1. Ensemble simulations with a Regional Earth System Model of the Arctic
CLIMARES WP 110
Ensemble simulations with a Regional Earth System Model of the Arctic
Klaus Dethloff, Annette Rinke, Wolfgang Dorn
Alfred Wegener Institute of Polar and Marine Research, Research Unit Potsdam, (AWI)
Planing Meeting, 21. Oktober 2009, Bergen 1

2. Main objective:
develop coupled Earth system models of the Arctic at the regional scale based on data:
comprising regional atmospheric models
with improved feedbacks and significantly
increased resolution
coupling to ocean, sea ice, land, soil, vegetation, chemistry, aerosols and ...
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
Soil
Vegetation
Sea ice 2

3. Arctic Planetary Boundary Layer and Clouds
Science Issues requiring special attention in Arctic regional and global models
Arctic Planetary Boundary Layer and Clouds
Snow and Land-Surface Processes
Arctic Sea Ice
Arctic Aerosols
Arctic Ozone (Tropo-Stratosphere)
Ocean dynamics and THC
Carbon and Methane Cycles
Horizontal and vertical resolution
Arctic vegetation systems
Glaciers

4. Polar 5 Measurements PAM-ARCMIP - ROUTE (30.03. - 28.04.2009)
Pan Arctic Measurements and Arctic Regional Climate Model Intercom. Proj.

5. Polar 5 Measurements PAM-ARCMIP - ROUTE (30.03. - 28.04.2009)

6. Sea ice thickness with POLAR5, Courtesy of C. Haas, Flight to NP 36.

7. Flight to NP 36.
Polar 5 on NP April 2009

8. Structure of the shallow stable APBL

9. RESM results are sensitive to the choice of
the integration domain,
lateral and lower boundary conditions,
horizontal and vertical resolution,
parameterizations.
Regionally coupled models of the climate system and improved data sets can contribute to the attribution of ongoing changes.
Development of new ideas (e.g. sea ice albedo, organic soil layer) for improving global models in the Arctic.
Two-way feedbacks has to be considered within a global model setup by regionally focused modelling of the area of interest.

10. Arctic components of the Earth system, © Dethloff 2009
Aerosols
Clouds
Momentum
Heat
Water
CH4
CO2 H L
Run-off
Tracer
Ozone O
Sea ice
Ocean currents 10

11. Synthesis of the regional model results will enable us to
Objectives:
Synthesis of the regional model results will enable us to
to understand recent regional climate changes through
simulation and validation,
to distinguish variability arising from Arctic-internal processes
and externally forced variability,
to carry out ensemble modelling of future regional climate
changes over the Arctic Ocean,
to reduce uncertainties of climate projections for the Arctic
Ocean. 11

12. 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 12

13. Coupled regional Atmosphere-Ocean-Sea Ice Model

14. Standard deviation of sea ice concentration (%)
Satellite observations
Coupled model simulations
Importance of internal (mainly atmospheric) variability in Arctic system
Standard deviation of sea ice concentration (%)
September , spin up time: , Dorn et al. 2007 14

15. (June-September) between “high-ice” minus “low-ice” years (1988-2000)
Differences in September sea level pressure (hPa) and ice drift vectors
(June-September) between “high-ice” minus “low-ice” years ( )
High-ice years are 1996, 1988, 1992, and 1994 in the observation and 1989, 1996, 1988, and 1997 in simulation,
Low-ice years are 1995, 1990, 1999, and 2000 in the observation and 1992, 1999, 1993, and 1991 in simulation.
Influence of summertime atmospheric circulation on sea ice drift
Cyclonic ice drift pattern during high-ice years
Anticyclonic ice drift pattern during low-ice year
Dorn et al. , Open Atmos. Sci. J. , 2, 2008

16. Sensitivity experiments over the Arctic Ocean Simulations from Dec
Sensitivity experiments over the Arctic Ocean Simulations from Dec Dec  Improved descriptions for sea ice growth, sea ice albedo parameterization and snow cover parameterization, Dorn, Dethloff, Rinke, Ocean Modelling 2009, 29,

17. Arctic Sea-Ice Extent, Dec. 1997-1998, Sensitivity Exp.
Already after one year there are model deviations in ice volume of up to 4500 km3 (one third of the total volume) as a result of altered sea ice- snow albedo parameterizations.

18. Sea-Ice Thickness (m), September 1998

19. Mean seasonal cycle of net surface heat flux (W/m2)
over the Arctic Ocean north of 70 ° N
June
Summer surface heat fluxes depends on parameterization schemes
for ice growth, sea ice albedo and snow cover
Importance of regional A-O-I feedbacks during the summer months
Changes in atmospheric static stability impact on baroclinic systems

20. Simulations with atmospheric and coupled A-O-I models for
future scenarios over the Arctic
Change in 2m air temperature [°C]. Colors show the changes "SCEN minus CTRL", isoline the present-day "CTRL".
Change in seasonal mean. The difference is significant at the 95th significance level for all grid points.
Change in seasonal standard deviation. The dotted areas represent the 95% significance., Rinke, Dethloff, 2008. 20

21. Milestones:
Improved sea ice and related feedbacks in an Arctic coupled A-O-I model.
Improved parameterizations of ice growth and onset of snow melt, of sea ice albedo, of snow cover and melt ponds
Analysis of factors for reproducing observed recent sea ice variability in an Arctic coupled Earth system model.
Ensemble of coupled RCM simulations to assess coupled feedbacks in the atmosphere-ice-ocean system.
Ensemble of uncoupled and coupled RCM simulations for future climate scenarios, until 2030 and until 2010. 21

22. Work plan
Realization of multi-decadal-long regional model simulations with the pure atmospheric HIRHAM and the coupled model system HIRHAM-NAOSIM.
Validation of regional model results in comparison with observations and identification of model deficiencies.
Improvements of feedbacks within the coupled Arctic climate system with respect to sea-ice and snow.
Ensemble simulations for future climate time slices with HIRHAM (Until 2030 and 2100) and HIRHAM-NAOSIM (until 2030 with different ocean-sea ice initial states).
Quantification of the impact of Arctic processes on projections of the future climate and Arctic passages based on GESM ECHAM-OM1 simulations
Estimation of the relative importance of regional processes inside the Arctic climate system and the large scale atmospheric circulation for the inter-annual climate variability of sea ice cover. 22

23. Deliverables of coupled Arctic RCMs: atmospheric component
mean sea level pressure
surface temperature / 2-m temperature
surface fluxes (sensible and latent heat fluxes, short-wave and long-wave radiative fluxes)
surface wind stress
cloud cover
precipitation

24. Deliverables of coupled Arctic RCMs: ocean ‒ ice component
ice concentration
ice thickness
snow thickness (+ snow cover fraction)
mixed layer temperature (+ salinity)
ice drift velocity
upper ocean current
Manpower: 2 PhD student positions

25. The end 25

26. Improved parameterization schemes
ICE GROWTH PARAMETERIZATION:
Old scheme: Melting of ice did not occur until the snow layer has disappeared
Snow disappeared too earlyleading to earlier ice melt
since the albedo of a snow free sea-ice cover is
higherdecreased energy input occured
 and thus lower ice melt.
New scheme: Possibility of ice melt even if snow layer is present
 Improved onset of snow melting
 More realistic representation of the two stage snow-albedo/
ice-albedo feedback
 improved simulation of summer maximum in net short-wave
radiative flux and the net surface heat flux

27. SEA ICE ALBEDO PARAMETERIZATION:
In Reality: Albedo of sea ice depends on overlying snow cover, ice age, ice
thickness, brine volume, melt ponds
Albedo of snow depends on grain size, affected by snow age,
contamination of snow by aerosol particles
Albedo is affected by angle of incident solar radiation and
clouds
Old scheme: Model albedo a linear parameterization function of the
ice/snow surface temperature
New scheme: (Køltzow et al., 2007)
3 different surface types (snow covered ice, bare sea-ice,
melt ponds and leads)
Stronger surface temperature dependent linear scheme
Reduced albedo for melting conditions, if snow already
disappeared.

28. SNOW COVER PARAMETERIZATION:
New scheme: This new snow cover scheme does not allow the formation of
melt ponds as long as the snow cover is still thick
New snow cover fraction on sea ice as a function of snow
thickness described by a tanh approach
 Validation against SHEBA measurements
Important for the initiation of the snow melt period in summer
The onset of ice decay and its strength depend on the time of
disappearance of the snow layer
If snow fraction is too low, melt ponds can form earlier with the result
of amplified melting further reduction of snow cover and too early
beginning of sea ice retreat
If melt ponds are neglected albedo in summer is too high, leading to
underestimated net surface heat fluxes and insufficient loss of ice
during the melting period

29. Improved ice growth scheme

30. Improved ice albedo parameterization

31. Improved snow cover parameterization