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Shiliu Chen, Kaighin A. McColl, Alexis Berg, and Yuefei Huang

Abstract

A recent theory proposes that inland continental regions are in a state of surface flux equilibrium (SFE), in which tight coupling between the land and atmosphere allow estimation of the Bowen ratio at daily to monthly time scales solely from atmospheric measurements, without calibration, even when the land surface strongly constrains the surface energy budget. However, since the theory has only been evaluated at quasi-point spatial scales using eddy covariance measurements with limited global coverage, it is unclear if it is applicable to the larger spatial scales relevant to studies of global climate. In this study, SFE estimates of the Bowen ratio are combined with satellite observations of surface net radiation to obtain large-scale estimates of latent heat flux λE. When evaluated against multiyear mean annual λE obtained from catchment water balance estimates from 221 catchments across the United States, the resulting error statistics are comparable to those in the catchment water balance estimates themselves. The theory is then used to diagnostically estimate λE using historical simulations from 26 CMIP6 models. The resulting SFE estimates are typically at least as accurate as the CMIP6 model’s simulated λE, when compared with catchment water balance estimates. Globally, there is broad spatial and temporal agreement between CMIP6 model SFE estimates and the CMIP6 model’s simulated λE, although SFE likely overestimates λE in some arid regions. We conclude that SFE applies reasonably at large spatial scales relevant to climate studies, and is broadly reproduced in climate models.

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Sha Zhou, Junyi Liang, Xingjie Lu, Qianyu Li, Lifen Jiang, Yao Zhang, Christopher R. Schwalm, Joshua B. Fisher, Jerry Tjiputra, Stephen Sitch, Anders Ahlström, Deborah N. Huntzinger, Yuefei Huang, Guangqian Wang, and Yiqi Luo

Abstract

Terrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land–Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. In this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (>90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.

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