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

陆地表面与其上方的大气强烈相互作用，因为大气以雨水和能量的形式向地表提供水，包括阳光和向下流动的红外辐射。 土地反过来又影响大气层，通过蒸发和蒸腾提供水蒸气，释放显热和上升的红外辐射，并用树木和其他障碍物阻挡风，等等。因此，陆-气相互作用是气候科学中的一个重要课题，这一领域的一个关键目标研究是了解陆面过程影响大气的反馈机制，从而对陆地产生进一步的影响，反之亦然。这一领域的大部分工作都集中在降水和土壤水分上，特别是蒸发作为后期降水来源的程度，后者进一步控制了土壤水分的数量和分布。在这里，PI超越了土壤水分-降水反馈，考虑了将陆面特征与云量联系起来的机制，以及随后的云量对地表的遮蔽效应。其中之一是一种反馈，即阳光照射在潮湿的土壤上会产生蒸发，从而导致云或雾的形成，从而遮蔽土壤并限制进一步的蒸发。PI以前的工作表明，这种负反馈机制在雨季限制亚马逊地区的蒸发方面发挥了重要作用。本研究中追求的另一个问题是，当近地面空气会聚并上升到干燥和温暖的地区时，表面覆盖的小规模差异（例如亚马逊邻近的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表示它们的能力有限。气候模式依赖于参数化来表示云和降水，而参数化很难捕捉云量的日周期。这对于研究云遮蔽对蒸发的影响是一个严重的限制，因为这种影响取决于云是在太阳在天空高处还是在地平线附近或下方形成的。气候模式中模拟的云也不太可能对小尺度的斑块状地表覆盖做出响应，因为模式只代表在每个方向上延伸至少数十公里的网格框上的总云量和地表条件。PI使用两种单独的模拟策略来克服这些困难，第一种是有限域云解析模式（天气研究和预报模型，或WRF）约束放松到指定的背景温度分布。这种配置基于弱温度梯度（WTG）近似，该近似假设由于热带地区（如亚马逊）的科里奥利力较弱，地表以上的温度水平均匀。WRF-WTG框架允许在有限的域上进行非常高分辨率的模拟（网格间距为一公里或两公里），在这些域上，感兴趣的过程可以以一定的现实主义来表示。第二种方法使用一种称为超参数化的技术，在这种技术中，在气候模式的每个网格列中放置一个稍微简化的云解析模式，创建一个既代表云尺度又代表大尺度的混合模式（参见AGS-0425247）。使用这两种建模策略，PI执行许多模型实验以确定所提出的机制的效果，包括实验中，陆面湍流热通量规定和模拟中，陆面通量的昼夜循环减少施加一个非常大的土壤热容量。模型实验还辅以对亚马逊地区一些观测站的相关观测结果的分析，这些观测站有些位于森林砍伐地区，有些代表了从潮湿到干燥的过渡条件。这项研究具有社会价值和科学意义，因为它试图提高对亚马逊地区气候变异和变化的理解，这是一个高度生物多样性的地区，在全球水和碳循环中发挥着重要作用。此外，各种教育和外展活动围绕工作组织，包括与高中学生在哈莱姆的工作，与设在加州州立大学洛杉矶的STEM中心的工作，并通过在罗格斯大学的科学与工程研究（RISE）计划的本科生招聘工作。该项目还为一名研究生和一名博士后提供支持和培训。

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.