Improving the Parameterization of Arctic Stomatal Traits in a Land Surface Model Using Empirical Field Observations and Optimality Theory
Authors
Kenneth J. Davidson1,2* (kdavidson@bnl.gov), Alistair Rogers1, Kim S. Ely1, Dedi Yang1,2, Fengming Yuan3, Benjamin N. Sulman3, Daniel M Ricciuto3, Jennifer A. Holm4, Shawn P. Serbin1,2, Colleen Iversen3
Institutions
1Department of Environmental and Climate Sciences, Brookhaven National Laboratory, Upton, NY; 2Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY; 3Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN; 4Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA
URLs
Abstract
The Arctic ecosystem is experiencing unparalleled warming as the result of global climate change, resulting in significant impacts to vegetation and soils and leading to an overall “greening” of the Arctic through increased shrub cover and accelerated permafrost thaw. Earth system models are the primary tool to forecast the complex coupling between the surface and atmosphere, where the underlying land surface models (LSMs) provide the means to simulate the contributions of the terrestrial ecosystems to nutrient, carbon (C), water (H20), and energy cycles. Of the vegetation processes simulated within LSMs, stomatal conductance (gs), the exchange of carbon dioxide (CO2) and H2O through small pores on vegetation called stomata, is one of the most critical processes to represent correctly as it fundamentally governs the exchange of C and H2O between the atmosphere and vegetative layer. Limitations imposed by stomata help set the upper limit on both H2O loss via transpiration (E), and photosynthesis (A) through stomatal limitation on C diffusion into the leaf. As such, gs is a strong determinant of gross primary productivity (GPP), with uncertainty in the model parameters related to stomatal conductance (the stomatal slope and stomatal intercept) contributing to some of the largest model uncertainties in current projections of C and H2O cycling in LSMs. In this study, researchers collected full-range stomatal response curves on eight arctic plant species, across a range of environmental conditions, and used these data, along with an accompanying foliar biochemical analysis, to estimate five key foliar model parameters (stomatal slope, stomatal intercept, SLA0, FLNR, and CNL) for the two default Arctic plant functional types (PFTs) used in the E3SM land model (ELM). By using these updated parameter values within ELM, researchers were able to assess the impact of revised parameters on modeled C cycling and evapotranspiration (ET). Further, using the newfound understanding of controls on Arctic plant stomatal conductance, along with widely held assumptions about stomatal optimality, researchers tested different assumptions for the way in which the soil moisture stress on gs and ET are represented in ELM, such as having soil moisture stress increase leaf-level water use efficiency. The team also evaluated the patterns in GPP, ET and soil moisture against satellite retrievals under the different simulation scenarios. The findings highlight the importance of accurately representing stomatal response to biotic and abiotic factors when modeling Arctic ecosystems and the importance of parameterizations that are specific to Arctic PFTs.