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.

土地表面与上面的大气相互作用，因为大气以雨水和能量的形式向表面提供水，包括阳光和倒塌的红外辐射。土地反过来又通过蒸发和蒸腾来提供水蒸气，散发出明智的热量和上升的红外辐射，并用树木和其他障碍物挡住风，以及其他效果，从而影响大气。因此，土地大气相互作用是气候科学中的一个重要主题，在该领域的关键目标研究是了解土地表面过程以对大气产生进一步影响土地的反馈机制，反之亦然。该地区的许多工作都集中在降水和土壤水分上，尤其是蒸发作为后期降水的范围，该降水的源头进一步控制了土壤水分的数量和分布。在此处，PIS超出了土壤水分湿度的反馈，以考虑将土地表面特征与云表面特征和云层覆盖层覆盖的机制联系起来。其中之一是一种反馈，其中阳光落在潮湿的土壤上会产生蒸发，从而导致云或雾的形成，从而遮盖土壤并限制进一步蒸发。 PIS的先前工作表明，这种负面反馈机制在限制雨季亚马逊蒸发方面起着重要作用。这项研究中提出的另一个问题是，在邻近的森林和亚马逊森林砍伐的斑块之间存在小规模的差异，因为近地面空气会融合到干燥的斑块，因此在较干燥的斑块上升高，因此在研究的关键关注点是有限的，气候模型的能力有限。气候模型依靠参数化来表示云和降水，并且参数化很难捕获毁灭性的昼夜周期。这是研究云阴影对蒸发的影响的严重限制，因为效果取决于当太阳在天空中还是附近或地平线以下时云发展。 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.这种构型基于弱温度梯度（WTG）近似，该近似值假设温度远高于表面，这是由于科里奥利力在亚马逊等热带区域上的肌酸力的弱点而水平均匀。 WRF-WTG框架允许在有限的域上进行非常高的分辨率模拟（一到两个公里的网格间距），在这些域中可以通过某些现实主义表示感兴趣的过程。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了一些简化的云解析模型，创建了代表云量表和大尺度的混合模型（请参阅AGS-04222247）。使用这些模型的模型效应的范围效应效应的范围效应的效应效应，构成了范围的范围，该模型构成了范围的效应，这些模型构成了范围的范围，这些效应构成了效应的效应效应。通过施加非常大的土壤热容量来减少土地表面通量的昼夜周期的规定和模拟。对模型实验进行了补充，并分析了亚马逊多个观察站的相关观察结果，有些在森林砍伐区域中，有些代表从湿润到更干燥的状况的过渡。研究具有社会价值以及科学的兴趣，因为它试图改善Amazon，carodials and Cary cy and Cery and Cery and Cary and Cary and Cary and Cary and Cary and car and car cy and car cy and can cy and cy cy and cy cy and car cy and cy and car。此外，围绕这项工作组织了各种教育和外展活动，包括与哈林的高中学生一起工作，与Cal State Los Angeles的STEM中心合作，以及通过Rutgers的科学与工程研究（RISE）研究的本科招聘工作。该项目还为研究生和博士后提供了支持和培训。