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A Partnership between the Bureau of Meteorology and CSIRO
The Centre for Australian Weather and Climate Research:
Surface wind-wave climate of the Pacific region: Variability, trends and future projections
Mark Hemer, Jack Katzfey and Galina Kelareva
SOPAC STAR Conference 2010
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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
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Coastal impacts and climate change
Warming Atmosphere and Oceans
Sea-level rise(0.2 – 0.8 m by 2100, IPCC AR4)
Changes to weather systems and storms
CHANGING RISK OF COASTAL IMPACTS
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, M.L. et al. (eds.)]. Cambridge University Press, Cambridge, Uniited Kingdon and New York, NY, USA.
IPCC AR4 (WG-2)
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contributor to coastal flooding eventsEROSION: wind-waves drive coastal sediment budgets. A shift in direction may lead to erosion.
LAGOON CIRCULATION: wind-wave setup drives lagoon circulation. Changing wave conditions may influence flushing.
Wind-wave influences on the Pacific Islands and Territories
DISTURBANCE: Marine habitats are characterised by the wave climate (energy) at that site.
Steep slope grid
30 56 wind speed (m/s)
Shallow slope grid
30 56 wind speed (m/s)
Sto
rm s
urg
e (
m)
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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 (2026-2045) and
end of century (2070-2099).
SOPAC STAR Conference 2010
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Phase 1 (Available data)
Satellite altimeter data(8 Missions)
Waverider buoy data (NDBC and SOPAC)
Global wave reanalysesECMWF ERA-40
ERA-Interim
Global wave models CSIRO (Phase 2 of project)
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Phase 1: Preliminary Results (ERA-Interim: 1989-2009)
Mean Annual Significant Wave Height (m)
High latitude storm belt
High latitude storm belt
SOPAC STAR Conference 2010
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Correlation Coefficient MapEFC-ERA40 components vs SOI (All monthly means)
-0.5 0 0.5Pearson’s correlation coefficient, R.
Bounded regions indicate significant correlation at 95% confidence level.
EF = E.cg = F (Hs, Tm, Dm), EF is a vector (eastwards component, EFu, northwards component EFv)
Hemer et al. (2010)
SOPAC STAR Conference 2010
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Comparisons of HS trends in Satellite Era.
-0.05 0 0.05
HS Trend (m/yr)
Ongoing work: Test robustness of these results using other available datasets,
with the focus being on the full Pacific basin
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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.
SOPAC STAR Conference 2010
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Map of current regional projections
Global projections: Wang & Swail, 2006 Mori et al., 2009
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Regional projections (methodology)
GCM1Scenario A
GCM2Scenario A
GCM1Scenario B
GCM2Scenario B
Regional Climate Model
RCM1Scenario A
RCM2Scenario A
RCM1Scenario B
RCM2Scenario B
Near surface winds force wave modelTypically for time slices (present, future)
Wave1Scenario A
Wave2Scenario A
Wave1Scenario B
Wave2Scenario B
Ensemble mean wave projectionScenario A
Ensemble mean wave projectionScenario B
Subset of CMIP outputs
SOPAC STAR Conference 2010
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Dynamical downscaling (PCCSP) and wave projection methodology
Bias correctedSST-only
GCM
Global 60 km
Using multiple global climate models (GCMs) to capture uncertainty of future climate change. (SRES A2 scenario)
1. CSIRO Mk3.5 4. ECHAM5
2. MIROC 5. HadCM3
3. GFDLcm2.0 6. 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
Surface Winds and Sea-Ice only
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Global wave model
H
S (
m)
CCAMECHAM5 – ERA-Interim: 10yr mean
WAVE MODELLING
Global 1 degree wave model
WaveWatch III (v3.14, default configuration)
Forced with global CCAM winds• 1st run. CCAMECHAM5 SRES A2
Two time-slices • Present – 1979-2009
• Future - 2080-2099
Mean Hs (m)
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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
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IPCC AR4 projections
Figure 3.2. Surface warming for SRES scenarios. Best estimates, and 2090-2099 likely rangesIPCC AR4 (2007) Synthesis Report
Likely ranges include uncertainties between: - climate models (multi-model ensembles), and- model versions (perturbed physics ensembles)
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Existing Global projectionsWang & Swail, 2006
Statistical Hs projn.Ensemble mean: CGCM2 (3PPE); HADCM3 (1PPE); ECHAM4/OPYC3 (1PPE)
SRES A2 scenario
2080-1990 diff.
Mori et al., 2009
Dynamical (SWAN) Hs projn.20km MRI/JMA AGCM IPCC AR4 (CMIP3) ensemble mean SST as BBforcing
SRES A1B scenario
2075-2099 mean – 1979-2003 mean diff
0.2
0.12
0.04 m-0.04
-0.12
-0.2
(m)
Available Global Wave Projections:
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SRES Scenario
Climate Modelling Centre A
Climate Modelling Centre B
Climate Modelling Centre C
…
PPE1 PPE2 PPE3 PPE1 PPE2 PPE1 PPE2 PPE3 …
Emission Scenario
Multi-ModelEnsembles
Perturbed Physics Ensembles
Wave Projection Ensembles
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, …
?? ??
Towards a coordinated approach to global wave projections (Hemer et al., 2010)
WCRP/JCOMM workshop on coordinated wave climate projections (April 2011, Geneva)http://www.jcomm.info/cowclip
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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.
SOPAC STAR Conference 2010
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Thankyou
Dr. Mark Hemer
A Partnership between the Bureau of Meteorology and CSIROThe Centre for Australian Weather and Climate Research:
SOPAC STAR Conference 2010
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IPCC AR4 (WG-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, M.L. et al. (eds.)]. Cambridge University Press, Cambridge, Uniited Kingdon and New York, NY, USA.
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).
SOPAC STAR Conference 2010
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Dominant contribution to coastal inundation on Pacific Islands is from wave setup
Wave driven inundation event, Cyclone Meena, Rarotonga, Feb 2005
Potential Impacts: FloodingInfrastructure damagefreshwater contamination, etc.
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Steep slope grid
30 56 wind speed (m/s)
Shallow slope gridSteep slope grid
30 56 wind speed (m/s)
Shallow slope grid
Steep slope grid
30 56 wind speed (m/s)
Shallow slope grid
30 56 wind speed (m/s)
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Waves are a key driver of geomorphological response
An Island with sandy beaches: No waves
SOPAC STAR Conference 2010
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Waves are a key driver of geomorphological response (erosion and accretion) of islands
An Island with sandy beaches: Southerly waves
Wave front Wave front
SOPAC STAR Conference 2010
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Waves are a key driver of geomorphological response (erosion and accretion) of islands
An Island with sandy beaches: Wave climate rotates to South-Westerly
Wave front
Wave front
Erosion
Accretion
Potential impacts: loss of useable land
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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.
SOPAC STAR Conference 2010
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conditions may alter flushing times, water quality and sand budgets.
From Lowe et al., 2009
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Phase 1: Available Data
Dataset Variables Time-span Resolution
Reanalyses:
ERA-40
(Uppala et al. 2005)
Hs, Tm, Dm 1957-2002 6-hr, 2.5° lat-lon
C-ERA-40 (Sterl and Caires, 2005)
Hs 1957-2002 6-hr, 1.5° lat-lon
ERA-Interim (ECMWF)
Hs, Tm, Dm 1989-2010 6-hr, 1.5° lat-lon
Buoy records:
US NDBC Hs, Tm, Tp (Dm, Dp) 1973-2010 Hourly, 5 buoys
SOPAC Hs, Tm, Tp XXXX-XXXX Hourly, X buoys
Satellite Altimeter
8 Missions
Hs 1985-2010 Variable
Global Wave Models
CSIRO (Phase II)
+ potential others
Hs, Tm, Dm 1979-2009 6-hr, 1° lat-lon
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Directional Distribution (ERA-40)
100
50 %
0
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Directional Distribution Trends (ERA-40: 1957-2002)
1
0 %/yr
-1
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Map of current regional projections
Global projections: Wang & Swail, 2006 Mori et al., 2009
Maldives
Marshall Islands
Tuvalu
Bahamas, Belize Cayman Islands, Turk and Caicos
Islands
Guyana, Suriname
Netherlands
Bangladesh
Djibouti
Egypt Vietnam
Gambia
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)
China, Japan
USA
India
Indonesia
Thailand
Philippines
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8 Km region selection criteria
Region Criterion 1 Criterion 2 Criterion 3 Criterion 4 Likely
impact Climate regime
Mountains Population
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
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8 Km domains
8 km domains in red Extra DDS domain in green