Cold Sea Surface Temperature Bias Research Articles

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.

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.

Comparisons of simulated radiation, surface wind stress and SST fields over tropical pacific by the GISS CMIP6 versions of global climate models with observations

This study compares the overall performance between versions 2.1 and 3 of National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) global climate models (referred to as GISS-E2.1 and GISS-E3, respectively), in simulating the present-day Pacific climate using the CMIP6 protocol. Model physical representations and configurations are extensively changed from GISS-E2.1 to GISS-E3, which result in greatly reduced discrepancies, including ice water path (IWP), ice water content (IWC), radiative fluxes, surface wind stress (TAU), sea surface temperature (SST), precipitation (PR) and column water vapor (PRW), relative to satellite-based observational products over south Pacific oceans. Cloud only IWP (CIWP) shows the largest change, decreasing biases from ∼400 g kg−1 in GISS-E2.1 to 10–20 g kg−1 in GISS-E3. The combination of improved CIWP and the inclusion of snow in GISS-E3 may play roles on reducing overestimated outgoing longwave radiation, overestimated reflected shortwave at the top of atmosphere, and underestimated surface downward shortwave in GISS-E2.1. Both models’ intertropical convergence zones (ITCZs) are, however, located far too north of the equator, as found in radiative fluxes, PR and PRW but not in SST relative to observations. This introduces biases in TAU, PR and PRW over north flank of the equator and north Pacific. Over south Pacific, especially the trade wind regions, the improvements of radiation fluxes, SST, PR and PRW appear to be due to improved TAU associated with inclusion of snow-radiative effects. In particular, GISS-E3 reduces a longstanding too warm SST bias over trade-wind regions, from 4 K in GISS-E2.1 to within 0.5 K, and too cold SST bias over north Pacific Ocean. Although GISS-E3 shows improved geographic patterns of the simulated fields in particular over south Pacific oceans compared to GISS-E2.1, our results suggest that the location of ITCZ needs to be further improved.

Mitigating Climate Biases in the Midlatitude North Atlantic by Increasing Model Resolution: SST Gradients and Their Relation to Blocking and the Jet

Abstract Starting to resolve the oceanic mesoscale in climate models is a step change in model fidelity. This study examines how certain obstinate biases in the midlatitude North Atlantic respond to increasing resolution (from 1° to 0.25° in the ocean) and how such biases in sea surface temperature (SST) affect the atmosphere. Using a multimodel ensemble of historical climate simulations run at different horizontal resolutions, it is shown that a severe cold SST bias in the central North Atlantic, common to many ocean models, is significantly reduced with increasing resolution. The associated bias in the time-mean meridional SST gradient is shown to relate to a positive bias in low-level baroclinicity, while the cold SST bias causes biases also in static stability and diabatic heating in the interior of the atmosphere. The changes in baroclinicity and diabatic heating brought by increasing resolution lead to improvements in European blocking and eddy-driven jet variability. Across the multimodel ensemble a clear relationship is found between the climatological meridional SST gradients in the broader Gulf Stream Extension area and two aspects of the atmospheric circulation: the frequency of high-latitude blocking and the southern-jet regime. This relationship is thought to reflect the two-way interaction (with a positive feedback) between the respective oceanic and atmospheric anomalies. These North Atlantic SST anomalies are shown to be important in forcing significant responses in the midlatitude atmospheric circulation, including jet variability and the storm track. Further increases in oceanic and atmospheric resolution are expected to lead to additional improvements in the representation of Euro-Atlantic climate.

Mean State of the Northern Hemisphere Stratospheric Polar Vortex in Three Generations of CMIP Models

Abstract Progress and persistent biases in the simulation of the stratospheric polar vortex from three generations of Coupled Model Intercomparison Projects (CMIPs) are assessed. On average, the stratospheric cold bias is largest in CMIP3, but is improved in CMIP5 and CMIP6. The climatological ridge in the stratosphere over the North Pacific is underestimated persistently across CMIPs. Four parameters of the stratospheric polar vortex are evaluated among models and among CMIPs, and some common biases are identified for most models, including the too-large size of the vortex periphery, overstrong strength, too-small aspect ratio of the vortex shape, and too-westward displaced a vortex centroid. Intermodel spread in some parameters is highly correlated with the sea surface temperature (SST) bias in northern tropical oceans. Namely, a cold SST bias in the northern tropical Pacific is associated with a too-strong and too-large polar vortex, and a cold SST bias in the northern tropical Atlantic seems to be related to the westward bias of the vortex centroid. The implications of biases in the mean state of the vortex for stratospheric events such as sudden stratospheric warmings (SSWs) are also identified in this study. Models with a climatological vortex that is too strong, has too low an aspect ratio, or has a westward-biased centroid usually produce fewer SSWs, whereas ones with weaker strength, high aspect ratio, and eastward-biased centroid produce more. Based on the multimodel mean, the biases of all parameters have improved across three generations of CMIPs, although persistent biases in some individual models exist across CMIPs. Intermodel relationships between the polar vortex and SST highlights the important role of the SST simulation for the stratosphere.

Comparing surface wind stress and sea surface temperature biases over the tropical and subtropical oceans in subsets of CMIP6 models categorized by frozen hydrometeors-radiation interactions

We evaluate the simulations of surface wind stress (TAU) and sea surface temperature (SST) over subtropical and tropical Pacific and Atlantic oceans in subsets of CMIP6 models that are categorized by frozen hydrometeors-radiation interactions. The CMIP6 models are divided into two subsets with combined (SON1) and separated (SON2) radiative properties of cloud ice and falling ice (snow) and compared to the set with cloud ice radiative effects only (NOS). There is evidence that these hydrometeors-radiation interaction treatments induce different atmospheric dynamic responses that influence the surface properties. Excessive westerly TAU and meridional TAU divergence away from convective zones are reduced significantly in SON1 and SON2 relative to NOS against QuikSCAT observations; while the differences between SON2 and SON1 are small. SON2 reduces cold SST biases over north oceans and equatorial zones drastically (1 to 2 K), and warm biases (up to 1 K) off the coasts of America and zonal TAU biases are reduced relative to NOS. Unlike SON2, SON1 improves SSTs mainly over south of Pacific Ocean and limited areas over the tropical belts relative to NOS although TAU is reduced drastically as in SON2, implying that other factors play a role in degrading the SST simulations in SON1 relative to SON2. SON2 outperforms NOS and SON1 in the seasonal cycles of SST mean biases and mean absolute biases averaged over the equatorial area, north ocean, and South Pacific against ERSST observations. Despite the significant improvements in TAU and SST simulations, SON2 models still exhibit non-trivial biases over south and north flanks of equatorial zones. These results suggest that there are direct linkages of TAU with SST changes resulting from the hydrometeors-radiation interactions in SON2, but not in SON1, relative to NOS, implying that a separated treatment of cloud ice and falling ice radiative properties in climate models is preferred.

Atmosphere-driven cold SST biases over the western North Pacific in the GloSea5 seasonal forecast system

The predictability of the sea surface temperature (SST) in seasonal forecast systems is crucial for accurate seasonal predictions. In this study, we evaluated the prediction of SST in the Global Seasonal forecast system version 5 (GloSea5) hindcast, particularly focusing on the western North Pacific (WNP), where the SST can modify atmospheric convection and the East Asian weather. GloSea5 has a cold SST bias in the WNP that grows over at least 7 months. The bias originates from the surface net heat flux. At the beginning of model integration, the ocean receives excessive heat from the atmosphere because of the predominant positive bias in the downward shortwave radiation (SW), which rapidly decreased within a few days as cloud cover builds. Then, the negative bias in the latent heat (LH) flux increases over time and induces a negative bias in the surface net heat flux. Although the magnitude of the negative bias in LH flux gradually decreases, it remains the most significant contributor to the negative bias in the net heat flux bias for more than 250 days. Uncoupled ocean model experiments showed that the ocean model is unlikely to be the primary source of the SST bias.

BCC-CSM2-HR: a high-resolution version of the Beijing Climate Center Climate System Model

Abstract. BCC-CSM2-HR is a high-resolution version of the Beijing Climate Center (BCC) Climate System Model (T266 in the atmosphere and 1/4∘ latitude × 1/4∘ longitude in the ocean). Its development is on the basis of the medium-resolution version BCC-CSM2-MR (T106 in the atmosphere and 1∘ latitude × 1∘ longitude in the ocean) which is the baseline for BCC participation in the Coupled Model Intercomparison Project Phase 6 (CMIP6). This study documents the high-resolution model, highlights major improvements in the representation of atmospheric dynamical core and physical processes. BCC-CSM2-HR is evaluated for historical climate simulations from 1950 to 2014, performed under CMIP6-prescribed historical forcing, in comparison with its previous medium-resolution version BCC-CSM2-MR. Observed global warming trends of surface air temperature from 1950 to 2014 are well captured by both BCC-CSM2-MR and BCC-CSM2-HR. Present-day basic atmospheric mean states during the period from 1995 to 2014 are then evaluated at global scale, followed by an assessment on climate variabilities in the tropics including the tropical cyclones (TCs), the El Niño–Southern Oscillation (ENSO), the Madden–Julian Oscillation (MJO), and the quasi-biennial oscillation (QBO) in the stratosphere. It is shown that BCC-CSM2-HR represents the global energy balance well and can realistically reproduce the main patterns of atmospheric temperature and wind, precipitation, land surface air temperature, and sea surface temperature (SST). It also improves the spatial patterns of sea ice and associated seasonal variations in both hemispheres. The bias of the double intertropical convergence zone (ITCZ), obvious in BCC-CSM2-MR, almost disappears in BCC-CSM2-HR. TC activity in the tropics is increased with resolution enhanced. The cycle of ENSO, the eastward propagative feature and convection intensity of MJO, and the downward propagation of QBO in BCC-CSM2-HR are all in a better agreement with observations than their counterparts in BCC-CSM2-MR. Some imperfections are, however, noted in BCC-CSM2-HR, such as the excessive cloudiness in the eastern basin of the tropical Pacific with cold SST biases and the insufficient number of tropical cyclones in the North Atlantic.

Comparison of MMCFS and SINTEX‐F2 for seasonal prediction of Indian summer monsoon rainfall

AbstractA comparison of the Indian summer Monsoon Rainfall (ISMR) in two different coupled models (viz., Scale Interaction Experiment‐Frontier‐F2; SINTEX‐F2, and Monsoon Mission Climate Forecast System; MMCFS) is carried out to ascertain the predictability sources in these models and their strengths and weaknesses. SINTEX‐F2 has a stronger cold sea surface temperature (SST) bias in the central equatorial Pacific, and it simulates mean ISMR better while underestimating the interannual variability of ISMR. On the other hand, MMCFS has warmer SST bias in the tropical Pacific off the equator, simulates a drier mean monsoon, but has a more realistic ISMR interannual variability. Further, the cold SST bias in the central tropical Pacific adversely affects the ability of the SINTEX‐F2 to capture the El Niño–Southern Oscillation (ENSO) related interannual variability and teleconnection patterns with monsoon by shifting it further westward in the tropical Pacific Ocean. The models' skill in simulating various climate indices are compared by considering criteria such as anomaly correlation coefficient (ACC), spread to root mean square error (RMSE) ratio, and signal to noise ratio (SNR). The prediction skill for ISMR in terms of ACC in MMCFS (.53) and SINTEX‐F2 (.45) are comparable for hindcasts initialized in February month. However, RMSE for ISMR from February initial conditions in SINTEX‐F2 (1.43 mm/day) is small compared to MMCFS (2.34 mm/day). A simple multi‐model ensemble prediction system based on MMCFS and SINTEX‐F2 results in better prediction skill in terms of ACC for tropical SST and ISMR. The spread/RMSE ratio for ISMR is similar in both the models but is better for ENSO indices in MMCFS at a longer lead time than in SINTEX‐F2. Considering the SNR as performance criteria, MMCFS has an advantage due to better predictability of SST and vertical wind shear in several parts of the Indo‐Pacific domain.

Impact of the tropical Pacific SST biases on the simulation and prediction of Indian summer monsoon rainfall in CFSv2, ECMWF-System4, and NMME models

Present study analyses the role of tropical Pacific sea surface temperature (SST) biases in simulating the mean and inter-annual variability of Indian summer monsoon rainfall (ISMR) using the hindcasts from Monsoon Mission CFSv2 (MMCFS) and ECMWF-SYSTEM4 (ES4) prediction systems. ES4 simulates the mean and annual cycle of ISMR better than MMCFS when initialized with February initial conditions (3-month lead, Feb IC). At the same time, interannual variability (IAV) and skill (correlation between the ensemble mean hindcast and observations) are the highest (least) for MMCFS (ES4). May IC (0-month lead) hindcasts of both the models have similar mean, annual cycle, IAV and skill. Further analysis shows that ES4_Feb IC hindcasts exhibit a strong cold bias (~ 2 °C) in the equatorial central Pacific, which is close to zero for MMCFS_Feb IC. Meanwhile, the 0-month lead (May IC) hindcast has a similar cold bias (~ 1.5 °C) in the equatorial Pacific for both the model hindcasts. Thus, model hindcasts with a better mean ISMR are having a strong cold bias in the equatorial Pacific and that having very less SST bias has higher IAV and skill for ISMR simulations. Additional investigation using North American National Multi-Model Ensemble Project (NMME) models is carried out and the following conclusion on the different roles of Pacific mean state bias is drawn. Hindcasts with strong cold SST bias in the tropical Pacific (ES4 hindcast and May IC hindcasts of MMCFS) tend to mimic the teleconnections associated with La Nina conditions reducing the dry bias over India, resulting in mean ISMR closer to the observed value, still less than the observed mean. At the same time, due to the same strong cold bias, the El Nino-Southern Oscillation (ENSO) induced rainfall and circulation pattern in the Pacific are weak and extended further northwestward. This weakens the ENSO induced heat sources over the Indian Ocean and monsoon region, resulting in reduced ENSO-ISMR teleconnections. These factors result in a poor simulation of interannual variability and skill of ISMR. On the other hand, model hindcasts with less cold bias in the equatorial Pacific (MMCFS_Feb IC) suffer from strong dry bias over India; however, the skill of ISMR is higher as a result of strong ENSO-Monsoon teleconnections. Thus the study confirms the differential role of SST bias in the tropical Pacific in simulating mean and IAV of ISMR in seasonal prediction models. A close to observed mean SST in the tropical Pacific and proper ENSO-Monsoon teleconnection is essential for the better skill of ISMR in the present generation seasonal prediction models.

Simulation and Improvements of Oceanic Circulation and Sea Ice by the Coupled Climate System Model FGOALS-f3-L

This study documents simulated oceanic circulations and sea ice by the coupled climate system model FGOALS-f3-L developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, under historical forcing from phase 6 of the Coupled Model Intercomparison Project (CMIP6). FGOALS-f3-L reproduces the fundamental features of global oceanic circulations, such as sea surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD), vertical temperature and salinity, and meridional overturning circulations. There are notable improvements compared with the previous version, FGOALS-s2, such as a reduction in warm SST biases near the western and eastern boundaries of oceans and salty SSS biases in the tropical western Atlantic and eastern boundaries, and a mitigation of deep MLD biases at high latitudes. However, several obvious biases remain. The most significant biases include cold SST biases in the northwestern Pacific (over 4°C), freshwater SSS biases and deep MLD biases in the subtropics, and temperature and salinity biases in deep ocean at high latitudes. The simulated sea ice shows a reasonable distribution but stronger seasonal cycle than observed. The spatial patterns of sea ice are more realistic in FGOALS-f3-L than its previous version because the latitude-longitude grid is replaced with a tripolar grid in the ocean and sea ice model. The most significant biases are the overestimated sea ice and underestimated SSS in the Labrador Sea and Barents Sea, which are related to the shallower MLD and weaker vertical mixing.

Analysis of ENSO simulation biases in FIO-ESM version 1.0

As the most significant interannual variability in the climate system, El Niño-Southern Oscillation (ENSO) has critical effects on global weather and climate patterns. To simulate and predict ENSO, coupled general circulation models (CGCMs) have become a key tool. However, the accurate simulation of ENSO is still a challenge for CGCMs. The performance of El Niño simulations conducted through FIO-ESM v1.0 is examined based on the outputs of the Coupled Model Intercomparsion Project phase 5 (CMIP5) historical experiments. The results show that FIO-ESM v1.0 suffers from similar common problems to other CMIP5 models, including an eastward shift in the central locations of El Niño, adopting a regular period of roughly 3 years, addressing excessively high amplitude, spurious eastward propagation of El Niño events, and Aborted El Niño events. El Niño composite and mixed layer heat budget analyses indicate that these simulation biases are mainly associated with the mean state biases, including a warm Sea Surface Temperature (SST) bias for the central-eastern Pacific, a cold SST bias for the western and central Pacific, seasonal cycles of SST of the equatorial eastern Pacific, and weaker trade winds. Weaker SST-cloud-shortwave radiation feedback in La Niña events than in El Niño events is what creates spurious ENSO amplitude symmetry in the model. We suggest that the improvement of El Niño simulations may be realized by focusing on the mean state and SST-cloud-shortwave radiation feedback in the tropical region. Specifically, further incremental improvements in the mean state of the tropical Pacific should constitute the first step to realizing more accurate ENSO simulation.

Impact of convective parameterization on the seasonal prediction skill of Indian summer monsoon

The sensitivity of seasonal predictions of the Indian summer monsoon (ISM) to convection parameterization schemes (CPS) is studied using 37 years of hindcast experiments. The predictions are quite sensitive to changes in these schemes and improve the skill by 18–28%. Though the mean state circulation and rainfall over India improves, the sea surface temperature (SST) biases increase in the sensitivity experiments compared to the control run. The ability of the model to realistically capture the teleconnections associated with monsoon such as the El-Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) also appears to change with different CPS. It is found that the suitability of a CPS for ISM in the Climate Forecast System version 2 (CFSv2) stems from its ability to capture cloud fractions realistically and keep the SST biases to a minimum. The revised Simplified-Arakawa–Schubert (SAS2, Han and Pan in Weather Forecast 26:520–533. https://doi.org/10.1175/waf-d-10-05038.1 , 2011) scheme gives better prediction skill for ISM compared to the skill score obtained from SAS2 with shallow convection (SAS2sc) primarily because it simulates realistic clouds, without aggravating the SST biases, particularly in the tropical Pacific Ocean, and captures the Indian Ocean teleconnections realistically. SAS2sc significantly under-estimates the low-level clouds over global equatorial region, despite simulating better mid and high-level clouds, higher Nino 3.4 skill, and better inter-annual variability of ISM. The cold SST bias in the tropical basins is large in SAS2sc. Therefore, to exploit the merits of SAS2sc, unrealistic suppression of low clouds needs to be addressed, and the cold SST biases need to be minimized.

The effect of the equatorial Pacific cold SST bias on simulated ENSO teleconnections to the North Pacific and California

Precipitation in California is modulated by variability in the tropical Pacific associated with El Nino/Southern Oscillation (ENSO): more rainfall is expected during El Nino episodes, and reduced rainfall during La Nina. It has been suggested that besides the shape and location of the sea surface temperature (SST) anomaly this remote connection depends on the strength and location of the atmospheric convection response in the tropical Pacific. Here we show in a perturbed physics ensemble of the Kiel Climate Model and CMIP5 models that due to a cold equatorial SST bias many climate models are in a La Nina-like mean state, resulting in a too westward position of the rising branch of the Pacific Walker Circulation. This in turn results in a convective response along the equator during ENSO events that is too far west in comparison to observations. This effect of the equatorial cold SST bias is not restricted to the tropics, moreover it leads to a too westward SLP response in the North Pacific and too westward precipitation response that does not reach California. Further we show that climate models with a reduced equatorial cold SST bias have a more realistic representation of the spatial asymmetry of the teleconnections between El Nino and La Nina.

Influence of Model Bias on Simulating North Atlantic Sea Surface Temperature During the Mid-Pliocene

Climate models generally underestimate the pronounced warming in the sea surface temperature (SST) over the North Atlantic during the mid‐Pliocene that is suggested by proxy data. Here we investigate the influence of the North Atlantic cold SST bias, which is observed in many climate models, on the simulation of mid‐Pliocene surface climate in a series of simulations with the Kiel Climate Model. A surface freshwater‐flux correction is applied over the North Atlantic, which considerably improves simulation of North Atlantic Ocean circulation and SST under present‐day conditions. Using reconstructed mid‐Pliocene boundary conditions with closed Bering and Arctic Archipelago Straits, the corrected model depicts significantly reduced model‐proxy SST discrepancy in comparison to the uncorrected model. A key factor in reducing the discrepancy is the stronger and more sensitive Atlantic Meridional Overturning Circulation and poleward heat transport. We conclude that simulations of mid‐Pliocene surface climate over the North Atlantic can considerably benefit from alleviating model biases in this region.

Origins of Biases in CMIP5 Models Simulating Northwest Pacific Summertime Atmospheric Circulation Anomalies during the Decaying Phase of ENSO

Abstract The present study documents the biases of summertime northwest Pacific (NWP) atmospheric circulation anomalies during the decaying phase of ENSO and investigates their plausible reasons in 32 models from phase 5 of the Coupled Model Intercomparison Project. Based on an intermodel empirical orthogonal function (EOF) analysis of El Niño–Southern Oscillation (ENSO)-related 850-hPa wind anomalies, the dominant modes of biases are extracted. The first EOF mode, explaining 21.3% of total intermodel variance, is characterized by a cyclone over the NWP, indicating a weaker NWP anticyclone. The cyclone appears to be a Rossby wave response to unrealistic equatorial western Pacific (WP) sea surface temperature (SST) anomalies related to excessive equatorial Pacific cold tongue in the models. On one hand, the cold SST biases increase the mean zonal SST gradient, which further intensifies warm zonal advection, favoring the development and persistence of equatorial WP SST anomalies. On the other hand, they reduce the anomalous convection caused by ENSO-related warming, and the resultant increase in downward shortwave radiation contributes to the SST anomalies there. The second EOF mode, explaining 18.6% of total intermodel variance, features an anticyclone over the NWP with location shifted northward. The related SST anomalies in the Indo-Pacific sector show a tripole structure, with warming in the tropical Indian Ocean and equatorial central and eastern Pacific and cooling in the NWP. The Indo-Pacific SST anomalies are highly controlled by ENSO amplitude, which is determined by the intensity of subtropical cells via the adjustment of meridional and vertical advection in the models.

SST biases over the Northwest Pacific and possible causes in CMIP5 models

In this paper, the features and possible causes of sea surface temperature (SST) biases over the Northwest Pacific are investigated based on a mixed-layer heat budget analysis in 21 coupled general circulation models (CGCMs) from phase 5 of the Coupled Model Inter-comparison Project (CMIP5). Most CMIP5 models show cold SST biases throughout the year over the Northwest Pacific. The largest biases appear during summer, and the smallest biases occur during winter. These cold SST biases are seen at the basin scale and are mainly located in the inner region of the low and mid-latitudes. According to the mixed-layer heat budget analysis, overestimation of upward net sea surface heat fluxes associated with atmospheric processes are primarily responsible for the cold SST biases. Among the different components of surface heat fluxes, overestimated upward latent heat fluxes induced by the excessively strong surface winds contribute the most to the cold SST biases during the spring, autumn, and winter seasons. Conversely, during the summer, overestimated upward latent heat fluxes and underestimated downward solar radiations at the sea surface are equally important. Further analysis suggests that the overly strong surface winds over the Northwest Pacific during winter and spring are associated with excessive precipitation over the Maritime Continent region, whereas those occurring during summer and autumn are associated with the excessive northward extension of the intertropical convergence zone (ITCZ). The excessive precipitation over the Maritime Continent region and the biases in the simulated ITCZ induce anomalous northeasterlies, which are in favor of enhancing low-level winds over the North Pacific. The enhanced surface wind increases the sea surface evaporation, which contributes to the excessive upward latent heat fluxes. Thus, the SST over the Northwest Pacific cools.

Impact of chlorophyll bias on the tropical Pacific mean climate in an earth system model

Climate modeling groups nowadays develop earth system models (ESMs) by incorporating biogeochemical processes in their climate models. The ESMs, however, often show substantial bias in simulated marine biogeochemistry which can potentially introduce an undesirable bias in physical ocean fields through biogeophysical interactions. This study examines how and how much the chlorophyll bias in a state-of-the-art ESM affects the mean and seasonal cycle of tropical Pacific sea-surface temperature (SST). The ESM used in the present study shows a sizeable positive bias in the simulated tropical chlorophyll. We found that the correction of the chlorophyll bias can reduce the ESM’s intrinsic cold SST mean bias in the equatorial Pacific. The biologically-induced cold SST bias is strongly affected by seasonally-dependent air–sea coupling strength. In addition, the correction of chlorophyll bias can improve the annual cycle of SST by up to 25%. This result suggests a possible modeling approach in understanding the two-way interactions between physical and chlorophyll biases by biogeophysical effects.

Origin of Indian summer monsoon rainfall biases in CMIP5 multimodel ensemble

Significant biases of coupled general circulation models (CGCMs) lead to considerable uncertainty in climate predictions and projections. Based on the historical simulations in the phase 5 of the Coupled Model Intercomparison Project (CMIP5), we identify that state-of-the-art CGCMs commonly simulate insufficient Indian summer monsoon (ISM) rainfall along with too weak monsoon circulations. Such ISM rainfall/circulation biases, however, are absent in the Atmospheric Model Intercomparison Project simulations forced by observed sea surface temperature (SST), suggesting that the common ISM biases are not intrinsic errors of the atmospheric models but arise from the interaction between the monsoon and the oceans. A multimodel statistical analysis further shows that the ISM rainfall/circulation biases in CMIP5 models can be traced back to the excessively cold SST over the northern Indian Ocean (NIO). The systemic cold SST biases in the NIO suppress local convective activity and reduce air temperature, resulting in a weak north–south thermal contrast in the mid-upper troposphere. This would induce an excessively weak ISM circulation and resultant insufficient monsoon rainfall. Furthermore, the dynamic effect of cold NIO SST biases on the ISM rainfall/circulation simulations is also confirmed through several sensitivity experiments by using the widely-applied Weather Research and Forecasting model. To the extent that the cold SST biases over the NIO may originate from an excessively strong Indian winter monsoon, improving the winter monsoon simulation is an important prerequisite for better summer climate simulations and predictions/projections over the broad ISM region.

Structure, characteristics, and simulation of monsoon low‐pressure systems in CFSv2 coupled model

AbstractIndian Summer Monsoon (ISM) synoptic scale systems (low‐pressure systems, LPS) are known to produce increased rainfall over central India (CI). Fidelity of the Climate Forecast System version 2 (CFSv2) at simulating the LPS and their characteristics is evaluated in this study using a feature tracking algorithm. The model is able to reproduce the clustering of LPS by monsoon intraseasonal oscillations and the associated precipitation over eastern‐central India. It is found that mean biases in circulation and moisture stem from cold sea surface temperature (SST) bias in the model which results in weak LPS linked rainfall events over central India. Two sensitivity experiments were carried out to study the effect of coupled dynamics of tropical basins on LPS. Suppression of active dynamics of the tropical Indian Ocean in CFSv2 causes a reduction in cold SST bias and enhanced cyclogenesis in the northern Bay of Bengal. The reduced low‐level anticyclonic bias and enhanced moisture availability result in a better simulation of LPS structure, and associated precipitation over CI. Suppression of active ocean dynamics in tropical Pacific Ocean causes a perennial El‐Niño type bias which restricts LPS propagation over the Indian landmass, possibly due to time‐mean subsidence induced by remote El‐Niño forcing. Sensitivity experiments indicate the need for improvements in the representation of tropical Indian Ocean coupled dynamics as well as convective parameterization schemes in the model for subsequent improvements in the simulation of ISM at various time scales.