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

基本信息

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

项目摘要

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（主要是可见阳光）与返回太空的红外辐射流之间的平衡决定的。因此，地球气候的模拟需要对这些流动进行近似表示，该表示要足够快、足够实用、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。鉴于大气对红外辐射的透明度可能从一个波长到另一个波长突然变化，这种表示法（称为辐射传输参数化或更简单地称为辐射方案）的开发具有挑战性。实用的辐射方案是存在的，但开发和更新它们具有挑战性，并且根据特定应用对其进行定制通常不可行。例如，尽管有新的光谱观测，2020 年推出的社区地球系统模型 (CESM) 版本仍使用 2008 年发布的辐射方案，并且同一版本用于全复杂性的当前模拟和理想化模拟这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，该工具包将大大增强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）的分布，涵盖特定应用中预期的条件范围，并且工具箱将使用复杂的逐行-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。然后，用户将能够为其应用程序选择成本和准确性之间的最佳权衡。此外，该项目还为特定研究目标创建了参数化：一项用于模拟高二氧化碳浓度的古气候，两项用于研究辐射与对流云之间的相互作用（一项强调速度，可以更频繁地调用，一项强调准确性）。第三个是与地球物理流体动力学实验室的合作者开发的（通过其他来源资助），针对当今气候的模拟和包括厄尔尼诺事件在内的气候波动的预测进行了优化。该项目还包括对辐射传输参数化的替代方法的研究，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频带表示。这些谱线是使用模拟退火等快速优化技术从光谱数据库中确定的。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而产生更广泛的影响。因此，这项工作将使依赖这些模型作为基础科学研究工具的全世界受益。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的价值，为关注气候变率和变化的决策者提供指导。该项目支持一名博士后和一名研究生，从而为气候模型开发的未来劳动力提供支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势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

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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