Coupled general circulation models (GCM), and their atmospheric, oceanic, land, and sea‐ice components have many parameters. Some parameters determine the numerics of the dynamical core, while others are based on our current understanding of the physical processes being simulated. Many of these parameters are poorly known, often globally defined, and are subject to pragmatic choices arising from a complex interplay between grid resolution and inherent model biases. To address this problem, we use an ensemble transform Kalman filter, to estimate spatiotemporally varying maps of ocean albedo and shortwave radiation e‐folding length scale in a coupled climate GCM. These parameters are designed to minimize the error between short term (3–28 days) forecasts of the climate model and a network of real world atmospheric, oceanic, and sea‐ice observations. The data assimilation system has an improved fit to observations when estimating ocean albedo and shortwave e‐folding length scale either individually or simultaneously. However, only individually estimated maps of shortwave e‐folding length scale are also shown to systematically reduce bias in longer multiyear climate forecasts during an out‐of‐sample period. The bias of the multiyear forecasts is reduced for parameter maps determined from longer DA cycle lengths.

中文翻译：

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耦合一般环流模型中减少偏差的海洋光学特性的集合卡尔曼滤波参数估计

耦合的一般环流模型（GCM）及其大气，海洋，陆地和海冰成分具有许多参数。一些参数确定了动态核心的数值，而其他参数则基于我们目前对模拟物理过程的理解。其中许多参数鲜为人知，通常是全局定义的，并且由于网格分辨率和固有模型偏差之间的复杂相互作用而受到实用选择的影响。为了解决这个问题，我们使用了集成变换卡尔曼滤波器，以估算耦合气候GCM中海洋反照率和短波辐射电子折叠长度尺度的时空变化图。这些参数旨在最大程度地减少对气候模型的短期（3–28天）预测与现实世界的大气，海洋和海冰观测网络之间的误差。当单独或同时估算海洋反照率和短波电子折叠长度尺度时，数据同化系统对观测的拟合度更高。但是，也仅显示了短波电子折叠长度尺度的单独估计图，可以在抽样期以外系统地减少较长年气候预报中的偏差。对于由更长的DA周期长度确定的参数图，可以减少多年预测的偏差。