Collaborative Research: Role of Cloud Albedo and Land-Atmosphere Interactions on Continental Tropical Climates

合作研究：云反照率和陆地-大气相互作用对大陆热带气候的作用

基本信息

批准号：
1734164
负责人：
Michael Pritchard
金额：
$ 26.11万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734164&HistoricalAwards=false
关键词：
Collaborative Research Role Cloud Albedo

项目摘要

The land surface interacts strongly with the atmosphere above it, as the atmosphere supplies water to the surface in the form of rain and energy, including sunlight and downwelling infrared radiation. The land in turn affects the atmosphere by providing water vapor through evaporation and transpiration, giving off sensible heat and upwelling infrared radiation, and blocking the wind with trees and other obstacles, among other effects. Land-atmosphere interactions are thus an important topic in climate science, and a key goal research in this area is to understand the feedback mechanisms through which land-surface processes influence the atmosphere in ways that produce further effects on the land and vice versa. Much of the work in this area is focused on precipitation and soil moisture, particularly the extent to which evaporation serves as a source for later precipitation which further controls the amount and distribution of soil moisture.Here the PIs go beyond soil moisture-precipitation feedback to consider mechanisms that link land surface characteristics to cloudiness and the subsequent shading effect of cloud cover on the surface. One of these is a feedback in which sunlight falling on moist soil produces evaporation, which leads to the formation of clouds or fog, which shades the soil and limits further evaporation. Previous work by the PIs suggests that this negative feedback mechanism plays an important role in limiting evaporation in the Amazon during the rainy season. An additional question pursued in this research is the extent to which small-scale differences in surface cover, such as exist between adjacent forested and deforested patches of the Amazon, produce differences in cloudiness as near-surface air converges into and rises above drier and hence warmer patches.A key concern in studying such effects is that climate models have limited ability to represent them. Climate models rely on parameterizations to represent clouds and precipitation, and parameterizations have difficulty capturing the diurnal cycle of cloudiness. This is a severe limitation for studying the effect of cloud shading on evaporation, as the effect depends on whether clouds develop when the sun is high in the sky or near or below the horizon. Clouds simulated in climate models are also unlikely to respond to small-scales patchiness in surface cover, as models only represent aggregate cloud cover and surface conditions over grid boxes which extend at least tens of kilometers in each direction.The PIs use two separate modeling strategies to circumvent these difficulties, the first of which is a limited domain cloud resolving model (the Weather Research and Forecasting model, or WRF) constrained to relax back to a specified background temperature profile. This configuration is based on the weak temperature gradient (WTG) approximation, which assumes that temperatures well above the surface are horizontally uniform due to the weakness of the Coriolis force over tropical regions such as the Amazon. The WRF-WTG framework allows for very high resolution simulations (grid spacing of one or two kilometers) over limited domains on which the processes of interest can be represented with some realism. The second approach uses a technique known as superparameterization, in which a somewhat simplified cloud resolving model is placed in each grid column of a climate model, creating a hybrid model which represents both the cloud scale and the large scale (see AGS-0425247).Using these two modeling strategies the PIs perform a number of model experiments to determine the effects of the proposed mechanisms, including experiments in which the land surface turbulent heat flux is prescribed and simulations in which the diurnal cycle of land surface fluxes is reduced by imposing a very large soil heat capacity. The model experiments are complemented with analysis of relevant observations from a number of observing stations in the Amazon, some in deforested regions and some representing the transition from wetter to drier conditions.The research has societal value as well as scientific interest, as it seeks to improve understanding of climate variability and change in the Amazon, a region of high biodiversity which plays a substantial role in the global water and carbon cycles. In addition, a variety of education and outreach activities are organized around the work, including work with high school students in Harlem, work with a STEM center housed at Cal State Los Angeles, and an undergraduate recruitment effort through the Research in Science and Engineering (RiSE) program at Rutgers. The project also provides support and training for a graduate student and a postdoc.

陆地表面与其上方的大气相互作用强烈，因为大气以雨水和能量（包括阳光和下降的红外辐射）的形式向地表提供水。土地反过来又通过蒸发和蒸腾提供水蒸气、释放显热和上涌的红外辐射、用树木和其他障碍物阻挡风等来影响大气。因此，陆地-大气相互作用是气候科学的一个重要课题，该领域研究的一个关键目标是了解地表过程影响大气的反馈机制，从而对土地产生进一步的影响，反之亦然。该领域的大部分工作都集中在降水和土壤湿度，特别是蒸发作为后期降水来源的程度，这进一步控制了土壤湿度的数量和分布。这里的 PI 超出了土壤湿度-降水反馈范围，考虑将地表特征与云量以及随后云层覆盖对地表的遮蔽效应联系起来的机制。其中之一是阳光照射在潮湿土壤上产生蒸发的反馈，从而导致云或雾的形成，从而遮蔽土壤并限制进一步的蒸发。 PI 之前的工作表明，这种负反馈机制在限制亚马逊雨季蒸发方面发挥着重要作用。这项研究提出的另一个问题是，当近地表空气汇聚并上升到干燥地区时，地表覆盖的小规模差异（例如亚马逊地区相邻森林和森林砍伐地区之间存在的差异）会在多大程度上产生云量差异。研究此类影响的一个关键问题是气候模型代表这些影响的能力有限。气候模型依靠参数化来表示云和降水，而参数化很难捕捉云量的昼夜周期。这对于研究云遮蔽对蒸发的影响来说是一个严重的限制，因为这种影响取决于当太阳高高在上、接近或低于地平线时云是否会形成。气候模型中模拟的云也不太可能对地表覆盖的小规模斑块做出反应，因为模型仅代表网格盒上的总云量和地表条件，这些网格盒在每个方向上延伸至少数十公里。PI 使用两种独立的建模策略为了规避这些困难，第一个困难是有限域云解析模型（天气研究和预报模型，或 WRF），限制放松回到指定的背景温度剖面。这种配置基于弱温度梯度 (WTG) 近似，该近似假设由于亚马逊等热带地区的科里奥利力较弱，远高于地表的温度在水平方向上是均匀的。 WRF-WTG 框架允许在有限的域上进行非常高分辨率的模拟（网格间距为一到两公里），在这些域上可以以一定的真实性表示感兴趣的过程。第二种方法使用一种称为超级参数化的技术，其中在气候模型的每个网格列中放置一个稍微简化的云解析模型，创建一个代表云尺度和大尺度的混合模型（参见 AGS-0425247）。使用这两种建模策略，PI 进行了许多模型实验，以确定所提出机制的效果，包括规定地表湍流热通量的实验和地表昼夜循环的模拟通过施加非常大的土壤热容来减少通量。模型实验还对亚马逊地区多个观测站的相关观测结果进行了补充，其中一些观测站位于森林砍伐地区，还有一些代表从湿润条件向干燥条件的转变。这项研究具有社会价值和科学意义，因为它旨在提高对亚马逊地区气候变异和变化的了解，亚马逊地区生物多样性丰富，在全球水和碳循环中发挥着重要作用。此外，围绕这项工作组织了各种教育和外展活动，包括与哈莱姆区的高中生合作、与加州州立大学洛杉矶分校的 STEM 中心合作，以及通过科学与工程研究 (Research in Science and Engineering) 进行本科生招募工作（ RiSE）罗格斯大学项目。该项目还为研究生和博士后提供支持和培训。