Reply on RC1

Abstract

The land data assimilation system, LDAS-Monde, developed by the Research Department of the French Meteorological service (Centre National de Recherches Météorologiques – CNRM) is capable of well representing Land Surface Variables (LSVs) from regional to global scales. It jointly assimilates satellite-derived observations of leaf area index (LAI) and surface soil moisture (SSM) into the Interactions between Soil Biosphere and Atmosphere (ISBA) land surface model (LSM), increasing the accuracy of the model simulations and forecasts of the LSVs. The assimilation of vegetation variables directly impacts RZSM through seven control variables consisting in soil moisture of seven soil layers from the soil surface to 1 m depth. This capability is particularly useful in dry conditions, where SSM and RZSM are decoupled to a large extent. However, this positive impact does not reach its full potential due to the low temporal availability of optical-based LAI observations, at best, every ten days, and can suffer from months of no data over regions and seasons with heavy cloud cover such as winter or monsoon conditions. In that context, this study investigates the assimilation of low frequency passive microwave vegetation optical depth (VOD), available in almost all weather conditions, as a proxy of LAI. The Vegetation Optical Depth Climate Archive (VODCA) dataset provides near-daily observations of vegetation conditions, far more frequently than optical based product such as LAI. This study's goal is to convert the more frequent X-band VOD observations into proxy-LAI observations through linear re-scaling and to assimilate them in place of direct LAI observations. Seven assimilation experiments were run from 2003 to 2018 over the contiguous United States (CONUS), with 1) no assimilation, the assimilation of 2) SSM, 3) LAI, 4) re-scaled VODX, 5) re-scaled VODX only when LAI observations available, 6) LAI + SSM, and 7) re-scaled VODX + SSM. This study analyzes these assimilation experiments by comparing to satellite derived observations and in situ measurements and is focused on the variables of LAI, SSM, gross primary production (GPP), and evapotranspiration (ET). Each experiment is driven by atmospheric forcing reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5. Results showed improved representation of GPP and ET by assimilating re-scaled VOD in place of LAI. Additionally, the joint assimilation of vegetation related variables (i.e. LAI or re-scaled VOD) and SSM demonstrates a small improvement in the representation of soil moisture over the assimilation of any dataset by itself.