JULES-GL7: The Global Land Configuration of the Joint UK Land Environment Simulation version 7.0

Wiltshire, Andrew J.; Rojas, Maria Carolina Duran; Edwards, John M.; Gedney, Nicola; Harper, Anna; Hartley, Andrew; Hendry, Margaret A.; Robertson, Eddy; Smout-Day, Kerry

doi:10.5194/gmd-2019-152

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…State‐of‐the‐art LSMs (e.g., Decharme et al, 2019; D. M. Lawrence et al, 2019; Wiltshire et al, 2019; Yokohata et al, 2019) typically provide a set of prognostic variables related to land‐mediated feedbacks on global biogeochemical cycles. In particular, the terrestrial carbon cycle, by partially controlling what fraction of CO 2 that humans emit remains in the atmosphere, has a role in determining the transient climate response to emissions and the remaining carbon emissions budget compatible with a given climate goal (Matthews et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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

Fisher

Koven

2020

J Adv Model Earth Syst

302

198

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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.

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Section: Introductionmentioning

confidence: 99%

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

Fisher

Koven

2020

J Adv Model Earth Syst

302

198

View full text Add to dashboard Cite

show abstract

“…Blyth et al, 2020;Fisher and Koven, 2020;Overgaard et al, 2006;and references therein). These are characterized by various levels of complexity such as, e.g., LSMs described in Niu et al, (2011), Maneta and Silverman (2013), Decharme et al, (2019), Lawrence et al, (2019), Wiltshire et al, (2019) or Yokohata et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Relative importance of uncertain model parameters driving water fluxes in a Land Surface Model

Luttenauer,

Dell'Oca,

Guadagnini

et al. 2024

Preprint

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Abstract. We focus on the way temporal distributions of key components of the water cycle are influenced by typically uncertain parameters embedded in a Land Surface Model. We rest on a joint analysis of multiple global sensitivity metrics to provide a comprehensive assessment of the ranking of the relative importance of uncertain factors of various origins on the hydrological system response. The latter is rendered in terms of the temporal dynamics of transpiration, evaporation, and groundwater recharge. The NIHM (Normally Integrated Hydrological Model) modular Land Surface model is applied to simulate realistic field conditions (in terms of, e.g., climate, vegetation, and soil type) associated with two watersheds in the Vosges region (France) across a one-year period. These watersheds are characterized by similar climatic conditions while being associated with differing soil types and vegetation. Uncertain model parameters we consider comprise monthly values of albedo and leaf area index, vegetation-related parameters, as well as parameters related to the soil types associated with the litter layer and root zone. Four diverse sensitivity indices are used to quantify impacts of uncertain model parameters on the whole probability distribution or given statistical moments of the density function of model outputs. Our results document that the strength of the relative importance of model parameters depends on the statistical moment considered. Evaporation is directly influenced by the energy flow through the canopy and by the parameters associated with the top litter layer. As one could expect, transpiration appears as mainly influenced by the vegetation characteristics and by albedo that influences the incoming radiation. Groundwater recharge is influenced only by a very limited number of model parameters. It mainly depends on soil-related parameters and is unexpectedly not sensible to any of the vegetation parameters considered, except the root layer thickness and the intercept.

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JULES-GL7: The Global Land Configuration of the Joint UK Land Environment Simulation version 7.0

Cited by 2 publications

References 19 publications

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

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

Relative importance of uncertain model parameters driving water fluxes in a Land Surface Model

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