Impacts of a Parameterization Deficiency on Offline and Coupled Land Surface Model Simulations

Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

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Scale Impact of Soil Moisture Observations to Noah-MP Land Surface Model Simulations

Remote Sensing ◽

10.3390/rs12071169 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1169

Author(s):

Jifu Yin ◽

Xiwu Zhan

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Synthetic Experiment ◽

Model Simulations ◽

Coarse Spatial Resolution ◽

Satellite Microwave ◽

National Water

Due to the limitations of satellite antenna technology, current operational microwave soil moisture (SM) data products are typically at tens of kilometers spatial resolutions. Many approaches have thus been proposed to generate finer resolution SM data using ancillary information, but it is still unknown if assimilation of the finer spatial resolution SM data has beneficial impacts on model skills. In this paper, a synthetic experiment is thus conducted to identify the benefits of SM observations at a finer spatial resolution on the Noah-MP land surface model. Results of this study show that the performance of the Noah-MP model is significantly improved with the benefits of assimilating 1 km SM observations in comparison with the assimilation of SM data at coarser resolutions. Downscaling satellite microwave SM observations from coarse spatial resolution to 1 km resolution is recommended, and the assimilation of 1 km remotely sensed SM retrievals is suggested for NOAA National Weather Service and National Water Center.

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Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

Geoscientific Model Development ◽

10.5194/gmd-10-2651-2017 ◽

2017 ◽

Vol 10 (7) ◽

pp. 2651-2670 ◽

Cited By ~ 21

Author(s):

Darren Slevin ◽

Simon F. B. Tett ◽

Jean-François Exbrayat ◽

A. Anthony Bloom ◽

Mathew Williams

Keyword(s):

Spatial Resolution ◽

Primary Productivity ◽

Land Surface ◽

Land Surface Model ◽

Gross Primary Productivity ◽

Surface Model ◽

Global Evaluation ◽

Annual Average ◽

Model Simulations ◽

The Tropics

Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate gross primary productivity (GPP) on regional and global scales for 2001–2010. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, when JULES was driven with the WFDEI-GPCC dataset (at 0. 5° × 0. 5° spatial resolution), the annual average global GPP simulated by JULES for 2001–2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 and 8 %, respectively, and CARDAMOM estimates by 23 %. JULES was able to simulate the standard deviation of monthly GPP fluxes compared to CARDAMOM and the observation-based estimates on global scales. Secondly, GPP simulated by JULES for various biomes (forests, grasslands and shrubs) on global and regional scales were compared. Differences among JULES, MODIS, FLUXNET-MTE and CARDAMOM on global scales were due to differences in simulated GPP in the tropics. Thirdly, it was shown that spatial resolution (0. 5° × 0. 5°, 1° × 1° and 2° × 2°) had little impact on simulated GPP on these large scales, with global GPP ranging from 140 to 142 PgC year−1. Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 3 PgC year−1. On regional scales, differences between the two were observed, with the WFDEI-GPCC-driven model simulations estimating higher GPP in the tropics (5° N–5° S) and the PRINCETON-driven model simulations estimating higher GPP in the extratropics (30–60° N).

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Applying Scaled Vegetation Greenness Metrics to Constrain Simulated Transpiration Anomalies: A Study over Australia*

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-070.1 ◽

2013 ◽

Vol 15 (4) ◽

pp. 1607-1623 ◽

Cited By ~ 3

Author(s):

Mark Decker ◽

Andy J. Pitman ◽

Jason Evans

Keyword(s):

Land Surface ◽

Climate Model ◽

Land Surface Model ◽

Surface Model ◽

Land Surface Models ◽

Model Simulations ◽

Vegetation Greenness ◽

Surface Models ◽

The Mean ◽

Climate Model Simulations

Abstract The feasibility of using vegetation greenness metrics as a proxy for transpiration variability over Australia is demonstrated. Several global evapotranspiration datasets, one of which provides transpiration data and is constructed independently of the vegetation greenness measurements, are compared to four satellite-based observations representative of the state of the vegetation over several regions in Australia. Further estimates of the transpiration are obtained by decomposing the evapotranspiration datasets using an ensemble of land surface model simulations. On monthly time scales, the greenness anomaly metrics show a near one-to-one relationship with the transpiration estimates when the time series are appropriately scaled by the mean. The authors demonstrate that anomalous vegetation greenness metrics, when properly scaled, provide a tool for evaluating transpiration variability simulated by land surface models and observation-based evapotranspiration datasets that include transpiration. These methods provide a new test to help constrain the dynamic behavior of the land surface in climate model simulations.

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