parameterization of arctic climate processes in canam knut von salzen canadian centre for climate...
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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
[email protected] 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)