%0 Journal Article %1 martin2020predicting %A Martin, Gill M. %A Dunstone, Nick J. %A Scaife, Adam A. %A Bett, Philip E. %D 2020 %J Advances in Atmospheric Sciences %K China MyYangtzeWork climateservices dynamics eastasianmonsoon monthly myown precip %N 1 %P 29--41 %R 10.1007/s00376-019-9051-8 %T Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin %U https://doi.org/10.1007/s00376-019-9051-8 %V 37 %X We demonstrate that there is significant skill in the GloSea5 operational seasonal forecasting system for predicting June mean rainfall in the middle/lower Yangtze River basin up to four months in advance. Much of the rainfall in this region during June is contributed by the mei-yu rain band. We find that similar skill exists for predicting the East Asian summer monsoon index (EASMI) on monthly time scales, and that the latter could be used as a proxy to predict the regional rainfall. However, there appears to be little to be gained from using the predicted EASMI as a proxy for regional rainfall on monthly time scales compared with predicting the rainfall directly. Although interannual variability of the June mean rainfall is affected by synoptic and intraseasonal variations, which may be inherently unpredictable on the seasonal forecasting time scale, the major influence of equatorial Pacific sea surface temperatures from the preceding winter on the June mean rainfall is captured by the model through their influence on the western North Pacific subtropical high. The ability to predict the June mean rainfall in the middle and lower Yangtze River basin at a lead time of up to 4 months suggests the potential for providing early information to contingency planners on the availability of water during the summer season.

@article{martin2020predicting, abstract = {We demonstrate that there is significant skill in the GloSea5 operational seasonal forecasting system for predicting June mean rainfall in the middle/lower Yangtze River basin up to four months in advance. Much of the rainfall in this region during June is contributed by the mei-yu rain band. We find that similar skill exists for predicting the East Asian summer monsoon index (EASMI) on monthly time scales, and that the latter could be used as a proxy to predict the regional rainfall. However, there appears to be little to be gained from using the predicted EASMI as a proxy for regional rainfall on monthly time scales compared with predicting the rainfall directly. Although interannual variability of the June mean rainfall is affected by synoptic and intraseasonal variations, which may be inherently unpredictable on the seasonal forecasting time scale, the major influence of equatorial Pacific sea surface temperatures from the preceding winter on the June mean rainfall is captured by the model through their influence on the western North Pacific subtropical high. The ability to predict the June mean rainfall in the middle and lower Yangtze River basin at a lead time of up to 4 months suggests the potential for providing early information to contingency planners on the availability of water during the summer season.}, added-at = {2019-12-11T16:49:15.000+0100}, author = {Martin, Gill M. and Dunstone, Nick J. and Scaife, Adam A. and Bett, Philip E.}, biburl = {https://www.bibsonomy.org/bibtex/28aa338b6310f6c2f1cb59fcb1ee92b61/pbett}, day = 01, description = {Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin | SpringerLink}, doi = {10.1007/s00376-019-9051-8}, interhash = {eeb581c0a20b92fc36b2e69a02a03507}, intrahash = {8aa338b6310f6c2f1cb59fcb1ee92b61}, issn = {1861-9533}, journal = {Advances in Atmospheric Sciences}, keywords = {China MyYangtzeWork climateservices dynamics eastasianmonsoon monthly myown precip}, month = jan, number = 1, pages = {29--41}, timestamp = {2019-12-11T16:49:15.000+0100}, title = {Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin}, url = {https://doi.org/10.1007/s00376-019-9051-8}, volume = 37, year = 2020 }