Surface wind-wave climate of the Pacific region: Variability, trends and future projections. Mark Hemer, Jack Katzfey and Galina Kelareva

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1 Surface wind-wave climate of the Pacific region: Variability, trends and future projections Mark Hemer, Jack Katzfey and Galina Kelareva The Centre for Australian Weather and Climate Research: A Partnership between the Bureau of Meteorology and CSIRO Talk outline Wind-waves in the climate context Project aims Phase 1: Climate drivers of historical wave climate variability Phase 2: Wave climate projections under future scenarios Summary 1

2 Coastal impacts and climate change Warming Atmosphere and Oceans Sea-level rise ( m by 2100, IPCC AR4) Changes to weather systems and storms CHANGING RISK OF COASTAL IMPACTS IPCC AR4 (WG-2) Chapter 6: Coastal systems and low-lying areas. 6.8 Key uncertainties, research gaps and priorities [On climate change impact assessments in the coastal zone] There also remains a strong focus on sea-level rise, which needs to be broadened to include all the climate drivers in the coastal zone (Table 6.2). Nicholls, R.J. et al. (2007) Coastal systems and low-lying areas. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Parry, SOPAC STAR Conference M.L. et 2010 al. (eds.)]. Cambridge University Press, Cambridge, Uniited Kingdon and New York, NY, USA. INUNDATION: wind wave setup is the dominant contributor to coastal flooding events EROSION: wind waves drive coastal sediment budgets. A shift in direction may lead to erosion. Storm surge (m) Steep slope grid wind speed (m/s) Shallow slope grid wind speed (m/s) Wind-wave influences on the Pacific Islands and Territories DISTURBANCE: Marine habitats are characterised by the wave climate (energy) at that site. LAGOON CIRCULATION: wind wave setup drives lagoon circulation. Changing wave conditions may influence flushing 2

3 Project Aims: Phase 1: Make use of existing available wave data in the Pacific basin, characterise mean seasonal conditions, the historical inter-annual variability and/or trends, and the key climatological drivers of variability in the present day wave climate. Phase 2: Develop wave climate projections under future climate scenarios, for the Pacific basin, consideration of near-term future ( ) and end of century ( ). Phase 1 (Available data) Satellite altimeter data (8 Missions) Global wave reanalyses ECMWF ERA-40 ERA-Interim Global wave models CSIRO (Phase 2 of project) Waverider buoy data (NDBC and SOPAC) 3

4 Phase 1: Preliminary Results (ERA-Interim: ) High latitude storm belt High latitude storm belt Mean Annual Significant Wave Height (m) Correlation Coefficient Map EF C-ERA40 components vs SOI (All monthly means) E F = E.c g = F (Hs, Tm, Dm), E F is a vector (eastwards component, EFu, northwards component EFv) Pearson s correlation coefficient, R. Bounded regions indicate significant correlation at 95% confidence level. Hemer et al. (2010) 4

5 Comparisons of H S trends in Satellite Era. Ongoing work: Test robustness of these results using other available datasets, with the focus being on the full Pacific basin H S Trend (m/yr) Phase 2: Wave climate projections IPCC AR4 (WG1) Box 11.5: Coastal Zone Climate Change Introduction. There is insufficient information on changes in waves or near-coastal currents to provide an assessment of the effects of climate change on erosion. Christensen, J.H. et al. (2007) Regional Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 5

6 Map of current regional projections Global projections: Wang & Swail, 2006 Mori et al., 2009 Regional projections (methodology) Subset of CMIP outputs GCM1 Scenario A GCM2 Scenario A GCM1 Scenario B GCM2 Scenario B Regional Climate Model RCM1 Scenario A RCM2 Scenario A RCM1 Scenario B RCM2 Scenario B Near surface winds force wave model Typically for time slices (present, future) Wave1 Scenario A Wave2 Scenario A Wave1 Scenario B Wave2 Scenario B Ensemble mean wave projection Scenario A Ensemble mean wave projection Scenario B 6

7 Dynamical downscaling (PCCSP) and wave projection methodology Using multiple global climate models (GCMs) to capture uncertainty of future climate change. (SRES A2 scenario) 1. CSIRO Mk ECHAM5 2. MIROC 5. HadCM3 3. GFDLcm GFDLcm2.1 The method used is: 1. Correct sea surface temperature biases from global climate models (GCMs) 2. Downscale to 60 km resolution (CSIRO CCAM model) 3. Use 60km resolution surface winds to force global 1 degree wave model GCM Bias corrected SST-only Global 60 km Surface Winds and Sea-Ice only Global wave model WAVE MODELLING Global 1 degree wave model WaveWatch III (v3.14, default configuration) Forced with global CCAM winds 1 st run. CCAM ECHAM5 SRES A2 Two time-slices Present Future Mean Hs (m) CCAM ECHAM5 ERA-Interim: 10yr mean Δ H S (m) 7

8 Phase 2: ongoing work Validation of climate model forced wave climate for present time-slice Repeat runs with other climate model forcing (assess uncertainty) Aiming for follow-on project to provide detailed coastal assessments for specific islands. PCCSP climate model downscaling to 8km at selected PICTs. Use these projections to generate high res wave projections IPCC AR4 projections Likely ranges include uncertainties between: - climate models (multi-model ensembles), and - model versions (perturbed physics ensembles) Figure 3.2. Surface warming for SRES scenarios. Best estimates, and likely ranges IPCC AR4 (2007) Synthesis Report 8

9 Available Global Wave Projections: Existing Global projections Wang & Swail, 2006 Statistical Hs proj n. Ensemble mean: CGCM2 (3PPE); HADCM3 (1PPE); ECHAM4/OPYC3 (1PPE) SRES A2 scenario diff m Mori et al., 2009 Dynamical (SWAN) Hs proj n. 20km MRI/JMA AGCM IPCC AR4 (CMIP3) ensemble mean SST as BBforcing SRES A1B scenario mean mean diff (m) Towards a coordinated approach to global wave projections (Hemer et al., 2010) Emission Scenario SRES Scenario Multi-Model Ensembles Climate Modelling Centre A Climate Modelling Centre B Climate Modelling Centre C Perturbed Physics Ensembles Wave Projection Ensembles PPE1 PPE2 PPE3 PPE1 PPE2 PPE1 PPE2 PPE3 Statistical wave projection?? Dynamical wave projection Wave Modelling Group A (model1) Wave Modelling Group B (model2) e.g.,???? 1. Raw or corrected forcing/covariate, 2. Perturbed physics in dynamic wave model, WCRP/JCOMM workshop on coordinated wave climate projections (April 2011, Geneva) 9

10 Summary Important to understand climatological influence on wind-waves for Pacific Island coastal impact assessments Phase 1 of project ongoing investigating key climatological drivers of historical wave climate variability Phase 2 of project ongoing projecting wave climate for the Pacific basin, with the framework of internationally coordinated global wave climate projections. Thankyou Dr. Mark Hemer The Centre for Australian Weather and Climate Research: A Partnership between the Bureau of Meteorology and CSIRO Mark.Hemer@csiro.au 10

11 IPCC AR4 (WG-2) Chapter 6: Coastal systems and low-lying areas. 6.8 Key uncertainties, research gaps and priorities [On climate change impact assessments in the coastal zone] There also remains a strong focus on sea-level rise, which needs to be broadened to include all the climate drivers in the coastal zone (Table 6.2). Nicholls, R.J. et al. (2007) Coastal systems and low-lying areas. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Parry, SOPAC STAR Conference M.L. et al (eds.)]. Cambridge University Press, Cambridge, Uniited Kingdon and New York, NY, USA. Dominant contribution to coastal inundation on Pacific Islands is from wave setup Wave driven inundation event, Cyclone Meena, Rarotonga, Feb 2005 Potential Impacts: Flooding Infrastructure damage freshwater contamination, etc. 11

12 Steep slope grid Steep slope grid Shallow slope grid wind speed (m/s) wind speed (m/s) Steep slope grid Shallow slope grid Shallow slope grid wind speed (m/s) wind speed (m/s) Waves are a key driver of geomorphological response An Island with sandy beaches: No waves 12

13 Waves are a key driver of geomorphological response (erosion and accretion) of islands An Island with sandy beaches: Southerly waves Wave front Wave front Waves are a key driver of geomorphological response (erosion and accretion) of islands An Island with sandy beaches: Wave climate rotates to South-Westerly Erosion Accretion Wave front Wave front Potential impacts: loss of useable land 13

14 Disturbance/Sediment supply Marine habitats are characterised by the energy of the site. Wave climate characterises large scale reef morphology, species distributions and nutrient uptake. Waves drive circulation within reef lagoons. Changed conditions may alter flushing times, water quality and sand budgets. From Lowe et al.,

15 Phase 1: Available Data Dataset Variables Time-span Resolution Reanalyses: ERA-40 (Uppala et al. 2005) Hs, Tm, Dm hr, 2.5 lat-lon C-ERA-40 (Sterl and Caires, 2005) Hs hr, 1.5 lat-lon ERA-Interim (ECMWF) Hs, Tm, Dm hr, 1.5 lat-lon Buoy records: US NDBC Hs, Tm, Tp (Dm, Dp) Hourly, 5 buoys SOPAC Hs, Tm, Tp XXXX-XXXX Hourly, X buoys Satellite Altimeter 8 Missions Hs Variable Global Wave Models CSIRO (Phase II) + potential others Hs, Tm, Dm hr, 1 lat-lon Directional Distribution (ERA-40) % 0 15

16 Directional Distribution Trends (ERA-40: ) 1 0 %/yr -1 Map of current regional projections Netherlands USA Bahamas, Belize Cayman Islands, Turk and Caicos Islands Gambia Guyana, Suriname Egypt Djibouti Bangladesh Vietnam India Thailand China, Japan Philippines Marshall Islands Tuvalu Maldives Indonesia Global projections: Wang & Swail, 2006 Mori et al., 2009 Countries with highest share of population within Low Elevation Coastal Zone (all countries) Countries with most population within Low Elevation Coastal Zone, McGranahan et al. (2007) 16

17 Projected change in mean Hs: 2070:2079 to 1979:1988 mean Hs: 1979: Km region selection criteria Region Criterion 1 Criterion 2 Criterion 3 Criterion 4 Likely Climate Mountains Population impact regime PNG high monsoon many 6,732,000 East Timor moderate monsoon many 1,134,000 Fiji moderate SPCZ many 849,000 Solomon Is. moderate SPCZ some 523,000 Vanuatu moderate SPCZ some 240,000 Samoa moderate SPCZ some 179,000 FSM low ITCZ few 111,000 Tonga low SPCZ few 104,000 Kiribati low ITCZ few 98,000 Marshall Is. low ITCZ few 62,000 Palau low monsoon few 20,000 Cook Is. low SPCZ few 20,000 Nauru low ITCZ few 10,000 Tuvalu low SPCZ few 10,000 Niue low SPCZ few 1,500 17

18 8 Km domains 8 km domains in red Extra DDS domain in green 18

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