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CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) – Part 2: Global benchmarking

机译:Classic V1.0:加拿大土地面积方案(类)和加拿大地面生态系统模型(CTEM)的开源社区后继人员 - 第2部分:全球基准测试

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摘要

The Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) is an open-source community model designed to address research questions that explore the role of the land surface in the global climate system. Here, we evaluate how well CLASSIC reproduces the energy, water, and carbon cycle when forced with quasi-observed meteorological data. Model skill scores summarize how well model output agrees with observation-based reference data across multiple statistical metrics. A lack of agreement may be due to deficiencies in the model, its forcing data, and/or reference data. To address uncertainties in the forcing, we evaluate an ensemble of CLASSIC runs that is based on three meteorological data sets. To account for observational uncertainty, we compute benchmark skill scores that quantify the level of agreement among independent reference data sets. The benchmark scores demonstrate what score values a model may realistically achieve given the uncertainties in the observations. Our results show that uncertainties associated with the forcing and observations are considerably large. For instance, for 10 out of 19 variables assessed in this study, the sign of the bias changes depending on what forcing and reference data are used. Benchmark scores are much lower than expected, implying large observational uncertainties. Model and benchmark score values are mostly similar, indicating that CLASSIC performs well when considering observational uncertainty. Future model development should address (i) a positive albedo bias and resulting shortwave radiation bias in parts of the Northern Hemisphere (NH) extratropics and Tibetan Plateau, (ii) an out-of-phase seasonal gross primary productivity cycle in the humid tropics of South America and Africa, (iii) a lacking spatial correlation of annual mean net ecosystem exchange with site-level measurements, (iv) an underestimation of fractional area burned and corresponding emissions in the boreal forests, (v) a negative soil organic carbon bias in high latitudes, and (vi) a time lag in seasonal leaf area index maxima in parts of the NH extratropics. Our results will serve as a baseline for guiding and monitoring future CLASSIC development.
机译:包括生物地球化学循环(经典)在内的加拿大土地面积是一个开源社区模型,旨在解决探索全球气候系统中的土地面的作用的研究问题。在这里,我们评估经典的再现能量,水和碳循环在迫使前观察到的气象数据时。模型技能分数总结了模型输出如何在多种统计指标上与基于观测的参考数据进行同意。缺乏协议可能是由于模型中的缺陷,它强制数据和/或参考数据。为了解决强制性的不确定性,我们评估基于三个气象数据集的经典运行的集合。要考虑观察性不确定性,我们计算基准技能分数,这些技能分数量化独立参考数据集之间的协议水平。基准分数展示了模型可以在考虑到观察中的不确定性的情况下实现的分数值。我们的研究结果表明,与矫正和观察相关的不确定性相当大。例如,对于在本研究中评估的19个变量中的10个中,偏差的符号根据使用的强制和参考数据而变化。基准得分远远低于预期,暗示了大的观测性不确定性。模型和基准分数值大多是相似的,表明在考虑观察性不确定性时经典表现良好。未来的模型开发应该解决(i)北半球(NH)级别(NH)级别(NH)级别和藏高高原(II)潮湿的热带季节性总初级生产力周期的阳性反玻璃偏置和产生的短波辐射偏差南美洲和非洲,(iii)缺乏与现场级别测量的年平均净生态系统交易所的空间相关性,(iv)低估了北森林(V)的北欧森林中烧毁和相应排放的低估,(v)负碳偏差在高纬度地区,(vi)在NH quortropics部分中的季节叶面积指数最大值的时间滞后。我们的结果将作为指导和监测未来经典发展的基准。

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