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The Atmospheric Response to Projected Terrestrial Snow Changes
in the Late Twenty-First Century
MICHAEL A. ALEXANDERNOAA/Earth System Research Laboratory,* Boulder, Colorado

ROBERT TOMAS, CLARA DESER, AND DAVID M. LAWRENCE
National Center for Atmospheric Research, Boulder, Colorado(Manuscript received 18 June 2010, in final form 3 August 2010)
ABSTRACTTwo atmospheric general circulation model experiments are conducted with specified terrestrial snow
conditions representative of 1980–99 and 2080–99. The snow states are obtained from twentieth-century andtwenty-first-century coupled climate model integrations under increasing greenhouse gas concentrations. Sea
surface temperatures, sea ice, and greenhouse gas concentrations are set to 1980–99 values in both atmospheric model experiments to isolate the effect of the snow changes. The reduction in snow cover in thetwenty-first century relative to the twentieth century increases the solar radiation absorbed by the surface, andit enhances the upward longwave radiation and latent and sensible fluxes that warm the overlying atmosphere.
The maximum twenty-first-century minus twentieth-century surface air temperature (SAT) differences are
relatively small (,38C) compared with those due to Arctic sea ice changes (;108C). However, they arecontinental in scale and are largest in fall and spring, when they make a significant contribution to the overallwarming over Eurasia and North America in the twenty-first century. The circulation response to the snowchanges, while of modest amplitude, involves multiple components, including a local low-level trough, remoteRossby wave trains, an annular pattern that is strongest in the stratosphere, and a hemispheric increase ingeopotential height.

1. Introduction
Terrestrial snow cover (SC) can influence the atmosphere via surface heat and radiative fluxes, as it is agood insulator and has both high albedo and thermal
emissivity. For example, changes in snow cover during
winter alter the surface fluxes, thereby changing thelower-tropospheric temperature (e.g., Walsh et al. 1982;
Vavrus 2007). In addition to this local effect, snow coveralso generates a remote response that differs dependingon the dynamical processes involved. Changes in the
Northern Hemisphere annular mode (NAM; Thompson* The National Center for Atmospheric Research is sponsoredby the National Science Foundation.

Corresponding author address: Michael Alexander, NOAA/
Earth System Research Laboratory, R/PSD1, 325 Broadway,
Boulder, CO 80305.
E-mail: michael.alexander@noaa.gov
DOI: 10.1175/2010JCLI3899.1
Ó 2010 American Meteorological Society

and Wallace 2000) have been linked to the distributionof snow over Siberia via vertically propagating Rossby
waves that break in the stratosphere and whose energy
subsequently propagates back down into the troposphere(Saito et al. 2001; Gong et al. 2003; Cohen et al. 2007;
Fletcher et al. 2009). In contrast to this zonal response,snow cover can also affect storm tracks and horizontal
Rossby wave propagation, creating alternating highs
and lows (Walland and Simmonds 1996; Vavrus 2007;
Orsolini and Kvamstø 2009; Sobolowski et al. 2010).
Model projections of climate forced by increased greenhouse gases (GHGs) show a number of changes, including
globally averaged warming, a more vigorous hydrologicalcycle, and an overall reduction in sea ice and snow cover(e.g., Solomon et al. 2007). Changes in the cryosphere,
however, do not just passively respond to anthropogenic
forcing but also feed back on the climate system. To
isolate the role of decreasing Arctic sea ice in climatechange, Deser et al. (2010) specified the projected sea icechanges in the late twenty-first century in an atmospheric1 DECEMBER 2010

ALEXANDER ET AL.

general circulation model (AGCM) while the SST and
GHGs were set to their twentieth-century values. In thiscomplementary study, we use a similar experiment design to investigate the seasonal response to projectedsnow cover changes.

2. Model experiments
To address the climatic effects of projected changes
in terrestrial snow cover, we have conducted two experiments with the National Center for AtmosphericResearch (NCAR) Community Atmosphere Model, version 3 (CAM3), coupled to the Community Land Model,
version 3 (CLM3), where the sea ice and sea surface
temperature (SST) are specified. The ‘‘control’’ experiment consists of a 60-yr integration with a specified repeating seasonal cycle of land snow fractional coverage,
depth, and water content based on the average over
1980–99, obtained from the seven-member ensemble
mean of the Community Climate System Model, version 3(CCSM3) ‘‘twentieth century’’ simulations, which includes ocean and sea ice models in addition to CAM3
and CLM3. The twentieth-century simulations wereforced with observed estimates of time-varying atmospheric chemical composition (greenhouse gases, ozone,
sulfate, and volcanic aerosols) and solar output as described in Meehl et al. (2006). The ‘‘perturbation’’ experiment consists of a 60-yr integration with a repeating
seasonal cycle of terrestrial snow fractional coverage,
depth, and water content for the period 2080–99, takenfrom the eight-member ensemble mean of twenty-firstcentury CCSM3 simulations under the Special Reporton Emissions Scenarios (SRES) A1B greenhouse-gasforcing scenario. In both simulations, the SSTs and sea
ice are set to those in the control experiment to isolate
the direct effect of the terrestrial snow changes. The response is obtained from the difference between the 60-yr
averages of the perturbation and control integrations;the statistical significance of the response is evaluatedusing a two-sided t test where each year is treated as anindependent sample.
CAM3 (Collins et al. 2006) and the CLM3 (Oleson
et al. 2004) have a horizontal resolution of ;1.48 latitude
and 1.48 longitude (T85). The CLM3 has been modified
so that the calculated values of snow depth (SD), fractional coverage, and water content are overwritten. The
snow state was interpolated to each model time stepusing monthly ensemble-averaged values from the coupled CCSM3 simulations. After determining that shallow overwritten snow depths were having unrealisticallylarge effects on the surface heat fluxes, all values lessthan 2 cm were set to zero.

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3. Results
The Northern Hemisphere terrestrial SC fraction andSD averaged over the period 1980–99 and the difference(D) between 2080 and 2099 (perturbation) and 1980 and1999 (control) are shown in Fig. 1 for October–December(OND), January–March (JFM), April–June (AMJ) andJuly–September (JAS). For the control experiment, snow
cover extends to central Asia and the central UnitedStates during JFM and retreats to Siberia and northwest
Canada by JAS, with maximum snow depth at highlatitudes and in the Himalayas and Rockies during thecold seasons. While CCSM3 tends to underestimate(overestimate) the mean SC (SD), its simulated change
in snow cover due to increased greenhouse gases is near
the middle of the Intergovernmental Panel on Climate
Change Fourth Assessment Report (IPCC AR4) model
distribution (Frei and Gong 2005). Compared to thecontrol, the perturbation experiment exhibits less snow
cover (DSC , 0) over the entire Northern Hemispherein all seasons because of the warmer climate induced byincreased GHGs, with the largest reductions on thesouthern flank of the twentieth-century snow distribution and in the mountains of North America and Eurasia(changes in snow cover were negligible over the entire
Southern Hemisphere). While snow depth also decreases
over most of the Northern Hemisphere, it increases overportions of Siberia and the Canadian Arctic during thecold seasons, especially in JFM. These increases occur inregions that receive more precipitation and remain
sufficiently cold for some of it to fall as snow. The increased moisture is primarily supplied by greater evaporation over the Arctic Ocean in the late twenty-first
century and the subsequent advection of the moisture
over the adjacent continents (Deser et al. 2010).
The specified snow cover/depth changes alter theseasonal differences in the nonsolar surface heat flux
(DQsfc)—that is, the sum of the sensible and latent fluxes
plus longwave radiation (Fig. 2, top row). The fluxes aremainly upward (DQsfc . 0) where the snow cover is reduced, with values generally ,10 W m22, except for highterrain, where they reach a maximum of ;30 W m22.
While DQsfc roughly scales with DSC rather than DSD,
the fluxes are not as strong as implied by the large decreases in snow cover over the northern portions ofEurasia and North America in OND (also see Fig. 4).
Regions with negative DQsfc values adjacent to large
positive values—for example, over eastern China and theUnited States during JFM—partly result from the advection of warmer air from upstream locations, where thesnow cover is reduced.
The decrease in snow cover also reduces the surface
albedo (not shown), thereby increasing the net shortwave

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FIG. 1. Seasonal distributions of the average terrestrial SC (%) for (first row) 1980–99 and (second row) 2080–99 2 1980–99 (D) and(third and fourth rows) the same distributions for SD (cm) obtained from the CCSM3 and used as boundary conditions for the experiments
performed here.

radiation into the surface (DQsw . 0; Fig. 2, middle).
However, DQsw is also influenced by the atmospheric
response to the reduction in snow, especially changes inlow clouds, which strongly reflect sunlight (Fig. 2, bottom). In general, low clouds tend to decrease along andto the south of where the snow cover decreases and increases farther north, although the causes of the lowcloud pattern are unclear. The increase in sunlight at the
top of the atmosphere at middle relative to high latitudes, also contributes to the southward enhancement of
DQsw in all seasons except summer. The latter two factors enhance and broaden the scale of DQsw in midlatitudes of Eurasia and North America.

The direct response of the terrestrial surface air temperature differences (DSAT) to snow cover changes
generally resembles DQsfc, although the former issmoother and of broader scale as the atmosphere advects and diffuses the surface heating (Fig. 3, top). The
largest and most extensive DSATs occur during OND
and AMJ (values of 1.08–3.08C), with a relative minimum in JFM and the smallest values in summer (also see
Fig. 4). During summer the largest anomalies are locatedat high latitudes in eastern Asia and western North
America, whereas in the other three seasons they arebroader in scale and centered farther south and/or located over high elevations.

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FIG. 2. The difference (D) in the seasonal (top) combined sensible, latent, and long wave fluxes at Qsfc (W m22, positive upward),(middle) Qsw (W m22, positive downward) and (c) low-level clouds (CLD; %) in response to the SC changes. Nearly all of the jDQsfcj andjDQswj values .3 W m22 are statistically significant at the 95% level as are jDCLDj values .2%.

The DSAT values from the reduced sea ice experimentsof Deser et al. (2010) over the same periods used here arealso shown in Fig. 3. While the maximum DSAT values
are much smaller in response to changes in snow compared to sea ice, the areal coverage of the SAT anomalies
is considerably broader than in the sea ice experiments,
where they are confined to the rim of the Arctic Ocean.
As a result, the changes in snow make a greater contribution to the DSAT south of ;558N in OND and JAS
than the changes in sea ice, and they are the dominantcryospheric forcing of DSAT over the Northern Hemisphere continents as a whole in AMJ and JAS. Thechanges in snow make an appreciable contribution to theoverall GHG-induced warming over the midlatitudes ofEurasia and North America in fall through spring andover Siberia and Alaska/western Canada in summer, accounting for 10%–30% of the continental-scale warming
but exceeding 70% in some regions, when compared to
DSAT in the full CCSM3 experiments (not shown).
The monthly evolution of DSC, DSAT, and the top
layer soil temperature (DTsoil) averaged over Eurasia

and North America north of 308N is shown in Fig. 4(top). For both regions, DSAT generally tracks 2DSC
(y axis reversed), where both exhibit maxima in fall andspring, with a relative minimum during winter and an
absolute minimum during late summer. Unlike DSAT,
DTsoil exhibits a strong annual cycle, where DTsoil is negative in winter and positive in summer. This variation in
DTsoil likely results from the insulating properties ofsnow; that is, less snow in the twenty-first century allows for the soil to cool more strongly in winter andwarm more in summer (Lawrence and Slater 2010).
The surface heat flux is balanced by Qsw and the flux
into the ground (Qgr; Qsfc 5 Qsw 2 Qgr), defined as theflux through a thin interfacial layer into the top full layerof the CLM, which contains snow and/or soil. While
DQsfc also exhibits a semiannual cycle over Eurasia andNorth America, the maxima are much larger in springthen than in fall (Fig. 4, bottom). The spring maxima
result from a combination of enhanced DQsw and DQgr,while the flux from the ground helps to maintain positive
DSAT values in winter.

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FIG. 3. The DSAT (8C) over land in the CAM–CLM (top) SC experiments performed here and (middle) CAM sea ice experiments(see Deser et al. 2010). All of the DSAT values .1.08C and most values .0.58C are statistically significant at the 95% level. (bottom)
Hovmöller diagrams of DSAT averaged over land as a function of latitude and calendar month in the (left) SC and (right) sea ice
experiments.

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FIG. 4. Seasonal cycle of (top) DSC (%; blue line, scale inverted), and DSAT (8C; black line) and DTsoil (8C, red line); (bottom) DQsfc,(W m22, positive upward, red line), DQsw (W m22, positive downward, orange line), and DQgr (W m22, positive downward, green line)
averaged over Eurasia and North America north of 308N. The original raw data for all variables have been smoothed in time witha 1-2-1 filter.

The vertical structure of the atmospheric temperature changes (DT) during November and May, themonths of maximum or near-maximum DSAT (Fig. 4top), over Eurasia and North America are shown in
Fig. 5. In both regions the response is considerablyshallower during November, when it is confined below;700 hPa, compared to May, when the response extends through the troposphere and exceeds 0.28C up to

300 hPa. The shallow DT maximum in November may
result from the greater vertical stability in the boundary layer during fall and winter, which limits the vertical penetration of the air warmed by the surface
heating. In contrast, the deeper and more uniform
DT profile in May results from convective heat releaseand an elevated heat source from melting snow overthe Himalayan Plateau and the Rocky Mountains in

FIG. 5. Vertical profiles of DT (8C) during November (solid line) and May (dashed line) overEurasia and North America. Values below the surface are treated as missing.

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FIG. 6. The Dz response at (bottom) 1000, (middle) 500, and (top) 50 hPa. The contour interval is 5 m with positive (negative) values in
red (blue), and the zero contour is omitted. Stippling indicates values that exceed the 95% confidence based on a two-sided Student’s t test;color shading denotes DSC (%).

spring, both of which warm the free troposphere (notshown).
The differences between the twenty-first-century andtwentieth-century geopotential height (Dz) at 50, 500,
and 1000 hPa are shown for the four seasons in Fig. 6.
While the response is of modest amplitude—for example, the maximum Dz values are ;25 m at 500 hPa—
they are comparable in magnitude to those in the sea ice
experiment (Deser et al. 2010), and many of the featuresare statistically significant, particularly in spring and fall
and at 50 hPa in summer. The response exhibits both
relatively small-scale local features and wavelike patterns, along with zonal or nearly hemispheric features.
Over most of the Northern Hemisphere continents, the1000-hPa anomalies indicate a trough where the snow
cover is reduced. The remote signal is generally equivalent barotropic through the troposphere (the responseat 200 hPa, not shown, has a similar pattern but slightly
larger amplitude compared with the anomalies at
500 hPa). The wavelike features, are most prominent at
500 hPa during OND, while a wave 3 pattern is readily
apparent from 500 to 50 hPa in AMJ. The stratosphericsignal (50 hPa) consists of a low at the pole ringed byhigher heights in midlatitudes with two centers in OND
and JFM, three centers in AMJ, and a nearly zonal pattern in JAS. Superimposed on these signals is a hemispheric increase in heights due to the general warming of
the troposphere, which is most apparent at upper levels
during spring and summer.

4. Discussion and conclusions
AGCM simulations were conducted with repeating terrestrial snow cover conditions representative of 1980–99

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and 2080–99, with the latter obtained from coupledmodel simulations using the SRES A1B greenhouse gas
scenario. The reduction in snow cover in the twenty-first
century relative to the twentieth century increases thesolar radiation absorbed by the surface and the upward
longwave radiation and latent and sensible heat fluxes,
which in turn warm the overlying atmosphere. Decreasing the amount of insulating snow cover increases theheat lost from the ground, resulting in colder soil temperatures but warmer air temperatures during winter.
While the maximum DSAT due to snow cover changes
is much smaller than that due to sea ice changes, theformer are broader in scale and more prominent in falland spring than the sea ice generated DSAT. In addition,
the SAT response found here may be relatively low, sincemodels that underestimate the present-day snow cover,such as the CCSM, also underestimate the climate sensitivity to snow cover change (Levis et al. 2007).
The atmospheric response to the terrestrial snowchanges, while of modest amplitude, appears to involve
multiple factors, including a near-surface low over theheating, consistent with the direct response to nearsurface thermal forcing in midlatitudes (Hoskins andKaroly 1981; Deser et al. 2007); a remote equivalentbarotropic response, which may result from storm-trackchanges and the horizontal propagation of Rossby wave
energy (Honda et al. 1999; Clark and Serreze 2000;
Sobolowski et al. 2010); the vertical propagation of energy via Rossby waves creating large-scale patterns inthe stratosphere (Cohen et al. 2007), as only long waves
are able to penetrate the tropopause (e.g., Holton 2004);
and an overall increase in heights due to the broad warming of the Northern Hemisphere. The response patterns
found here do not resemble the northern annular mode,as found in some previous AGCM simulations forced byanomalous snow cover or sea ice boundary conditions(e.g., see Deser et al. 2010). Finally, the direct response to
the snow cover changes shown here may be altered by
feedbacks with other components of the climate system.
Acknowledgments. We gratefully acknowledge support from the National Science Foundation’s Arctic
System Science Program.
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