globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2142
论文题名:
Fewer large waves projected for eastern Australia due to decreasing storminess
作者: Andrew J. Dowdy
刊名: Nature Climate Change
ISSN: 1758-1384X
EISSN: 1758-7504
出版年: 2014-03-09
卷: Volume:4, 页码:Pages:283;286 (2014)
语种: 英语
英文关键词: Projection and prediction ; Atmospheric dynamics ; Physical oceanography ; Climate-change impacts
英文摘要:

Extratropical cyclones are the main generators of the strong winds that cause large ocean waves in temperate regions of the world. The severity of the winds associated with these storms is poorly represented by the coarse resolution of current global climate models (GCMs), making it challenging to produce projections of the future climate of large waves. Wind data from GCMs can be downscaled in resolution using dynamical methods, resulting in a successful reproduction of the mean wave climate, but a suboptimal reproduction of the storm wave climate1. Projections of large wave occurrence can also be produced using statistical downscaling methods, although such methods have previously been applied only to three or less GCMs2, 3, preventing a robust assessment of confidence in projections based on variation between models. Consequently, considerable uncertainty remains in projections of the future storm wave climate. Here we apply a statistical diagnostic of large wave occurrence in eastern Australia to 18 different GCMs, allowing model variations to be examined in greater detail than previously possible. Results are remarkably consistent between different GCMs, allowing anthropogenic influences to be clearly demonstrated, with fewer days with large waves expected to occur in eastern Australia due to increasing greenhouse gas concentrations.

There is growing interest in understanding the climatology of surface ocean waves, partly due to their role in coastal erosion and inundation when coupled with rising sea levels4, as well as their potential for renewable energy generation5. Although tropical cyclones can have some influence on the occurrence of large waves in subtropical regions, the largest waves along the central east coast of Australia are most commonly attributable to extratropical cyclones6. The large waves caused by these storms can have severe impacts on coastal regions, such as being a major contributor to elevated water levels due to wave set-up7. Large waves can also have desirable benefits for coastal areas including recreational pursuits such as surfing, as well as influencing biodiversity within ocean ecosystems8. Any projected change in the future wave height spectrum could therefore be expected to have both desirable and undesirable impacts on coastal regions.

The diagnostic method used here to produce projections of large wave occurrence is based on geopotential height in the upper troposphere. Previous studies have examined contour maps of geopotential height for eastern Australia, finding that a strong curvature of the contours provides a good indication of the likelihood of extratropical cyclone occurrence9, 10, 11. To examine whether or not this is also the case for large wave occurrence, Fig. 1 shows contour maps of geopotential height (at the 500 hPa pressure level obtained from the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalyses12) for four different wave height ranges: 6 m or larger, 4–6 m, 2–4 m and 2 m or smaller. Wave height is calculated as the largest wave height observed on a given day at any one of five ocean buoys13 along the central east coast of Australia. The contour maps represent the average of all days when the wave height was within the specified range, calculated for the period of available wave observations (from 1992 to 2010).

Figure 1: Contour maps of geopotential height over eastern Australia for different wave height ranges.
Contour maps of geopotential height over eastern Australia for different wave height ranges.

The contour maps represent the average for all days with wave heights in the following ranges: a, 6 m or larger; b, 4–6 m; c, 2–4 m; and d, 2 m or smaller. Wave height is calculated as the largest value recorded from any one of five offshore buoys (grey squares) on a given day during the period 1992–2010. The Australian coastline is shown, as is the region used to calculate the diagnostic (black rectangle).

For a detailed description of the methods see Supplementary Methods.

Wave observations.

Wave height data were obtained from a series of buoys13 located about 6–12 km off the coast in deep water (~70 m). The buoys use an accelerometer to measure changes in vertical motion as they move with the water surface. Daily significant wave height data are used here, representing the mean wave height of the highest third of the wave data. Data were available for use in this study during the time period 1992–2010 from five buoys located between 30° S and 38° S (Fig. 1 and Supplementary Fig. 1, Table 1).

Tropical cyclone observations.

Tropical cyclone observations were obtained from a data set created and maintained by the National Climate Centre of the Australian Bureau of Meteorology, as described in previous studies15, 16. The wave height distribution for days on which tropical cyclones occurred (Fig. 2) is based on tropical cyclone observations in the South Pacific region from 150° E to 180.

Description of the diagnostic method.

The diagnostic method is based on 500 hPa geostrophic vorticity calculated as the Laplacian of geopotential divided by the Coriolis parameter10. First, geostrophic vorticity at a given time is calculated at every individual location (using gridded data) within a geographic region of 15° in longitude and 10.5° in latitude (shown as the black rectangles in Fig. 1). Second, a daily time series is produced of the maximum magnitude of cyclonic (that is, negative in the Southern Hemisphere) geostrophic vorticity within this geographic region. Third, the 90th percentile of this daily time series is calculated. Fourth, days on which the time series exceeds its 90th percentile are defined as diagnostic event days. Diagnostic event days therefore represent the 10% of days with the strongest cyclonic geostrophic vorticity at any location within the diagnostic region. Previous studies have detailed the application of the diagnostic method to a variety of different reanalyses and GCMs10, 11.

Application to reanalyses.

The diagnostic method is applied here to ERA-Interim reanalyses12 with 1.5 degree resolution in both latitude and longitude and six-hourly temporal resolution. A one-day running mean is applied to the six-hourly data to reduce small-scale temporal variability. The diagnostic is produced here using a time lag of 6 h with respect to the timing of the wave observations, as this produces the best diagnostic skill (Supplementary Fig. 2).

Application to GCMs.

The application of the diagnostic to GCMs requires spatial fields of 500 hPa geopotential height with daily (or shorter) temporal resolution. In conjunction with the Intergovernmental Panel on Climate Change, a set of GCM experiments has been produced: the World Climate Research Program CMIP517. Of the more than 50 models in the CMIP5 data set, 22 had archived daily 500 hPa geopotential height fields. Four of these 22 models were not consistent with the requirements of the study method (for reasons detailed in Supplementary Information), such that 18 GCMs were available for use in this study (as listed in Supplementary Table 2).

The diagnostic method is applied to the 18 GCMs to examine projected changes in the frequency of occurrence of diagnostic event days. A change in the frequency of occurrence of diagnostic event days will also produce a change of equal magnitude and opposite sign in the occurrence frequency of non-diagnostic event days, so as to conserve the total number of days. This condition is described by equation (1), based on applying a projected change of X% (that is, −25% for RCP4.5 and −42% for RCP8.5) over the historical distribution of diagnostic event days (as shown in Fig. 2) and applying a change of − X/9% over the historical distribution of non-diagnostic event days (noting that there are nine times more non-diagnostic event days than diagnostic event days during the historical period).

where h is wave height, Wproj(h) is the projected wave height distribution, Whist(h) is the historical wave height distribution, Dhist(h) is the historical wave height distribution for diagnostic event days and X is the projected change in the number of diagnostic event days, noting that [Whist(h) − Dhist(h)] represents the historical wave height distribution for non-diagnostic event days.

  1. Hemer, M. A., McInnes, K. L. & Ranasinghe, R. Climate and variability bias adjustment of climate model-derived winds for a southeast Australian dynamical wave model. Ocean Dynam. 62, 87104 (2012).
  2. Wang, X. L. & Swail, V. R. Climate change signal and uncertainty in projections of ocean wave heights. Clim. Dynam. 26, 109126 (2006).
  3. Caires, S., Swail, V. R. & Wang, X. L. Projection and analysis of extreme wave climate. J. Clim. 19, 55815605 (2006).
  4. Webb, E. L. et al. A global standard for monitoring coastal wetland vulnerability to accelerated sea-level rise. Nature Clim. Change 3, 458465 (2013).
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  6. Short, A. D. & Trenaman, N. L. Wave climate of the Sydney region, an energetic and highly variable ocean wave regime. Aust. J. Mar. Freshwat. Res. 43, 765791 (1992).
  7. McInnes, K. L., Hubbert, G. D., Abbs, D. J. & Oliver, S. E. A numerical modelling study of coastal flooding. Met. Atmos. Phys. 80, 217233 (2002).
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  9. Mills, G. A. et al. Centre for Australian Weather and Climate Research, Australia, CAWCR Technical Report 23 The Pasha Bulker east coast low of 8 June 2007. (Australian Weather and Climate Research, (2010).
  10. Dowdy, A. J., Mills, G. A. & Timbal, B. Large-scale diagnostics of extratropical cyclogenesis in eastern Australia. Int. J. Climatol. 33, 23182327 (2013).
  11. Dowdy, A. J., Mills, G. A., Timbal, B. & Wang, Y. Changes in the risk of extratropical cyclones in eastern Australia. J. Clim. 26, 14031417 (2013).
  12. Dee, D. P. et al. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553597 (2011). URL:
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/5205
Appears in Collections:气候变化事实与影响
科学计划与规划
气候变化与战略

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Andrew J. Dowdy. Fewer large waves projected for eastern Australia due to decreasing storminess[J]. Nature Climate Change,2014-03-09,Volume:4:Pages:283;286 (2014).
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