All India summer monsoon rainfall prediction using an artificial neural network

The prediction of Indian summer monsoon rainfall (ISMR) on a seasonal time scales has been attempted by various research groups using different techniques including artificial neural networks. The prediction of ISMR on monthly and seasonal time scales is not only scientifically challenging but is also important for planning and devising agricultural strategies. This article describes the artificial neural network (ANN) technique with error- back-propagation algorithm to provide prediction (hindcast) of ISMR on monthly and seasonal time scales. The ANN technique is applied to the five time series of June, July, August, September monthly means and seasonal mean (June + July + August + September) rainfall from 1871 to 1994 based on Parthasarathy data set. The previous five years values from all the five time-series were used to train the ANN to predict for the next year. The details of the models used are discussed. Various statistics are calculated to examine the performance of the models and it is found that the models could be used as a forecasting tool on seasonal and monthly time scales. It is observed by various researchers that with the passage of time the relationships between various predictors and Indian monsoon are changing, leading to changes in monsoon predictability. This issue is discussed and it is found that the monsoon system inherently has a decadal scale variation in predictability. Received: 13 March 1999 / Accepted: 31 August 1999     

Validation of the experimental hindcasts produced by the GloSea4 seasonal prediction system

Using 14 year (1996–2009) ensemble hindcast runs produced with the Global Seasonal Forecasting System version 4 (GloSea4), this study evaluates the spatial and temporal structure of the hindcast climatology and the prediction skill of major climate variability. A special focus is on the fidelity of the system to reproduce and to forecast phenomena that are closely related to the East Asian climate. Overall the GloSea4 system exhibits realistic representations of the basic climate even though a few model deficiencies are identified in the sea surface temperature and precipitation. In particular, the capability of GloSea4 to capture the seasonal migration of rain belt associated with Changma implies a good potential for the Asian summer monsoon prediction. It is found that GloSea4 is as skillful as other state-of-the-art seasonal prediction systems in forecasting climate variability including the El-Nino/southern oscillation (ENSO), the East Asian summer monsoon, the Arctic Oscillation (AO), and the Madden-Julian Oscillation (MJO). The results presented in this study will provide benchmark evaluation for next seasonal prediction systems to be developed at the Korea Meteorological Administration.     

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

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

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

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

The relationship between lightning activity and surface wet bulb temperature and its variation with latitude in Australia

Summary Convective Available Potential Energy (CAPE) is the driving force for thunderstorm development. CAPE is closely controlled by wet bulb temperature. The lightning activity measured by a network of ten lightning flash counters widely distributed across continental Australia was studied as a function of wet bulb temperature. At each of the stations, the monthly total of lightning ground flashes, N, increased sharply with the increase of the monthly mean daily maximum wet bulb temperature, T_w, max. The dependence was strongest in the tropics and became less pronounced at temperate latitudes. In Darwin (latitude 12° S), the lightning ground flash activity increased by over three orders of magnitude over a 7 °C range of T_w, max. The corresponding increases for Coffs Harbour (latitude 30° S) and for Melbourne (latitude 38° S) were about one and a half orders of magnitude and about half an order of magnitude, respectively, each over a 10 °C range of T_w, max. Power law approximations were derived for each of the ten stations and showed that the logarithm of N was directly proportional to the power, P, of T_w, max. The value of P showed a sharp exponential decrease with increasing latitude away from the equator.     

Skill as a function of time scale in ensembles of seasonal hindcasts

Forecast skill as a function of time lead and time averaging is examined in two 6-member ensembles of seasonal hindcasts. One ensemble is produced with the second generation general circulation model of the Canadian Centre for Climate Modelling and Analysis (GCM2) and the other with a reduced resolution version of the numerical weather prediction model of the Canadian Meteorological Centre (SEF). The integrations are initiated from the NCEP/NCAR reanalyzed data. Monthly sea surface temperature anomalies observed prior to the forecast period are maintained throughout the forecast season. A statistical forecast improvement technique, based on the singular value decomposition of forecast and reanalyzed fields, is discussed and evaluated. A simple analogue of the hindcast integrations is used to examine the behavior of two common skill scores, the correlation skill score and the explained variance skill score. The maximal skill score and the corresponding optimal forecast in this analogue are identified. The total skill of the optimal forecast is a sum of two terms, one associated with the initial conditions and the other with the lower boundary forcing. The two sources of skill operate on different time scales, with initial conditions being more important in the first one-two weeks and the atmospheric response to the boundary forcing becoming more dominant for longer time leads and time averages. This suggests that these sources of skill should be considered separately in forecast optimization. The statistical technique is moderately successful in improving the skill of monthly to seasonal forecasts of 500 hPa height (Z ₅₀₀) and 700 hPa temperature (T ₇₀₀) in the Northern Hemisphere and in the North Pacific/North America sector. The improvement is better when the forecasts for the first week and for the rest of the season are optimized separately. The SEF model produces better Z ₅₀₀ and T ₇₀₀ forecasts than GCM2 in the first one-two weeks whereas GCM2 performs slightly better at longer time leads. The skill of zero time lead forecast decays rapidly with averaging interval for time averages up to about 30–45 days and stabilizes, or even rises, for longer time averages. Excluding the first week from seasonal forecasts results in substantial degradation of predictive skill. Received: 1 November 1999 / Accepted: 24 May 2000     

Cyclone track prediction by a quasi-Lagrangian limited area model

Summary. ?Cyclone track predictions in the Indian seas (Bay of Bengal and Arabian Sea) with a quasi-Lagrangian model (QLM) have been attempted. QLM has a horizontal resolution of 40 km and 16 sigma levels in the vertical. It is integrated in a domain of about 4400 × 4400 km². A new initialization procedure to provide initial fields for running the model has been designed. The initialization procedure consists of updating the global model forecasts, used as first guess, provided by the National Center for Medium Range Weather Forecasting (NCMRWF), New Delhi. A new version of IMD’s operational optimum interpolation scheme has been created to suit the QLM grid structure. Lateral boundary conditions are computed from the extended forecasts of NCMRWF. The track forecasts in each case show a reasonable skill of the forecast model in predicting the direction of movement within acceptable limits of forecast errors, which are comparable to some of the best models operated by advanced NWP centers of the world. Even the recurving storms are well predicted. Evolution of the vertical motion fields are also studied which reveal some interesting features, which are described in detail in the text. The composited vertical motion fields are projected against observed rainfall distribution, which show a good spatial correspondence. Received August 9, 2001; revised March 12, 2002; accepted June 17, 2002 Published online: May 8, 2003     

Recent trends of mean maximum and minimum air temperatures in the western half of Iran

In this study, the trends of the annual, seasonal and monthly maximum (T _max) and minimum (T _min) air temperatures time series were investigated for 20 stations in the western half of Iran during 1966?C2005. Three statistical tests including Mann?CKendall, Sen??s slope estimator and linear regression were used for the analysis. The annual T _max and T _min series showed a positive trend in 85% of the stations and a negative trend in 15% of the stations in the study region. The highest increase of T _max and T _min values were obtained over Kermanshah and Ahwaz at the rates of (+)0.597°C/decade and (+)0.911°C/decade, respectively. On the seasonal scale, the strongest increasing trends were identified in T _max and T _min data in summer. The highest numbers of stations with positive significant trends occurred in the monthly T _max and T _min series in August. In contrast, the lowest numbers of stations with significant positive trends were observed between November and March. Overall, the results showed similar increasing trends for the study variables, although T _min generally increased at a higher rate than T _max in the study period.     

Comparison of extended medium-range forecast skill between KMA ensemble,ocean coupled ensemble,and GloSea5

This article describes a three way inter-comparison of forecast skill on an extended medium-range time scale using the Korea Meteorological Administration (KMA) operational ensemble numerical weather prediction (NWP) systems (i.e., atmosphere-only global ensemble prediction system (EPSG) and ocean-atmosphere coupledEPSG) and KMA operational seasonal prediction system, the Global Seasonal forecast system version 5 (GloSea5). The main motivation is to investigate whether the ensemble NWP system can provide advantage over the existing seasonal prediction system for the extended medium-range forecast (30 days) even with putting extra resources in extended integration or coupling with ocean with NWP system. Two types of evaluation statistics are examined: the basic verification statistics - the anomaly correlation and RMSE of 500-hPa geopotential height and 1.5-meter surface temperature for the global and East Asia area, and the other is the Real-time Multivariate Madden and Julian Oscillation (MJO) indices (RMM1 and RMM2) - which is used to examine the MJO prediction skill. The MJO is regarded as a main source of forecast skill in the tropics linked to the mid-latitude weather on monthly time scale. Under limited number of experiment cases, the coupled NWP extends the forecast skill of the NWP by a few more days, and thereafter such forecast skill is overtaken by that of the seasonal prediction system. At present stage, it seems there is little gain from the coupled NWP even though more resources are put into it. Considering this, the best combination of numerical product guidance for operational forecasters for an extended medium-range is extension of the forecast lead time of the current ensemble NWP (EPSG) up to 20 days and use of the seasonal prediction system (GloSea5) forecast thereafter, though there exists a matter of consistency between the two systems.     

Climate change scenarios for surface temperature in Emilia-Romagna (Italy) obtained using statistical downscaling models

Summary Possible changes of mean climate and the frequency of extreme temperature events in Emilia-Romagna, over the period 2070–2100 compared to 1960–1990, are assessed. A statistical downscaling technique, applied to HadAM3P experiments (control, A2 and B2 scenarios) performed at the Hadley Centre, is used to achieve this objective. The method applied consists of a multivariate regression based on Canonical Correlation Analysis (CCA), using as possible predictors mean sea level pressure (MSLP), geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850), and as predictands the seasonal mean values of minimum and maximum surface temperature (T_min and T_max), 90^th percentile of maximum temperature (T_max90), 10^th percentile of minimum temperature (T_min10), number of frost days (T_nfd) and heat wave duration (HWD) at the station level. First, the statistical model is optimised and calibrated using NCEP/NCAR reanalysis to evaluate the large-scale predictors. The observational data at 32 stations uniformly distributed over Emilia-Romagna are used to compute the local predictands. The results of the optimisation procedure reveal that T850 is the best predictor in most cases, and in combination with MSLP, is an optimum predictor for winter T_max90 and autumn T_min10. Finally, MSLP is the best predictor for spring T_min while Z500 is the best predictor for spring T_max90 and heat wave duration index, except during autumn. The ability of HadAM3P to simulate the present day spatial and temporal variability of the chosen predictors is tested using the control experiments. Finally, the downscaling model is applied to all model output experiments to obtain simulated present day and A2 and B2 scenario results at the local scale. Results show that significant increases can be expected to occur under scenario conditions in both maximum and minimum temperature, associated with a decrease in the number of frost days and with an increase in the heat wave duration index. The magnitude of the change is more significant for the A2 scenario than for the B2 scenario.     

Measurements of surface ozone at semi-arid site Anantapur (14.62°N, 77.65°E, 331 m asl) in India

In the present study, an attempt has been made to examine the governing photochemical processes of surface ozone (O₃) formation in rural site. For this purpose, measurements of surface ozone and selected meteorological parameters have been made at Anantapur (14.62°N, 77.65°E, 331 m asl), a semi-arid zone in India from January 2002 to December 2003. The annual average diurnal variation of O₃ shows maximum concentration 46 ppbv at noon and minimum 25 ppbv in the morning with 1σ standard deviation. The average seasonal variation of ozone mixing ratios are observed to be maximum (about 60 ppbv) during summer and minimum (about 22 ppbv) in the monsoon period. The monthly daytime and nighttime average surface ozone concentration shows a maximum (55 ± 7 ppbv; 37 ± 7.3 ppbv) in March and minimum (28 ± 3.4 ppbv; 22 ± 2.3 ppbv) in August during the study period. The monthly average high (low) O₃ 48.9 ± 7.7 ppbv (26.2 ± 3.5 ppbv) observed at noon in March (August) is due to the possible increase in precursor gas concentration by anthropogenic activity and the influence of meteorological parameters. The rate of increase of surface ozone is high (1.52 ppbv/h) in March and lower (0.40 ppbv/h) in July. The average rate of increase of O₃ from midnight to midday is 1 ppbv/h. Surface temperature is highest (43–44°C) during March and April months leading to higher photochemical production. On the other hand, relative humidity, which is higher during the rainy season, shows negative correlation with temperature and ozone mixing ratio. It can be seen that among the two parameters are measured, correlation of surface ozone with wind speed is better (R ²=0.84) in compare with relative humidity (R ²=0.66).     

A Regression-based Assessment of the Predictability of New Zealand Climate Anomalies

Summary Estimates of the predictability of New Zealand monthly and seasonal temperature and rainfall anomalies are calculated using a cross-validated linear regression procedure. Predictors are indices of the large scale circulation, sea-surface temperatures, the Southern Oscillation Index and persistence. Statistical significance is estimated through a series of Monte Carlo trials. No significant forecast relationships are found for rainfall anomalies at either the monthly or seasonal time scale. Temperature forecasts are however considered to exhibit significant skill, with variance reductions of the order of 10–20% in independent trials. Temperature anomalies are most skilfully predicted over the North Island, and skill is greatest in Spring and Summer in most areas. At the monthly time scale, predictors local to the New Zealand region account for most of the forecast skill, while at the seasonal time scale, skill depends strongly upon “remote” predictors defined over regions of the southern hemisphere distant from New Zealand. Indices of meridional flow over the Tasman Sea/New Zealand region are found to be useful predictors, especially for monthly forecasts, perhaps as a proxy for atmospherically-forced sea surface temperature anomalies. Sea surface temperature anomalies to the west of New Zealand and in the tropical Indian Ocean are also useful, especially for seasonal predictions. Forecast skill is more reliably estimated at the monthly time scale than at the seasonal time scale, as a result of the larger sample size of monthly mean data. While long-term mean levels of skill may be estimated reliably over the whole data set, statistically significant decadal-scale variations are found in the predictability of temperature anomalies. Therefore, even if long-term forecast skill levels are reliably estimated, it may be impossible to predict the short-term skill of operational seasonal climate forecasts. Implications for operational climate predictions in mid-latitudes are discussed. Received July 18, 1997 Revised April 2, 1998     

Seasonality of tropospheric ozone and water vapor over Delhi,India: a study based on MOZAIC measurement data

Tropospheric distributions of ozone (O₃) and water vapor (H₂O) have been presented based on the Measurements of OZone and water vapor by Airbus In-Service AirCraft (MOZAIC) data over the metro and capital city of Delhi, India during 1996–2001. The vertical mixing ratios of both O₃ and H₂O show strong seasonal variations. The mixing ratios of O₃ were often below 40 ppbv near the surface and higher values were observed in the free troposphere during the seasons of winter and spring. In the free troposphere, the high mixing ratio of O₃ during the seasons of winter and spring are mainly due to the long-range transport of O₃ and its precursors associated with the westerly-northwesterly circulation. In the lower and middle troposphere, the low mixing ratios of ∼20–30 ppbv observed during the months of July–September are mainly due to prevailing summer monsoon circulation over Indian subcontinent. The summer monsoon circulation, southwest (SW) wind flow, transports the O₃-poor marine air from the Arabian Sea and Indian Ocean. The monthly averages of rainfall and mixing ratio of H₂O show opposite seasonal cycles to that of O₃ mixing ratio in the lower and middle troposphere. The change in the transport pattern also causes substantial seasonal variation in the mixing ratio of H₂O of 3–27 g/kg in the lower troposphere over Delhi. Except for some small-scale anomalies, the similar annual patterns in the mixing ratios of O₃ and H₂O are repeated during the different years of 1996–2001. The case studies based on the profiles of O₃, relative humidity (RH) and temperature show distinct features of vertical distribution over Delhi. The impacts of long range transport of air mass from Africa, the Middle East, Indian Ocean and intrusions of stratospheric O₃ have also been demonstrated using the back trajectory model and remote sensing data for biomass burning and forest fire activities.     

国家气候中心短期气候预测模式系统业务化进展

该文简要介绍了国家气候中心短期气候预测模式系统的研发成果,并侧重于从海洋资料同化系统、陆面资料同化系统、月动力延伸预测模式系统、季节气候预测模式系统4个方面介绍了第2代短期气候预测模式系统的业务化进展。第2代海洋资料同化系统已初步建成,其对温盐的同化效果总体上优于第1代同化系统;陆面资料同化系统正在研发中,目前已完成其中的多源降水融合子系统的业务建设工作,可为陆面分量提供实时的大气降水强迫分析场;第2代月动力延伸预测系统基于国家气候中心大气环流模式BCC_AGCM2.2建立,已于2012年8月进入准业务运行阶段;第2代季节预测模式系统基于国家气候中心气候系统模式BCC_CSM1.1(m) 建立,将于2013年底投入准业务运行。初步评估表明:第2代月动力延伸预测模式系统和季节气候预测模式系统分别对候、旬、月和季节、年际时间尺度的气候变率体现出了一定的预测能力,其对降水、气温、环流等要素的预测技巧总体上要高于第1代预测系统。     

Climatology and variability of historical Soviet snow depth data: some new perspectives in snow – Indian monsoon teleconnections

 This study presents the monthly climatology and variability of the historical soviet snow depth data. This data set was developed under the bilateral data exchange agreement between United States of America and the former Union of Soviet Socialist Republics. The original data is for 284 stations for periods varying from 1881 upto 1985. The seasonal cycle of the mean snow depth has been presented both as spatial maps and as averages over key locations. The deepest snow (=80 cms/day) areas are found over Siberia (in Particular over 80′–100 ′E, 55′–70 ′N) during March. Over the course of the annual cycle average snow depth over this region changes dramatically from about 10 cms in October to about 80 cms in March. The variability is presented in the form of spatial maps of standard deviation. To investigate the interaction of snow depth with Indian monsoon rainfall (IMR), lag and lead correlation coefficients are computed. Results reveal that the winter-time snow depth over western Eurasia surrounding Moscow (eastern Eurasia in central Siberia) shows significant negative (positive) relationship with subsequent IMR. Following the monsoon the signs of relationship reverse over both the regions. This correlation structure is indicative of a midlatitude longwave pattern with an anomalous ridge (trough) over Asia during the winter prior to a strong (weak) monsoon. As the time progresses from winter to spring, the coherent areas of significant relationship show southeastward propagation. Empirical orthogonal function analysis of the snow depth reveal that the first mode describes a dipole-type structure with one centre around Moscow and the other over central Siberia, depicting similar pattern as the spatial correlation structure. The decadal-scale IMR variations seem to be more associated with the Northern Hemisphere midlatitude snow depth variations rather than with the tropical ENSO (El Nino Southern Oscillation) variability. Received: 16 March 1998 / Accepted: 24 December 1998     

The Relationship Between some Large-scale Atmospheric Parameters and Rainfall over Southeast Asia: A Comparison with Features over India

Summary Monthly rainfall data for 135 stations for periods varying from 25 to 125 years are utilised to investigate the rainfall climatology over the southeast Asian monsoon regime. Monthly rainfall patterns for the regions north of equator show that maximum rainfall along the west coasts occurs during the summer monsoon period, while the maximum along the east coasts is observed during the northeast monsoon period. Over the Indonesian region (south of the equator) maximum rainfall is observed west of 125 °E during northern winter and east of 125 °E during northern summer. The spatial relationships of the seasonal rainfall (June to September) with the large scale parameters – the Subtropical Ridge (STR) position over the Indian and the west Pacific regions, the Darwin Pressure Tendency (DPT) and the Northern Hemisphere Surface Temperature (NHST) – reveal that within the Asian monsoon regime, not only are there any regions which are in-phase with Indian monsoon rainfall, but there are also regions which are out-of-phase. The spatial patterns of correlation coefficients with all the parameters are similar, with in-phase relationships occurring over the Indian region, some inland regions of Thailand, central parts of Brunei and the Indonesian region lying between 120° to 140 °E. However, northwest Philippines and some southern parts of Kampuchea and Vietnam show an out-of-phase relationship. Even the first Empirical Orthogonal Function of seasonal rainfall shows similar spatial configuration, suggesting that the spatial correlation patterns depict the most dominant mode of interannual rainfall variability. The influence of STR and DPT (NHST) penetrates (does not penetrate) upto the equatorial regions. Possible dynamic causes leading to the observed correlation structure are also discussed. Received October 10, 1996 Revised February 25, 1997     

Evaluating spatial scales of climate variability in sub-Saharan Africa

Summary Spatial scales of variability in seasonal rainfall over Africa are investigated by means of statistical and numerical techniques. In the statistical analysis spatial structure is studied using gridded 0.5° resolution monthly data in the period 1948–1998. The de-seasonalized time series are subjected to successive principal component (PC) analysis, allowing the number of modes to vary from 10 to 24, producing cells of varying dimension. Then the original rainfall data within each cell are cross-correlated (internal), then averaged and compared with the adjacent cells (external) for each PC solution. By considering the ratio of internal to external correlation, the spatial scales of rainfall variability are evaluated and an optimum solution is found whose cell dimensions are approximately 10⁶ km². The aspect of scale is further studied for southern Africa by consideration of numerical model ensemble simulations over the period 1985–1999 forced with observed sea surface temperatures (SSTs). The hindcast products are compared with observed January to March (JFM) rainfall, based on a station-satellite merged analysis of precipitation (CMAP) data at 2.5° resolution. Validations for different sized areas indicate that cumulative standardized errors are greatest at the scale of a single grid cell (10⁴ km²) and decrease 20–30% by averaging over successively larger areas (10⁶ km²).     

The association of surface wind stresses over Indian Ocean with monsoon rainfall

Summary Variability of Indian summer monsoon rainfall is examined with respect to variability of surface wind stresses over Indian Ocean. The Indian Ocean region extending from 40°–120° E, and 30° S–25° N, has been divided into 8 homogeneous subregions, viz (1) Arabian Sea (AS), (2) Bay of Bengal (BB), (3) West-equatorial Indian Ocean (WEIO), (4) Central-equatorial Indian Ocean (CEIO), (5) East-equatorial Indian Ocean (EEIO), (6) South-west Indian Ocean (SWIO), (7) South-central Indian Ocean (SCIO), and (8) South-east Indian Ocean (SEIO). The period of study extends for 13 years from 1982–1994. Monthly NCEP surface wind stress data of five months – May through September, have been used in the study. The spatial variability of seasonal and monthly surface wind stresses shows very low values over CEIO and EEIO and very high values over AS, SWIO, and SEIO regions. On the seasonal scale, all India summer monsoon rainfall (AISMR) shows concurrent positive relationships with the surface wind stresses over AS, BB, WEIO, SWIO and SCIO and negative relationships with the surface wind stresses over EEIO and SEIO. The relationships of AISMR with the surface wind stresses over AS and WEIO are significant at 5% level. The concurrent relationships between monthly surface wind stresses over these 8 oceanic sub-regions and monthly subdivisional rainfalls over 29 sub-divisions have been studied. The rainfalls over the subdivisions in the central India and on the west coast of India are found to be significantly related with surface wind stresses over AS, SWIO, SCIO. Monthly subdivisional rainfalls of four subdivisions in the peninsular India show negative relationship with BB surface wind stresses. May surface wind stresses over AS, BB, WEIO, CEIO and SWIO have been found to be positively related with ensuing AISMR. The relationship with AS wind stresses is significant at 5% level and hence may be considered as a potential predictor of AISMR. Received May 21, 2001 Revised October 8, 2001     

Seasonal forecasting of tropical cyclones in the North Indian Ocean region: the role of El Niño-Southern Oscillation

In this study, we have investigated the contribution of El Niño-Southern Oscillation (ENSO) to the North Indian Ocean (NIO) tropical cyclone (TC) activity and seasonal predictability. A statistical seasonal prediction model was developed for the NIO region tropical cyclone genesis, trajectories and landfalls using the Southern Oscillation index (SOI: as a metric of ENSO) as a predictor. The forecast model utilised kernel density estimation (KDE), a generalised additive model (GAM), Euler integration, and a country mask. TCs from the Joint Typhoon Warning Centre were analysed over the 35-year period from 1979 to 2013. KDE was used to model the distribution of cyclone genesis points and the cyclone tracks were estimated using the GAM, with velocities fit as smooth functions of location according to ENSO phase and TC season. The best predictor lead time scales for TC forecast potential were assessed from 1 to 6 months. We found that the SOI (as a proxy for ENSO) is a good predictor of TC behaviour 2-months in advance (70% skill). Two hindcast validation methods were applied to assess the reliability of the model. The model was found to be skillful in hindcasting NIO region TC activity for the pre and post monsoon season. The distribution of TC genesis, movement and landfall probabilities over the study period, as well as the hindcast probabilities of TC landfall during ENSO events, matched well against observations over most of the study domain. Overall, we found that the phase of ENSO has the potential to improve NIO region TC seasonal forecast skill by about 15% over climatological persistence.     

North Sea – Caspian Pattern (NCP) – an upper level atmospheric teleconnection affecting the eastern Mediterranean – implications on the regional climate

Summary ?A calendar of the negative and positive phases of the North Sea – Caspian Pattern (NCP) for the period 1958–1998 was used to analyse the implication of the NCP upper level teleconnections on the regional climate of the eastern Mediterranean basin. Series of monthly mean air temperature and monthly total rainfall from 33 stations across Greece, Turkey and Israel, for the same period, were used. For each month, from October to April, averages of the monthly mean temperatures and the monthly rainfall totals as well as the standardized values of both parameters were calculated separately for the negative (NCP (−)) and the positive (NCP (+)) phases of the NCP. At all stations and in all months, temperature values were significantly higher during the NCP (−) as compared with the NCP (+). Furthermore, apart from very few exceptions, the absolute monthly mean maximum and monthly mean minimum values were obtained during the NCP (−) and the NCP (+) phases, respectively. The maximum impact of the NCP on mean air temperature was detected in the continental Anatolian Plateau, where the mean seasonal differences are around 3.5 °C. This influence decreases westwards and southwards. The influence on the rainfall regime is more complex. Regions exposed to the southern maritime trajectories, in Greece and in Turkey, receive more rainfall during the NCP (−) phase, whereas in the regions exposed to the northern maritime trajectories, such as Crete in Greece, the Black Sea region in Turkey, and in all regions of Israel, there is more rainfall during the NCP (+) phase. The accumulated rainfall differences between the two phases are over 50% of the seasonal average for some stations. A comparison of the capabilities of the NCP, the North Atlantic Oscillation (NAO) and the Southern Oscillation (SO) indices to differentiate between below and above normal temperatures was made. The results have placed the NCP, as the best by far of all three teleconnections in its ability to differentiate between below or above normal temperatures and as the main teleconnection affecting the climate of the Balkans, the Anatolian Peninsula and the Middle East. These results may serve to downscale General Circulation Model (GCM) scenarios to a regional scale and provide forecasts regarding eventual temperature and/or precipitation changes. Received June 25, 2001; revised February 25, 2002; accepted March 3, 2002