QBO influence on extratropical predictive skill

The quasi-biennial oscillation (QBO) in the zonal wind in the tropical stratosphere is one of the most predictable aspects of the circulation anywhere in the atmosphere and can be accurately forecast for many months in advance. If the stratospheric QBO systematically (and significantly) affects the tropospheric circulation, it potentially provides a predictable signal useful for seasonal forecasting. The stratospheric QBO itself is generally not well represented in current numerical models, however, including those used for seasonal prediction and this potential may not be exploited by current numerical-model based forecast systems. The purpose of the present study is to ascertain if a knowledge of the state of the QBO can contribute to extratropical boreal winter seasonal forecast skill and, if so, to motivate further research in this area. The investigation is in the context of the second Historical Forecasting Project (HFP2), a state-of-the-art multimodel two-tier ensemble seasonal forecasting system. The first tier, consisting of a prediction of sea surface temperature anomalies (SSTAs), is followed by the second tier which is a prediction of the state of the atmosphere and surface using an AGCM initialized from atmospheric analyses and using the predicted SSTs as boundary conditions. The HFP2 forecasts are successful in capturing the extratropical effects of sea surface temperature anomalies in the equatorial Pacific to the extent that a linear statistical correction based on the NINO3.4 index does not provide additional extratropical skill. By contrast, knowledge of the state of the stratospheric QBO can be used statistically to add extratropical skill centred in the region of the North Atlantic Oscillation. Although the additional skill is modest, the result supports the contention that taking account of the QBO could improve extratropical seasonal forecasting skill. This might be done statistically after the fact, by forcing the QBO state into the forecast model as it runs or, preferably, by using models which correctly represent the physical processes and behaviour of the QBO.     

Skill assessment of seasonal hindcasts from the Canadian historical forecast project

Abstract

The performance of seasonal hindcasts produced with four global atmospheric models in the second phase of the Canadian Historical Forecasting Project is evaluated. Deterministic and probabilistic forecast skill assessments are carried out using common verification measures. Several methods of combining multi‐model output to produce deterministic and probabilistic forecasts of near‐surface air temperature, 500 hPa geopotential height, and 700 hPa temperature for zero‐month and one‐month leads are considered. A variance‐based weighting modestly improves the skill of deterministic and probabilistic hindcasts in some cases. A parametric Gaussian probability estimator is superior to a non‐parametric count‐method estimator for producing multi‐model probability forecasts. Statistical adjustment is beneficial for deterministic and probabilistic hindcasts of near‐surface temperature over the ocean but not always over land. Skill improves with the number of different models used for a given total ensemble size. The four‐model ensemble is shown to be a reasonable multi‐model configuration.     

Climatology and interannual variability simulated by the ARPEGE-OPA coupled model

A 10-year simulation with a coupled ocean-atmosphere general circulation model (CGCM) is presented. The model consists of the climate version of the Météo-France global forecasting model, ARPEGE, coupled to the LODYC oceanic model, OPA, by the CERFACS coupling package OASIS. The oceanic component is dynamically active over the tropical Pacific, while climatological time-dependent sea surface temperatures (SSTs) are prescribed outside of the Pacific domain. The coupled model shows little drift and exhibits a very regular seasonal cycle. The climatological mean state and seasonal cycle are well simulated by the coupled model. In particular, the oceanic surface current pattern is accurately depicted and the location and intensity of the Equatorial Undercurrent (EUC) are in good agreement with available data. The seasonal cycle of equatorial SSTs captures quite realistically the annual harmonic. Some deficiencies remain including a weak zonal equatorial SST gradient, underestimated wind stress over the Pacific equatorial band and an additional inter-tropical convergence zone (ITCZ) south of the equator in northern winter and spring. Weak interannual variability is present in the equatorial SST signal with a maximum amplitude of 0.5°C.     

用一种动力－统计方法提高中国夏季降水的季节预报

A dynamical-statistical post-processing approach is applied to seasonal precipitation forecasts in China during the summer. The data are ensemble-mean seasonal forecasts in summer (June-August) from four atmospheric general circulation models (GCMs) in the second phase of the Canadian Historical Forecasting Project (HFP2) from 1969 to 2001. This dynamical-statistical approach is designed based on the relationship between the 500 geopotential height (Z500) forecast and the observed sea surface temperature (SST) to calibrate the precipitation forecasts. The results show that the post-processing can improve summer precipitation forecasts for many areas in China. Further examination shows that this post-processing approach is very effective in reducing the model-dependent part of the errors, which are associated with GCMs. The possible mechanisms behind the forecast's improvements are investigated.     

ENSO预测的目标观测敏感区在热带太平洋海温的多模式集合预报中的应用

本文将ENSO预测的目标观测敏感区与多模式集合预报方法相结合, 提出了一种能够有效提高预报技巧且又具有较小计算成本的多模式集合预报方法。该方法在目标观测敏感区内采用模式不等权的多模式超级集合预报方法(SUP), 而在其他区域采用相对简单的等权的多模式消除偏差集合平均方法(BREM)。利用CMIP5中15个气候系统模式的工业革命前参照试验(pi-Control)数据, 针对热带太平洋海温的长期演变开展了理想预报试验。将新集合预报方法与现有的多模式集合预报方法进行了比较。结果表明, 在所考察的预报期内(即1～20年), 新集合预报方法与整个热带太平洋区域使用SUP方法具有相当的预报技巧, 但前者的计算成本明显小于后者, 计算时间仅为后者的1/4。可见, 新方法是一个具有较高预报技巧且计算成本较小的多模式集合预报方法。同时, 其较高的预报技巧强调了热带太平洋SST预测对ENSO目标观测敏感区内的模式误差也是极端敏感的, 也正因如此, 多模式集合预报方法才能够有效过滤模式误差的影响, 具有较高的预报技巧。     

Heat budget of the south-central equatorial Pacific in CMIP3 models

ABSTRACT Using data from 17 coupled models and nine sets of corresponding Atmospheric Model Intercomparison Project （AMIP） results, we investigated annual and seasonal variation biases in the upper 50 m of the south-central equatorial Pacific, with a focus on the double-ITCZ bias, and examined the causes for the amplitude biases by using heat budget analysis. The results showed that, in the research region, most of the models simulate SSTs that are higher than or similar to observed. The simulated seasonal phase is close to that observed, but the amplitudes of more than half of the model results are larger than or equal to observations. Heat budget analysis demonstrated that strong shortwave radiation in individual atmospheric models is the main factor that leads to high SST values and that weak southward cold advection is an important mechanism for maintaining a high SST. For seasonal circulation, large surface shortwave radiation amplitudes cause large SST amplitudes.     

Seasonal predictions based on two dynamical models

Abstract

Two dynamical models are used to perform a series of seasonal predictions. One model, referred to as GCM2, was designed as a general circulation model for climate studies, while the second one, SEF, was designed for numerical weather prediction. The seasonal predictions cover the 26‐year period 1969–1994. For each of the four seasons, ensembles of six forecasts are produced with each model, the six runs starting from initial conditions six hours apart. The sea surface temperature (SST) anomaly for the month prior to the start of the forecast is persisted through the three‐month prediction period, and added to a monthly‐varying climatological SST field.

The ensemble‐mean predictions for each of the models are verified independently, and the two ensembles are blended together in two different ways: as a simple average of the two models, denoted GCMSEF, and with weights statistically determined to minimize the mean‐square error (the Best Linear Unbiased Estimate (BLUE) method).

The GCMSEF winter and spring predictions show a Pacific/North American (PNA) response to a warm tropical SST anomaly. The temporal anomaly correlation between the zero‐lead GCMSEF mean‐seasonal predictions and observations of the 500‐hPa height field (Z₅₀₀) shows statistically significant forecast skill over parts of the PNA area for all seasons, but there is a notable seasonal variability in the distribution of the skill. The GCMSEF predictions are more skilful than those of either model in winter, and about as skilful as the better of the two models in the other seasons.

The zero‐lead surface air temperature GCMSEF forecasts over Canada are found to be skilful (a) over the west coast in all seasons except fall, (b) over most of Canada in summer, and (c) over Manitoba, Ontario and Quebec in the fall. In winter the skill of the BLUE forecasts is substantially better than that of the GCMSEF predictions, while for the other seasons the difference in skill is not statistically significant.

When the Z₅₀₀ forecasts are averaged over months two and three of the seasons (one‐month lead predictions), they show skill in winter over the north‐eastern Pacific, western Canada and eastern North America, a skill that comes from those years with strong SST anomalies of the El Niño/La Niña type. For the other seasons, predictions averaged over months two and three show little skill in Z₅₀₀ in the mid‐latitudes. In the tropics, predictive skill is found in Z₅₀₀ in all seasons when a strong SST anomaly of the El Niño/La Niña type is observed. In the absence of SST anomalies of this type, tropical forecast skill is still found over much of the tropics in months two and three of the northern hemisphere spring and summer, but not in winter and fall.     

Simulations of precipitation and atmospheric circulation changes associated with warm SSTs: results from an ensemble of long term integrations with idealized anomalies

A general circulation model is used to simulate the atmospheric response to a prescribed, idealized time varying warm sea surface temperature anomaly (SSTA) in the equatorial Pacific characteristic of ENSO events. The model incorporates the full seasonal cycle and an ensemble of five anomaly simulations, each of 14 months duration, is performed and the results compared with those from a ten year control simulation involving climatological SSTs. In a test of the model, simulated perturbations to precipitation patterns are compared with the analysis by Ropelewski and Halpert which identifies regions where precipitation perturbations are associated with the ENSO cycle. Strong and statistically significant perturbations are simulated throughout much of the equatorial Pacific and the Australian region which agree with the analysis. Perturbations over the Americas and the Indian sub-continent show part agreement with observations, while elsewhere, at more remote and high latitude locations, there is less agreement, providing some indication of model limitations in simulating the hydrological cycle. Over the equatorial Pacific most of the anomalous precipitation is accounted for by moisture flux convergence. Only in the vicinity of the maximum anomaly, located in the eastern Pacific, do warmer SSTs contribute to the perturbed circulation. Elsewhere, anomalous wind speeds mainly determine anomalous heat fluxes. As a result, the large scale perturbations to wind, moisture and precipitation appear to be forced indirectly, rather than directly as assumed in simple ocean-atmosphere models.     

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。     

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.     

Mechanisms for projected future changes in south Asian monsoon precipitation

Results are first presented from an analysis of a global coupled climate model regarding changes in future mean and variability of south Asian monsoon precipitation due to increased atmospheric CO₂ for doubled (2 × CO₂) and quadrupled (4 × CO₂) present-day amounts. Results from the coupled model show that, in agreement with previous studies, mean area-averaged south Asian monsoon precipitation increases with greater CO₂ concentrations, as does the interannual variability. Mechanisms producing these changes are then examined in a series of AMIP2-style sensitivity experiments using the atmospheric model (taken from the coupled model) run with specified SSTs. Three sets of ensemble experiments are run with SST anomalies superimposed on the AMIP2 SSTs from 1979–97: (1) anomalously warm Indian Ocean SSTs, (2) anomalously warm Pacific Ocean SSTs, and (3) anomalously warm Indian and Pacific Ocean SSTs. Results from these experiments show that the greater mean monsoon precipitation is due to increased moisture source from the warmer Indian Ocean. Increased south Asian monsoon interannual variability is primarily due to warmer Pacific Ocean SSTs with enhanced evaporation variability, with the warmer Indian Ocean SSTs a contributing but secondary factor. That is, for a given interannual tropical Pacific SST fluctuation with warmer mean SSTs in the future climate, there is enhanced evaporation and precipitation variability that is communicated via the Walker Circulation in the atmosphere to the south Asian monsoon to increase interannual precipitation variability there. This enhanced monsoon variability occurs even with no change in interannual SST variability in the tropical Pacific.     

Interpreting observed northern hemisphere snow trends with large ensembles of climate simulations

Simulated variability and trends in Northern Hemisphere seasonal snow cover are analyzed in large ensembles of climate integrations of the National Center for Atmospheric Research’s Community Earth System Model. Two 40-member ensembles driven by historical radiative forcings are generated, one coupled to a dynamical ocean and the other driven by observed sea surface temperatures (SSTs) over the period 1981–2010. The simulations reproduce many aspects of the observed climatology and variability of snow cover extent as characterized by the NOAA snow chart climate data record. Major features of the simulated snow water equivalent (SWE) also agree with observations (GlobSnow Northern Hemisphere SWE data record), although with a lesser degree of fidelity. Ensemble spread in the climate response quantifies the impact of natural climate variability in the presence and absence of coupling to the ocean. Both coupled and uncoupled ensembles indicate an overall decrease in springtime snow cover that is consistent with observations, although springtime trends in most climate realizations are weaker than observed. In the coupled ensemble, a tendency towards excessive warming in wintertime leads to a strong wintertime snow cover loss that is not found in observations. The wintertime warming bias and snow cover reduction trends are reduced in the uncoupled ensemble with observed SSTs. Natural climate variability generates widely different regional patterns of snow trends across realizations; these patterns are related in an intuitive way to temperature, precipitation and circulation trends in individual realizations. In particular, regional snow loss over North America in individual realizations is strongly influenced by North Pacific SST trends (manifested as Pacific Decadal Oscillation variability) and by sea level pressure trends in the North Pacific/North Atlantic sectors.     

Factors that affect the amplitude of El Nino in global coupled climate models

Historically, El Nino-like events simulated in global coupled climate models have had reduced amplitude compared to observations. Here, El Nino-like phenomena are compared in ten sensitivity experiments using two recent global coupled models. These models have various combinations of horizontal and vertical ocean resolution, ocean physics, and atmospheric model resolution. It is demonstrated that the lower the value of the ocean background vertical diffusivity, the greater the amplitude of El Nino variability which is related primarily to a sharper equatorial thermocline. Among models with low background vertical diffusivity, stronger equatorial zonal wind stress is associated with relatively higher amplitude El Nino variability along with more realistic east–west sea surface temperature (SST) gradient along the equator. The SST seasonal cycle in the eastern tropical Pacific has too much of a semiannual component with a double intertropical convergence zone (ITCZ) in all experiments, and thus does not affect, nor is it affected by, the amplitude of El Nino variability. Systematic errors affecting the spatial variability of El Nino in the experiments are characterized by the eastern equatorial Pacific cold tongue regime extending too far westward into the warm pool. The time scales of interannual variability (as represented by time series of Nino3 SSTs) show significant power in the 3–4 year ENSO band and 2–2.5 year tropospheric biennial oscillation (TBO) band in the model experiments. The TBO periods in the models agree well with the observations, while the ENSO periods are near the short end of the range of 3–6 years observed during the period 1950–94. The close association between interannual variability of equatorial eastern Pacific SSTs and large-scale SST patterns is represented by significant correlations between Nino3 time series and the PC time series of the first EOFs of near-global SSTs in the models and observations. Received: 17 April 2000 / Accepted: 17 August 2000     

Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO₂ concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2–5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6–9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2–5 years and 6–9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6–9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions.     

Performance of the seasonal forecasting of the Asian summer monsoon by BCC_CSM1.1(m)

This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC_CSM1.1(m). For the South and Southeast Asian summer monsoon, reasonable skill is found in the model's forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts. Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Niño3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and El Niño-Southern Oscillation.     

Interdecadal modulation of Australian rainfall

Interdecadal variability is investigated in a 300 year run of the Parallel Climate Model, a global coupled atmosphere-land-ocean-sea ice model. The model simulates El Niño variability of realistic magnitude and is found to produce interdecadal characteristics similar to those observed, both in frequency, spatial patterns and amplitude. Modulation of Australian rainfall on interdecadal time scales is similar to observed and is found to have contributions from both the modulation of ENSO, changes in the position of the Walker circulation and variations in western Pacific SSTs. A slackening of the equatorial Pacific thermocline slope is associated with diminished ENSO variability during interdecadal periods of positive tropical Pacific SSTs. These interdecadal changes to ENSO and shifts in the position of the Walker circulation are physical mechanisms that contribute to the weakened correlations between the SOI and Australian climate during interdecadal periods of positive tropical Pacific SSTs. Warm anomalies in the western Pacific also contribute to a decrease in Australian rainfall in the model on interdecadal time scales.     

Predictability of the western North Pacific summer climate associated with different ENSO phases by ENSEMBLES multi-model seasonal forecasts

Predictability of the western North Pacific (WNP) summer climate associated with different El Niño–Southern Oscillation (ENSO) phases is investigated in this study based on the 1-month lead retrospective forecasts of five state-of-the-art coupled models from ENSEMBLES. During the period from 1960 to 2005, the models well capture the WNP summer climate anomalies during most of years in different ENSO phases except the La Niña decaying summers. In the El Niño developing, El Niño decaying and La Niña developing summers, the prediction skills are high for the WNP summer monsoon index (WNPMI), with the prediction correlation larger than 0.7. The high prediction skills of the lower-tropospheric circulation during these phases are found mainly over the tropical western Pacific Ocean, South China Sea and subtropical WNP. These good predictions correspond well to their close teleconnection with ENSO and the high prediction skills of tropical SSTs. By contrast, for the La Niña decaying summers, the prediction skills are considerably low with the prediction correlation for the WNPMI near to zero and low prediction skills around the Philippines and subtropical WNP. These poor predictions relate to the weak summer anomalies of the WNPMI during the La Niña decaying years and no significant connections between the WNP lower-tropospheric circulation anomalies and the SSTs over the tropical central and eastern Pacific Ocean in observations. However, the models tend to predict an apparent anomalous cyclone over the WNP during the La Niña decaying years, indicating a linearity of the circulation response over WNP in the models prediction in comparison with that during the El Niño decaying years which differs from observations. In addition, the models show considerable capability in describing the WNP summer anomalies during the ENSO neutral summers. These anomalies are related to the positive feedback between the WNP lower-tropospheric circulation and the local SSTs. The models can capture this positive feedback but with some uncertainties from different ensemble members during the ENSO neutral summers.     

Statistical Downscaling for Multi-Model Ensemble Prediction of Summer Monsoon Rainfall in the Asia-Pacific Region Using Geopotential Height Field

The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, wh     

Indian summer monsoon rainfall variability in global coupled ocean-atmospheric models

Performance of seven fully coupled models in simulating Indian summer monsoon climatology as well as the inter-annual variability was assessed using multi member 1 month lead hindcasts made by several European climate groups as part of the program called Development of a European multi-model ensemble system for seasonal-to-inter-annual prediction (DEMETER). Dependency of the model simulated Indian summer monsoon rainfall and global sea surface temperatures on model formulation and initial conditions have been studied in detail using the nine ensemble member simulations of the seven different coupled ocean–atmosphere models participated in the DEMETER program. It was found that the skills of the monsoon predictions in these hindcasts are generally positive though they are very modest. Model simulations of India summer monsoon rainfall for the earlier period (1959–1979) are closer to the ‘perfect model’ (attainable) score but, large differences are observed between ‘actual’ skill and ‘perfect model’ skill in the recent period (1980–2001). Spread among the ensemble members are found to be large in simulations of India summer monsoon rainfall (ISMR) and Indian ocean dipole mode (IODM), indicating strong dependency of model simulated Indian summer monsoon on initial conditions. Multi-model ensemble performs better than the individual models in simulating ENSO indices, but does not perform better than the individual models in simulating ISMR and IODM. Decreased skill of multi-model ensemble over the region indicates amplification of errors due to existence of similar errors in the individual models. It appears that large biases in predicted SSTs over Indian Ocean region and the not so perfect ENSO-monsoon (IODM-monsoon) tele-connections are some of the possible reasons for such lower than expected skills in the recent period. The low skill of multi-model ensemble, large spread among the ensemble members of individual models and the not so perfect monsoon tele-connection with global SSTs points towards the importance of improving individual models for better simulation of the Indian monsoon.     

ENSO variability and atmospheric response in a global coupled atmosphere-ocean GCM

The interannual variability associated with the El Ni?o/Southern Oscillation (ENSO) cycle is investigated using a relatively high-resolution (T42) coupled general circulation model (CGCM) of the atmosphere and ocean. Although the flux correction is restricted to annual means of heat and freshwater, the annual as well as the seasonal climate of the CGCM is in good agreement with that of the atmospheric model component forced with observed sea surface temperatures (SSTs). During a 100-year simulation of the present-day climate, the model is able to capture many features of the observed interannual SST variability in the tropical Pacific. This includes amplitude, lifetime and frequency of occurrence of El Ni?o events and also the phase locking of the SST anomalies to the annual cycle. Although the SST warming during the evolution of El Ni?os is too confined spatially, and the warming along the Peruvian coast is much too weak, the patterns and magnitudes of key atmospheric anomalies such as westerly wind stress and precipitation, and also their eastward migration from the western to the central equatorial Pacific is in accord with observations. There is also a qualitative agreement with the results obtained from the atmospheric model forced with observed SSTs from 1979 through 1994. The large-scale dynamic response during the mature phase of ENSO (December through February) is characterized by an eastward displacement and weakening of the Walker cell in the Pacific while the Hadley cell intensifies and moves equatorward. Similar to the observations, there is a positive correlation between tropical Pacific SST and the winter circulation in the North Pacific. The deepening of the Aleutian low during the ENSO winters is well captured by the model as well as the cooling in the central North Pacific and the warming over Canada and Alaska. However, there are indications that the anomalies of both SST and atmospheric circulation are overemphasized in the North Pacific. Finally, there is evidence of a coherent downstream effect over the North Atlantic as indicated by negative correlations between the PNA index and the NAO index, for example. The weakening of the westerlies across the North Atlantic in ENSO winters which is related to a weakening and southwestward displacement of the Icelandic low, is in broad agreement with the observations, as well as the weak tendency for colder than normal winters in Europe. Received: 31 October 1995 / Accepted: 29 May 1996