English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Optimisation methods were successfully used to calibrate parameters\udin an atmospheric component of a climate model using two variants of the\udGauss–Newton line-search algorithm: (1) a standard Gauss–Newton algorithm\udin which, in each iteration, all parameters were perturbed and (2)\uda randomised block-coordinate variant in which, in each iteration, a random\udsub-set of parameters was perturbed. The cost function to be minimised used\udmultiple large-scale multi-annual average observations and was constrained to\udproduce net radiative fluxes close to those observed. These algorithms were\udused to calibrate the HadAM3 (third Hadley Centre Atmospheric Model) model at\udN48 resolution and the HadAM3P model at N96 resolution. For the HadAM3 model, cases with 7 and 14 parameters were tried. All\udten 7-parameter cases using HadAM3\udconverged to cost function values similar to that of the standard\udconfiguration. For the 14-parameter cases several failed to converge, with\udthe random variant in which 6 parameters were perturbed being most\udsuccessful. Multiple sets of parameter values were found that produced\udmultiple models very similar to the standard configuration. HadAM3 cases that\udconverged were coupled to an ocean model and run for 20 years starting\udfrom a pre-industrial HadCM3 (3rd Hadley Centre Coupled model) state\udresulting in several models whose global-average temperatures were consistent\udwith pre-industrial estimates. For the 7-parameter cases the Gauss–Newton\udalgorithm converged in about 70 evaluations. For the 14-parameter algorithm,\udwith 6 parameters being randomly perturbed, about 80 evaluations were needed\udfor convergence. However, when 8 parameters were randomly perturbed,\udalgorithm performance was poor. Our results suggest the computational cost\udfor the Gauss–Newton algorithm scales between P and P2, where P is\udthe 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 hand-tuned 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 human-driven trial and error. However, convergence and costs are likely sensitive to details of the algorithm

Calibrating climate models using inverse methods: case studies with HadAM3, HadAM3P and HadCM3