Isolating mesoscale coupled ocean–atmosphere interactions in the Kuroshio Extension region

The Kuroshio Extension region is characterized by energetic oceanic mesoscale and frontal variability that alters the air–sea fluxes that can influence large-scale climate variability in the North Pacific. We investigate this mesoscale air-sea coupling using a regional eddy-resolving coupled ocean–atmosphere (OA) model that downscales the observed large-scale climate variability from 2001 to 2007. The model simulates many aspects of the observed seasonal cycle of OA coupling strength for both momentum and turbulent heat fluxes. We introduce a new modeling approach to study the scale-dependence of two well-known mechanisms for the surface wind response to mesoscale sea surface temperatures (SSTs), namely, the ‘vertical mixing mechanism’ (VMM) and the ‘pressure adjustment mechanism’ (PAM). We compare the fully coupled model to the same model with an online, 2-D spatial smoother applied to remove the mesoscale SST field felt by the atmosphere. Both VMM and PAM are found to be active during the strong wintertime peak seen in the coupling strength in both the model and observations. For VMM, large-scale SST gradients surprisingly generate coupling between downwind SST gradient and wind stress divergence that is often stronger than the coupling on the mesoscale, indicating their joint importance in OA interaction in this region. In contrast, VMM coupling between crosswind SST gradient and wind stress curl occurs only on the mesoscale, and not over large-scale SST gradients, indicating the essential role of the ocean mesocale. For PAM, the model results indicate that coupling between the Laplacian of sea level pressure and surface wind convergence occurs for both mesoscale and large-scale processes, but inclusion of the mesoscale roughly doubles the coupling strength. Coupling between latent heat flux and SST is found to be significant throughout the entire seasonal cycle in both fully coupled mode and large-scale coupled mode, with peak coupling during winter months. The atmospheric response to the oceanic mesoscale SST is also studied by comparing the fully coupled run to an uncoupled atmospheric model forced with smoothed SST prescribed from the coupled run. Precipitation anomalies are found to be forced by surface wind convergence patterns that are driven by mesoscale SST gradients, indicating the importance of the ocean forcing the atmosphere at this scale.     

Interdecadal Variability of the East Asian Summer Monsoon in an AGCM

It is well known that significant interdecadal variation of the East Asian summer monsoon （EASM） occurred around the end of the 1970s. Whether these variations can be attributed to the evolution of global sea surface temperature （SST） and sea ice concentration distribution is investigated with an atmospheric general circulation model （AGCM）. The model is forced with observed monthly global SST and sea ice evolution through 1958-1999. A total of four integrations starting from different initial conditions are carried out. It is found that only one of these reproduces the observed interdecadal changes of the EASM after the 1970s, including weakened low-level meridional wind, decreased surface air temperature and increased sea level pressure in central China, as well as the southwestward shift of the western Pacific subtropical high ridge and the strengthened 200-hPa westerlies. This discrepancy among these simulated results suggests that the interdecadal variation of the EASM cannot be accounted for by historical global SST and sea ice evolution. Thus, the possibility that the interdecadal timescale change of monsoon is a natural variability of the coupled climate system evolution cannot be excluded.     

La Plata basin precipitation variability in spring: role of remote SST forcing as simulated by GCM experiments

An ensemble of nine experiments with the same interannually varying sea surface temperature (SST), as boundary forcing, and different initial conditions is used to investigate the role of tropical oceans in modulating precipitation variability in the region of La Plata Basin (LPB). The results from the ensemble are compared with a twentieth-century experiment performed with a coupled ocean-atmosphere model, sharing the same atmospheric component. A rotated empirical orthogonal functions analysis of South America precipitation shows that the dominant mode of variability in spring is realistically captured in both experiments. Its principal component (RPC1) correlated with global SST and atmospheric fields identifies the pattern related to El Niño Southern Oscillation and its large-scale teleconnections. Overall the pattern is well simulated in the tropical southern Pacific Ocean, mainly in the ensemble, but it is absent or too weak in other oceanic areas. The coupled model experiment shows a more realistic correlation in the subtropical South Atlantic where air-sea interactions contribute to the relationship between LPB precipitation and SST. The correspondence between model and data is much improved when the composite analysis of SST and atmospheric fields is done over the ensemble members having an RPC1 in agreement with the observations: the improvement relies on avoiding climate noise by averaging only over members that are statistically similar. Furthermore, the result suggests the presence of a high level of uncertainty due to internal atmospheric variability. The analysis of some individual years selected from the model and data RPC1 comparison reveals interesting differences among rainy springs in LPB. For example, 1982, which corresponds to a strong El Niño year, represents a clean case with a distinct wave train propagating from the central Pacific and merging with another one from the eastern tropical south Indian Ocean. The year 2003 is an example of a rainy spring in LPB not directly driven by remote SST forcing. In this case the internal variability has a dominant role, as the model is not able to reproduce the correct local precipitation pattern.     

The impact of internal atmospheric variability on the North Pacific SST variability

The impact of internal atmospheric variability on North Pacific sea surface temperature (SST) variability is examined based on three coupled general circulation model simulations. The three simulations differ only in the level of atmospheric noise occuring over the ocean at the air-sea interface. The amplitude of atmospheric noise is controlled by use of the interactive ensemble technique. This technique simultaneously couples multiple realizations of a single atmospheric model to a single realization of an ocean model. The atmospheric component models all experience the same SST, but the ocean component is forced by the ensemble averaged fluxes thereby reducing the impact of internal atmospheric dynamics at the air-sea interface. The ensemble averaging is only applied at the air-sea interface so that the internal atmospheric dynamics (i.e., transients) of each atmospheric ensemble member is unaffected. This interactive ensemble technique significantly reduces the SST variance throughout the North Pacific. The reduction in SST variance is proportional to the number of ensemble members indicating that most of the variability can simply be explained as the response to atmospheric stochastic forcing. In addition, the impact of the internal atmospheric dynamics at the air-sea interface masks out much of the tropical-midlatitude SST teleconnections on interannual time scales. Once this interference is reduced (i.e., applying the interactive ensemble technique), tropical-midlatitude SST teleconnections are easily detected.     

Impact of global SST on decadal shift of East Asian summer climate

East Asia experienced a significant interdecadal climate shift around the late 1970s, with more floods in the valley of the Yangtze River of central-eastern China and more severe drought in North China since then. Whether global SST variations have played a role in this shift is unclear. In the present study, this issue is investigated by ensemble experiments of an atmospheric general circulation model (AGCM), the GFDL AM2, since one validation reveals that the model simulates the observed East Asian Summer Monsoon (EASM) well. The results suggest that decadal global SST variations may have played a substantial role in this climate shift. Further examination of the associated atmospheric circulation shows that these results are physically reasonable.     

A further assessment of vegetation feedback on decadal Sahel rainfall variability

The effect of vegetation feedback on decadal-scale Sahel rainfall variability is analyzed using an ensemble of climate model simulations in which the atmospheric general circulation model ICTPAGCM (“SPEEDY”) is coupled to the dynamic vegetation model VEGAS to represent feedbacks from surface albedo change and evapotranspiration, forced externally by observed sea surface temperature (SST) changes. In the control experiment, where the full vegetation feedback is included, the ensemble is consistent with the observed decadal rainfall variability, with a forced component 60 % of the observed variability. In a sensitivity experiment where climatological vegetation cover and albedo are prescribed from the control experiment, the ensemble of simulations is not consistent with the observations because of strongly reduced amplitude of decadal rainfall variability, and the forced component drops to 35 % of the observed variability. The decadal rainfall variability is driven by SST forcing, but significantly enhanced by land-surface feedbacks. Both, local evaporation and moisture flux convergence changes are important for the total rainfall response. Also the internal decadal variability across the ensemble members (not SST-forced) is much stronger in the control experiment compared with the one where vegetation cover and albedo are prescribed. It is further shown that this positive vegetation feedback is physically related to the albedo feedback, supporting the Charney hypothesis.     

Influence of SST biases on future climate change projections

We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977?C1999 in the historical period and 2077?C2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean?Catmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection.     

On the importance of initializing the stochastic part of the atmosphere for forecasting the 1997/1998 El Ni?o

The importance of initializing atmospheric intra-seasonal (stochastic) variations for prediction of the onset of the 1997/1998 El Ni?o is examined using the Australian Bureau of Meteorology coupled seasonal forecast model. A suite of 9-month forecasts was initialized on the 1st December 1996. Observed ocean initial conditions were used together with five different atmospheric initial conditions that sample a range of possible initial states of intra-seasonal (stochastic) variability, especially the Madden-Julian Oscillation (MJO), which is considered the primary stochastic variability of relevance to El Ni?o evolution. The atmospheric initial states were generated from a suite of atmosphere-only integrations forced by observed sea surface temperatures (SST). To the extent that low frequency variability of the tropical atmosphere is forced by slow variations in SST, these atmospheric states should all represent realistic low frequency atmospheric variability that was present in December 1996. However, to the extent that intra-seasonal variability is not constrained by SST, they should capture a range of intra-seasonal states, especially variations in the activity, phase and amplitude of the MJO. For each of these five states, a 20-member ensemble of coupled model forecasts was generated by the addition of small random perturbations to the SST field at the initial time. The ensemble mean from all five sets of forecasts resulted in El Ni?o but three of the sets produced substantially greater warming by months 4?C5 in the NINO3.4 region compared to the other two. The warmer group stemmed from stronger intra-seasonal westerly wind anomalies associated with the MJO that propagated eastward into the central Pacific during the first 1?C2?months of the forecast. These were largely absent in the colder group; the weakest of the colder group developed strong easterly wind anomalies, relative to the grand ensemble mean, that propagated into the central Pacific early in the forecast, thereby generating significantly weaker downwelling Kelvin waves in comparison to the warmer group. The strong reduction in downwelling Kelvin waves in the weakest case acted to limit the warming in the eastern Pacific, resulting in a ??Modoki?? type El Ni?o that is more focused in the central Pacific. Our results suggest that the intra-seasonal stochastic component of the atmospheric initial condition has an important and potentially predictable impact on the forecasts of the initial warming and flavour of the 1997/1998 El Ni?o. However, to the extent that atmospheric intra-seasonal variability is not predictable beyond a month or two, these results imply a limit to the accuracy with which the strength and perhaps the spatial distribution of an El Ni?o can ultimately be predicted. These results do not preclude a predictable role of the MJO and other intra-seasonal stochastic variability at longer lead times if some aspects of the stochastic variability are preconditioned by the evolving state of El Ni?o or other low frequency boundary forcing.     

The relationship between sea surface temperature anomalies and atmospheric circulation in GCM experiments

Several 19-year integrations of the Hamburg version of the ECMWF/T21 general circulation model driven by the monthly mean sea surface temperature (SST) observed in 1970–1988 were examined to study extratropical response of the atmospheric circulation to SST anomalies in the Northern Hemisphere in winter. In the first 19-years run SST anomalies were prescribed globally (GAGO run), and in two others SST monthly variability was limited to extratropical regions (MOGA run) and to tropics (TOGA run), respectively. A canonical correlation analysis (CCA), which select from two time-dependent fields optimally correlated pairs of patterns, was applied to monthly anomalies of SST in the North Alantic and Pacific Oceans and monthly anomalies of sea level pressure and 500 hPa geopotential height in the Northern Hemisphere. In the GAGO run the best correlated atmospheric pattern is global and is characterized by north-south dipole structures of the same polarity in the North Atlantic and the North Pacific sectors. In the MOGA and TOGA experiments the atmospehric response is more local than in the GAGO run with main centers in the North Atlantic and North Pacific, respectively. The extratropical response in the GAGO run is not equal to the sum of the responses in the MOGA and TOGA runs. The artificial meridional SST gradients at 25°–30°N probably influence the results of the MOGA and TOGA runs. The atmopsheric modes found by the CCA were compared with the normal modes of the barotropic vorticity equation linearized about the 500 hPa. winter climate. The normal modes with smallest eigenvalues are similar to the model leading variability modes and canonical patterns of 500 hPa geopotential height. The corresponding eigenvectors of the adjoint operator, which represent an external forcing optimal for exciting normal modes, have a longitudinal structure with maxima in regions characterized by enhanced high frequency baroclinic activity over both oceans.     

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.     

不同海温强迫的月动力延伸集合预报试验

利用全球谱模式T106L19和增长模繁殖法（BGM）建立了月动力延伸集合预报系统,基于气候海表面温度（SST）和预测海表面温度,设计了三组集合预报试验,一组为气候SST作为模式下边界条件的集合预报试验（CSST试验）,另一组为预测SST作为模式的下边界条件的集合预报试验（FSST试验）,第三组为前两组试验的集合预报结果之和（AVE30试验）,对两种海温强迫分别进行了48个月的试验,并对预报结果进行了检验和分析。结果表明：相对于单一的控制预报,不管是CSST试验还是FSST试验,利用BGM方法制作的初值集合预报能显著提高月平均环流的预报技巧,集合预报对PNA区域的预报技巧改进显著,特别是预测SST强迫有正的贡献;同时考虑初值和边值不确定性影响的集合预报试验（AVE30试验）,其全球预报技巧不仅高于控制预报,也分别高于FSST试验和CSST试验,这说明要提高月延伸预报技巧,必须同时考虑初值和边值的影响;大气对SST强迫的响应在模式积分10天开始显著,SST对第二旬和第三旬的作用直接影响月平均环流的预报效果,而SST对第二旬和第三旬预报的影响不仅与SST本身变化有关,还与初值有关,不同的初值其作用不同;集合预报对我国夏季月平均温度分布具有较强预报能力,采用预报海温强迫的预报结果,总体上优于气候海温强迫的结果。     

FGOALS海洋同化试验对西北太平洋夏季SST—降水关系的模拟评估

评估了耦合气候系统模式FGOALS海洋同化试验对西北太平洋夏季降水和SST相关关系的模拟技巧,并对比了相应的观测海温强迫试验（AMIP）和历史气候模拟试验结果。结果显示,FGOALS海洋同化试验对亚洲季风区大部分海域夏季SST年际变化有较高的模拟技巧,但其对菲律宾以东海域模拟技巧较低。在西北太平洋夏季降水-SST相关关系方面,同化试验部分地再现了南海和菲律宾以东海域降水超前SST变化1个月和同时二者的负相关关系,优于AMIP试验但逊于自由耦合模拟试验。同化试验对SST倾向-降水相关关系的模拟技巧亦介于AMIP试验和自由耦合试验之间。观测中,西北太平洋夏季降水与环流异常受日界线附近和赤道东印度洋海洋大陆地区海温异常的遥强迫,并通过改变到达海表的净短波辐射通量影响局地SST异常,导致局地海温-降水和局地海温倾向-降水的负相关关系。在AMIP试验中,遥强迫导致的西北太平洋地区环流异常较之观测偏弱,由于缺少局地海气耦合过程,在西北太平洋多数地区表现为海温对大气的强迫作用,即SST-降水正相关关系。FGOALS同化试验和自由耦合试验考虑了局地海气耦合过程,虽然低估了遥强迫对西北太平洋地区夏季环流异常的影响,依然部分模拟出局地降水-SST负相关关系但较之观测偏弱。同时,自由耦合试验高估了西北太平洋20°N以南地区海温异常对大气环流异常的强迫,使得其对中国南海和日本岛以南海域SST-降水负相关关系的模拟稍优于同化试验。     

Modes of variability of Southern Hemisphere atmospheric circulation estimated by AGCMs

The seasonal mean variability of the atmospheric circulation is affected by processes with time scales from less than seasonal to interannual or longer. Using monthly mean data from an ensemble of Atmospheric General Circulation Model (AGCM) realisations, the interannual variability of the seasonal mean is separated into intraseasonal, and slowly varying components. For the first time, using a recently developed method, the slowly varying component in multiple AGCM ensembles is further separated into internal and externally forced components. This is done for Southern Hemisphere 500?hPa geopotential height from five AGCMs in the CLIVAR International Climate of the Twentieth Century project for the summer and winter seasons. In both seasons, the intraseasonal and slow modes of variability are qualitatively well reproduced by the models when compared with reanalysis data, with a relative metric finding little overall difference between the models. The Southern Annular Mode (SAM) is by far the dominant mode of slowly varying internal atmospheric variability. Two slow-external modes of variability are related to El Ni?o-Southern Oscillation (ENSO) variability, and a third is the atmospheric response to trends in external forcing. An ENSO-SAM relationship is found in the model slow modes of variability, similar to that found by earlier studies using reanalysis data. There is a greater spread in the representation of model slow-external modes in winter than summer, particularly in the atmospheric response to external forcing trends. This may be attributable to weaker external forcing constraints on SH atmospheric circulation in winter.     

Roles of Indian and Pacific Ocean air–sea coupling in tropical atmospheric variability

Sea surface temperature (SST) variations include negative feedbacks from the atmosphere, whereas SST anomalies are specified in stand-alone atmospheric general circulation simulations. Is the SST forced response the same as the coupled response? In this study, the importance of air–sea coupling in the Indian and Pacific Oceans for tropical atmospheric variability is investigated through numerical experiments with a coupled atmosphere-ocean general circulation model. The local and remote impacts of the Indian and Pacific Ocean coupling are obtained by comparing a coupled simulation with an experiment in which the SST forcing from the coupled simulation is specified in either the Indian or the Pacific Ocean. It is found that the Indian Ocean coupling is critical for atmospheric variability over the Pacific Ocean. Without the Indian Ocean coupling, the rainfall and SST variations are completely different throughout most of the Pacific Ocean basin. Without the Pacific Ocean coupling, part of the rainfall and SST variations in the Indian Ocean are reproduced in the forced run. In regions of large mean rainfall where the atmospheric negative feedback is strong, such as the North Indian Ocean and the western North Pacific in boreal summer, the atmospheric variability is significantly enhanced when air–sea coupling is replaced by specified SST forcing. This enhancement is due to the lack of the negative feedback in the forced SST simulation. In these regions, erroneous atmospheric anomalies could be induced by specified SST anomalies derived from the coupled model. The ENSO variability is reduced by about 20% when the Indian Ocean air–sea coupling is replaced by specified SST forcing. This change is attributed to the interfering roles of the Indian Ocean SST and Indian monsoon in western and central equatorial Pacific surface wind variations.     

Evolution of model systematic errors in the Tropical Atlantic Basin from coupled climate hindcasts

Significant systematic errors in the tropical Atlantic Ocean are common in state-of-the-art coupled ocean–atmosphere general circulation models. In this study, a set of ensemble hindcasts from the NCEP coupled forecast system (CFS) is used to examine the initial growth of the coupled model bias. These CFS hindcasts are 9-month integrations starting from perturbed real-time oceanic and atmospheric analyses for 1981–2003. The large number of integrations from a variety of initial states covering all months provides a good opportunity to examine how the model systematic errors grow. The monthly climatologies of ensemble hindcasts from various initial months are compared with both observed and analyzed oceanic and atmospheric datasets. Our analyses show that two error patterns are dominant in the hindcasts. One is the warming of the sea surface temperature (SST) in the southeastern tropical Atlantic Ocean. This error grows faster in boreal summer and fall and peaks in November–December at round 2°C in the open ocean. It is caused by an excessive model surface shortwave radiative flux in this region, especially from boreal summer to fall. The excessive radiative forcing is in turn caused by the CFS inability to reproduce the observed amount of low cloud cover in the southeastern ocean and its seasonal increase. According to a comparison between the seasonal climatologies from the CFS hindcasts and a long-term simulation of the atmospheric model forced with observed SST, the CFS low cloud and radiation errors are inherent to its atmospheric component. On the other hand, the SST error in CFS is a major cause of the model’s southward bias of the intertropical convergence zone (ITCZ) in boreal winter and spring. An analysis of the SST errors of the 6-month ensemble hindcasts by seven coupled models in the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction project shows that this SST error pattern is common in coupled climate hindcasts. The second error pattern is an excessive deepening of the model thermocline depth to the north of the equator from the western coast toward the central ocean. This error grows fastest in boreal summer. It is forced by an overly strong local anticyclonic surface wind stress curl and is in turn related to the weakened northeast trade winds in summer and fall. The thermocline error in the northwest delays the annual shoaling of the equatorial thermocline in the Gulf of Guinea remotely through the equatorial waveguide.     

国家气候中心两个CMIP6模式模拟的东亚夏季风的季节内演变

分析了国家气候中心两个参加第六次国际耦合模式比较计划（CMIP6）的模式BCC-CSM2-MR和BCC-ESM1对东亚夏季风季节内演变的模拟情况,包括气候态特征以及在ENSO（El Ni?o and Southern Oscillation）循环不同位相下的特征。本文同时对比分析了观测海温海冰驱动大气环流模式试验（AMIP试验）以及耦合模式的历史气候模拟试验（Historical试验）的结果。结果表明,模式能够合理地模拟出东亚夏季风环流和降水的气候态特征。相比大气模式,耦合模式能够明显改善对气候态的模拟,特别是耦合模式能够较好地模拟出副热带高压从6～8月向北以及向东移动的季节内演变特征。对于El Ni?o衰减年和La Ni?a年合成来说,大气模式能够在一定程度上模拟出El Ni?o衰减年（La Ni?a年）副高偏西（东）、对流减弱（增强）的特征,但是对于位置和强度的模拟存在偏差,特别是对于其季节内尺度的演变。耦合模式相比大气模式来说,并没有改善对于ENSO循环影响东亚夏季风季节内演变的模拟,这可能和耦合模式模拟的ENSO本身的偏差有关。因此要想改善对于东亚夏季风季节内演变及其年际差异的模拟,除了考虑海气相互作用之外,还需要改进模式对于ENSO的模拟效果。     

Simulation of the Indian monsoon using the RegCM3–ROMS regional coupled model

A regional coupled atmosphere–ocean model was developed to study the role of air–sea interactions in the simulation of the Indian summer monsoon. The coupled model includes the regional climate model (RegCM3) as atmospheric component and the regional ocean modeling system (ROMS) as oceanic component. The two-way coupled model system exchanges sea surface temperature (SST) from the ocean to the atmospheric model and surface wind stress and energy fluxes from the atmosphere to the ocean model. The coupled model is run for four years 1997, 1998, 2002 and 2003 and the results are compared with observations and atmosphere-only model runs employing Reynolds SSTs as lower boundary condition. It is found that the coupled model captures the main features of the Indian monsoon and simulates a substantially more realistic spatial and temporal distribution of monsoon rainfall compared to the uncoupled atmosphere-only model. The intraseasonal oscillations are also better simulated in the coupled model compared to the atmosphere-only model. These improvements are due to a better representation of the feedbacks between the SST and convection and highlight the importance of air–sea coupling in the simulation of the Indian monsoon.     

Seasonal and Intraseasonal Variations of East Asian Summer Monsoon Precipitation Simulated by a Regional Air-Sea Coupled Model

The performance of a regional air-sea coupled model, comprising the Regional Integrated Environment Model System (RIEMS) and the Princeton Ocean Model (POM), in simulating the seasonal and intraseasonal variations of East Asian summer monsoon (EASM) rainfall was investigated. Through comparisons of the model results among the coupled model, the uncoupled RIEMS, and observations, the impact of air-sea coupling on simulating the EASM was also evaluated. Results showed that the regional air-sea coupled climate model performed better in simulating the spatial pattern of the precipitation climatology and produced more realistic variations of the EASM rainfall in terms of its amplitude and principal EOF modes. The coupled model also showed greater skill than the uncoupled RIEMS in reproducing the principal features of climatological intraseasonal oscillation (CISO) of EASM rainfall, including its dominant period, intensity, and northward propagation. Further analysis indicated that the improvements in the simulation of the EASM rainfall climatology and its seasonal variation in the coupled model were due to better simulation of the western North Pacific Subtropical High, while the improvements of CISO simulation were owing to the realistic phase relationship between the intraseasonal convection and the underlying SST resulting from the air-sea coupling.     

An evaluation of the CMIP3 and CMIP5 simulations in their skill of simulating the spatial structure of SST variability

The natural sea surface temperature (SST) variability in the global oceans is evaluated in simulations of the Climate Model Intercomparison Project Phase 3 (CMIP3) and CMIP5 models. In this evaluation, we examine how well the spatial structure of the SST variability matches between the observations and simulations on the basis of their leading empirical orthogonal functions-modes. Here we focus on the high-pass filter monthly mean time scales and the longer 5 years running mean time scales. We will compare the models and observations against simple null hypotheses, such as isotropic diffusion (red noise) or a slab ocean model, to illustrate the models skill in simulating realistic patterns of variability. Some models show good skill in simulating the observed spatial structure of the SST variability in the tropical domains and less so in the extra-tropical domains. However, most models show substantial deviations from the observations and from each other in most domains and particularly in the North Atlantic and Southern Ocean on the longer (5 years running mean) time scale. In many cases the simple spatial red noise null hypothesis is closer to the observed structure than most models, despite the fact that the observed SST variability shows significant deviations from this simple spatial red noise null hypothesis. The CMIP models tend to largely overestimate the effective spatial number degrees of freedom and simulate too strongly localized patterns of SST variability at the wrong locations with structures that are different from the observed. However, the CMIP5 ensemble shows some improvement over the CMIP3 ensemble, mostly in the tropical domains. Further, the spatial structure of the SST modes of the CMIP3 and CMIP5 super ensemble is more realistic than any single model, if the relative explained variances of these modes are scaled by the observed eigenvalues.