Warm Sea Surface Temperature Bias Research Articles

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.

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.

California margin temperatures modulate regional circulation and extreme summer precipitation in the desert Southwest

In August 2022, Death Valley, the driest place in North America, experienced record flooding from summertime rainfall associated with the North American monsoon (NAM). Given the socioeconomic cost of these type of events, there is a dire need to understand their drivers and future statistics. Existing theory predicts that increases in the intensity of precipitation is a robust response to anthropogenic warming. Paleoclimatic evidence suggests that northeast Pacific (NEP) sea surface temperature (SST) variability could further intensify summertime NAM rainfall over the desert southwest. Drawing on this paleoclimatic evidence, we use historical observations and reanalyzes to test the hypothesis that warm SSTs on the southern California margin are linked to more frequent extreme precipitation events in the NAM domain. We find that summers with above-average coastal SSTs are more favorable to moist convection in the northern edge of the NAM domain (southern California, Arizona, New Mexico, and the southern Great Basin). This is because warmer SSTs drive circulation changes that increase moisture flux into the desert southwest, driving more frequent precipitation extremes and increases in seasonal rainfall totals. These results, which are robust across observational products, establish a linkage between marine and terrestrial extremes, since summers with anomalously warm SSTs on the California margin have been linked to seasonal or multi-year NEP marine heatwaves. However, current generation earth system models (ESMs) struggle to reproduce the observed relationship between coastal SSTs and NAM precipitation. Across models, there is a strong negative relationship between the magnitude of an ESM’s warm SST bias on the California margin and its skill at reproducing the correlation with desert southwest rainfall. Given persistent NEP SST biases in ESMs, our results suggest that efforts to improve representation of climatological SSTs are crucial for accurately predicting future changes in hydroclimate extremes in the desert southwest.

Origins of Southern Ocean warm sea surface temperature bias in CMIP6 models

The warm sea surface temperature (SST) bias in the Southern Ocean (SO) has persisted in several generations of Coupled Model Inter-comparison Project (CMIP) models, yet the origins of such a bias remain controversial. Using the latest CMIP6 models, here we find that the warm SST bias in the SO features a zonally oriented non-uniform pattern mainly located between the northern and southern fronts of the Antarctic Circumpolar Current. This common bias is not likely to be caused by the biases in the surface heat flux or the strength of the Atlantic meridional overturning circulation (AMOC) — the two previously suggested sources of the SO bias based on CMIP5 models. Instead, it is linked to the robust common warm bias in the Northern Atlantic deep ocean through the AMOC transport as an adiabatic process. Our findings indicate that remote oceanic biases that are dynamically connected to the SO should be taken into account to reduce the SO SST bias in climate models.

The Impact of Tropical SST Biases on the S2S Precipitation Forecast Skill over the Contiguous United States in the UFS Global Coupled Model

Abstract This work investigates the impact of tropical sea surface temperature (SST) biases on the Subseasonal to Seasonal Prediction project (S2S) precipitation forecast skill over the contiguous United States (CONUS) in the Unified Forecast System (UFS) coupled model Prototype 6. Boreal summer (June–September) and winter (December–March) for 2011–18 were analyzed. The impact of tropical west Pacific (WP) and tropical North Atlantic (TNA) warm SST biases is evaluated using multivariate linear regression analysis. A warm SST bias over the WP influences the CONUS precipitation remotely through a Rossby wave train in both seasons. During boreal winter, a warm SST bias over the TNA partly affects the magnitude of the North Atlantic subtropical high (NASH)’s center, which in the reforecasts is weaker than in reanalysis. The weaker NASH favors an enhanced moisture transport from the Gulf of Mexico, leading to increased precipitation over the Southeast United States. Compared to reanalysis, during boreal summer, the NASH’s center is also weaker and in addition, its position is displaced to the northeast. The displacement further affects the CONUS summer precipitation. The SST biases over the two tropical regions and their impacts become stronger as the forecast lead increases from week 1 to 4. These tropical biases explain up to 10% of the CONUS precipitation biases on the S2S time scale.

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.

Impact of the Benguela coastal low-level jet on the southeast tropical Atlantic SST bias in a regional ocean model

The current generation of coupled ocean–atmosphere climate models, which are widely used for studying the dynamics of the climate system and to make projections of future climate, suffers from erroneously warm (exceeding 5 °C) sea surface temperature (SST) simulation in the eastern boundary upwelling systems (EBUS) in the Atlantic and Pacific oceans. While recent improvements in the horizontal resolution of coupled model components helped to alleviate this issue in the North and South Pacific and North Atlantic oceans, the warm bias in the Southeast Tropical Atlantic (SETA) region still remains above 5 °C. Recent studies have highlighted the importance of inaccuracies in the near-coastal winds, especially in the Benguela low-level coastal jet (BLLCJ), in the formation of this warm SST bias. In addition, unique ocean circulation features like the poleward flowing coastal current and the presence of a strong coastal SST front were argued to contribute to the complexity in simulating realistic SST in the SETA region. In this study, we investigate how ocean model resolution and the spatial structure and strength of the BLLCJ affect the SST warm bias in the SETA. We conducted a suite of ocean model experiments with varying horizontal resolutions (81–3 km) and different atmospheric forcing products. We found that the accuracy in the magnitude of BLLCJ’s alongshore component and in the spatial structure of BLLCJ are the primary factors in reducing the warm SST bias in SETA and the ocean model horizontal resolution is of only secondary importance. A weaker alongshore component of the BLLCJ leads to a weaker upwelling through Ekman transport and fails to generate a downwind equatorward surface jet in the ocean. When the core of the BLLCJ is too far offshore, the broad wind stress curl zone gives rise to anomalous poleward depth integrated flow through Sverdrup balance and weakens the wind stress curl induced Ekman pumping near the coast. Both the presence of a poleward current, which can transport warm equatorial waters farther south, and the absence of the equatorward surface jet contributes to the warm SST bias. Heat budget analysis shows that the contribution from the anomalous poleward flow is a bigger contributor to the warm bias than the weak upwelling. With more realistic, high-resolution winds, an ocean model resolution increase from 27 to 9 km slightly reduces the extent and magnitude of SST bias. However, no such improvement occurs with coarse resolution less realistic winds. Sensitivity experiments using atmospheric forcing fields from different sources confirm that the major contribution to the warm SST bias is in fact from the erroneous winds rather than other atmospheric forcing fields like net shortwave and downward longwave radiation. Further studies are needed to understand whether eddy heat advection or mean flow heat advection primarily cause the warm SST bias.

An overview of the performance of CMIP6 models in the tropical Atlantic: mean state, variability, and remote impacts

General circulation models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) are examined with respect to their ability to simulate the mean state and variability of the tropical Atlantic and its linkage to the tropical Pacific. While, on average, mean state biases have improved little, relative to the previous intercomparison (CMIP5), there are now a few models with very small biases. In particular the equatorial Atlantic warm SST and westerly wind biases are mostly eliminated in these models. Furthermore, interannual variability in the equatorial and subtropical Atlantic is quite realistic in a number of CMIP6 models, which suggests that they should be useful tools for understanding and predicting variability patterns. The evolution of equatorial Atlantic biases follows the same pattern as in previous model generations, with westerly wind biases during boreal spring preceding warm sea-surface temperature (SST) biases in the east during boreal summer. A substantial portion of the westerly wind bias exists already in atmosphere-only simulations forced with observed SST, suggesting an atmospheric origin. While variability is relatively realistic in many models, SSTs seem less responsive to wind forcing than observed, both on the equator and in the subtropics, possibly due to an excessively deep mixed layer originating in the oceanic component. Thus models with realistic SST amplitude tend to have excessive wind amplitude. The models with the smallest mean state biases all have relatively high resolution but there are also a few low-resolution models that perform similarly well, indicating that resolution is not the only way toward reducing tropical Atlantic biases. The results also show a relatively weak link between mean state biases and the quality of the simulated variability. The linkage to the tropical Pacific shows a wide range of behaviors across models, indicating the need for further model improvement.

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.

The Southeastern Tropical Atlantic SST Bias Investigated with a Coupled Atmosphere–Ocean Single-Column Model at a PIRATA Mooring Site

AbstractWarm sea surface temperature (SST) biases in the tropical Atlantic Ocean form a longstanding problem in coupled general circulation models (CGCMs). Considerable efforts to understand the origins of these biases and alleviate them have been undertaken, but state-of-the-art CGCMs still suffer from biases that are very similar to those of the generation of models before. In this study, we use a powerful combination of in situ moored buoy observations and a new coupled ocean–atmosphere single-column model (SCM) with parameterization that is identical to that of a three-dimensional CGCM to investigate the SST bias. We place the SCM at the location of a Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) mooring in the southeastern tropical Atlantic, where large SST biases occur in CGCMs. The SCM version of the EC-Earth state-of-the-art coupled GCM performs well for the first five days of the simulation. Then, it develops an SST bias that is very similar to that of its three-dimensional counterpart. Through a series of sensitivity experiments we demonstrate that the SST bias can be reduced by 70%. We achieve this result by enhancing the turbulent vertical ocean mixing efficiency in the ocean parameterization scheme. The underrepresentation of vertical mixing in three-dimensional CGCMs is a candidate for causing the warm SST bias. We further show that surface shortwave radiation does not cause the SST bias at the location of the PIRATA mooring. Rather, a warm atmospheric near-surface temperature bias and a wet moisture bias contribute to it. Strongly nudging the atmosphere to profiles from reanalysis data reduces the SST bias by 40%.

The importance of North Atlantic Ocean transports for seasonal forecasts

The Atlantic Meridional Overturning Circulation (AMOC) is a main driver for predictability at decadal time scales, but has been largely ignored in the context of seasonal forecasts. Here, we show compelling evidence that AMOC initialization can have a direct and strong impact on seasonal forecasts. Winter reforecasts with SEAS5, the current operational seasonal forecasting system by the European Centre for Medium-Range Weather Forecasts, exhibit errors of sea-surface temperature (SST) in the western part of the North Atlantic Subpolar Gyre that are strongly correlated with decadal variations in the AMOC initial conditions. In the early reforecast period 1981–1996, too warm SST coincide with an overly strong AMOC transporting excessive heat into the region. In the ocean reanalyses providing the forecast initial conditions, excessive heat transport is balanced by additional surface cooling from relaxing towards observed SST, and therefore the fit to observations is acceptable. However, the additional surface cooling contributes to enhanced deep convection and strengthens the AMOC, thereby establishing a feedback loop. In the forecasts, where the SST relaxation is absent, the balance is disrupted, and fast growth of SST errors ensues. The warm SST bias has a strong local impact on surface air temperature, mean sea-level pressure, and precipitation patterns, but remote impact is small. In the late reforecast period 2001–2016, neither the SST in the western North Atlantic nor the AMOC show large biases. The non-stationarity of the bias prevents an effective forecast calibration and causes an apparent loss of skill in the affected region. The case presented here demonstrates the importance of correctly initializing slowly varying aspects of the Earth System such as the AMOC in order to improve forecasts on seasonal and shorter time scales.

FIO‐ESM Version 2.0: Model Description and Evaluation

AbstractThe First Institute of Oceanography Earth System Model (FIO‐ESM) version 2.0 was developed and participated in the Climate Model Intercomparison Project phase 6 (CMIP6). In comparison with FIO‐ESM v1.0, all component models of FIO‐ESM v2.0 are updated, and their resolutions are fined. In addition to the non‐breaking surface wave‐induced mixing (Bv), which has also been included in FIO‐ESM v1.0, there are three more distinctive physical processes in FIO‐ESM v2.0, including the effect of surface wave Stokes drifts on air–sea momentum and heat fluxes, the effect of wave‐induced sea spray on air–sea heat fluxes and the effect of sea surface temperature (SST) diurnal cycle on air–sea heat and gas fluxes. The FIO‐ESM v2.0 has conducted the CMIP6 Diagnostic, Evaluation, and Characterization of Klima (DECK) and historical experiments. The historical simulation of FIO‐ESM v2.0 for 1850–2014 is evaluated, including the surface air temperature (SAT), precipitation, SST, Atlantic Meridional Overturning Circulation (AMOC), El Niño‐Southern Oscillation (ENSO), and so forth. The climate changes with respect to SAT and SST global warming and decreasing AMOC are well reproduced by FIO‐ESM v2.0. The correlation coefficient of the global annual mean SAT anomaly can reach 0.92 with observations. In particular, the large warm SST bias at the east coast of tropical Pacific from FIO‐ESM v1.0, which is a common challenge for all climate models, is dramatically reduced in FIO‐ESM v2.0 and the ENSO period within the range of 2–7 years is well reproduced with the largest variation of SST anomalies occurring in boreal winter, which is consistent with observations.

Role of wind stress in driving SST biases in the Tropical Atlantic

Coupled climate models used for long-term future climate projections and seasonal or decadal predictions share a systematic and persistent warm sea surface temperature (SST) bias in the tropical Atlantic. This study attempts to better understand the physical mechanisms responsible for the development of systematic biases in the tropical Atlantic using the so-called Transpose-CMIP protocol in a multi-model context. Six global climate models have been used to perform seasonal forecasts starting both in May and February over the period 2000–2009. In all models, the growth of SST biases is rapid. Significant biases are seen in the first month of forecast and, by 6 months, the root-mean-square SST bias is 80% of the climatological bias. These control experiments show that the equatorial warm SST bias is not driven by surface heat flux biases in all models, whereas in the south-eastern Atlantic the solar heat flux could explain the setup of an initial warm bias in the first few days. A set of sensitivity experiments with prescribed wind stress confirm the leading role of wind stress biases in driving the equatorial SST bias, even if the amplitude of the SST bias is model dependent. A reduced SST bias leads to a reduced precipitation bias locally, but there is no robust remote effect on West African Monsoon rainfall. Over the south-eastern part of the basin, local wind biases tend to have an impact on the local SST bias (except in the high resolution model). However, there is also a non-local effect of equatorial wind correction in two models. This can be explained by sub-surface advection of water from the equator, which is colder when the bias in equatorial wind stress is corrected. In terms of variability, it is also shown that improving the mean state in the equatorial Atlantic leads to a beneficial intensification of the Bjerknes feedback loop. In conclusion, we show a robust effect of wind stress biases on tropical mean climate and variability in multiple climate models.

The impact of stochastic physics on the El Ni\xf1o Southern Oscillation in the EC-Earth coupled model

The impact of stochastic physics on El Niño Southern Oscillation (ENSO) is investigated in the EC-Earth coupled climate model. By comparing an ensemble of three members of control historical simulations with three ensemble members that include stochastics physics in the atmosphere, we find that in EC-Earth the implementation of stochastic physics improves the excessively weak representation of ENSO. Specifically, the amplitude of both El Niño and, to a lesser extent, La Niña increases. Stochastic physics also ameliorates the temporal variability of ENSO at interannual time scales, demonstrated by the emergence of peaks in the power spectrum with periods of 5–7 years and 3–4 years. Based on the analogy with the behaviour of an idealized delayed oscillator model (DO) with stochastic noise, we find that when the atmosphere–ocean coupling is small (large) the amplitude of ENSO increases (decreases) following an amplification of the noise amplitude. The underestimated ENSO variability in the EC-Earth control runs and the associated amplification due to stochastic physics could be therefore consistent with an excessively weak atmosphere–ocean coupling. The activation of stochastic physics in the atmosphere increases westerly wind burst (WWB) occurrences (i.e. amplification of noise amplitude) that could trigger more and stronger El Niño events (i.e. increase of ENSO oscillation) in the coupled EC-Earth model. Further analysis of the mean state bias of EC-Earth suggests that a cold sea surface temperature (SST) and dry precipitation bias in the central tropical Pacific together with a warm SST and wet precipitation bias in the western tropical Pacific are responsible for the coupled feedback bias (weak coupling) in the tropical Pacific that is related to the weak ENSO simulation. The same analysis of the ENSO behaviour is carried out in a future scenario experiment (RCP8.5 forcing), highlighting that in a coupled model with an extreme warm SST, characterized by a strong coupling, the effect of stochastic physics on the ENSO representation is opposite. This corroborates the hypothesis that the mean state bias of the tropical Pacific region is the main reason for the ENSO representation deficiency in EC-Earth.

Origin of Warm SST Bias over the Atlantic Cold Tongue in the Coupled Climate Model FGOALS-g2

Most of the coupled models contain a strong warm bias in sea surface temperature (SST) over the Atlantic Cold Tongue (ACT) region (10° S–3° N, 20° W–10° E) during June–August (JJA) and September–November (SON). In this study, the origins of the ACT SST bias and their relative contributions to the bias are explored by conducting a set of sensitivity experiments, which are based on an ocean-ice model, and by ignoring the nonlinear effects of each origin. The origins for the warm bias over the ACT in the coupled climate model during JJA are estimated as follows: westerly wind bias along the equator (5° S–5° N) during March–May (MAM; contributes approximately 32.6% of the warm bias), northerly bias over the southern tropical Atlantic (25° S–3° N, 40° W–20° E) during MAM and JJA (21.4%), bias in the surface specific humidity and surface air temperature (11.9%), and downward shortwave radiation bias (6.5%). The origins of the ACT bias during SON are as follows: northerly bias over the southern tropical Atlantic during SON (31.2%), bias in the surface specific humidity and surface air temperature (27.9%), downward shortwave radiation bias (17.4%), and zonal wind bias (13.4%). Note that these contribution ratios of these origins may be model-dependent. In addition, the local and non-local effects of the zonal wind bias are explored explicitly, while those of all the other biases are examined implicitly. Therefore, a better-performing atmospheric component is crucial when simulating zonal winds during MAM along the equator (5° S–5° N) and meridional winds during MAM, JJA, and SON over the southern tropical Atlantic, which will alleviate the warm bias over the ACT region in the coupled climate model.

The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations

AbstractThe Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Among other applications, GC3 is the basis of the United Kingdom's submission to the Coupled Model Intercomparison Project 6 (CMIP6). This paper documents the model components that make up the configuration (although the scientific descriptions of these components are in companion papers) and details the coupling between them. The performance of GC3 is assessed in terms of mean biases and variability in long climate simulations using present‐day forcing. The suitability of the configuration for predictability on shorter time scales (weather and seasonal forecasting) is also briefly discussed. The performance of GC3 is compared against GC2, the previous Met Office coupled model configuration, and against an older configuration (HadGEM2‐AO) which was the submission to CMIP5. In many respects, the performance of GC3 is comparable with GC2, however, there is a notable improvement in the Southern Ocean warm sea surface temperature bias which has been reduced by 75%, and there are improvements in cloud amount and some aspects of tropical variability. Relative to HadGEM2‐AO, many aspects of the present‐day climate are improved in GC3 including tropospheric and stratospheric temperature structure, most aspects of tropical and extratropical variability and top‐of‐atmosphere and surface fluxes. A number of outstanding errors are identified including a residual asymmetric sea surface temperature bias (cool northern hemisphere, warm Southern Ocean), an overly strong global hydrological cycle and insufficient European blocking.

Origin of the warm eastern tropical Atlantic SST bias in a climate model

The substantial warm sea surface temperature bias in the eastern Tropical Atlantic reported in most CMIP5 climate simulations with various models, in particular along the coast of Namibia and Angola, remains an issue in more recent and CMIP6-ready versions of climate models such as EC-Earth. A complete and original set of experiments with EC-Earth3.1 is performed to investigate the causes and mechanisms responsible for the emergence and persistence of this bias. The fully-developed bias is studied in a historical experiment that has reached quasi-equilibrium, while retrospective prediction experiments are used to highlight the development/growth from an observed initial state. Prediction experiments are performed at both low and high resolution to assess the possible dependence of the bias on horizontal resolution. Standalone experiments with the ocean and the atmosphere components of EC-Earth are also analyzed to separate the respective contributions of the ocean and atmosphere to the development of the bias. EC-Earth3.1 exhibits a bias similar to that reported in most climate models that took part in CMIP5. The magnitude of this bias, however, is weaker than most CMIP5 models by few degrees. Increased horizontal resolution only leads to a minor reduction of the bias in EC-Earth. The warm SST bias is found to be the result of an excessive solar absorption in the ocean mixed layer, which can be linked to the excessive solar insolation due to unrealistically low cloud cover, and the absence of spatial and temporal variability of the biological productivity in the ocean component. The warm SST bias is further linked to deficient turbulent vertical mixing of cold water to the mixed layer. Our study points at a need for better representation of clouds in the vicinity of eastern boundaries in atmosphere models, and better representation of solar penetration and turbulent mixing in the ocean models in order to eliminate the Tropical Atlantic biases.

Causes of the large warm bias in the Angola\u2013Benguela Frontal Zone in the Norwegian Earth System Model

We have investigated the causes of the sea surface temperature (SST) bias in the Angola–Benguela Frontal Zone (ABFZ) of the southeastern Atlantic Ocean simulated by the Norwegian Earth System Model (NorESM). Similar to other coupled-models, NorESM has a warm SST bias in the ABFZ of up to 8 °C in the annual mean. Our analysis of NorESM reveals that a cyclonic surface wind bias over the ABFZ drives a locally excessively strong southward (0.05 m/s (relative to observation)) Angola Current displacing the ABFZ southward. A series of uncoupled stand-alone atmosphere and ocean model simulations are performed to investigate the cause of the coupled model bias. The stand-alone atmosphere model driven with observed SST exhibits a similar cyclonic surface circulation bias; while the stand-alone ocean model forced with the reanalysis data produces a warm SST in the ABFZ with a magnitude approximately half of that in the coupled NorESM simulation. An additional uncoupled sensitivity experiment shows that the atmospheric model’s local negative surface wind curl generates anomalously strong Angola Current at the ocean surface. Consequently, this contributes to the warm SST bias in the ABFZ by 2 °C (compared to the reanalysis forced simulation). There is no evidence that local air-sea feedbacks among wind stress curl, SST, and sea level pressure (SLP) affect the ABFZ SST bias. Turbulent surface heat flux differences between coupled and uncoupled experiments explain the remaining 2 °C warm SST bias in NorESM. Ocean circulation, upwelling and turbulent heat flux errors all modulate the intensity and the seasonality of the ABFZ errors.

On the link between mean state biases and prediction skill in the tropics: an atmospheric perspective

The present study examines how mean state biases in sea-surface temperature (SST), surface wind and precipitation affect model skill in reproducing surface wind and precipitation anomalies in the tropics. This is done using theoretical arguments, atmosphere-only experiments in the Coupled Model Intercomparison Project Phase 5, and customized sensitivity tests with the SINTEX-F general circulation model. Theoretical arguments suggest that under certain conditions the root mean square error (RMSE) of a variable can be related to its variance and its mean, which indicates a direct link between bias and skill. The anomaly correlation coefficient (ACC), on the other hand, is generally not related to either the mean state or its variance, as several examples document. Multi-model atmosphere-only experiments with prescribed SST warming suggest that both ACC and RMSE of surface wind and precipitation are rather insensitive to warming on the order of 4 K. When SST biases from a free-running control simulation are prescribed in SINTEX-F, the ACC of surface wind is almost unaffected in the equatorial Pacific and Atlantic, while that of precipitation decreases noticeably in some regions but also increases in others. The RMSE of both fields shows widespread deterioration. There is a tendency for warm SST biases to increase the signal-to-noise ratio and sometimes ACC as well. The results suggest that, in the context of atmosphere-only simulations, improving SST and precipitation biases does not necessarily improve the skill in reproducing anomalies of surface wind and precipitation.