Multivariable evaluation of land surface processes in forced and coupled modes reveals new error sources to the simulated water cycle in the IPSL (Institute Pierre Simon Laplace) climate model

Hiroki Mizuochi,Agnès Ducharne,Philippe Peylin,Jean-Pierre Wigneron,Vladislav Bastrikov,Amen Al-Yaari,Fabienne Maignan,Josefine Ghattas,Nicolas Vuichard,Frédérique Cheruy

doi:10.5194/hess-25-2199-2021

Abstract

Abstract. Evaluating land surface models (LSMs) using available observations is important for understanding the potential and limitations of current Earth system models in simulating water- and carbon-related variables. To reveal the error sources of a LSM, five essential climate variables have been evaluated in this paper (i.e., surface soil moisture, evapotranspiration, leaf area index, surface albedo, and precipitation) via simulations with the IPSL (Institute Pierre Simon Laplace) LSM ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) model, particularly focusing on the difference between (i) forced simulations with atmospheric forcing data (WATCH Forcing Data ERA-Interim – WFDEI) and (ii) coupled simulations with the IPSL atmospheric general circulation model. Results from statistical evaluation, using satellite- and ground-based reference data, show that ORCHIDEE is well equipped to represent spatiotemporal patterns of all variables in general. However, further analysis against various landscape and meteorological factors (e.g., plant functional type, slope, precipitation, and irrigation) suggests potential uncertainty relating to freezing and/or snowmelt, temperate plant phenology, irrigation, and contrasted responses between forced and coupled mode simulations. The biases in the simulated variables are amplified in the coupled mode via surface–atmosphere interactions, indicating a strong link between irrigation–precipitation and a relatively complex link between precipitation–evapotranspiration that reflects the hydrometeorological regime of the region (energy limited or water limited) and snow albedo feedback in mountainous and boreal regions. The different results between forced and coupled modes imply the importance of model evaluation under both modes to isolate potential sources of uncertainty in the model.

Highlights

Land surface models (LSMs) are essential for understanding the large-scale exchange of energy, water, and carbon between the land surface and the atmosphere
This paper has presented an in-depth evaluation of five interlinked essential climate variables simulated by ORCHIDEE land surface model under different simulation modes
Statistical evaluation was conducted using various reference data sources (ESA CCI, upscaled FLUXNET, Global Inventory Modeling and Mapping Studies (GIMMS) 3g, Moderate Resolution Imaging Spectroradiometer (MODIS) products, and GPCC), and factor analysis was conducted against various landscape factors

Summary

Introduction

Land surface models (LSMs) are essential for understanding the large-scale exchange of energy, water, and carbon between the land surface and the atmosphere. Uncertainties associated with LSMs can arise from a deficiency in model physics and parameterization (Liu et al, 2003), errors in atmospheric forcing data (Guo et al, 2006; Nasonova et al, 2011; Yin et al, 2018), boundary conditions, including vegetation and land use changes (Guimberteau et al, 2017; Boisier et al, 2014), and/or error propagation through land–atmosphere coupling (so-called “climate drift”; Dirmeyer, 2001). Such errors occur at short timescales (i.e., several days) up to seasonal timescales (Dirmeyer, 2001) via the interlinkage of hydrological variables (e.g., rainfall, SSM, ET, and infiltration) in the LSM scheme and thermal variables (Cheruy et al, 2017; Ait-Mesbah et al, 2015)

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