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

Stochastic methods are increasingly used in global coupled model climateforecasting systems to account for model uncertainties. In this paper, wedescribe in more detail the stochastic dynamics technique introduced byBatté andDéqué (2012) in the ARPEGE-Climate atmospheric model. We presentnew results with an updated version of CNRM-CM using ARPEGE-Climate v6.1, andshow that the technique can be used both as a means of analyzing model errorstatistics and accounting for model inadequacies in a seasonal forecastingframework.  The perturbations are designed as corrections of model drift errors estimatedfrom a preliminary weakly nudged re-forecast run over an extended referenceperiod of 34 boreal winter seasons. A detailed statistical analysis of thesecorrections is provided, and shows that they are mainly made of intra-monthvariance, thereby justifying their use as in-run perturbations of the modelin seasonal forecasts. However, the interannual and systematic errorcorrection terms cannot be neglected. Time correlation of the errors islimited, but some consistency is found between the errors of up to 3consecutive days.  These findings encourage us to test several settings of the random draws ofperturbations in seasonal forecast mode. Perturbations are drawn randomly butconsistently for all three prognostic variables perturbed. We explore theimpact of using monthly mean perturbations throughout a given forecast monthin a first ensemble re-forecast (SMM, for stochastic monthly means), and testthe use of 5-day sequences of perturbations in a second ensemble re-forecast(S5D, for stochastic 5-day sequences). Both experiments are compared in thelight of a REF reference ensemble with initial perturbations only. Results interms of forecast quality are contrasted depending on the region and variableof interest, but very few areas exhibit a clear degradation of forecastingskill with the introduction of stochastic dynamics. We highlight somepositive impacts of the method, mainly on Northern Hemisphere extra-tropics.The 500 hPa geopotential height bias is reduced, and improvements projectonto the representation of North Atlantic weather regimes. A modest impact onensemble spread is found over most regions, which suggests that this methodcould be complemented by other stochastic perturbation techniques in seasonalforecasting mode

Randomly correcting model errors in the ARPEGE-Climate v6.1 component of CNRM-CM: applications for seasonal forecasts