Evaluating wind extremes in CMIP5 climate models
| LEADER | caa a22 4500 | ||
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| 001 | 605472130 | ||
| 003 | CHVBK | ||
| 005 | 20210128100336.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150701xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00382-014-2306-2 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00382-014-2306-2 | ||
| 245 | 0 | 0 | |a Evaluating wind extremes in CMIP5 climate models |h [Elektronische Daten] |c [Devashish Kumar, Vimal Mishra, Auroop Ganguly] |
| 520 | 3 | |a Wind extremes have consequences for renewable energy sectors, critical infrastructures, coastal ecosystems, and insurance industry. Considerable debates remain regarding the impacts of climate change on wind extremes. While climate models have occasionally shown increases in regional wind extremes, a decline in the magnitude of mean and extreme near-surface wind speeds has been recently reported overmost regions of the Northern Hemisphere using observed data. Previous studies of wind extremes under climate change have focused on selected regions and employed outputs from theregional climate models(RCMs). However, RCMsultimately rely on the outputs of global circulation models(GCMs), and the value-addition from the former over the latter has been questioned. Regional model runs rarely employ the full suite of GCM ensembles, and hence may not be able to encapsulate the most likely projections or their variability. Here we evaluate the performance of the latest generation of GCMs, the Coupled Model Intercomparison Project phase 5 (CMIP5), in simulating extreme winds. We find that the multimodel ensemble (MME) mean captures the spatial variability of annual maximum wind speeds over most regions except over the mountainous terrains. However, the historical temporal trends in annual maximum wind speeds forthe reanalysis data,ERA-Interim, are not well represented in the GCMs. The historical trendsin extreme winds fromGCMs are statistically not significant over most regions. The MME model simulates the spatial patterns of extreme winds for 25-100year return periods. The projected extreme winds from GCMs exhibit statistically less significant trends compared to the historical reference period. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a CMIP5 models |2 nationallicence | |
| 690 | 7 | |a Wind extremes |2 nationallicence | |
| 690 | 7 | |a Gumbel distribution |2 nationallicence | |
| 690 | 7 | |a Model evaluation |2 nationallicence | |
| 700 | 1 | |a Kumar |D Devashish |u Sustainability and Data Sciences Laboratory, Civil and Environmental Engineering, Northeastern University, 400 SN, 360 Huntington Avenue, 02115, Boston, MA, USA |4 aut | |
| 700 | 1 | |a Mishra |D Vimal |u Sustainability and Data Sciences Laboratory, Civil and Environmental Engineering, Northeastern University, 400 SN, 360 Huntington Avenue, 02115, Boston, MA, USA |4 aut | |
| 700 | 1 | |a Ganguly |D Auroop |u Sustainability and Data Sciences Laboratory, Civil and Environmental Engineering, Northeastern University, 400 SN, 360 Huntington Avenue, 02115, Boston, MA, USA |4 aut | |
| 773 | 0 | |t Climate Dynamics |d Springer Berlin Heidelberg |g 45/1-2(2015-07-01), 441-453 |x 0930-7575 |q 45:1-2<441 |1 2015 |2 45 |o 382 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00382-014-2306-2 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00382-014-2306-2 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Kumar |D Devashish |u Sustainability and Data Sciences Laboratory, Civil and Environmental Engineering, Northeastern University, 400 SN, 360 Huntington Avenue, 02115, Boston, MA, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Mishra |D Vimal |u Sustainability and Data Sciences Laboratory, Civil and Environmental Engineering, Northeastern University, 400 SN, 360 Huntington Avenue, 02115, Boston, MA, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Ganguly |D Auroop |u Sustainability and Data Sciences Laboratory, Civil and Environmental Engineering, Northeastern University, 400 SN, 360 Huntington Avenue, 02115, Boston, MA, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Climate Dynamics |d Springer Berlin Heidelberg |g 45/1-2(2015-07-01), 441-453 |x 0930-7575 |q 45:1-2<441 |1 2015 |2 45 |o 382 | ||