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.     

South Australian rainfall variability and climate extremes

Rainfall extremes over South Australia are connected with broad-scale atmospheric rearrangements associated with strong meridional sea surface temperature (SST) gradients in the eastern Indian Ocean. Thirty-seven years of winter radiosonde data is used to calculate a time series of precipitable water (PW) and convective available potential energy (CAPE) in the atmosphere. Principle component analysis on the parameters of CAPE and PW identify key modes of variability that are spatially and seasonally consistent with tropospheric processes over Australia. The correlation of the leading principle component of winter PW to winter rainfall anomalies reveal the spatial structure of the northwest cloudband and fronts that cross the southern half of the continent during winter. Similarly the second and third principle components, respectively, reveal the structures of the less frequent northern and continental cloudbands with remarkable consistency. 850 hPa-level wind analysis shows that during dry seasons, anomalous offshore flow over the northwest of Australia inhibits advection of moisture into the northwest, while enhanced subsidence from stronger anticyclonic circulation over the southern half of the continent reduces CAPE. This coincides with a southward shift of the subtropical ridge resulting in frontal systems passing well to the south of the continent, thus producing less frequent interaction with moist air advected from the tropics. Wet winters are the reverse, where a weaker meridional pressure gradient to the south of the continent allows rain-bearing fronts to reach lower latitudes. The analysis of SSTs in the Indian Ocean indicate that anomalous warm (cool) waters in the southeast Indian Ocean coincide with a southward (northward) shift in the subtropical ridge during dry (wet) seasons.     

Simulating the diurnal cycle of rainfall in global climate models: resolution versus parameterization

The effects of horizontal resolution and the treatment of convection on simulation of the diurnal cycle of precipitation during boreal summer are analyzed in several innovative weather and climate model integrations. The simulations include: season-long integrations of the Non-hydrostatic Icosahedral Atmospheric Model (NICAM) with explicit clouds and convection; year-long integrations of the operational Integrated Forecast System (IFS) from the European Centre for Medium-range Weather Forecasts at three resolutions (125, 39 and 16 km); seasonal simulations of the same model at 10 km resolution; and seasonal simulations of the National Center for Atmospheric Research (NCAR) low-resolution climate model with and without an embedded two-dimensional cloud-resolving model in each grid box. NICAM with explicit convection simulates best the phase of the diurnal cycle, as well as many regional features such as rainfall triggered by advancing sea breezes or high topography. However, NICAM greatly overestimates mean rainfall and the magnitude of the diurnal cycle. Introduction of an embedded cloud model within the NCAR model significantly improves global statistics of the seasonal mean and diurnal cycle of rainfall, as well as many regional features. However, errors often remain larger than for the other higher-resolution models. Increasing resolution alone has little impact on the timing of daily rainfall in IFS with parameterized convection, yet the amplitude of the diurnal cycle does improve along with the representation of mean rainfall. Variations during the day in atmospheric prognostic fields appear quite similar among models, suggesting that the distinctive treatments of model physics account for the differences in representing the diurnal cycle of precipitation.     

Changes in rainfall regime over Burkina Faso under the climate change conditions simulated by 5 regional climate models

Sahelian rainfall has recorded a high variability during the last century with a significant decrease (more than 20 %) in the annual rainfall amount since 1970. Using a linear regression model, the fluctuations of the annual rainfall from the observations over Burkina Faso during 1961–2009 period are described through the changes in the characteristics of the rainy season. The methodology is then applied to simulated rainfall data produced by five regional climate models under A1B scenario over two periods: 1971–2000 as reference period and 2021–2050 as projection period. As found with other climate models, the projected change in annual rainfall for West Africa is very uncertain. However, the present study shows that some features of the impact of climate change on rainfall regime in the region are robust. The number of the low rainfall events (0.1–5 mm/d) is projected to decrease by 3 % and the number of strong rainfall events (>50 mm/d) is expected to increase by 15 % on average. In addition, the rainy season onset is projected by all models to be delayed by one week on average and a consensus exists on the lengthening of the dry spells at about 20 %. Furthermore, the simulated relationship between changed annual rainfall amounts and the number of rain days or their intensity varies strongly from one model to another and some changes do not correspond to what is observed for the rainfall variability over the last 50 years.     

基于全球及区域气候模式的江苏省降水变化趋势预估

利用气象观测资料,8个全球耦合气候系统模式的集合平均以及区域气候模式(RegCM4)的结果,通过方差分析、相关分析、趋势分析、扰动法等方法对模式性能进行了评估,并对江苏省在未来RCP8.5高端排放情景下降水的变化趋势进行了预估。结果表明,在RCP8.5情景下,至2020、2030和2050年,全球模式模拟的江苏省年平均降水在未来有逐渐增加的趋势,线性增加率约为7 mm/(10 a)。至2050年,江苏省年平均降水量将增加2%左右;区域模式模拟的年平均降水在未来线性增加率为1.5 mm/(10 a),变化不显著。区域模式模拟的夏季降水在未来有所增加,最多可增加20%～30%,但增幅随时间逐渐减小;全球模式模拟的夏季降水比现在有所减少,至2050年,减少了大约10%。区域模式模拟的冬季降水在未来不同时间段均比现在有所减少,同现在相比,最多可减少30%～40%;而全球模式模拟的冬季降水在未来则是先减少,后增加,至2050年,比现在大约增加10%。对于不同季节,总体而言,南部地区降水量的变化较北部地区显著。对于极端降水事件来说,江苏省未来小雨日数将减少,而暴雨日数则微弱增加。但由于全球模式本身的性能、区域模式对全球模式的依赖性以及温室气体排放的不确定性使上述预估结果仍具有不确定性。     

Bias corrections of global models for regional climate simulations of high-impact weather

All global circulation models (GCMs) suffer from some form of bias, which when used as boundary conditions for regional climate models may impact the simulations, perhaps severely. Here we present a bias correction method that corrects the mean error in the GCM, but retains the six-hourly weather, longer-period climate-variability and climate change from the GCM. We utilize six different bias correction experiments; each correcting different bias components. The impact of the full bias correction and the individual components are examined in relation to tropical cyclones, precipitation and temperature. We show that correcting of all boundary data provides the greatest improvement.     

Interannual variability of rainfall over the Sahel based on multiple regional climate models simulations

We analyse the interannual variability of the averaged summer monsoon rainfall over the Sahel from multiple regional climate models driven by the ERA-interim reanalysis and seek to provide effective information for future modelling work. We find that the majority of the models are able to reproduce the rainfall variability with correlation coefficient exceeding 0.5 compared with observations. This is due to a good representation of the dynamics of the main monsoon features of the West African climate such as the monsoon flux, African Easterly Jet (AEJ) and Tropical Easterly Jet (TEJ). Among the models, only HIRHAM fails to reproduce the rainfall variability exhibiting hence a correlation coefficient of ?0.2. This deficiency originates from the fact that HIRHAM does not properly capture the variability of monsoon flow and the relationship between rainfall and the AEJ dynamic. We conclude that a good performance of a regional climate model in simulating the monsoon dynamical features variability is of primary importance for a better representation of the interannual variability of rainfall over the Sahel.     

从全球到区域气候变化

尽管气候变化是全球性的现象,但其表现和结果随区域不同而不同,因此区域气候信息对于气候变化的作用和风险评估很重要.基于此,IPCC第六次评估报告(AR6)第一工作组(WGI)报告第十章对如何从全球链接到区域气候变化方面进行了评估.区域气候变化是对自然强迫和人类活动的区域响应、对大尺度气候系统内部变率的响应和区域气候本身反...     

Analysis of rainfall seasonality from observations and climate models

Two new indicators of rainfall seasonality based on information entropy, the relative entropy (RE) and the dimensionless seasonality index (DSI), together with the mean annual rainfall, are evaluated on a global scale for recently updated precipitation gridded datasets and for historical simulations from coupled atmosphere–ocean general circulation models. The RE provides a measure of the number of wet months and, for precipitation regimes featuring a distinct wet and dry season, it is directly related to the duration of the wet season. The DSI combines the rainfall intensity with its degree of seasonality and it is an indicator of the extent of the global monsoon region. We show that the RE and the DSI are fairly independent of the time resolution of the precipitation data, thereby allowing objective metrics for model intercomparison and ranking. Regions with different precipitation regimes are classified and characterized in terms of RE and DSI. Comparison of different land observational datasets reveals substantial difference in their local representation of seasonality. It is shown that two-dimensional maps of RE provide an easy way to compare rainfall seasonality from various datasets and to determine areas of interest. Models participating to the Coupled Model Intercomparison Project platform, Phase 5, consistently overestimate the RE over tropical Latin America and underestimate it in West Africa, western Mexico and East Asia. It is demonstrated that positive RE biases in a general circulation model are associated with excessively peaked monthly precipitation fractions, too large during the wet months and too small in the months preceding and following the wet season; negative biases are instead due, in most cases, to an excess of rainfall during the premonsoonal months.     

Internal variability of regional climate models

 Two regional climate models have been applied to the task of generating an ensemble of realizations of the year 1982 with observed boundary conditions in areas covering parts of the Mediterranean countries. These realizations were generated by applying boundary conditions from the ECMWF ERA reanalysis project consecutively, carrying over the soil variables from the regional models from one iteration to the next. Monthly mean fields for six iterations of each model have been used as statistical ensembles in order to investigate the internal variability of the regional model dynamics. This internal variability is a necessary consequence of the non-linear physical feedback mechanisms of the RCM being active. A small value of internal variability will give better statistics for climate sensitivity signals, but will make these results less credible. The internal variability is important for the quantitative assessment of a climate sensitivity signal. With the present choice of models and integration domains the internal variabilities of surface fields and precipitation do reach levels that are less than, but in summer of comparable order of magnitude to, corresponding atmospheric variabilities of an atmospheric general circulation model. Received: 26 October 1999 / Accepted: 18 December 2000     

Simulation of rainfall anomalies leading to the 2005 drought in Amazonia using the CLARIS LPB regional climate models

The meteorological characteristics of the drought of 2005 in Amazonia, one of the most severe in the last 100 years were assessed using a suite of seven regional models obtained from the CLARIS LPB project. The models were forced with the ERA-Interim reanalyses as boundary conditions. We used a combination of rainfall and temperature observations and the low-level circulation and evaporation fields from the reanalyses to determine the climatic and meteorological characteristics of this particular drought. The models reproduce in some degree the observed annual cycle of precipitation and the geographical distribution of negative rainfall anomalies during the summer months of 2005. With respect to the evolution of rainfall during 2004–2006, some of the models were able to simulate the negative rainfall departures during early summer of 2005 (December 2004 to February 2005). The interannual variability of rainfall anomalies for both austral summer and fall over northern and southern Amazonia show a large spread among models, with some of them capable of reproducing the 2005 observed negative rainfall departures (four out of seven models in southern Amazonia during DJF). In comparison, all models simulated the observed southern Amazonia negative rainfall and positive air temperature anomalies during the El Nino-related drought in 1998. The spatial structure of the simulated rainfall and temperature anomalies in DJF and MAM 2005 shows biases that are different among models. While some models simulated the observed negative rainfall anomalies over parts of western and southern Amazonia during DJF, others simulated positive rainfall departures over central Amazonia. The simulated circulation patterns indicate a weaker northeasterly flow from the tropical North Atlantic into Amazonia, and reduced flows from southern Amazonia into the La Plata basin in DJF, which is consistent with observations. In general, we can say that in some degree the regional models are able to capture the response to the forcing from the tropical Atlantic during the drought of 2005 in Amazonia. Moreover, extreme climatic conditions in response to anomalous low-level circulation features are also well captured, since the boundary conditions come from reanalysis and the models are largely constrained by the information provided at the boundaries. The analysis of the 2005 drought suggests that when the forcing leading to extreme anomalous conditions is associated with both local and non-local mechanisms (soil moisture feedbacks and remote SST anomalies, respectively) the models are not fully capable of representing these feedbacks and hence, the associated anomalies. The reason may be a deficient reproduction of the land–atmosphere interactions.     

Northern African climate at the end of the twenty-first century: an integrated application of regional and global climate models

A method for simulating future climate on regional space scales is developed and applied to northern Africa. Simulation with a regional model allows for the horizontal resolution needed to resolve the region’s strong meridional gradients and the optimization of parameterizations and land-surface model. The control simulation is constrained by reanalysis data, and realistically represents the present day climate. Atmosphere–ocean general circulation model (AOGCM) output provides SST and lateral boundary condition anomalies for 2081–2100 under a business-as-usual emissions scenario, and the atmospheric CO₂ concentration is increased to 757 ppmv. A nine-member ensemble of future climate projections is generated by using output from nine AOGCMs. The consistency of precipitation projections for the end of the twenty-first century is much greater for the regional model ensemble than among the AOGCMs. More than 77% of ensemble members produce the same sign rainfall anomaly over much of northern Africa. For West Africa, the regional model projects wetter conditions in spring, but a mid-summer drought develops during June and July, and the heat stoke risk increases across the Sahel. Wetter conditions resume in late summer, and the likelihood of flooding increases. The regional model generally projects wetter conditions over eastern Central Africa in June and drying during August through September. Severe drought impacts parts of East Africa in late summer. Conditions become wetter in October, but the enhanced rainfall does not compensate for the summertime deficit. The risk of heat stroke increases over this region, although the threat is not projected to be as great as in the Sahel.     

Contrasting regional and global climate simulations over South Asia

Climate Dynamics - Two ensembles of climate simulations, one global and one regional, are used to investigate model errors and projected climate change in seasonal mean temperature and...     

An evaluation of the CORDEX regional climate models in simulating future rainfall and extreme events over Mzingwane catchment,Zimbabwe

The study evaluated CORDEX RCMs’ ability to project future rainfall and extreme events in the Mzingwane catchment using an ensemble average of three RCMs (RCA4, REMO2009 and CRCM5). Model validation employed the statistical mean and Pearson correlation, while trends in projected rainfall and number of rainy days were computed using the Mann-Kendall trend test and the magnitudes of trends were determined by Sen’s slope estimator. Temporal and spatial distribution of future extreme dryness and wetness was established by using the Standard Precipitation Index (SPI). The results show that RCMs adequately represented annual and inter-annual rainfall variability and the ensemble average outperformed individual models. Trend results for the projected rainfall suggest a significant decreasing trend in future rainfall (2016–2100) for all stations at p < 0.05. In addition, a general decreasing trend in the number of rainy days is projected for future climate, although the significance and magnitude varied with station location. Model results suggest an increased occurrence of future extreme events, particularly towards the end of the century. The findings are important for developing proactive sustainable strategies for future climate change adaption and mitigation.     

Multi-variable error correction of regional climate models

Climate change impact research needs regional climate scenarios of multiple meteorological variables. Those variables are available from regional climate models (RCMs), but affected by considerable biases. We evaluate the application of an empirical-statistical error correction method, quantile mapping (QM), for a small ensemble of RCMs and six meteorological variables. Annual and monthly biases are reduced to close to zero by QM for all variables in most cases. Exceptions are found, if non-stationarity of the model’s error characteristics occur. Even in the worst cases of non-stationarity, QM clearly improves the biases of raw RCMs. In addition, QM successfully adjusts the distributions of the analysed variables. To approach the question whether time series and inter-variable relationships are still plausible after correction, we evaluate the root-mean-square error (RMSE), autocorrelation and inter-variable correlation. We found improvement or no clear effect in RMSE and autocorrelation, and no clear effect on the correlation between meteorological variables. These results demonstrate that QM retains the quality of the temporal structure in time series and the inter-variable dependencies of RCMs. It has to be emphasised that this cannot be interpreted as an improvement and that deficiencies of the RCMs in those features are retained as well. Our results give some indication for the performance of QM applied to future scenarios, since our evaluation relies on independent calibration and evaluation periods, which are affected by climate variability and change. The effect of non-stationarity, however, can be expected to be larger in far future. We demonstrate the retainment of the RCM’s temporal structure and inter-variable dependencies, and large improvements in biases. This qualifies QM as a valuable, though not perfect, method in the interface between climate models and climate change impact research. Nonetheless, in case of no correlation between re-analysis driven RCM and observation, one should consider that QM does not correct this correlation.     

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.