Implementation of an optimal stomatal conductance model in the Australian Community Climate Earth Systems Simulator (ACCESS1.3b)

Abstract

Abstract. We implement a new stomatal conductance model, based on the optimality approach, within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. Coupled land-atmosphere simulations are then performed using CABLE within the Australian Community Climate and Earth Systems Simulator (ACCESS) with prescribed sea surface temperatures. As in most land surface models, the default stomatal conductance scheme only accounts for differences in model parameters in relation to the photosynthetic pathway, but not in relation to plant functional types. The new scheme allows model parameters to vary by plant functional type, based on a global synthesis of observations of stomatal conductance under different climate regimes over a wide range of species. We show that the new scheme reduces the latent heat flux from the land surface over the boreal forests during the Northern Hemisphere summer by 0.5 to 1.0 mm day-1. This leads to warmer daily maximum and minimum temperatures by up to 1.0 °C and warmer extreme maximum temperatures by up to 1.5 °C. These changes generally improve the climate model's climatology and improve existing biases by 10–20 %. The change in the surface energy balance also affects net primary productivity and the terrestrial carbon balance. We conclude that the improvements in the global climate model which result from the new stomatal scheme, constrained by a global synthesis of experimental data, provide a valuable advance in the long-term development of the ACCESS modelling system.

Optimal stomatal behavior with competition for water and risk of hydraulic impairment

“…1, when applied to biophysical models of photosynthesis and transpiration, closely matches the functional forms of empirical stomatal models, which themselves have extraordinary empirical support (2,5,7,27,34). For this reason, the WUEH is widely used to guard against out-of-sample behavior in the novel climates created in ecosystem models (35)(36)(37)(38)(39). Finally, several notable efforts have been made to extend WUEH to situations including soil moisture stress (11,12,28,33,(40)(41)(42) and competition (43) and to develop a theory of the value of λ over the short time scales during which it should be approximately constant and how λ should change over weeks, months, or seasons (9,11,12,28,32).…”

“…In particular, knowledge of the large costs associated with low xylem potential, which was obtained after 1977, might have obviated the need for the assumption of a fixed water supply in CF77 (6). On the other hand, WUEH is widely applied where such costs are thought to be high, as in ecosystem models under climate change (35)(36)(37)(38)(39). We include all combinations of i-iii simply to explore the impact of each component on the predicted stomatal behavior and in recognition of the fact that Eq.…”

Comparing Australian heat waves in the CMIP5 models through cluster analysis

“…It is also notable that ACCESS1‐0 and ACCESS1.3 occupy quite different branches on the dendrogram. Since a major difference between these models is the updated land surface scheme in ACCESS1.3 [ Bi et al ., ], this may imply that heat waves are sensitive to the way land surface processes are represented in these models, in agreement with the findings of previous studies [e.g., Kala et al ., ; Kala et al ., ]. However, it has been shown that differences in the atmospheric configurations between these models, in particular the simulation of clouds, may influence the seasonality of ENSO [ Rashid and Hirst , ] and therefore possibly the heat wave climatology.…”

Woody plants optimise stomatal behaviour relative to hydraulic risk

“…2015a; Kala et al . ). Thus, performing all tests at both a daily and seasonal‐to‐yearly timescale is a relevant and crucial comparison.…”

“…Thus, WUE hypothesis has become the dominant hypothesis of stomatal regulation for 40 years (Ball et al 1987;Medlyn et al 2011Medlyn et al , 2013Lin et al 2015;Buckley 2017), and is widely thought to help avoid errors in predictions of climate models in future (i.e. out-of-training-sample) conditions (Bonan et al 2014;Kala et al 2015;Xu et al 2016).…”