Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-09-16

AUTHORS

Emma E. Aalbers, Geert Lenderink, Erik van Meijgaard, Bart J. J. M. van den Hurk

ABSTRACT

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—‘noise’, intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM–GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability. More... »

PAGES

4745-4766

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    http://scigraph.springernature.com/pub.10.1007/s00382-017-3901-9

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    http://dx.doi.org/10.1007/s00382-017-3901-9

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    79 local impacts
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    81 members
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    83 more confidence
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    86 noise
    87 oceanic processes
    88 precipitation
    89 predictability
    90 probability
    91 probability of events
    92 process
    93 regional climate model
    94 regional differences
    95 regional downscaling
    96 representativeness
    97 resolution
    98 respect
    99 response
    100 results
    101 scale
    102 signals
    103 simulations
    104 single model
    105 small spatial scales
    106 spatial scales
    107 strength
    108 system
    109 terms
    110 uncertainty
    111 values
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