Parameterization of Arctic Climate Processes in CanAM

Knut von Salzen

Canadian Centre for Climate Modelling and Analysis (CCCma) Environment Canada, Victoria, British Columbia, Canada

Acknowledgements: J. Cole, M. Namazi, Y. Peng, X. Ma, J. Scinocca, J. Li, N. McFarlane, D. Verseghy,P. Bartlett, C. Derksen, M. Lazare, L. Solheim

knut.vonsalzen@ec.gc.ca www.cccma.ec.gc.ca

General features

• Resolution: T63 (ca. 2.8°), 49 levels to approx. 1hPa

• Spectral advection, hybridization of tracer variable, physics filter

• Orographic and non-orographic gravity wave drag

• Radiation: Correlated-k distribution and Monte carlo Independent Column Approximation (McICA) methods

• Local and non-local turbulent mixing

• Mass flux schemes for deep and shallow convection

• Prognostic cloud liquid water and ice, statistical cloud scheme

New features

• Most recent version of the CLASS land surface scheme (version 3.6)

• Parameterizations for snow microphysics and snow albedo

• Prognostic aerosol microphysics (size distributions) for sulphate, sea salt, mineral dust, hydrophobic and hydrophilic black and organic carbon

• Improved direct radiative aerosol forcings (internally mixed aerosol)

• 1st and 2nd aerosol indirect effects, using online non-adiabatic parcel model

• Absorption of solar radiation by black carbon in cloud droplets

Canadian Atmospheric Global Climate Model (CanAM4.2)

- Strong cooling of Arctic climate by aerosols largely offsets warming influence of GHGs

- Simulated trends are sensitive to treatment of aerosols in models

Fyfe et al., Nature Sci. Reports (2013),

adapted

Human Influence on Arctic Climate

Observations

Reductions in Snow Cover from Black Carbon (BC)

Flanner et al. (2009), adapted

Equilibrium snow cover changes over land for

March-May between pre-industrial and present-day

from simulations with CAM3.1 + CLM + slab ocean

Similar reductions in springtime snow cover from

- absorption of solar radiation by BC in snow

- increased CO2

gravitationalsettling

wet depositionSinks

dry deposition

coagulation& condensation

condensationnucleation

& coagulation

inorganic & organicvapours

mechanical production(sea salt, mineral dust)

approx. dry particle radius (µm)

emissions

Sources

Aerosol Microphysical Processes in CanAM4.2

droplets/cm3

Cloud Droplet NumberConcentration in low Clouds

for JJA

Obs: MODIS, 2001(Bennartz, pers. comm.)

Improved Simulation of Cloud Droplets and Aerosol Forcings

Satellite observations

CanAM with aerosol microphysics CanAM with bulk aerosol scheme

- Lookup table function of: SWE, underlying surface albedo, solar zenith angle, snow grain size, BC concentration, wavelength interval

- Diffuse albedo, direct albedo, diffuse transmission, and direct transmission

- Single layer of snow over bare ground (consistent with CLASS)

- Detailed offline DISORT calculations at 280 wavelengths. Results averaged over CCCma solar radiation bands

- Total albedo for each band is weighted average (based on incident radiation) of direct and diffuse albedo

SWE (kg/m2) SWE (kg/m2)

Gra

in s

ize

(mic

rons

)

Diffuse albedo Diffuse trans

Parameterization of Snow Albedo

Means for0.2-0.69 microns, black surface, θ=0o

dry + melt-freeze

metamorphism

Atmosphere

Surface Snow Layer

snowfallBC dry + wet

deposition

BC melt waterscavenging

Parameterizations for Snow Microphysics

Clear-Sky Planetary Albedo BiasesMarch-April-May (MAM) June-July-August (JJA)

New snow albedoparameterization

CLASS 3.6

(Anomalies vs. CERESEBAF V2.7, 2003-2008,

masked by modelled SWE)

Improved biases from new parameterizations for snow albedo

Arctic BC Snow Mass Mixing Ratios: Model vs. Observations

Observations: Doherty et al. (2010)

Comparisons for nearest grid point,snow layer depth of 20 cm,

monthly mean values, 2003-2008

Assessment of Arctic Black Carbon and Climate

Quinn et al. (2011), adapted

Assessment by Expert Group on Short-Lived

Climate Forcers,Arctic Monitoring and

Assessment Programme,Arctic Council

- BC burdens and BC radiative forcings in the Arctic dominated by human activities

- Upcoming assessment report in 2015, with assessment of temperature changes

Three experimental activities feed new measurements to improve climate and chemical transport models

NETCARE – Network on Climate and Aerosols:Addressing Key Uncertainties in Remote Canadian Environments

Collaborators Howard Barker, EC Jason Cole, EC Daniel Cziczo, MIT Mark Flanner, U Michigan Sunling Gong, EC Wanmin Gong, ECYves Gratton, INRS-ETEAndreas Herber, Alfred Wegener InstituteLin Huang, EC Ron Kiene, U South Alabama  Alexei Korolev, ECRichard Leaitch, EC Peter Liu, EC Anne Marie Macdonald, EC Lisa Miller, DFOTim Papakyriakou, U ManitobaJeff Pierce, Dal/CSUKim Prather, UCSD Lynn Russell, ScrippsMichael Scarratt, DFO Sangeeta Sharma, EC Corinne Schiller, EC Ralf Staebler, EC Kevin Strawbridge, EC Jean-Éric Tremblay, U LavalSvein Vagle, DFO

Principal Investigator and Research Activity Leaders Jon Abbatt - Network PI, University of TorontoAllan Bertram, University of British Columbia Maurice Levasseur, Université LavalRandall Martin, Dalhousie University

Co-ApplicantsJean-Pierre Blanchet, UQAMGreg Evans, UofTChristopher Fletcher, U Waterloo Michel Gosselin, UQAREric Girard, UQAMCharles Jia, UofTJennifer Murphy, UofTAnn-Lise Norman, U Calgary Norm O’Neill, U SherbrookeNadja Steiner, U Victoria/DFOKnut von Salzen, U Victoria/EC

Collaborating InstitutionsEnvironment CanadaDepartment of Fisheries and OceansAlfred Wegener Institute (Germany)

NETCARE Team

Backup slides

NETCARE – Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments

“To improve the accuracy of climate predictions, the direct radiative effects of aerosol and the impacts of aerosol on clouds and precipitation have to be resolved; it is well recognized that aerosol effects represent the largest uncertainty in present-day radiative forcing estimates.”

And so, NETCARE was established to:

i) address key uncertainties in predictions of aerosol effects on climate by using a variety of observational and modeling approaches, and

ii) use that increased knowledge to improve the accuracy of Canadian climate and Earth system model predictions of aerosol radiative forcing

Focus on remote regions given the potential impacts that anthropogenic input may have on pristine environments; urban regions are much better studied.

NETCARE – Structure

Three experimental activities feed new measurements to improve climate and chemical transport models:

– Scientific research on attribution of historic climate change to SLCFs (aerosols, CH4, trop. O3) and mitigation of future climate change are becoming increasingly important for climate policy development (e.g. Climate and Clean Air Coalition).

– Shindell et al. (2012) highlight potential benefits of SLCF mitigation for reducing global climate change in the short term.

– Fundamental scientific uncertainties still exist, especially regarding the magnitude of regional radiative forcings and climate responses, including the Arctic.

Shindell et al. (2012)

Short-Lived Climate Forcers:How Important are They for Climate?

Potentially large impacts of SLCFs on global climate.

But is there an Influence on Climate Change in the Arctic?

Land

BC

Ocean

BCBC

Black Carbon Sources + Sinks in CanESM4.2

BC

hydrophobic

hydrophilic

1 h (day) 24 h (night)

Cloud Microphysical Processes in AGCM4

Water vapour

Cloud liquidwater

Cloud ice

Rain SnowQmlts

Qagg

Qsaci

Qaut

Qracl Qsacl

Qmlti

Qfrh

Qfrk Qfrs

Qcnd Qdep

Qevp Qsub

Lohmann and Roeckner (1996), Rotstayn (1997), Khairoutdinov and Kogan (2000),Chaboureau and Bechtold (2002)

Black Carbon Emissions for IPCC AR5Historic

(Lamarque et al., 2010)Future

(Moss et al., 2010)

Anthropo-genic

VegetationFires

FSUN AmericaEurope

S+E Asia

Other

FSUN AmericaEuropeS+E Asia

Other

RCP6.0

RCP8.5

RCP6.0

RCP2.6

RCP8.5

RCP2.6

Model Evidence for Warming Effect of BC in the Arctic Variations in simulated zonally averaged near-surface

temperature with respect to pre-industrial values

unit: K

GHG – all well-mixed greenhouse gasesOA – other anthropogenic (aerosols, ozone, etc.)fBC – fossil- and biofuel black carbonNATURAL – anything else (solar variations, volcanoes)

Jones et al. (2011)

Impact of SLCFs on Arctic Climate

Observations

Global Climate Models

- Mainly SCLFs

Adapted from Fyfe, von Salzen, Gillett,Arora, Flato, McConnell, Nature ScientificReports (2013)

Large contribution of SCLFs to Arctic temperature changes in the 20th century

– Research has previously focussed on global radiative forcings, which are still uncertain for aerosols compared to GHGs.

– Regional radiative forcings are much less certain.

– A strong sea ice-albedo feedback and other climate feedbacks makes the Arctic particularly vulnerable to changes in radiative forcings.

– Arctic climate appears to be very sensitive to the location and type of forcing agent (GHG, SLCF). However, responses of climate to regional forcings are very uncertain.

Adapted from Shindell and Faluvegi (2009)

Radiative Forcing and Climate Response in the Arctic

Near-Surface Concentration of BC, 2000-2004

unit: kg/m3

Source: CMIP5/IPCC AR5 model data archive at PCMDI

Jun-Aug (JJA)

Dec-Feb (DJF)

BC Concentration Measurements

Near-Surface Concentrations, 2003-2008

Sulphate (+1%) Black Carbon (-54%) Organic Aerosol (-64%)

GCM underestimates mean BC concentrations and variability in North America and Europe. Larger underestimates in China.

CanAM4-PAM vs. network data

Mean BC Near-Surface Concentration, 2003-2008

Alert Ny-Ålesund

Barrow Reasonably good agreement between simulated and observed concentrations for CanAM4-PAM

Observations provided by S. Sharma, Env. CanadaAlert: 1989-2008Barrow: 1989-2007Ny-Ålesund: 2001-2007gray shading indicates range of observations

> 60°NPAMARCMIP 2009+2011

BC Concentration Profiles from Aircraft Campaigns

GlobalHIPPO 1-5, PAMARCMIP 2009+2011

> 60°NHIPPO 1-5, PAMARCMIP 2009+2011

Concentrations and standard deviations for all aircraft samples and months

Full lines – mean conc.Dashed lines – median conc.

PAMARCMIP data courtesy of Andreas Herber

Model overpredicts concentrations below ca. 5000 m, especially in the Arctic (different from Bond et al. , 2013)

Caveats: Freely running model, only 1 ensemble member

Droplet Activation and Growth in PAM

25 cm/s50 cm/s100 cm/s 200 cm/s

updraft wind speedCircles: New numerical solutionBullets: Detailed parcel model (Shantz and Leaitch)

Water-solubleorganics in aerosol

Water-insoluble organics in aerosol

heig

ht

(m)

supersaturation (%) supersaturation (%)

CDNC (m-3)

CDNC (m-3)

cloud layer

adiabaticair parcel

heig

ht

(m)