CMIP6 for the Seven U.S. National Climate Assessment Regions

Abstract Observation-based climate model evaluation and future projections help policymakers in developing action plans for efficient management of water resources and mitigation of the impacts of hazardous extremes. Apart from this socioeconomic importance, the scientific value cannot be overstated, especially in light of the upcoming Fifth U.S. National Climate Assessment (NCA) report. In this study, we evaluate the realism of hydroclimate variability in the historical simulations of a suite of coupled general circulation models (CGCMs) participating in the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). Our results demonstrate systematic biases in the simulated seasonal precipitation—most prominently, wet bias over the mountainous western United States in winter, and dry bias over the U.S. central plains in summer. A distinctive feature of this work is our focus on the examination of the atmospheric water budget, in particular the relative importance of remote and local contributions—convergence of moisture fluxes and local land surface processes (evapotranspiration) respectively—in helping produce precipitation. This diagnosis reveals that the leading contribution of the remote influence in winter is overestimated by the CMIP6 multimodel mean (MMM), whereas the local influence, which is more influential in summer, is underestimated. Our results aid in understanding the drivers of seasonal precipitation over the United States, where precipitation will likely increase by the end of the century but with significant model disagreement for the summer and fall. In support of ongoing NCA efforts, our study aims to contribute a comprehensive, regional-level analysis of the moisture budget and emphasizes the importance of realistically simulating its major components in CGCMs.

Role of Pacific preconditioning in modulating the relationship between the spring North Tropical Atlantic SST and the ensuing El Niño

Previous literature has indicated that a spring cold sea surface temperature (SST) anomaly in the North Tropical Atlantic (NTA) could induce an ensuing El Niño event. However, observations show that this relationship is not always robust. For example, the spring cold NTA SST anomalies are not necessarily accompanied by El Niño events in the ensuing winter. Two types of cases are further classified for comparison: a spring cold NTA SST anomaly accompanied by an El Niño (tagged as NTA-El Niño) and a spring cold NTA SST anomaly accompanied by no El Niño (tagged as NTA-Normal). Statistical analysis indicates that Pacific preconditioning acts an important role in modulating the NTA SST and El Niño relationship. The zonal wind anomaly over the eastern subtropical Pacific serves as an important medium linking the spring NTA SST anomaly to the subsequent development of El Niño, and this part of the zonal wind anomaly in spring and summer is largely associated with the preceding winter tropical Pacific SST anomaly. The proposed mechanism is verified by two groups of Coupled General Circulation Models (CGCMs) runs with a weak and strong La Niña condition. Compared with the strong La Niña case, under a weak La Niña case in the previous winter, the spring cold NTA SST anomaly is more capable of modulating the development of the ensuing El Niño event. The present study suggests the importance of Pacific preconditioning while forecasting ENSO with the precursor of the spring NTA SST anomaly.

Representation of the Mozambique channel trough and its link to southern African rainfall in CMIP6 models

AbstractThe topography of Madagascar and the strength of the Mozambique Channel Trough (MCT) modulate summer rainfall over southern Africa. A strong MCT hinders the penetration of moisture bearing easterlies from the South Indian Ocean into the mainland, thus reducing rainfall there and vice versa for weak MCT summers. Given the link between the MCT and rainfall, it is important to analyse how climate models represent the trough. Here, output from 20 models within the CMIP6 ensemble of Coupled General Circulation Models (CGCMs) are analyzed to investigate how state-of-the-art CGCMs represent the MCT and its link to southern African rainfall. Overall, the ensemble mean insignificantly underestimates the observed MCT. There is a large spread among the models, with the strength of the MCT significantly correlated with the Froude number based on the mountain height over Madagascar. In models, the vorticity tendency in the MCT area is dominated by the stretching and friction terms, whereas the vertical advection, tilting and residual terms dominate in the ERA5 reanalysis. The link between MCT and rainfall in the southern African subcontinent is missing in the models. Large rainfall biases are depicted over mainland even in models with a very strong MCT. It is found that the impacts of the MCT in the models could be masked by a complex mix of processes such as the strength of the Angola low, moisture fluxes from the Indian and South Atlantic Oceans as well as overestimated convection in the Mozambique Channel area.

Improved ENSO and PDO Prediction Skill Resulting from Finer Parameterization Schemes in a CGCM

Coupled general circulation models (CGCMs), as tools of predicting climate variability, are constantly being improved due to their immense value in a host of theoretical and practical, real-world problems. Consequently, four new parameterization schemes are introduced in the First Institute of Oceanography Earth System Model (FIO-ESM), and a new climate prediction System (CPS) is built up based on modified and original FIO-ESM. Here, turbulence from the sea surface to deep ocean were fully described, and seasonal forecasts of El Niño-Southern Oscillation (ENSO) and year-to-year prediction of Pacific Decadal Oscillation (PDO) were made with both the modified and original FIO-ESM-CPS. The results illustrate that the anomaly correlation coefficient (ACC) of the Niño 3.4 index significantly increased, and the root mean square error (RMSE) significantly decreased, respectively, in the modified FIO-ESM-CPS as compared to the original. The RMSE is improved by over 20% at 4- and 5-month lead times. Over longer leads, and in the modified FIO-ESM-CPS, forecast ENSO amplitudes are far closer to observations than the original CGCM, which significantly overestimates amplitudes. PDO prediction skill is also improved in the modified FIO-ESM-CPS with ACC improving by 36% at the 4-year lead time and RMSE decreasing by 21% at the 3-year lead time.

Critical Role of Tropical North Atlantic SSTA in Boreal Summer in Affecting Subsequent ENSO Evolution

AbstractThe season‐dependent impacts of the tropical North Atlantic (TNA) sea surface temperature anomaly (SSTA) on subsequent El Niño‐Southern Oscillation (ENSO) evolution were investigated through observational and modeling studies. The results indicate that, although the maximum amplitude of the TNA SSTA occurs during boreal spring, the TNA SSTA in boreal summer generates a stronger rainfall response in situ, which can further induce a significantly stronger zonal wind anomaly over the equatorial western Pacific via Kelvin and Rossby wave processes. The cause of a stronger precipitation response in boreal summer is attributed to the northward migration of the climatological Atlantic warm pool and the Inter‐Tropical Convergence Zone. Idealized Coupled General Circulation Model experiments further demonstrate that a persisting TNA SSTA forcing up to boreal summer is critical in conveying the TNA impact to subsequent ENSO evolutions in the Pacific.

Key process diagnostics for monsoon intraseasonal oscillation over the Indian Ocean in coupled CMIP6 models

The simulations of the monsoon intraseasonal oscillation (MISO) during the Indian summer monsoon (ISM) are evaluated with 19 atmosphere–ocean coupled general circulation models (CGCMs) from phase 6 of the Coupled Model Inter-comparison Project (CMIP6). The focus is on the northward propagation of MISO. The CMIP6 models have great improvement in simulating the mean rainfall, as 17 out of 19 models can reasonably simulate the mean rainfall. However, many models fail to reproduce the realistic patterns of the mean rainfall and the MISO amplitude, particularly over land in the monsoon region. The underestimation of the MISO amplitude is still a notable model bias in CMIP6. Moreover, 9 out of 19 models cannot generate realistic northward propagation features, and some even reproduce a stationary MISO pattern. Process diagnostics based on the seasonal mean vertical zonal wind shear, low-level mean moisture, and vortex tilting are also examined. It is found that the accuracy of model simulations of vortex tilting is strongly associated with the northward propagation of MISO. In contrast, the model fidelity in MISO is not dependent on the simulation skill for the seasonal mean state. In addition, decomposition analysis of vortex tilting illustrates that the meridional shear of the intraseasonal vertical velocity is crucial to the tilting simulation. The poor model fidelity in vortex tiling is caused by the weak convection, particularly the absence of downdraft to the north of the convection center. The coupling between the moisture in the boundary layer and the tilting in the free troposphere may be responsible for capturing the vertical motions. In summary, vortex tilting can be a useful metric for evaluating the northward propagation of MISO.

Variability and extremes: statistical validation of the Alfred Wegener Institute Earth System Model (AWI-ESM)

Abstract. Coupled general circulation models are of paramount importance to quantitatively assessing the magnitude of future climate change. Usual methods for validating climate models include the evaluation of mean values and covariances, but less attention is directed to the evaluation of extremal behaviour. This is a problem because many severe consequences of climate change are due to climate extremes. We present a method for model validation in terms of extreme values based on classical extreme value theory. We further discuss a clustering algorithm to detect spatial dependencies and tendencies for concurrent extremes. To illustrate these methods, we analyse precipitation extremes of the Alfred Wegener Institute Earth System Model (AWI-ESM) global climate model and from other models that take part in the Coupled Model Intercomparison Project CMIP6 and compare them to the reanalysis data set CRU TS4.04. The clustering algorithm presented here can be used to determine regions of the climate system that are then subjected to a further in-depth analysis, and there may also be applications in palaeoclimatology.

Balancing environmental impacts and economic benefits of agriculture under the climate change through an integrated optimization system

The present study proposes a framework to mitigate impact of climate change on the rice production by maximizing the yield while the energy use and ecological impacts on the river ecosystem as the irrigation source are mitigated. Coupled general circulation model- soil and water assessment tool (SWAT) was utilized to project the impact of climate change on the stream flow. Fuzzy physical habitat simulation was applied to develop the ecological impact function of the river. Moreover, a data-driven model was developed to predict the rice yield through changing water and energy consumption. Finally, all the simulations were utilized in the structure of the optimization model in which minimizing loss of the production, greenhouse gas emission by reducing energy use and physical habitat loss were considered as the objectives. Based on the results, the Nash–Sutcliffe model efficiency coefficient of the SWAT is 0.7 that demonstrates its reliability for simulating the impact of climate change on river flow. The optimization model is able to reduce the impact of climate change on yield of production by balancing water and energy use. In the most pessimistic scenario, water use should approximately be reduced 25% for protecting river ecosystem. However, the optimization model approximately increased energy use 16% for preserving the yield of the rice. Conversely, model decreased the energy use 40% compared with the current condition due to increasing water supply. Moreover, physical habitat loss is less than 50% that means the combined optimization model is able to protect river habitats properly.

A study of capturing Atlantic meridional overturning circulation (AMOC) regime transition through observation-constrained model parameters

Abstract. The multiple equilibria are an outstanding characteristic of the Atlantic meridional overturning circulation (AMOC) that has important impacts on the Earth climate system appearing as regime transitions. The AMOC can be simulated in different models, but the behavior deviates from the real world due to the existence of model errors. Here, we first combine a general AMOC model with an ensemble Kalman filter to form an ensemble coupled model data assimilation and parameter estimation (CDAPE) system and derive the general methodology to capture the observed AMOC regime transitions through utilization of observational information. Then we apply this methodology designed within a “twin” experiment framework with a simple conceptual model that simulates the transition phenomenon of AMOC multiple equilibria as well as a more physics-based MOC box model to reconstruct the “observed” AMOC multiple equilibria. The results show that the coupled model parameter estimation with observations can significantly mitigate the model deviations, thus capturing regime transitions of the AMOC. This simple model study serves as a guideline when a coupled general circulation model is used to incorporate observations to reconstruct the AMOC historical states and make multi-decadal climate predictions.

Assessing the role of air-sea coupling in predicting Madden-Julian Oscillation with an atmosphere-ocean coupled model

AbstractThe Madden-Julian Oscillation (MJO) provides an important source of sub-seasonal to seasonal (S2S) predictability. Improved MJO prediction can be beneficial to S2S prediction of global climate and associated weather extremes. In this study, hindcasts based on an atmosphere-ocean coupled general circulation model (CGCM) are compared to those based on atmosphere general circulation models (AGCMs) to investigate influences of air-sea interactions on MJO prediction. Our results suggest that MJO prediction skill can be extended about one week longer in the CGCM hindcasts than AGCM-only experiments, particularly for boreal winter predictions.Further analysis suggests that improved MJO prediction in the CGCM is closely associated with improved representation of moistening processes. Compared to the AGCM experiments, the CGCM better predicts the boundary-layer moisture preconditioning to the east of MJO convection, which is generally considered crucial for triggering MJO deep convection. Meanwhile, the widely extended east-west asymmetric structure in free-tropospheric moisture tendency anomalies relative to the MJO convection center as seen in the observations is also well predicted in the CGCM. Improved prediction of MJO moisture processes in CGCM is closely associated with better representation of the zonal scale of MJO circulation and stronger Kelvin waves to the east of MJO convection, both of which have been recently suggested conductive for MJO eastward propagation. The above improvements by including air-sea coupling could be largely attributed to the realistic MJO-induced SST fluctuations through the convection-SST feedback. This study confirms a critical role of atmosphere-ocean coupling for the improvement of MJO prediction.

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

AbstractA common deficiency in coupled atmosphere‐ocean models is the lack of stratocumulus clouds near the west coasts of continents and shallow cumulus clouds over the trade wind regions. In this study, we examine changes of large‐scale trade wind environment associated with hydrometeors‐radiation‐circulation interactions, focusing over the south‐central Pacific, by carrying out experiments with falling ice (snow) radiative effects (FIREs) on or off using the CESM1‐CAM5 coupled model. Compared to observations/reanalysis data and an experiment with FIREs on (SON), an experiment with FIREs off (NOS) tends to have too‐high (0.3–0.9 K) sea surface temperature (SST) over the eastern half of the Pacific, and weaker surface wind stress over the northeast side but stronger stress over the southwest side of the study domain. The difference between NOS and SON shows lower‐tropospheric wind fields with a cyclonic‐like wind pattern, which is similar to surface wind stress, accompanied with weaker subsidence over the northeast side. The cyclonic‐like wind pattern causes stronger moisture convergence, accompanied with horizontal warm and moist advections, and stronger effective ascending motion over the northeast side of the study domain, building large‐scale environmental conditions to facilitate middle‐ and high‐clouds instead of shallow cumulus clouds in nature. This large‐scale environment in conjunction with the higher SSTs may be also responsible for the lack of the stratocumulus clouds near the coast of America. Remaining biases in SON such as the double intertropical convergence zone and parameterization deficiencies may also prevent a more realistic simulation of trade wind clouds and their associated large‐scale environments.

Radiative Impacts of Low-Level Clouds on the Summertime Subtropical High in the South Indian Ocean Simulated in a Coupled General Circulation Model

AbstractOver the south Indian Ocean, the coupled system of the subtropical Mascarene high and low-level clouds exhibits marked seasonality. To investigate this seasonality, the present study assesses radiative impacts of low-level clouds on the summertime Mascarene high with a coupled general circulation model. Comparison between a fully coupled control simulation and a “no-low-cloud simulation,” where the radiative effects of low-level clouds are artificially turned off, demonstrates that they act to reinforce the Mascarene high. Their impacts are so significant that the summertime Mascarene high almost disappears in the no-low-cloud experiment, suggesting their essential role in the existence of the summertime Mascarene high. As the primary mechanism, lowered sea surface temperature by the cloud albedo effect suppresses deep convective precipitation, inducing a Matsuno–Gill type response that reinforces the high, as verified through an atmospheric dynamical model diagnosis. Associated reduction of high-top clouds, as well as increased low-level clouds, augments in-atmosphere radiative cooling, which further reinforces the high. The present study reveals that low-level clouds constitute a tight positive feedback system with the subtropical high via sea surface temperature over the summertime south Indian Ocean.

Assessing the Impact of Climate Change on Future Water Demand using Weather Data

Assessing the impact of climate change on water demand is a challenging task. This paper proposes a novel methodology that quantifies this impact by establishing a link between water demand and weather based on climate change scenarios, via Coupled General Circulation Models. These models simulate the response of the global climate system to increasing greenhouse gas concentrations by reproducing atmospheric and ocean processes. In order to establish the link between water demand and weather, Random Forest models based on weather variables were used. This methodology was applied to a district metered area in Naples (Italy). Results demonstrate that the total district water demand may increase by 9–10% during the weeks with the highest temperatures. Furthermore, results show that the increase in water demand changes depending on the social characteristics of the users. The water demand of employed users with high education may increase by 13–15% when the highest temperatures occur. These increases can seriously affect the capacity and operation of existing water systems.

Ensemble Kalman Filter Parameter Estimation of Ocean Optical Properties for Reduced Biases in a Coupled General Circulation Model

AbstractCoupled general circulation models (GCM), and their atmospheric, oceanic, land, and sea‐ice components have many parameters. Some parameters determine the numerics of the dynamical core, while others are based on our current understanding of the physical processes being simulated. Many of these parameters are poorly known, often globally defined, and are subject to pragmatic choices arising from a complex interplay between grid resolution and inherent model biases. To address this problem, we use an ensemble transform Kalman filter, to estimate spatiotemporally varying maps of ocean albedo and shortwave radiation e‐folding length scale in a coupled climate GCM. These parameters are designed to minimize the error between short term (3–28 days) forecasts of the climate model and a network of real world atmospheric, oceanic, and sea‐ice observations. The data assimilation system has an improved fit to observations when estimating ocean albedo and shortwave e‐folding length scale either individually or simultaneously. However, only individually estimated maps of shortwave e‐folding length scale are also shown to systematically reduce bias in longer multiyear climate forecasts during an out‐of‐sample period. The bias of the multiyear forecasts is reduced for parameter maps determined from longer DA cycle lengths.

Impact of air\u2013sea coupling on the probability of occurrence of heat waves in Japan

In extreme event attribution, which aims to answer whether and to what extent a particular extreme weather event can be attributed to global warming, the probability of an event is generally estimated through large ensemble simulations, using an atmospheric general circulation model (AGCM). In islands, such as Japan, it has been considered that surface air temperature (SAT) can be significantly affected by the surrounding sea surface temperature (SST), which mostly is affected by atmospheric circulation at mid- and high-latitudes. Therefore, the absence of SST responses to atmospheric variability in AGCMs impacts the estimation of the occurrence of extreme events, such as heat waves in Japan. In this study, we examined the impact of air–sea coupling on the probability of occurrence of severe heat waves that occurred in Japan in the summer of 2010 by analyzing the probability differences obtained from AGCM and coupled general circulation model (CGCM) large-ensemble experiments. The observed ocean temperature, salinity, and sea ice were assimilated in the 100-member CGCM experiments, as they were assigned as boundary conditions in the 100-member AGCM experiments. The SAT around Japan in the northern summer is largely related to the Bonin high, whose interannual variability is largely affected by the Silk Road and Pacific-Japan (PJ) pattern teleconnections in the real world. The SAT anomaly over Japan was related to the pressure variability due to the Silk Road and PJ patterns in the CGCM experiment. By contrast, the SAT over Japan simulated by AGCM was less sensitive to such pressure variability, and the SAT ensemble spread became narrower in AGCM. The results suggest that the probability of occurrence of the 2010 heat wave in Japan would tend to be underestimated by the AGCM ensemble compared to the CGCM ensemble, provided that the ensemble averages of the SAT anomalies were equal between CGCM and AGCM experiments. This study raised the issue of the absence of SST response to atmospheric variability in AGCMs, which can critically impact the estimation of extreme event probability, particularly in mid-latitude islands, such as Japan.

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.