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

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 evaluationmore » stages. Furthermore, the new improved parameters for JULES are presented along with the associated uncertainties for each parameter.« less

Authors:
 [1];  [1];  [1];  [1]
  1. Univ. of Exeter, Exeter (United Kingdom)
Publication Date:
Research Org.:
Oregon State Univ., Corvallis, OR (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1361533
Grant/Contract Number:  
FG02-04ER63917; FG02-04ER63911
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Name: Geoscientific Model Development (Online); Journal Volume: 9; Journal Issue: 8; Journal ID: ISSN 1991-9603
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES

Citation Formats

Raoult, Nina M., Jupp, Tim E., Cox, Peter M., and Luke, Catherine M.. Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0. United States: N. p., 2016. Web. doi:10.5194/gmd-9-2833-2016.
Raoult, Nina M., Jupp, Tim E., Cox, Peter M., & Luke, Catherine M.. Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0. United States. doi:10.5194/gmd-9-2833-2016.
Raoult, Nina M., Jupp, Tim E., Cox, Peter M., and Luke, Catherine M.. Thu . "Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0". United States. doi:10.5194/gmd-9-2833-2016. https://www.osti.gov/servlets/purl/1361533.
@article{osti_1361533,
title = {Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0},
author = {Raoult, Nina M. and Jupp, Tim E. and Cox, Peter M. and Luke, Catherine M.},
abstractNote = {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. Furthermore, the new improved parameters for JULES are presented along with the associated uncertainties for each parameter.},
doi = {10.5194/gmd-9-2833-2016},
journal = {Geoscientific Model Development (Online)},
number = 8,
volume = 9,
place = {United States},
year = {Thu Aug 25 00:00:00 EDT 2016},
month = {Thu Aug 25 00:00:00 EDT 2016}
}

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