Embedding trend into seasonal temperature forecasts through statistical calibration of GCM outputs,International Journal of Climatology

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Embedding trend into seasonal temperature forecasts through statistical calibration of GCM outputs
International Journal of Climatology ( IF 3.5 ) Pub Date : 2020-09-01 , DOI: 10.1002/joc.6788
Yawen Shao ₁ , Quan J. Wang ₁ , Andrew Schepen ₂ , Dongryeol Ryu ₁

Affiliation

Accurate and reliable seasonal climate forecasts are frequently sought by climate‐sensitive sectors to support decision‐making under climate variability and change. Temperature trend is discernible globally over the past decades, but seasonal forecasts produced by a global climate model (GCM) generally underestimate such trend. Current statistical methods used for calibrating seasonal climate forecasts mostly do not explicitly account for climate trends. Consequently, the calibrated forecasts also fail to capture the observed trend. Solving this problem can enhance user confidence in seasonal climate forecasts. In this study, we extend the capability of the Bayesian joint probability (BJP) modelling approach for statistical calibration of seasonal climate forecasts. A trend component is introduced into the BJP algorithm for embedding the observed trend into calibrated ensemble forecasts. We apply the new model (named BJP‐t) to three test stations in Australia. Seasonal forecasts of daily maximum temperatures from the SEAS5 model, operated by the European Centre for Medium‐Range Weather Forecasts (ECMWF), are calibrated and evaluated. The BJP‐t calibrated ensemble forecasts can reproduce the observed trend, when the raw ensemble forecasts and the BJP calibrated ensemble forecasts both fail to do so. The BJP‐t calibration leads to more skilful, more reliable and sharper forecasts than the BJP calibration.

中文翻译：

通过 GCM 输出的统计校准将趋势嵌入到季节性温度预测中

气候敏感部门经常寻求准确可靠的季节性气候预测，以支持气候变率和变化下的决策。在过去的几十年里，全球范围内的温度趋势是显而易见的，但全球气候模型 (GCM) 产生的季节性预测通常低估了这种趋势。当前用于校准季节性气候预测的统计方法大多没有明确考虑气候趋势。因此，校准的预测也无法捕捉观察到的趋势。解决这个问题可以增强用户对季节性气候预测的信心。在这项研究中，我们扩展了贝叶斯联合概率 (BJP) 建模方法的能力，用于季节性气候预测的统计校准。趋势分量被引入到 BJP 算法中，用于将观察到的趋势嵌入到校准的集合预测中。我们将新模型（名为 BJP-t）应用于澳大利亚的三个测试站。欧洲中期天气预报中心 (ECMWF) 运营的 SEAS5 模型对每日最高气温的季节性预测进行了校准和评估。BJP-t 校准集合预测可以重现观察到的趋势，当原始集合预测和 BJP 校准集合预测都不能这样做时。BJP-t 校准导致比 BJP 校准更熟练、更可靠和更清晰的预测。由欧洲中期天气预报中心 (ECMWF) 运营，进行校准和评估。BJP-t 校准集合预测可以重现观察到的趋势，当原始集合预测和 BJP 校准集合预测都不能这样做时。BJP-t 校准导致比 BJP 校准更熟练、更可靠和更清晰的预测。由欧洲中期天气预报中心 (ECMWF) 运营，进行校准和评估。BJP-t 校准的集合预测可以重现观察到的趋势，当原始集合预测和 BJP 校准的集合预测都不能这样做时。与 BJP 校准相比，BJP-t 校准导致更熟练、更可靠和更清晰的预测。

更新日期：2020-09-01

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