The adjoint method is used to calibrate the medium complexity climate model “Planet Simulator” through parameter estimation. Identical twin experiments demonstrate that this method can retrieve default values of the control parameters when using a long assimilation window of the order of 2 months. Chaos synchronization through nudging, required to overcome limits in the temporal assimilation window in the adjoint method, is employed successfully to reach this assimilation window length. When assimilating ERA‐Interim reanalysis data, the observations of air temperature and the radiative fluxes are the most important data for adjusting the control parameters. The global mean net longwave fluxes at the surface and at the top of the atmosphere are significantly improved by tuning two model parameters controlling the absorption of clouds and water vapor. The global mean net shortwave radiation at the surface is improved by optimizing three model parameters controlling cloud optical properties. The optimized parameters improve the free model (without nudging terms) simulation in a way similar to that in the assimilation experiments. Results suggest a promising way for tuning uncertain parameters in nonlinear coupled climate models.

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基于伴随的气候模型调整：在行星模拟器中的应用

伴随方法用于通过参数估计来校准中等复杂性气候模型“ Planet Simulator”。完全相同的孪生实验表明，当使用2个月量级的长同化窗口时，此方法可以检索控制参数的默认值。克服伴随方法中的时间同化窗口中的限制所需要的通过微调的混沌同步，成功地达到了该同化窗口的长度。当吸收ERA-Interim重新分析数据时，对空气温度和辐射通量的观测是调整控制参数最重要的数据。通过调整控制云和水蒸气吸收的两个模型参数，可以显着改善地表和大气顶部的全球平均净长波通量。通过优化控制云光学特性的三个模型参数，可以改善表面的全局平均净短波辐射。优化的参数以与同化实验中相似的方式改善了自由模型（无小项）的仿真。结果表明，在非线性耦合气候模型中调整不确定参数是一种有前途的方法。优化的参数以与同化实验中相似的方式改善了自由模型（无小项）的仿真。结果表明，在非线性耦合气候模型中调整不确定参数是一种有前途的方法。优化的参数以与同化实验中相似的方式改善了自由模型（无小项）的仿真。结果表明，在非线性耦合气候模型中调整不确定参数是一种有前途的方法。