Is long-term climate memory important in temperature/precipitation predictions over China?
Long-term climate memory is ubiquitous in climate systems, but its contribution to climate prediction has not been assessed systematically. We used an integral fractional statistical model (FISM) to quantify climate memories in different variables over China. Their contributions to climate prediction were estimated using explained variances. We found different climate memory effects for different variables in different regions. The contribution of climate memory to climate variability is stronger in temperature than in precipitation records. For temperatures (including both air temperature and land temperature), the average variance explained by climate memory is around 3∼4%. For precipitation, the average explained variance was 0.6%. The low values for explained variances indicate that, on average, the contributions of climate memory to temperature and precipitation predictions are small. But in specific regions, higher climate memory effects may occur. For precipitation, climate memory can contribute 3% of the variance in southeast China. For temperature, climate memory can explain ≥ 10% of the variance in northeast and southwest China, which is not low and should be considered in prediction. Therefore, for more accurate climate prediction, we suggest first determining the contribution of climate memory. For variables or regions with strong climate memory effects, a scheme considering climate memory effects may help improve future climate predictions.
KeywordsLong-term memory Climate memory effects Integral fractional statistical model Climate prediction
The homogenized air temperature and precipitation data used in this research are provided by the Information Center of China Meteorological Administration. The land temperature data are obtained from the China Meteorological Data Sharing Service System (http://data.cma.cn). We thank LetPub for its linguistic assistance during the preparation of this manuscript.
This study is supported by the National Key R&D Program of China (2016YFA0600404 and 2016YFA0601504), the National Natural Science Foundation of China (No. 41675088, No. 41705041, and No. 41675068), and the CAS Pioneer Hundred Talents Program.
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