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

. Energy and mass-balance modelling of glaciers is a key tool for climate impactstudies of future glacier behaviour. By incorporating many of the physicalprocesses responsible for surface accumulation and ablation, they offer moreinsight than simpler statistical models and are believed to suffer less fromproblems of stationarity when applied under changing climate conditions.However, this view is challenged by the widespread use of parameterizationsfor some physical processes which introduces a statistical calibration step.We argue that the reported uncertainty in modelled mass balance (andassociated energy flux components) are likely to be understated in modellingstudies that do not use spatio-temporal cross-validation and use a singleperformance measure for model optimization. To demonstrate the importance ofthese principles, we present a rigorous sensitivity and uncertaintyassessment workflow applied to a modelling study of two glaciers in theEuropean Alps, extending classical best guess approaches. The procedurebegins with a reduction of the model parameter space using a globalsensitivity assessment that identifies the parameters to which the modelresponds most sensitively. We find that the model sensitivity to individualparameters varies considerably in space and time, indicating that a singlestated model sensitivity value is unlikely to be realistic. The model is mostsensitive to parameters related to snow albedo and vertical gradients of themeteorological forcing data. We then apply a Monte Carlo multi-objectiveoptimization based on three performance measures: model bias and meanabsolute deviation in the upper and lower glacier parts, with glaciologicalmass balance data measured at individual stake locations used as reference.This procedure generates an ensemble of optimal parameter solutions which areequally valid. The range of parameters associated with these ensemble membersare used to estimate the cross-validated uncertainty of the model output andcomputed energy components. The parameter values for the optimal solutionsvary widely, and considering longer calibration periods does notsystematically result in better constrained parameter choices. The resultingmass balance uncertainties reach up to 1300 kg m −2 , with thespatial and temporal transfer errors having the same order of magnitude. Theuncertainty of surface energy flux components over the ensemble at the pointscale reached up to 50 % of the computed flux. The largest absoluteuncertainties originate from the short-wave radiation and the albedoparameterizations, followed by the turbulent fluxes. Our study highlights theneed for due caution and realistic error quantification when applying suchmodels to regional glacier modelling efforts, or for projections of glaciermass balance in climate settings that are substantially different from theconditions in which the model was optimized.

Robust uncertainty assessment of the spatio-temporal transferability of glacier mass and energy balance models