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

协作研究：可追溯至基准的灵活辐射参数化框架

• 批准号: 1916908
• 负责人: Robert Pincus
• 金额: $ 102.33万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916908&HistoricalAwards=false
• 关键词: Collaborative; Research; Flexible; Framework; Radiation

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.

地球的气候是由流入大气层的辐射能（主要是可见光）和返回太空的红外辐射之间的平衡决定的。因此，对地球气候的模拟需要对这些流动的近似表示，该近似表示足够快以实用并且足够准确以捕获感兴趣的现象，例如二氧化碳（CO2）的变暖效应。 这种表示的发展，称为辐射传输参数化或更简单地称为辐射方案，是具有挑战性的，因为大气对红外辐射的透明度可以从一个波长突然变化到另一个波长。实际的辐射计划是存在的，但它们的开发和更新具有挑战性，而且通常不可行，使其适合特定的应用。例如，2020年可用的共同体地球系统模型（CESM）版本使用了2008年发布的辐射方案，尽管有新的光谱观测，同样的版本也用于全复杂性呈现天的模拟和理想化的模拟，将受益于更快，但不太准确的版本。这个奖项支持创建一个工具包，将大大提高能力，气候研究人员需要创建适合他们需要的辐射传输参数化。 用户将提供一套基准大气，具体说明为温度和成分（例如水蒸气和二氧化碳）的分布图，涵盖特定应用中预期的各种条件，工具箱将使用复杂的逐线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，用户就能够在成本和准确性之间为他们的应用选择最佳的折衷方案。 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CO2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，可以更频繁地调用，另一个强调准确性）。 第三种是与地球物理流体动力学实验室的合作者共同开发的（由其他来源供资），该项目最适合于模拟当今气候和预测包括厄尔尼诺现象在内的气候波动。该项目还包括关于辐射转移参数化的替代方法的工作，其中一种方法是离散频率近似法，其中使用一组最佳选择的严格单色谱线来代替根据频带的表示。 使用诸如模拟退火的快速优化技术从光谱数据库确定谱线。 第二个是机器学习方法，旨在模拟辐射传输方程的基本精确解。这项工作通过开发天气和气候模型的关键基础设施产生了更广泛的影响。 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型的预测和预测作为对关注气候变异性和变化的决策者的指导的价值。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人才。该奖项反映了NSF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。

项目成果

Sparse, Empirically Optimized Quadrature for Broadband Spectral Integration

用于宽带频谱积分的稀疏、经验优化正交

• DOI: 10.1029/2023ms003819
• 发表时间: 2023
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Czarnecki, Paulina;Polvani, Lorenzo;Pincus, Robert
• 通讯作者: Pincus, Robert

A New Halocarbon Absorption Model Based on HITRAN Cross‐Section Data and New Estimates of Halocarbon Instantaneous Clear‐Sky Radiative Forcing

基于 HITRAN 截面数据的新卤碳吸收模型和卤碳瞬时清晰天空辐射强迫的新估计

• DOI: 10.1029/2022ms003239
• 发表时间: 2022
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Buehler, Stefan A.;Brath, Manfred;Lemke, Oliver;Hodnebrog, Øivind;Pincus, Robert;Eriksson, Patrick;Gordon, Iouli;Larsson, Richard
• 通讯作者: Larsson, Richard

How Moisture Shapes Low‐Level Radiative Cooling in Subsidence Regimes

水分如何在沉降区域形成低水平辐射冷却

• DOI: 10.1029/2023av000880
• 发表时间: 2023
• 期刊: AGU Advances
• 影响因子: 8.4
• 作者: Fildier, B.;Muller, C.;Pincus, R.;Fueglistaler, S.
• 通讯作者: Fueglistaler, S.