Abstract
Abstract. Vegetation plays a fundamental role not only in the energy and carbon cycles but also in the global water balance by controlling surface evapotranspiration (ET). Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water, energy, and carbon cycles. This study aims to assess the extent to which a land surface model (LSM) 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 into an LSM through an ensemble Kalman filter (EnKF) to estimate LAI, ET, canopy-interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework not only effectively reduces errors in LAI model simulations but also improves all the modeled 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 conditions). However, it tends to worsen some of the modeled 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 LSM is conservative, and the LAI assimilation introduces more vegetation, which requires more water than what is available within the soil.
Highlights
Terrestrial vegetation plays a vital role in the global water cycle, as it controls the surface evapotranspiration (ET) and the state of the carbon cycle
This study evaluates the efficiency of assimilating vegetation information (i.e., leaf area index (LAI) synthetic observations) within a land surface model (Noah-MP 3.6) when the precipitation forcing data are strongly biased
Two observing system simulation experiments (OSSEs) that use an ensemble Kalman filter (EnKF) algorithm for LAI assimilation are performed at a global scale for the period from June 2011 to May 2013
Summary
Terrestrial vegetation plays a vital role in the global water cycle, as it controls the surface evapotranspiration (ET) and the state of the carbon cycle. In early generation land surface models (LSMs), the development stage of vegetation was prescribed by regularly updating vegetation variables, based on fixed lookup tables to simplify the model computation (Foley et al, 1996). This approach uses constant vegetation indices, e.g., the leaf area index (LAI), while in reality the growth of vegetation continuously changes in response to weather and climate conditions. New generation LSMs are coupled with dynamic vegetation modules that comprehensively simulate several biogeochemical processes (Woodward and Lomas, 2004; Gibelin et al, 2006; Fisher et al, 2018) and that are able to capture more detailed variations in plant productivity than traditional methods (Kucharik et al, 2000; Arora, 2002; Krinner et al, 2005)
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