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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.

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Presentation on theme: "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."— Presentation transcript:

1 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

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

3 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 Vegetation Soil

4 Measurements and process studies, RESM and GESM

5 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

6 Momentum, Energy, H 2 O, CO 2 Land HD JSBACH Atmosphere ECHAM6 T63/L47 T159/L95 Solar variations Volcanic aerosol CO 2 emissions Natural forcing Anthropogenic forcing Land use change CH 4, N 2 O, CFC conc. Ocean MPIOM 1°/L40 0.4°/L80 HAMOCC MPI - IPCC AR5 Earth System Model

7 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 Flux Water Flux Momentum Flux Global Atmosphere Global Ocean & Sea-Ice SST Sea-ice The high resolution CMCC-MODEL High-resolution, short-term (decadal) prediction experiments

8 Aerosols Clouds Momentum Heat Water CH 4 CO 2 H L H H Run-off Tracer Ozone O OO Sea ice Ocean currents Arctic components of the Earth system, © Dethloff 2009

9 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)

10 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

11 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


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