Cloud_ICA: A deterministic cloud-overlap algorithm for generating a complete set of independent column atmospheres

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 phas..., A unique and useful advantage of Cloud-ICA is that with low computational cost it readily generates a complete set of ICAs such that the sum of wtICAs equals one.  Cloud-ICA can then calculate a set of up-to-four quadrature column atmospheres (QCAs) that can be used to approximate the integral over the full set of ICAs.  The method of parsing partially overlapping clouds within a single column atmosphere (SCA) into a large number of ICAs and then into a set of only four QCAs originated in Neu, Prather & Penner (2007) and was fully developed with observation-based overlap models in the chemistry photolysis code Cloud-J v7.3c in Prather (2015).  Cloud-J has continued to develop (Prather & Hsu, 2019;  2019d; Hsu & Prather, 2021) and the current Cloud-ICA code is taken from Cloud-J v8.0c (Prather, 2023). 

Note added (2024/05/20):  The program as written limits the number of ICAs to the dimension size ICA_ = 20,000 = 2 x 104.  If using the preferred 6 max-overlap vertical blocks..., , # Assessing Uncertainties and Approximations in Solar Heating of the Climate System

[https://doi.org/10.5061/dryad.w9ghx3fxz](https://doi.org/10.5061/dryad.w9ghx3fxz)

The primary goal of the computer code published here is to provide an algorithm for sorting an atmosphere of overlapping clouds into a complete and finite set of independent column atmospheres (ICAs), wherein each layer is horizontally homogenous (i.e., plane parallel) and either uniformly cloudy or clear.  Many current solar heating codes can only estimate the distribution of ICAs with Monte Carlo sampling.  This algorithm gets around that problem.  It has been in use with UCI photochemistry codes, but it was so deeply embedded as to make it unusable by others.  Here we extract that algorithm to enable ready use in other Earth system modeling codes. 

This work presents a stand-alone FORTRAN 90 computer code Cloud-ICA that reads in a single column atmosphere (p at edges, T in layer) where each layer includes a cloud fra...

在太阳辐射计算中，气候模型采用了诸多简化手段——部分是为了降低计算成本、推动气候模拟的可行性，部分则源于对关键大气信息的认知不足。无论误差是已知还是未知，学界共同关注的核心问题均为：这些简化手段会如何影响模拟得到的气候结果。
这类简化手段已广为人知，且多数已有相关研究对其开展评估，但受限于单个研究的局限性，难以实现不同简化方案间的横向对比。本研究收集了辐射传输建模或大气条件中涉及的大量此类简化手段，并在适配气候相关尺度的统一框架内评估其潜在误差。我们围绕一款兼具化学光解功能的太阳加热代码（Solar-J）搭建了基准测试框架，该代码可便捷适配以纳入其他误差与不确定性因素。本次涵盖的简化手段大类包括：使用宽波段对光谱特征进行积分、改变相位的散射近似……

Cloud-ICA的一项独特且实用的优势在于，其凭借极低的计算成本即可生成完整的独立大气柱集合（Independent Column Atmospheres, ICAs），且权重独立大气柱（weighted ICAs, wtICAs）的总和恒为1。随后Cloud-ICA可计算最多四套正交柱大气（Quadrature Column Atmospheres, QCAs），用于近似完整ICA集合的积分结果。将单柱大气（Single Column Atmosphere, SCA）内部分重叠云团拆解为大量ICA，再整合为仅含四套QCAs的方法，最早由Neu、Prather与Penner（2007）提出，并于Prather（2015）的研究中，借助基于观测的云团重叠模型在化学光解代码Cloud-J v7.3c中完成了完整开发。Cloud-J至今仍在持续更新（Prather & Hsu, 2019; 2019d; Hsu & Prather, 2021），当前的Cloud-ICA代码源自Cloud-J v8.0c（Prather, 2023）。

2024年5月20日补充说明：当前编写的程序将ICA的数量限制为维度大小ICA_=20000=2×10⁴。若采用推荐的最大重叠垂直块数为6的设置……
# 评估气候系统太阳加热过程中的不确定性与近似方案
[https://doi.org/10.5061/dryad.w9ghx3fxz](https://doi.org/10.5061/dryad.w9ghx3fxz)

本次发布的计算机代码的核心目标，是提供一种算法，可将包含重叠云团的大气拆解为完整且有限的独立大气柱集合（ICAs），其中每个大气层均为水平均一（即平面平行）状态，要么完全多云，要么完全晴朗。当前多数太阳加热代码仅能通过蒙特卡洛采样估算ICA的分布，而本算法可突破这一局限。该算法此前已与UCI光化学代码结合使用，但由于其嵌入过深，无法被其他研究者直接复用。本次研究将该算法从原代码中剥离，使其可便捷应用于其他地球系统模拟代码中。

本研究提供了一款独立的FORTRAN 90计算机代码Cloud-ICA，其可读取单柱大气数据（边界层气压为p，层内气温为T），其中每个大气层均包含云的占比（原文此处内容截断）……