Robustness and uncertainties in global multivariate wind-wave climate projections

Morim, Joao; Hemer, Mark; Wang, Xiaolan L.; Cartwright, Nick; Trenham, Claire; Semedo, Álvaro; Young, Ian R.; Bricheno, Lucy; Camus, Paula; Casas-Prat, Mercè; Erikson, Li H.; Mentaschi, Lorenzo; Mori, Nobuhito; Shimura, Tomoya; Timmermans, Ben; Aarnes, Ole Johan; Breivik, Øyvind; Behrens, Arno; Dobrynin, Mikhail; Menéndez, M.; Staneva, Joanna; Wehner, Michael; Wolf, Judith; Kamranzad, Bahareh; Webb, Adrean; Stopa, Justin E.; Andutta, Fernando Pinheiro

doi:10.1038/s41558-019-0542-5

Cited by 246 publications

(246 citation statements)

References 75 publications

(91 reference statements)

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“…A similar argument applies to the wave model used (see next section): any meaningful change must come from the driving atmosphere, not the wave model used to downscale it. Consistent with this, Morim et al (2019) find that uncertainty in projections of changing wave climate is dominated by climate model-driven uncertainty, rather than uncertainties in wave modelling. The wave model used here was included in the Morim et al (2019) study, where the sensitivity to wave model used is discussed in more detail.…”

Section: Surgesupporting

confidence: 61%

Contributions to 21st century projections of extreme sea-level change around the UK

Howard

Palmer

Bricheno

2019

Environ. Res. Commun.

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We provide a synthesis of results of a recent government-funded initiative to make projections of 21st century change in extreme sea levels around the coast of the United Kingdom. We compare four factors that influence future coastal flood risk: (i) time-mean sea-level (MSL) rise; (ii) changes in storm surge activity; (iii) changes in the offshore wave climate; (iv) changes in tidal amplitude arising from the increase in MSL. Our projections are dominated by the effects of MSL rise, which is typically more than five times larger than any of the other contributions. MSL is projected to rise by about 53 to 115centimetres at the mouth of the Thames and 30 to 90centimetres at Edinburgh (5th to 95th percentiles at 2100 relative to 1981-2000 average). Surge model projections disagree on the sign of future changes. Typical simulated changes are around +/−7centimetres. Because of the disagreement, our best estimate is of no change from this contribution, although we cannot rule out changes of either sign. Wave model projections suggest a decrease in significant wave height of the order of 7centimetres over the 21st century. However, the limited sample size and uncertainty in projections of changes in atmospheric circulation means that we cannot be confident about the sign of future changes in wave climate. MSL rise may induce changes in tidal amplitude of more than 15centimetres over the 21st century for the Bristol Channel. However, models disagree on the sign of change there. Elsewhere, our projected tidal amplitude changes are mostly less than 7centimetres. Whilst changes in MSL dominate, we have shown the potential for all processes considered here to make nonnegligible contributions over the 21st century.

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Section: Surgesupporting

confidence: 61%

Contributions to 21st century projections of extreme sea-level change around the UK

Howard

Palmer

Bricheno

2019

Environ. Res. Commun.

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“…The importance of the region as a major generation source for swell that is ubiquitous across the Indian, Pacific, and South Atlantic Oceans has also become clear following model and satellite studies (Young 1994a(Young , 1999Semedo et al 2011;Alves 2006). More recently, it has become clear that this is also a region where the wave (and wind) climate has been changing and is projected to change in the future (Young et al 2011;Young and Ribal 2019;Morim et al 2019;Meucci et al 2018).…”

Section: Introductionmentioning

confidence: 99%

The Wave Climate of the Southern Ocean

Young

Fontaine

Liu

et al. 2020

Journal of Physical Oceanography

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The wave climate of the Southern Ocean is investigated using a combined dataset from 33 years of altimeter data, in situ buoy measurements at five locations, and numerical wave model hindcasts. The analysis defines the seasonal variation in wind speed and significant wave height, as well as wind speed and significant wave height for a 1-in-100-year return period. The buoy data include an individual wave with a trough to crest height of 26.4 m and suggest that waves in excess of 30 m would occur in the region. The extremely long fetches, persistent westerly winds, and procession of low pressure systems that traverse the region generate wave spectra that are unique. These spectra are unimodal but with peak frequencies that propagate much faster than the local wind. This situation results in a unique energy balance in which waves at the spectra peak grow as a result of nonlinear transfer without any input from the local wind.

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“…Statistical confidence in GCM simulations depends on heavily on their ability to represent historical observations (Xie et al, 2015). Consequently, understanding how current CMIP5 GCM models in represent historical surface winds is an integral part of building reliable projections of future wind climate (Flato et al, 2013, Fricker et al, 2013 as well as and other variables (terrestrial and marine)-which rely on GCM-derived wind forcing ( Figure 1) (Prospero, 1999;Otero and Ruiz-Villarreal, 2008;Hemer et al, 2013;Cronin and Tozuka, 2016;Yang et al, 2017;Morim et al, 2018;Morim et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Skill and uncertainty in surface wind fields from general circulation models: Intercomparison of bias between AGCM, AOGCM and ESM global simulations

Morim

Hemer

Andutta

et al. 2019

Intl Journal of Climatology

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Understanding the reliability of global climate models (GCMs) to reproduce the historical surface wind fields is integral part of building robust projections of surface wind‐climate, and other wind‐dependent geophysical climatic variables. Understanding the skill of atmosphere‐only models (AGCM), coupled atmosphere–ocean models (AOGCM) and fully coupled earth system models (ESM) is likewise paramount to assess any systematic model improvements. In this paper, we systematically assess whether surface wind fields obtained from 28 CMIP5 GCMs can represent large‐scale spatial patterns and temporal variability of historical surface winds. We show that inter‐model uncertainty is typically 2–4 times larger than the uncertainty associated with GCM internal variability, although the latter can be significant within specific regions. We also find that CMIP5 models are typically capable of reliably reproducing large‐scale spatial patterns of historical near‐surface winds, but considerable uncertainty lies within the CMIP5 ensemble with strong latitudinal dependence. CMIP5 models show limitations in their ability to reliably represent inter‐annual and inter‐seasonal variability particularly within tropical‐cyclone‐affected regions. In further analysis, we quantify and intercompare historical wind bias from different types of models with different dynamical cores, based on multiple CMIP5 diagnostic experiments. We find that bias in surface wind fields are largely intrinsic to the atmospheric components of the models, and that the inclusion of carbon‐cycle dynamics has insignificant effect on simulated surface winds (at decadal time‐scales). Inconsistencies between AGCM and AOGCM simulations are largely driven by errors in sea surface temperatures (SST); though such differences are not statistically significant relative to the inter‐model uncertainty within the CMIP5 ensemble. These results show that the dominant source of bias in simulated wind fields lies in the underlying physics of the atmospheric component of the models.

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Robustness and uncertainties in global multivariate wind-wave climate projections

Cited by 246 publications

References 75 publications

Contributions to 21st century projections of extreme sea-level change around the UK

Contributions to 21st century projections of extreme sea-level change around the UK

The Wave Climate of the Southern Ocean

Skill and uncertainty in surface wind fields from general circulation models: Intercomparison of bias between AGCM, AOGCM and ESM global simulations

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