Influence of Leaf Area Index Prescriptions on Simulations of Heat, Moisture, and Carbon Fluxes

Abstract Leaf area index (LAI), the total one-sided surface area of leaf per ground surface area, is a key component of land surface models. The authors investigate the influence of differing, plausible LAI prescriptions on heat, moisture, and carbon fluxes simulated by the Community Atmosphere Biosphere Land Exchange version 1.4b (CABLEv1.4b) model over the Australian continent. A 15-member ensemble monthly LAI dataset is generated using the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and gridded observations of temperature and precipitation. Offline simulations lasting 29 years (1980–2008) are carried out at 25-km resolution with the composite monthly means from the MODIS LAI product (control simulation) and compared with simulations using each of the 15-member ensemble monthly varying LAI datasets generated. The imposed changes in LAI did not strongly influence the sensible and latent fluxes, but the carbon fluxes were more strongly affected. Croplands showed the largest sensitivity in gross primary production with differences ranging from −90% to 60%. Plant function types (PFTs) with high absolute LAI and low interannual variability, such as evergreen broadleaf trees, showed the least response to the different LAI prescriptions, while those with lower absolute LAI and higher interannual variability, such as croplands, were more sensitive. The authors show that reliance on a single LAI prescription may not accurately reflect the uncertainty in the simulation of terrestrial carbon fluxes, especially for PFTs with high interannual variability. The study highlights that accurate representation of LAI in land surface models is key to the simulation of the terrestrial carbon cycle. Hence, this will become critical in quantifying the uncertainty in future changes in primary production.

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Ecosystem sensitivity to land-surface models and leaf area index

Global and Planetary Change ◽

10.1016/0921-8181(95)00040-2 ◽

1996 ◽

Vol 13 (1-4) ◽

pp. 89-98 ◽

Cited By ~ 17

Author(s):

W.J. Parton ◽

A. Haxeltine ◽

P. Thornton ◽

R. Anne ◽

Melannie Hartman

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Land Surface ◽

Land Surface Models ◽

Area Index ◽

Ecosystem Sensitivity ◽

Surface Models

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Evaluation of Land Surface Models in Reproducing Satellite Derived Leaf Area Index over the High-Latitude Northern Hemisphere. Part II: Earth System Models

Remote Sensing ◽

10.3390/rs5083637 ◽

2013 ◽

Vol 5 (8) ◽

pp. 3637-3661 ◽

Cited By ~ 50

Author(s):

Alessandro Anav ◽

Guillermo Murray-Tortarolo ◽

Pierre Friedlingstein ◽

Stephen Sitch ◽

Shilong Piao ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Northern Hemisphere ◽

High Latitude ◽

Land Surface ◽

Earth System ◽

Land Surface Models ◽

Area Index ◽

Surface Models ◽

Earth System Models

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Spatial variability of aboveground net primary production for a forested landscape in northern Wisconsin

Canadian Journal of Forest Research ◽

10.1139/x03-124 ◽

2003 ◽

Vol 33 (10) ◽

pp. 2007-2018 ◽

Cited By ~ 25

Author(s):

S N Burrows ◽

S T Gower ◽

J M Norman ◽

G Diak ◽

D S Mackay ◽

...

Keyword(s):

Primary Production ◽

Leaf Area Index ◽

Leaf Area ◽

Spatial Patterns ◽

Vegetation Cover ◽

Land Surface ◽

Net Primary Production ◽

Mixed Forest ◽

Forested Wetlands ◽

Area Index

Quantifying forest net primary production (NPP) is critical to understanding the global carbon cycle because forests are responsible for a large portion of the total terrestrial NPP. The objectives of this study were to measure above ground NPP (NPPA) for a land surface in northern Wisconsin, examine the spatial patterns of NPPA and its components, and correlate NPPA with vegetation cover types and leaf area index. Mean NPPA for aspen, hardwoods, mixed forest, upland conifers, nonforested wetlands, and forested wetlands was 7.8, 7.2, 5.7, 4.9, 5.0, and 4.5 t dry mass·ha1·year1, respectively. There were significant (p = 0.01) spatial patterns in wood, foliage, and understory NPP components and NPPA (p = 0.03) when the vegetation cover type was included in the model. The spatial range estimates for the three NPP components and NPPA differed significantly from each other, suggesting that different factors are influencing the components of NPP. NPPA was significantly correlated with leaf area index (p = 0.01) for the major vegetation cover types. The mean NPPA for the 3 km × 2 km site was 5.8 t dry mass·ha1·year1.

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Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0237.1 ◽

2019 ◽

Vol 20 (7) ◽

pp. 1359-1377 ◽

Cited By ~ 14

Author(s):

Sujay V. Kumar ◽

David M. Mocko ◽

Shugong Wang ◽

Christa D. Peters-Lidard ◽

Jordan Borak

Keyword(s):

United States ◽

Leaf Area Index ◽

Leaf Area ◽

Land Surface ◽

Land Surface Model ◽

Carbon Fluxes ◽

Surface Model ◽

Vegetation Changes ◽

Area Index ◽

Terrestrial Water

Abstract Accurate representation of vegetation states is required for the modeling of terrestrial water–energy–carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing–based leaf area index (LAI) retrievals over the continental United States in the Noah-MP land surface model, during a time period of 2000–17. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms, such as soil moisture, evapotranspiration, snow depth, terrestrial water storage, and streamflow, when compared with a large suite of reference datasets. In addition, the assimilation of LAI is also found to improve the carbon fluxes of gross primary production (GPP) and net ecosystem exchange (NEE). Most prominent improvements in the water and carbon variables are observed over the agricultural areas of the United States, where assimilation improves the representation of vegetation seasonality impacted by cropping schedules. The systematic, added improvements from assimilation in a configuration that employs high-quality boundary conditions highlight the significant utility of LAI data assimilation in capturing the impacts of vegetation changes.

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On the Proper Use of Satellite-Derived Leaf Area Index in Climate Modeling

Journal of Climate ◽

10.1175/2009jcli2868.1 ◽

2009 ◽

Vol 22 (16) ◽

pp. 4427-4433 ◽

Cited By ~ 4

Author(s):

Jianjun Ge

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Land Surface ◽

Climate Modeling ◽

Heat Fluxes ◽

Sensible Heat ◽

Area Index ◽

Simulated Precipitation ◽

Surface Models ◽

Improper Use

Abstract Satellite-observed leaf area index (LAI) is increasingly being used in climate modeling. In common land surface models, LAI is specified for the vegetated part only. In contrast, satellite LAI is defined for the total area including both vegetated and nonvegetated fractions. Some recent modeling studies and model developments have not noticed this difference, which resulted in improper use of satellite LAI. This paper clarified this issue. A sensitivity test was carried out using a regional model to investigate the impacts of LAI definitions on simulated climates. This study showed that use of satellite LAI without considering the inconsistency in definition caused much smaller LAI values in the model. As a result, partitioning of surface energy into latent and sensible heat fluxes, as well as the model-simulated precipitation, was affected substantially. Overall, improper use of satellite LAI increased the model biases in simulated precipitation.

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