Arctic atmosphere-ocean-sea ice-land system modelling at AWI

Klaus Dethloff, Annette Rinke, Wolfgang Dorn, Rüdiger Gerdes, Matthias Läuter, Dörthe Handorf

Alfred Wegener Institute for Polar- and Marine Research, Research Unit Potsdam, AWI

Arctic System Model Workshop Montreal, 16-17 July 2009

This talk:

1. Motivation2. Atmospheric RCM3. Coupled A-O-I RCM4. Coupled A-L-S RCM5. Regionally focused global model

Aerosols

Clouds

Momentum

Heat

Water

CH4

CO2

H

L

H

H

Run-off

Tracer

Ozone

OOO

Sea ice

The Arctic in the global Earth system

Process studies, Regional and Global Earth System ModelsRCM as magnifying glass due to higher resolutionReduction of uncertainties in attribution of current climate changes Improved climate model projections for next IPPC Report

Regional climate model, Arctic integration areaHigh horizontal resolution of regional topographic structures at the surface,

Improved simulation of hydrodynamical instabilities and baroclinic cyclones

GCM (ERA40) RCM HIRHAM, 50 km

Initial & boundary conditionsfor the RCM provided by ERA-40 data

(m)

This talk:

1. Motivation2. Atmospheric RCM3. Coupled A-O-I RCM4. Coupled A-L-S RCM5. Regionally focused global model

NP35:

North Pole drifting station No. 35operated by AARI St. Petersburgfrom October 2007 until July 2008 Contribution to IPY

North Pole drifting station NP 35

Atmospheric observations:

Radiosonde (up to 30 km) (twice every day, at noon and midnight)Tethered balloon (lowest 500 m) (if weather conditions allowed it, at 55 days)Synoptic weather recording (at surface) (four times per day, every 6 hours)

HIRHAM regional climate model

Pan Arctic domain, 110x100 grid points 50 km horizontal resolution 25 vertical levels (lowest level at 10 m, 10 levels in lowest 1 km) boundary forcing by ECMWF operational analyses

Regional atmospheric climate model simulations

Trajectory of NP35 ice campNovember 2007-March 2008

How to compare simulation output with single point observations?

Regional climate model simulations with 2 different setups:

1) HIRHAM in forecast mode - simulation with initialization every 12 hours

2) HIRHAM in climate mode with ensemble approach - initialization only at the beginning of the month - series of simulations with slightly different initial conditions - 5 ensemble members (ctrl, ±6 and ±12 hours initial state) - ensemble mean & across-ensemble member scatter

Regional atmospheric climate model simulations to compare with NP35 data set

Meteorological evolution at NP35 during February 2008-evolution of temperature profile in simulations -

HIRHAM Forecast12

HIRHAM Climate Ensemble mean

Temperature [°C] Temperature bias [°C] HIRHAM F12 - Obs

Temperature [°C]Temperature [°C]

Observation

HIRHAM Bias

Pre

ssu

re [

hP

a]

Pre

ssu

re [

hP

a]

Pre

ssu

re [

hP

a]

Pre

ssu

re [

hP

a]

Stable PBL

This talk:

1. Motivation2. Atmospheric RCM3. Coupled A-O-I RCM4. Coupled A-L-S RCM5. Regionally focused global model

Coupled regional Atmosphere-Ocean-Sea Ice Model

Atmosphere modelAtmosphere model HIRHAM- parallelized versionparallelized version- 110×100 grid points110×100 grid points- horizontal resolution 0.5°horizontal resolution 0.5°- 19 vertical levels19 vertical levels

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

dynamicsdynamics- 242×169 grid points242×169 grid points- horizontal resolution 0.25°horizontal resolution 0.25°- 30 vertical levels30 vertical levels

Boundary forcing ERA-40Boundary forcing ERA-40

High horizontal resolution of regional topographic structures at the surface, Improved simulation of hydro-dynamical instabilities and baroclinic cyclones Sea ice is an integrator of oceanic and atmospheric changes

Sea ice anomaly in Beaufort Sea well simulated by the coupled model

Atmospheric circulation Anticyclonic flow in the Beaufort Sea

Ice growth parameterization during winter Influence on simulated sea ice

Ice albedo parameterization crucial factor for ice melting during summer

Simulation of sea ice concentration anomaly of September 1998 over the Beaufort sea

(SSMI and 12 year long simulations after spin up time of 7 years)

SSMI: Special Sensor Microwave Imager

Coupled RCM

Standard deviation of sea ice concentration (%)

in September 1988-2000, spin up time: 1980-1987

Dorn et al. OASJ 2008Satellite observations Coupled Arctic climate model

Importance of internal variability due to atmospheric processes

Differences in September sea level pressure (hPa) and ice drift vectors (June-September) between “high-ice” minus “low-ice” years (1988-2000)

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.

Cyclonic ice drift pattern during high-ice yearsAnticyclonic ice drift pattern during low-ice years

Strong influence of summertime atmospheric circulation on sea ice drift. Dorn et al. , OASJ, 2, 2008

Arctic Sea-Ice Extent, Dec. 1997-1998, Sensitivity Experiments

Already after one year there are modeldeviations in ice volume of up to 4500 km3

(one third of the total volume) as a result ofaltered sea ice- snow albedo parameterizations.

A-O-I RCM

Combination of improved parameterizations for ice growth, sea ice albedo and snow cover improves the simulation of summer sea ice.

Dorn et al., JGR, 2007, OASJ 2008, OM 2009.

Change from HIRHAM 4 to HIRHAM 5 using the ECHAM 5 physical parameterizations New formulation of long-wave radiation and cloud physics.

Long term simulations up to the year 2008/09 with NCEP and ERA boundary forcing and validation using recent measurements on NP 35 and NP 36 in progress.

This talk:

1. Motivation2. Atmospheric RCM3. Coupled A-O-I RCM4. Coupled A-L-S RCM5. Regionally focused global model

Coupled atmosphere-land surface-soil model

• An important component of the climate and environmental system poorly represented in RCMs

characteristics of land surface (e.g., roughness, albedo, emissivity, soil texture, vegetation type, snow and ice cover extent, leaf area index, and seasonality)

states of soil properties over land (e.g., soil moisture, soil temperature, canopy temperature, snow water equivalent)

exchanges of momentum, energy, water vapour, and trace gases between land surface, soil and the overlying atmosphere

A-L-S RCMCoupled atmosphere-

land surface-soil

model horiz. resolution 0.25° or 0.5°

Atmosphere

ECHAM4 parameterizations

19 vertical levels

Land surface-soil

LSM module from NCAR 6 layers (total depth of 6 m)

Topography (m)

Non-wood tundra

0-10 cm peat

Forest tundra

0-10 cm moss

10-30 cm peat

0-10 cmmoss/lichen

Forest

Modified land surface model Inclusion of a soil organic layer

Original LSM ground column treated as mineral ground texture (sand, silt, clay)

1) Moss, peat, lichen are included as 3 additional texture types thermal and hydraulic parameters are specified according to Beringer et al. (2001),

30 times lower thermal conductivity, 10 times higher hydraulic parameters

2) different textures are specified for each layer Top organic layer prescribed according to 3 land surface types in model domain

Forest

Forest Tundra

Non-Wood Tundra

A-L-S RCM Sensitivity of Arctic climate

simulation to a soil organic layer

21-year-long run 1979-1999driven by ERA-40.

Different land surface types in RCM describe fractional cover of plant types influence surface fluxes

Top organic layer has been prescribed according to land types:

° only mineral soil texture (orig. model) “CTRL”° top soil organic layer included “SOL”

first 11 years neglected: spin-up time of deep ground conditions

10 years (1990-1999) are analyzedClimatic effect of soil layer“SOL minus CTRL”

Rinke et al., GRL 2008

Changes in atmosphere (“SOL minus CTRL”), Winter

2m air temperature [K] Sea level pressure (hPa)SLP bias (GCM composite minus ERA40),1981-2000, 14 GCMs (Chapman & Walsh, 2007)

[hPa]

Remote influences due to soil propertiesReduction of GCM bias in the Arctic.

Top organic layer reduces ground soil temperatures by 0.5 ° C up to 8 °CChanges in the surface heat fluxes affects regional atmospheric circulation

This talk:

1. Motivation2. Atmospheric RCM3. Coupled A-O-I RCM4. Coupled A-L-S RCM5. Regionally focused global model

Atmospheric dynamical core on unstructured triangular grids Shallow

water model

• Adaptive triangular grid

• Spherical geometry and spherical

triangular coordinates

• Feedbacks between planetary

and synoptic-scale waves

• Approximation of curvature of the finite

elements in space by polynomial order k

• Explicit Runge-Kutta time step

Describe two-way dynamical feedbacks between regions with

high and low horizontal resolution Arctic and rest of globe

• Deviations in geopotential heights (gpm) from geostrop.

balanced field after 30 days due to orogr. perturbation

• Reference simulation (R) without Greenland topography

• Sensitivity simulation (S) with Greenland topography

• Simulation with uniform grid x=133 km

• Simulation with with regionally resolved grid x = 67 km

(S)-(R), Regionally resol. Grid(S)-(R), Uniform Grid Day 30

Two-way coupling between regional and global scales

Impact of the regionally resolved area on global circulation structures

Summary:

RCM 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 Arctic climate system and improved data sets can contribute to the attribution of ongoing changes.

Development of new ideas (e.g. sea ice albedo, organic top 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.

Difference in atmosphere “HIRHAM-LSM minus HIRHAM”

1979-93, Saha et al. (2006)

[Pa]

[Pa]

Mean sea level pressure (Pa) and 10m wind (m/s) Remote influences

Summer (JJA) Winter (DJF)

RCM HIR_LSMERA-40

Standard deviation (°C) of surface air temperature, winter (1958-2001), Matthes et al., 2009, submitted

°C

Atmosphere-surface-soil feedbacks Impacts over Siberia and Alaska

Ts temperature at the interface between atmosphere and surface

Snow

Layer 1, dz=0.10 m

Layer 5, dz=1.60 m

Layer 4, dz=0.80 m

Layer 3, dz=0.40 m

Layer 2, dz=0.20 m

New soil scheme from Land Surface Model LSM (NCAR)

Hea

t co

nd

uct

ion

eq

uat

ion

to c

alcu

late

Ts

oil

(z)

atmospheric radiative + turbulent fluxes at the surface precipitation + evaporation

6.30 mLayer 6, dz=3.20 m

Conservation equationto calculate soil water

content WSsoil (z)

runoffVegetation

Old soil scheme ECHAM4 (Roeckner et al., 1996) with a soil moisture bucket model

Thawing & Freezing

Atmospheric shallow-water modelInfluence of horizontal resolution on normalised model

error

• Hyperbolic system for layer depth, momentum

• Spherical geometry and spherical triangular coordinates

• Polynomial order k approximation of curvature of the finite elements in space

• Polynomial degree k convergence order k+1 k

2

4

6

8

Test case: Barotropic instability of a geostrophic jet at 30 ° N

September sea level pressure (hPa) for “high-ice” and “low-ice” years within 1988-2000 in ERA-40 and CRCM

LLLL

High ice cover low sea level pressure cyclonic conditionsMore ice transport into the Beaufort Sea more sea ice to the Laptev SeaWeaker transpolar drift weaker sea ice outflow through Fram Strait

HHHH

Low ice cover high sea level pressure anticyclonic conditionsStronger transpolar drift Sea ice export through the Fram Strait

Coupled regional Atmosphere-Ocean-Sea Ice Model

HIRHAM Forecast12 HIRHAM Climate Ensemble mean

ECMWF

Atmospheric circulation February 2008

X

X X

Sea level pressure (hPa; color)500 hPa geopotential height (m; isoline)

X Position of NP 35 in February 2008

Land-surface-soil and PBL turbulence closure

Surface layer energy budget:

Rn Radiative fluxesH0 Heat fluxesE0 Humidity fluxesHm Ground heat fluxes

compute surface fluxes and update surface temperature and humidity by solving soil model and surface energy budget

Soil

Atmosphere

Vorticity, Jet streamDay 3 Day 6

• Zonal wind jet with initial perturbation

• Regulär grid x = 30 km

• Development of filaments

• Meridional mixing

Läuter et al., J. Computational Physics 2008

Multiple scale interaction: Barotropic instability