Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions
Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions
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DOI：
10.1007/s00382-012-1531-9
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发表时间：
2013-08-01
期刊：
影响因子：
4.6
通讯作者：
Underwood, Fiona M.
中科院分区：
文献类型：
--
作者：
Ho, Chun Kit;Hawkins, Ed;Underwood, Fiona M.
准确的十年气候预测可以用来为适应气候变化的行动提供信息。可以通过与仅基于历史观测的统计模型的预测进行比较，来评估来自初始化的动力全球气候模型(GCM)的此类预测的技能。本文提出了两个基准统计模式，用于在十年时间尺度上预报年平均海表面温度(SSTs)的辐射强迫趋势和内部变率。对于这两个统计模式，与辐射强迫有关的趋势都是利用每个网格盒的SST时间序列在等效全球平均大气CO2浓度时间序列上的线性回归来模拟的。然后，通过(1)一阶自回归模型(AR1)和(2)构建的模拟模型(CA)来模拟剩余内部变异性。对1960-2005年46个回溯预报与起始年的检验表明，在热带外北大西洋、印度洋和西太平洋的部分地区，用AR1趋势预报的距平相关系数大于0.7。这主要与对强迫趋势的预测有关。更重要的是，CA和AR1都巧妙地预测了远北大西洋亚极涡旋区域海温的内部变率，相关系数大于0.5。对于亚极地涡旋和南大西洋部分地区，CA的提前时间为6-9年，优于AR1。这些统计预测也与初始GCM DePreSys的总体平均回溯性预测进行了比较。在北大西洋大部分地区，DePreSys的表现优于统计模型，提前期为2-5年和6-9年，但在北大西洋海流区，AR1的趋势通常优于DePreSys，而在南大西洋的部分地区，CA的趋势优于DePreSys，提前期为6-9年。这些发现鼓励进一步发展基准统计十年预测模型，以及结合不同预测的方法。
Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO2 concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2-5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6-9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2-5 years and 6-9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6-9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions.