Indian summer monsoon onset forecast skill in the UK Met Office initialized coupled seasonal forecasting system (GloSea5-GC2)
Indian summer monsoon onset forecast skill in the UK Met Office initialized coupled seasonal forecasting system (GloSea5-GC2)
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DOI：
10.1007/s00382-018-4536-1
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发表时间：
2018-11
期刊：
影响因子：
4.6
通讯作者：
A. Chevuturi;A. Turner;S. Woolnough;G. Martin;C. MacLachlan
中科院分区：
文献类型：
--
作者：
A. Chevuturi;A. Turner;S. Woolnough;G. Martin;C. MacLachlan
准确和准确地预报印度季风对印度的社会经济安全很重要，农业和相关部门因季风爆发的预报而有所改善。在这项研究中，我们建立了英国气象局联合初始化的全球季节预报系统GloSea5-GC2在预测印度季风爆发方面的技能。我们在以前工作的基础上，证明了GloSea5在使用大尺度环流和当地降雨量来预测印度季风季节平均年际变化方面的良好技能。我们分析了为期20年(1992-2011)的夏季后播，从4月底/5月初的一组三个春季开始日期开始。后播集合每年至少有15个集合成员，并使用五个不同的目标季风指数进行分析。这些指数旨在检验大范围和局部尺度的季风环流、水文变化、对流层温度梯度或降雨量的单值(面积平均)或印度季风爆发的网格点测量。模型中的发病日期与再分析中发现的发病日期之间存在显著的相关性。基于大尺度动力和热力指数的指数比基于局地尺度动力和水文指数更能更好地估计模式中的季风爆发。这可以归因于与局部尺度特征相比，该模型对大尺度动力学的更好表示。GloSea5可能无法预测印度季风爆发的确切日期，但这项研究表明，该模式在预测季风爆发的类别方面有很好的能力，使用早期、正常或晚期季风类别。使用网格点当地降雨开始指数，我们注意到，预报技巧在印度中部部分地区、恒河平原和印度沿海部分地区最高--这些地区都是印度的大规模农业区。模式中的厄尔尼诺南方涛动(ENSO)强迫提高了使用大尺度环流指数预报季风爆发的技巧，晚季风爆发与厄尔尼诺条件相吻合，而早季风爆发在拉尼娜年更常见。研究结果表明，尽管有系统的模式误差，GloSea5的S集合平均预报可以提前一个月用于可靠的印度季风爆发预报。
Accurate and precise forecasting of the Indian monsoon is important for the socio-economic security of India, with improvements in agriculture and associated sectors from prediction of the monsoon onset. In this study we establish the skill of the UK Met Office coupled initialized global seasonal forecasting system, GloSea5-GC2, in forecasting Indian monsoon onset. We build on previous work that has demonstrated the good skill of GloSea5 at forecasting interannual variations of the seasonal mean Indian monsoon using measures of large-scale circulation and local precipitation. We analyze the summer hindcasts from a set of three springtime start-dates in late April/early May for the 20-year hindcast period (1992–2011). The hindcast set features at least fifteen ensemble members for each year and is analyzed using five different objective monsoon indices. These indices are designed to examine large and local-scale measures of the monsoon circulation, hydrological changes, tropospheric temperature gradient, or rainfall for single value (area-averaged) or grid-point measures of the Indian monsoon onset. There is significant correlation between onset dates in the model and those found in reanalysis. Indices based on large-scale dynamic and thermodynamic indices are better at estimating monsoon onset in the model rather than local-scale dynamical and hydrological indices. This can be attributed to the model’s better representation of large-scale dynamics compared to local-scale features. GloSea5 may not be able to predict the exact date of monsoon onset over India, but this study shows that the model has a good ability at predicting category-wise monsoon onset, using early, normal or late tercile categories. Using a grid-point local rainfall onset index, we note that the forecast skill is highest over parts of central India, the Gangetic plains, and parts of coastal India—all zones of extensive agriculture in India. El Niño Southern Oscillation (ENSO) forcing in the model improves the forecast skill of monsoon onset when using a large-scale circulation index, with late monsoon onset coinciding with El Niño conditions and early monsoon onset more common in La Niña years. The results of this study suggest that GloSea5’s ensemble-mean forecast may be used for reliable Indian monsoon onset prediction a month in advance despite systematic model errors.