Dirty Snow and Arctic Climate Charlie Zender and Mark Flanner Department of Earth System Science, University of California, Irvine Laboratoire de Glaciologie Géophysique de l Environnement, (LGGE), Grenoble, France National Center for Atmospheric Research (NCAR), Boulder, Colorado Collaborators: M. Andreae (MPI), T. Bond (UIUC), F. Dominé (LGGE), N. Mahowald (NCAR), D. Muhs (USGS), T. Painter (NSIDC/UU), P. Rasch (NCAR), T. Roush (NASA) Presented to: Short-lived Pollutants and Arctic Climate (SPAC) Workshop Kjeller, Norway, November 5 8, 2007 (Web: http://dust.ess.uci.edu/smn/smn snw spac 200711.pdf)
Abstract 1. Motivations to understand snow-albedo feedback (SAF) 2. SAF enhancement by snowpack vertical heating profile 3. Linking snowpack microphysics and aging to albedo 4. Snowpack forcing by and response to aerosol impurities 5. Related problems and projects
Monitoring to Quantify BC Cryosphere Interactions Make Continual or Regular-interval surface measurements at snow observatories to characterize 1. Reflectance dependence on specific surface area, impurity content 2. Surface-atmosphere exchange/deposition velocity 3. Reflectance diurnal cycle, variability 4. Closure of broadband surface reflectance Useful measurements include: 1. Snow and air temperatures, humidity, wind, precipitation, liquid 2. Aerosol Optical Depth (e.g., AERONET) 3. Multi-channel spectral and broadband solar reflectance Use spectral reflectances to estimate: 1. Snow specific surface area (SSA) 2. Impurity concentration (using SSA and visible channels) Replicates in perennial and seasonal snowpacks, e.g., Summit and Barrow.
Episodic Measurements of BC Cryosphere Interactions Make Opportunitistic and Episodic (e.g., pre/post aerosol plume) field measurements at observatories to 1. Calibrate optical BC estimates against mass-based measurements 2. Understand BC optical property aging since emission 3. Quantify BC scavenging above/within snowpack 4. Force and falsify snow evolution models used in GCMs Useful measurements include: 1. Snow BC optical properties and mixing state 2. Near-surface air BC concentration and optical properties 3. Snowpack density, BC concentration, temperature profiles Use chemical transport models to link field measurements to complementary laboratory studies and to regional and synoptic observations and campaigns involving aircraft/satellites. Use GCMs to extend across space and time scales.
Laboratory Measurements of BC Cryosphere Interactions Make controlled, complementary Laboratory measurements to: 1. Verify snow/ice reflectance perturbation by impurities with known microphysical optical properties 2. Devise/assess optical methods to estimate/segregate BC, OC, dust 3. Characterize metamorphism and hysteresis near melt-point 4. Melt-scavenging of impurities with known size/hygroscopic properties Useful Measurements include: 1. Spectral reflectance and SSA for snow with/out known impurities 2. Spectral reflectance and SSA evolution for different temperatures, temperature gradients, densities, and near-melt conditions 3. Impurity mass before/after melt events Laboratory data help to formulate, calibrate, and extend (beyond field conditions) the predictive relationships that models use to simulate aerosol cryosphere interactions.
FIGURE SPM-2. Global-average radiative forcing (RF) estimates and ranges in 2005 for anthropogenic carbon Figure dioxide 1: (CO Global-mean 2 ), methane radiative (CH 4 ), nitrous forcing oxide estimates, (N 2 O) and scale, other and important certainty agents in 2005. and mechanisms, (IPCC, 2007) together with the typical geographical extent (spatial scale) of the forcing and the assessed level of scientific understanding (LOSU). The net anthropogenic radiative forcing and its range are also shown.
Table 1: Arctic Response to Short-Lived Pollutants a Forcing Mechanism Temperature Response T [ C] Winter Spring Summer Fall Aerosol Direct Effect 1.4 0.93 0.47 1.1 Aerosol Indirect Effect 0.77 0.68 0.45 0.89 Thin Cloud Emissivity 1.0 1.6 Black Carbon Snow Albedo 0.37 0.51 0.21 0.49 BC/Snow Our Estimate 0.62 0.97 0.54 0.93 Tropospheric Ozone 0.43 0.31 0.11 0.26 Methane 0.34 0.27 0.15 0.35 Total 0.27 0.52 0.45 0.89 Total Our Estimate 0.52 0.06 0.12 0.45 a Sources: Quinn et al. (2007, Submitted to Eos), Hansen et al. (2007, Clim. Dyn.), and Flanner et al. (2007, JGR)
H land for the y is for F. The ] were e same found period s from e (solid b) The er NH iments, iments r T s )is a s (T s ) eighted hough climate lues of Figure 3. Scatterplot of simulated springtime Da s /DT s values in climate change (ordinate) vs. simulated springtime Figure 2: Simulated springtime A s / T s north of 30 N under IPCC AR4 720 ppm scenario (ordinate) and present springtime seasonal cycle A s / T s (abscissa). (Hall and Qu, 2006, GRL)
1. References References Flanner, M. G., C. S. Zender, J. T. Randerson and P. J. Rasch, 2007: Presentday climate forcing and response from black carbon in snow. J. Geophys. Res., 112, D11202, doi:10.1029/2006jd008003. Hall, A. and X. Qu, 2006: Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys. Res. Lett., 33(L03502), doi:10.1029/2005gl025127. Hansen, J., M. Sato, R. Ruedy, P. Kharecha, A. Lacis, R. Miller, L. Nazarenko, K. Lo, G. A. Schmidt, G. Russell, I. Aleinov, S. Bauer, E. Baum, B. Cairns, V. Canuto, M. Chandler, Y. Cheng, A. Cohen, A. D. Genio, G. Faluvegi, E. Fleming, A. Friend, T. Hall, C. Jackman, J. Jonas, M. Kelley, N. Y. Kiang, D. Koch, G. Labow, J. Lerner, S. Menon, T. Novakov, V. Oinas, J. Perlwitz, J. Perlwitz, D. Rind, A. Romanou, R. Schmunk, D. Shindell, P. Stone, S. Sun, D. Streets, N. Tausnev, D. Thresher, N. Unger, M. Yao and S. Zhang, 2007: Climate simulations
for 1880 2003 with GISS modele. Clim. Dyn., pp. doi:10.1007/s00382 007 0255 8. IPCC, 2007: Climate change 2007: The physical science basis. Contribution of Working Group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. p. 996. Cambridge Univ. Press, Cambridge, UK, and New York, NY, USA. Painter, T. H., J. Dozier, D. A. Roberts, R. E. Davis and R. O. Green, 2003: Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data. Rem. Sens. Environ., 85, 64 77, doi:10.1016/s0034 4257(02)00187 6. Quinn, P. K., T. S. Bates, E. Baum, N. Doubleday, A. Fiore, M. Flanner, A. Fridlind, T. Garrett, D. Koch, S. Menon, D. Shindell, A. Stohl and s. G. Warren, 2007: Short-lived pollutants in the Arctic: Their climate impact and possible mitigation strategies. Submitted to Eos.
2. Problem: Can Dirty Snow be Remotely Sensed Snow grain-size has been remotely sensed with radiance-ratio techniques (e.g., Painter et al., 2003). Can snow impurities be monitored from space? Steve Warren is dubious because: Cloud contamination! Pristine sky NIR radiance ratios can yield information on snow grain size, which, combined with visible radiance, estimates surface soot concentration. Iterative procedure required because anisotropic reflectance factor (ARF) depends on wavelength, snow grain-size, and soot concentration. ARF sensitive to surface roughness, e.g., ripples, sastrugi, suncups Soot mimicked by thin snow, vegetation, sea-ice leads Atmospheric soot has similar spectral signature to surface soot