Collaborative Research: A Flexible Framework for Radiation Parameterizations Traceable to Benchmarks

地球的气候是由进入大气层的辐射能量流（主要是可见光）和返回太空的红外辐射流之间的平衡决定的。因此，对地球气候的模拟需要这些流动的近似表示，这种表示要足够快，以便于实际，并且要足够精确，以捕捉感兴趣的现象，例如二氧化碳的变暖效应。考虑到大气对红外辐射的透明度可能从一个波长突然变化到另一个波长，这种表述的发展（称为辐射传输参数化或更简单地称为辐射方案）具有挑战性。实际的辐射方案是存在的，但它们在发展和更新方面具有挑战性，而且根据具体应用进行调整通常是不可行的。例如，2020年可用的社区地球系统模型（CESM）版本使用2008年发布的辐射方案，尽管有新的光谱观测，但同样的版本用于完全复杂的当今模拟和理想化的模拟，这些模拟将受益于更快但不太准确的版本。该奖项支持创建一个工具包，该工具包将大大提高气候研究人员创建适合其需求的辐射传输参数化的能力。用户将提供一组基准大气，指定为温度和成分（例如水蒸气和二氧化碳）的剖面，涵盖特定应用中预期的条件范围，工具箱将使用复杂的逐行辐射传输模型和最新光谱观测数据库，以创建具有不同精度和计算成本的参数化选项。这样，用户就可以在成本和准确性之间做出最佳选择。此外，该项目还为特定的研究目标创建了参数化：一个用于模拟高二氧化碳浓度的古气候，两个用于研究辐射和对流云之间的相互作用（一个强调速度，这可以更频繁地调用，另一个强调准确性）。第三个是与地球物理流体动力学实验室（由其他来源资助）的合作者共同开发的，用于模拟当今气候和预测包括厄尔尼诺事件在内的气候波动。该项目还包括研究参数化辐射传输的替代方法，其中之一是离散频率近似，其中使用一组最佳选择的严格单色谱线，而不是根据频带表示。利用模拟退火等快速优化技术从光谱数据库中确定谱线。第二种是一种机器学习方法，旨在模拟辐射传递方程的基本精确解。通过开发天气和气候模型的关键基础设施，这项工作具有更广泛的影响。因此，这项工作将有益于依赖这些模型作为基础科学研究工具的国际社会。支持研讨会和教程，以促进社区参与。这项工作还提高了基于模式的预测和预估的价值，为关注气候变率和变化的决策者提供了指导。该项目支持一名博士后和一名研究生，从而为未来的气候模型开发提供劳动力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Earth's climate is set by the balance between the flow of radiant energy into the atmosphere, largely as visible sunlight, and the return flow of infrared radiation to space. Simulations of earth's climate thus require an approximate representation of these flows which is fast enough to be practical and accurate enough to capture phenomena of interest, such as the warming effect of carbon dioxide (CO2). The development of such representations, referred to as radiative transfer parameterizations or more simply as radiation schemes, is challenging given that the transparency of the atmosphere to infrared radiation can vary abruptly from one wavelength to another. Practical radiation schemes exist, but they are challenging to develop and update and it is not generally feasible to tailor them to specific applications. For example the version of the Community Earth System Model (CESM) available in 2020 uses a radiation scheme published in 2008, despite the availability of new spectroscopic observations, and the same version is used for full-complexity present-day simulations and for idealized simulations that would benefit from a faster but less accurate version.This award supports the creation of a toolkit that would greatly enhance the ability of climate researchers to create radiative transfer parameterizations suitable for their needs. The user would supply a set of benchmark atmospheres, specified as profiles of temperature and composition (water vapor and CO2, for instance), that cover the range of conditions anticipated in the particular application, and the toolbox would use a sophisticated line-by-line radiative transfer model and a database of up-to-date spectroscopic observations to create parameterization options with varying degrees of accuracy and computational cost. Users would then be able to choose the best trade-off between cost and accuracy for their application. In addition, the project creates parameterizations for specific research goals: one for simulations of paleoclimates with high CO2 concentrations and two for studies of the interaction between radiation and convective clouds (one emphasizing speed, which can be invoked more frequently, and one emphasizing accuracy). A third, developed with collaborators at the Geophysical Fluid Dynamics Laboratory (funded through other sources), is optimized for simulations of present-day climate and prediction of climate fluctuations including El Nino events.The project also includes work on alternative methods for parameterizing radiative transfer, one of which is a discrete frequency approximation, in which an optimally chosen set of strictly monochromatic spectral lines is used instead of a representation in terms of frequency bands. The lines are determined from the spectral database using a fast optimization technique such as simulated annealing. The second is a machine learning approach designed to emulate the underlying exact solutions to the radiative transfer equations.The work has broader impacts through the development of a key piece of infrastructure for weather and climate models. The work will thus have benefit for the worldwide community that relies on these models as tools for basic science research. Workshops and tutorials are supported to facilitate community engagement. The work also enhances the value of model-based predictions and projections as guidance for decision makers concerned with climate variability and change. The project supports a postdoc and a graduate student, thus providing for the future workforce in climate model development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.