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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,

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Presentation on theme: "Parameterization of Arctic Climate Processes in CanAM Knut von Salzen Canadian Centre for Climate Modelling and Analysis (CCCma)‏ Environment Canada, Victoria,"— Presentation transcript:

1 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

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) 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) 1 st and 2 nd 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)

3 - 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

4 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 CO 2

5 gravitational settling wet deposition Sinks dry deposition coagulation & condensation condensation nucleation & coagulation inorganic & organic vapours mechanical production (sea salt, mineral dust) approx. dry particle radius (µm) emissions Sources Aerosol Microphysical Processes in CanAM4.2

6 droplets/cm 3 Cloud Droplet Number Concentration in low Clouds for JJA Obs: MODIS, 2001 (Bennartz, pers. comm.) Improved Simulation of Cloud Droplets and Aerosol Forcings Satellite observations CanAM with aerosol microphysicsCanAM with bulk aerosol scheme

7 - 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/m 2 ) Grain size (microns) Diffuse albedo Diffuse trans Parameterization of Snow Albedo Means for microns, black surface, θ=0 o

8 dry + melt-freeze metamorphism Atmosphere Surface Snow Layer snowfall BC dry + wet deposition BC melt water scavenging Parameterizations for Snow Microphysics

9 Clear-Sky Planetary Albedo Biases March-April-May (MAM) June-July-August (JJA) New snow albedo parameterization CLASS 3.6 (Anomalies vs. CERES EBAF V2.7, , masked by modelled SWE) Improved biases from new parameterizations for snow albedo

10 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,

11 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

12 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

13 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 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) NETCARE Team

14 Backup slides

15 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.

16 NETCARE – Structure Three experimental activities feed new measurements to improve climate and chemical transport models:

17 –Scientific research on attribution of historic climate change to SLCFs (aerosols, CH 4, trop. O 3 ) 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?

18 Land BC Ocean BC Black Carbon Sources + Sinks in CanESM4.2 BC hydrophobic hydrophilic 1 h (day) 24 h (night)

19 Cloud Microphysical Processes in AGCM4 Water vapour Cloud liquid water Cloud ice Rain Snow Q mlts Q agg Q saci Q aut Q racl Q sacl Q mlti Q frh Q frk Q frs Q cnd Q dep Q evp Q sub Lohmann and Roeckner (1996), Rotstayn (1997), Khairoutdinov and Kogan (2000), Chaboureau and Bechtold (2002)

20 Black Carbon Emissions for IPCC AR5 Historic (Lamarque et al., 2010) Future (Moss et al., 2010) Anthropo- genic Vegetation Fires FSU N America Europe S+E Asia Other FSU N America Europe S+E Asia Other RCP6.0 RCP8.5 RCP6.0 RCP2.6 RCP8.5 RCP2.6

21 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.) fBC – fossil- and biofuel black carbon NATURAL – anything else (solar variations, volcanoes) Jones et al. (2011)

22 Impact of SLCFs on Arctic Climate Observations Global Climate Models - Mainly SCLFs Adapted from Fyfe, von Salzen, Gillett, Arora, Flato, McConnell, Nature Scientific Reports (2013) Large contribution of SCLFs to Arctic temperature changes in the 20 th century

23 –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

24 Near-Surface Concentration of BC, unit: kg/m 3 Source: CMIP5/IPCC AR5 model data archive at PCMDI Jun-Aug (JJA) Dec-Feb (DJF)

25 BC Concentration Measurements

26 Near-Surface Concentrations, 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

27 Mean BC Near-Surface Concentration, Alert Ny-Ålesund Barrow Reasonably good agreement between simulated and observed concentrations for CanAM4-PAM Observations provided by S. Sharma, Env. Canada Alert: Barrow: Ny-Ålesund: gray shading indicates range of observations

28 > 60°N PAMARCMIP BC Concentration Profiles from Aircraft Campaigns Global HIPPO 1-5, PAMARCMIP > 60°N HIPPO 1-5, PAMARCMIP 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 m, especially in the Arctic (different from Bond et al., 2013) Caveats: Freely running model, only 1 ensemble member

29 Droplet Activation and Growth in PAM 25 cm/s 50 cm/s 100 cm/s 200 cm/s updraft wind speed Circles: New numerical solution Bullets: Detailed parcel model (Shantz and Leaitch) Water-soluble organics in aerosol Water-insoluble organics in aerosol height (m) supersaturation (%) CDNC (m -3 ) cloud layer adiabati c air parcel height (m)


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