Recently, study in past trends of climate variables gained significant consideration because of its contribution in adaptions and mitigation strategies for potential future changes in climate, primarily in the area of...Recently, study in past trends of climate variables gained significant consideration because of its contribution in adaptions and mitigation strategies for potential future changes in climate, primarily in the area of water resource management. Future interannual and inter-seasonal variations in maximum and minimum temperature may bring significant changes in hydrological systems and affect regional water resources. The present study has been performed to observe past(1970-2010) as well as future(2011-2100)spatial and temporal variability in temperature(maximum and minimum) over selected stations of Sutlej basin located in North-Western Himalayan region in India. The generation of future time series of temperature data at different stations is done using statistical downscaling technique. The nonparametric test methods, modified Mann-Kendall test and Cumulative Sum chart are used for detecting monotonic trend and sequential shift in time series of maximum and minimum temperature. Sen's slope estimator test is used to detect the magnitude of change over a period of time on annual and seasonal basis. The cooling experienced in annual TMax and TMin at Kasol in past(1970-2010) would be replaced by warming in future as increasing trends are detected in TMax during 2020 s and 2050 s and in TMin during 2020 s, 2050 s and 2080 s under A1 B and A2 scenarios. Similar results of warming are also predicted at Sunnifor annual TMin in future under both scenarios which witnessed cooling during 1970-2010. The rise in TMin at Rampur is predicted to be continued in future as increasing trends are obtained under both the scenarios. Seasonal trend analysis reveals large variability in trends of TMax and TMin over these stations for the future periods.展开更多
In this study we validate the raw ensemble mean forecasts of the CCCma's GCM2 model against surface temperature and precipitation data obtained from 160 Chinese stations.It is found that despite the lagre biases,t...In this study we validate the raw ensemble mean forecasts of the CCCma's GCM2 model against surface temperature and precipitation data obtained from 160 Chinese stations.It is found that despite the lagre biases,the model was able to produce seasonal anomalies that have properties that are reasonably close to those that are observed.This anomaly is the quantity of interest when forecasting seasonal climatic conditions.The root mean squared difference(RMSD) between the forecast and observed anomaly leads us to be modestly optimistic about the prospects for using dynamical models to forecast the interannual variability of some meteorological elements. The correlation analysis of the forecast and observation also supports the result given by the RMSD analysis and provides a tool for identify the forecast confidence level in various regions,展开更多
基金financial support in the form of fellowship provided by University Grant Commission (UGC), Government of India to Mr. Dharmaveer Singh as Research Fellow for carrying out the research
文摘Recently, study in past trends of climate variables gained significant consideration because of its contribution in adaptions and mitigation strategies for potential future changes in climate, primarily in the area of water resource management. Future interannual and inter-seasonal variations in maximum and minimum temperature may bring significant changes in hydrological systems and affect regional water resources. The present study has been performed to observe past(1970-2010) as well as future(2011-2100)spatial and temporal variability in temperature(maximum and minimum) over selected stations of Sutlej basin located in North-Western Himalayan region in India. The generation of future time series of temperature data at different stations is done using statistical downscaling technique. The nonparametric test methods, modified Mann-Kendall test and Cumulative Sum chart are used for detecting monotonic trend and sequential shift in time series of maximum and minimum temperature. Sen's slope estimator test is used to detect the magnitude of change over a period of time on annual and seasonal basis. The cooling experienced in annual TMax and TMin at Kasol in past(1970-2010) would be replaced by warming in future as increasing trends are detected in TMax during 2020 s and 2050 s and in TMin during 2020 s, 2050 s and 2080 s under A1 B and A2 scenarios. Similar results of warming are also predicted at Sunnifor annual TMin in future under both scenarios which witnessed cooling during 1970-2010. The rise in TMin at Rampur is predicted to be continued in future as increasing trends are obtained under both the scenarios. Seasonal trend analysis reveals large variability in trends of TMax and TMin over these stations for the future periods.
文摘In this study we validate the raw ensemble mean forecasts of the CCCma's GCM2 model against surface temperature and precipitation data obtained from 160 Chinese stations.It is found that despite the lagre biases,the model was able to produce seasonal anomalies that have properties that are reasonably close to those that are observed.This anomaly is the quantity of interest when forecasting seasonal climatic conditions.The root mean squared difference(RMSD) between the forecast and observed anomaly leads us to be modestly optimistic about the prospects for using dynamical models to forecast the interannual variability of some meteorological elements. The correlation analysis of the forecast and observation also supports the result given by the RMSD analysis and provides a tool for identify the forecast confidence level in various regions,
