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.     

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.     

Projected changes in South Asian summer monsoon by multi-model global warming experiments

South Asian summer monsoon (June through September) rainfall simulation and its potential future changes are evaluated in a multi-model ensemble of global coupled climate models outputs under World Climate Research Program Coupled Model Intercomparison Project (WCRP CMIP3) dataset. The response of South Asian summer monsoon to a transient increase in future anthropogenic radiative forcing is investigated for two time slices, middle (2031–2050) and end of the twenty-first century (2081–2100), in the non-mitigated Special Report on Emission Scenarios B1, A1B and A2 .There is large inter-model variability in the simulation of spatial characteristics of seasonal monsoon precipitation. Ten out of the 25 models are able to simulate space–time characteristics of the South Asian monsoon precipitation reasonably well. The response of these selected ten models has been examined for projected changes in seasonal monsoon rainfall. The multi-model ensemble of these ten models projects a significant increase in monsoon precipitation with global warming. The substantial increase in precipitation is observed over western equatorial Indian Ocean and southern parts of India. However, the monsoon circulation weakens significantly under all the three climate change experiments. Possible mechanisms for the projected increase in precipitation and for precipitation–wind paradox have been discussed. The surface temperature over Asian landmass increases in pre-monsoon months due to global warming and heat low over northwest India intensifies. The dipole snow configuration over Eurasian continent strengthens in warmer atmosphere, which is conducive for the enhancement in precipitation over Indian landmass. No notable changes have been projected in the El Niño–Monsoon relationship, which is useful for predicting interannual variations of the monsoon.     

Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China

National Centers for Environmental Prediction recently upgraded its operational seasonal forecast system to the fully coupled climate modeling system referred to as CFSv2. CFSv2 has been used to make seasonal climate forecast retrospectively between 1982 and 2009 before it became operational. In this study, we evaluate the model’s ability to predict the summer temperature and precipitation over China using the 120 9-month reforecast runs initialized between January 1 and May 26 during each year of the reforecast period. These 120 reforecast runs are evaluated as an ensemble forecast using both deterministic and probabilistic metrics. The overall forecast skill for summer temperature is high while that for summer precipitation is much lower. The ensemble mean reforecasts have reduced spatial variability of the climatology. For temperature, the reforecast bias is lead time-dependent, i.e., reforecast JJA temperature become warmer when lead time is shorter. The lead time dependent bias suggests that the initial condition of temperature is somehow biased towards a warmer condition. CFSv2 is able to predict the summer temperature anomaly in China, although there is an obvious upward trend in both the observation and the reforecast. Forecasts of summer precipitation with dynamical models like CFSv2 at the seasonal time scale and a catchment scale still remain challenge, so it is necessary to improve the model physics and parameterizations for better prediction of Asian monsoon rainfall. The probabilistic skills of temperature and precipitation are quite limited. Only the spatially averaged quantities such as averaged summer temperature over the Northeast China of CFSv2 show higher forecast skill, of which is able to discriminate between event and non-event for three categorical forecasts. The potential forecast skill shows that the above and below normal events can be better forecasted than normal events. Although the shorter the forecast lead time is, the higher deterministic prediction skill appears, the probabilistic prediction skill does not increase with decreased lead time. The ensemble size does not play a significant role in affecting the overall probabilistic forecast skill although adding more members improves the probabilistic forecast skill slightly.     

How are seasonal prediction skills related to models’ performance on mean state and annual cycle?

Given observed initial conditions, how well do coupled atmosphere–ocean models predict precipitation climatology with 1-month lead forecast? And how do the models’ biases in climatology in turn affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981–2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models’ ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian–Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated.     

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

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

Probabilistic prediction of Indian summer monsoon rainfall using global climate models

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low.     

Reproducibility and predictability of the Asian summer monsoon in the ECHAM4-GCM

The mean evolution of the Asian summer monsoon and its interannual variability have been studied using three simulations (from 1961 to 1994) with the ECHAM4 General Circulation Model (GCM). The results have been compared with observational data and with two reanalyses data sets: the ECMWF Reanalysis (ERA) and the NCEP-NCAR Reanalysis. The South Asian summer monsoon (SASM) has been studied in terms of mean precipitation and circulation patterns. The model is successful in simulating the mean circulation of the SASM, though precipitation is generally weaker than observed in India, but closer to the observed values over the Indian Ocean and the Philippines. The ECHAM4 model also shows a capability to capture the interannual variability of the monsoon as it is measured by two different indices, the EIMR (Extended Indian Monsoon Rainfall) index and the DMI (Dynamical Monsoon Index). An analysis of NINO3 SSTA anomalies and of the Asian summer monsoon indices showed that the model is able to capture rather well the interdecadal variation of the correlation between them. A large ensemble of 25 members, forced with interannually varying SST from 1979 to 1993, has been used to test the potential predictability of the Indian summer monsoon and the dependence of the skill on the ensemble size. Results indicate that a minimum ensemble size of 16 members is needed to capture the variability of the monsoon indices.     

CMIP5全球气候模式对青藏高原地区气候模拟能力评估

青藏高原是气候变化的敏感和脆弱区,全球气候模式对于这一地区气候态的模拟能力如何尚不清楚。为此,本文使用国际耦合模式比较计划第五阶段（CMIP5）的历史模拟试验数据,评估了44 个全球气候模式对1986~2005 年青藏高原地区地表气温和降水两个基本气象要素的模拟能力。结果表明,CMIP5 模式低估了青藏高原地区年和季节平均地表气温,年均平均偏低2.3℃,秋季和冬季冷偏差相对更大;模式可较好地模拟年和季节平均地表气温分布型,但模拟的空间变率总体偏大;地形效应校正能够有效订正地表气温结果。CMIP5 模式对青藏高原地区降水模拟能力较差。尽管它们能够模拟出年均降水自西北向东南渐增的分布型,但模拟的年和季节降水量普遍偏大,年均降水平均偏多1.3 mm d-1,这主要是源于春季和夏季降水被高估。同时,模式模拟的年和季节降水空间变率也普遍大于观测值,尤其表现在春季和冬季。相比较而言,44 个模式集合平均性能总体上要优于大多数单个模式;等权重集合平均方案要优于中位数平均;对择优挑选的模式进行集合平均能够提高总体的模拟能力,其中对降水模拟的改进更为显著。     

Multiyear hindcast simulations of summer monsoon over South Asia using a nested regional climate model��BCC_RegCM1.0

Multiyear (1983?C2006) hindcast simulation of summer monsoon over South Asia has been carried out using the regional climate model of the Beijing Climate Centre (BCC_RegCM1.0). The regional climate model (hereafter BCC RCM) is nested into the global climate model of the Beijing Climate Centre BCC_CGCM1.0 (here after CGCM). The regional climate model is initialized on 01 May and integrated up to the end of the September for 24?years. Compared to the driving CGCM the BCC RCM reproduces reasonably well the intensity and magnitude of the large-scale features associated with the South Asia summer monsoon such as the upper level anticyclone at 200?hPa, the mid-tropospheric warming over the Tibetan plateau, the surface heat low and the 850?hPa moisture transport from ocean to the land. Both models, i.e., BCC RCM and the driving CGCM overestimates (underestimates) the 850?hPa southwesterly flow over the northern (southern) Arabian Sea. Moreover, both models overestimate the seasonal mean precipitation over much of the South Asia region compared to the observations. However, the precipitation biases are significantly reduced in the BCC RCM simulations. Furthermore, both models simulate reasonably the interannual variability of the summer monsoon over India. The precipitation index simulated by BCC RCM shows significant correlation (0.62) with the observed one. The BCC RCM simulates reasonably well the spatial and temporal variation of the precipitation and surface air temperature compared to the driving CGCM. Further, the temperature biases are significantly reduced (1?C4°C) in the BCC RCM simulations. The simulated vertical structure of the atmosphere show biases above the four sub-regions, however, these biases are significantly reduced in the BCC RCM simulations compared to the driving CGCM. Compared to the driving CGCM, the evolution processes of the onset of summer monsoon, e.g., the meridional temperature gradient and the vertical wind shear are well simulated by the BCC RCM. The 24-year simulations also show that with a little exception the BCC RCM is capable to reproduce the monsoon active and break phases and the intraseasonal precipitation variation over the Indian subcontinent.     

Indian summer monsoon rainfall predictability and variability associated with Northwest Pacific circulation in a suit of coupled model hindcasts

The Northwest Pacific (NWP) circulation (subtropical high) is an important component of the East Asian summer monsoon system. During summer (June–August), anomalous lower tropospheric anticyclonic (cyclonic) circulation appears over NWP in some years, which is an indicative of stronger (weaker) than normal subtropical high. The anomalous NWP cyclonic (anticyclonic) circulation years are associated with negative (positive) precipitation anomalies over most of Indian summer monsoon rainfall (ISMR) region. This indicates concurrent relationship between NWP circulation and convection over the ISMR region. Dry wind advection from subtropical land regions and moisture divergence over the southern peninsular India during the NWP cyclonic circulation years are mainly responsible for the negative rainfall anomalies over the ISMR region. In contrast, during anticyclonic years, warm north Indian Ocean and moisture divergence over the head Bay of Bengal-Gangetic Plain region support moisture instability and convergence in the southern flank of ridge region, which favors positive rainfall over most of the ISMR region. The interaction between NWP circulation (anticyclonic or cyclonic) and ISMR and their predictability during these anomalous years are examined in the present study. Seven coupled ocean–atmosphere general circulation models from the Asia-Pacific Economic Cooperation Climate Center and their multimodel ensemble mean skills in predicting the seasonal rainfall and circulation anomalies over the ISMR region and NWP for the period 1982–2004 are assessed. Analysis reveals that three (two) out of seven models are unable to predict negative (positive) precipitation anomalies over the Indian subcontinent during the NWP cyclonic (anticyclonic) circulation years at 1-month lead (model is initialized on 1 May). The limited westward extension of the NWP circulation and misrepresentation of SST anomalies over the north Indian Ocean are found to be the main reasons for the poor skill (of some models) in rainfall prediction over the Indian subcontinent. This study demonstrates the importance of the NWP circulation variability in predicting summer monsoon precipitation over South Asia. Considering the predictability of the NWP circulation, the current study provides an insight into the predictability of ISMR. Long lead prediction of the ISMR associated with anomalous NWP circulation is also discussed.     

Influence of Eurasian snow on Indian summer monsoon in NCEP CFSv2 freerun

The latest version of the state-of-the-art global land–atmosphere–ocean coupled climate forecast system of NCEP has shown considerable improvement in various aspects of the Indian summer monsoon. However, climatological mean dry bias over the Indian sub-continent is further increased as compared to the previous version. Here we have attempted to link this dry bias with climatological mean bias in the Eurasian winter/spring snow, which is one of the important predictors of the Indian summer monsoon rainfall (ISMR). Simulation of interannual variability of the Eurasian snow and its teleconnection with the ISMR are quite reasonable in the model. Using composite analysis it is shown that a positive snow anomaly, which is comparable to the systematic bias in the model, results into significant decrease in the summer monsoon rainfall over the central India and part of the Equatorial Indian Ocean. Decrease in the summer monsoon rainfall is also found to be linked with weaker northward propagation of intraseasonal oscillation (ISO). A barotropic stationary wave triggered by positive snow anomaly over west Eurasia weakens the upper level monsoon circulation, which in turn reduces the zonal wind shear and hence, weakens the northward propagation of summer monsoon ISOs. A sensitivity experiment by reducing snow fall over Eurasian region causes decrease in winter and spring snow depth, which in turn leads to decrease in Indian summer monsoon rainfall. Results from the sensitivity experiment corroborate with those of composite analysis based on long free run. This study suggests that further improvements in the snow parametrization schemes as well as Arctic sea ice are needed to reduce the Eurasian snow bias during winter/spring, which may reduce the dry bias over Indian sub-continent and hence predictability aspect of the model.     

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.     

亚洲季风降水的多模式模拟结果分析

利用参加政府间气候变化委员会(IPCC)第四次评估报告(AR4)的多个大气模式(包括中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室新发展的全球格点大气模式GAMIL)的AMIP-II(大气模式比较计划-II)积分的集合平均结果(MMEA),研究了当前大气模式对亚洲季风降水的平均模拟能力,同时也评估了GAMIL的模拟水平。对多年平均冬夏季降水的模拟研究发现:MMEA和GAMIL对冬季降水的模拟好于夏季。与以往的结果相比,MMEA对夏季印度洋和西太平洋地区降水的模拟改进不明显;部分模式能够模拟出夏季东亚副热带地区从中国东海到中太平洋的带状梅雨降水,但大部分模式的模拟强度还不够。可以看出GAMIL除了冬季印度洋和夏季菲律宾模拟的降水稍弱外,与MMEA的结果很接近。降水场的误差与环流场的误差对应。此外,作者还研究了降水的年际变化和季风爆发撤退过程的模拟能力。MMEA与观测在印度季风区降水的相关系数不如在东亚热带和东亚副热带季风区的好。各模式冬季的相关系数一般好于夏季,特别是东亚热带季风区冬季的相关系数普遍较高,而印度季风区夏季的相关系数普遍较低。MMEA对标准差的模拟并不总比单个模式的好。各个模式对东亚热带季风区冬季的降水距平同号率和降水距平百分率模拟得最好。季风爆发、撤退时降水推移的模拟也还有待于进一步提高。     

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.     

Monsoon precipitation in the AMIP runs

 We present an analysis of the seasonal precipitation associated with the African, Indian and the Australian-Indonesian monsoon and the interannual variation of the Indian monsoon simulated by 30 atmospheric general circulation models undertaken as a special diagnostic subproject of the Atmospheric Model Intercomparison Project (AMIP). The seasonal migration of the major rainbelt observed over the African region, is reasonably well simulated by almost all the models. The Asia West Pacific region is more complex because of the presence of warm oceans equatorward of heated continents. Whereas some models simulate the observed seasonal migration of the primary rainbelt, in several others this rainbelt remains over the equatorial oceans in all seasons. Thus, the models fall into two distinct classes on the basis of the seasonal variation of the major rainbelt over the Asia West Pacific sector, the first (class I) are models with a realistic simulation of the seasonal migration and the major rainbelt over the continent in the boreal summer; and the second (class II) are models with a smaller amplitude of seasonal migration than observed. The mean rainfall pattern over the Indian region for July-August (the peak monsoon months) is even more complex because, in addition to the primary rainbelt over the Indian monsoon zone (the monsoon rainbelt) and the secondary one over the equatorial Indian ocean, another zone with significant rainfall occurs over the foothills of Himalayas just north of the monsoon zone. Eleven models simulate the monsoon rainbelt reasonably realistically. Of these, in the simulations of five belonging to class I, the monsoon rainbelt over India in the summer is a manifestation of the seasonal migration of the planetary scale system. However in those belonging to class II it is associated with a more localised system. In several models, the oceanic rainbelt dominates the continental one. On the whole, the skill in simulation of excess/deficit summer monsoon rainfall over the Indian region is found to be much larger for models of class I than II, particularly for the ENSO associated seasons. Thus, the classification based on seasonal mean patterns is found to be useful for interpreting the simulation of interannual variation. The mean rainfall pattern of models of class I is closer to the observed and has a higher pattern correlation coefficient than that of class II. This supports Sperber and Palmer’s (1996) result of the association of better simulation of interannual variability with better simulation of the mean rainfall pattern. The hypothesis, that the skill of simulation of the interannual variation of the all-India monsoon rainfall in association with ENSO depends upon the skill of simulation of the seasonal variation over the Asia West Pacific sector, is supported by a case in which we have two versions of the model where NCEP1 is in class II and NCEP2 is in class I. The simulation of the interannual variation of the local response over the central Pacific as well as the all-India monsoon rainfall are good for NCEP2 and poor for NCEP1. Our results suggest that when the model climatology is reasonably close to observations, to achieve a realistic simulation of the interannual variation of all-India monsoon rainfall associated with ENSO, the focus should be on improvement of the simulation of the seasonal variation over the Asia West Pacific sector rather than further improvement of the simulation of the mean rainfall pattern over the Indian region. Received: 2 June 1997 / Accepted: 8 January 1998     

Performance of a multi-RCM ensemble for South Eastern South America

The ability of four regional climate models to reproduce the present-day South American climate is examined with emphasis on La Plata Basin. Models were integrated for the period 1991–2000 with initial and lateral boundary conditions from ERA-40 Reanalysis. The ensemble sea level pressure, maximum and minimum temperatures and precipitation are evaluated in terms of seasonal means and extreme indices based on a percentile approach. Dispersion among the individual models and uncertainties when comparing the ensemble mean with different climatologies are also discussed. The ensemble mean is warmer than the observations in South Eastern South America (SESA), especially for minimum winter temperatures with errors increasing in magnitude towards the tails of the distributions. The ensemble mean reproduces the broad spatial pattern of precipitation, but overestimates the convective precipitation in the tropics and the orographic precipitation along the Andes and over the Brazilian Highlands, and underestimates the precipitation near the monsoon core region. The models overestimate the number of wet days and underestimate the daily intensity of rainfall for both seasons suggesting a premature triggering of convection. The skill of models to simulate the intensity of convective precipitation in summer in SESA and the variability associated with heavy precipitation events (the upper quartile daily precipitation) is far from satisfactory. Owing to the sparseness of the observing network, ensemble and observations uncertainties in seasonal means are comparable for some regions and seasons.     

An Assessment on the Performance of IPCC AR4 Climate Models in Simulating Interdecadal Variations of the East Asian Summer Monsoon

Observations from several data centers together with a categorization method are used to evaluate the IPCC AR4 （Intergovernmental Panel on Climate Change, the Fourth Assessment Report） climate models＇ performance in simulating the interdecadal variations of summer precipitation and monsoon circulation in East Asia. Out of 19 models under examination, 9 models can relatively well reproduce the 1979-1999 mean June-July-August （JJA） precipitation in East Asia, but only 3 models （Category-1 models） can capture the interdecadal variation of precipitation in East Asia. These 3 models are： GFDL-CM2.0, MIROC3.2 （hires）, and MIROC3.2 （medres）, among which the GFDL-CM2.0 gives the best performance. The reason for the poor performance of most models in simulating the East Asian summer monsoon interdecadal variation lies in that the key dynamic and thermal-dynamic mechanisms behind the East Asian monsoon change are missed by the models, e.g., the large-scale tropospheric cooling and drying over East Asia. In contrast, the Category-1 models relatively well reproduce the variations in vertical velocity and water vapor over East Asia and thus show a better agreement with observations in simulating the pattern of ＂wet south and dry north＂ in China in the past 20 years.
It is assessed that a single model＇s performance in simulating a particular variable has great impacts on the ensemble results. More realistic outputs can be obtained when the multi-model ensemble is carried out using a suite of well-performing models for a specific variable, rather than using all available models. This indicates that although a multi-model ensemble is in general better than a single model, the best ensemble mean cannot be achieved without looking into each member model＇s performance.     

The general circulation model precipitation bias over the southwestern equatorial Indian Ocean and its implications for simulating the South Asian monsoon

Most of current general circulation models (GCMs) show a remarkable positive precipitation bias over the southwestern equatorial Indian Ocean (SWEIO), which can be thought of as a westward expansion of the simulated IO convergence zone toward the coast of Africa. The bias is common to both coupled and uncoupled models, suggesting that its origin does not stem from the way boundary conditions are specified. The spatio-temporal evolution of the precipitation and associated three-dimensional atmospheric circulation biases is comprehensively characterized by comparing the GFDL AM3 atmospheric model to observations. It is shown that the oceanic bias, which develops in spring and reduces during the monsoon season, is associated to a consistent precipitation and circulation anomalous pattern over the whole Indian region. In the vertical, the areas are linked by an anomalous Hadley-type meridional circulation, whose northern branch subsides over northeastern India significantly affecting the monsoon evolution (e.g., delaying its onset). This study makes the case that the precipitation bias over the SWEIO is forced by the model excess response to the local meridional sea surface temperature (SST) gradient through enhanced near-surface meridional wind convergence. This is suggested by observational evidence and supported by AM3 sensitivity experiments. The latter show that relaxing the magnitude of the meridional SST gradient in the SWEIO can lead to a significant reduction of both local and large-scale precipitation and circulation biases. The ability of local anomalies over the SWEIO to force a large-scale remote response to the north is further supported by numerical experiments with the GFDL spectral dry dynamical core model. By imposing a realistic anomalous heating source over the SWEIO the model is able to reproduce the main dynamical features of the AM3 bias. These results indicate that improved GCM simulations of the South Asian summer monsoon could be achieved by reducing the springtime model bias over the SWEIO. Deficiencies in the atmospheric model, and in particular in the convective parameterization, are suggested to play a key role. Finally, the important mechanism controlling the simulated precipitation distribution over South Asia found here should be considered in the interpretation and attribution of regional precipitation variation under climate change.     

Mid-Holocene monsoons: a multi-model analysis of the inter-hemispheric differences in the responses to orbital forcing and ocean feedbacks

The response of monsoon circulation in the northern and southern hemisphere to 6?ka orbital forcing has been examined in 17 atmospheric general circulation models and 11 coupled ocean–atmosphere general circulation models. The atmospheric response to increased summer insolation at 6?ka in the northern subtropics strengthens the northern-hemisphere summer monsoons and leads to increased monsoonal precipitation in western North America, northern Africa and China; ocean feedbacks amplify this response and lead to further increase in monsoon precipitation in these three regions. The atmospheric response to reduced summer insolation at 6?ka in the southern subtropics weakens the southern-hemisphere summer monsoons and leads to decreased monsoonal precipitation in northern South America, southern Africa and northern Australia; ocean feedbacks weaken this response so that the decrease in rainfall is smaller than might otherwise be expected. The role of the ocean in monsoonal circulation in other regions is more complex. There is no discernable impact of orbital forcing in the monsoon region of North America in the atmosphere-only simulations but a strong increase in precipitation in the ocean–atmosphere simulations. In contrast, there is a strong atmospheric response to orbital forcing over northern India but ocean feedback reduces the strength of the change in the monsoon although it still remains stronger than today. Although there are differences in magnitude and exact location of regional precipitation changes from model to model, the same basic mechanisms are involved in the oceanic modulation of the response to orbital forcing and this gives rise to a robust ensemble response for each of the monsoon systems. Comparison of simulated and reconstructed changes in regional climate suggest that the coupled ocean–atmosphere simulations produce more realistic changes in the northern-hemisphere monsoons than atmosphere-only simulations, though they underestimate the observed changes in precipitation in all regions. Evaluation of the southern-hemisphere monsoons is limited by lack of quantitative reconstructions, but suggest that model skill in simulating these monsoons is limited.