Regional climate modeling of the Arctic, Antarctica and ...€¦ · Standard scheme (from ECHO-G)...

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:


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:


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:


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:


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:


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:


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:


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:


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

Regional climate modeling of the Arctic, Antarctica and ...€¦ · Standard scheme (from ECHO-G)...

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Transcript of Regional climate modeling of the Arctic, Antarctica and ...€¦ · Standard scheme (from ECHO-G)...