Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Changes in climate extremes and their impacts on the natural physical environment: An overview of the IPCC SREX report

[1] Arctic amplification (AA) – the observed enhanced warming in high northern latitudes relative to the northern hemisphere – is evident in lower-tropospheric temperatures and in 1000-to-500 hPa thicknesses. Daily fields of 500 hPa heights from the National Centers for Environmental Prediction Reanalysis are analyzed over N. America and the N. Atlantic to assess changes in north-south (Rossby) wave characteristics associated with AA and the relaxation of poleward thickness gradients. Two effects are identified that each contribute to a slower eastward progression of Rossby waves in the upper-level flow: 1) weakened zonal winds, and 2) increased wave amplitude. These effects are particularly evident in autumn and winter consistent with sea-ice loss, but are also apparent in summer, possibly related to earlier snow melt on high-latitude land. Slower progression of upper-level waves would cause associated weather patterns in mid-latitudes to be more persistent, which may lead to an increased probability of extreme weather events that result from prolonged conditions, such as drought, flooding, cold spells, and heat waves.

Climate phenomena and their relevance for future regional climate change

A state-of-art regional climate modelling system, known as PRECIS (Providing Regional Climates for Impacts Studies) developed by the Hadley Centre for Climate Prediction and Research, is applied for India to develop high-resolution climate change scenarios. The present-day simulation (1961-1990) with PRECIS is evaluated, including an examination of the impact of enhanced resolution and an identification of biases. The RCM is able to resolve features on finer scales than those resolved by the GCM, particularly those related to improved resolution of the topography. The most notable advantage of using the RCM is a more realistic representation of the spatial patterns of summer monsoon rainfall such as the maximum along the windward side of the Western Ghats. There are notable quantitative biases in precipitation over some regions, mainly due to similar biases in the driving GCM. PRECIS simulations under scenarios of increasing greenhouse gas concentrations and sulphate aerosols indicate marked increase in both rainfall and temperature towards the end of the 21st century. Surface air temperature and rainfall show similar patterns of projected changes under A2 and B2 scenarios, but the B2 scenario shows slightly lower magnitudes of the projected change. The warming is monotonously widespread over the country, but there are substantial spatial differences in the projected rainfall changes. West central India shows maximum expected increase in rainfall. Extremes in maximum and minimum temperatures are also expected to increase into the future, but the night temperatures are increasing faster than the day temperatures. Extreme precipitation shows substantial increases over a large area, and particularly over the west coast of India and west central India.

High-resolution climate change scenarios for India for the 21st century

Future projection of Indian summer monsoon variability under climate change scenario: An assessment from CMIP5 climate models

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

All India summer monsoon rainfall prediction using an artificial neural network

The southeastern parts of India and Sri Lanka receive substantial rainfall from the northeast monsoon (NEM) during October through December. The interannual variability in NEM rainfall is known to be significantly influenced by the El-Nino/Southern Oscillation (ENSO). Unlike the southwest monsoon (SWM), the NEM rainfall is enhanced during the warm ENSO events, and vice versa. In the context of the recent weakening of the inverse relationship between Southwest Monsoon (SWM) and ENSO, we examine the secular variations in the positive relationship between ENSO and NEM rainfall over South Asia, showing that their relationship has strengthened over the recent years. Based on the analysis of GISST, IMD/CRU precipitation and NCEP/NCAR reanalysis data, we suggest that this secular variation of the relationship is due to epochal changes in the tropospheric circulation associated with ENSO over the region.

On the recent strengthening of the relationship between ENSO and northeast monsoon rainfall over South Asia

A methodology is presented for making optimum use of the global sea surface temperature (SST) field for long lead prediction of the Indian summer monsoon rainfall (ISMR). To avoid the node phase of the biennial oscillation of El Nino Southern Oscillation (ENSO)-monsoon system and also to include the multiyear ENSO variability, the ISMR-SST relationship was examined from three seasons lag prior to the start of the monsoon season up to four years lag. First, a correlation analysis is used to identify the regions and seasonal lags for which SST is highly correlated with ISMR. The correlation patterns show a slow and consistent temporal evolution, suggesting the existence of SST oscillations that produce significant correlation even when no direct physical relationship between SST and ISMR, at such long lags, is plausible. As a second step, a strategy for selecting the best 14 predictors (hot spots of the global oceans) is investigated. An experimental prediction of ISMR is made using the SST anomalies in these 14 hot spots. The prediction is consistent for the 105 years (1875 to 1979) of the model development and the 22 years (1980 to 2001) of the model verification period (these last years were not included in the correlation analysis or in the computation of the regression model). The predicted values explain about 80% of the observed variance of ISMR in the model verification period. It is shown that, to a large extent, the behavior of the ensuing ISMR can be determined nine months in advance using SST only. The consistent and skillful prediction for more than a century is not a product of chance. Thus the anomalously strong and weak monsoon seasons are parts of longer period and broader scale circulation patterns, which result from interactions in the ocean–atmosphere coupled system over many seasons in the past. It is argued that despite the weakening of the ENSO-ISMR relationship in recent years, most of the variability of ISMR can still be attributed to SST in a global perspective.

A. K. Sahai

Papers

High-resolution climate change scenarios for India for the 21st century

Future projection of Indian summer monsoon variability under climate change scenario: An assessment from CMIP5 climate models

All India summer monsoon rainfall prediction using an artificial neural network

On the recent strengthening of the relationship between ENSO and northeast monsoon rainfall over South Asia

Long-lead prediction of Indian summer monsoon rainfall from global SST evolution