Inflated Uncertainty in Multimodel‐Based Regional Climate Projections

Marianne Sloth Madsen; Peter L. Langen; Fredrik Boberg; Jens Hesselbjerg Christensen

doi:10.1002/2017GL075627

Inflated Uncertainty in Multimodel‐Based Regional Climate Projections

Marianne Sloth Madsen, Peter L. Langen, Fredrik Boberg, Jens Hesselbjerg Christensen

Klima- og Geofysik / Climate and Geophysics

14 Citationer (Scopus)

82 Downloads (Pure)

Abstract

Multimodel ensembles are widely analyzed to estimate the range of future regional climate change projections. For an ensemble of climate models, the result is often portrayed by showing maps of the geographical distribution of the multimodel mean results and associated uncertainties represented by model spread at the grid point scale. Here we use a set of CMIP5 models to show that presenting statistics this way results in an overestimation of the projected range leading to physically implausible patterns of change on global but also on regional scales. We point out that similar inconsistencies occur in impact analyses relying on multimodel information extracted using statistics at the regional scale, for example, when a subset of CMIP models is selected to represent regional model spread. Consequently, the risk of unwanted impacts may be overestimated at larger scales as climate change impacts will never be realized as the worst (or best) case everywhere.

Originalsprog	Engelsk
Tidsskrift	Geophysical Research Letters
Vol/bind	44
Udgave nummer	22
Sider (fra-til)	606-613
ISSN	0094-8276
DOI	https://doi.org/10.1002/2017GL075627
Status	Udgivet - 28 nov. 2017

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Madsen_et_al-2017-Geophysical_Research_Letters(1)Forlagets udgivne version, 1,31 MBLicens: CC BY

Citationsformater

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AB - Multimodel ensembles are widely analyzed to estimate the range of future regional climate change projections. For an ensemble of climate models, the result is often portrayed by showing maps of the geographical distribution of the multimodel mean results and associated uncertainties represented by model spread at the grid point scale. Here we use a set of CMIP5 models to show that presenting statistics this way results in an overestimation of the projected range leading to physically implausible patterns of change on global but also on regional scales. We point out that similar inconsistencies occur in impact analyses relying on multimodel information extracted using statistics at the regional scale, for example, when a subset of CMIP models is selected to represent regional model spread. Consequently, the risk of unwanted impacts may be overestimated at larger scales as climate change impacts will never be realized as the worst (or best) case everywhere.

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Inflated Uncertainty in Multimodel‐Based Regional Climate Projections

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