The role of large-scale modes of climate variability on the Cape Point wave record

Transcription

1 GODAE OceanView 5th COSS-TT meeting, Cape Town 2017 The role of large-scale modes of climate variability on the Cape Point wave record Jennifer Veitch1, Andrew Birkett2, Juliet Hermes1, Christo Rautenbach, Chris Reason2, Marius Roussouw4 and Daniel Wilson2 1. South African Environmental and Observational Network (SAEON), Egagasini node 2. Department of Oceanography, University of Cape Town 4. WSP Parsons Brinckerhoff Coastal and Port Engineering, Stellenbosch

2 Atmospheric forcing SLP: NCEP reanalysis climatology (1948-present)

3 Atmospheric forcing Seasonal cyclone density* based on NCEP data 1000/degree2 * The cyclone statistics were obtained from the University of Melbourne Automatic Cyclone Tracking web page ( These are computed using the automatic cyclone tracking scheme of Murray and Simmonds (Murray and Simmonds 1991a; Murray and Simmonds 1991b; Simmonds et al. 1999; Simmonds and Murray 1999; Simmonds et al. 2003; Lim and Simmonds 2007).

4 Atmospheric forcing Dominant modes of large-scale atmospheric variability Colberg (2014): ENSO results in N-S movements of the SAA, causing a intensified (weakened) westerly wind belt during El Nino (La Nina). Southern Annular Mode (SAM) is characterized in N-S shifts in the westerly wind belt. A positive (negative) SAM is associated with a poleward (equatorward) shift.

5 Operational monitoring Four real-time monitoring buoys Van der Westhuysen, 2002

6 Operational monitoring

7 Operational monitoring The Cape Point wave record 1978-present Slangkop: Cape Point: 1994-present 3-hourly averages Joubert, 2008

8 Operational monitoring The Cape Point wave record: climatology

9 Operational monitoring The Cape Point wave record 1978-present* *data courtesy of Transnet and CSIR

10 Operational monitoring The Cape Point wave record 1978-present data coverage: 72% new position (100 m shallower) data coverage: 92% several different instruments

11 Operational monitoring The Cape Point wave record 1978-present data coverage: 72% new position (100 m shallower) data coverage: 92% several different instruments

12 Wave model CSIRO Wavewatch III model data* Forced with: 0.3o CFSR hourly winds and daily sea-ice Horizontal resolution: 0.4o global grid Closest grid point: -34oS, 18oE Durrant et al, 2013 Source: Bureau of Meteorology and CSIRO 2013

13 Wave model vs buoy data Model output underestimates period and overestimates height Why the discrepencies? Problems with dissipation Lack of wave-current interaction in the model Deficiencies in capturing the full wave spectrum

14 Wave model vs buoy data Model overestimates height Wave-current interaction can cause wave refraction or, as in the case of 'Rogue' waves off the southern African east coast, amplification of the waves. Currents are not included in the model [The Benguela Jet is] 'a river in the sea' - Marius Roussouw

15 Wave model vs buoy data

16 Wave model climatology Wave roses: significant wave height and direction Waves tend to be bigger during winter months and with a more westerly component

17 Wave model climatology Histogram of significant wave heights Waves tend to be bigger during winter months and with a more westerly component

18 SAM and ENSO

19 SAM and ENSO

20 ENSO Direction Scatter plot of model direction and height, El Nińo in red, La Nina in blue Significant wave height

21 SAM Direction Scatter plot of model direction and height, +SAM in red, -SAM in blue Significant wave height

22 SAM and ENSO Scatter plot of ENSO and SAM for all DJF months pre and beyond - ENSO, +SAM more frequent since 1998

23 SAM and ENSO Scatter plot of ENSO and SAM with wave model data in colour When ENSO is negative (positive) and SAM is positive (negative), wave direction tends to be more southerly

24 SAM and ENSO Scatter plot of ENSO and SAM with wave model data in colour When ENSO is negative (positive) and SAM is positive (negative), wave height tends to decrease When ENSO is negative (positive) and SAM is positive (negative), wave direction tends to be more southerly

25 SAM and ENSO Scatter plot of ENSO and SAM with wave model data in colour When ENSO is negative (positive) and SAM is positive (negative), wave height tends to decrease When ENSO is negative (positive) and SAM is positive (negative), wave direction tends to be more southerly When ENSO is negative (positive) and SAM is positive (negative), wave period tends to decrease

26 SAM and ENSO +ENSO, -SAM ENSO Model DJF wave rose composties for various combinations of SAM and ENSO +ENSO, +SAM DJF climatology SAM -ENSO, -SAM -ENSO, +SAM

27 SAM and ENSO Model DJF histograms of wave height for various combinations of SAM and ENSO +ENSO, -SAM +ENSO, +SAM Climatology in red -ENSO, -SAM -ENSO, +SAM

28 SAM and ENSO DJF cyclone density anomalies: + ENSO, - SAM Climatology in red -ENSO, +SAM

29 SAM and ENSO DJF cyclone density anomalies: - ENSO, + SAM Climatology in red -ENSO, +SAM

30 Extreme events: wave model vs buoy Significant wave height > 6.5 m for > 6 hours (our seasonal expectation)

31 Extreme events Significant wave height > 6.5 m for > 6 hours more westerly? more southerly? a decreasing trend? These trends are consistent with increasing instances of +SAM, -ENSO

32 Synthesis +ENSO, +SAM +ENSO, -SAM More westerly direction Wider spread Narrower spread -ENSO, +SAM Smaller waves -ENSO, -SAM Bigger waves SAM seems to have the dominant effect on wave height throughout the year More southerly direction When out of phase with SAM, ENSO influences the swell direction

33 Last word Relevance in the context of operational oceanography - CSIROs Wavewatch III hindcast model reproduces wave anomalies and extreme events well but underestimates wave period and overestimates wave height, suggesting the importance of wave-current interactions - Large-scale modes of climate variability interact to either amplify or reduce the frequency and height of extreme waves and therefore need to be properly resolved in operational models Ongoing work A rigorous statistical analysis of the relationship between atmospheric variables associated with ENSO and SAM patterns and the CP wave record An investigation of the large-scale atmospheric setting of extreme wave events

34 Thank you! References Colberg, F., C. Reason and K. Rodgers (2004): South Atlantic response to El Nino-Southern Oscillation induced climate variability in an ocean general circulation model. Journal of Geophysical Research, 109. Durrant, Thomas; Hemer, Mark; Trenham, Claire; Greenslade, Diana (2013): CAWCR Wave Hindcast v7. CSIRO. Data Collection. Joubert, J.R., MSc (2008) An investigation of the wave energy resource on the South African coast, focusing on the spatial distribution OF the South West Coast. Stellenosch University Murray, R. J., and I. Simmonds, 1991a: A numerical scheme for tracking cyclone centres from digital data. Part I: Development and operation of the scheme. Australian Meteorological Magazine, 39, Van der Westhuysen, A.J. (2002): The application of the numerical wind-wave model SWAN to a selected filed case on the South African coast. PhD thesis, University of Stellenbosch.