Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems

Abstract

Land surface models (LSMs) are a vital tool for understanding, projecting, and predicting the dynamics of the land surface and its role within the Earth system, under global change. Driven by the need to address a set of key questions, LSMs have grown in complexity from simplified representations of land surface biophysics to encompass a broad set of interrelated processes spanning the disciplines of biophysics, biogeochemistry, hydrology, ecosystem ecology, community ecology, human management, and societal impacts. This vast scope and complexity, while warranted by the problems LSMs are designed to solve, has led to enormous challenges in understanding and attributing differences between LSM predictions. Meanwhile, the wide range of spatial scales that govern land surface heterogeneity, and the broad spectrum of timescales in land surface dynamics, create challenges in tractably representing processes in LSMs. We identify three "grand challenges" in the development and use of LSMs, based around these issues: managing process complexity, representing land surface heterogeneity, and understanding parametric dynamics across the broad set of problems asked of LSMs in a changing world. In this review, we discuss progress that has been made, as well as promising directions forward, for each of these challenges.Plain Language Summary Land surface models (LSMs) are the part of climate models that simulate processes happening at the Earth's surface. These include reflection of the sunlight, evaporation from ecosystems, and the amount of carbon from human emissions that the land takes up. LSMs also need to simulate how human management of the land surface changes the climate both directly (e.g., via the effect on evaporation) and in the long term (via changing the amount of carbon stored in wood and soil). Not surprisingly, trying to make a single mathematical representation of all of these different parts of the Earth system is difficult. Here we discuss themes that repeatedly affect all teams developing LSMs: how to manage the increasing number of complicated model components, how to represent the high degree of variability of the land surface, and how to predict how the properties of the surface (particularly those of plant communities) will change. These are large problems, with no obvious easy solutions. We hope to spark discussion and investment into their resolution, concomitant with the increasing importance of LSMs as our best tools for translating possible trajectories of climate change into impacts on humans, ecosystems, food and water supplies, and river systems.

Forest responses to simulated elevated CO2 under alternate hypotheses of size‐ and age‐dependent mortality

“…For this analysis we ran FATES in a novel ‘prescribed physiology mode’, a reduced complexity configuration that bypasses many of the physiological mechanisms of the model, following the modular complexity approach to LSM design described in Fisher and Koven (2020). In the full FATES model, plant productivity is the result of a cascade of processes including light interception, photosynthesis, stomatal conductance, surface energy balance and plant respiration.…”

Benchmarking and parameter sensitivity of physiological and vegetation dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) at Barro Colorado Island, Panama

“…The PPA (Purves et al, 2008) describes an approach of organizing trees (or, equivalently, cohorts) into discrete canopy strata by rank-ordering the trees from tallest to shortest and defining canopy trees as those whose cumulative crown area equals that of the ground (or, when combined with ED, patch area) that they occupy. Fisher et al (2010) added a modified form of the PPA, whereby the cohorts, rather than being strictly rank-ordered in their separation between canopy and understory, were probabilistically sorted into the canopy and understory based on a function of their height.…”

“…As described above, the original PPA (Purves et al, 2008) used a deterministic ranking of trees based on their heights and separated them in each time step based on whether their height was above or below the height, z * , equal to the tree whose cumulative crown area equaled the area of the ground that trees occupied. Fisher et al (2010) modified this to create a probabilistic PPA whereby the relative probability of trees in a cohort (or, equivalently, the fractional number density of trees of a given cohort) being assigned to the canopy was proportional to their size raised to a parameter called the competitive exclusion parameter c excl . In FATES, we generalized the height sorting so that it can use either the deterministicor probabilistic-sorting approach to the PPA and discuss both versions below.…”

Technical note: Low meteorological influence found in 2019 Amazonia fires

“…Between September 2019 and February 2020, fires across south-eastern Australian burnt around 18.6 × 10 6 ha, destroyed over 5900 buildings and killed at least 34 people (Boer et al, 2020;RFS, 2019;Sanderson and Fisher, 2020). Unusual fire events such as these are expected to increase in frequency in the future from both changes in climate and socio-economic pressures on the landscape (Fonseca et al, 2019;Jones et al, 2020). Given the concerns raised and the extent to which much of these fire events captured the attention of the public and press in recent months, in the aftermath, it is important to look at these events objectively.…”