Stratospheric zonal wind and temperature in relation to summer monsoon rainfall over India

Summary  The interannual variability of the Indian summer monsoon (June–September) rainfall is examined in relation to the stratospheric zonal wind and temperature fluctuations at three stations, widely spaced apart. The data analyzed are for Balboa, Ascension and Singapore, equatorial stations using recent period (1964–1994) data, at each of the 10, 30 and 50 hPa levels. The 10 hPa zonal wind for Balboa and Ascension during January and the 30 hPa zonal wind for Balboa during April are found to be positively correlated with the subsequent Indian summer monsoon rainfall, whereas the temperature at 10 hPa for Ascension during May is negatively correlated with Indian summer monsoon rainfall. The relationship with stratospheric temperatures appears to be the best, and is found to be stable over the period of analysis. Stratospheric temperature is also significantly correlated with the summer monsoon rainfall over a large and coherent region, in the north-west of India. Thus, the 10 hPa temperature for Ascension in May appears to be useful for forecasting summer monsoon rainfall for not only the whole of India, but also for a smaller region lying to the north-west of India. Received July 30, 1999 Revised March 17, 2000     

Simple Multiple Regression Model for long range forecasting of Indian Summer Monsoon Rainfall

Summary The relationship between the Indian Ocean Sea-Surface Temperature Anomalies (SSTA) and the Indian Summer Monsoon Rainfall (ISMR) have been examined for the period, 1983–2006. High and positive correlation (0.51; significant at >99% level) is noticed between ISMR and SSTA over southeastern Arabian Sea (AS) in the preceding January. Significant and positive correlation (0.61: significant at >99% level) is also observed with the SSTA over northwest of Australia (NWA) in the preceding February. The combined SSTA index (AS + NWA) showed a very high correlation of 0.71 with the ISMR. The correlation between East Asia sea-level pressure (average during February and March in the region, 35° N–45° N; 120° E–130° E) and ISMR is found to be 0.62. The multiple correlation using the above two parameters is 0.85 which explains 72% variance in ISMR. Using the above two parameters a linear multiple regression model to predict ISMR is developed. Our results are comparable with those obtained from the power regression (developed with 16, 8 and 10 parameters) and ensemble models (using 3 to 6 parameters) of the Indian Meteorological Department (IMD) (Rajeevan et al. 2004; 2006). The rainfall during 2002 and 2004 could be predicted accurately from the present model. It is well known fact that most of the dynamical/statistical methods failed to predict the rainfall in 2002. However, as for associations between SST and ISMR, the index is quite susceptible to inter decadal fluctuations and markedly reduced skill is found in the decades preceding 1983. The RMS error for 24 years is 5.56 (% of long period average, LPA) and the correlation between the predicted and observed rainfall is 0.79. Correspondence: Y. Sadhuram, Regional Centre, National Institute of Oceanography, 176, Lawson’s Bay Colony, Visakhapatnam-530017, India     

Long Range Forecasting Indian Summer Monsoon Rainfallby a Hybrid Principal Component Neural Network Model

Summary  The existing methods based on statistical techniques for long range forecasts of Indian monsoon rainfall have shown reasonably accurate performance, for last 11 years. Because of the limitation of such statistical techniques, new techniques may have to be tried to obtain better results. In this paper, we discuss the results of an artificial neural network model by combining two different neural networks, one explaining assumed deterministic dynamics within the time series of Indian monsoon rainfall (Model I) and other using eight regional and global predictors (Model II). The model I has been developed by using the data of past 50 years (1901–50) and the data for recent period (1951–97) has been used for verification. The model II has been developed by using the 30 year (1958–87) data and the verification of this model has been carried out using the independent data of 10 year period (1988–97). In model II, instead of using eight parameters directly as inputs, we have carried out Principal Component Analysis (PCA) of the eight parameters with 30 years of data, 1958–87, and the first five principal components are included as input parameters. By combining model I and model II, a hybrid principal component neural network model (Model III) has been developed by using 30 year (1958–87) data as training period and recent 10 year period (1988–97) as verification period. Performance of the hybrid model (Model III) has been found the best among all three models developed. Rootmean square error (RMSE) of this hybrid model during the independent period (1988–97) is 4.93% as against 6.83%of the operational forecasts of the India Meteorological Department (IMD) using the 16 parameter Power Regression model. As this hybrid model is showing good results, it is now used by the IMD for experimental long-range forecasts of summer monsoon rainfall over India as a whole. Received August 20, 1998/Revised April 20, 1999     

Northwest Pacific tropical cyclone activity and July rainfall over India

Summary In 2002, India had experienced one of the most severe droughts. The severe drought conditions were caused by the unprecedented deficient rainfall in July 2002, in which only 49% of the normal rainfall was received. One of the major circulation anomalies observed during July 2002, was the active monsoon trough over Northwest (NW) Pacific and enhanced typhoon activity over this region. The present study was designed to examine the long-term relationships between Tropical Cyclone (TC) activity over NW Pacific and monsoon rainfall over India in July. A statistically significant negative correlation between TC days over NW Pacific and July rainfall over India was observed. Spatial dependence of the relationship revealed that TCs forming over NW Pacific east of 150° E and moving northwards have an adverse effect on Indian monsoon rainfall. It was observed that TCs forming over the South China Sea and moving westwards may have a positive impact on monsoon rainfall over India. Enhanced TC activity over NW Pacific during July 2002 induced weaker monsoon circulation over the Indian region due to large-scale subsidence.     

Intense rainfall events over the west coast of India

Summary The west coast of the Indian peninsula receives very heavy rainfall during the summer Monsoon (June–September) season with average rainfall over some parts exceeding 250 cm. Heavy rainfall events with rainfall more than 15 cm day⁻¹ at one or more stations along the west coast of India occur frequently and cause considerable damage. A special observational programme, Arabian Sea Monsoon Experiment, was carried out during the monsoon season of 2002 to study these events. The spatial and temporal distributions of intense rainfall events, presented here, were used for the planning of this observational campaign. The present study using daily rainfall data for summer monsoon season of 37 years (1951–1987) shows that the probability of getting intense rainfall is the maximum between 14° N–16° N and near 19° N. The probability of occurrence of these intense rainfall events is high from mid June to mid August, with a dip in early July. It has been believed for a long time that offshore troughs and vortices are responsible for these intense rainfall events. However, analysis of the characteristics of cloud systems associated with the intense rainfall events during 1985–1988 using very high resolution brightness temperature data from INSAT-IB satellite shows that the cloud systems during these events are characterized by large spatial scales and high cloud tops. Further study using daily satellite derived outgoing longwave radiation (OLR) data over a longer period (1975–1998) shows that, most of these events (about 62%) are associated with systems organized on synoptic and larger scales. We find that most of the offshore convective systems responsible for intense rainfall along the west coast of India are linked to the atmospheric conditions over equatorial Indian Ocean.     

Projections of Extreme Rainfall in Hong Kong in the 21st Century

The possible changes in the frequency of extreme rainfall events in Hong Kong in the 21st century wereinvestigated by statistically downscaling 30 sets of the daily global climate model projections (involvinga combination of 12 models and 3 greenhouse gas emission scenarios,namely,A2,A1B,and B1) of theFourth Assessment Report of the Intergovernmental Panel on Climate Change.To cater for the intermittentand skewed character of the daily rainfall,multiple stepwise logistic regression and multiple stepwise linearregression were employed to develop the downscaling models for predicting rainfall occurrence and rainfallamount,respectively.Verification of the simulation of the 1971-2000 climate reveals that the models ingeneral have an acceptable skill in reproducing past statistics of extreme rainfall events in Hong Kong.Theprojection results suggest that,in the 21st century,the annual number of rain days in Hong Kong is expectedto decrease while the daily rainfall intensity will increase,concurrent with the expected increase in annualrainfall.Based on the multi-model scenario ensemble mean,the annual number of rain day is expected todrop from 104 days in 1980-1999 to about 77 days in 2090-2099.For extreme rainfall events,about 90% ofthe model-scenario combinations indicate an increase in the annual number of days with daily rainfall 100mm (R100) towards the end of the 21st century.The mean number of R100 is expected to increase from 3.5days in 1980-1999 to about 5.3 days in 2090-2099.The projected changes in other extreme rainfall indicesalso suggest that the rainfall in Hong Kong in the 21st century may also become more extreme with moreuneven distributions of wet and dry periods.While most of the model-emission scenarios in general projectconsistent trends in the change of rainfall extremes in the 21st century,there is a large divergence in theprojections among different model/emission scenarios.This reflects that there are still large uncertainties inmodel simulations of future extreme rainfall events.     

South Asian summer monsoon precipitation variability: Coupled climate model simulations and projections under IPCC AR4

Summary South Asian summer monsoon precipitation and its variability are examined from the outputs of the coupled climate models assessed as part of the Intergovernmental Panel on Climate Change Fourth Assessment. Out of the 22 models examined, 19 are able to capture the maximum rainfall during the summer monsoon period (June through September) with varying amplitude. While two models are unable to reproduce the annual cycle well, one model is unable to simulate the summer monsoon season. The simulated inter-annual variability from the 19 models is examined with respect to the mean precipitation, coefficient of variation, long-term trends and the biennial tendency. The model simulated mean precipitation varies from 500 mm to 900 mm and coefficient of variation from 3 to 13%. While seven models exhibit long-term trends, eight are able to simulate the biennial nature of the monsoon rainfall. Six models, which generate the most realistic 20th century monsoon climate over south Asia, are selected to examine future projections under the doubling CO₂ scenario. Projections reveal a significant increase in mean monsoon precipitation of 8% and a possible extension of the monsoon period based on the multi-model ensemble technique. Extreme excess and deficient monsoons are projected to intensify. The projected increase in precipitation could be attributed to the projected intensification of the heat low over northwest India, the trough of low pressure over the Indo-Gangetic plains, and the land–ocean pressure gradient during the establishment phase of the monsoon. The intensification of these pressure systems could be attributed to the decline in winter/spring snowfall. Furthermore, a decrease of winter snowfall over western Eurasia is also projected along with an increase of winter snowfall over Siberia/eastern Eurasia. This projected dipole snow configuration during winter could imply changes in mid-latitude circulation conducive to subsequent summer monsoon precipitation activity. An increase in precipitable water of 12–16% is projected over major parts of India. A maximum increase of about 20–24% is found over the Arabian Peninsula, adjoining regions of Pakistan, northwest India and Nepal. Although the projected summer monsoon circulation appears to weaken, the projected anomalous flow over the Bay of Bengal (Arabian Sea) will support oceanic moisture convergence towards the southern parts of India and Sri Lanka (northwest India and adjoining regions). The ENSO-Monsoon relationship is also projected to weaken.     

Validation of general circulation climate models and projectionsof temperature and rainfall changes in Cameroon and someof its neighbouring areas

Summary  Four coupled atmosphere-ocean general circulation models were examined for the ability of their control runs to simulate present climate given present forcings. The area of study is mainly Cameroon and some of its surrounding areas (0–25° E, 5° S-30° N). These models are from the UK Meteorological Office Hadley Centre (HadCM2), the German Max-Planck-Institut für Meteorologie (ECHAM4), the Canadian Centre for Climate Modelling and Analysis (CGCM1) and the Australian Commonwealth Science and Industrial Research Organisation (CSIRO-Mk2). The ability of the models to reproduce the observed spatial and temporal patterns was studied. ECHAM4 and HadCM2 were found to reproduce the spatial pattern well, with a correlation of more than 90%. They also simulated the main annual features of both temperature and rainfall. The CSIRO-Mk2 model was slightly less successful and the CGCM1 had the worst results for the area, especially as concern rainfall. In view of these results, ECHAM4 and HADCM2 were used to evaluate projected changes in rainfall and temperature resulting from increased concentration of greenhouse gases in the atmosphere for the 30 year period 2040 to 2070. Received February 15, 1999/Revised March 10, 2000     

Periodicity in intraseasonal variation of summer monsoon rainfall over Orissa, India in relation to synoptic disturbances

Summary The summer monsoon rainfall over Orissa, a state of eastern India, shows characteristic intraseasonal and interannual variability, due to interaction of basic westerly flow with orography and the synoptic scale monsoon disturbances including low-pressure systems and cyclonic circulations extending upto mid-tropospheric level (LPSC). These systems normally develop over the north Bay of Bengal and move west-northwestwards along the monsoon trough. The essence of this study is to find out the main features of the intraseasonal variability of daily monsoon rainfall over Orissa in relation to synoptic systems like LPSC and its implication on the interannual variation of rainfall. For this purpose, the actual and mean daily rainfall data of 31 uniformly distributed stations, six homogeneous regions and Orissa as a whole during monsoon season (June–September) over a period of 20 years (1980–1999) are subjected to auto-correlation and power spectrum analyses. The actual and average daily scores of significant EOFs and actual daily occurrence along with daily probability of occurrence of the LPSC influencing rainfall over Orissa during the same period are also subjected to auto-correlation and power spectrum analyses. The intraseasonal variation of monsoon rainfall over Orissa and different homogeneous regions is dominated by the synoptic mode (3–9 days) of variation due to the similar mode of variation in the occurrence of LPSC influencing the rainfall. The seasonal rainfall and hence the interannual variation depends on the intraseasonal variation of rainfall modulated with the synoptic mode of variation in the occurrence of the LPSC. The occurrence of LPSC over the northwest (NW) Bay/NW and adjoining northeast (NE) Bay and its subsequent movement and persistence over Orissa and east Madhya Pradesh & Chhattisgarh in synoptic mode (3–6 days) alongwith absence of similar mode in the occurrence of the LPSC over NE Bay, Gangetic West Bengal (GWB) in the north and west central (WC) Bay to the south leads to excess rainfall over different homogeneous regions and Orissa as a whole. The reverse is the case in deficient years over Orissa and all homogeneous regions except southwest Orissa. The occurrence of the LPSC over GWB in synoptic mode (about 5 days) alongwith absence of synoptic mode in the occurrence of the LPSC over NW Bay leads to deficient rainfall year over southwest Orissa. Correspondence: U. C. Mohanty, Centre for Atmospheric Sciences, Indian Institute of Technology, Delhi Hauz Khas, New Delhi 110016, India     

Features of ozone quasi-biennial oscillation in the vertical structure of tropics and subtropics

Summary Latitude-altitude structure of ozone QBO over the tropical-subtropical stratosphere (40° S–40° N) has been explored by analyzing Microwave Limb Sounder (MLS) aboard Upper Atmospheric Research Satellite (UARS) data for the period 1992–1999 using the multifunctional regression model. The inferred ozone QBO shows two maxima located at 22 hPa and 10 hPa with coefficient of 2–3% per 10 m/s centered at the equator. The equatorial maxima are out of phase with each other. Subtropics exhibit two peak structure near 14 hPa but of opposite sign to that of equatorial maximum near 10 hPa. Over the equatorial region, positive (zonal winds westerly) coefficients overlay negative (zonal winds easterlies) coefficients which descend with time. A pattern of equatorial maximum and two subtropical minima appears in the months December to February near 10 hpa and it propagates upward with progression of seasons. Equatorial QBO is seasonally asynchronous while subtropical QBO is seasonally synchronous. Correspondence: Suvarna Fadnavis, Physical Meteorology and Aerology Division, Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune 411008, India     

Peninsular Indian rainfall and its association with meteorological and oceanic parameters over the Indian Ocean

The present study attempts to formulate a regression model to predict summer rainfall over Peninsular India (PIR) using some regional predictors. Parameters having significant correlation (99%) with PIR were identified for the period 1975–1997 (training), and a 15-year sliding correlation (90%) was found to check the consistency of the relationship between PIR and predictors. From a set of 14 candidate predictors, 4 were selected using a stepwise regression method and tested over a period from 1998 to 2006. The predictors selected are sea surface temperature during March over Indian Ocean, air temperature at 850?hPa during May over Peninsular India, zonal, and meridional wind at 700?hPa during February and January, respectively, over the Arabian Sea. The model captures a variance of 77.7% and has a multiple correlation of 0.88. The root mean square error, absolute mean error, and bias for the training (test) period were 7.6% (21.5%), 6.6% (17.9%), and 0% (11.4%) of mean rainfall, respectively. Results of the climatological predictions show that the model developed is useful.     

Temperature variability over the Indian Ocean and its relationship with Indian summer monsoon rainfall

Summary The present study examines the long term trend in sea surface temperatures (SSTs) of the Arabian Sea, Bay of Bengal and Equatorial South India Ocean in the context of global warming for the period 1901–2002 and for a subset period 1971–2002. An attempt has also been made to identify the relationship between SST variations over three different ocean areas, and All-India and homogeneous region summer monsoon rainfall variability, including the role of El-Ni?o/Southern Oscillation (ENSO). Annual sea surface temperatures of the Arabian Sea, Bay of Bengal and Equatorial South India Ocean show a significant warming trend of 0.7 °C, 0.6 °C and 0.5 °C per hundred years, respectively, and a relatively accelerated warming of 0.16 °C, 0.14 °C and 0.14 °C per decade during the 1971–2002 period. There is a positive and statistically significant relationship between SSTs over the Arabian Sea from the preceding November to the current February, and Indian monsoon rainfall during the period 1901–2002. The correlation coefficient increases from October and peaks in December, decreasing from February to September. This significant relationship is also found in the recent period 1971–2002, whereas, during 1901–70, the relationship is not significant. On the seasonal scale, Arabian Sea winter SSTs are positively and significantly correlated with Indian monsoon rainfall, while spring SSTs have no significant positive relationship. Nino3 spring SSTs have a negative significant relationship with Indian monsoon rainfall and it is postulated that there is a combined effect of Nino3 and Arabian Sea SSTs on Indian monsoon. If the Nino3 SST effect is removed, the spring SSTs over the Arabian Sea also have a significant relationship with monsoon rainfall. Similarly, the Bay of Bengal and Equatorial South Indian Ocean spring SSTs are significantly and positively correlated with Indian monsoon rainfall after removing the Nino3 effect, and correlation values are more pronounced than for the Arabian Sea. Authors’ address: Dr. D. R. Kothawale, A. A. Munot, H. P. Borgaonkar, Climatology and Hydrometeorology divisions, Indian Institute of Tropical Meteorology, Pune 411008, India.     

Interannual and long-term variability in the North Atlantic Oscillation and Indian Summer monsoon rainfall

Summary The interannual and decadal scale variability in the North Atlantic Oscillation (NAO) and its relationship with Indian Summer monsoon rainfall has been investigated using 108 years (1881–1988) of data. The analysis is carried out for two homogeneous regions in India, (Peninsular India and Northwest India) and the whole of India. The analysis reveals that the NAO of the preceding year in January has a statistically significant inverse relationship with the summer monsoon rainfall for the whole of India and Peninsular India, but not with the rainfall of Northwest India. The decadal scale analysis reveals that the NAO during winter (December–January–February) and spring (March–April–May) has a statistically significant inverse relationship with the summer monsoon rainfall of Northwest India, Peninsular India and the whole of India. The highest correlation is observed with the winter NAO. The NAO and Northwest India rainfall relationship is stronger than that for the Peninsular and whole of India rainfall on climatological and sub-climatological scales.Trend analysis of summer monsoon rainfall over the three regions has also been carried out. From the early 1930s the Peninsular India and whole of India rainfall show a significant decreasing trend (1% level) whereas the Northwest India rainfall shows an increasing trend from 1896 onwards.Interestingly, the NAO on both climatological and subclimatological scales during winter, reveals periods of trends very similar to that of Northwest Indian summer monsoon rainfall but with opposite phases.The decadal scale variability in ridge position at 500 hPa over India in April at 75° E (an important parameter used for the long-range forecast of monsoon) and NAO is also investigated.With 4 Figures     

Simulation of Indian summer monsoon circulation and rainfall using RegCM3

Summary The Regional Climate Model RegCM3 has been used to examine its suitability in simulating the Indian summer monsoon circulation features and associated rainfall. The model is integrated at 55 km horizontal resolution over a South Asia domain for the period April–September of the years 1993 to 1996. The characteristics of wind at 850 hPa and 200 hPa, temperature at 500 hPa, surface pressure and rainfall simulated by the model over the Indian region are examined for two convective schemes (a Kuo-type and a mass flux scheme). The monsoon circulation features simulated by RegCM3 are compared with those of the NCEP/NCAR reanalysis and the simulated rainfall is validated against observations from the Global Precipitation Climatology Centre (GPCC) and the India Meteorological Department (IMD). Validation of the wind and temperature fields shows that the use of the Grell convection scheme yields results close to the NCEP/NCAR reanalysis. Similarly, the Indian Summer Monsoon Rainfall (ISMR) simulated by the model with the Grell convection scheme is close to the corresponding observed values. In order to test the model response to land surface changes such as the Tibetan snow depth, a sensitivity study has also been conducted. For such sensitivity experiment, NIMBUS-7 SMMR snow depth data in spring are used as initial conditions in the RegCM3. Preliminary results indicate that RegCM3 is very much sensitive to Tibetan snow. The model simulated Indian summer monsoon circulation becomes weaker and the associated rainfall is reduced by about 30% with the introduction of 10 cm of snow over the Tibetan region in the month of April.     

New statistical models for long-range forecasting of southwest monsoon rainfall over India

The India Meteorological Department (IMD) has been issuing long-range forecasts (LRF) based on statistical methods for the southwest monsoon rainfall over India (ISMR) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the recent deficient monsoon years of 2002 and 2004. In this paper, we report the improved results of new experimental statistical models developed for LRF of southwest monsoon seasonal (June–September) rainfall. These models were developed to facilitate the IMD’s present two-stage operational forecast strategy. Models based on the ensemble multiple linear regression (EMR) and projection pursuit regression (PPR) techniques were developed to forecast the ISMR. These models used new methods of predictor selection and model development. After carrying out a detailed analysis of various global climate data sets; two predictor sets, each consisting of six predictors were selected. Our model performance was evaluated for the period from 1981 to 2004 by sliding the model training period with a window length of 23 years. The new models showed better performance in their hindcast, compared to the model based on climatology. The Heidke scores for the three category forecasts during the verification period by the first stage models based on EMR and PPR methods were 0.5 and 0.44, respectively, and those of June models were 0.63 and 0.38, respectively. Root mean square error of these models during the verification period (1981–2004) varied between 4.56 and 6.75% from long period average (LPA) as against 10.0% from the LPA of the model based on climatology alone. These models were able to provide correct forecasts of the recent two deficient monsoon rainfall events (2002 and 2004). The experimental forecasts for the 2005 southwest monsoon season based on these models were also found to be accurate.     

Numerical case study of an extreme rainfall event during 9–11 December 2004 over the east coast of Peninsular Malaysia

Summary An exceptional rainstorm affected the eastern coast of Peninsular Malaysia during 9–11 December 2004 as a result of a westward propagating tropical disturbance known as the Borneo vortex. Rainfall totals near the storm center exceeded 600 mm and led to flash floods, loss of life and severe damage in the area. This study presents the results of a numerical simulation of this event using the fifth generation of the Penn State – NCAR Mesoscale Model (MM5). The model successfully simulated the synoptic circulation and reproduced the episode with comparable spatial patterns and total accumulated amount of precipitation to the observed. Various sensitivity experiments showed that the local topography is decisive in shaping the rainfall distribution during the storm episode. The role of the terrain elevation appears to be to block the westward progression of the system and inhibit excessive rainfall in the inland areas of Peninsular Malaysia. To the north of the storm center where coastal terrain elevation is relatively high, orography plays an important role in the rainfall by providing an additional forcing for moist air lifting. An additional fake dry simulation suggested that latent heat release is crucial for the development of the storm. Without latent heating, the vertical coupling of low-level convergence and upper level divergence is weakened and the vertical motion associated with the storm is suppressed.     

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).     

An analogue-based method to downscale surface air temperature: application for Australia

 A statistical model (SM) has been developed to downscale large-scale predictors given by general circulation models (GCMs); emphasis has been put on local surface air temperature in two areas of interest: the south-west corner (SWC) of Western Australia and the Murray-Darling Basin (MDB) in southeastern Australia. This is a complementary approach to the dynamical modelling of climate change using high-resolution nested regional models. The analogue technique was chosen for this study as it has proven successful in the past for mid-latitude climate and, in particular, for forecasting in Australia. Furthermore, the analogue technique is successful in reproducing spells of anomalous events. The development and validation datasets used for both predictors and predictants cover the 1970–1993 period. Predictors are extracted from a dataset of operational analyses for the Australian region. Several predictors have been assessed alone and combined. Mean sea level pressure and temperature at 850 hPa have been identified as the most useful combination. Predictants have come from quality controlled stations with daily temperature extremes for the 1970–1993 period. Twenty two stations in the SWC and 29 in the MDB have been selected. The sensitivity of the SM has been tested to several internal parameters. The number of atmospheric predictors and the geographical domain on which large-scale fields are used are key factors that maximise the skill of the SM. Several metrics have been tested taking into account the state of the predictors on the day or, in order to describe the evolution of the atmosphere, over several days. This latter has been particularly useful in improving the representation of anomalous spells as it partially incorporates the auto-correlation of surface temperature. The correlation obtained between the observed local temperature series and the reconstructed series ranges between 0.5 and 0.8. Best results are obtained in summer and for maximum temperature. The reproduction of spells is satisfactory for most stations. The SM is then applied to large-scale fields obtained from a GCM forced by observed sea surface temperature; the improvement gained when using the SM instead of relying on the surface temperature calculated by the GCM is shown. Received: 14 December 1999 / Accepted: 10 January 2001     

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     

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