Learning from Observations: The Case for a New Generation of Land Surface Models

A new generation of land surface models (LSMs) have been developed in the framework of the EU-funded CRESCENDO project aiming to improve understanding of the Earth system as part of the community CMIP6 effort.&#160; These new LSMs explicitly represent key processes in the carbon and nitrogen cycles, enabling more realistic vegetation-climate interactions to be simulated. For instance, vegetation phenology, the seasonality of vegetation, is explicitly represented in all these new LSMs. Intra- and inter-annual variations in vegetation phenology can substantially influence land-atmosphere exchanges of energy, moisture and carbon. Changes in phenological events also provide clear indicators of climate impacts on ecosystems.&#160; Results are presented on the evaluation of phenological variability from offline runs of this new generation of LSMs. In particular, the timing of growing season onset and offset at global scale, and the Leaf Area Index (LAI) peak timing are investigated using monthly mean outputs. Three satellite-derived LAI datasets are used as benchmark observations for this evaluation. In general, LSMs exhibit high skill in reproducing the observed phenology cycle in the North hemisphere mid- and high-latitudes, while lower skill is obtained in the South hemisphere. All LSMs simulate an offset in the timing of the active vegetative season characterized by later onset and LAI peak. Offset timings are slightly better captured by the LSMs. For these reasons, further development of the representation of phenology is required in LSMs, especially in the South hemisphere, where more complex vegetation and reduced in-situ observations are available.

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ON THE ROLE OF INTEGRATED GROUNDWATER-LAND SURFACE MODELS FOR GROUNDWATER SUSTAINABILITY ASSESSMENTS

10.1130/abs/2019am-338177 ◽

2019 ◽

Author(s):

Hoori Ajami ◽

◽

Matthew F Mccabe ◽

Jason P. Evans ◽

Simon Stisen

Keyword(s):

Land Surface ◽

Land Surface Models ◽

Groundwater Sustainability ◽

Surface Models ◽

Sustainability Assessments

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Eco‐evolutionary optimality as a means to improve vegetation and land‐surface models

New Phytologist ◽

10.1111/nph.17558 ◽

2021 ◽

Author(s):

Sandy P. Harrison ◽

Wolfgang Cramer ◽

Oskar Franklin ◽

Iain Colin Prentice ◽

Han Wang ◽

...

Keyword(s):

Land Surface ◽

Land Surface Models ◽

Surface Models ◽

Evolutionary Optimality

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The CarboEurope Regional Experiment Strategy

Bulletin of the American Meteorological Society ◽

10.1175/bams-87-10-1367 ◽

2006 ◽

Vol 87 (10) ◽

pp. 1367-1380 ◽

Cited By ~ 92

Author(s):

A. J. Dolman ◽

J. Noilhan ◽

P. Durand ◽

C. Sarrat ◽

A. Brut ◽

...

Keyword(s):

Land Surface ◽

Major Product ◽

State Variables ◽

Land Surface Models ◽

Quality Metric ◽

Key Characteristics ◽

Surface Models ◽

Global Soil ◽

Standard Deviations ◽

Model Agreement

The Second Global Soil Wetness Project (GSWP-2) is an initiative to compare and evaluate 10-year simulations by a broad range of land surface models under controlled conditions. A major product of GSWP-2 is the first global gridded multimodel analysis of land surface state variables and fluxes for use by meteorologists, hydrologists, engineers, biogeochemists, agronomists, botanists, ecologists, geographers, climatologists, and educators. Simulations by 13 land models from five nations have gone into production of the analysis. The models are driven by forcing data derived from a combination of gridded atmospheric reanalyses and observations. The resulting analysis consists of multimodel means and standard deviations on the monthly time scale, including profiles of soil moisture and temperature at six levels, as well as daily and climatological (mean annual cycle) fields for over 50 land surface variables. The monthly standard deviations provide a measure of model agreement that may be used as a quality metric. An overview of key characteristics of the analysis is presented here, along with information on obtaining the data.

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On the utility of land surface models for agricultural drought monitoring

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3451-2012 ◽

2012 ◽

Vol 16 (9) ◽

pp. 3451-3460 ◽

Cited By ~ 42

Author(s):

W. T. Crow ◽

S. V. Kumar ◽

J. D. Bolten

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Cross Correlation ◽

Root Zone ◽

Vegetation Indices ◽

Agricultural Drought ◽

Drought Monitoring ◽

Global Evaluation ◽

Land Surface Models ◽

Surface Models

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

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Estimating the human contribution to groundwater depletion in the Middle East, from GRACE data, land surface models, and well observations

Water Resources Research ◽

10.1002/2013wr014633 ◽

2014 ◽

Vol 50 (3) ◽

pp. 2679-2692 ◽

Cited By ~ 110

Author(s):

Gholamreza Joodaki ◽

John Wahr ◽

Sean Swenson

Keyword(s):

Middle East ◽

Land Surface ◽

Groundwater Depletion ◽

Land Surface Models ◽

Grace Data ◽

Surface Models

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Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0053.1 ◽

2017 ◽

Vol 18 (3) ◽

pp. 897-915 ◽

Cited By ~ 20

Author(s):

Jennifer L. Jefferson ◽

Reed M. Maxwell ◽

Paul G. Constantine

Keyword(s):

Land Surface ◽

Stomatal Resistance ◽

Hydrologic Model ◽

Surface Model ◽

Land Surface Models ◽

Common Land Model ◽

Surface Models ◽

Input Parameters ◽

Rate Of Photosynthesis ◽

Quantities Of Interest

Abstract Land surface models, like the Common Land Model component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California, Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO2 Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.

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