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The statistics of the annual Rh (g C m−2) based on the partial least squares (PLS) regression

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posted on 2013-09-23, 00:00 authored by Pu Shao, Xubin Zeng, David J P Moore, Xiaodong Zeng

Table 2.  The statistics of the annual Rh (g C m−2) based on the partial least squares (PLS) regression. (Note: covariates for the prediction of ln(Rh) include the annual mean surface temperature (T, ° C), and the logarithms of annual precipitation (Pr, mm), net primary production (NPP, g C m−2), and soil organic carbon (SOC, g Cm−2). The r2 and RMSE values are the coefficient of determination and root mean square error between Rh and fitted values, respectively.)


Soil microbial respiration (Rh) is a large but uncertain component of the terrestrial carbon cycle. Carbon–climate feedbacks associated with changes to Rh are likely, but Rh parameterization in Earth System Models (ESMs) has not been rigorously evaluated largely due to a lack of appropriate measurements. Here we assess, for the first time, Rh estimates from eight ESMs and their environmental drivers across several biomes against a comprehensive soil respiration database (SRDB-V2). Climatic, vegetation, and edaphic factors exert strong controls on annual Rh in ESMs, but these simple controls are not as apparent in the observations. This raises questions regarding the robustness of ESM projections of Rh in response to future climate change. Since there are many more soil respiration (Rs) observations than Rh data, two 'reality checks' for ESMs are also created using the Rs data. Guidance is also provided on the Rh improvement in ESMs.