Vegetation Effects on Rainfall in West Africa (VERA)
Vegetation Effects on Rainfall in West Africa (VERA)
批准号:
NE/M004295/1
负责人:
Christopher Taylor
金额:
$34.58万
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2015
资助国家:
英国
项目状态:
已结题
起止时间:
2015 至 --
中文摘要
降雨量是热带大陆人口最重要的气候参数。季风雨的到来推动了地貌的快速变化,使农作物得以生长,河网得以重新填满。然而,预测热带地区何时何地会下雨是一个出了名的难题。在预测厄尔尼诺等偏远海洋条件如何影响热带不同地区的降雨量方面取得了进展。然而,植被等当地因素也起到了作用。例如,当热带森林被砍伐用于农业时,我们有证据表明,这会影响当地和整个邻国的降雨量。事实上,气候科学家在评估21世纪热带气候将如何变化时,必须考虑未来的森林砍伐率和温室气体排放。植物通过蒸腾过程影响降雨量。当植物吸收二氧化碳进行光合作用时,它们的叶子就会失去水分。树木能够利用它们深深的根部从地表以下几米处抽取这些水,使它们能够在没有降雨的情况下继续进行几个月的光合作用。另一方面,农作物和牧草在干旱期间开始耗尽土壤水分,这会减少蒸腾作用。相反,被植物树冠吸收的太阳辐射提高了气温。用农作物和草地取代森林会改变大气的增湿和升温速度,特别是当这种浅根物种开始耗尽土壤水分的时候。这些变化反过来影响风、云和雨的发展。大气对植被的反应细节是一个重大的科学争论领域。首先,有证据表明,砍伐成片的森林可能会增加砍伐地区的降雨量,减少剩余森林的降雨量,这取决于特定的天气模式。另一方面,新的结果表明,当气团穿过大陆时,它们从森林中吸收了额外的水分,然后导致更多的降雨,再往下几百公里。最后,通过控制大气加热和增湿之间的平衡,植被可以影响将潮湿空气带离海洋的风,延迟或延长热带气候的雨季。尽管这三种植被效应都已知会影响降雨量,但我们依赖植被和大气的计算机模型来了解它们如何结合起来。捕捉模型中的基本物理过程是非常具有挑战性的。特别是,在模式中对积雨云风暴(雷暴,它主导了许多热带地区的降雨)的描述存在很大和长期的不确定性。然而,由于最近计算能力的进步,我们现在能够在整个季节运行这些模型,具有足够的空间细节来适当地捕捉风暴。在这个项目中,我们将使用来自卫星的数据和最新的天气和气候模型来深入了解植被如何影响降雨。我们将重点关注西非,这是世界上气候最敏感的地区之一,我们将研究过去30年的云和植被观测,以发现森林砍伐在哪里改变了降雨量,以及每年大草原的快速绿化如何影响季风雨。我们将进行新的计算机模拟,结合数千个单独风暴的详细发展,并检查当我们在模型中人工砍伐一个地区时会发生什么。这些结果将使我们能够评估用于预测全球气候变化的更粗略的模型的表现。通过专注于气候系统中的特定过程,我们的结果将有助于改进这些模型,同时提供关于森林砍伐的强有力的结论,以指导土地管理者。
英文摘要
Rainfall is the climatic parameter of greatest importance to the populations of the tropical continents. The arrival of monsoon rains drives a rapid transformation of the landscape, allowing crops to grow and river networks to refill. Yet predicting where and when rain will fall in the tropics is a notoriously difficult problem. Progress has been made in predicting how remote ocean conditions, such as El Nino, can affect rainfall in different parts of the tropics. However local factors such as vegetation also play a role. For example, when tropical forests are cut down for agriculture, we have evidence that this affects rainfall both locally, and across neighbouring countries. Indeed, climate scientists have to take into account future deforestation rates as well as greenhouse gas emissions when they assess how tropical climate will change in the 21st century.Vegetation affects rainfall through the process of transpiration. When plants absorb carbon dioxide for photosynthesis, they lose water from their leaves. Trees are able to extract this water from several metres below the surface using their deep roots, allowing them to continue photosynthesising for months without rainfall. Crops and grasses on the other hand start to run out of soil water during dry spells, which reduces transpiration. Instead the solar radiation absorbed by the plant canopy raises the air temperature. Replacing forests with crops and grasslands changes the rates of moistening and heating of the atmosphere, particularly when the shallow-rooted species start to run out of soil water. These changes in turn affect the development of winds, cloud and rain.The details of how the atmosphere responds to vegetation is an area of significant scientific debate. Firstly, there is evidence that clearing patches of forest may increase rainfall over the cleared area and reduce it over the remaining forest, depending on the particular weather patterns. On the other hand, new results have shown that as air masses cross the continent, they pick up additional moisture from forests, which then leads to more rain several hundred kilometres further downwind. Finally, by controlling the balance between heating and moistening of the atmosphere, the vegetation can affect the winds bringing moist air off the ocean, delaying or extending the rainy seasons which characterise tropical climate.Although these 3 vegetation effects are each known to affect rainfall, we rely on computer models of the vegetation and atmosphere to understand how they might work in combination. Capturing the essential physical processes within a model is very challenging. In particular, there are large and long-standing uncertainties in the description of cumulonimbus storms (thunderstorms, which dominate the rainfall of many tropical regions) within the models. However through recent advances in computing power, we are now able to run these models for entire seasons with sufficient spatial detail to properly capture storms.In this project we will use data from satellites and the latest weather and climate models to get to the heart of how vegetation affects rainfall. Focusing on West Africa, one of the most climatically sensitive regions of the world, we will examine cloud and vegetation observations from the last 30 years to detect where deforestation has changed rainfall, and how the rapid greening of the savannah each year affects the monsoon rains. We will perform new computer simulations, incorporating the detailed development of thousands of individual storms, and examine what happens when we artificially deforest a region in the model. These results will allow us to assess the performance of the somewhat cruder models used to forecast climate change globally. By focusing on specific processes in the climate system, our results will help to improve these models, and at the same time provide robust conclusions on deforestation to guide land managers.
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The Global Monsoon System - Research and Forecast
全球季风系统 - 研究与预测
DOI:
10.1142/9789813200913_0004
发表时间:
2017
期刊:
影响因子:
--
作者:
[Lafore J]
通讯作者:
Lafore J
DOI:
10.1002/qj.3340
发表时间:
2018-10
期刊:
Quarterly Journal of the Royal Meteorological Society
影响因子:
8.9
作者:
[M. Bhowmick;D. Parker]
通讯作者:
M. Bhowmick;D. Parker
DOI:
10.1175/bams-d-16-0273.1
发表时间:
2019-11-01
期刊:
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
影响因子:
8
作者:
[Cornforth, Rosalind, Parker, Douglas J., Tompkins, Adrian]
通讯作者:
Tompkins, Adrian
DOI:
10.1175/jcli-d-19-0380.1
发表时间:
2020-04-01
期刊:
JOURNAL OF CLIMATE
影响因子:
4.9
作者:
[Fitzpatrick, Rory G. J., Parker, Douglas J., Tucker, Simon]
通讯作者:
Tucker, Simon
DOI:
10.1016/j.agrformet.2016.03.001
发表时间:
2016-05-28
期刊:
AGRICULTURAL AND FOREST METEOROLOGY
影响因子:
6.2
作者:
[Hartley, Andrew J., Parker, Douglas J., Webster, Stuart]
通讯作者:
Webster, Stuart
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Functional Genomics of Transfer Cells
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Dynamic Credit Rating with Feedback Effects
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