Evaluating aspects of the community land and atmosphere models (CLM3 and CAM3) using a Dynamic Global Vegetation Model
Evaluating aspects of the community land and atmosphere models (CLM3 and CAM3) using a Dynamic Global Vegetation Model
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DOI：
10.1175/jcli3741.1
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发表时间：
2006-06-01
影响因子：
4.9
通讯作者：
Levis, Samuel
中科院分区：
文献类型：
--
作者：
Bonan, Gordon B.;Levis, Samuel
社区土地模型版本 3 (CLM3) 动态全球植被模型 (CLM-DGVM) 用于诊断性地识别导致模拟植被偏差的土地和大气模型偏差。由观测到的大气数据（离线模拟）驱动的 CLM-DGVM 低估了全球森林覆盖率，高估了草地，并低估了全球净初级生产。这些结果与早期发现的 CLM3 土壤过于干燥的结果一致。在离线模拟中，通过改变该变量对土壤湿度的依赖性和减少冠层拦截的降水来增加模拟蒸腾量，从而改善全球植物生物地理和全球净初级生产。当 CLM-DGVM 与社区大气模型版本 3 (CAM3) 耦合时，相同的修改并不能改善出现最严重植被偏差的美国东部和亚马逊流域的模拟植被。美国东部降水的干燥偏差非常严重，以至于模拟植被对水文循环的变化不敏感。在亚马逊流域，土壤湿度、植被、蒸散量和降水之间的强耦合产生了高度复杂的水文循环，其中植被加剧了降水的微小扰动。亚马逊流域的这些相互作用导致降水量急剧减少和森林崩溃。这些结果表明，对流的准确参数化给包括动态植被的气候模型带来了复杂且具有挑战性的科学问题。结果还强调了耦合任何两个仅进行非耦合测试的高度非线性系统时可能出现的困难。
The Community Land Model version 3 (CLM3) Dynamic Global Vegetation Model (CLM-DGVM) is used diagnostically to identify land and atmospheric model biases that lead to biases in the simulated vegetation. The CLM-DGVM driven with observed atmospheric data (offline simulation) underestimates global forest cover, overestimates grasslands, and underestimates global net primary production. These results are consistent with earlier findings that the soils in CLM3 are too dry. In the offline simulation an increase in simulated transpiration by changing this variable's soil moisture dependence and by decreasing canopy-intercepted precipitation results in better global plant biogeography and global net primary production. When CLM-DGVM is coupled to the Community Atmosphere Model version 3 (CAM3), the same modifications do not improve simulated vegetation in the eastern United States and Amazonia where the most serious vegetation biases appear. The dry bias in eastern U.S. precipitation is so severe that the simulated vegetation is insensitive to changes in the hydrologic cycle. In Amazonia, strong coupling among soil moisture, vegetation, evapotranspiration, and precipitation produces a highly complex hydrologic cycle in which small perturbations in precipitation are accentuated by vegetation. These interactions in Amazonia lead to a dramatic precipitation decrease and a collapse of the forest. These results suggest that the accurate parameterization of convection poses a complex and challenging scientific issue for climate models that include dynamic vegetation. The results also emphasize the difficulties that may arise when coupling any two highly nonlinear systems that have only been tested uncoupled.