Impact of observation‐optimized model parameters on decadal predictions: Simulation with a simple pycnocline prediction model
Impact of observation‐optimized model parameters on decadal predictions: Simulation with a simple pycnocline prediction model
复制标题
DOI：
10.1029/2010gl046133
复制
发表时间：
2011-01
影响因子：
5.2
通讯作者：
S. Zhang
中科院分区：
文献类型：
--
作者：
S. Zhang
一个熟练的年代际预测，预测不同的区域气候条件在季节-年际到几十年的时间尺度是社会意义。然而，从气候观测系统初始化的预测往往漂移远离观测状态的不完善的模式气候，由于模式偏差所产生的不完善的模式方程，数值方案和物理参数化，以及在模式参数值的误差。在这里，我展示了如何减轻模式偏差，通过优化模式参数，利用观测，以限制模式漂移的气候预测与一个简单的年代际预测模式。结果表明，与观测值的耦合状态参数优化大大提高了耦合模型的可预报性。虽然有效的“大气”预测延长了5倍以上，但“深海”的十年可预测性几乎增加了一倍。优化的模型参数和状态的一致性对于改善长时间尺度的预测是至关重要的。
A skillful decadal prediction that foretells varying regional climate conditions over seasonal‐interannual to multidecadal time scales is of societal significance. However, predictions initialized from the climate observing system tend to drift away from observed states towards the imperfect model climate due to model biases arising from imperfect model equations, numeric schemes and physical parameterizations, as well as the errors in the values of model parameters. Here I show how to mitigate the model bias through optimizing model parameters using observations so as to constrain the model drift in climate predictions with a simple decadal prediction model. Results show that the coupled state‐parameter optimization with observations greatly enhances the predictability of the coupled model. While valid “atmospheric” forecasts are extended by more than 5 times, the decadal predictability of the “deep ocean” is almost doubled. The coherence of optimized model parameters and states is critical to improve the long time scale predictions.