Abstract
Abstract. Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the “soil14_psi” experiments), when the critical threshold value for inducing soil moisture stress was reduced (“soil14_p0”), and when plants were able to access soil moisture in deeper soil layers (“soil14_dr*2”). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes.
Highlights
This study aimed to evaluate the simulation of gross primary productivity (GPP) and latent energy flux (LE) for a range of biomes and climates, to diagnose sites and seasons when soil moisture stress affects the results, and to evaluate different methods for representing soil moisture stress in Joint UK Land Environment Simulator (JULES) as a first step in improving the simulated plant responses to low soil moisture content (SMC) in global applications of JULES
JULES matched the pattern of observed seasonal cycle of GPP well for sites in non-agricultural biomes in temperate and cold climates (Fig. 4, Table 3)
In terms of model biases, the normalized absolute error (NAE) was lowest for GPP at tropical evergreen forest and temperate woody savanna sites, while NAE was highest in tropical grassland, tropical savanna, and cold grassland sites (Fig. 5)
Summary
Drought has a range of impacts on terrestrial ecosystems (Allen et al, 2010; Choat et al, 2012), plays a role in feedbacks on the weather and climate systems across scales (Seneviratne et al, 2013; Lemordant et al, 2016; Miralles et al, 2019; Lian et al, 2020), and affects the global carbon cycle (Green et al, 2017; Humphrey et al, 2018; Peters et al, 2018) These impacts and feedbacks have the potential to affect society, either directly through moisture availability effects on crops or indirectly by adjusting near-surface temperatures or forcing large-scale variations to the climate system. The models used to represent biogeophysical and biogeochemical processes in Earth system models (ESMs) are often unable to properly capture observed responses to soil moisture stress (Beer et al, 2010; Powell et al, 2013; Medlyn et al, 2016; Restrepo-Coupe et al, 2017; De Kauwe et al, 2017; Peters et al, 2018; Paschalis et al, 2020)
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