Interannual Variations and Prediction of Spring Precipitation over China
The interannual variations and the prediction of the leading two empirical orthogonal function (EOF) modes of spring (April–May) precipitation over China for the period from 1951 to 2014 are investigated using both observational data and the seasonal forecast made by six coupled climate models. The...
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| Veröffentlicht in: | Journal of climate 2018-01, Vol.31 (2), p.655-670 |
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| description | The interannual variations and the prediction of the leading two empirical orthogonal function (EOF) modes of spring (April–May) precipitation over China for the period from 1951 to 2014 are investigated using both observational data and the seasonal forecast made by six coupled climate models. The leading EOF mode of spring precipitation over China (EOF1-prec) features a monosign pattern, with the maximum loading located over southern China. The ENSO-related tropical Pacific SST anomalies in the previous winter can serve as a precursor for EOF1-prec. The second EOF mode of spring precipitation (EOF2-prec) over China is characterized by a dipole structure, with one pole near the Yangtze River and the other one with opposite sign over the Pearl River delta. A North Atlantic sea surface temperature (SST) anomaly dipole in the preceding March is found contribute to the prec-EOF2 and can serve as its predictor. A physics-based empirical (P-E) model is then formulated using the two precursors revealed by the observational analysis to forecast the variations of EOF1-prec and EOF2-prec. Compared to coupled climate models, which have little skill in forecasting the time variations of the two EOF modes, this P-E model can significantly improve the forecast skill of their time variations. Alinear regression model is further established using the time series forecast by the P-E model to forecast the spring precipitation over China. Results suggest that the seasonal forecast skill of the spring precipitation over southeastern China, especially over the Yangtze River area, can be significantly improved by the regression model. |
| doi_str_mv | 10.1175/JCLI-D-17-0233.1 |
| format | Article |
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The leading EOF mode of spring precipitation over China (EOF1-prec) features a monosign pattern, with the maximum loading located over southern China. The ENSO-related tropical Pacific SST anomalies in the previous winter can serve as a precursor for EOF1-prec. The second EOF mode of spring precipitation (EOF2-prec) over China is characterized by a dipole structure, with one pole near the Yangtze River and the other one with opposite sign over the Pearl River delta. A North Atlantic sea surface temperature (SST) anomaly dipole in the preceding March is found contribute to the prec-EOF2 and can serve as its predictor. A physics-based empirical (P-E) model is then formulated using the two precursors revealed by the observational analysis to forecast the variations of EOF1-prec and EOF2-prec. Compared to coupled climate models, which have little skill in forecasting the time variations of the two EOF modes, this P-E model can significantly improve the forecast skill of their time variations. Alinear regression model is further established using the time series forecast by the P-E model to forecast the spring precipitation over China. Results suggest that the seasonal forecast skill of the spring precipitation over southeastern China, especially over the Yangtze River area, can be significantly improved by the regression model.</description><identifier>ISSN: 0894-8755</identifier><identifier>EISSN: 1520-0442</identifier><identifier>DOI: 10.1175/JCLI-D-17-0233.1</identifier><language>eng</language><publisher>Boston: American Meteorological Society</publisher><subject>Annual variations ; Anomalies ; Boundary conditions ; Climate ; Climate models ; Dipoles ; Earth science ; El Nino ; El Nino phenomena ; El Nino-Southern Oscillation event ; Empirical analysis ; Interannual variations ; Orthogonal functions ; Phase transitions ; Physics ; Precipitation ; Precursors ; Regression analysis ; Regression models ; Rivers ; Sea surface ; Sea surface temperature ; Seasons ; Southern Oscillation ; Spring ; Spring precipitation ; Standard deviation ; Studies ; Summer ; Surface temperature ; Trends ; Tropical climate ; Variation ; Weather forecasting ; Wind ; Winter</subject><ispartof>Journal of climate, 2018-01, Vol.31 (2), p.655-670</ispartof><rights>2018 American Meteorological Society</rights><rights>Copyright American Meteorological Society Jan 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c340t-ca2f0491a3fb403b8378c7de17cb9a8348e4c6907a708c9310f52b9316aa9cc13</citedby><cites>FETCH-LOGICAL-c340t-ca2f0491a3fb403b8378c7de17cb9a8348e4c6907a708c9310f52b9316aa9cc13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26388795$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26388795$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,3681,27924,27925,58017,58250</link.rule.ids></links><search><creatorcontrib>You, Yujia</creatorcontrib><creatorcontrib>Jia, Xiaojing</creatorcontrib><title>Interannual Variations and Prediction of Spring Precipitation over China</title><title>Journal of climate</title><description>The interannual variations and the prediction of the leading two empirical orthogonal function (EOF) modes of spring (April–May) precipitation over China for the period from 1951 to 2014 are investigated using both observational data and the seasonal forecast made by six coupled climate models. The leading EOF mode of spring precipitation over China (EOF1-prec) features a monosign pattern, with the maximum loading located over southern China. The ENSO-related tropical Pacific SST anomalies in the previous winter can serve as a precursor for EOF1-prec. The second EOF mode of spring precipitation (EOF2-prec) over China is characterized by a dipole structure, with one pole near the Yangtze River and the other one with opposite sign over the Pearl River delta. A North Atlantic sea surface temperature (SST) anomaly dipole in the preceding March is found contribute to the prec-EOF2 and can serve as its predictor. A physics-based empirical (P-E) model is then formulated using the two precursors revealed by the observational analysis to forecast the variations of EOF1-prec and EOF2-prec. Compared to coupled climate models, which have little skill in forecasting the time variations of the two EOF modes, this P-E model can significantly improve the forecast skill of their time variations. Alinear regression model is further established using the time series forecast by the P-E model to forecast the spring precipitation over China. Results suggest that the seasonal forecast skill of the spring precipitation over southeastern China, especially over the Yangtze River area, can be significantly improved by the regression model.</description><subject>Annual variations</subject><subject>Anomalies</subject><subject>Boundary conditions</subject><subject>Climate</subject><subject>Climate models</subject><subject>Dipoles</subject><subject>Earth science</subject><subject>El Nino</subject><subject>El Nino phenomena</subject><subject>El Nino-Southern Oscillation event</subject><subject>Empirical analysis</subject><subject>Interannual variations</subject><subject>Orthogonal functions</subject><subject>Phase transitions</subject><subject>Physics</subject><subject>Precipitation</subject><subject>Precursors</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Rivers</subject><subject>Sea surface</subject><subject>Sea surface temperature</subject><subject>Seasons</subject><subject>Southern Oscillation</subject><subject>Spring</subject><subject>Spring precipitation</subject><subject>Standard deviation</subject><subject>Studies</subject><subject>Summer</subject><subject>Surface temperature</subject><subject>Trends</subject><subject>Tropical climate</subject><subject>Variation</subject><subject>Weather 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climate</jtitle><date>2018-01-01</date><risdate>2018</risdate><volume>31</volume><issue>2</issue><spage>655</spage><epage>670</epage><pages>655-670</pages><issn>0894-8755</issn><eissn>1520-0442</eissn><abstract>The interannual variations and the prediction of the leading two empirical orthogonal function (EOF) modes of spring (April–May) precipitation over China for the period from 1951 to 2014 are investigated using both observational data and the seasonal forecast made by six coupled climate models. The leading EOF mode of spring precipitation over China (EOF1-prec) features a monosign pattern, with the maximum loading located over southern China. The ENSO-related tropical Pacific SST anomalies in the previous winter can serve as a precursor for EOF1-prec. The second EOF mode of spring precipitation (EOF2-prec) over China is characterized by a dipole structure, with one pole near the Yangtze River and the other one with opposite sign over the Pearl River delta. A North Atlantic sea surface temperature (SST) anomaly dipole in the preceding March is found contribute to the prec-EOF2 and can serve as its predictor. A physics-based empirical (P-E) model is then formulated using the two precursors revealed by the observational analysis to forecast the variations of EOF1-prec and EOF2-prec. Compared to coupled climate models, which have little skill in forecasting the time variations of the two EOF modes, this P-E model can significantly improve the forecast skill of their time variations. Alinear regression model is further established using the time series forecast by the P-E model to forecast the spring precipitation over China. Results suggest that the seasonal forecast skill of the spring precipitation over southeastern China, especially over the Yangtze River area, can be significantly improved by the regression model.</abstract><cop>Boston</cop><pub>American Meteorological Society</pub><doi>10.1175/JCLI-D-17-0233.1</doi><tpages>16</tpages></addata></record> |
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| subjects | Annual variations Anomalies Boundary conditions Climate Climate models Dipoles Earth science El Nino El Nino phenomena El Nino-Southern Oscillation event Empirical analysis Interannual variations Orthogonal functions Phase transitions Physics Precipitation Precursors Regression analysis Regression models Rivers Sea surface Sea surface temperature Seasons Southern Oscillation Spring Spring precipitation Standard deviation Studies Summer Surface temperature Trends Tropical climate Variation Weather forecasting Wind Winter |
| title | Interannual Variations and Prediction of Spring Precipitation over China |
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