Assessment of Indian summer monsoon simulation by Community Atmosphere Model (CAM3)

Seasonal prediction of Indian Summer Monsoon (ISM) has been attempted for the current year 2011 using Community Atmosphere Model (CAM) developed at the National Centre for Atmospheric Research (NCAR). First, 30?years of model climatology starting from 1981 to 2010 has been generated to capture the variability of ISM over the Indian region using 30 seasonal simulations. The simulated model climatology has been validated with different sets of observed climatology, and it was observed that the simulated climatological rainfall is affected by model bias. Subsequently, a bias correction procedure using the Tropical Rainfall Measuring Mission (TRMM) 3B43 rainfall has been proposed. The bias-corrected rainfall climatology shows both spatial and temporal variability of ISM satisfactorily. Further, four sets of 10-member ensemble simulations of ISM 2009 and 2010 have been performed in hindcast mode using observed sea surface temperature (SST) and persistence of April SST anomaly, and it has been found that the bias-corrected model rainfall captures the seasonal variability of ISM reasonably well with some discrepancies in these two contrasting monsoon years. With this positive background, the seasonal prediction of ISM 2011 has been carried out in forecast mode with the assumption of persistence of May SST anomaly from June through September 2011. The model assessment shows an 11% deficiency in All-India Rainfall (AIR) of ISM 2011. In particular, the monthly accumulated rains are predicted to be 101% (17.6?cm), 86% (24.3?cm), 83% (21.0?cm) and 95% (15.5?cm) of normal AIR for the months of June, July, August and September, respectively.     

Seasonality and time scales in the relationship between global SST and African rainfall

The main goal of this study is to determine the oceanic regions corresponding to variability in African rainfall and seasonal differences in the atmospheric teleconnections. Canonical correlation analysis (CCA) has been applied in order to extract the dominant patterns of linear covariability. An ensemble of six simulations with the global atmospheric general circulation model ECHAM4, forced with observed sea surface temperatures (SSTs) and sea ice boundary variability, is used in order to focus on the SST-related part of African rainfall variability. Our main finding is that the boreal summer rainfall (June–September mean) over Africa is more affected by SST changes than in boreal winter (December–March mean). In winter, there is a highly significant link between tropical African rainfall and Indian Ocean and eastern tropical Pacific SST anomalies, which is closely related to El Niño-Southern Oscillation (ENSO). However, long-term changes are found to be associated with SST changes in the Indian and tropical Atlantic Oceans, thus, showing that the tropical Atlantic plays a critical role in determining the position of the intertropical convergence zone (ITCZ). Since ENSO is less in summer, the tropical Pacific and the Indian Oceans are less important for African rainfall. The African summer monsoon is strongly influenced by SST variations in the Gulf of Guinea, with a response of opposite sign over the Sahelian zone and the Guinean coast region. SST changes in the subtropical and extratropical oceans mostly take place on decadal time scales and are responsible for low-frequency rainfall fluctuations over West Africa. The modelled teleconnections are highly consistent with the observations. The agreement for most of the teleconnection patterns is remarkable and suggests that the modelled rainfall anomalies serve as suitable predictors for the observed changes.     

Prediction of summer monsoon rainfall over India using the NCEP climate forecast system

The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the Indian region during June to September (JJAS). The evaluation is based on the National Centre for Environmental Prediction’s (NCEP) climate forecast system (CFS) initialized during March, April and May and integrated for a period of 9 months with a 15 ensemble members for 25 years period from 1981 to 2005. The CFS’s hindcast climatology during JJAS of March (lag-3), April (lag-2) and May (lag-1) initial conditions show mostly an identical pattern of rainfall similar to that of verification climatology with the rainfall maxima (one over the west-coast of India and the other over the head Bay of Bengal region) well simulated. The pattern correlation between verification and forecast climatology over the global tropics and Indian monsoon region (IMR) bounded by 50°E–110°E and 10°S–35°N shows significant correlation coefficient (CCs). The skill of simulation of broad scale monsoon circulation index (Webster and Yang; WY index) is quite good in the CFS with highly significant CC between the observed and predicted by the CFS from the March, April and May forecasts. High skill in forecasting El Nino event is also noted for the CFS March, April and May initial conditions, whereas, the skill of the simulation of Indian Ocean Dipole is poor and is basically due to the poor skill of prediction of sea surface temperature (SST) anomalies over the eastern equatorial Indian Ocean. Over the IMR the skill of monsoon rainfall forecast during JJAS as measured by the spatial Anomaly CC between forecast rainfall anomaly and the observed rainfall anomaly during 1991, 1994, 1997 and 1998 is high (almost of the order of 0.6), whereas, during the year 1982, 1984, 1985, 1987 and 1989 the ACC is only around 0.3. By using lower and upper tropospheric forecast winds during JJAS over the regions of significant CCs as predictors for the All India Summer Monsoon Rainfall (AISMR; only the land stations of India during JJAS), the predicted mean AISMR with March, April and May initial conditions is found to be well correlated with actual AISMR and is found to provide skillful prediction. Thus, the calibrated CFS forecast could be used as a better tool for the real time prediction of AISMR.     

Seasonal prediction of African precipitation with ECHAM4–T42 ensemble simulations using a multivariate MOS re-calibration scheme

A 15 member ensemble of 20th century simulations using the ECHAM4–T42 atmospheric GCM is utilized to investigate the potential predictability of interannual variations of seasonal rainfall over Africa. Common boundary conditions are the global sea surface temperatures (SST) and sea ice extent. A canonical correlation analysis (CCA) between observed and ensemble mean ECHAM4 precipitation over Africa is applied in order to identify the most predictable anomaly patterns of precipitation and the related SST anomalies. The CCA is then used to formulate a re-calibration approach similar to model output statistics (MOS) and to derive precipitation forecasts over Africa. Predictand is the climate research unit (CRU) gridded precipitation over Africa. As predictor we use observed SST anomalies, ensemble mean precipitation over Africa and a combined vector of mean sea level pressure, streamfunction and velocity potential at 850 hPa. The different forecast approaches are compared. Most skill for African precipitation forecasts is provided by tropical Atlantic (Gulf of Guinea) SST anomalies which mainly affect rainfall over the Guinean coast and Sahel. The El Niño/Southern Oscillation (ENSO) influences southern and East Africa, however with a lower skill. Indian Ocean SST anomalies, partly independent from ENSO, have an impact particularly on East Africa. As suggested by the large agreement between the simulated and observed precipitation, the ECHAM4 rainfall provides a skillful predictor for CRU precipitation over Africa. However, MOS re-calibration is needed in order to provide skillful forecasts. Forecasts using MOS re-calibrated model precipitation are at least as skillful as forecast using dynamical variables from the model or instantaneous SST. In many cases, MOS re-calibrated precipitation forecasts provide more skill. However, differences are not systematic for all regions and seasons, and often small.     

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.     

Coupling between the El-Niño and planetary-scale waves and their linkage with the Indian monsoon rainfall

Summary The relationships between the El-Niño phenomenon and the planetary-scale waves, and the interannual variations in the Indian monsoon (June–September) rainfall have been analysed in order to investigate how the sea surface temperature (SST) in the equatorial eastern Pacific associated with the El-Niño can produce reduced monsoon rainfall over India by teleconnections.The longitude of ridge location over the Indian region of the integrated planetary waves (numbers 1–3) along 15° N latitude circle in the height field of 200 mb pressure level in May is significantly (r=0.93, significant at 98% CL) related to the May SST anomaly at Puerto Chicama. This implies that warmer (colder) SST anomalies are associated with eastward (westward) longitude of the ridge location. The variations of the ridge location in May appear to be significantly inversely (r=–0.95, significant at 99% CL) related to the Indian monsoon rainfall, with rainfall tending to be less (more) than normal during eastward (westward) longitude of the ridge location suggesting some predictive value for the Indian monsoon rainfall. The Indian monsoon rainfall and May SST anomaly at Puerto Chicama are inversely related (r=–0.90, significant at 96% CL).In terms of the observed relationships, a plausible mechanism for linking El-Niño with the reduced Indian monsoon rainfall is discussed. The relationships noted suggest that excessive warm SST anomalies associated with El-Niño induce an eastward shift in the planetary waves which in turn reduce the Indian monsoon rainfall.With 8 Figures     

Predictability of Indian Ocean sea surface temperature using canonical correlation analysis

There is strong evidence that Indian Ocean sea surface temperatures (SSTs) influence the climate variability of Southern Asia and Africa; hence, accurate prediction of these SSTs is a high priority. In this study, we use canonical correlation analysis (CCA) to design empirical models to assess the predictability of tropical Indian Ocean SST from sea level pressure (SLP) and SST themselves with lead-times up to one year. One model uses the first twelve empirical orthogonal functions (EOFs) of SLP over the Indian Ocean using different lead-times to predict SST. A CCA model with EOFs of SST as the predictor at the same lead-times is compared to SLP as a predictor and shows the auto-correlation of the system. A CCA using the first five extended empirical orthogonal functions (EEOFs) of sea level pressure over the Indian Ocean basin for an interval of two years combined with SST EOFs as predictors is found to produce the greatest correlation between forecast and observed SSTs. This model obtains higher skill by explicitly considering the development in time of SLP anomalies in the region. The skill of this model, assessed from retroactive forecasts of an 18 year period, shows improvement relative to other empirical forecasts particularly for the central and eastern Indian Ocean and boreal autumn months preceding the Southern Hemisphere summer rainfall season. This is likely due to the limited domain of this model identifying modes of variability that are more pronounced in these areas during this season. Finally, a nonlinear canonical correlation analysis (NLCCA) derived from a neural network is used to analyze the leading nonlinear modes. These nonlinear modes differ from the linear CCA modes with distinct cold and warm SST phases suggesting a nonlinear relationship between SST and SLP over the tropical Indian Ocean.     

一个全球耦合模式的ENSO后报试验

A group of seasonal hindcast experiments are conducted using a coupled model known as the Flexible Global Ocean-Atmosphere-Land System Model-gamil1.11 (FGOALS-g1.11) developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG). Two steps are included in our El Niño-Southern Oscillation (ENSO) hindcast experiments. The first step is to integrate the coupled GCM with the Sea Surface Temperature (SST) strongly nudged towards the observation from 1971 to 2006. The second step is to remove the SST nudging term. We carried out a one-year hindcast by adopting the initial values from SST nudging experiments from the first step on January 1st, April 1st, July 1st, and October 1st from 1982 to 2005. In the SST nudging experiment, the model can reproduce the observed equatorial thermocline anomalies and zonal wind stress anomalies in the Pacific, which demonstrates that the SST nudging approach can provide realistic atmospheric and oceanic initial conditions for seasonal prediction experiments. The model also demonstrates a high Anomaly Correlation Coefficient (ACC) score for SST in most of the tropical Pacific, Atlantic Ocean, and some Indian Ocean regions with a 3-month lead. Compared with the persistence ACC score, this model shows much higher ACC scores for the Nino3.4 index for a 9-month lead.     

一个全球耦合模式的ENSO后报试验

A group of seasonal hindcast experiments are conducted using a coupled model known as the Flexible Global Ocean-Atmosphere-Land System Modelgamil1.11 (FGOALS-g1.11) developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG).Two steps are included in our ElNi o-Southern Oscillation (ENSO) hindcast experiments.The first step is to integrate the coupled GCM with the Sea Surface Temperature (SST) strongly nudged towards the observation from 1971 to 2006.The second step is to remove the SST nudging term.The authors carried out a one-year hindcast by adopting the initial values from SST nudging experiments from the first step on January 1st,April 1st,July 1st,and October 1st from 1982 to 2005.In the SST nudging experiment,the model can reproduce the observed equatorial thermocline anomalies and zonal wind stress anomalies in the Pacific,which demonstrates that the SST nudging approach can provide realistic atmospheric and oceanic initial conditions for seasonal prediction experiments.The model also demonstrates a high Anomaly Correlation Coefficient (ACC) score for SST in most of the tropical Pacific,Atlantic Ocean,and some Indian Ocean regions with a 3-month lead.Compared with the persistence ACC score,this model shows much higher ACC scores for the Ni o-3.4 index for a 9-month lead.     

CoPIVEP: a theory-based analysis of coupled processes and interannual variability in the Equatorial Pacific in four coupled GCMs

 The interannual variability over the tropical Pacific and a possible link with the mean state or the seasonal cycle is examined in four coupled ocean-atmosphere general circulation models (GCM). Each model is composed of a high-resolution ocean GCM of either the tropical Pacific or near-global oceans coupled to a moderate-resolution atmospheric GCM, without using flux correction. The oceanic subsurface is considered to describe the mean state or the seasonal cycle through the analytical formulations of some potential coupled processes. These coupled processes characterise the zonal gradient of sea surface temperature (hereafter SST), the oceanic vertical gradient of temperature and the equatorial upwelling. The simulated SST patterns of the mean state and the interannual signals are generally too narrow. The grid of the oceanic model could control the structure of the SST interannual signals while the behaviour of the atmospheric model could be important in the link between the oceanic surface and the subsurface. The first SST EOFs are different between the coupled models, however, the second SST EOFs are quite similar and could correspond to the return to the normal state while that of the observations (COADS) could favour the initial anomaly. All the models seem to simulate a similar equatorial wave-like dynamics to return to the normal state. The more the basic state is unstable from the coupled processes point of view, the more the interannual signal are high. It seems that the basic state could control the intensity of the interannual variability. Two models, which have a significant seasonal variation of the interannual variance, also have a significant seasonal variation of the instability with a few months lag. The potential seasonal phase locking of the interannual fluctuations need to be examined in more models to confirm its existence in current tropical GCMs. Received: 30 July 1999 / Accepted: 25 April 2000     

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.     

Evaluation of the ENSEMBLES multi-model seasonal forecasts of Indian summer monsoon variability

The performance of the new multi-model seasonal prediction system developed in the frame work of the ENSEMBLES EU project for the seasonal forecasts of India summer monsoon variability is compared with the results from the previous EU project, DEMETER. We have considered the results of six participating ocean-atmosphere coupled models with 9 ensemble members each for the common period of 1960–2005 with May initial conditions. The ENSEMBLES multi-model ensemble (MME) results show systematic biases in the representation of mean monsoon seasonal rainfall over the Indian region, which are similar to that of DEMETER. The ENSEMBLES coupled models are characterized by an excessive oceanic forcing on the atmosphere over the equatorial Indian Ocean. The skill of the seasonal forecasts of Indian summer monsoon rainfall by the ENSEMBLES MME has however improved significantly compared to the DEMETER MME. Its performance in the drought years like 1972, 1974, 1982 and the excess year of 1961 was in particular better than the DEMETER MME. The ENSEMBLES MME could not capture the recent weakening of the ENSO-Indian monsoon relationship resulting in a decrease in the prediction skill compared to the “perfect model” skill during the recent years. The ENSEMBLES MME however correctly captures the north Atlantic-Indian monsoon teleconnections, which are independent of ENSO.     

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.     

我国短期气候预测技术进展

经过近60年的发展,我国短期气候预测技术和方法也有了长足进步。近年来,一些新的预报技术和机理认识不断应用于短期气候预测业务。ARGO海洋观测资料的使用大大提高了业务模式的预测技巧,新一代气候预测模式系统已经投入准业务化运行,研发了多种模式降尺度释用技术,多模式气候预测产品解释应用集成系统(MODES)和动力-统计结合的季节预测系统(FODAS)逐渐应用于业务中,大气季节内振荡(MJO)逐步在延伸期预报中得到应用。近年来,对全球海洋、北极海冰、欧亚积雪、南半球环流系统对东亚季风影响的新认识也不断引入到短期气候预测业务中。这些新技术和新认识的应用极大提高了我国短期气候预测的业务能力。     

Simulation and prediction of blocking in the Australian region and its influence on intra-seasonal rainfall in POAMA-2

We assess the depiction and prediction of blocking at 140°E and its impact on Australian intra-seasonal climate variability in the Bureau of Meteorology’s dynamical intra-seasonal/seasonal forecast model Predictive Ocean Atmosphere Model for Australia version 2 (POAMA-2). The model simulates well the strong seasonality of blocking but underestimates its strength and frequency increasingly with lead time, particularly after the first fortnight of the hindcast, in connection with the model’s drifting basic state. POAMA-2 reproduces well the large-scale structure of weekly-mean blocking anomalies and associated rainfall anomalies over Australia; the depiction of total blocking in POAMA-2 may be improved with the reduction of biases in the distribution of Indian Ocean rainfall via a tropical-extratropical wave teleconnection linking blocking activity at 140°E with tropical variability near Indonesia. POAMA-2 demonstrates the ability to skilfully predict the daily blocking index out to 16 days lead time for the ensemble mean hindcast, surpassing the average predictive skill of the individual hindcast members (5 days), the skill obtained from persistence of observed (2 days), and the decorrelation timescale of blocking (3 days). This skilful prediction of the blocking index, together with effective simulation of blocking rainfall anomalies, translates into higher skill in forecasting rainfall in weeks 2 and 3 over much of Australia when blocking is high at the initial time of the hindcast, compared to when the blocking index is small. POAMA-2 is thus capable of providing forecast skill for blocking rainfall on the intra-seasonal timescale to meet the needs of Australian farming communities, whose management practises often rely upon decisions being made a few weeks ahead.     

Seasonality in the relationship between El Nino and Indian Ocean dipole

The seasonal change in the relationship between El Nino and Indian Ocean dipole (IOD) is examined using the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), and the twentieth century simulations (20c3m) from the Geophysical Fluid Dynamics Laboratory Coupled Model, version 2.1. It is found that, both in ERA-40 and the model simulations, the correlation between El Nino (Nino3 index) and the eastern part of the IOD (90?C110°E; 10°S-equator) is predominantly positive from January to June, and then changes to negative from July to December. Correlation maps of atmospheric and oceanic variables with respect to the Nino3 index are constructed for each season in order to examine the spatial structure of their seasonal response to El Nino. The occurrence of El Nino conditions during January to March induces low-level anti-cyclonic circulation anomalies over the southeastern Indian Ocean, which counteracts the climatological cyclonic circulation in that region. As a result, evaporation decreases and the southeastern Indian Ocean warms up as the El Nino proceeds, and weaken the development of a positive phase of an IOD. This warming of the southeastern Indian Ocean associated with the El Nino does not exist past June because the climatological winds there develop into the monsoon-type flow, enhancing the anomalous circulation over the region. Furthermore, the development of El Nino from July to September induces upwelling in the southeastern Indian Ocean, thereby contributing to further cooling of the region during the summer season. This results in the enhancement of a positive phase of an IOD. Once the climatological circulation shifts from the boreal summer to winter mode, the negative correlation between El Nino and SST of the southeastern Indian Ocean changes back to a positive one.     

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     

Evolution of geophysical parameters over the Indian Ocean region during contrasting monsoon years of 2002 and 2003 using TRMM/TMI data

Summary The evolution of geophysical parameters over Indian Ocean during two contrasting monsoon years 2002 (drought) and 2003 (normal) were studied using TRMM/TMI satellite data. Analysis indicates that there was a lack of total water vapour (TWV) build up over Western Indian Ocean (WIO) during May 2002 (drought) when compared to 2003 (normal). Negative (positive) TWV anomalies were found over the WIO in May 2002 (2003). In 2002, negative SST anomaly of ∼1.5 °C is found over entire WIO when compared to 2003. Anomalously high sea surface wind speed (SWS) anomaly over the South West Indian Ocean (SWIO) and WIO would have resulted in cooling of the sea surface in May 2002 in comparison to 2003. In 2003 the wind speed anomaly over entire WIO and Arabian Sea (AS) was negative, whereas sea surface temperature (SST) anomaly was positive over the same region, which would have resulted in higher moisture availability over these regions. A negative (positive) TWV anomaly over Eastern Arabian Sea (EAS) and positive (negative) anomaly over WIO forms a dipole structure. In the month of June no major difference is seen in all these parameters over the Indian Ocean. In July 2002 the entire WIO and AS was drier by 10–15 mm as compared to 2003. The pentad (5 day) average TWV values shows high (>55 mm) TWV convergence over EAS and Bay of Bengal (BoB) during active periods of 2003, which gives high rainfall over these regions. However, during 2002 although TWV over BoB was >55 mm but it was ∼45–55 mm over EAS during entire July and hence less rainfall. The evaporation has been calculated from the bulk aerodynamic formula using TRMM/TMI geophysical products. It has been seen that the major portion of evaporative moisture flux is coming from southern Indian Ocean (SIO) between 15 and 25° S. Evaporation in June was more over AS and SIO in 2003 when compared to 2002 which may lead to reduce moisture supply in July 2002 and hence less rainfall compared to July 2003.     

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

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

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.