Predictability of SST forced climate signals in two atmospheric general circulation models

 The predictability of atmospheric responses to global sea surface temperature (SST) anomalies is evaluated using ensemble simulations of two general circulation models (GCMs): the GENESIS version 1.5 (GEN) and the ECMWF cycle 36 (ECM). The integrations incorporate observed SST variations but start from different initial land and atmospheric states. Five GEN 1980–1992 and six ECM 1980–1988 realizations are compared with observations to distinguish predictable SST forced climate signals from internal variability. To facilitate the study, correlation analysis and significance evaluation techniques are developed on the basis of time series permutations. It is found that the annual mean global area with realistic signals is variable dependent and ranges from 3 to 20% in GEN and 6 to 28% in ECM. More than 95% of these signal areas occur between 35 °S–35 °N. Due to the existence of model biases, robust responses, which are independent of initial condition, are identified over broader areas. Both GCMs demonstrate that the sensitivity to initial conditions decreases and the predictability of SST forced responses increases, in order, from 850 hPa zonal wind, outgoing longwave radiation, 200 hPa zonal wind, sea-level pressure to 500 hPa height. The predictable signals are concentrated in the tropical and subtropical Pacific Ocean and are identified with typical El Ni?o/ Southern Oscillation phenomena that occur in response to SST and diabatic heating anomalies over the equatorial central Pacific. ECM is less sensitive to initial conditions and better predicts SST forced climate changes. This results from (1) a more realistic basic climatology, especially of the upper-level wind circulation, that produces more realistic interactions between the mean flow, stationary waves and tropical forcing; (2) a more vigorous hydrologic cycle that amplifies the tropical forcing signals, which can exceed internal variability and be more efficiently transported from the forcing region. Differences between the models and observations are identified. For GEN during El Ni?o, the convection does not carry energy to a sufficiently high altitude, while the spread of the tropospheric warming along the equator is slower and the anomaly magnitude smaller than observed. This impacts model ability to simulate realistic responses over Eurasia and the Indian Ocean. Similar biases exist in the ECM responses. In addition, the relationships between upper and lower tropospheric wind responses to SST forcing are not well reproduced by either model. The identification of these model biases leads to the conclusion that improvements in convective heat and momentum transport parametrizations and basic climate simulations could substantially increase predictive skill. Received: 25 April 1996 / Accepted: 9 December 1996     

Contribution of realistic soil moisture initial conditions to boreal summer climate predictability

Seasonal climate forecasts mainly rely on the atmospheric sensitivity to its lower boundary conditions and on their own predictability. Besides sea surface temperature (SST), soil moisture (SM) may be an additional source of climate predictability particularly during boreal summer in the mid-latitudes. In this work, we investigate the role of SM initial conditions on near-surface climate predictability during ten boreal summer seasons using three complementary ensembles of AMIP-type simulations performed with the Arpège-Climat atmospheric general circulation model. First we have conducted an assessment of the SM predictability itself through a comparison of simple empirical SM models with Arpège-Climat. The statistical and dynamical models reveal similar SM prediction skill patterns but the Arpège-Climat reaches higher scores suggesting that it is at least suitable to explore the influence of SM initialization on atmospheric predictability. Then we evaluate the relationships between SM predictability and some near surface atmospheric predictability. While SM initialization obviously improves the predictability of land surface evaporation, it has no systematic influence on the precipitation and near surface temperature skills. Nevertheless, the summer hindcast skill is clearly improved during specific years and over certain regions (mainly north America and eastern Europe in the Arpège-Climat model), when and where the SM forcing is sufficiently widespread and strong. In this case, a significant impact is also found on the occurrence of heat waves and heavy rains, whose predictability at the seasonal timescale is a crucial challenge for years to come.     

Soil moisture memory and West African monsoon predictability: artefact or reality?

Besides sea surface temperature (SST), soil moisture (SM) exhibits a significant memory and is likely to contribute to atmospheric predictability at the seasonal timescale. In this respect, West Africa was recently highlighted as a “hot spot” where the land–atmosphere coupling could play an important role, through the recycling of precipitation and the modulation of the meridional gradient of moist static energy. Particularly intriguing is the observed relationship between summer monsoon rainfall over Sahel and the previous second rainy season over the Guinean Coast, suggesting the possibility of a soil moisture memory beyond the seasonal timescale. The present study is aimed at revisiting this question through a detailed analysis of the instrumental record and a set of numerical sensitivity experiments. Three ensembles of global atmospheric simulations have been designed to assess the relative influence of SST and SM boundary conditions on the West African monsoon predictability over the 1986–1995 period. On the one hand, the results indicate that SM contributes to rainfall predictability at the end and just after the rainy season over the Sahel, through a positive soil-precipitation feedback that is consistent with the “hot spot” hypothesis. On the other hand, SM memory decreases very rapidly during the dry season and does not contribute to the predictability of the all-summer monsoon rainfall. Though possibly model dependent, this conclusion is reinforced by the statistical analysis of the summer monsoon rainfall variability over the Sahel and its link with tropical SSTs. Our results indeed suggest that the apparent relationship with the previous second rainy season over the Guinean Coast is mainly an artefact of rainfall teleconnections with tropical modes of SST variability both at interannual and multi-decadal timescales.     

Assessment of regional seasonal rainfall predictability using the CPTEC/COLA atmospheric GCM

This is a study of the annual and interannual variability of regional rainfall produced by the Center for Weather Forecasts and Climate Studies/Center for Ocean, Land and Atmospheric Studies (CPTEC/COLA) atmospheric global climate model. An evaluation is made of a 9-member ensemble run of the model forced by observed global sea surface temperature (SST) anomalies for the 10-year period 1982–1991. The Brier skill score and, Relative Operating Characteristics (ROC) are used to assess the predictability of rainfall and to validate rainfall simulations, in several regions world wide. In general, the annual cycle of precipitation is well simulated by the model for several continental and oceanic regions in the tropics and mid latitudes. Interannual variability of rainfall during the peak rainy season is realistically simulated in Northeast Brazil, Amazonia, central Chile, and southern Argentina–Uruguay, Eastern Africa, and tropical Pacific regions, where the model shows good skill. Some regions, such as northwest Peru–Ecuador, and southern Brazil exhibit a realistic simulation of rainfall anomalies associated with extreme El Niño warming conditions, while in years with neutral or La Niña conditions, the agreement between observed and simulated rainfall anomalies is not always present. In the monsoon regions of the world and in southern Africa, even though the model reproduces the annual cycle of rainfall, the skill of the model is low for the simulation of the interannual variability. This is indicative of mechanisms other than the external SST forcing, such as the effect of land–surface moisture and snow feedbacks or the representation of sub-grid scale processes, indicating the important role of factors other than external boundary forcing. The model captures the well-known signatures of rainfall anomalies of El Niño in 1982–83 and 1986–87, indicating its sensitivity to strong external forcing. In normal years, internal climate variability can affect the predictability of climate in some regions, especially in monsoon areas of the world.     

Climate simulations for 1880–2003 with GISS modelE

We carry out climate simulations for 1880–2003 with GISS modelE driven by ten measured or estimated climate forcings. An ensemble of climate model runs is carried out for each forcing acting individually and for all forcing mechanisms acting together. We compare side-by-side simulated climate change for each forcing, all forcings, observations, unforced variability among model ensemble members, and, if available, observed variability. Discrepancies between observations and simulations with all forcings are due to model deficiencies, inaccurate or incomplete forcings, and imperfect observations. Although there are notable discrepancies between model and observations, the fidelity is sufficient to encourage use of the model for simulations of future climate change. By using a fixed well-documented model and accurately defining the 1880–2003 forcings, we aim to provide a benchmark against which the effect of improvements in the model, climate forcings, and observations can be tested. Principal model deficiencies include unrealistically weak tropical El Nino-like variability and a poor distribution of sea ice, with too much sea ice in the Northern Hemisphere and too little in the Southern Hemisphere. Greatest uncertainties in the forcings are the temporal and spatial variations of anthropogenic aerosols and their indirect effects on clouds. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Decadal predictability and prediction skill of sea surface temperatures in the South Pacific region

The South Pacific Ocean is a key driver of climate variability within the Southern Hemisphere at different time scales. Previous studies have characterized the main mode of interannual sea surface temperature (SST) variability in that region as a dipolar pattern of SST anomalies that cover subtropical and extratropical latitudes (the South Pacific Ocean Dipole, or SPOD), which is related to precipitation and temperature anomalies over several regions throughout the Southern Hemisphere. Using that relationship and the reported low predictive skill of precipitation anomalies over the Southern Hemisphere, this work explores the predictability and prediction skill of the SPOD in near-term climate hindcasts using a set of state-of-the-art forecast systems. Results show that predictability greatly benefits from initializing the hindcasts beyond the prescribed radiative forcing, and is modulated by known modes of climate variability, namely El Niño-Southern Oscillation and the Interdecadal Pacific Oscillation. Furthermore, the models are capable of simulating the spatial pattern of the observed SPOD even without initialization, which suggests that the key dynamical processes are properly represented. However, the hindcast of the actual phase of the mode is only achieved when the forecast systems are initialized, pointing at SPOD variability to not be radiatively forced but probably internally generated. The comparison with the performance of an empirical prediction based on persistence suggests that initialization may provide skillful information for SST anomalies, outperforming damping processes, up to 2–3 years into the future.     

Dynamics of the boreal summer African monsoon in the NSIPP1 atmospheric model

A nine-member ensemble of simulations with a state-of-the-art atmospheric model forced only by the observed record of sea surface temperature (SST) over 1930–2000 is shown to capture the dominant patterns of variability of boreal summer African rainfall. One pattern represents variability along the Gulf of Guinea, between the equator and 10°N. It connects rainfall over Africa to the Atlantic marine Intertropical Convergence Zone, is controlled by local, i.e., eastern equatorial Atlantic, SSTs, and is interannual in time scale. The other represents variability in the semi-arid Sahel, between 10°N and 20°N. It is a continental pattern, capturing the essence of the African summer monsoon, while at the same time displaying high sensitivity to SSTs in the global tropics. A land–atmosphere feedback associated with this pattern translates precipitation anomalies into coherent surface temperature and evaporation anomalies, as highlighted by a simulation where soil moisture is held fixed to climatology. As a consequence of such feedback, it is shown that the recent positive trend in surface temperature is consistent with the ocean-forced negative trend in precipitation, without the need to invoke the direct effect of the observed increase in anthropogenic greenhouse gases. We advance plausible mechanisms by which the balance between land–ocean temperature contrast and moisture availability that defines the monsoon could have been altered in recent decades, resulting in persistent drought. This discussion also serves to illustrate ways in which the monsoon may be perturbed, or may already have been perturbed, by anthropogenic climate change.     

Relative contribution of soil moisture and snow mass to seasonal climate predictability: a pilot study

Land surface hydrology (LSH) is a potential source of long-range atmospheric predictability that has received less attention than sea surface temperature (SST). In this study, we carry out ensemble atmospheric simulations driven by observed or climatological SST in which the LSH is either interactive or nudged towards a global monthly re-analysis. The main objective is to evaluate the impact of soil moisture or snow mass anomalies on seasonal climate variability and predictability over the 1986–1995 period. We first analyse the annual cycle of zonal mean potential (perfect model approach) and effective (simulated vs. observed climate) predictability in order to identify the seasons and latitudes where land surface initialization is potentially relevant. Results highlight the influence of soil moisture boundary conditions in the summer mid-latitudes and the role of snow boundary conditions in the northern high latitudes. Then, we focus on the Eurasian continent and we contrast seasons with opposite land surface anomalies. In addition to the nudged experiments, we conduct ensembles of seasonal hindcasts in which the relaxation is switched off at the end of spring or winter in order to evaluate the impact of soil moisture or snow mass initialization. LSH appears as an effective source of surface air temperature and precipitation predictability over Eurasia (as well as North America), at least as important as SST in spring and summer. Cloud feedbacks and large-scale dynamics contribute to amplify the regional temperature response, which is however, mainly found at the lowest model levels and only represents a small fraction of the observed variability in the upper troposphere.     

Variability in global land surface energy budgets during 1987–1988 simulated by an off-line land surface model

The International Satellite Land-Surface Climatology Project (ISLSCP) Initiative-I 1-degree 1987–1988 data were used to drive a land surface model (LSM) to simulate global surface energy budgets. Simulated surface heat fluxes show remarkable spatial variability and seem to capture well their annual and interannual variability. A shift of maximum evaporation across the equator is more closely related to the seasonal shifting of precipitation pattern than to surface radiation changes. The NCEP/NCAR reanalysis did not reflect this shift, presumably due to its dominant rainfall maximum in the Southern Hemisphere. To assess the “reliability” of these fields, both Global Soil Wetness Project (GSWP) and reanalysis were verified against observations, at two sites. Monthly mean ISLSCP forcing conditions agree fairly well with observations, but its precipitation is usually lower during spring and summer. Low summer GSWP evaporation may be due to low precipitation and incorrect specification of vegetation and soil conditions. The reanalysis had larger seasonal variability than GSWP and observations, and overestimated summer heat fluxes because of its large rainfall and surface radiation. Despite uncertainty in ISLSCP data, an LSM with a modest treatment of vegetation was able to capture reasonably well the seasonal variations in surface heat fluxes at global scales. With some caution, these types of simulations can be used as “pseudo-observations” to evaluate climate-model simulations and to investigate global energy budgets. For the next phase of ISLSCP data development, higher resolution data, which can reflect local heterogeneity of vegetation and soil characteristics, include more rain gauge data are highly desirable to improve model simulations.     

Seasonal differences of model predictability and the impact of SST in the Pacific

Both seasonal potential predictability and the impact of SST in the Pacific on the forecast skill over China are investigated by using a 9-level global atmospheric general circulation model developed at the Institute of Atmospheric Physics under the Chinese Academy of Sciences (IAP9L-AGCM). For each year during 1970 to 1999, the ensemble consists of seven integrations started from consecutive observational daily atmospheric fields and forced by observational monthly SST. For boreal winter, spring and summer,the variance ratios of the SST-forced variability to the total variability and the differences in the spatial correlation coefficients of seasonal mean fields in special years versus normal years are computed respectively. It follows that there are slightly inter-seasonal differences in the model potential predictability in the Tropics. At northern middle and high latitudes, prediction skill is generally low in spring and relatively high either in summer for surface air temperature and middle and upper tropospheric geopotential height or in winter for wind and precipitation. In general, prediction skill rises notably in western China, especially in northwestern China, when SST anomalies (SSTA) in the Nino-3 region are significant. Moreover,particular attention should be paid to the SSTA in the North Pacific (NP) if one aims to predict summer climate over the eastern part of China, i.e., northeastern China, North China and southeastern China.     

Seasonal to interannual climate predictability in mid and high northern latitudes in a global coupled model

The upper limit of climate predictability in mid and high northern latitudes on seasonal to interannual time scales is investigated by performing two perfect ensemble experiments with the global coupled atmosphere–ocean–sea ice model ECHAM5/MPI-OM. The ensembles consist of six members and are initialized in January and July from different years of the model’s 300-year control integration. The potential prognostic predictability is analyzed for a set of oceanic and atmospheric climate parameters. The predictability of the atmospheric circulation is small except for southeastern Europe, parts of North America and the North Pacific, where significant predictability occurs with a lead time of up to half a year. The predictability of 2 m air temperature shows a large land–sea contrast with highest predictabilities over the sub polar North Atlantic and North Pacific. A combination of relatively high persistence and advection of sea surface temperature anomalies into these areas leads to large predictability. Air temperature over Europe, parts of North America and Asia shows significant predictability of up to half a year in advance. Over the ice-covered Arctic, air temperature is not predictable at time scales exceeding 2 months. Sea ice thickness is highly predictable in the central Arctic mainly due to persistence and in the Labrador Sea due to dynamics. Surface salinity is highly predictable in the Arctic Ocean, northern North Atlantic and North Pacific for several years in advance. We compare the results to the predictability due to persistence and show the importance of dynamical processes for the predictability.     

Internal variability in multidecadal trends of surface air temperature over antarctica in austral winter in model simulations

The surface air temperature (SAT) exhibits pronounced warming over West Antarctica in recent decades, especially in austral spring and winter. Using a 30-member ensemble of simulations by Community Earth System Model (CESM), two reanalysis datasets, and observed station data, this study investigates the relative contributions of internally generated low-frequency climate variability and externally forced climate change to the austral winter SAT trend in Antarctica. Although these simulations share the same external forcing, the SAT trends during 1979–2005 show large diversity among the individual members in the CESM ensemble simulations, suggesting that internally generated variability contributes a considerable part to the multidecadal SAT change in Antarctica. Quantitatively, the total forced contribution to the SAT (1979–2005) change is about 0.53 k/27 yr, and the internal variability can be strong enough to double or cancel the externally forced warming trend. A method called “dynamical adjustment” is utilized to further divide the forced response. We find both the forced thermodynamically-induced and the forced dynamically-induced SAT trends are positive over all the regions in Antarctica, with the regional mean values of 0.20 k /27 yr and 0.33 k/27 yr, respectively. The diversity of SAT trends among the simulations is closely linked to a Southern hemisphere Annular Mode (SAM)-like atmospheric circulation multidecadal change in the Southern Hemisphere. When there exists a positive–negative seesaw of pressure trend between Antarctica and the mid-latitudes, the SAT trend is positive over most of Antarctica but negative over the Antarctic Peninsula, and vice versa. The SAM-like atmospheric circulation multidecadal change mainly arises from atmospheric internal variability rather than remote tropical Sea Surface Temperature (SST).

     

An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC

Decadal climate predictability is examined in hindcast experiments by a multi-model ensemble using three versions of the coupled atmosphere-ocean model MIROC. In these hindcast experiments, initial conditions are obtained from an anomaly assimilation procedure using the observed oceanic temperature and salinity with prescribed natural and anthropogenic forcings on the basis of the historical data and future emission scenarios in the Intergovernmental Panel of Climate Change. Results of the multi-model ensemble in our hindcast experiments show that predictability of surface air temperature (SAT) anomalies on decadal timescales mostly originates from externally forced variability. Although the predictable component of internally generated variability has considerably smaller SAT variance than that of externally forced variability, ocean subsurface temperature variability has predictive skills over almost a decade, particularly in the North Pacific and the North Atlantic where dominant signals associated with Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) are observed. Initialization enhances the predictive skills of AMO and PDO indices and slightly improves those of global mean temperature anomalies. Improvement of these predictive skills in the multi-model ensemble is higher than that in a single-model ensemble.     

Response of the Western European climate to a collapse of the thermohaline circulation

Two ensemble simulations with the ECHAM5/MPI-OM climate model have been investigated for the atmospheric response to a thermohaline circulation (THC) collapse. The model forcing was specified from observations between 1950 and 2000 and it followed a rising greenhouse gases emission scenario from 2001 to 2100. In one ensemble, a THC collapse was induced by adding freshwater in the northern North Atlantic, from 2001 onwards. After about 20 years, an almost stationary response pattern develops, that is, after the THC collapse, global mean temperature rises equally fast in both ensembles with the hosing ensemble displaying a constant offset. The atmospheric response to the freshwater hosing features a strong zonal gradient in the anomalous 2-m air temperature over Western Europe, associated with a strong land–sea contrast. Since Western Europe climate features a strong marine impact due to the prevailing westerlies, the question arises how such a strong land–sea contrast can be maintained. We show that a strong secondary cloud response is set up with increased cloud cover over sea and decreased cloud cover over land. Also, the marine impact on Western European climate decreases, which results from a reduced transport of moist static energy from sea to land. As a result, the change in lapse rate over the cold sea surface temperature (SST) anomalies west of the continent is much larger than over land, dominated by changes in moisture content rather than temperature.     

Sub-Saharan Northern African climate at the end of the twenty-first century: forcing factors and climate change processes

A regional climate model, the Weather Research and Forecasting (WRF) Model, is forced with increased atmospheric CO₂ and anomalous SSTs and lateral boundary conditions derived from nine coupled atmosphere–ocean general circulation models to produce an ensemble set of nine future climate simulations for northern Africa at the end of the twenty-first century. A well validated control simulation, agreement among ensemble members, and a physical understanding of the future climate change enhance confidence in the predictions. The regional model ensembles produce consistent precipitation projections over much of northern tropical Africa. A moisture budget analysis is used to identify the circulation changes that support future precipitation anomalies. The projected midsummer drought over the Guinean Coast region is related partly to weakened monsoon flow. Since the rainfall maximum demonstrates a southward bias in the control simulation in July–August, this may be indicative of future summer drying over the Sahel. Wetter conditions in late summer over the Sahel are associated with enhanced moisture transport by the West African westerly jet, a strengthening of the jet itself, and moisture transport from the Mediterranean. Severe drought in East Africa during August and September is accompanied by a weakened Indian monsoon and Somali jet. Simulations with projected and idealized SST forcing suggest that overall SST warming in part supports this regional model ensemble agreement, although changes in SST gradients are important over West Africa in spring and fall. Simulations which isolate the role of individual climate forcings suggest that the spatial distribution of the rainfall predictions is controlled by the anomalous SST and lateral boundary conditions, while CO₂ forcing within the regional model domain plays an important secondary role and generally produces wetter conditions.     

土壤湿度对东亚夏季气候潜在可预报性影响的数值模拟

利用全球大气环流模式NCARCAM3进行了在给定的观测海温条件下的22a（1979—2000年）5—8月的2组集合试验。运用方差分析方法,分析了在气候态和年际变化的表层土壤湿度情况下,CAM3模式模拟的东亚夏季气候潜在可预报性及其差异。结果表明：在给定的观测海温条件下,采用气候态的土壤湿度时,CAM3模式模拟的东亚夏季气候的潜在可预报性偏低;而采用年际变化的土壤湿度时,模拟的夏季气候潜在可预报性有所提高,尤其是在中国西北地区;后者模拟的中国西北地区夏季降水和气温的潜在可预报性比前者的模拟结果提高0．1以上。其原因可能是：采用年际变化的土壤湿度时,模式可以更好地模拟出中国西北地区的地表蒸发量和湍流热通量的年际变化,进而使得模式对该地区夏季气候的预报技巧得到提高。     

Assessing the Seasonal Predictability of Summer Precipitation over the Huaihe River Basin with Multiple APCC Models

Seasonal rainfall predictability over the Huaihe River basin is evaluated in this paper on the basis of 23-year(1981-2003) retrospective forecasts by 10 climate models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) prediction system.It is found that the summer rainfall variance in this basin is largely internal,which leads to lower rainfall predictability for most individual climate models.By dividing the 10 models into three categories according to their sea surface temperature(SST) boundary conditions including observed,predicted,and persistent SSTs,the MME deterministic predictive skill of summer rainfall over Huaihe River basin is investigated.It is shown that the MME is effective for increasing the current seasonal forecast skill.Further analysis shows that the MME averaged over predicted SST models has the highest rainfall prediction skill,which is closely related to model’s capability in reproducing the observed dominant modes of the summer rainfall anomalies in Huaihe River basin.This result can be further ascribed to the fact that the predicted SST MME is the most effective model ensemble for capturing the relationship between the summer rainfall anomalies over Huaihe River basin and the SST anomalies(SSTAs) in equatorial oceans.     

The influence of tropical Pacific forcing on the Arctic Oscillation

The potential role of tropical Pacific forcing in driving the seasonal variability of the Arctic Oscillation (AO) is explored using both observational data and a simple general circulation model (SGCM). A lead–lag regression technique is first applied to the monthly averaged sea surface temperature (SST) and the AO index. The AO maximum is found to be related to a negative SST anomaly over the tropical Pacific three months earlier. A singular value decomposition (SVD) analysis is then performed on the tropical Pacific SST and the sea level pressure (SLP) over the Northern Hemisphere. An AO-like atmospheric pattern and its associated SST appear as the second pair of SVD modes. Ensemble integrations are carried out with the SGCM to test the atmospheric response to different tropical Pacific forcings. The atmospheric response to the linear fit of the model’s empirical forcing associated with the SST variability in the second SVD modes strongly projects onto the AO. Idealized thermal forcings are then designed based on the regression of the seasonally averaged tropical Pacific precipitation against the AO index. Results indicate that forcing anomalies over the western tropical Pacific are more effective in generating an AO-like response while those over the eastern tropical Pacific tend to produce a Pacific-North American (PNA)-like response. The physical mechanisms responsible for the energy transport from the tropical Pacific to the extratropical North Atlantic are investigated using wave activity flux and vorticity forcing formalisms. The energy from the western tropical Pacific forcing tends to propagate zonally to the North Atlantic because of the jet stream waveguide effect while the transport of the energy from the eastern tropical Pacific forcing mostly concentrates over the PNA area. The linearized SGCM results show that nonlinear processes are involved in the generation of the forced AO-like pattern.     

Covariability of SST and surface heat fluxes in reanalyses and CMIP3 climate models

The generation and dissipation of SST anomalies is mediated by the covariability of SST and surface heat fluxes. The connection between the variability of heat flux (including its radiative and turbulent components) and that of SST is investigated using the NCEP-NCAR and ERA-40 reanalyses and the CMIP3 multi-model collection of climate simulations. The covariance patterns of SST and heat flux are broadly similar in the two reanalyses. The upward heat fluxes are positively correlated with the SST anomalies in the tropics, the northern Pacific mid-latitudes, and over the Gulf Stream, and negatively correlated in the northern subtropics and the SPCZ region. Common covariance features are seen in all climate models in the tropics and the subtropics, while covariances differ considerably among models at northern mid-latitudes, where weak values of the ensemble mean are seen. Lagged covariances are broadly similar in the two reanalyses and among the models, implying that heat flux feedback is also similar. The heat flux feedback parameter is determined from the lagged cross-covariances together with the auto-covariance of SST. Feedback is generally negative and is dominated by the turbulent component. The strongest feedback is found at mid-latitudes in both hemispheres, with the largest values occurring in the western and central portions of the oceans with extensions to higher latitudes. The latter are also areas with large inter-model differences. The heat flux feedback strengthens in winter and fall and weakens in spring and summer. The magnitudes of the annual and seasonal feedback parameters are slightly weaker in most models compared to the reanalysis-based estimates. The mean model feedback parameter has the best pattern correlation and the smallest mean square difference compared to the reanalysis-based values, although spatial variances are weak. Model resolution shows no relationship with the heat flux feedback parameters obtained from model results. The SST-heat flux covariance is decomposed into components associated with surface heat flux feedback and atmospheric forcing processes. Heat flux feedback dominates over the atmospheric forcing and heat flux damps SST anomalies on average at northern Pacific mid-latitudes and southern Atlantic mid-latitudes; while the reverse occurs in the SPCZ and northern Atlantic mid-latitudes.