Regional climate modeling of the Arctic, Antarctica and the 3. Pole
Klaus DethloffAnnette Rinke, Wolfgang Dorn, Moritz Mielke,
Daniel Klaus, Heidrun Matthes, Stefan Polanski, Dörthe Handorf
Alfred Wegener Institute for Polar‐ and Marine Research Potsdam& Institute of Physics and Astronomy, University Potsdam
Alpine Summer School, Valsavarenche, 20. June 2012
This talk:
1. Motivation 2. Regional climate models3. Equations, parameterizations and forcing4. Arctic RCM5. Antarctic RCM 6. Tibetan plateau RCM7. Coupled A-O-I model of the Arctic8. Coupled A-S-L model of the Arctic
Dethloff K., et al., Arctic regional climate models, 324-356, In Lemke, Jacobi, Arctic Climate Change, Springer, 2012
This talk:
1. Motivation 2. Regional climate models3. Equations, parameterizations and forcing4. Arctic RCM5. Antarctic RCM 6. Tibetan plateau RCM7. Coupled A-O-I model of the Arctic8. Coupled A-S-L model of the Arctic
AWI- Research section: Atmospheric Circulations
Modeling
Observation
Arctic Climate System
Arctic Changes
Atmospheric Processes
AAlfred WWegener IInstitute for Polar and Marine Research (Germany)
IInstitut Polaire Français PPaul EEmile VVictor (France)
Atmospheric ObservatoryKoldewey
Rabot Corbel
Atmospheric CirculationsAtmospheric Circulations
stratospheric ozoneaerosols and cloudsplanetary boundary layermeteorological monitoring
Arctic Climate Change
(1900-2100; September Ice Extent; 21 Models of IPCC Reports 2007)
Arctic September Sea Ice ExtentObservations Model Runs
Observations
Mean and range of IPCC models
underestimated decadal climate variabiliy ?stronger anthropogenic signal (more CO2, less aerosol) ?missing processes and regional feedbacks ?
Arctic: Energy Sink of the Earth
Run-offSea
ice
Ocean currents
H
L
H
H
Momentum
HeatWater
Tracer
Clouds
Ozone
O OO
AerosolsCH4
CO2
Global models show largest biases in polar regionsArctic RCM with high resolution as magnifying glass
This talk:
1. Motivation 2. Regional climate models3. Equations, parameterizations and forcing4. Arctic RCM5. Antarctic RCM 6. Tibetan plateau RCM7. Coupled A-O-I model of the Arctic8. Coupled A-S-L model of the Arctic
Weather Climate
Weather forecast, seasonal forecast, climate scenariosNonlinear dynamics, physical parameterizationsDependence on IC and BC, ensemble approach
Initial conditions Boundary Conditions
Hours
7 days
14 days
Years
Months
2 weeks
Predictability at regional scalesMeso-scale spatial scale features developed in RCMs are attributed to four types of sources:
• The surface forcing - A better representation of small scale forcings such as topography and other surface heterogeneities contributes to the increase of details in high-resolution simulations.
• The nonlinearities presented in the atmospheric dynamical equations - The nonlinear dynamics also play an important role. Internal atmospheric dynamics exhibit a nonlinear downscale energy cascade by stirring and stretching the flow, and this phenomenonwould occur even in the absence of surface forcings.
• Hydrodynamic instabilities - Shear and buoyancy in the flow can also, through hydrodynamic instabilities, produce mesoscale features without the help of surface forcings.
• The noise generated at the lateral boundaries and model errors. - The abrupt change of model resolution by several times at the lateral boundaries can distort wave propagation and reflection properties. The noise introduced at the lateral boundaries and the model errors may contaminate the predictions.
Measurements in the (European) Arctic
permanent stationNy-Ålesund 78.9°N, 11.9°E
drifting ice stationsNP-35 (2007-2008)cooperation with AARI, Russia
airborne campaignsAWI airplane Polar-5
1. Surface energy budget components2. Sea ice thickness and ice properties3. Snow depths and properties on sea ice and land4. Convective plumes due to leads in the sea-ice
5. Atmospheric radiative and turbulent fluxes 6. Inversions, low level jets and decoupling7. Planetary boundary layer structure, APBL heights
8. APBL feedbacks with baroclinic cyclones (including Polar lows)
9. Vertical profiles of ozone and carbon dioxide10. Vertical aerosol concentrations and compositions 11. Cloud properties and vertical distribution12. Precipitation occurrence and type
13. Coupling tropo-stratosphere-mesosphere 14. Gravity wave drag
An list of Arctic atmospheric measurements (single points and satellites) to validate sub-grid scale parameterizations and to improve the performance of RESM and GESM
A-O-I inter-actions
Coupled ABLS
Feedbacks with the troposphere
Feedbacks with strato- and meso-sphere
This talk:
1. Motivation 2. Regional climate models3. Equations, parameterizations and forcing4. Arctic RCM5. Antarctic RCM 6. Tibetan plateau RCM7. Coupled A-O-I model of the Arctic8. Coupled A-S-L model of the Arctic
Momentum conservation Momentum equations
Mass conservation Continuity equation
Energy conservation Thermodynamic equation
State equation of ideal gasHydrostatic equation
Primitive equations in spherical coordinates
Radiation
Surfacetemperature
Snowtemperature
Winds
Snow
Resolved and parameterised sub-grid scale model processes
AdiabaticprocessesPressure
Momentumflux
Sensibleheat flux
Surfaceroughness
Cumulusconvection
Cloud-water
Diffusion
Water vapour
Grid scaleprecipitation
Interceptionstorage
Latentheat flux
Snowmelt
Temperature
Groundhumidiy
Cloud-ice
Ozone
Examples of parameterisations
• Radiation• Convection• Diffusion• Clouds • Precipitation• Gravity wave drag(Friction)
• Soil‐snow‐vegetationWhat is their impact on decadel-scale climate variability?
Standard scheme (from ECHO-G)
• New schema (Køltzow et al., 2003), 3 different surface types (snow covered ice, bare sea-ice, melt ponds and leads), surface temperature dependent linear scheme.
Reduced albedo for melting conditions, especially if snow already disappeared.
Uncertain parameterization of sea-ice albedoIImproved scheme based on SHEBA & satellite data, used in RCM
Lower albedo limit in case of no snow cover
Upper albedo limit in case of completesnow cover
Regional Downscaling Method
1. Grid rotation Rotated spherical grid metric coefficients
Equations in Cartesian coordinates
2.Boundary relaxation lateral forcing from coarse resolution
model. Nudging to the RCM model values via a relaxation zone (10 points) with a relaxation weight to join the boundary forcing data linearly with the model data
3. Vertical discretizationHybrid sigma‐pressure coordinate
system with terrain following coordinates 19, 25 or more vertical levels up to 10 hPa
CFL Criteriatime step ~ 120 s
Regional Downscaling Method
4. Horizontal discretization
5. Time discretization
xxxxx
x ΔΔ−−Δ+
≅∂∂
2)()( ψψψ
Central difference scheme
⇒=∂∂ F
tψ
).(211ψψψ SFt nnn −Δ+= −+
nnne Ft.211 Δ+= −+ ψψ ψψψ Stn
en .211 Δ+= ++
05.0)2( ;11 =+++= +−f
nnnff
nnf εψψψεψψ
Semi‐implicit “Leap‐Frog” scheme Semi‐implicit correction term
1) Explicit scheme solved 2) Final solution
3) Time filter1≤
ΔΔxtu
HIRHAM model descriptionModel dynamics from regional model HIRLAM
Prognostic variables:horizontal wind, temperature, specific humidity, cloud water, surface pressure
Integration area:pan-Arctic domain (north of 65°N, 110x100 grid points)
Grid:- Arakawa-C; horizontal resol. 0.5° (25 km);
- 19-25 unequally spaced vertical levels (hybrid σ–p)
Time step:
- 300 sec, semi-implicit leapfrog
Physical parametrizations from atmospheric circulation model ECHAM4:
radiation, land-surface-soil processes, turbulent fluxes in planetary boundary layer, cumulus convection, condensation, precipitation, evaporation, momentum transport due to gravity waves
Forcing with ERA40 data:Lateral boundary (every 6h): all prognostic variables, relaxation ina 10 grid point wide boundary zoneLower boundary (every 24h): SST (sea surface temperature), sea ice fraction
This talk:
1. Motivation 2. Regional climate models3. Equations, parameterizations and forcing4. Arctic RCM5. Antarctic RCM 6. Tibetan plateau RCM7. Coupled A-O-I model of the Arctic8. Coupled A-S-L model of the Arctic
Arctic Regional Climate Model Intercomparison Project - ARCMIP
Participating RCMs: ARCSyM (USA),
COAMPS (S, USA), HIRHAM (D, DK),
CRCM (C), RCA (S), RegCM (N),
REMO (D), PolarMM5 (USA)
SHEBA:Surface Heat Budget of the Arctic OceanMeasurements: Sept.1997-Sept.1998SHEBA trajectory Beaufort Sea
Validation and improvement of RCMs:Same domain, Same horizontal resolution Same lower and lateral boundary conditionDifferent dynamics and physics
NP 35 Sept. 2007-July 2008NP 36 Sept. 2008-July 2009Measurements in the European Arctic
Rinke et al., 2006, Climate Dynamics
Monthly averages of the SHEBA year 1997/98 for the models participating in ARCMIP
Data: thick dashed line
Biggest uncertainties:
Shortwave radiationSurface albedoCloudsPBL turbulence
Wyser et al., Clim. Dyn., 2007
Uncertainties in Arctic climate model simulations
Shortwave down radiation
Longwave down radiation
Surface albedo
Cloud cover
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 simulationsto compare with NP35 data set
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
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
ssur
e[h
Pa]
Pre
ssur
e[h
Pa]
Pre
ssur
e[h
Pa]
Pre
ssur
e[h
Pa]
APBL
International Study: Pan Arctic Measurements and Arctic Regional Climate Model Simulations PAM-ARCMIP
AWI Aircraft Polar 5 Route (30.3.- 28.4.2009)Russian North Pole drifting station NP 36
Stone R., et al. JGR, 2010, Atmospheric aerosol measurementsHaas C., et al., GRL, 2010, Sea ice thickness measurements with EM bird
This talk:
1. Motivation 2. Regional climate models3. Equations, parameterizations and forcing4. Arctic RCM5. Antarctic RCM 6. Tibetan plateau RCM7. Coupled A-O-I model of the Arctic8. Coupled A-S-L model of the Arctic
Motivation for Antarctic RCM
Chapman and Walsh (2005)
Decadal-scale temperature changes
Cooling over CentralAntarctica
Warming over Antarctic Peninsula
Identification and understanding of key processes for recent climate changes
Linear trends of annual mean surface air temperature (°C/decade) for the period 1958-2002 (observation data)
Antarctic application of the HIRHAM model
HIRHAM• Forcing at lateral and lower
boundaries:
ERA40 (Re-analyses)
• Integration area based on experience in pan-Arctic RCM domain
• Horizontal resolution : 0.5° × 0.5°
• Vertical resolution : 25 levels between surface and 10hPa, 10 levels in the lowest 850 m
• Time step :120s
Neumayerstation
Model validation : Seasonal MSLP 1958-1998 [hPa]
HIRHAMERA40 WINTER
HIRHAMSUMMERERA40
1981-1998
CORRELATION of MSLP withwith 12 station‘s data
HIRHAM ERA 40 NCEP
AVERAGE 0,92 0,90 0,89
Model validation: MSLP [hPa] and 2m temperature [°C]
Neumayer Neumayer stationstation, , 2000 2000 –– 20012001MSLPMSLP
OD - Operational DataHIRHAM(OD) - HIRHAM driven by Operational DataAVHRR - Advanced Very High Resolution RadiometerNCEP - National Centres for Environmental PredictionERA40 - ECMWF Re-AnalysisHIRHAM(ERA40) - HIRHAM driven by ERA40
[[ hPahP
a ]]
2m temperature2m temperature
[[ °°C
]C]
Model validation: Vertical temperature profile [°C]Neumayer Neumayer stationstation, , 1991 1991 –– 19981998
-14 -12 -10 -8 -6 -4
0
250
500
750
1000
1250
1500
1750
2000
2250
2500
-26 -24 -22 -20 -18 -16
0
250
500
750
1000
1250
1500
1750
2000
2250
2500
WINTERWINTER SUMMERSUMMER
HIRHAMSTATION DATA
[m]
[m]
[m]
[m]
[[ººC]C] [[ººC]C]
This talk:
1. Motivation 2. Regional climate models3. Equations, parameterizations and forcing4. Arctic RCM5. Antarctic RCM 6. Tibetan plateau RCM7. Coupled A-O-I model of the Arctic8. Coupled A-S-L model of the Arctic
The Indian Monsoon Circulation
H
ITCZ
NE-Monso
on
Sum
mer
Mon
soon
(JJA
S)W
inte
r Mon
soon
(DJF
)
T ITCZ
SW-MonsoonS
S
N
N
Tibetan Plateau
Tibetan Plateau
T
H
SW-Monsoon
Tropical Jet (E) Polar Jet (W)
Indo-Gangetic Plains
Indo-Gangetic Plains
H
NE-Monsoon
TPolar Jet (W)
Orography
Third Pole HIRHAM
110 grid points = 5500 km
100
grid
poi
nts
= 50
00 k
m
HIRHAM vs. ERA40: 2m-Temperature in ºC (summer monsoon JJAS 1958-2001)
HIRHAM HIRHAM – ERA40
DATASET PATCOR BIAS (K) RMSE (K)
HIRHAM - ERA40 0.96 0.3 2.3
Climatology – seasonal meanHIRHAM vs. ERA40: Mean monsoonal wind fields (wind vectors)
Wind speed [850 hPa] in m/s (colours)
(summer monsoon JJAS 1958-2001)
HIRHAM ERA40
HIRHAM vs. ERA40: Mean upper air wind fields (wind vectors)
Wind speed [300 hPa] in m/s (colours)
(summer monsoon JJAS 1958-2001)
HIRHAM ERA40
This talk:
1. Motivation 2. Regional climate models3. Equations, parameterizations and forcing4. Arctic RCM5. Antarctic RCM 6. Tibetan plateau RCM7. Coupled A-O-I model of the Arctic8. Coupled A-S-L model of the Arctic
Coupled regional Atmosphere-Ocean-Sea Ice Model
Atmosphere model HIRHAM- parallelized version- 110×100 grid points- horizontal resolution 0.5°- 19 vertical levels
Ocean–ice model NAOSIM- based on MOM-2 (+EVP)- Elastic viscous plastic
ice rheology- 242×169 grid points- horizontal resolution 0.25°- 30 vertical levels
Boundary 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
HIRHAM NAOSIMEquations hydrostatic; prognostic equations
for u, v, T, q, qw, ps;diagnostic equations for ω, Ф.
Ocean: hydrostatic; progn. eq. for u, v, T, S; diagn. eq. for w, ρ, p.Ice: progn. eq. for u, v, c, h, hsn.
Physical parame-terizations
radiation, land surface processes, sea surface sea-ice processes, vertical diffusion (PBL), gravity wave drag, cumulus convection, large-scale condensation.
Ocean: const. vertical diffusion coefficients, convection, salt restoring, no river inflow.Ice: EVP rheology, one layer with optional snow cover.
Grid Arakawa-C; horizontal res. 0.5°(50 km); 19 unequally spaced vertical levels (hybrid σ–p).
Arakawa-B; horizontal res. 0.25°(25 km); 30 unequally spaced vertical levels (z-coordinate).
Timing Time step: 240 s, semi-implicit leapfrog; A–O coupling 3600 s.
Time step: 900 s (for O, I, and O–I coupling); O–A coupling 3600 s.
Boundary conditions
Lateral: ECMWF re-analyses (ERA) (updated every 6 h).Lower: out of ocean domain ERA (updated daily).Start: normal mode initialization using ERA.
Lateral: open southern boundary following Stevens (1991), else closed (O), open for outflow (I).Upper: out of atmos. domain ERA.Start: restart from uncoupled run driven by ERA.
Atmosphere-ocean-sea ice coupling
Spin-up of about 6–10 years to reach quasi-stationary ice volumeYear to year variations in ice volume for HIRHAM–NAOSIM and ORCM shows similar fluctuations (consequence of variability in external atmospheric forcing)
Total volume of Arctic seaTotal volume of Arctic sea--ice 1989ice 1989––20002000
Blue: two HIRHAM–NAOSIM experiments with different initial sea-ice thickness (Dorn et al., 2006)
Black: ORCM experiment (Debernard et al., 2006)
Standard deviation of sea ice conzentration (%) in September 1988-2000,
Dorn, Dethloff et al. OASJ 2008Satellite observations Coupled Arctic climate model
Importance of internal variability due to atmospheric processes
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
ICE GROWTH PARAMETERIZATIONPossibility of ice melt even if snow layer is present
Improved onset of snow melting
IMPROVED SEA ICE ALBEDODifferent surface types and melt ponds (Schmelzwassertümpel)(snow covered ice, bare sea-ice, melt ponds and leads)
Stronger surface temperature dependent linear scheme
SNOW COVER PARAMETERIZATIONNew snow cover scheme does now allow the formation of melt ponds as long as the snow cover is still thick
Improves initiation of snow melt period in summer
Improved descriptions for sea ice growth, sea ice albedoparameterization and snow cover parameterization,
Dorn et al., Ocean Modeling 2009, 29, 103-114.
10 20 30 40 50 60 70 80 90
Satelliten-Messung
10 20 30 40 50 60 70 80 90
Standard-Modell
10 20 30 40 50 60 70 80 90
VerbessertesModell
Sea ice concentration (%) in SeptemberSSMI, Standard model, Improved model
Simulations from 1980-2000RCM spin up time: 1980-1987, Results for: 1988-2000
SSMI data Standard RCM Improved RCM
Ensemble of hindcast simulations 1948-2008
• Hydrostatic HIRHAM-NAOSIM simulations with improved Arcticparameterizations
• Atmospheric forcing at lateral boundaries from NCEP reanalysis• 6 ensemble members with different initialization of ocean and
sea ice fields provided by a NAOSIM spin-up run.
Arctic sea ice extent (September) in A-O-I model & data
Strong variability of sea ice extent Decadal memory effectsSea ice extent (September) decrease after 2003 in the coupled model
Dorn et al. , Present limitations of coupled RCM reproduction of observedArctic sea ice retreat, The Cryosphere discussions, 2012,
1978 satellite data
Special Sensor Microwave Imager SSMI
This talk:
1. Motivation 2. Regional climate models3. Equations, parameterizations and forcing4. Arctic RCM5. Antarctic RCM 6. Tibetan plateau RCM7. Coupled A-O-I model of the Arctic8. Coupled A-S-L model of the Arctic
Atmosphere-Land-Soil RCMCoupled atmosphere-land surface-soil model
horiz. resolution 0.25° or 0.5°
AtmosphereECHAM4 parameterizations19 vertical levels
Land surface-soilLSM module from NCAR6 layers (total depth of 6 m)
Topography (m)
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 budgetSoil
Atmosphere
Interface land surface and soil
• An important component of the weather, climate and environmental system.
exchanges of momentum, energy, water vapor, CO2, and other trace gases between land surface, soil and the overlying atmosphere
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)
• Critical for weather, climate, hydrological, and environmental forecasts.
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
Soil scheme from Land Surface Model LSM (NCAR)
Hea
t con
duct
ion
equa
tion
to c
alcu
late
Tso
il(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
Freezing- and thawing index Accumulated degree days as annual mean on the basis of 15 years 1979-93
[°C]
Freezing index, accumulated days < 0 °C describes the permafrost area
Difference in atmosphere “HIRHAM-LSM minus HIRHAM”
1979-93, Saha et al. (2006)
[Pa][Pa]
Mean sea level pressure (hPa) and 10m wind (m/s)Remote influences
Summer (JJA) Winter (DJF)
REKLIM Regional Arctic System Models AWI, SMHI, DMI, CIRES
Dorn et al. 2009Dethloff et al., 2010Rinke et al., 2011Matthes et al., 2011Dorn et al., 2012
Remember: RCMs are sensitive to choice of
• Regional integration domain,
• Lateral boundary conditions large-scale & cyclonic forcing,
• Lower boundary conditions topography, SST & sea ice,
• Horizontal and vertical resolution,
• Sub-grid scale parameterizations.
Regionally models of the climate system and improved data sets contribute to attribution of on- going changes.
RCMs one-way feedbacks Two-way feedbacks has to be considered within a global model by regionally focused modelling of the area of interest.
Thank you