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Abstract
Optimisation methods were successfully used to calibrate parameters in an atmospheric component of a climate model using two variants of the Gauss–Newton linesearch algorithm: (1) a standard Gauss–Newton algorithm in which, in each iteration, all parameters were perturbed and (2) a randomised blockcoordinate variant in which, in each iteration, a random subset of parameters was perturbed. The cost function to be minimised used multiple largescale multiannual average observations and was constrained to produce net radiative fluxes close to those observed. These algorithms were used to calibrate the HadAM3 (third Hadley Centre Atmospheric Model) model at N48 resolution and the HadAM3P model at N96 resolution.
For the HadAM3 model, cases with 7 and 14 parameters were tried. All ten 7parameter cases using HadAM3 converged to cost function values similar to that of the standard configuration. For the 14parameter cases several failed to converge, with the random variant in which 6 parameters were perturbed being most successful. Multiple sets of parameter values were found that produced multiple models very similar to the standard configuration. HadAM3 cases that converged were coupled to an ocean model and run for 20 years starting from a preindustrial HadCM3 (3rd Hadley Centre Coupled model) state resulting in several models whose globalaverage temperatures were consistent with preindustrial estimates. For the 7parameter cases the Gauss–Newton algorithm converged in about 70 evaluations. For the 14parameter algorithm, with 6 parameters being randomly perturbed, about 80 evaluations were needed for convergence. However, when 8 parameters were randomly perturbed, algorithm performance was poor. Our results suggest the computational cost for the Gauss–Newton algorithm scales between P and P2, where P is the number of parameters being calibrated.
For the HadAM3P model three algorithms were tested. Algorithms in which seven parameters were perturbed and three out of seven parameters randomly perturbed produced final configurations comparable to the standard handtuned configuration. An algorithm in which 6 out of 13 parameters were randomly perturbed failed to converge.
These results suggest that automatic parameter calibration using atmospheric models is feasible and that the resulting coupled models are stable. Thus, automatic calibration could replace humandriven trial and error. However, convergence and costs are likely sensitive to details of the algorithm.
For the HadAM3 model, cases with 7 and 14 parameters were tried. All ten 7parameter cases using HadAM3 converged to cost function values similar to that of the standard configuration. For the 14parameter cases several failed to converge, with the random variant in which 6 parameters were perturbed being most successful. Multiple sets of parameter values were found that produced multiple models very similar to the standard configuration. HadAM3 cases that converged were coupled to an ocean model and run for 20 years starting from a preindustrial HadCM3 (3rd Hadley Centre Coupled model) state resulting in several models whose globalaverage temperatures were consistent with preindustrial estimates. For the 7parameter cases the Gauss–Newton algorithm converged in about 70 evaluations. For the 14parameter algorithm, with 6 parameters being randomly perturbed, about 80 evaluations were needed for convergence. However, when 8 parameters were randomly perturbed, algorithm performance was poor. Our results suggest the computational cost for the Gauss–Newton algorithm scales between P and P2, where P is the number of parameters being calibrated.
For the HadAM3P model three algorithms were tested. Algorithms in which seven parameters were perturbed and three out of seven parameters randomly perturbed produced final configurations comparable to the standard handtuned configuration. An algorithm in which 6 out of 13 parameters were randomly perturbed failed to converge.
These results suggest that automatic parameter calibration using atmospheric models is feasible and that the resulting coupled models are stable. Thus, automatic calibration could replace humandriven trial and error. However, convergence and costs are likely sensitive to details of the algorithm.
Original language  English 

Pages (fromto)  35673589 
Number of pages  23 
Journal  Geoscientific Model Development 
Volume  10 
Issue number  9 
DOIs  
Publication status  Published  28 Sep 2017 
Keywords
 DATA ASSIMILATION
 UNCERTAINTY ESTIMATION
 PARAMETER VARIATIONS
 SKILL OPTIMIZATION
 FLUX ADJUSTMENTS
 PREDICTIONS
 CIRCULATION
 CLOSURE
 IMPACT
 QUANTIFICATION
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Simulations to explore impact of calibration of model parameters on climate sensitivity
Tett, S. (Creator) & Mineter, M. (Data Manager), Edinburgh DataVault, 31 Aug 2020
DOI: 10.7488/84b585fc57d24e5ab3a3694f70534a02
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