cowclip coordinated ocean wave climate projections: an ... · coordinated ocean wave climate...
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Coordinated Ocean Wave Climate Projections: COWCLIP
Mark Hemer CSIRO Wealth from Oceans Flagship
and Centre for Australian Weather and Climate Research:
A partnership between CSIRO and the Bureau of MeteorologyHobart, TAS, Australia
With input from:Xiaolan Wang and Val Swail – Environment CanadaRalf Weisse – Helmholtz-Zentrum Geesthacht, GermanyAlvaro Semedo – Escola Naval-CINAV, Portugal and Uppsala University, SwedenNobuhito Mori – Kyoto University, JapanYalin Fan – GFDL, USAand other COWCLIP participants (see http://www.jcomm.info/COWCLIP)
Coastal Impacts of Climate Change
Warming Oceans and Climate
Sea-level rise(0.2 – 0.8 m by 2100, IPCC AR4)
Changes to weather systems and storms
CHANGING RISK OF COASTAL IMPACTS(Felt most acutely via extreme events –
storm surges and large wave events)
Disturbance (aquatic & terrestrial) Inundation (SLR/Surge/Setup) Erosion (SLR/runup/wave dirn changes)
Map of current regional projections
Global projections: Wang & Swail, 2006Mori et al., 2009
Maldives
Marshall Islands
Tuvalu
Bahamas, Belize Cayman Islands, Turk and Caicos
IslandsGuyana, 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
Thailand
Philippines
Summarising current limitations of wave projections
• Lack of global coverage– Regions of higher risk (vulnerability or hazard) may be overlooked
• Poor sampling of uncertainty- Limited scenario, inter and intra model ensembles considered
• Inconsistencies in output parameters makes intercomparison difficult– No community standard for scenarios/projection
periods/variables/
COWCLIP Aims:To generate global wave climate projections and aid comprehensive
assessments of their cascading uncertainty by:• Providing a systematic, community-based framework and infrastructure to
support validation, intercomparison, documentation and data access for wave climate projections forced from CMIP5 datasets,
• To describe best practice for regional wave projections• Engaging interests of the wind-wave community into wider climate
community and ultimately moving to coupled wind-wave AOGCM models, enabling quantitative estimates of wave-driven feedbacks in coupled climate system.
WCRP/JCOMM COWCLIP April 2011 Workshop Outcomes: http://www.jcomm.info/COWCLIP
The COWCLIP strawman
RCP Pathway
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,
…
?? ??
COWCLIP pilot phase (AR5 time-scale)• Quantitative intercomparison of available global wave climate
projections.• Contributors
– CAWCR (Hemer)– Environment Canada (Wang)– Escola Naval-CINAV, Portugal, Uppsala Univ, HZG, MPI-M, U. Reading (Semedo)– GFDL (Fan)– Kyoto Univ (Mori)
• Not a designed approach– Overlooks differences in experimental design (scenario, GCM, period, …)
• Aims– Assess robustness of available wave climate projections– Raise the profile of wave climate issues in the climate community– Develop a collaborative framework for COWCLIP production phase– Produce recommendations of how COWCLIP can be best supported, and contribute.– Compile details on CPU and disk space for processing and archive requirements– Encourage uptake of greater community involvement in ongoing COWCLIP activities
COWCLIP Contributions
SRES A2
GCMs: ECHAM5
Mk3.5
RCM: 60km CCAM
WW3 WW3
BA SST
BA U10
1979-20092070-2099
2m EM 2m EM
SRES A1B
GCMs: 23GCM EM
MRI/JMA AGCM20km
SWAN
1979-20052075-2099
Waves
SRES A1B
GCMs: ECHAM5
0.5
WAM
1959-19902059-2100
Waves
SRES A1B
GCMs:18GCM EM
HiRAM
WW3
1979-20092070-2099
Waves
GCM:GFDL2.1
HiRAM
Waves
WW3
Coupled Wave-Atm
SRES A1B
GCMs: CGCM2 (3PPE); HADCM3 (1PPE);
ECHAM4/OPYC3 (1PPE)
Hs
MSLPERA40
Statistical
1990 (1958-2001)
2080
Hemer et al Mori et al Semedo et al Fan et al Wang and Swail
ERA-Interim
Mean Annual HS Bias (Present Climate) (Model-ERA) Mean Annual HS Bias (Model-ERA) (Percentage Error)
Mean Annual HS Difference (Future-Present)
∆t > σe
∆t > 2σe
JFM JAS
AnnualHSN=20
JFM JAS
AnnualDMN=5
∆t > σe
∆t > σe
∆t > 2σe
JFM JAS
AnnualTMN=5
JAS
Mean Annual Cycle: Tasman
Month of Year
Month of Year
MW
D (°
N)
ΔM
WD
(°N
)CAWCRuaWW3
CAWCRbaWW3
SWANmori
GFDLens18
GFDLcm21
Future time-slice
Hemer et al. Regional proj.
Ensemble mean
Future Ensmean
ERA-Interim
DM
Including regional wave climate projection studies
Ongoing COWCLIP activities (1)• COWCLIP wave climate ensemble
– Produce a community ensemble of wave climate projections to aid comprehensive assessment of uncertainty.
– Designed approach – agreed RCP scenarios, time-slices defined by CMIP5 sub-daily surface winds archives (1979-2009, 2026-2045, 2080-2100).
– What benefit can be extracted from the current generation of climate models for marine-meteorological applications?
– How do different methods of deriving wave climate projections inter-compare?– What is the magnitude of uncertainty surrounding projected wave climate change? What
sources dominate?
Wind-wave dependent processes in the coupled climate system
MomentumFluxes
HeatFluxes
MassFluxes
Albedowhitecapping
waves MIZ Extent
Wave dependent momentum flux
e.g., Janssen
& Viterbo,
1997
Sensible and Latent Heate.g.,
Veron et al., 2008
Wave driven turbulence and langmuir circulations
e.g., Babanin et al., 2006, 2009
Marine Aerosol Production
e.g., O’Dowd & de Leeuw,
2007
Bubble injection by breaking waves
e.g., Woolf, 1993
e.g., Frouin et al., 2001
e.g., Squire et al., 1995
Ongoing COWCLIP activities (2)• Support coupling waves into AOGCMs, with the aim to:
• Quantify the magnitude of wind-wave driven feedbacks in the coupled climate system• Several groups working on different aspects of the system (UC-Boulder, FIO-China, GFDL, CAWCR, MPI-M, ….)
Concluding remarks• COWCLIP is providing a framework for quantitative
intercomparison of future wave climate projections, providing information on robustness within available ensemble
• COWCLIP is connecting the wind-wave and climate communities, to establish climate impacts on waves, and the impact of waves on climate.
• COWCLIP Discussion, here after talks Tuesday. All welcome.
Thanks. Contact: [email protected]
A changing wave climate
Linear trends in Hs from VOS data (1950-2002)
Source: IPCC AR4 (Trenberth et al., 2007)
IPCC AR4 (WG-I)
Box 11.5: Coastal Zone Climate Change
IntroductionClimate change has the potential to interact with the coastal zone in a number
of ways including inundation, erosion and salt-water intrusion into thewater table....... There is insufficient information on changes in wavesor near-coastal currents to provide an assessment of the effects ofclimate change on erosion.
…Changes in storm surges and wave heights have been addressed for only a
limited set of models. Thus, we cannot reliably quantify the range of uncertainty in estimates of future coastal flooding and can only make crude estimates of the minimum values (Lowe and Gregory, 2005).
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.
Regional projections (dynamical method)
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
Bias-adjustment?
Bias-adjustment?
Bias-adjustment?
Regional projections (statistical method)
ReanalysisGlobal/Regional
Predictor(MSLP, U10, …)
Predictand(HS, EF, JPD(HS,TP), S(f,θ),…)
Historical climate
F(x,t)Establish a statistical relationship between predictor and predictand
Projected climate GCM/RCM
Predictor(MSLP, U10, …)
Predictand(HS, EF, JPD(HS,TP), S(f,θ),…)F(x,t)
Use statistical relationship and projected predictor to project predictand
Also projections being carried out based on projected diagnostics (e.g., teleconnection indices, etc)
Cascading uncertainty in prediction problems
e.g.
IPCC emissions scenarios
GCMsimulations
RCM simulations
Ballooning Ballooning
How do we quantify uncertainty in this environment?
Wave Model Simulations
Ballooning
A2
A1B
B2
Cascading uncertainty in prediction problems
• To fully quantify variance of complete sample space is potentially very computationally expensive.
e.g.
IPCC emissions scenarios
GCMsimulations
RCM simulations
Wave Model Simulations
Cascading uncertainty in prediction problems
• To fully quantify variance of complete sample space is potentially very computationally expensive.
e.g.
IPCC emissions scenarios
GCMsimulations
RCM simulations
Wave Model Simulations
A need for designed approach to sample variance
2080-1990 diff. Statistical Hs projectionEnsemble mean:
CGCM2 (3PPE); HADCM3 (1PPE); ECHAM4/OPYC3 (1PPE)
SRES A2 scenario
2075-2099 mean – 1979-2003 mean diffDynamical (SWAN) Hs projection20km MRI/JMA AGCM
CMIP3 ensemble mean SST as BB forcing
SRES A1B scenario
0.2
0.12
0.04-0.04
-0.12
-0.2
(m)
Wang & Swail, 2006 Mori et al., 2009