On the origin of equatorial Atlantic biases in coupled general circulation models

Many coupled ocean–atmosphere general circulation models (GCMs) suffer serious biases in the tropical Atlantic including a southward shift of the intertropical convergence zone (ITCZ) in the annual mean, a westerly bias in equatorial surface winds, and a failure to reproduce the eastern equatorial cold tongue in boreal summer. The present study examines an ensemble of coupled GCMs and their uncoupled atmospheric component to identify common sources of error. It is found that the westerly wind bias also exists in the atmospheric GCMs forced with observed sea surface temperature, but only in boreal spring. During this time sea-level pressure is anomalously high (low) in the western (eastern) equatorial Atlantic, which appears to be related to deficient (excessive) precipitation over tropical South America (Africa). In coupled simulations, this westerly bias leads to a deepening of the thermocline in the east, which prevents the equatorial cold tongue from developing in boreal summer. Thus reducing atmospheric model errors during boreal spring may lead to improved coupled simulations of tropical Atlantic climate.     

Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons

This paper shows demonstrable improvement in the global seasonal climate predictability of boreal summer (at zero lead) and fall (at one season lead) seasonal mean precipitation and surface temperature from a two-tiered seasonal hindcast forced with forecasted SST relative to two other contemporary operational coupled ocean–atmosphere climate models. The results from an extensive set of seasonal hindcasts are analyzed to come to this conclusion. This improvement is attributed to: (1) The multi-model bias corrected SST used to force the atmospheric model. (2) The global atmospheric model which is run at a relatively high resolution of 50 km grid resolution compared to the two other coupled ocean–atmosphere models. (3) The physics of the atmospheric model, especially that related to the convective parameterization scheme. The results of the seasonal hindcast are analyzed for both deterministic and probabilistic skill. The probabilistic skill analysis shows that significant forecast skill can be harvested from these seasonal hindcasts relative to the deterministic skill analysis. The paper concludes that the coupled ocean–atmosphere seasonal hindcasts have reached a reasonable fidelity to exploit their SST anomaly forecasts to force such relatively higher resolution two tier prediction experiments to glean further boreal summer and fall seasonal prediction skill.     

The variation of ENSO characteristics associated with atmospheric parameter perturbations in a coupled model

We analyse the differences in the properties of the El Niño Southern Oscillation (ENSO) in a set of 17 coupled integrations with the flux-adjusted, 19-level HadCM3 model with perturbed atmospheric parameters. Within this ensemble, the standard deviation of the NINO3.4 deseasonalised SSTs ranges from 0.6 to 1.3 K. The systematic changes in the properties of the ENSO with increasing amplitude confirm that ENSO in HadCM3 is prevalently a surface (or SST) mode. The tropical-Pacific SST variability in the ensemble of coupled integrations correlates positively with the SST variability in the corresponding ensemble of atmosphere models coupled with a static mixed-layer ocean (“slab” models) perturbed with the same changes in atmospheric parameters. Comparison with the respective coupled ENSO-neutral climatologies and with the slab-model climatologies indicates low-cloud cover to be an important controlling factor of the strength of the ENSO within the ensemble. Our analysis suggests that, in the HadCM3 model, increased SST variability localised in the south-east tropical Pacific, not originating from ENSO and associated with increased amounts of tropical stratocumulus cloud, causes increased ENSO variability via an atmospheric bridge mechanism. The relationship with cloud cover also results in a negative correlation between the ENSO activity and the model’s climate sensitivity to doubling CO₂.     

Impacts of Indian Ocean SST biases on the Indian Monsoon: as simulated in a global coupled model

In this study, the impact of the ocean–atmosphere coupling on the atmospheric mean state over the Indian Ocean and the Indian Summer Monsoon (ISM) is examined in the framework of the SINTEX-F2 coupled model through forced and coupled control simulations and several sensitivity coupled experiments. During boreal winter and spring, most of the Indian Ocean biases are common in forced and coupled simulations, suggesting that the errors originate from the atmospheric model, especially a dry islands bias in the Maritime Continent. During boreal summer, the air-sea coupling decreases the ISM rainfall over South India and the monsoon strength to realistic amplitude, but at the expense of important degradations of the rainfall and Sea Surface Temperature (SST) mean states in the Indian Ocean. Strong SST biases of opposite sign are observed over the western (WIO) and eastern (EIO) tropical Indian Ocean. Rainfall amounts over the ocean (land) are systematically higher (lower) in the northern hemisphere and the south equatorial Indian Ocean rainfall band is missing in the control coupled simulation. During boreal fall, positive dipole-like errors emerge in the mean state of the coupled model, with warm and wet (cold and dry) biases in the WIO (EIO), suggesting again a significant impact of the SST errors. The exact contributions and the distinct roles of these SST errors in the seasonal mean atmospheric state of the coupled model have been further assessed with two sensitivity coupled experiments, in which the SST biases are replaced by observed climatology either in the WIO (warm bias) or EIO (cold bias). The correction of the WIO warm bias leads to a global decrease of rainfall in the monsoon region, which confirms that the WIO is an important source of moisture for the ISM. On the other hand, the correction of the EIO cold bias leads to a global improvement of precipitation and circulation mean state during summer and fall. Nevertheless, all these improvements due to SST corrections seem drastically limited by the atmosphere intrinsic biases, including prominently the unimodal oceanic position of the ITCZ (Inter Tropical Convergence Zone) during summer and the enhanced westward wind stress along the equator during fall.     

Remotely forced variability in the tropical Atlantic Ocean

An ensemble of eight hindcasts has been conducted using an ocean-atmosphere general circulation model fully coupled only within the Atlantic basin, with prescribed observational sea surface temperature (SST) for 1950–1998 in the global ocean outside the Atlantic basin. The purpose of these experiments is to understand the influence of the external SST anomalies on the interannual variability in the tropical Atlantic Ocean. Statistical methods, including empirical orthogonal function analysis with maximized signal-to-noise ratio, have been used to extract the remotely forced Atlantic signals from the ensemble of simulations. It is found that the leading external source on the interannual time scales is the El Niño/Southern Oscillation (ENSO) in the Pacific Ocean. The ENSO signal in the tropical Atlantic shows a distinct progression from season to season. During the boreal winter of a maturing El Niño event, the model shows a major warm center in the southern subtropical Atlantic together with warm anomalies in the northern subtropical Atlantic. The southern subtropical SST anomalies is caused by a weakening of the southeast trade winds, which are partly associated with the influence of an atmospheric wave train generated in the western Pacific Ocean and propagating into the Atlantic basin in the Southern Hemisphere during boreal fall. In the boreal spring, the northern tropical Atlantic Ocean is warmed up by a weakening of the northeast trade winds, which is also associated with a wave train generated in the central tropical Pacific during the winter season of an El Niño event. Apart from the atmospheric planetary waves, these SST anomalies are also related to the sea level pressure (SLP) increase in the eastern tropical Atlantic due to the global adjustment to the maturing El Niño in the tropical Pacific. The tropical SLP anomalies are further enhanced in boreal spring, which induce anomalous easterlies on and to the south of the equator and lead to a dynamical oceanic response that causes cold SST anomalies in the eastern and equatorial Atlantic from boreal spring to summer. Most of these SST anomalies persist into the boreal fall season.

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Forecasting extreme events in the tropical Indian Ocean sector climate

Prospects for forecasting climate variability over the tropical Indian Ocean sector, specifically extreme positive events of the Indian Dipole Mode (IDM), with lead times of a season or more are investigated using the NASA Seasonal-to-Interannual Prediction Project (NSIPP) coupled-model system. The coupled system presents biases in its climatology over the Indian Ocean sector, which include (i) warmest sea-surface temperatures (SSTs) occurring in the central equatorial basin rather than on the eastside with the eastern (western) tropical SSTs up to 1 °C too cool (warm), (ii) a too northwest lying InterTropical Convergence Zonal over the ocean in boreal fall, (iii) a thermocline shallower (deeper) than observed west of Sumatra-Java (north of Madagascar), (iv) a delay of about a month in the onset (cessation) of the southwest (southeast) monsoon in the west (east) in boreal spring (fall). These biases affect the effectiveness of the SST-clouds-shortwave radiation negative feedback, the sensitivity of SST to wind-stress perturbations, and the character of equatorial coupled ocean-atmosphere modes. Despite these biases, ensemble hindcasts of the SST anomalies averaged over the eastern and western poles of the IDM for the decade 1993–2002, which included extreme positive events in 1994 and 1997/1998, are encouragingly good at 3-months lead. The onset of the 1997/1998-event is delayed by about a month, though the peak and decay are correctly timed. At 6-months lead-time, the forecast at the eastern pole deteriorates with either positive or negative false alarms generated each boreal fall. The forecast at the western pole remains good.     

ENSO phase-locking to the boreal winter in CMIP3 and CMIP5 models

In this study, the El Nino-Southern Oscillation (ENSO) phase-locking to the boreal winter in CMIP3 and CMIP5 models is examined. It is found that the models that are poor at simulating the winter ENSO peak tend to simulate colder seasonal-mean sea-surface temperature (SST) during the boreal summer and associated shallower thermocline depth over the eastern Pacific. These models tend to amplify zonal advection and thermocline depth feedback during boreal summer. In addition, the colder eastern Pacific SST in the model can reduce the summertime mean local convective activity, which tends to weaken the atmospheric response to the ENSO SST forcing. It is also revealed that these models have more serious climatological biases over the tropical Pacific, implying that a realistic simulation of the climatological fields may help to simulate winter ENSO peak better. The models that are poor at simulating ENSO peak in winter also show excessive anomalous SST warming over the western Pacific during boreal winter of the El Nino events, which leads to strong local convective anomalies. This prevents the southward shift of El Nino-related westerly during boreal winter season. Therefore, equatorial westerly is prevailed over the western Pacific to further development of ENSO-related SST during boreal winter. This bias in the SST anomaly is partly due to the climatological dry biases over the central Pacific, which confines ENSO-related precipitation and westerly responses over the western Pacific.     

Shortwave radiation�CSST relationship over the mid-latitude North Pacific during boreal summer in climate models

Over the mid-latitude North Pacific, there is a close relationship between interannual variations of the sea surface temperature (SST) and surface shortwave radiation during boreal summer. The present study evaluates this relationship in coupled model simulations, forced model simulations, and retrospective forecasts. It is found that the simulation of this relationship in climate models is closely related to the model biases in the meridional gradients of mean SST and surface shortwave radiation. A southward shift in the region of large mean meridional gradients leads to a similar southward shift in the region of large correlation between the SST and shortwave radiation variations. The relationship is enhanced (weakened) when the mean meridional gradients are stronger (weaker) compared to observations. The shortwave radiation?CSST correlation is weak in individual forced simulations because of the interference of internally generated shortwave radiation variations. The shortwave radiation?CSST correlation increases significantly in the ensemble mean due to reduction of internally generated variability. The long-lead Climate Forecast System (CFS) forecasts have a better simulation of the shortwave radiation?CSST correlation compared to the short-lead forecasts. Estimation based on the CFS ensemble forecasts indicates that the high-frequency atmospheric variations contribute importantly to the SST variability over the mid-latitude North Pacific during boreal summer.     

Tropical Atlantic biases and their relation to surface wind stress and terrestrial precipitation

Most coupled general circulation models (GCMs) perform poorly in the tropical Atlantic in terms of climatological seasonal cycle and interannual variability. The reasons for this poor performance are investigated in a suite of sensitivity experiments with the Geophysical Fluid Dynamics Laboratory (GFDL) coupled GCM. The experiments show that a significant portion of the equatorial SST biases in the model is due to weaker than observed equatorial easterlies during boreal spring. Due to these weak easterlies, the tilt of the equatorial thermocline is reduced, with shoaling in the west and deepening in the east. The erroneously deep thermocline in the east prevents cold tongue formation in the following season despite vigorous upwelling, thus inhibiting the Bjerknes feedback. It is further shown that the surface wind errors are due, in part, to deficient precipitation over equatorial South America and excessive precipitation over equatorial Africa, which already exist in the uncoupled atmospheric GCM. Additional tests indicate that the precipitation biases are highly sensitive to land surface conditions such as albedo and soil moisture. This suggests that improving the representation of land surface processes in GCMs offers a way of improving their performance in the tropical Atlantic. The weaker than observed equatorial easterlies also contribute remotely, via equatorial and coastal Kelvin waves, to the severe warm SST biases along the southwest African coast. However, the strength of the subtropical anticyclone and along-shore winds also play an important role.     

An investigation of tropical Atlantic bias in a high-resolution coupled regional climate model

Coupled atmosphere–ocean general circulation models (AOGCMs) commonly fail to simulate the eastern equatorial Atlantic boreal summer cold tongue and produce a westerly equatorial trade wind bias. This tropical Atlantic bias problem is investigated with a high-resolution (27-km atmosphere represented by the Weather Research and Forecasting Model, 9-km ocean represented by the Regional Ocean Modeling System) coupled regional climate model. Uncoupled atmospheric simulations test climate sensitivity to cumulus, land-surface, planetary boundary layer, microphysics, and radiation parameterizations and reveal that the radiation scheme has a pronounced impact in the tropical Atlantic. The CAM radiation simulates a dry precipitation (up to ?90%) and cold land-surface temperature (up to ?8?K) bias over the Amazon related to an over-representation of low-level clouds and almost basin-wide westerly trade wind bias. The Rapid Radiative Transfer Model and Goddard radiation simulates doubled Amazon and Congo Basin precipitation rates and a weak eastern Atlantic trade wind bias. Season-long high-resolution coupled regional model experiments indicate that the initiation of the warm eastern equatorial Atlantic sea surface temperature (SST) bias is more sensitive to the local rather than basin-wide trade wind bias and to a wet Congo Basin instead of dry Amazon—which differs from AOGCM simulations. Comparisons between coupled and uncoupled simulations suggest a regional Bjerknes feedback confined to the eastern equatorial Atlantic amplifies the initial SST, wind, and deepened thermocline bias, while barrier layer feedbacks are relatively unimportant. The SST bias in some CRCM simulations resembles the typical AOGCM bias indicating that increasing resolution is unlikely a simple solution to this problem.     

What controls phase-locking of ENSO to boreal winter in coupled GCMs?

In this study, the phase-locking of El Nino Southern Oscillation (ENSO) in a coupled model with different physical parameter values is investigated. It is found that there is a dramatic change in ENSO phase-locking in response to a slight change in the Tokioka parameter, which is a minimum entrainment rate threshold in the cumulus parameterization. With a smaller Tokioka parameter, the model simulates ENSO peak in the boreal summer season rather than in the winter season as observed. It is revealed that the differences in climatological zonal sea surface temperature (SST) gradient and its associated mean state changes are crucial to determine the phase-locking of ENSO. In the simulations with smaller Tokioka parameter values, climatological zonal SST gradient during the boreal summer is excessively large, because the zonally-asymmetric SST change (i.e., SST increase is relatively smaller over the eastern Pacific) is maximum during the boreal summer when the eastern Pacific SST is the coolest of the year. The enhanced climatological zonal SST gradient in boreal summer reduces the convection over the eastern Pacific, which leads to a weakening of air–sea coupling strength. The minimum coupling strength during summer prevents SST anomalies from further development in the following season, which favors SST maximum during summer. In addition, enhanced zonal SST gradient and resultant thermocline shoaling over the eastern Pacific lead to excessive zonal advective feedback and thermocline feedback. Atmospheric damping is also weakened during boreal summer season. These changes due to feedback processes allow an excessive development of SST anomalies during the summer time, and lead to a summer peak of ENSO. The importance of basic state change for the ENSO phase-locking is also validated in a multi-model framework using the Coupled Model Intercomparison Project phase-3 archive. It is found that several of the climate models have the same problem in producing a summer peak of ENSO. Consistent with the simulations with different physical parameter values, these models have minimum air–sea coupling strength during the boreal summer season. Also, they have stronger climatological zonal SST gradient and shallower climatological thermocline depth over the eastern Pacific during the boreal summer season.     

利用ARGO资料改进ENSO和我国夏季降水气候预测

全球海洋ARGO资料的获取为气候预测的研究提供了前所未有的海洋资料。该文首先利用ARGO资料改进次表层参数化方案后的Zebiak-Cane海洋模式, 与统计大气模式耦合, 建立了热带动力海洋-统计大气耦合模式。通过比较应用和未应用ARGO资料改进的海洋模式, 进行了耦合模式的长期回报试验。结果表明:ARGO资料的应用极大地改善了耦合模式对热带太平洋海面温度异常的预测能力, 提前3个月和6个月的回报结果都有很大的改进, 基本上回报出了Ni?o3.4区海面温度异常的演变特征, 对厄尔尼诺和拉尼娜都能够给出较准确的回报, 回报结果与观测之间的相关性在整个热带太平洋区域明显提高。该文还利用国家气候中心 (NCC) 全球海气耦合模式, 对我国夏季降水进行了个例和多年季度回报模拟试验, 探讨了包含和不包含ARGO观测资料的同化资料作为初始场对我国夏季降水预测的影响, 表明采用带有ARGO观测资料的海洋同化初始场, 回报的我国夏季降水分布形式与观测更一致, 回报结果与观测之间的正相关区域变大, 对我国夏季降水的回报水平比采用没有ARGO观测资料的海洋同化初始场时有明显提高。     

Impact of improved assimilation of temperature and salinity for coupled model seasonal forecasts

We assess the impact of improved ocean initial conditions for predicting El Niño-Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) using the Bureau of Meteorology’s Predictive Ocean Atmosphere Model for Australia (POAMA) coupled seasonal prediction model for the period 1982–2006. The new ocean initial conditions are provided by an ensemble-based analysis system that assimilates subsurface temperatures and salinity and which is a clear improvement over the previous optimal interpolation system which used static error covariances and was univariate (temperature only). Hindcasts using the new ocean initial conditions have better skill at predicting sea surface temperature (SST) variations associated with ENSO than do the hindcasts initialized with the old ocean analyses. The improvement derives from better prediction of subsurface temperatures and the largest improvements come during ENSO–IOD neutral years. We show that improved prediction of the Niño3.4 SST index derives from improved initial depiction of the thermocline and halocline in the equatorial Pacific but as lead time increases the improved depiction of the initial salinity field in the western Pacific become more important. Improved ocean initial conditions do not translate into improved skill for predicting the IOD but we do see an improvement in the prediction of subsurface temperatures in the Indian Ocean (IO). This result reflects that the coupling between subsurface and surface temperature variations is weaker in the IO than in the Pacific, but coupled model errors may also be limiting predictive skill in the IO.     

The seasonal predictability of the Asian summer monsoon in a two-tiered forecast system

An extensive set of boreal summer seasonal hindcasts from a two tier system is compared with corresponding seasonal hindcasts from two other coupled ocean–atmosphere models for their seasonal prediction skill (for precipitation and surface temperature) of the Asian summer monsoon. The unique aspect of the two-tier system is that it is at relatively high resolution and the SST forcing is uniquely bias corrected from the multi-model averaged forecasted SST from the two coupled ocean–atmosphere models. Our analysis reveals: (a) The two-tier forecast system has seasonal prediction skill for precipitation that is comparable (over the Southeast Asian monsoon) or even higher (over the South Asian monsoon) than the coupled ocean–atmosphere. For seasonal anomalies of the surface temperature the results are more comparable across models, with all of them showing higher skill than that for precipitation. (b) Despite the improvement from the uncoupled AGCM all models in this study display a deterministic skill for seasonal precipitation anomalies over the Asian summer monsoon region to be weak. But there is useful probabilistic skill for tercile anomalies of precipitation and surface temperature that could be harvested from both the coupled and the uncoupled climate models. (c) Seasonal predictability of the South Asian summer monsoon (rainfall and temperature) does seem to stem from the remote ENSO forcing especially over the Indian monsoon region and the relatively weaker seasonal predictability in the Southeast Asian summer monsoon could be related to the comparatively weaker teleconnection with ENSO. The uncoupled AGCM with the bias corrected SST is able to leverage this teleconnection for improved seasonal prediction skill of the South Asian monsoon relative to the coupled models which display large systematic errors of the tropical SST’s.     

An assessment of the ENSO forecast skill of GEOS-5 system

The seasonal forecast skill of the NASA Global Modeling and Assimilation Office atmosphere–ocean coupled global climate model (AOGCM) is evaluated based on an ensemble of 9-month lead forecasts for the period 1993 to 2010. The results from the current version (V2) of the AOGCM consisting of the GEOS-5 AGCM coupled to the MOM4 ocean model are compared with those from an earlier version (V1) in which the AGCM (the NSIPP model) was coupled to the Poseidon Ocean Model. It was found that the correlation skill of the Sea Surface Temperature (SST) forecasts is generally better in V2, especially over the sub-tropical and tropical central and eastern Pacific, Atlantic, and Indian Ocean. Furthermore, the improvement in skill in V2 mainly comes from better forecasts of the developing phase of ENSO from boreal spring to summer. The skill of ENSO forecasts initiated during the boreal winter season, however, shows no improvement in terms of correlation skill, and is in fact slightly worse in terms of root mean square error (RMSE). The degradation of skill is found to be due to an excessive ENSO amplitude. For V1, the ENSO amplitude is too strong in forecasts starting in boreal spring and summer, which causes large RMSE in the forecast. For V2, the ENSO amplitude is slightly stronger than that in observations and V1 for forecasts starting in boreal winter season. An analysis of the terms in the SST tendency equation, shows that this is mainly due to an excessive zonal advective feedback. In addition, V2 forecasts that are initiated during boreal winter season, exhibit a slower phase transition of El Nino, which is consistent with larger amplitude of ENSO after the ENSO peak season. It is found that this is due to weak discharge of equatorial Warm Water Volume (WWV). In both observations and V1, the discharge of equatorial WWV leads the equatorial geostrophic easterly current so as to damp the El Nino starting in January. This process is delayed by about 2 months in V2 due to the slower phase transition of the equatorial zonal current from westerly to easterly.     

A systematic approach to identify the sources of tropical SST errors in coupled models using the adjustment of initialised experiments

Understanding the sources of systematic errors in climate models is challenging because of coupled feedbacks and errors compensation. The developing seamless approach proposes that the identification and the correction of short term climate model errors have the potential to improve the modeled climate on longer time scales. In previous studies, initialised atmospheric simulations of a few days have been used to compare fast physics processes (convection, cloud processes) among models. The present study explores how initialised seasonal to decadal hindcasts (re-forecasts) relate transient week-to-month errors of the ocean and atmospheric components to the coupled model long-term pervasive SST errors. A protocol is designed to attribute the SST biases to the source processes. It includes five steps: (1) identify and describe biases in a coupled stabilized simulation, (2) determine the time scale of the advent of the bias and its propagation, (3) find the geographical origin of the bias, (4) evaluate the degree of coupling in the development of the bias, (5) find the field responsible for the bias. This strategy has been implemented with a set of experiments based on the initial adjustment of initialised simulations and exploring various degrees of coupling. In particular, hindcasts give the time scale of biases advent, regionally restored experiments show the geographical origin and ocean-only simulations isolate the field responsible for the bias and evaluate the degree of coupling in the bias development. This strategy is applied to four prominent SST biases of the IPSLCM5A-LR coupled model in the tropical Pacific, that are largely shared by other coupled models, including the Southeast Pacific warm bias and the equatorial cold tongue bias. Using the proposed protocol, we demonstrate that the East Pacific warm bias appears in a few months and is caused by a lack of upwelling due to too weak meridional coastal winds off Peru. The cold equatorial bias, which surprisingly takes 30 years to develop, is the result of an equatorward advection of midlatitude cold SST errors. Despite large development efforts, the current generation of coupled models shows only little improvement. The strategy proposed in this study is a further step to move from the current random ad hoc approach, to a bias-targeted, priority setting, systematic model development approach.     

Hindcast skill and predictability for precipitation and two-meter air temperature anomalies in global circulation models over the Southeast United States

This paper presents an assessment of the seasonal prediction skill of current global circulation models, with a focus on the two-meter air temperature and precipitation over the Southeast United States. The model seasonal hindcasts are analyzed using measures of potential predictability, anomaly correlation, Brier skill score, and Gerrity skill score. The systematic differences in prediction skill of coupled ocean–atmosphere models versus models using prescribed (either observed or predicted) sea surface temperatures (SSTs) are documented. It is found that the predictability and the hindcast skill of the models vary seasonally and spatially. The largest potential predictability (signal-to-noise ratio) of precipitation anywhere in the United States is found in the Southeast in the spring and winter seasons. The maxima in the potential predictability of two-meter air temperature, however, reside outside the Southeast in all seasons. The largest deterministic hindcast skill over the Southeast is found in wintertime precipitation. At the same time, the boreal winter two-meter air temperature hindcasts have the smallest skill. The large wintertime precipitation skill, the lack of corresponding two-meter air temperature hindcast skill, and a lack of precipitation skill in any other season are features common to all three types of models (atmospheric models forced with observed SSTs, atmospheric models forced with predicted SSTs, and coupled ocean–atmosphere models). Atmospheric models with observed SST forcing demonstrate a moderate skill in hindcasting spring-and summertime two-meter air temperature anomalies, whereas coupled models and atmospheric models forced with predicted SSTs lack similar skill. Probabilistic and categorical hindcasts mirror the deterministic findings, i.e., there is very high skill for winter precipitation and none for summer precipitation. When skillful, the models are conservative, such that low-probability hindcasts tend to be overestimates, whereas high-probability hindcasts tend to be underestimates.     

What controls equatorial Atlantic winds in boreal spring?

The factors controlling equatorial Atlantic winds in boreal spring are examined using both observations and general circulation model (GCM) simulations from the coupled model intercomparison phase 5. The results show that the prevailing surface easterlies flow against the attendant pressure gradient and must therefore be maintained by other terms in the momentum budget. An important contribution comes from meridional advection of zonal momentum but the dominant contribution is the vertical transport of zonal momentum from the free troposphere to the surface. This implies that surface winds are strongly influenced by conditions in the free troposphere, chiefly pressure gradients and, to a lesser extent, meridional advection. Both factors are linked to the patterns of deep convection. Applying these findings to GCM errors indicates, that, consistent with the results of previous studies, the persistent westerly surface wind bias found in most GCMs is due mostly to precipitation errors, in particular excessive precipitation south of the equator over the ocean and deficient precipitation over equatorial South America. Free tropospheric influences also dominate the interannual variability of surface winds in boreal spring. GCM experiments with prescribed climatological sea-surface temperatures (SSTs) indicate that the free tropospheric influences are mostly associated with internal atmospheric variability. Since the surface wind anomalies in boreal spring are crucial to the development of warm SST events (Atlantic Niños), the results imply that interannual variability in the region may rely far less on coupled air–sea feedbacks than is the case in the tropical Pacific.     

Indian Ocean and monsoon coupled interactions in a warming environment

Several studies have drawn attention to the steady warming of the equatorial and tropical Indian Ocean (IO) sea surface temperature (SST) observed during recent decades. An intriguing aspect of the IO SST warming trend is that it is has been accompanied by a pronounced weakening of the large-scale boreal summer monsoon circulation. Based on a detailed diagnostic analysis of observed datasets, reanalysis products and IPCC AR4 coupled model output, this study examines how the observed changes in the summer monsoon circulation could have contributed to this SST warming trend. The present results reveal that the weakening trend of the summer monsoon cross-equatorial flow has favored a reorientation of surface westerlies towards the equatorial IO during recent decades, relative to summer monsoons of earlier decades, which were dominated by stronger cross-equatorial flow. Our analysis suggests that the weakening of the summer monsoon cross-equatorial flow has in turn significantly accelerated the SST warming in the central equatorial IO. While the trend in the equatorial westerlies has promoted downwelling and thermocline deepening in the eastern equatorial IO, the central equatorial IO warming is attributed to reduced upwelling in response to a weakening trend of the wind-stress curl. The observed trends in Indian monsoon rainfall and the near-equatorial SST warming are shown to be closely related to variations in the meridional gradient of the monsoon zonal winds. An examination of the twentieth century simulations from 22 IPCC AR4 models, suggests that some models capture the recent equatorial IO warming associated with the weakened summer monsoon circulation reasonably well. The individual member models, however, exhibit significant inter-model variations in representing the observed response of the IO and monsoon coupled system.     

Characteristics of tropical Pacific SST predictability in coupled GCM forecasts using the NCEP CFS

The limits of predictability of El Niño and the Southern Oscillation (ENSO) in coupled models are investigated based on retrospective forecasts of sea surface temperature (SST) made with the National Centers for Environmental Prediction (NCEP) coupled forecast system (CFS). The influence of initial uncertainties and model errors associated with coupled ENSO dynamics on forecast error growth are discussed. The total forecast error has maximum values in the equatorial Pacific and its growth is a strong function of season irrespective of lead time. The largest growth of systematic error of SST occurs mainly over the equatorial central and eastern Pacific and near the southeastern coast of the Americas associated with ENSO events. After subtracting the systematic error, the root-mean-square error of the retrospective forecast SST anomaly also shows a clear seasonal dependency associated with what is called spring barrier. The predictability with respect to ENSO phase shows that the phase locking of ENSO to the mean annual cycle has an influence on the seasonal dependence of skill, since the growth phase of ENSO events is more predictable than the decay phase. The overall characteristics of predictability in the coupled system are assessed by comparing the forecast error growth and the error growth between two model forecasts whose initial conditions are 1 month apart. For the ensemble mean, there is fast growth of error associated with initial uncertainties, becoming saturated within 2 months. The subsequent error growth follows the slow coupled mode related the model’s incorrect ENSO dynamics. As a result, the Lorenz curve of the ensemble mean NINO3 index does not grow, because the systematic error is identical to the same target month. In contrast, the errors of individual members grow as fast as forecast error due to the large instability of the coupled system. Because the model errors are so systematic, their influence on the forecast skill is investigated by analyzing the erroneous features in a long simulation. For the ENSO forecasts in CFS, a constant phase shift with respect to lead month is clear, using monthly forecast composite data. This feature is related to the typical ENSO behavior produced by the model that, unlike the observations, has a long life cycle with a JJA peak. Therefore, the systematic errors in the long run are reflected in the forecast skill as a major factor limiting predictability after the impact of initial uncertainties fades out.