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: Mark.Hemer@csiro.au

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