Transcript Parameterization of Snow Albedo

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
Canadian Atmospheric Global Climate Model (CanAM4.2)
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)
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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
Human Influence on Arctic Climate
Observations
- 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
Reductions in Snow Cover from Black Carbon (BC)
Equilibrium snow cover
changes over land for
March-May between preindustrial 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
Flanner et al. (2009), adapted
Aerosol Microphysical Processes in CanAM4.2
inorganic & organic
vapours
condensation
nucleation
& coagulation
Sources
mechanical production
(sea salt, mineral dust)
emissions
coagulation
& condensation
dry deposition
Sinks
gravitational
settling
wet deposition
approx. dry particle
radius (µm)
Improved Simulation of Cloud Droplets and Aerosol Forcings
CanAM with aerosol microphysics
Cloud Droplet Number
Concentration in low Clouds
for JJA
CanAM with bulk aerosol scheme
Satellite observations
Obs: MODIS, 2001
(Bennartz, pers. comm.)
droplets/cm3
Parameterization of Snow Albedo
- 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
Diffuse albedo
Diffuse trans
Grain size (microns)
Means for
0.2-0.69 microns,
black surface,
θ=0o
SWE (kg/m2)
SWE (kg/m2)
Parameterizations for Snow Microphysics
snowfall
Atmosphere
BC dry + wet
deposition
Surface Snow Layer
dry + melt-freeze
metamorphism
BC melt water
scavenging
Clear-Sky Planetary Albedo Biases
March-April-May (MAM)
New snow albedo
parameterization
Improved biases from
new parameterizations
for snow albedo
CLASS 3.6
(Anomalies vs. CERES
EBAF V2.7, 2003-2008,
masked by modelled SWE)
June-July-August (JJA)
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
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
Quinn et al. (2011), adapted
NETCARE – Network on Climate and Aerosols:
Addressing Key Uncertainties in Remote Canadian Environments
Three experimental activities feed new measurements to improve
climate and chemical transport models
NETCARE Team
Principal Investigator and Research Activity Leaders
Jon Abbatt - Network PI, University of Toronto
Allan Bertram, University of British Columbia
Maurice Levasseur, Université Laval
Randall Martin, Dalhousie University
Co-Applicants
Jean-Pierre Blanchet, UQAM
Greg Evans, UofT
Christopher Fletcher, U Waterloo
Michel Gosselin, UQAR
Eric Girard, UQAM
Charles Jia, UofT
Jennifer Murphy, UofT
Ann-Lise Norman, U Calgary
Norm O’Neill, U Sherbrooke
Nadja Steiner, U Victoria/DFO
Knut von Salzen, U Victoria/EC
Collaborating Institutions
Environment Canada
Department of Fisheries and Oceans
Alfred Wegener Institute (Germany)
Collaborators
Howard Barker, EC
Jason Cole, EC
Daniel Cziczo, MIT
Mark Flanner, U Michigan
Sunling Gong, EC
Wanmin Gong, EC
Yves Gratton, INRS-ETE
Andreas Herber, Alfred Wegener Institute
Lin Huang, EC
Ron Kiene, U South Alabama
Alexei Korolev, EC
Richard Leaitch, EC
Peter Liu, EC
Anne Marie Macdonald, EC
Lisa Miller, DFO
Tim Papakyriakou, U Manitoba
Jeff Pierce, Dal/CSU
Kim Prather, UCSD
Lynn Russell, Scripps
Michael Scarratt, DFO
Sangeeta Sharma, EC
Corinne Schiller, EC
Ralf Staebler, EC
Kevin Strawbridge, EC
Jean-Éric Tremblay, U Laval
Svein Vagle, DFO
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:
Short-Lived Climate Forcers:
How Important are They for Climate?
– 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).
Potentially large impacts of
SLCFs on global climate.
But is there an Influence on
Climate Change in the Arctic?
– 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)
Black Carbon Sources + Sinks in CanESM4.2
BC
BC
BC
BC
1 h (day)
24 h (night)
hydrophilic
hydrophobic
Land
Ocean
Cloud Microphysical Processes in AGCM4
Water
vapour
Qevp
Qcnd
Cloud liquid
water
Qaut
Qracl
Rain
Qfrh
Qsub
Qdep
Qfrk Qfrs
Cloud ice
Qmlti
Qsacl
Qmlts
Qagg
Qsaci
Snow
Lohmann and Roeckner (1996), Rotstayn (1997), Khairoutdinov and Kogan (2000),
Chaboureau and Bechtold (2002)
Black Carbon Emissions for IPCC AR5
Future
(Moss et al., 2010)
Historic
(Lamarque et al., 2010)
FSU
N America
Europe
RCP6.0
RCP8.5
S+E Asia
Anthropogenic
RCP2.6
Other
Vegetation
Fires
FSU
N America
Europe
S+E Asia
Other
RCP6.0
RCP2.6
RCP8.5
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 gases
OA – other anthropogenic (aerosols, ozone, etc.)
Jones et al. (2011)
fBC – fossil- and biofuel black carbon
NATURAL – anything else (solar variations, volcanoes)
Impact of SLCFs on Arctic Climate
Observations
Global Climate Models
Large contribution of
SCLFs to Arctic
temperature changes
in the 20th century
- Mainly SCLFs
Adapted from Fyfe, von Salzen, Gillett,
Arora, Flato, McConnell, Nature Scientific
Reports (2013)
Radiative Forcing and Climate Response in the Arctic
– 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)
Near-Surface Concentration of BC, 2000-2004
Jun-Aug
(JJA)
Dec-Feb
(DJF)
unit: kg/m3
Source: CMIP5/IPCC AR5 model data archive at PCMDI
BC Concentration Measurements
Near-Surface Concentrations, 2003-2008
CanAM4-PAM vs. network data
Sulphate (+1%)
Black Carbon (-54%)
GCM underestimates mean
BC concentrations and
variability in North America
and Europe. Larger
underestimates in China.
Organic Aerosol (-64%)
Mean BC Near-Surface Concentration, 2003-2008
Alert
Barrow
Ny-Ålesund
Reasonably good agreement
between simulated and
observed concentrations for
CanAM4-PAM
Observations provided by S. Sharma, Env. Canada
Alert: 1989-2008
Barrow: 1989-2007
Ny-Ålesund: 2001-2007
gray shading indicates range of observations
BC Concentration Profiles from Aircraft Campaigns
Global
> 60°N
> 60°N
HIPPO 1-5, PAMARCMIP 2009+2011
HIPPO 1-5, PAMARCMIP 2009+2011
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
cloud layer
supersaturation (%)
height (m)
supersaturation (%)
adiabati
c
air
parcel
Water-insoluble
organics in aerosol
height (m)
Water-soluble
organics in aerosol
CDNC (m-3)
updraft wind speed
CDNC (m-3)
25 cm/s
50 cm/s
100 cm/s
200 cm/s
Circles: New numerical solution
Bullets: Detailed parcel model
(Shantz and Leaitch)