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A missing source of uncertainty: forcing-dependent model parameter sensitivity
Author(s) -
Xiuhua Zhu
Publication year - 2021
Publication title -
environmental research communications
Language(s) - English
Resource type - Journals
ISSN - 2515-7620
DOI - 10.1088/2515-7620/abfe18
Subject(s) - forcing (mathematics) , sensitivity (control systems) , climate sensitivity , climate model , range (aeronautics) , climate change , resolution (logic) , environmental science , radiative forcing , econometrics , computer science , climatology , mathematics , geology , oceanography , materials science , electronic engineering , artificial intelligence , engineering , composite material
Climate modelling groups usually work with a handful of model versions (parameter combinations) that reproduce certain targeted aspects of observed climate within a certain validity range and apply them for studying future climate change. What is of concern is whether these retained model versions, with respect to their de-selected counterparts, continue being optimal for future climate that is supposed to distinctly differ from the present one. Extrapolating model performances beyond their validity range requires model parameter sensitivity (i.e., changes in model output due to changes in model parameters) remains more or less stationary despite different forcing conditions. This requirement, however, is shown to be ill-grounded by exemplified analyses of resolution sensitivity in an Earth System Model under different forcing conditions, whereby model resolution is handled as a model parameter in a wider sense: (i) Model resolution sensitivity depends on the forcing conditions applied; moreover, (ii) The further the forcing deviates from a reference state, the earlier one can detect a systematic change in model resolution sensitivity, in particular, in its spatial details. Therefore, model parameter sensitivity and forcing conditions should be evaluated as a compound; failure to account for this relation leads to a systematic underestimation of uncertainty in forced responses of climate models, thereby imposing hazardous impacts on practical applications of CMIP outputs.

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