Spatiotemporal Distribution Of The Monthly Temperature And Precipitation Predictability Limit In China

By using observational daily temperature and precipitation and nonlinear dynamical systemtheory, the predictability limit of monthly temperature and precipitation was quantitativelyestimated. We further analyzed the relationship between the monthly temperature predictabilitylimit and the skill of persistence prediction and monthly dynamic extended range forecast（DERF） of monthly temperature.Based on the nonlinear Lyapunov exponent and nonlinear error growth dynamics, thespatiotemporal distribution and decadal change of the monthly temperature predictability limit（MTPL） in China is quantitatively estimated. Data used are daily temperature of518stationsfrom1960to2011in China. The results are summarized as follows: The spatial distribution ofMTPL varies regionally. MTPL is higher in most areas of Northeast China, the southwest ofYunnan Province and eastern Northwest China. MTPL is lower in the middle and lower reachesof Yangtze River and Huang-huai Basin. The spatial distribution of MTPL varies distinctly withseasons. MTPL is higher in boreal summer than in boreal winter. MTPL has had distinct decadalchanges in China, with increase since the1970s and decrease since2000. Especially in thenortheast part of the country, MTPL has significantly increased since1986. Decadal change ofMTPL in Northwest China, Northeast China and the Huang-huai Basin may have a closerelationship with the persistence of temperature anomaly. Since the beginning of the21st century,MTPL has decreased slowly in most of the country, except for the south.The spatial distribution of MTPL varies obviously with seasons. MTPL is greater inSummer and in Autumn but less in Spring and in Winter. The length of temperature series hassome impact on the value of MTPL. This result is highly coherent with the prediction score ofpersistence prediction and monthly dynamic extended range forecast（DERF）. The predictionscore of monthly temperature is higher in boreal summer than that in winter. Spatialdistribution of prediction score and MTPL are similar in these two seasons. The data length hasgreater impact on MTPL in Northwest China、Northeast China and Central China and it hasless impact on MTPL in eastern Northwest China, Yunnan Province and south of YangtzeRiver. The annual cycle of temperature also affects MTPL values. It affects MTPL much inNortheast China, North China, South China and the source area of Yangtze River and YellowRiver. It affects MTPL less in Yangtze River Basin. Based on the nonlinear Lyapunov exponent and nonlinear error growth dynamics, themonthly standardized precipitation index Predictability Limit （MSPL） in China isquantitatively estimated. The data used is daily precipitation of527stations from1960to2012in China. Firstly, we transform the non-normal precipitation into normal standardizedprecipitation index （SPI）. Then we divided precipitation into three kinds: drought state,normal state and flood state according to the value of SPI. Later, the annual mean MSPL andseasonal mean MSPL of these three kinds of states are estimated respectively. It implies thatthe spatial distribution characteristics of annual MSPL is not significant. But after theprecipitation is divided into three kinds, the annual MSPL of each kind has a much clearerspatial distribution pattern. The seasonal mean MSPL varies a lot in different regions. Underthe200mm isohyet, the MSPL of drought is higher, meanwhile the MSPL of flood isrelatively low. To the south of Yangtze River, the MSPL of flood is higher, meanwhile theMSPL of drought is relatively low.The research provides a scientific foundation to understand the mechanism of monthlytemperature and precipitation anomalies and an important reference for the improvement ofmonthly temperature and precipitation prediction.