Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

Nina M Raoult,Peter M Cox,Catherine M Luke,Tim E Jupp

doi:10.5194/gmd-9-2833-2016

Abstract

Abstract. Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate–carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model–data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85 % of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter.

Highlights

Land-surface models (LSMs) have formed an important component of climate models for many decades (Pitman, 2003)
The results presented in this paper highlight the need to reassess Joint UK Land Environment Simulator (JULES) and other land-surface models for predominantly C4 landscapes
This study introduces the adJULES system, which has been developed to tune the internal parameters of the JULES landsurface model. adJULES enables objective calibration of JULES against observational data, providing best-fit internal parameters and associated uncertainty ranges

Summary

Introduction

Land-surface models (LSMs) have formed an important component of climate models for many decades (Pitman, 2003). In the early 2000s, climate modelling groups began to use the carbon fluxes simulated by LSMs within first generation climate–carbon cycle models (Cox et al, 2000; Friedlingstein et al, 2001). These early results, and a subsequent model inter-comparison (Friedlingstein et al, 2006), highlighted the uncertainties associated with land carbon–climate feedbacks. Any future decreased ability of the land surface to drawdown atmospheric CO2 could imply smaller “compatible emissions” in order to stay below key warming thresholds such as 2 ◦

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