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|Other Titles:||Evaluation of Seasonal Potential Predictability of Temperature Extremes in Taiwan and the Influence of Climatic Warming|
Department of Geography, NTNU
The daily weather variability is considered as a noise for the seasonal climate prediction and thus its influence needs to be alleviated as much as possible. This study proposes a numerical method that relaxes the assumptions normally made in weather noise estimations within a season, thereby likely increasing seasonal potential predictability(SPP). The proposed numerical method is first applied to the centennial de-trended monthly-mean surface temperature maximum(Tmax) and minimum(Tmin) data inTaiwan’s measurement stations.The numerical solutions show that both the monthly mean variances of weather noise,not necessarily modeled by linear/stationary assumptions, and their inter-monthly correlations loosen the stationary assumption or register as non-zero values (if one month apart or even more, e.g. between January and March). Compared to the analytic solution of simplified equations, the numerical solutions in this study generally have much better coherencies with those estimated directly using the daily data. The estimated SPP of Tmax is slightly lower in winter but higher in other seasons. The estimated SPP of Tmin is slightly lower in fall and winter but higher in spring and summer. The overall means in both Tmax and Tmin(47.6%) are comparable to the estimations of the analytic solution(46.2%), but significant differences appear at individual stations and seasons. The warming trend effects on the SPP are examined by applying the new method to the trend series. The results showed that coherency between a warming trend and an increased SPP is significantly higher in Tmin than inTmax. On average, the SPP of Tmin in summer(winter) is 86.3% (66.8%), and22.7% (20.3%) higher than that of the de-trended SPP estimations. The overall mean of the trending SPP of Tmin is around 75%, a 27.4% increase compared to the de-trended SPP. Conversely, the overall mean of the trending SPP of Tmax (48.5%) is similar to that of the de-trended estimate. The all-season regression shows that the warming efficiency, which is defined as the linear slope of the changed SPP (trend-remained minus de-trended) regressed on the trend values of seasonal-mean station temperature extremes, shown an increase of around16% (2%) per 1.0℃/100-yr warming for Tmin(Tmax). Conclusively, the climatic warming is an external source of SPP for temperature extremes, especially for theTmin.
|Appears in Collections:||地理研究|
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