标题： 利用长记忆和循环模式进行气温极端值的随机预测 显示英文标题

标题： Utilizing long memory and circulation patterns for stochastic forecasts of temperature extremes

摘要： 长记忆和环流模式是次季节到季节预测的潜在来源。 在这里，我们推断了一维非线性随机模型的每日温度，这些模型既捕捉了长记忆，又受到北极涛动（AO）指数的外部驱动。 为此，我们采用了一种数据驱动的方法，结合了分数微积分和随机差分方程。 对AO和北大西洋涛动指数以及欧洲每日极端温度的因果分析显示，AO指数对南斯堪的纳维亚冬季温度的影响最大。 瑞典维斯比机场的随机温度预测显示，长记忆模型的性能显著提高。 二进制温度预测对于最高（最低）每日温度的预测时效可达20（11）天（66%置信区间），而AR(1)模型对于每日最高（最低）温度的预测时效为8（3）天（66%置信区间）。 我们的结果表明，长记忆和环流模式在极端温度预测中具有潜力。 显示英文摘要

摘要： Long memory and circulation patterns are potential sources of subseasonal-to-seasonal predictions. Here, we infer one-dimensional nonlinear stochastic models of daily temperature which capture both long memory and external driving by the Arctic Oscillation (AO) index. To this end, we employ a data-driven method which combines fractional calculus and stochastic difference equations. A causal analysis of AO and North-Atlantic Oscillation indices and European daily extreme temperatures reveals the largest influence of the AO index on winter temperature in southern Scandinavia. Stochastic temperature forecasts for Visby Flygplats, Sweden, show significantly improved performance for long memory models. Binary temperature forecasts show predictive power for up to 20 (11) days lead time for maximum (minimum) daily temperature (66% CI) while an AR(1) model possesses predictive power for 8 (3) days lead time for daily maximum (minimum) temperature (66% CI). Our results show the potential of long memory and circulation patterns for extreme temperature forecasts.