In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate‐relevant scales. We build benchmarking capability around a solar heating code (Solar‐J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice‐cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m⁻² using a full climate re‐creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ∼2 W m⁻² with greater latitudinal biases and are likely to affect a model's ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.

中文翻译：

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评估气候系统太阳采暖的不确定性和近似值

在计算太阳辐射时，气候模型进行了许多简化，部分是为了减少计算成本并启用气候模型，部分是由于对关键的大气信息缺乏了解。无论是已知错误还是未知错误，社区关注的是这些因素如何影响模拟气候。简化是众所周知的，并且大多数已经发表了评估它们的研究，但是与单独的研究很难比较。在这里，我们在辐射传递模型或大气条件下收集了各种各样的简化方法，并在与气候有关的尺度的一致框架内评估了潜在的误差。我们围绕太阳能加热代码（Solar-J）建立基准测试功能，该代码可以兼作化学的光解代码，并且可以很容易地考虑其他误差和不确定性。这里的大类包括：使用宽波段来整合光谱特征；散射近似值，会改变云和气体的相位函数和光学深度；冰云光学的不确定性; 处理部分云层，包括重叠；和海洋表面反照率的变化。我们在地理上绘制W m中的误差 和海洋表面反照率的变化。我们在地理上绘制W m中的误差 和海洋表面反照率的变化。我们在地理上绘制W m中的误差⁻²使用天气预报模型得出的2015年1月的完整气候再造数据。对于此处评估的许多近似值，平均误差约为2 W m ^-2且具有更大的纬向偏差，并且可能会影响模型匹配当前气候状态的能力。将这项工作与以前的研究相结合，我们根据错误的幅度以及修复的难易程度或计算成本为修复这些简化提出优先级建议。