Performance of Multi Model Canonical Correlation Analysis (MMCCA) for prediction of Indian summer monsoon rainfall using GCMs output

The emerging advances in the field of dynamical prediction of monsoon using state-of-the-art General Circulation Models (GCMs) have led to the development of various multi model ensemble techniques (MMEs). In the present study, the concept of Canonical Correlation Analysis is used for making MME (referred as Multi Model Canonical Correlation Analysis or MMCCA) for the prediction of Indian summer monsoon rainfall (ISMR) during June-July-August-September (JJAS). This method has been employed on the rainfall outputs of six different GCMs for the period 1982 to 2008. The prediction skill of ISMR by MMCCA is compared with the simple composite method (SCM) (i.e. arithmetic mean of all GCMs), which is taken as a benchmark. After a rigorous analysis through different skill metrics such as correlation coefficient and index of agreement, the superiority of MMCCA over SCM is illustrated. Performance of both models is also evaluated during six typical monsoon years and the results indicate the potential of MMCCA over SCM in capturing the spatial pattern during extreme years.     

Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India

The northeast (NE) monsoon season (October, November and December) is the major period of rainfall activity over south peninsular India. This study is mainly focused on the prediction of northeast monsoon rainfall using lead-1 products (forecasts for the season issued in beginning of September) of seven general circulation models (GCMs). An examination of the performances of these GCMs during hindcast runs (1982–2008) indicates that these models are not able to simulate the observed interannual variability of rainfall. Inaccurate response of the models to sea surface temperatures may be one of the probable reasons for the poor performance of these models to predict seasonal mean rainfall anomalies over the study domain. An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among these three schemes, SVD based MME has more skill than other MME schemes as well as member models.     

Determination and prediction of standardized precipitation index (SPI) using TRMM data in arid ecosystems

Drought over a period threatens the water resources, agriculture, and socioeconomic activities. Therefore, it is crucial for decision makers to have a realistic anticipation of drought events to mitigate its impacts. Hence, this research aims at using the standardized precipitation index (SPI) to predict drought through time series analysis techniques. These adopted techniques are autoregressive integrating moving average (ARIMA) and feed-forward backpropagation neural network (FBNN) with different activation functions (sigmoid, bipolar sigmoid, and hyperbolic tangent). After that, the adequacy of these two techniques in predicting the drought conditions has been examined under arid ecosystems. The monthly precipitation data used in calculating the SPI time series (SPI 3, 6, 12, and 24 timescales) have been obtained from the tropical rainfall measuring mission (TRMM). The prediction of SPI was carried out and compared over six lead times from 1 to 6 using the model performance statistics (coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE)). The overall results prove an excellent performance of both predicting models for anticipating the drought conditions concerning model accuracy measures. Despite this, the FBNN models remain somewhat better than ARIMA models with R?≥?0.7865, MAE?≤?1.0637, and RMSE?≤?1.2466. Additionally, the FBNN based on hyperbolic tangent activation function demonstrated the best similarity between actual and predicted for SPI 24 by 98.44%. Eventually, all the activation function of FBNN models has good results respecting the SPI prediction with a small degree of variation among timescales. Therefore, any of these activation functions can be used equally even if the sigmoid and bipolar sigmoid functions are manifesting less adjusted R² and higher errors (MAE and RMSE). In conclusion, the FBNN can be considered a promising technique for predicting the SPI as a drought monitoring index under arid ecosystems.     

Probabilistic multi-model ensemble prediction of Indian summer monsoon rainfall using general circulation models: A non-parametric approach

Probabilistic prediction has the ability to convey the intrinsic uncertainty of forecast that helps the decision makers to manage the climate risk more efficiently than deterministic forecasts. In recent times, probabilistic predictions obtained from the products from General Circulation Models (GCMs) have gained considerable attention. The probabilistic forecast can be generated in parametric (assuming Gaussian distribution) as well as non-parametric (counting method) ways. The present study deals with the non-parametric approach that requires no assumption about the form of the forecast distribution for the prediction of Indian summer monsoon rainfall (ISMR) based on the hindcast run of seven general circulation models from 1982 to 2008. Probabilistic prediction from each of the GCM products has been generated by non-parametric methods for tercile categories (viz. below normal (BN), near-normal (NN), and above normal (AN)) and evaluation of their skill is assessed against observed data. Five different types of PMME schemes have been used for combining probabilities from each GCM to improve the forecast skill as compared to the individual GCMs. These schemes are different in nature of assigning the weights for combining probabilities. After a rigorous analysis through Rank Probability Skill Score (RPSS) and relative operating characteristic (ROC) curve, the superiority of PMME has been established over climatological probability. It is also found that, the performances of PMME1 and PMME3 are better than all the other methods whereas PMME3 has showed more improvement over PMME1.     

Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects

Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models.     

Climate change impact projections at the catchment scale in Tunisia using the multi-model ensemble mean approach

In this work, we developed a mean projection for climate change and assessed its impact on some hydro-meteorological indicators relevant to climatic condition, precipitation extremes magnitude and frequency for the Siliana catchment in Tunisia based on an ensemble of seven combinations of global circulation models (GCMs) and regional climate models (RCMs) derived from the EU-FP6 ENSEMBLES project. We performed quantile-based mapping (QM) bias correction technique of climate model projection using local observations. Because there is no warranty that the best climate model based on its performances in reproducing historic climate will be superior to other models in simulating future climate, we used the multi-model ensemble (MME) mean approach to derive a mean projection as the best guess for climate change projection for the Siliana catchment. We also quantified the uncertainty of the MME in the projected change in the selected indicators by comparing their values in the reference period (1981–2010) to these in the future period (2041–2070). Results reveal that the Siliana catchment will be prone to drier and warmer climate in the future with less rainy days for each month. The uncertainty associated with the MME projection suggests that no clear general tendency for extreme rainy days in the future is expected. These findings highlight the need to consider an ensemble of multi-climate models with an uncertainty framework if reliable climate change impact study is sought at the catchment scale.     

The relationship between the isotopic content of precipitation and the precipitation amount in tropical regions

General circulation models (GCMs) fitted with stable isotope schemes are widely used to interpret the isotope–climate relationship. However, previous studies have found that the spatiotemporal isotope/precipitation correlation simulated by GCMs is stronger and more widespread than the observed value. To understand the reason for this failure, we investigated the factors influencing the empirically well-known isotope/precipitation relationship, or precipitation amount effect, in the tropics using newly obtained daily precipitation isotope monitoring data over Asia. As in previous studies, we found an apparent correlation between the long-term monthly mean isotopic content and the corresponding precipitation amount (local precipitation) observed at sub-tropical island stations. Furthermore, on a monthly timescale, the isotopic variability of precipitation for these stations was more clearly related to the regional precipitation amount than to local precipitation. This correlation of isotopic content with the regional precipitation amount was observed at the equatorial (Maritime Continent) stations. For these stations, isotope/local precipitation relationships only appeared over longer timescales, with different regression line slopes at each station. However, at the coastal stations, there was a strong linear relationship between the monthly mean isotopic content and corresponding regional precipitation, and regression line slopes were spatially uniform. For the two sub-tropical terrestrial (Indochina Peninsula) stations, the isotopic minimum appeared without any relationship to rainfall amount but usually occurred at the leeward station during the rainy season. These results suggest that the isotopic variations of precipitation did not depend on the ’local’ rain-out history but on the rain-out process in the surrounding region. However, local rainfall events were associated not only with large-scale disturbances but also with regional circulation. Thus, the scale difference of controlling factors between local rainfall amount and isotopic value results in the weakening of the rainfall amount effect at the observation site and in the discrepancy between GCM simulations and observations. This finding suggests that regional precipitation–isotope relationships should be compared with GCM results. Additionally, because the isotope signal reflects the rain-out history at a regional scale, evaluation of the isotopic field using isotopic GCMs will be useful not only to reconstruct paleoclimate conditions but also to examine how GCMs can reproduce real atmospheric circulation over the tropics.     

Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis

Quality and reliable drought prediction is essential for mitigation strategies and planning in disaster-stricken regions globally. Prediction models such as empirical or data-driven models play a fundamental role in forecasting drought. However, selecting a suitable prediction model remains a challenge because of the lack of succinct information available on model performance. Therefore, this review evaluated the best model for drought forecasting and determined which differences if any were present in model performance using standardised precipitation index (SPI). In addition, the most effective combination of the SPI with its respective timescale and lead time was investigated. The effectiveness of data-driven models was analysed using meta-regression analysis by applying a linear mixed model to the coefficient of determination and the root mean square error of the validated model results. Wavelet-transformed neural networks had superior performance with the highest correlation and minimum error. Preprocessing data to eliminate non-stationarity performed substantially better than did the regular artificial neural network (ANN) model. Additionally, the best timescale to calculate the SPI was 24 and 12 months and a lead time of 1–3 months provided the most accurate forecasts. Studies from China and Sicily had the most variation based on geographical location as a random effect; while studies from India rendered consistent results overall. Variation in the result can be attributed to geographical differences, seasonal influence, incorporation of climate indices and author bias. Conclusively, this review recommends use of the wavelet-based ANN (WANN) model to forecast drought indices.

     

Construction of Statistical-Dynamic Prediction Model for the Asian-Australian Summer Monsoon Based on the Predictable Mode Analysis Method and Assessment of Its Predictive Skills

Due to the limitations of model performances, the predictive skills of current climate models for the Asian-Australian summer monsoon precipitation are still poor. The prediction based on the combination of statistical and dynamic approaches is an effective way to improve the predictive skills. We used such method to identify the predictable modes of the Asian-Australian summer monsoon precipitation with clear physical interpretation from the historical observational data. Then we combined the principal components time series of these modes predicted by the coupled models, which is derived from the seasonal prediction experiments in the ENSEMBLES project, and the corresponding spatial patterns derived from the above observational analysis to reconstruct the precipitation field. These formed a statistical-dynamic seasonal prediction model for the Asian-Australian summer monsoon precipitation. We analyzed the predictive skills of the model at 1-, 4-and 7-month leads. The result shows that the forecast skills of the statistical-dynamic prediction model are higher than those of the simple dynamic predictions. In addition, the predictive skills of the Multi-Model Ensemble (MME) mean are superior to those of any individual models. Therefore, it is very necessary to implement multi-model ensemble prediction for the monsoon precipitation.     

Characteristics of meteorological drought in Bangladesh

Meteorological drought events occur in Bangladesh are diagnosed using monthly rainfall and mean air temperature from the surface observations and Regional Climate Model (RegCM) by calculating Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI) for the period 1961?C1990. The historical records of drought event obtained from the Bangladesh Bureau of Statistics and International Disaster Database are used to verify the SPI and PDSI detected events. The SPI and monthly PDSI are obtained for 27 station data across Bangladesh as well as for two subregions over the country. Result based on the observed data shows that regional information is better in drought diagnosis compared to the point information. The regional analysis is able to detect about 80?% of the drought events occurred during the study period. Frequency of moderate drought is higher for all over the country. The SPI calculated from RegCM rainfall shows that the detection of moderate drought events is 10, 7, and 21?% overestimated for 1-, 3-, and 6-month length, respectively, compared to using of observed data. For extreme drought cases, detection is overestimated (underestimated) by 25?% (79?%) for 1-month (6-month) length. The PDSI results for model and observed data are nearly same to SPI calculations. Model monthly PDSI result is overestimated (underestimated) by 29?% (50?%) for moderate (severe) drought events with reference to the observed PDSI. Hence, RegCM output may be useful to detect 3?C6-month (monthly to seasonal) length moderate drought events over a heavy rainfall region likely Bangladesh.     

Sedimentological evaluation of general circulation model simulations for the “greenhouse” Earth: Cretaceous and Jurassic case studies

Conceptual climate models, based on the workings of the present-day climate system, provided a first-order approach to ancient climate systems. They are potentially very subjective in character. Their main drawback was that they involved the relocation of continents beneath a stable atmospheric circulation modelled upon that of the present. General circulation models (GCMs) use the laws of physics and an understanding of past geography to simulate climatic responses. They are objective in character. However, they require super computers to handle vast numbers of calculations. Nonetheless it is now possible to compare results from different GCMs for a range of times and over a wide range of parameterisations. GCMs are currently producing simulated climate predictions which compare favourably with the distributions of climatically sensitive facies (e.g. coals, evaporites and palaeosols). They have been used effectively in the prediction of oceanic upwelling sites and the distribution of petroleum source-rocks and phosphorites. Parameterisation is the main weakness in GCMs (e.g. sea-surface temperature, orography, cloud behaviour). Sensitivity experiments can be run on GCMs which simulate the effects of Milankovitch forcing and thus provide insights into possible patterns of climate change both globally and locally (i.e. provide predictions that can be evaluated against the rock record). Future use of GCMs could be in the forward modelling of sequence stratigraphic evolution and in the prediction of the diagenetic characteristics of reservoir units in frontier exploration areas. The sedimentary record provides the only way that GCMs may themselves be evaluated and this is important because these same GCMs are being used currently to predict possible changes in future climate.     

Investigation of hydrological drought using Cumulative Standardized Precipitation Index (SPI 30) in the eastern Mediterranean region (Damascus,Syria)

The Eastern Mediterranean region has been exposed to drought episodes, which have been occurring more frequently during the last decades. The objective of the present paper is to study the precipitation regime of the Damascus (Mazzeh) meteoric station by analysing drought characteristics using the Standardized Precipitation Index (SPI) and comparing this with the drought in Cyprus. The cumulative drought conceptis proposed to characterize long-term hydrologic drought, which affects the shallow groundwater productivity in terms of quantity and quality. Gamma probability distribution was fitted to the long-term annual precipitation in Damascus from 1918–1919 to 2007–2008 (n = 90 years). Generally, a decreasing trend of 17% to the mean annual rainfall of Damascus and 13% to the mean annual rainfall of Cyprus was estimated between 1970 and 2000. The SPI identifies three major extended drought periods: (1) 9 years of severe drought (1954–1963) with an average 20% precipitation deficit per year compared to the mean. (2) 8 years of severe drought (1983–1991) with a 27% deficit per year on average. (3) 9 years of extreme drought (1993–2002) with a 31% deficit per year on average. The cumulative standardized precipitation index (SPI 30) demonstrates positive values for the first period and is indicative of having no effect on the global water balance. SPI 30 exhibits sensitive equilibrium with near zero values / a near zero value (±1.5) for the second period. For the third period, however, the SPI 30 decreases below ?10 indicating an extreme hydrological drought that has negative consequences on the recent groundwater recharge. It is required to develop and implement a sustainable groundwater management strategy to reduce long-terms drought risks. Generally, the SPI 30 in Cyprus is parallel to that in Damascus with a 3–5 year delay. Thus, the central zone of the Eastern Mediterranean region is facing big challenges and has been suffering from three decades of moderate to severe hydrological drought (SPI 30=?5 to ?10) causing a severe decrease in springs discharges of the region. Therefore, in order to reduce the climate change effects on water resources, it is necessary to adopt a sustainable proactive management plan during the frequent severe droughts.     

短期气候预测的可预报性与不确定性

分析总结了近年来国内外短期气候预测业务、预测试验研究及可预报性研究的成果。指出无论用大气环流模式（AGAM）还是用统计方法,月平均环流预报与观测实况的相关系数均在0.2～0.3之间。用统计方法所作的气温预报水平与之相当,降水预报水平还要略低一些。季度预报大多依靠统计方法。近年来我国汛期降水预报水平有明显提高,但也只相当于相关系数0.2～0.3。用耦合环流模式（CGCM）积分作季度预报仅仅才开始试验。用各种模式作ENSO预报时表现出一定技巧,预报时效可达半年以上,但仍有春季预报障碍等问题。短期内气候预测业务可能仍然以统计方法为主。但必须大力开展气候系统机理的研究,并建立相应的模式。不了解气候变率形成的物理机制, 短期气候预测水平不可能有显著提高。     

Improving the performance of precipitation outputs from Global Climate Models to predict monthly and seasonal rainfall over the Indian subcontinent

Skilful prediction of the monthly and seasonal summer monsoon rainfall over India at a smaller spatial scale is a major challenge for the scientific community. The present study is aimed at achieving this objective by hybridising two mathematical techniques, namely synthetic superensemble (SSE) and supervised principal component regression (SPCR) on six state-of-the art Global Climate Models (GCMs). The performance of the mathematical model is evaluated using correlation analysis, the root mean square error, and the Nash–Sutcliffe efficiency index. Results feature reasonable improvement over central India, which is a zone of maximum rainfall activity in the summer monsoon season. The study also highlights improvement in the monthly prediction of rainfall over raw GCMs (15–20% improvement) with exceptional improvement in July. The developed model is also examined for anomalous years of monsoon and it is found that the model is able to capture the signs of anomalies over different gridpoints of the Indian domain.     

Forecasting of cyclone Viyaru and Phailin by NWP-based cyclone prediction system (CPS) of IMD – an evaluation

An objective NWP-based cyclone prediction system (CPS) was implemented for the operational cyclone forecasting work over the Indian seas. The method comprises of five forecast components, namely (a) Cyclone Genesis Potential Parameter (GPP), (b) Multi-Model Ensemble (MME) technique for cyclone track prediction, (c) cyclone intensity prediction, (d) rapid intensification, and (e) predicting decaying intensity after the landfall. GPP is derived based on dynamical and thermodynamical parameters from the model output of IMD operational Global Forecast System. The MME technique for the cyclone track prediction is based on multiple linear regression technique. The predictor selected for the MME are forecast latitude and longitude positions of cyclone at 12-hr intervals up to 120 hours forecasts from five NWP models namely, IMD-GFS, IMD-WRF, NCEP-GFS, UKMO, and JMA. A statistical cyclone intensity prediction (SCIP) model for predicting 12 hourly cyclone intensity (up to 72 hours) is developed applying multiple linear regression technique. Various dynamical and thermodynamical parameters as predictors are derived from the model outputs of IMD operational Global Forecast System and these parameters are also used for the prediction of rapid intensification. For forecast of inland wind after the landfall of a cyclone, an empirical technique is developed. This paper briefly describes the forecast system CPS and evaluates the performance skill for two recent cyclones Viyaru (non-intensifying) and Phailin (rapid intensifying), converse in nature in terms of track and intensity formed over Bay of Bengal in 2013. The evaluation of performance shows that the GPP analysis at early stages of development of a low pressure system indicated the potential of the system for further intensification. The 12-hourly track forecast by MME, intensity forecast by SCIP model and rapid intensification forecasts are found to be consistent and very useful to the operational forecasters. The error statistics of the decay model shows that the model was able to predict the decaying intensity after landfall with reasonable accuracy. The performance statistics demonstrates the potential of the system for improving operational cyclone forecast service over the Indian seas.     

Prediction and error growth in the daily forecast of precipitation from the NCEP CFSv2 over the subdivisions of Indian subcontinent

This study investigates the forecast skill and predictability of various indices of south Asian monsoon as well as the subdivisions of the Indian subcontinent during JJAS season for the time domain of 2001–2013 using NCEP CFSv2 output. It has been observed that the daily mean climatology of precipitation over the land points of India is underestimated in the model forecast as compared to observation. The monthly model bias of precipitation shows the dry bias over the land points of India and also over the Bay of Bengal, whereas the Himalayan and Arabian Sea regions show the wet bias. We have divided the Indian landmass into five subdivisions namely central India, southern India, Western Ghat, northeast and southern Bay of Bengal regions based on the spatial variation of observed mean precipitation in JJAS season. The underestimation over the land points of India during mature phase was originated from the central India, southern Bay of Bengal, southern India and Western Ghat regions. The error growth in June forecast is slower as compared to July forecast in all the regions. The predictability error also grows slowly in June forecast as compared to July forecast in most of the regions. The doubling time of predictability error was estimated to be in the range of 3–5 days for all the regions. Southern India and Western Ghats are more predictable in the July forecast as compared to June forecast, whereas IMR, northeast, central India and southern Bay of Bengal regions have the opposite nature.     

The performance of SPI and PNPI in analyzing the spatial and temporal trend of dry and wet periods over Iran

Drought monitoring is carried out using various drought indices, including SPI, to generate time series of dry and wet periods. Furthermore, the dispersion of dry and wet periods was embossed with different intensities (high, medium, and low) over the data record years. Although these results were very necessary for planning and predicting future droughts, it appeared that the application of any trend over dry and wet periods could provide more accurate and unbiased or safer predictions in terms of analysis process. Generally, most of the researchers believed that the results of a drought trend analysis have been influenced by short-term persistence or significant autocorrelation with different lags on drought event time series and the mentioned impact should be preferably removed. Accordingly, drought monitoring was accomplished using SPI and PNPI drought indices to extract time series of dry and wet periods in terms of 50-year (1965–2014) annual rainfall data of 40 synoptic stations over Iran. Having used the basic and modified Mann–Kendall nonparametric tests, it was attempted to analyze the trend of dry and wet periods extracted from mentioned indices. The results represent the relative advantage of using the modified Mann–Kendall test in drought trend analysis. Furthermore, it was shown that the trend of dry and wet periods was negative in the majority of selected stations and that this trend was significant at 95% confidence level in northwest of Iran. Also, the results indicated the similar performance of SPI and PNPI indices in trend analysis of dry and wet periods.

     

基于条件分布模型的气象水文干旱传递分析

气象干旱发展到一定程度可以传递为水文干旱。以潘家口水库流域1961—2010年逐月平均降水数据和潘家口水库的入库径流序列为基础数据,分别计算了1、3、6、12个月时间尺度的标准化降水指数（SPI）和标准化径流指数（SRI）,以表征研究区域的气象干旱和水文干旱。基于条件分布模型,分析了不同时间尺度的气象干旱传递到未来的不同等级和不同的预测期（或滞后期）的水文干旱的概率。结果表明,当SPI时间尺度较短或预测期（滞后期）较短时,其对应的SRI水文干旱等级越倾向于维持与SPI相同的干旱等级;随着SPI时间尺度的增长或预测期（滞后期）延长,其对应的SRI水文干旱等级略低于气象干旱或恢复到正常状态。     

Potential impacts of global warming on the frequency and magnitude of heavy precipitation

It is now widely recognised that the most significant impacts of global warming are likely to be experienced through changes in the frequency of extreme events, including flooding. This paper reviews physical and empirical arguments which suggest that global warming may result in a more intense hydrological cycle, with an associated increase in the frequency and/or magnitude of heavy precipitation. Results derived from enhanced-greenhouse experiments using global climate models (GCMs) are shown to be consistent with these physical and empirical arguments. Detailed analysis of output from three GCMs indicates the possibility of substantial increases in the frequency and magnitude of extreme daily precipitation, with amplification of the effect as the return period increases. Moreover, return period analyses for locations in Australia, Europe, India, China and the USA indicate that the results are global in scope. Subsequent discussion of the limitations of GCMs for this sort of analysis highlights the need for caution when interpreting the precipitation results presented here. However, the consistency between physically-based expectations, empirical observations, and GCM results is considered sufficient for the GCM results to be taken seriously, at least in a qualitative sense, especially considering that the alternative seems to be reliance by planners on the fundamentally flawed concept of a stationary climate.     

District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI)

District-wide drought climatology over India for the southwest monsoon season (June–September) has been examined using two simple drought indices; Percent of Normal Precipitation (PNP) and Standardized Precipitation Index (SPI). The season drought indices were computed using long times series (1901–2003) of southwest monsoon season rainfall data of 458 districts over the country. Identification of all India (nation-wide) drought incidences using both PNP and SPI yielded nearly similar results. However, the district-wide climatology based on PNP was biased by the aridity of the region. Whereas district-wide drought climatology based on SPI was not biased by aridity. This study shows that SPI is a better drought index than PNP for the district-wide drought monitoring over the country. SPI is also suitable for examining break and active events in the southwest monsoon rainfall over the country. The trend analysis of district-wide season (June–September) SPI series showed significant negative trends over several districts from Chattisgarh, Bihar, Kerala, Jharkhand, Assam and Meghalaya, Uttaranchal, east Madhya Pradesh, Vidarbha etc., Whereas significant positive trends in the SPI series were observed over several districts from west Uttar Pradesh, west Madhya Pradesh, South & north Interior Karnataka, Konkan and Goa, Madhya Maharashtra, Tamil Nadu, East Uttar Pradesh, Punjab, Gujarat etc.