Dust and Soot Aerosol Effects on Snow and ClimateCharlie Zender1,2, Mark Flanner1,3,Florent Domine2, Joe McConnell4

1Department of Earth System Science, University of California, Irvine2Laboratoire de Glaciologie Geophysique de l’Environnement (LGGE), Grenoble, France

3Advanced Study Program, NCAR, 4Desert Research Institute, Reno, NV

Collaborators:T. Bond (UIUC), J.-C. Gallet (LGGE),

N. Mahowald (NCAR), T. Painter (NSIDC/UU), G. Picard (LGGE),J. Randerson (UCI), P. Rasch (NCAR)

Presented to:21st GEWEX Scientific Steering Group (SSG) Meeting

Irvine, California, January 19, 2009(Web: http://dust.ess.uci.edu/smn/smn_snw_gewex_200901.pdf)

Abstract

1. Trends in temperature, snow extent2. Seasonal-to-climate scales: Snow-Albedo feedback (SAF)3. Estimating regional SRF timeseries from ice cores4. Global dirty snow effects in PI, PD, 2050 climates5. Testing GCM representations with field/lab measurements

Figure 1: “Blue Marble”—Earth without clouds in February (earthobservatory.nasa.gov).

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Fig. 4. Timeseries of observed springtime (March–May) temperature anomalies and snow cover over Eurasiaand North America (averaged over land north of 30◦N). Temperature data are from NASA GISS (Hansen et al.,2001) and CRUTEM3 (Brohan et al., 2006). Anomalies are relative to different mean periods, explaining theslight offset between GISS and CRUTEM3 data. Snow cover extent is from NOAA/Rutgers (Robinson andFrei, 2000). Linear trends are shown for 1979–2008 data.

23

Figure 2: Continental 1979–2008 T and SCE north of 30 ◦N (Flanner et al., 2008, In Press in ACPD).

Figure 3: Photo unavailable for public viewing due to copyright restrictions.

Figure 4: Dust plumes emanating from Iceland, October 2000. (Photo: NASA MODIS)

Does boreal forest fire warm or cool climate?

Donnelly Flats Fire, June 11-18th, 1999, Delta Junction, Alaska(Photos: Jim Randerson, UCI)

Figure 5: February 15 dust-fall in snowpack near Niwot Ridge, Colorado on May 17, 2006. (Courtesy TomPainter, NSIDC/UU)

[15] During the winter/spring 2005, aBB dropped in twoperiods from 0.85 (subalpine and alpine) to 0.45 (subalpine)and 0.51 (alpine), respectively, and aVIS dropped in thesame periods from 0.92 to 0.50 and 0.61, respectively, dueto the accumulation of dust at the snow surface (AuxiliaryMaterial Figure S1). In 2006, aBB dropped quasi-monoton-ically from 0.85 to 0.40 and 0.46, respectively, and aVIS

dropped likewise from 0.92 to 0.45 and 0.58, respectively(Auxiliary Material Figure S1).[16] In 2005, the radiative forcing had two periods of

mean forcings of 30–50 (subalpine) and 20–40 (alpine)W m�2 as dust layers were exposed at the surface, coveredby snowfall and exposed again through melt (AuxiliaryMaterial Figure S2). In 2006, the radiative forcing increasedsteadily from early April to maxima of 80 (subalpine) and60 (alpine) W m�2 later in ablation despite frequent butsmall snowfalls (Auxiliary Material Figure S3). For theperiod March 21 to June 21 (spring), mean daily radiativeforcing in 2005 ranged from 31 to 37 W m�2 (subalpine)and 14 to 19 W m�2 (alpine), respectively, and in 2006ranged from 56 to 64 W m�2 and 36 to 42 W m�2,respectively. Radiative forcings in the alpine are generallylower than those in the subalpine because dust concentra-tions were consistently lower at the wind-exposed alpine

tower. However, other slopes and aspects in the alpinecollect scoured dust and have higher concentrations.[17] The combination of more frequent and heavy dust

events, less post-event snowfall and thus greater exposure,and clearer skies in 2006 compared with in 2005 resulted indistinctly larger radiative forcing and shorter snow coverduration in 2006 than in 2005. Figure 2 presents thecumulative radiative forcings and the time series of theratios of daily mean forcings for year 2006 to 2005 for bothsites. The ratio generally increased at both sites lyingbetween 1.5 and 2.5 for most of the ablation season,indicating that the net shortwave energy made availableby dust for melting was approximately doubled in 2006.Ratios less than 1 result from small but non-zero dustforcing before late April 2005.[18] SNOBAL snowmelt simulations indicate that in

2005 the subalpine melted out 22 to 32 days earlier relativeto the dust free cases and the alpine snow cover melted out23 to 33 days earlier (Figure 3). In 2006, the subalpinemelted out completely 24 to 35 days earlier and the alpinemelted out 18 to 27 days earlier. Far smaller peak snowwater equivalents in 2006 relative to 2005 (67% in alpine,85% in subalpine) resulted in the small differences between2005 and 2006 with respect to snow cover duration despite

Figure 3. Time series of snow water equivalent scenarios with number of days difference from observed indicated:snowmelt model results for subalpine 2005 in measured case (black), dust free Kdmin

* (thin), and dust free Kdmax+i1* (thick),

where differences in snow cover duration are indicated for respective cases above the horizontal arrows; subalpinesnowmelt in 2006; alpine snowmelt in 2005; and alpine snowmelt in 2006.

L12502 PAINTER ET AL.: IMPACT OF DESERT DUST ON SNOW DURATION L12502

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Figure 6: Snow darkening by dust shortened Subalpine and Alpine seasonal snow cover duration in San JuanMountains, Colorado, USA, in 2006 by 18–35 days. (Painter et al., 2007, GRL)

Nordenskiold on Greenland (1883):“Everywhere where the snow from lastwinter has melted away, a fine dust,gray in color, and, when wet, blackor dark brown, is distributed over theinland ice in a layer which I shouldestimate at from 0.1 to 1 millimetre.”(Garrett and Verzella, 2008, BAMS)

MARCH 2008|300

alarmed to find the sky was not

the deep blue he expected, but

that it displayed a whitish haze;

two years of sun photometer

measurements showed skies

sometimes as murky as those

seen in cities.

Shaw suspected the haze was

pollution from well outside the

Arctic, but this hypothesis was

met with overwhelming skepti-

cism from colleagues. So, for

follow-up work Shaw teamed

with University of Rhode Island

chemists Kenneth Rahn and

Randolph Borys. The first set of

results, obtained in spring 1976,

showed that background aerosol

had unnaturally high vanadium

concentrations indicative of

heavy oil combustion, and that

when it was most hazy, the com-

position was consistent with

plumes of soil dust, blown to the

Arctic from the Gobi Desert.

Later studies of winter haze confirmed a clear

anthropogenic signature, revealing high levels of such

heavy metals as titanium, chromium, manganese,

iron, and nickel, principally from Eurasia. Rahn and

Shaw concluded that “Arctic haze is the end product

of massive transport of air pollution from various

midlatitude sources to the northern polar regions, on

a scale that could have never been imagined, even by

the most pessimistic observer.”

While the work of Shaw, Borys, and Rahn has

laid the foundation for the current IPY Arctic pol-

lution studies, it has a distant and underappreciated

precedent. Like Shaw, late 1800s Arctic explorers and

scientists were also astonished by Arctic aerosols—so

much so that in a 1906 lecture, British scientist

George C. Simpson remarked, “All who have traveled

in Arctic regions know the peculiar haze which fills

the air when the temperature falls very low and gives

the ‘cold’ aspect to Arctic scenes. Such a haze, which

is not a mist or fog, was frequent during the winter

in Karasjok [69°N in Norway]. On the other hand,

at the end of the summer the air reached a degree of

transparency which I have never seen equaled in any

other place.”

Even the First IPY appears to support indications

of a seasonal haze. Under the command of Lt. P. H.

Ray, the U. S. Army organized a

formal IPY expedition to Alas-

ka’s northernmost point, Point

Barrow, arriving by sail in Sep-

tember 1881. A permanent site

was established, and from 18

October 1881 to 27 August 1883,

hourly meteorological data were

recorded. Among these detailed

records, a “light” or “dense”

haze is often mentioned. While

it is not entirely clear from

these qualitative indicators

whether the “haze” was aerosol

or rather tenuous ice crystal

precipitation, precipitation was

not noted concurrently, and

the haze was noted under both

clear and cloudy conditions. It

was not a fog, either, as this was

noted only during summer and

fall. The haze events occurred

regularly in stretches of several

days between November and

April. This seasonality observed

by Ray in Barrow, and also by Simpson in Karasjok, is

a telltale sign of Arctic haze, and one that continues

to be echoed in quantitative measurements at these

sites today, such as reported in a paper by Quinn et

al. in Tellus last year.

However, it is perhaps Swedish geologist (Nils) Ad-

olf Erik Nordenskiöld who was first to explicitly draw

attention to the haze phenomenon. Nordenskiöld

earned renown for his extensive Arctic explorations,

in particular for being the first to successfully navi-

gate the Northeast Passage to Asia from the Atlantic.

His 1883 expedition to Greenland is described in a

fascinating article from an 1883 issue of Science. His

notes from 22 July at 2:30 a.m. contain this descrip-

tion: “The sky was covered with a thin veil of clouds,

through which the sun shone warmly, at times even

scorchingly. From time to time this veil of clouds, or

haze, descended to the surface of the ice, and hid the

view over the expanse; but it was, remarkably enough,

not wet, but dry—yes, so dry that our wet clothes

absolutely dried in it.”

Nordenskiöld also described a “kryokonite” (cryo-

conite or ice-dust), something he had observed during

an earlier 1870 Greenland expedition: “Everywhere

where the snow from last winter has melted away, a

fine dust, gray in color, and, when wet, black or dark

FIG. 2 . Polar voyager Adolf Erik Nordenskiöld (1832–1901) believed a metallic soot from space settled “imperceptibly and continuously” over the Arctic.

Southern Hemisphere (28) and Patagonian (29) air tempera-tures during recent decades to centuries (Fig. 3). For monthlyaveraged aluminum concentration and flux, correlations toSouthern Hemisphere air temperatures during the past 150 yearsare (Pearson’s r) 0.398 (P � 0.0001) and 0.362 (P � 0.0001),respectively. For annual averages, correlations are 0.649 (P �0.0001) and 0.615 (P � 0.0001), respectively. Crustal dustmobilization depends on a number of factors including windvelocity and surface conditions (e.g., type and density of vege-tation cover), soil moisture content, and soil composition (22–25), so both land-use and climate changes impact dust mobilityand export. A plausible explanation for this close agreementbetween air temperatures and dust levels is that observedwarming during the 20th century, possibly amplified by land-usechanges (30), has resulted in decreased soil moisture and alteredvegetation cover, enhancing dust mobility in the likely sourceregions for dust arriving at James Ross Island.

Located at the northeastern tip of the Antarctic Peninsula,James Ross Island (Fig. 1) is an ideal site from which to developa long-term record of atmospheric dust concentration. It issituated relatively near major dust-producing regions of southernSouth America, which have experienced significant desertifica-tion during the 20th century (31, 32), and also is surrounded byocean water and thus is distant from any significant local sourcesof dust.

The loess region of Argentina, the most extensive in theSouthern Hemisphere, covering �1.0 � 106 km2, is thought to bethe source of crustal dust found in East Antarctica during the lastglacial maximum (11, 16), whereas atmospheric aerosol studiesidentify Patagonia as the modern source region for crustal dustin the northern Antarctic Peninsula (23, 32). Rock outcrops thatmay act as local sources of crustal dust are relatively close to theDalinger Dome ice coring site on James Ross Island, althoughthey are also much farther away than on alpine glaciers where

rock outcrops can be a few kilometers or less from coring sites.Atmospheric transport results in particle-size sorting becausesmaller particles (0.1 to �6.0 �m) stay suspended in the air fordays to weeks and can be transported long distances. Conversely,larger particles (�6.0 �m) have atmospheric lifetimes of hoursto days. Although episodic long-range transport of very largeparticles has been reported (33), larger particles are not gener-ally transported long distances (23).

To determine whether dust found in the James Ross Island icecore is primarily from local sources or more distant sources as theresult of long-range transport, we evaluated continuous micro-particle count and size distribution measurements (34) made onthe same ice core samples throughout the size range of 0.8–10.0�m. Although these continuous measurements were not cali-brated with Coulter counter measurements on discrete samples(34), they are in agreement with earlier studies of particles in icecores (17) and generally consistent with long-range transport ofcrustal dust to the James Ross Island ice cap (23). Particleconcentrations are approximately log-normally distributed witha volume-weighted mean particle size of �1.7 �m and a geo-metric standard deviation of �4.6 �m (34). Comparison withother ice core measurements suggests that the relative concen-tration of larger particles is greater at James Ross Island than atsites in West and East Antarctica, which are much more distalfrom potential lower latitude dust sources, but that the distri-bution is similar to those measured in ice cores from the centralGreenland ice sheet where long-range transport is implicit.

To further evaluate possible local dust sources, we comparedthe James Ross Island aluminum concentration record with airtemperatures in the northern Antarctic Peninsula to see whetherlocal warming may have resulted in decreased snow cover, thus

Fig. 1. Map showing the location of James Ross Island at the northeasternend of the Antarctic Peninsula and its proximity to the dust-producing regionsof Patagonia and Argentina. Fig. 2. Monthly averaged aluminum and aluminosilicate dust concentration

(A) and flux (B) from 1832 to 1991 measured in James Ross Island ice cores. Theheavy red line shows annual averages. Aluminosilicate dust was computedfrom the aluminum measurements by using the mean crustal abundance bymass of 8.04%.

5744 � www.pnas.org�cgi�doi�10.1073�pnas.0607657104 McConnell et al.

Figure 7: Al-inferred dust (a) concentration and (b) flux at James Ross Island, Antarctica. (McConnell et al.,2006, PNAS)

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Figure 8: BC and CO2 forcings in Greenland. (McConnell et al., Science, 2007)

Summary for Policymakers IPCC WGI Fourth Assessment Report

Page 16 of 21

FIGURE SPM-2. Global-average radiative forcing (RF) estimates and ranges in 2005 for anthropogenic carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and other important agents and mechanisms, 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. These require summing asymmetric uncertainty estimates from the component terms, and cannot be obtained by simple addition. Additional forcing factors not included here are considered to have a very low LOSU. Volcanic aerosols contribute an additional natural forcing but are not included in this figure due to their episodic nature. Range for linear contrails does not include other possible effects of aviation on cloudiness. {2.9, Figure 2.20}

Figure 9: Global-mean radiative forcing estimates, scale, and certainty in 2005. (IPCC, 2007)

Figure 10: Snow-cryoconite boundary in Greenland. (Photo: Richard Brandt, U. Washington)

Figure 11: Photo unavailable for public viewing due to copyright restrictions.

Figure 12: High SSA stellar dendrite (snowcrystals.com) and low SSA wind-crust. (SEM: Florent Domine,LGGE, and Werner Kuhs, U. Gottingen)

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NCAR CLMVerseghy, 1991

Niwot, Jan2−Jan12, 11:00Niwot, Jan2−Jan12, 12:00Niwot, Jan2−Jan12, 13:00Niwot, Jan2−Jan12, 14:00

Figure 13: Observed and modeled albedo decay at Niwot Ridge January 2, 2001. (Flanner and Zender, 2006,JGR)

Snow in a Warmer, Sootier, Less Dusty World

Competing processes as climate warms:

• Warmer T accelerates metamorphism increases re: Positive feedback• Thinner snowpack reveals darker surfaces: Positive feedback• Melt scavenging concentrates aerosol near surface: Positive feedback• Warmer Ts reduces dT/dz, aging: Negative feedback• Increased P frequency can “recharge” SSA: Negative feedback

Emissions Assumptions:Industrialization continues to increase soot emissionsMore CO2→ more vegetation→ less dust since Pre-IndustrialNo explicit representation of anthropogenic dust sources

Figure 14: Snow BC concentration [ng g−1] for (a) Present, (b) Pre-industrial, (c) 2050A2, and (d) LGM.

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Figure 15: Observed and simulated BC concentrations (Flanner et al., 2007, JGR)

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Figure 16: Present day response to BC and dust in snowpack.

Figure 17: Predicted global mean temperature response [K] to snowpack heating by BC and dust duringPre-Industrial, Present Day, and 2050 IPCC A2 climates.

Figure 18: Predicted global mean forcing [W m−2], response [K], and efficacy of snowpack heating by BCand dust during Pre-Industrial, Present Day, and 2050 IPCC A2 climates.

definition (Table 1) and 2.37 W/m2 for the tropopause usedby Hansen et al. [2002]. Thus DTs/Fa � 0.463�C/W/m2 for1.5 � CO2, with this specific numerical relation being validfor the model III version of GISS modelE.[152] The principal uncertainty in Ea is in the calculated

DTS for the given forcing agent, which depends upon thenumber of simulations carried out as well as upon therealism of the representation of the forcing agent andthe climate model. The standard deviation in the Escolumn of Tables 1–4, obtained from the variability ofDTS in 5-member ensembles of experiments, applies also tothe Ea and Ei columns, after scaling in proportion to theefficacy value.[153] We use the same normalization, i.e., DTS(CO2)/

Fa(CO2) � 0.463�C/(W/m2), for Es and Ei as for Ea. Thusalthough Fs (1.5 � CO2) � Fa (1.5 � CO2) so that Es(1.5 � CO2) � 1.00, Fi (1.5 � CO2) is �10% larger than Fa(1.5� CO2), and as a result Ei (1.5� CO2)� 0.90 (Table 1).[154] Figure 25b shows the efficacies, Es, based on the

fixed SST forcings, Fs:

Es ¼ DTS=Fs

DTS CO2ð Þ=Fa CO2ð Þ �DTS=Fs

0:463 C=W=m2: ð6Þ

The uncertainty in Es depends mainly on the uncertainty inthe calculated DTS for the given forcing agent, as the

unforced variability in the calculation of Fs in a 100-yearrun with fixed SST is smaller than the variability in thecalculation of DTS with the coupled atmosphere-oceanmodel.[155] In this section we discuss three implications of

Figure 25. First, there is the positive slope with increasingforcing, for either increasing CO2 or increasing solar irra-diance. This positive slope implies that climate, or at least100-year climate response as simulated by the GISS climatemodel, is more sensitive to a positive forcing than to anegative forcing. Second, non-CO2 gases are more effectiveat producing global warming than is CO2 for an equalforcing, with the standard (Fa) definition of forcing. Third,absorbing aerosols (black carbon) are less effective thanCO2 at producing global warming.

5.1. Climate Sensitivity Versus Magnitude of Forcing

[156] Efficacy increases as the forcing increases, albeitonly slightly, for forcings that do not take the climate too faraway from the current climate, as shown in Figure 25 for avariety of CO2 and solar irradiance changes. Efficacy isexpected to vary as the climate state varies, because thestrength of climate feedbacks changes with the climate state.For example, the positive sea ice feedback eventuallydisappears as sea ice area disappears. Thus, by itself, thesea ice feedback probably would cause the efficacy to slope

Figure 25. Efficacy of various climate forcing agents for producing global temperature change relativeto the global temperature change produced by an equal CO2 forcing at today’s CO2 amount (mean for 1 �CO2 to 1.5 � CO2). The effective forcing is the product of the efficacy and the forcing. (a) Uses thestandard definition of climate forcing, Fa, the adjusted forcing; (b) uses the fixed SST forcing, Fs. Thefact that the different forcing agents cluster closer to the E = 1 line for fixed SST definition of forcingindicates that Fs provides a better measure of expected climate response than does Fa. The positive slopeof efficacy curves for changes of solar irradiance or CO2 amount indicates that (in our climate model,with fixed ice sheet area and fixed vegetation distribution) the 100-year climate response becomes moresensitive as the planet becomes warmer. Upturns in the efficacy at very small and very large solarirradiances or CO2 amounts correspond to the snowball Earth and runaway greenhouse effects.

D18104 HANSEN ET AL.: EFFICACY OF CLIMATE FORCINGS

32 of 45

D18104

Figure 19: Hansen et al. (2005) forcing efficacies.

Efficacy Ea: Response relative to re-sponse to equivalent CO2 forcing

Ea ≡λ(dirty snow)

λ(∆CO2)

=(∆Ts/∆F Trp

R )dirty snow(2.47 K)/(3.58 W m−2)

=(∆Ts/∆F Trp

R )dirty snow0.69 K (W m−2)−1

Snow impurity “direct” effect efficacyis ∼ 1.7–2× CO2.Snow impurity “direct”+“semi-direct”effects efficacy is ∼ 3–5× CO2.

Summary for Policymakers IPCC WGI Fourth Assessment Report

Page 16 of 21

FIGURE SPM-2. Global-average radiative forcing (RF) estimates and ranges in 2005 for anthropogenic carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and other important agents and mechanisms, 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. These require summing asymmetric uncertainty estimates from the component terms, and cannot be obtained by simple addition. Additional forcing factors not included here are considered to have a very low LOSU. Volcanic aerosols contribute an additional natural forcing but are not included in this figure due to their episodic nature. Range for linear contrails does not include other possible effects of aviation on cloudiness. {2.9, Figure 2.20}

Figure 20: Global-mean radiative forcing estimates, scale, and certainty in 2005. (IPCC, 2007)

Figure 21: Clean snow (left) doped with ∼250 ppb Monarch120 BC (right).

Figure 22: Measured and modeled snowpack reflectance for varying soot (BC) concentration.

Figure 23: Measured vs. modeled snowpack reflectance for varying soot (BC) concentration. Estimated SSA.

Summary: Consequences of Dirty Snow

Processes across scales:

• Seasonal snow albedo feedback (SAF) predicts climate change SAF• Dirty snow accelerates regional/seasonal snow-melt• Ice core impurity RF often exceeds local-regional GHG RF• Lab snow darkening by BC adequately predicted by GCM methods

Global trends:

• Dirty snow is most efficient climate forcing agent known• BC (not dust) now dominant component of dirty snow• Present day forcing, response greatest despite increasing BC emissions

Figure 24: Photo unavailable for public viewing due to copyright restrictions.