Download 0302_Hemer_STAR_2010_WindWave

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
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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
SOPAC STAR Conference 2010
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
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,
M.L. et 2010
al. (eds.)]. Cambridge University Press, Cambridge, Uniited Kingdon and New York, NY, USA.
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Storm surge (m)
INUNDATION: wind-wave setup is the dominant
contributor to coastal flooding events
Steep slope grid
30
56
wind speed (m/s)
EROSION: wind-waves drive coastal sediment budgets.
A shift in direction may lead to erosion.
Shallow slope grid
30
56
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.
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LAGOON CIRCULATION: wind-wave setup drives lagoon
circulation. Changing wave conditions may influence flushing.
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).
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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)
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Phase 1: Preliminary Results (ERA-Interim: 1989-2009)
High latitude storm belt
High latitude storm belt
Mean Annual Significant Wave Height (m)
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Correlation Coefficient Map
EFC-ERA40 components vs SOI (All monthly means)
EF = E.cg = F (Hs, Tm, Dm),
EF is a vector (eastwards component, EFu, northwards component EFv)
-0.5
0
Pearson’s correlation coefficient, R.
0.5
Bounded regions indicate significant correlation at 95% confidence level.
SOPAC STAR Conference 2010
Hemer et al. (2010)
Comparisons of HS trends in Satellite Era.
Ongoing work:
Test robustness of these results using other available datasets,
with the focus being on the full Pacific basin
-0.05
0
HS Trend (m/yr)
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0.05
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.
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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)
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
Ensemble mean wave projection
Scenario A
SOPAC STAR Conference 2010
Wave1
Scenario B
Wave2
Scenario B
Ensemble mean wave projection
Scenario B
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 Mk3.5
4. ECHAM5
2. MIROC
5. HadCM3
3. GFDLcm2.0
6. GFDLcm2.1
GCM
Bias corrected
SST-only
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
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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
• 1st run. CCAMECHAM5 SRES A2
Two time-slices
• Present – 1979-2009
• Future - 2080-2099
CCAMECHAM5 – ERA-Interim: 10yr mean
D HS (m)
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
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 2090-2099 likely ranges
IPCC AR4 (2007) Synthesis Report
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Available Global Wave Projections:
Wang & Swail, 2006
Existing Global projections
Statistical Hs projn.
Ensemble mean:
CGCM2 (3PPE);
HADCM3 (1PPE);
ECHAM4/OPYC3 (1PPE)
SRES A2 scenario
2080-1990 diff.
0.2
0.12
0.04 m
-0.04
-0.12
-0.2
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
(m)
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Towards a coordinated approach to global wave
projections (Hemer et al., 2010)
Emission
Scenario
SRES Scenario
Multi-Model
Ensembles
Climate Modelling
Centre A
Perturbed
Physics
Ensembles
PPE1 PPE2
Wave
Projection
Ensembles
PPE3
Statistical wave
projection
??
e.g.,
Climate Modelling
Centre B
Climate Modelling
Centre C
PPE1 PPE2 PPE1 PPE2
PPE3
Dynamical wave
projection
Wave Modelling
Group A (model1)
??
1. Raw or corrected forcing/covariate,
2. Perturbed physics in dynamic wave model,
…
Wave Modelling
Group B (model2)
??
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.
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Thankyou
Dr. Mark Hemer
The Centre for Australian Weather and Climate Research:
A Partnership between the Bureau of Meteorology and CSIRO
[email protected]
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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,
M.L. et al.
(eds.)]. Cambridge University Press, Cambridge, Uniited Kingdon and New York, NY, USA.
SOPAC STAR Conference
2010
Dominant contribution to coastal inundation on Pacific
Islands is from wave setup
Wave driven inundation event, Cyclone Meena, Rarotonga, Feb 2005
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Potential Impacts: Flooding
Infrastructure damage
freshwater contamination, etc.
Steep slope grid
Steep slope grid
30
56
wind speed (m/s)
Shallow slope grid
30
56
wind speed (m/s)
Steep slope grid
Shallow slope grid
Shallow slope grid
30
56
wind speed (m/s)
SOPAC STAR Conference 2010
30
56
wind speed (m/s)
Waves are a key driver of geomorphological response
An Island with sandy beaches: No waves
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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
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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
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.
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Waves drive circulation within reef lagoons. Changed
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
Hs
1957-2002
6-hr, 1.5° lat-lon
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
Hs, Tm, Dm
1979-2009
6-hr, 1° lat-lon
(Sterl and Caires, 2005)
ERA-Interim
(ECMWF)
Global Wave Models
CSIRO (Phase II)
+ potential others
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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
Netherlands
USA
Bahamas, Belize
Cayman Islands,
Turk and Caicos
Islands Gambia
Guyana,
Suriname
Bangladesh
Vietnam
Egypt
India
Djibouti
China,
Japan
Philippines
Marshall Islands
Thailand
Tuvalu
Maldives
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)
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8 Km region selection criteria
Region
PNG
East Timor
Fiji
Solomon Is.
Vanuatu
Samoa
FSM
Tonga
Kiribati
Marshall Is.
Palau
Cook Is.
Nauru
Tuvalu
Niue
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Criterion 1
Likely
impact
high
moderate
moderate
moderate
moderate
moderate
low
low
low
low
low
low
low
low
low
Criterion 2
Climate
regime
monsoon
monsoon
SPCZ
SPCZ
SPCZ
SPCZ
ITCZ
SPCZ
ITCZ
ITCZ
monsoon
SPCZ
ITCZ
SPCZ
SPCZ
Criterion 3 Criterion 4
Mountains Population
many
many
many
some
some
some
few
few
few
few
few
few
few
few
few
6,732,000
1,134,000
849,000
523,000
240,000
179,000
111,000
104,000
98,000
62,000
20,000
20,000
10,000
10,000
1,500
8 Km domains
8 km domains in red
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Extra DDS domain in green