CLIMARES WP 110

Climate model scenarios for the Arctic region for the next decades

Current state: Klaus Dethloff, AWI

WP Leader: Erich Roeckner, MPI

Planing Meeting, 21. Oktober 2009, Bergen

Palmer et al. BAMS 2008

Climate feedbacks and chains, Palmer et al., BAMS, 2008

Main objective:

GESM and RESM with special focus on improvements in the Arctic based on data:

comprising global and regional atmospheric models with improved feedbacks and significantly increased resolution coupling to ocean, sea ice, land, soil, vegetation, chemistry, aerosols etc.

These models need to be set-up: provided with adequate parameterizations validated in order to describe past, ongoing and future regional climate changes in spatial and temporal details needed for different scientific & managerial applications, Northern Sea routes

Sea ice

VegetationSoil

Measurements and process studies, RESM and GESM

Relative importance of sources of uncertainty (total, scenario, model physics, internal variability) in decadal mean surface temperature projections. Fractional uncertainty (the 90% confidence level divided by the mean prediction) for the British Isles mean, relative to the warming from the 1971–2000 mean. The importance of model uncertainty is visible for all policy relevant timescales. Internal variability grows in importance for the smaller region Added value of RCM

Sources of uncertainties in decadal surface temperature prediction

Hawkins & Sutton, BAMS 2009

Momentum, Energy, H2O, CO2

LandHD

JSBACH

AtmosphereECHAM6 T63/L47

T159/L95

Solar variations

Volcanic aerosolCO2 emissions

Natural forcingAnthropogenic forcing

Land use change

CH4, N2O, CFC conc.

OceanMPIOM 1°/L40

0.4°/L80HAMOCC

MPI - IPCC AR5 Earth System Model

OCEAN (dynamics and physics) NEMO/ORCA2 (Barnier et al. 2006)

SEA-ICE: LIM (Timmermann et al. 2005)

ATMOSPHERE (dynamics, physics, prescribed gases and aerosols)

ECHAM5 T159 - L31 Roeckner et al. (2006)

T63-L95 (stratosphere resolving) (Manzini et al. 2006)

COUPLER Oasis 3

Valcke et al. (2004) COUPLER

Heat FluxWater Flux Momentum Flux

Global Atmosphere

Global Ocean& Sea-Ice

SSTSea-ice

The high resolution CMCC-MODEL

High-resolution, short-term (decadal) prediction experiments

Aerosols

Clouds

Momentum

Heat

Water

CH4

CO2

H

L

H

H

Run-off

Tracer

Ozone

O OO

Sea ice

Ocean currents

Arctic components of the Earth system, © Dethloff 2009

Regional climate model, Arctic integration area

High horizontal resolution, improved simulation of hydrodynamical instabilities and baroclinic cyclones

GCM (ERA40) RCM HIRHAM, 25 or 50 km Initial & boundary conditions for the RCM provided by ERA40 data

(m)

Relative importance of internal versus external processes Coupled Regional Atmosphere-Ocean-Sea Ice Model of the Arctic Sea ice is an integrator of oceanic and atmospheric changes

Atmosphere model HIRHAM- parallelized version- 110×100 grid points- horizontal resolution 0.5°- 19 vertical levels

Ocean–ice model NAOSIM- based on MOM-2 - Elastic-Viscous Plastic ice

dynamics- 242×169 grid points- horizontal resolution 0.25°- 30 vertical levels

Boundary forcing ERA-40 or NCEP

WP 110: Topics and Contributors

MPI : Global climate simulations and new sea ice model in ECHAM5-OM1, E. Roeckner

CMCC : Short term climate change projections with a GESM, S. Gualdi

AWI : Ensemble simulations with a regional ESM of the Arctic, K. Dethloff

MGO : Ensemble simulation of regional climate in the Arctic, V. Meleshko

AARI : Boundary layer over and under sea ice for improved parameterizations, ???

RIHMI-WDC : Meteorol. & oceanog. data sets for verification of climate models, N. Michailov

MMBI : Bio-oceanological data bases, D. Moisseev

GEUS : Use of paleoclimatic data for modelling future scenarios, N. Mikkelsen