Spring Onset Predictability in the North American Multimodel Ensemble
Spring Onset Predictability in the North American Multimodel Ensemble
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DOI：
10.1029/2018jd028597
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发表时间：
2018-06-16
影响因子：
4.4
通讯作者：
Wilks, Daniel S.
中科院分区：
文献类型：
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作者：
Carrillo, Carlos M.;Ault, Toby R.;Wilks, Daniel S.
利用其年际变率指数（“扩展春季指数”或SI-x）和北美多模式集合重预报试验的输出来评估春季开始的可预测性。计算SI-x的输入数据采用每日联合偏差校正方法进行处理，从北美多模型集成计算的SI-x输出使用集成模型输出统计方法非齐次高斯回归进行后处理。该集合模型输出统计方法用于量化训练周期长度和集合大小对预测技能的影响。预测春季来临时间的提前时间为10 ~ 60天，该范围的上限位于美国东部北纬35度至北纬45度之间的狭窄带。使用连续等级概率得分和技能得分（SS）阈值，本研究表明SI-x的正可预测性范围分为两类：10-40天和40-60天。使用更高的技能阈值（SS等于0.1和0.2），可预测性被限制在10-30天左右的较低范围内。使用联合偏置校正的后处理工作相对于未处理的输入数据集提高了SI-x的预测技能。使用非齐次高斯回归，在联合偏差校正的技能显示出改善证据的区域，SS的正变化被注意到。这些发现表明，春季的开始可以在季节内的时间范围内预测，这反过来可能对农民，种植者和利益相关者在这些时间尺度上做出决策有用。
The predictability of spring onset is assessed using an index of its interannual variability (the "extended spring index" or SI-x) and output from the North American Multimodel Ensemble reforecast experiment. The input data to compute SI-x were treated with a daily joint bias correction approach, and the SI-x outputs computed from the North American Multimodel Ensemble were postprocessed using an ensemble model output statistic approachnonhomogeneous Gaussian regression. This ensemble model output statistic approach was used to quantify the effects of training period length and ensemble size on forecast skill. The lead time for predicting the timing of spring onset is found to be from 10 to 60 days, with the higher end of this range located along a narrow band between 35 degrees N to 45 degrees N in the eastern United States. Using continuous rank probability scores and skill score (SS) thresholds, this study demonstrates that ranges of positive predictability of SI-x fall into two categories: 10-40 and 40-60 days. Using higher skill thresholds (SS equal to 0.1 and 0.2), predictability is confined to a lower range with values around 10-30 days. The postprocessing work using joint bias correction improves the predictive skill for SI-x relative to the untreated input data set. Using nonhomogeneous Gaussian regression, a positive change in the SS is noted in regions where the skill with joint bias correction shows evidence of improvement. These findings suggest that the start of spring might be predictable on intraseasonal time horizons, which in turn could be useful for farmers, growers, and stakeholders making decisions on these time scales.