Assessing the Spatiotemporal Variability of Leaf Functional Traits and Their Drivers Across Multiple Amazon Evergreen Forest Sites: A Stochastic Parameterization Approach With Land‐Surface Modeling
Assessing the Spatiotemporal Variability of Leaf Functional Traits and Their Drivers Across Multiple Amazon Evergreen Forest Sites: A Stochastic Parameterization Approach With Land‐Surface Modeling
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DOI：
10.1029/2020jg006228
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发表时间：
2021-05
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通讯作者：
Shaoqing Liu;G. Ng
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作者：
Shaoqing Liu;G. Ng
大多数地球系统模型未能捕获亚马逊地区辐射有限的热带万年青森林（TEF）中碳通量的季节性。Kim等人（2012，https：//doi.org/10.1111/j.1365-2486.2011.02629.x）首次在统计学上将光控物候学模块纳入生态系统模型，以改善一个TEF站点的碳通量模拟。然而，目前尚不清楚他们的方法如何扩展到具有不同气候条件的其他TEF站点。在这里，我们评估了植物功能性状的时间变异性在三个不同的TEF网站使用数据条件随机参数化方法。我们发现，以前研究的季节性光合有效辐射（PAR）和特性Vcmax 25和叶片寿命之间的联系发生在不同地点。我们进一步确定，季节性PAR可以类似地驱动气孔导度斜率参数的变化。不同地点之间的时间特性估计发现的差异表明，动态特性参数不能均匀地应用于空间，但它可能是可以推断出它们的气候因素的基础上。受最近观察到的生理能力随着叶子成熟而发展的影响，我们建立了新的回归模型来预测性状，不仅包括PAR，还包括自回归滞后项，以捕获PAR驱动的物候变化背后观察到的生理延迟。我们的随机参数化，我们预测这三个网站是碳中性或碳汇RCP 8.5未来气候情景下。相比之下，使用标准静态性状参数的预测显示，亚马逊TEF地区的大部分地区成为碳源。我们进一步估计，可变特征可能允许亚马逊地区辐射限制的TEF地区至少有三分之一成为未来的净碳汇。
Most earth system models fail to capture the seasonality of carbon fluxes in radiation‐limited tropical evergreen forests (TEF) in the Amazon. Kim et al. (2012, https://doi.org/10.1111/j.1365-2486.2011.02629.x) first statistically incorporated a light‐controlled phenology module into an ecosystem model to improve carbon flux simulations at one TEF site. However, it is not clear how their approach can be extended to other TEF sites with different climatic conditions. Here we evaluated temporal variability in plant functional traits at three different TEF sites using a data‐conditioned stochastic parameterization method. We showed that previously studied links—between seasonal photosynthetically active radiation (PAR) and the traits Vcmax25 and leaf longevity—occur across sites. We further determined that seasonal PAR could similarly drive variations in the stomatal conductance slope parameter. Differences found in temporal trait estimates among sites indicate that dynamic trait parameters cannot be applied uniformly over space, but it may be possible to extrapolate them based on climatic factors. Motivated by recent observations that physiological capacity develops as leaves mature, we built new regression models for predicting traits that not only include PAR but also an autoregressive lag term to capture observed physiological delays behind PAR‐driven phenology shifts. With our stochastic parameterization, we predicted the three sites to be carbon neutral or carbon sinks under the RCP 8.5 future climate scenario. In contrast, projections using standard static trait parameters show most of the Amazonian TEF region becoming a carbon source. We further approximated that variable traits may allow at least a third of the radiation‐limited TEF region in the Amazon to serve as a future net carbon sink.