Skill metrics for confronting global upper ocean ecosystem-biogeochemistry models against field and remote sensing data
Skill metrics for confronting global upper ocean ecosystem-biogeochemistry models against field and remote sensing data
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DOI：
10.1016/j.jmarsys.2008.05.015
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发表时间：
2009-02-20
影响因子：
2.8
通讯作者：
Takahashi, Taro
中科院分区：
文献类型：
--
作者：
Doney, Scott C.;Lima, Ivan;Takahashi, Taro
我们提出了一个通用的框架，评估全球上层海洋生态系统-地球化学模型对现场数据和卫星观测的技能。我们说明的方法，利用社区气候系统模型（CCSM-3）海洋碳模型进行了几十年（1979-2004年）的后报实验。CCSM-3海洋碳模式将一个多营养物、多浮游植物功能群生态系统模块与嵌入全球三维海洋环流模式的碳、氧、氮、磷、硅和铁地球化学模块相结合。该模式的强迫与大气再分析和卫星数据产品和随时间变化的大气尘埃沉降的物理气候强迫。基于数据的技能指标被用来评估模拟的时间平均空间格局，季节性周期的振幅和相位，以及年际变化。评价数据包括：海洋表层温度和混合层深度;卫星获取的表层海洋叶绿素、初级生产力、浮游植物生长率和碳生物量;表层营养物、pCO（2）、海气CO2和O2通量的大尺度气候学;以及全球海洋通量联合研究的时间序列数据。在数据充足的情况下，我们使用模型数据残差、时空相关性、均方根误差和泰勒图构建定量技能指标。(C)2008 Elsevier B. V.保留所有权利。
We present a generalized framework for assessing the skill of global upper ocean ecosystem-biogeochemical models against in-situ field data and satellite observations. We illustrate the approach utilizing a multi-decade (1979-2004) hindcast experiment conducted with the Community Climate System Model (CCSM-3) ocean carbon model. The CCSM-3 ocean carbon model incorporates a multi-nutrient, multi-phytoplankton functional group ecosystem module coupled with a carbon, oxygen, nitrogen, phosphorus, silicon, and iron biogeochemistry module embedded in a global, three-dimensional ocean general circulation model. The model is forced with physical climate forcing from atmospheric reanalysis and satellite data products and time-varying atmospheric dust deposition. Data-based skill metrics are used to evaluate the simulated time-mean spatial patterns, seasonal cycle amplitude and phase, and subannual to interannual variability. Evaluation data include: sea surface temperature and mixed layer depth; satellite-derived surface ocean chlorophyll, primary productivity, phytoplankton growth rate and carbon biomass; large-scale climatologies of surface nutrients, pCO(2), and air-sea CO2 and 02 flux; and time-series data from the joint Global Ocean Flux Study (JGOFS). Where the data is sufficient, we construct quantitative skill metrics using: model-data residuals, timespace correlation, root mean square error, and Taylor diagrams. (C) 2008 Elsevier B.V. All rights reserved.