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Part 1. Black Carbon in Arctic snow: concentrations and effect on surface albedo Tom Grenfell & Steve Warren University of Washington Tony Clarke (University.

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Presentation on theme: "Part 1. Black Carbon in Arctic snow: concentrations and effect on surface albedo Tom Grenfell & Steve Warren University of Washington Tony Clarke (University."— Presentation transcript:

1 Part 1. Black Carbon in Arctic snow: concentrations and effect on surface albedo Tom Grenfell & Steve Warren University of Washington Tony Clarke (University of Hawaii) Vladimir Radionov (AARI, St. Petersburg) Other UW participants: Dean Hegg, Richard Brandt, Sarah Doherty, Steve Hudson, Mike Town, Hyun-Seung Kim, Lora Koenig, Ron Sletten (ESS) Jamie Morison, Andy Heiberg, Mike Steele (APL) Project website: Intro 1

2 Snow Albedo 0.5ppm 0.05ppm 5 ppm 0.05 ppm 0.5 ppm Primary influence of BC on Spectral Albedo was first characterized by Warren and Wiscombe (i) visible wavelengths (ii) grain radius

3 Where and when does variation of snow albedo matter for climate? Whenever large areas of snow are exposed to significant solar energy Arctic snow - Tundra in spring - Sea ice in spring (covered with snow) - Greenland Ice Sheet in spring (cold snow) - Greenland Ice Sheet in summer (melting snow) Glacier ice and sea ice: - Ablation zone of Greenland Ice Sheet in summer - Arctic sea ice in summer Non-Arctic snow - Great Plains of North America - Steppes of Asia: Kazakhstan, Mongolia, Xinjiang, Tibet Where and when does this matter (?)

4 Pioneering Effort – 1983/4 Survey

5 1983 Soot in snow (Clarke & Noone) Most amounts are 5-50 parts per billion. Clarke & Noone Sites

6 Warren & Wiscombe (1985); Warren & Clarke (1986) Soot contents from Clarke & Noone (1985) Expected magnitude of albedo reduction

7 Difficulties in the use of remote sensing to determine BC's effect on snow albedo 1. It's hard to distinguish snow from clouds-over-snow, which hide the surface. Thin near- surface layers of atmospheric ice crystals ("diamond-dust") are common in the Arctic. 2. The bidirectional reflectance (BRDF) is affected by: a. small-scale surface roughness: ripples, sastrugi, suncups, pressure-ridges. (The effects of sastrugi on BRDF are different at different wavelengths, because they depend on the ratio of sastrugi width to flux-penetration depth.) b. when thin surface-fog (or diamond-dust layer) covers the rough snow, the forward peak is enhanced and the nadir view is darker. This darkening at nadir could be mistaken for BC contamination. c. Grain shape 3. Albedo reduction by BC in snow can be mimicked by: - thin snow. Sooty snow has the same spectral signature as thin snow. - increase of grain size with depth (common situation) preferentially reduces visible albedo - sub-grid-scale leads in the Arctic Ocean. - BC in the atmosphere above the snow (Arctic haze). Difficulties with remote sensing

8 Our 4-year project (begun in spring 2006): a comprehensive surface- based survey of BC in Arctic snow, to repeat and extend Clarke & Noone’s survey from 1983/4. Our Sites

9 Sampling Profiles

10 Filter Apparatus deployed in the field

11 Filters are compared to standard calibration filters. They will be scanned with a spectrophotometer to quantify BC, dust, & other components – different spectral absorption curves. Filters

12 BC in snow (ppb) Median values K. Steffen automatic weather stations + Greenland Sector

13 BC in Snow (ppb) M. Sturm (CRREL) + Canada Sector

14 T. Grenfell and Steve Hudson, Western Arctic Russia March-May 2007 Permissions were granted to enter restricted border areas; International Polar Year (IPY) has prominence in Russia. Russian Sector

15 Representative Profile - Khatanga

16 IPY News Information Bulletin June 2007 Stephen Hudson (left), a graduate student at the University of Washington, traveling up the Khatanga River Новости МПГ

17 Table of Results *strong haze event *

18 (1) Do particles collect at the surface as the snow melts? Greenland (Dye-2) August 2007, melting snow: surface 9 ppb, subsurface 3 ppb Enhancements (2) Snow grain size increases markedly with spring melt onset magnifying the effect of a given soot load – accelerating melt. Δ(albedo) changes from to for 35 ngC/g

19 Spectral albedo of snow observed at selected sites for closure - soot observations, RT modeling, and spectral albedo. Svalbard, March 2007

20 New Snow Loading and Scavenging Experiments - Tony Clarke

21 January: Artificial snowpack to quantify effect of soot on snow albedo with homogeneous grain size and known BC loading - (Rich Brandt, Steve Warren – Adirondacks) March-May: Snow sampling in Eastern Siberia (Grenfell & Warren) April: Albedo & BC intercomparison with Norwegian Polar Institute (Gerland, Brandt) April-May: Redistribution of BC during melt (Sanja Forsstrøm at Tromsø) July: Greenland melting-snow zone: redistribution study - fine vertical BC sampling of top 20 cm; spectral albedo (Brandt & Warren) Calibrate new spectrophotometer; quantify BC, dust, other components (Sarah Doherty, Tom Grenfell); further comparisons with SP2 (Joe McConnell, Tony Clarke) Scanning Electron Microscope and chemical analysis of samples to investigate source signatures (Hegg, Grenfell, Warren) Plans for 2008

22 Jim Hansen for inspiring us to take on this project Clean Air Task Force and NSF Arctic Program for support Thanks to:

23 This project has benefited from the increased scientific activity in the Arctic, Collaborations: Norwegian Polar Institute (Svalbard) Sebastian Gerland Danish Polar Center (Northeast Greenland) Carl-Egede Bøggild Arctic and Antarctic Research Institute (Russia) Vladimir Radionov Volunteers who have collected snow for this project in 2007: Konrad Steffen & Thomas Phillips (Univ. Colorado). Automatic weather stations in Greenland Matthew Sturm (U.S. Army Cold Regions Lab, Fairbanks, Alaska). Snowmobile traverse of Arctic Alaska and Canada Jacqueline Richter-Menge (U.S. Army Cold Regions Lab, Hanover, NH). Snow on sea ice in the Beaufort Sea Jamie Morison, Andy Heiberg & Mike Steele (UW Applied Physics Lab). North Pole Environmental Observatory and Switchyard Expt, Arctic Ocean. Matt Nolan (Univ. Alaska). McCall Glacier, northern Alaska Von Walden (Univ. Idaho). Ellesmere Island, Canada Shawn Marshall (Univ. Calgary). Devon Island Ice Cap, Canada. International Polar Year Collaborations

24 Part 2. Source Attribution of Black Carbon in Arctic Snow Dean Hegg, Tom Grenfell, Steve Warren U. of Washington, Seattle, WA

25 Current Data Base 36 sites - Canada, Greenland, Russia, North Pole BC estimates from filter samples 26 soluble co-analytes from filtered, melted snow a. Anions – ion chromatography b. Hydrocarbons – liquid chromatography, mass spectrometer detection c. Elements – ICP-OES (inductively coupled plasma with optical emission spectroscopy)

26 BC concentration, 3 most highly correlated analytes, and a biomass fire tracer (Levoglucosan) Levoglucosan is not simply correlated with BC but is identified by the factor analysis.

27 PMF (Positive matrix factorization) model results (tentative) for available data base. The five most significant factors explained 90% of variance. 90 % of the mass of BC is associated with this and the next factor.

28 PMF Results continued. Factor shown had next highest BC loading. These two factors accounted for over 90% of the BC 90 % of the mass of BC is associated with this and the previous factor.

29 Preliminary Interpretation Both factors had appreciable levoglucosan, suggesting a strong biomass component to the BC

30 Preliminary Interpretation Both factors had appreciable levoglucosan, suggesting a strong biomass component to the BC The 1 st factor was associated primarily with the Russian sites, the 2 nd with the Canadian sites

31 Preliminary Interpretation Both factors had appreciable levoglucosan, suggesting a strong biomass component to the BC The 1 st factor was associated primarily with the Russian sites, the 2 nd with the Canadian sites Both factors also indicated a pollution component of different composition for the two locales. This is expected and may be a geographic discriminator. More species are needed to explore the attribution in detail.

32 Further Analysis Analysis of non-filtered snow melt Chemical analysis of snow filters for insoluble tracers. In particular, analysis of filter deposits for PAH’s (polycyclic aromatic hydrocarbons). More elaborate receptor modeling


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