A machine learning correction model of the winter clear-sky temperature bias over the Arctic sea ice in atmospheric reanalyses
A machine learning correction model of the winter clear-sky temperature bias over the Arctic sea ice in atmospheric reanalyses
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DOI：
10.1175/mwr-d-22-0130.1
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发表时间：
2023-03
影响因子：
3.2
通讯作者：
L. Zampieri;G. Arduini;M. Holland;S. Keeley;K. Mogensen;M. Shupe;S. Tietsche
中科院分区：
文献类型：
--
作者：
L. Zampieri;G. Arduini;M. Holland;S. Keeley;K. Mogensen;M. Shupe;S. Tietsche
大气再分析被广泛用于估算海冰上过去的大气近地表状态。它们为海冰和海洋数值模拟提供了边界条件，并为研究极地变异性和人为气候变化提供了相关信息。以前的研究表明，在当前一代大气再分析中，北极海冰上存在较大的近地表温度偏差(主要是温暖的)，这与海冰上的雪和用于进行再分析的预报模式中稳定分层的边界层有关。这些错误可能会影响使用再分析产品来支持极地研究。在这里，我们训练一个完全连接的神经网络，它从遥感红外温度观测中学习，以基于一组海冰和大气预报器修正现有的未耦合大气再分析(ERA5，JRA-55)，这些预报器本身就是再分析产品。与以前的校正方案相比，建议的校正方案的优点是考虑了天气和云态，预报器与造成偏差的机制相一致，以及与北极海冰状况下降相一致的自我出现的季节性和多年代际趋势。与马赛克活动的独立现场观测(在晴天条件下分别为32%和10%)相比，校正导致ERA5和JRA-55的温度偏差平均减少了27%和7%。这些改进对强迫海冰和海洋模拟是有益的，它们依赖于重新分析作为边界条件的表面场。
Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multi-decadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in-situ observations from the MOSAiC campaign (respectively 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.