Sea Surface Temperature Biases Research Articles

Constraints on the Projected Tropical Pacific Sea Surface Temperature Warming Pattern by the Tropical North Atlantic Cold SST Bias in CMIP6 Models

AbstractReliable projections of the tropical Pacific sea surface temperature (SST) warming (TPSW) patterns are critically important for exploring the future climate change. However, climate models suffer from long‐standing common biases in simulating the present‐day climate, raising doubts about the model projected TPSW patterns. Here by using outputs from 30 CMIP6 models, we find the projected TPSW patterns are significantly correlated with the simulated present‐day SST in the tropical North Atlantic (TNA), with higher present‐day TNA SSTs tending to project more weakened zonal SST gradients by producing more present‐day low‐level clouds and the resultant positive cloud–shortwave–SST feedbacks over the eastern equatorial Pacific. An emergent constraint using observed TNA SST reveals a consistent El Niño‐like warming pattern in all models with more weakened zonal SST gradient than before in most models, together with a reduction of the inter‐model uncertainty in the zonal SST gradient change by more than 20%.

Greenland summer blocking characteristics: an evaluation of a high-resolution multi-model ensemble

AbstractAtmospheric blocking is a phenomenon that can lead to extreme weather events over a large region, yet its causes are not fully understood. Global climate models show limitations in representing Northern Hemisphere blocking, especially its frequency, and decadal variability in Greenland blocking in summer in the recent decades. In this study we evaluate the ability of high-resolution (HighResMIP) Earth System Models (ESMs) to simulate summer blocking over the Greenland area, using different but complementary methods to describe the characteristics of blocking. We find that the HighResMIP ensemble can reproduce the spatial pattern of Greenland blocking events, albeit with systematic biases, and capture the relative frequencies of the main blocking patterns: namely the wave breaking structure, North Atlantic ridge, and omega-type blocking. However, the HighResMIP ensemble fails to simulate the observed temporal variations of Greenland blocking index (GB2) and the extremely high values of daily GB2 observed in recent decades. In addition, we do not find clearly superior representation of blocking features from higher-resolution in HighResMIP models compared with lower-resolution models. We also find large sea surface temperature (SST) biases over the North Atlantic and seas surrounding Greenland, and biases in moisture transport over the North Atlantic toward Greenland, especially over the western flank of blocking areas, which might together contribute to model biases in the representation of blocking magnitude.

Structural and Dynamical Quality Assessment of Gap‐Filled Sea Surface Temperature Products

AbstractPrevious studies that intercompared global Level‐4 (L4) sea surface temperature (SST) analyses were centered on the assessment of the accuracy and bias of SST by comparing them with independent near‐surface Argo profile temperature data. This type of assessment is centered on the absolute value of SST rather than on SST spatial properties (gradients), which is more relevant to the study of oceanographic features (e.g., fronts, eddies, etc.) and ocean dynamics. Here, we use, for the first time, the spectrum of singularity exponents to assess the structural and dynamical quality of different L4 gap‐filled products based on the multifractal theory of turbulence. Singularity exponents represent the geometrical projection of the turbulence cascade, and its singular spectrum can be related to the probability density function of the singularity exponents normalized by the scale. Our results reveal that the different schemes used to produce the L4 SST products generate different singularity spectra, which are then used to identify a potential loss of dynamical information or structural coherence. This new diagnostic constitutes a valuable tool to assess the structural quality of SST products and can support data satellite SST producers efforts to improve the interpolation schemes used to generate gap‐filled SST products.

MJO Diversity in CMIP6 Models

Abstract Recent studies have shown that individual Madden–Julian oscillation (MJO) events can be categorized into four types based on their propagation characteristics: standing, jumping, slow-propagating, and fast-propagating MJOs. While their structures and impacts are well documented in observations, their representation in state-of-the-art climate models has not been investigated. This study evaluates the four types of MJOs in phase 6 of Coupled Model Intercomparison Project (CMIP6) models. While most models show reasonable MJO propagation characteristics, their performance varies among MJO types, with better performance for the propagating MJO types. To identify the factors that drive each MJO type, background conditions at the surface, troposphere, and stratosphere are examined. We found that the standing and fast MJOs are closely associated with sea surface temperature (SST) and precipitable water (PW) distributions. Specifically, negative and positive SST and PW anomalies are observed over the equatorial Pacific during the standing and fast-propagating MJOs, respectively. In contrast, the jumping and slow-propagating MJOs do not show noticeable SST and PW distributions, suggesting that they may be more strongly influenced by the internal dynamics than by the background conditions. It is further found that the models with more frequent standing MJO than fast MJO generally have stronger negative biases in both SST and PW over the equatorial western Pacific. Although the standing MJO is also preferred when the stratospheric wind at 50 hPa is westerly, such a relationship is absent in the models. These results suggest that MJO diversity in climate models could be improved by better simulation of the mean state, especially for the standing and fast-propagating MJOs.

Assessment of the Southern Ocean Sea Surface Temperature Biases in CMIP5 and CMIP6 Models

Assessment of the Southern Ocean Sea Surface Temperature Biases in CMIP5 and CMIP6 Models

Generation of state‐dependent ensemble perturbations based on time‐varying seawater density for GloSea5 initialization

AbstractIn this study, we developed a flow‐dependent oceanic initialization system for initializing the oceanic temperature and salinity in the Global Seasonal forecast system version 5 (GloSea5). Our algorithm overcomes the limitation of stationary perturbations for Ensemble Optimal Interpolation (EnOI) by spreading observed information along isopycnal lines to create three‐dimensional snapshot density states. The proposed algorithm, which we call state‐dependent ensemble‐based EnOI (SD‐EnOI), takes into account changes in the background error covariance over time without relying on ensemble model simulations. To evaluate the quality of the oceanic initial conditions (ICs) produced by SD‐EnOI, we compared them with those generated by the Global Ocean Data Assimilation and Prediction System version 1 (GODAPS1) operated by the Korea Meteorological Agency (KMA) throughout January 2017 to December 2017. Our findings show that the thermal construction of the SD‐EnOI ICs is more realistic than that of GODAPS1, particularly in the tropical Pacific region. The strong warm bias in sea surface temperature (SST) and the shallow mixed‐layer depth bias observed in the GODAPS1 ICs are not shown in SD‐EnOI. Due to the more realistic oceanic thermal structure present in the SD‐EnOI ICs, their use in retrospective forecast experiments resulted in a systematic reduction in climatological SST drift in the central‐eastern Pacific for forecasts up to four lead months compared to using GODAPS1 ICs. This demonstrates the significant impact of the initialization process on the quality of dynamical seasonal forecasts.

Using NIW Observations to Assess Mixed Layer Parameterizations: A Case Study in the Tropical Atlantic

AbstractTropical sea surface temperature (SST) biases can cause atmospheric biases on global scales, hence SST needs to be represented well in climate models. A major source of uncertainties is the representation of turbulent mixing in the oceanic boundary layer, or mixed layer (ML). In the present study we focus on near‐inertial wave (NIW) induced mixing. The performance of two mixing schemes, Turbulent Kinetic Energy and K‐profile parameterization (KPP), is assessed at two sites (11.5°N, 23°W and 15°N, 38°W) in the tropical Atlantic. At 11.5°N, turbulence observations (eddy diffusivities, shear and stratification) are available for comparison. We find that the schemes differ in their representation of NIWs, but both under‐represent the observed enhanced diffusivities below the observed ML. However, we find that the models do mix below the ML at 15°N when a storm passes nearby. The near‐inertial oscillations remain below the ML for the following 10 days. Near‐inertial kinetic energy (NIKE) biases in the models are not directly correlated with the wind speed, the MLD biases, or the stratification at the ML base. Instead, NIKE biases are sensitive to the vertical mixing scheme parameterization. NIKE biases are lowest when the KPP scheme is used.

Eastern Boundary Upwelling Systems in Ocean–Sea Ice Simulations Forced by CORE and JRA55-do: Mean State and Variability at the Surface

Abstract In this paper we summarize improvements in climate model simulation of eastern boundary upwelling systems (EBUS) when changing the forcing dataset from the Coordinated Ocean-Ice Reference Experiments (CORE; ∼2° winds) to the higher-resolution Japanese 55-year Atmospheric Reanalysis for driving ocean–sea ice models (JRA55-do, ∼0.5°) and also due to refining ocean grid spacing from 1° to 0.1°. The focus is on sea surface temperature (SST), a key variable for climate studies, and which is typically too warm in climate model representation of EBUS. The change in forcing leads to a better-defined atmospheric low-level coastal jet, leading to more equatorward ocean flow and coastal upwelling, both in turn acting to reduce SST over the upwelling regions off the west coast of North America, Peru, and Chile. The refinement of ocean resolution then leads to narrower and stronger alongshore ocean flow and coastal upwelling, and the emergence of strong across-shore temperature gradients not seen with the coarse ocean model. Off northwest Africa the SST bias mainly improves with ocean resolution but not with forcing, while in the Benguela, JRA55-do with high-resolution ocean leads to lower SST but a substantial bias relative to observations remains. Reasons for the Benguela bias are discussed in the context of companion regional ocean model simulations. Finally, we address to what extent improvements in mean state lead to changes to the monthly to interannual variability. It is found that large-scale SST variability in EBUS on monthly and longer time scales is largely governed by teleconnections from climate modes and less sensitive to model resolution and forcing than the mean state.

Development of Indian summer monsoon precipitation biases in two seasonal forecasting systems and their response to large-scale drivers

Abstract. The Met Office Global Coupled Model (GC) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) are both widely used for predicting and simulating the Indian summer monsoon (ISM), and previous studies have demonstrated similarities in the biases in both systems at a range of timescales from weather forecasting to climate simulation. In this study, ISM biases are studied in seasonal forecasting setups of the two systems in order to provide insight into how they develop across timescales. Similarities are found in the development of the biases between the two systems, with an initial reduction in precipitation followed by a recovery associated with an increasingly cyclonic wind field to the north-east of India. However, this occurs on longer timescales in CFSv2, with a much stronger recovery followed by a second reduction associated with sea surface temperature (SST) biases so that the bias at longer lead times is of a similar magnitude to that in GC. In GC, the precipitation bias is almost fully developed within a lead time of just 8 d, suggesting that carrying out simulations with short time integrations may be sufficient for obtaining substantial insight into the biases in much longer simulations. The relationship between the precipitation and SST biases in GC seems to be more complex than in CFSv2 and differs between the early part of the monsoon season and the later part of the monsoon season. The relationship of the bias with large-scale drivers is also investigated, using the boreal summer intraseasonal oscillation (BSISO) index as a measure of whether the large-scale dynamics favour increasing, active, decreasing or break monsoon conditions. Both models simulate decreasing conditions the best and increasing conditions the worst, in agreement with previous studies and extending these previous results to include CFSv2 and multiple BSISO cycles.

Impact of atmospheric forcing on SST biases in the LETKF-based ocean research analysis (LORA)

In the previous study, the authors have produced an eddy-resolving ocean ensemble analysis product called the local ensemble transform Kalman filter (LETKF)-based ocean research analysis (LORA) over the western North Pacific and Maritime Continent regions using an ocean data assimilation system driven by the Japanese operational atmospheric reanalysis dataset known as the JRA-55. However, the LORA includes warm biases in sea surface temperatures (SSTs) in coastal regions during the boreal winter. In this study, we perform sensitivity experiments with atmospheric forcing using an ocean forcing dataset known as the JRA55-do, which adjusts the JRA-55 to high-quality reference datasets to reduce biases and uncertainties. The results show that the nearshore warm SST biases are significantly improved by the JRA55-do. During the boreal autumn, the improvement comes from mainly two factors: (i) enhancement of surface cooling by latent heat releases caused by removing contamination of weak winds at the land grid cells, and (ii) weakening surface heating by downward shortwave radiation through the adjustment in the JRA55-do.During the boreal winter, enhanced cooling by the analysis increments suppresses the growth of the warm SST biases when the JRA55-do is used. However, if the JRA-55 dataset is used, the adaptive observation error inflation (AOEI) scheme acts negatively to keep the nearshore SST biases in winter. Based on the innovation statistics, the AOEI inflates the observation errors when the differences between the squared observation-minus-forecast innovations and the squared forecast ensemble spreads are larger than the prescribed observation error variance, and improves the accuracy in the open ocean, especially around the frontal regions. However, when substantial warm SST biases are formed in the previous season, AOEI's observation error inflation makes the analysis increments smaller and cannot suppress the warm biases.We also validate the analysis accuracy using various data such as sea surface height and horizontal velocities and find that the JRA55-do has significant advantages. Therefore, continuous maintenance and development of ocean forcing datasets are essential for ocean modeling and data assimilation.

Tropical Cyclogenesis Bias over the Central North Pacific in CMIP6 Simulations

Abstract Current coupled climate models contain large biases in simulating tropical cyclogenesis, reducing the confidence in tropical cyclone (TC) projection. In this study, we investigated the influence of sea surface temperature (SST) biases on TC genesis in the Coupled Model Intercomparison Project phase 6 simulations from 1979 to 2014. Positive TC genesis biases were found over the tropical central North Pacific (CNP) in most of climate models, including the high-resolution models. Compared to coupled models, TC genesis density (TCGD) simulations over CNP in uncoupled models forced by observational SST improved obviously. A warm SST bias over the tropical CNP in the coupled models is the main cause of TC genesis biases. The SST bias–induced diabatic heating leads to an anomalous Gill-type atmospheric circulation response, which contributes to a series of favorable environmental conditions for TC formation over the CNP. Numerical experiments were also performed with HiRAM to demonstrate the influence of SST biases on the TCGD simulation, further confirming our conclusion. The current results highlight the importance of improving TC simulation in state-of-the-art climate models by reducing SST simulation bias.

Inconsistent Atlantic Links to Precipitation Extremes over the Humid Tropics

This study investigates extreme wet and dry conditions over the humid tropics and their connections to the variability of the tropical ocean basins using observations and a multi-model ensemble of 24 state-of-the-art coupled climate models, for the 1930–2014 period. The extreme wet (dry) conditions are consistently linked to Central Pacific La Niña (Eastern Pacific El Niño), the weakest being the Congo basin, and homogeneous patterns of sea surface temperature (SST) variability in the tropical Indian Ocean. The Atlantic exhibits markedly varying configurations of SST anomalies, including the Atlantic Niño and pan-Atlantic decadal oscillation, with non-symmetrical patterns between the wet and dry conditions. The oceanic influences are associated with anomalous convection and diabatic heating partly related to variations in the strength of the Walker Circulation. The observed connection between the Amazon basin, as well as the Maritime continent, and the Indo-Pacific variability are better simulated than that of the Congo basin. The observed signs of the Pacific and Indian SST anomalies are reversed for the modelled Congo basin extreme conditions which are, instead, tied to the Atlantic Niño/Niña variability. This Atlantic–Congo basin connection is related to a too southerly location of the simulated inter-tropical convergence zone that is associated with warm SST biases over the Atlantic cold tongue. This study highlights important teleconnections and model improvements necessary for the skillful prediction of extreme precipitation over the humid tropics.

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.

Understanding Biases in Indian Ocean Seasonal SST in CMIP6 Models

AbstractThe latest generation of climate models continue to exhibit biases in their representation of climatological sea surface temperature (SST), affecting their ability to simulate climate variability including the Indian Ocean Dipole which is typically too strong. Here, we analyze the surface layer heat budget of the Indian Ocean to diagnose the processes leading to biases in climatological SST biases in 20 Coupled Model Intercomparison Project Phase 6 (CMIP6) models, in comparison to a suite of observational and reanalysis products. In the western tropical Indian Ocean, we find that weaker than observed winds reduce the strength of surface currents leading to warm SST bias. In the south‐eastern tropical Indian Ocean, overly strong southeasterly winds are associated with overestimated coastal upwelling that increases cooling across CMIP6 models. In the Arabian Sea, overly strong surface winds increase latent heat loss and leads to cool SST biases. We also analyze other regions like the Bay of Bengal, where a persistent cool bias cannot be explained by seasonal heat budgets, and southern Indian Ocean, where overly strong surface winds are responsible for cool SST biases. These biases in surface processes are supported by intermodel relationships between relevant variables, thus explaining differences in simulating the climatological SSTs across the model ensemble. Our analysis suggests that biases in atmospheric processes in particular surface winds are a primary cause of biases in Indian Ocean SST. Reducing these biases would improve the simulation of climate variability in the Indian Ocean toward more reliable climate projections and predictions.

Signal‐to‐noise errors in free‐running atmospheric simulations and their dependence on model resolution

AbstractEnsemble forecasts have been shown to better predict observed Atlantic climate variability than that of their own ensemble members. This phenomenon—termed the signal‐to‐noise paradox—is found to be widespread across models, timescales, and climate variables, and has wide implications. The signal‐to‐noise paradox can be interpreted as forecasts underestimating the amplitude of predictable signals on seasonal‐to‐decadal timescales. The cause of this remains unknown. Here, we examine sea level pressure variability from a very large multi‐model ensemble of uninitialized atmosphere‐only simulations, focusing on boreal winter. To assess signal‐to‐noise errors, the ratio of predictable components (RPC) is examined globally, as well as for regional climate indices: the North Atlantic Oscillation, Arctic Oscillation, Southern Annular Mode, and an Arctic index. Our analyses reveal significant correlations between the multi‐model ensemble‐mean and observations over large portions of the globe, particularly the tropics, North Atlantic, and North Pacific. However, RPC values greater than one are apparent over many extratropical regions and in all four climate indices. Higher‐resolution models produce greater observation‐model correlations and greater RPC values than lower‐resolution models in all four climate indices. We find that signal‐to‐noise errors emerge more clearly at higher resolution, but the amplitudes of predictable signals do not increase with resolution, at least across the range of resolutions considered here. Our results suggest that free‐running atmospheric models underestimate predictable signals in the absence of sea surface temperature biases, implying that signal‐to‐noise errors originate in the atmosphere or in ocean–atmosphere coupling.

Air-sea coupling influence on projected changes in major Atlantic hurricane events

Tropical cyclone (TC) projections with atmosphere-only models are associated with uncertainties due to their inability to represent TC-ocean interactions. However, global coupled models, which represent TC-ocean interactions, can produce basin-scale sea surface temperature biases in seasonal to centennial simulations that lead to challenges in representing TC activity. Therefore, focusing on recent individual major hurricane events, we investigated the influence of TC-ocean coupling on the response of TCs to anthropogenic change using atmosphere-only and coupled atmosphere-ocean regional model simulations. Under an extremely warm scenario, coupling does not influence the signs of projected TC rainfall and intensity responses. Coupling, however, does influence the magnitude of projected intensity and especially rainfall. Within a 500 km radius region of the TCs, the projected rainfall increases in coupled simulations are 3–59 % less than in the atmosphere-only simulations, driven by enhanced TC-induced sea surface temperature cooling in the former. However, the influence of coupling on the magnitude of projected rainfall could vary considerably over the regions of highest rainfall generated by TCs.

Causes of Diverse Impacts of ENSO on the Southeast Asian Summer Monsoon among CMIP6 Models

Abstract This study examines the fidelity of 47 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in representing the influence of El Niño–Southern Oscillation (ENSO) on the Southeast Asian summer monsoon (SEASM) during the ENSO decaying summer. The response of the SEASM to ENSO shows a large model spread among the models, some of which even simulate opposite signs of SEASM anomalies compared to the observed values. The bad-performance models (BPMs) are therefore selected to be compared with both the good-performance models (GPMs) and observations to explore the possible causes of the deficiency. Results show that in the BPMs, the ENSO-related warm sea surface temperature (SST) anomalies extend too far westward in the western equatorial Pacific (WEP) and they do not dissipate in the El Niño decaying summer in comparison with those in the GPMs and observations, interfering with the effect of ENSO on the SEASM. The slow decay of WEP SST anomalies from the El Niño mature winter to the decaying summer in the BPMs is mainly caused by a weak negative shortwave radiation feedback due to a low sensitivity of convection to local SST anomalies, which is related to the cold bias in climatological SST over this region. On the other hand, from the mature winter to the decaying summer of El Niño, the El Niño–related anomalous eastward current does not reverse to a westward current in the BPMs, which also contributes to the slow decay of WEP SST anomalies via inducing excessively persistent warm zonal advection. Significance Statement We investigate the possible causes of the diverse impacts of El Niño–Southern Oscillation (ENSO) on the Southeast Asian summer monsoon (SEASM) among 47 CMIP6 models. We find that a plausible reason for the deficiency of some models in simulating the influence of ENSO on the monsoon is that the sea surface temperature (SST) anomalies associated with ENSO are unrealistic in the western equatorial Pacific (WEP) in these models. Further diagnoses indicate that the unrealistic WEP SST anomalies are related to the cold bias of the climatological SST, which could lead to a weak negative shortwave radiation feedback and excessively persistent warm zonal advection. The information provided in this study is useful for improving the skill of the climate models in representing the ENSO–SEASM relationship.

How Well Do CMIP6 Models Simulate Salinity Barrier Layers in the North Indian Ocean?

Abstract Previous studies have hypothesized that climatologically thick salinity-stratified barrier layers (BLs) in the north Indian Ocean (NIO) influence the upper ocean heat budget, sea surface temperature (SST), and monsoons. Here, we investigate how state-of-the-art Coupled Model Intercomparison Project phase 6 (CMIP6) climate models simulate the NIO barrier layer thickness (BLT). CMIP6 models generally reproduce the BLT seasonal cycle and spatial distribution, but with shallow November–February (NDJF) biases in regions with thick observed BLT: the eastern equatorial Indian Ocean (EEIO), Bay of Bengal (BoB), and southeastern Arabian Sea (SEAS). We show that the intensity of the CMIP6 equatorial easterly wind bias controls the EEIO shallow isothermal layer depth (ILD) and BLT biases. It also controls the BoB shallow BLT bias, both through the propagation of the EEIO shallow ILD bias into the NIO coastal waveguide and because it is linked to the BoB dry and cold bias through the Bjerknes feedback, hence also controlling the mixed layer depth (MLD) deep bias there. Finally, the SEAS shallow BLT bias is due to a too-deep MLD, in response to subdued monsoonal currents around India, which do not bring enough BoB low-salinity water. The BL insulating effect mentioned in literature does not seem to dominate in CMIP6. Rather, the CMIP6 salinity-related deep MLD biases diminish the BoB cooling rate by winter upward surface heat fluxes, reducing cold SST biases. This suggests that salinity effects alleviate the easterly equatorial wind, cold, and dry BoB biases that develop through the positive Bjerknes feedback loop in CMIP6.

Inter-model spread in East Asian Summer Monsoon onset timing linked to the tropical Pacific and Indian oceans' mean state in CMIP6 models

A major challenge for Earth system climate models in simulating the onset of the East Asian summer monsoon (EASM) is the wide inter-model spread of outputs, which causes considerable uncertainty in projections. Based on 31 models of the Coupled Model Inter-comparison Project Phase 6 (CMIP6), this study investigated the inter-model spread of EASM onset biases and its physical connection with simulated atmospheric circulation and tropical Pacific mean state biases. The inter-model leading mode of EASM onset biases exhibits a meridional seesaw pattern, with inverse patterns for South China and the Yangtze–Yellow River basins. The leading mode is closely related to the simulated meridional displacement of the East Asian subtropical westerly jet (EASWJ) and the Northwest Pacific subtropical anticyclone (WNPAC). The northward EASWJ and WNPAC respectively cause ascending or descending motions with moisture convergence or divergence in the Yangtze–Yellow River basins of South China. The leading mode can be attributed to simulated biases in the zonal tripole sea surface temperature (SST) in the tropical Pacific Ocean. Statistical analysis and numerical experiments based on CAM5.0 indicate that the SST bias stimulates “double-loop” zonal vertical circulation in the tropical Pacific and Indian oceans, influencing convection precipitation in the tropical central–western Pacific. The altered convection then triggers a Pacific–Japan-like pattern anomaly, forming meridional seesaw circulation and precipitation anomalies in East Asia. Such meridional seesaw circulation modulates the meridional displacement of the EASWJ and WNPAC, thus affecting EASM onset. Our results provide potential indicators of inter-model uncertainty of EASM onset.

Local and Remote Causes of the Equatorial Pacific Cold Sea Surface Temperature Bias in the Kiel Climate Model

Abstract We investigate the origin of the equatorial Pacific cold sea surface temperature (SST) bias and its link to wind biases, local and remote, in the Kiel Climate Model (KCM). The cold bias is common in climate models participating in phases 5 and 6 of the Coupled Model Intercomparison Project. In the coupled experiments with the KCM, the interannually varying NCEP/CFSR wind stress is prescribed over four spatial domains: globally, over the equatorial Pacific (EP), the northern Pacific (NP), and the southern Pacific (SP). The corresponding EP SST bias is reduced by 100%, 52%, 12%, and 23%, respectively. Thus, the EP SST bias is mainly attributed to the local wind bias, with small but not negligible contributions from the extratropical regions. Erroneous ocean circulation driven by overly strong winds causes the cold SST bias, while the surface heat flux counteracts it. Extratropical Pacific SST biases contribute to the EP cold bias via the oceanic subtropical gyres, which is further enhanced by dynamical coupling in the equatorial region. The origin of the wind biases is examined by forcing the atmospheric component of the KCM in a stand-alone mode with observed SSTs and simulated SSTs from the coupled experiments. Wind biases over the EP, NP, and SP regions originate in the atmosphere model. The cold EP SST bias substantially enhances the wind biases over all three regions, while the NP and SP SST biases support local amplification of the wind bias. This study suggests that improving surface wind stress, at and off the equator, is a key to improve mean-state equatorial Pacific SST in climate models.