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

2009 ◽  
Vol 22 (16) ◽  
pp. 4427-4433 ◽  
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.

Ecosystem sensitivity to land-surface models and leaf area index

1996 ◽  
Vol 13 (1-4) ◽  
pp. 89-98 ◽  
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

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

2013 ◽  
Vol 5 (8) ◽  
pp. 3637-3661 ◽  
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

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

2014 ◽  
Vol 15 (1) ◽  
pp. 489-503 ◽  
Author(s):  
Jatin Kala ◽  
Mark Decker ◽  
Jean-François Exbrayat ◽  
Andy J. Pitman ◽  
Claire Carouge ◽  
...  
Keyword(s):  
Primary Production ◽  
Leaf Area Index ◽  
Interannual Variability ◽  
Leaf Area ◽  
Land Surface ◽  
Carbon Fluxes ◽  
Land Surface Models ◽  
Area Index ◽  
Surface Models ◽  
Modis Lai

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.

Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling

2011 ◽  
Vol 115 (5) ◽  
pp. 1171-1187 ◽  
Author(s):  
Hua Yuan ◽  
Yongjiu Dai ◽  
Zhiqiang Xiao ◽  
Duoying Ji ◽  
Wei Shangguan
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Climate Modelling ◽  
Area Index

scholarly journals Ability of the land surface model ISBA-A-gs to simulate leaf area index at the global scale: Comparison with satellites products

2006 ◽  
Vol 111 (D18) ◽  
Author(s):  
Anne-Laure Gibelin ◽  
Jean-Christophe Calvet ◽  
Jean-Louis Roujean ◽  
Lionel Jarlan ◽  
Sietse O. Los
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Area Index

Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance

2016 ◽  
Vol 54 (9) ◽  
pp. 5301-5318 ◽  
Author(s):  
Zhiqiang Xiao ◽  
Shunlin Liang ◽  
Jindi Wang ◽  
Yang Xiang ◽  
Xiang Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Surface Reflectance ◽  
Area Index ◽  
Long Time Series ◽  
Global Land ◽  
Long Time

scholarly journals ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models

2013 ◽  
Vol 6 (2) ◽  
pp. 563-582 ◽  
Author(s):  
S. Faroux ◽  
A. T. Kaptué Tchuenté ◽  
J.-L. Roujean ◽  
V. Masson ◽  
E. Martin ◽  
...  
Keyword(s):  
Land Cover ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Climate Models ◽  
Area Index ◽  
Surface Parameters ◽  
Climatic Environment

Abstract. The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo). Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover) and GLC2000 (Global Land Cover) by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography). The leaf area index (LAI) from MODIS and normalized difference vegetation index (NDVI) from SPOT/Vegetation (a global monitoring system of vegetation) yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas) and, inside the nature tile, fractions of 12 plant functional types (PFTs) representing generic vegetation types – principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land – as incorporated by the SVAT model ISBA (Interactions Surface Biosphere Atmosphere) developed at Météo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of versatile land occupation nomenclatures.

scholarly journals An improved parameterization of leaf area index (LAI) seasonality in the Canadian Land Surface Scheme (CLASS) and Canadian Terrestrial Ecosystem Model (CTEM) modelling framework

2018 ◽  
Author(s):  
Ali Asaadi ◽  
Vivek K. Arora ◽  
Joe R. Melton ◽  
Paul Bartlett
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Terrestrial Ecosystem ◽  
Ecosystem Model ◽  
Area Index ◽  
Terrestrial Ecosystem Model ◽  
Modelling Framework ◽  
Canadian Land Surface Scheme ◽  
Surface Scheme

Abstract. Leaf area index (LAI) and its seasonal dynamics are key determinants of vegetation productivity in nature and as represented in terrestrial biosphere models seeking to understand land-surface atmosphere flux dynamics and its response to climate change. Non-structural carbohydrates (NSCs) and their seasonal variability are known to play a crucial role in seasonal variation of leaf phenology and growth and functioning of plants. The carbon stored in NSC pools provides a buffer during times when supply and demand of carbon are asynchronous. An example of this role is illustrated when NSCs from previous years are used to initiate leaf onset at the arrival of favourable weather conditions. In this study, we incorporate NSC pools and associated parameterizations of new processes in the modelling framework of the Canadian Land Surface Scheme-Canadian Terrestrial Ecosystem Model (CLASS-CTEM) with an aim to improve the seasonality of simulated LAI. The performance of these new parameterizations is evaluated by comparing simulated LAI and atmosphere-land CO2 fluxes, to their observation-based estimates, at three sites characterized by broadleaf cold deciduous trees selected from the Fluxnet database. Results show an improvement in leaf onset and offset times with about 2 weeks shift towards earlier times during the year in better agreement with observations. These improvements in simulated LAI help to improve the simulated seasonal cycle of gross primary productivity (GPP) and as a result simulated net ecosystem productivity (NEP) as well.

scholarly journals The Influence of Assimilating Leaf Area Index in a Land Surface Model on Global Water Fluxes and Storages

2019 ◽  
Author(s):  
Xinxuan Zhang ◽  
Viviana Maggioni ◽  
Azbina Rahman ◽  
Paul Houser ◽  
Yuan Xue ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Global Water

Abstract. Vegetation plays a fundamental role not only in the energy and carbon cycle, but also the global water balance by controlling surface evapotranspiration. Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water and carbon cycles. This study aims to assess to what extent a land surface model can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI through an Ensemble Kalman Filter (EnKF) to estimate LAI, evapotranspiration (ET), interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework effectively reduces errors in LAI simulations. LAI assimilation also improves the model estimates of all the water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet condition). However, it tends to worsen some of the model estimated water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the land surface model is conservative and the LAI assimilation introduces more vegetation, which requires more water than what available within the soil. Future work should investigate a multi-variate data assimilation system that concurrently merges both LAI and soil moisture (or TWS) observations.

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