Regional climate model simulation of U.S. soil temperature and moisture during 1982-2002
Regional climate model simulation of U.S. soil temperature and moisture during 1982-2002
复制标题
1982-2002年美国土壤温度和湿度的区域气候模型模拟
DOI：
10.1029/2005jd006472
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发表时间：
2005
影响因子：
--
通讯作者：
Xin‐Zhong Liang
中科院分区：
文献类型：
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作者：
Jinhong Zhu;Xin‐Zhong Liang
b[1]通过比较NCEP-DOE AMIP II再分析（R-2）驱动的1982-2002年持续整合与观测资料、R-2导数和北美陆地数据同化系统（NLDAS）产品，评价了基于第五代PSU-NCAR中尺度模式（MM5）的区域气候模式（CMM5）模拟美国土壤温度和土壤湿度年循环和年际变率的能力。对于年周期，CMM5比驱动R-2和NLDAS输出产生更真实的区域细节和总体较小的偏差。CMM5还忠实地模拟了美国中部土壤温度和伊利诺伊州和爱荷华州土壤湿度的年际变化，这些地区有观测数据。现有的CMM5与土壤温度（湿度）观测值的差异不能完全用地表气温（降水）的模式偏差来解释。短草下测量值与其他土地覆盖类型下模型表示值之间的不一致可能起重要作用。特别是，这种测量高估了夏季和秋季的土壤温度，同时在模型中相对于农田的土壤湿度年循环中产生了1个月的阶段领先。结果表明，需要对模型评价和土壤温度和土壤湿度的偏差认识进行更全面的研究。
[1] The fifth-generation PSU-NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) capability in simulating the U.S. soil temperature and soil moisture annual cycle and interannual variability is evaluated by comparing the 1982–2002 continuous integration driven by the NCEP-DOE AMIP II reanalysis (R-2) with observations, the R-2 derivatives and North American Land Data Assimilation System (NLDAS) products. For the annual cycle, the CMM5 produces more realistic regional details and overall smaller biases than the driving R-2 and NLDAS outputs. The CMM5 also faithfully simulates interannual variations of soil temperature over the central United States and soil moisture in Illinois and Iowa, where observational data are available. The existing CMM5 differences from observations in soil temperature (moisture) cannot be fully explained by model biases in surface air temperature (precipitation). Inconsistencies between measurements taken under short grass versus model representations beneath other land cover types may play an important role. In particular, such measurements overestimate soil temperature in summer and fall while generating a 1-month phase lead in the soil moisture annual cycle with respect to croplands in the model. The result emphasizes the need for more comprehensive study on model evaluation and bias understanding of soil temperature and soil moisture.