Final Author Version, published inClim Dyn (2014) 43:345359DOI 10.1007/s00382-013-1954-y

Interpreting Observed Northern Hemisphere Snow Trends with LargeEnsembles of Climate Simulations

L. R. Mudryk · P. J. Kushner · C. Derksen

Received: 5 March 2013 / Accepted: 23 September 2013 / Published online: 18 October 2013

Abstract Simulated variability and trends in Northern Hemi-sphere seasonal snow cover are analyzed in large ensemblesof climate integrations of the National Center for Atmo-spheric Research’s Community Earth System Model. Two40-member ensembles driven by historical radiative forcingsare generated, one coupled to a dynamical ocean and theother driven by observed sea surface temperatures (SSTs)over the period 1981-2010. The simulations reproduce manyaspects of the observed climatology and variability of snowcover extent as characterized by the NOAA snow chart cli-mate data record. Major features of the simulated snow wa-ter equivalent (SWE) also agree with observations (Glob-Snow Northern Hemisphere SWE data record), although witha lesser degree of fidelity. Ensemble spread in the climateresponse quantifies the impact of natural climate variabil-ity in the presence and absence of coupling to the ocean.Both coupled and uncoupled ensembles indicate an overalldecrease in springtime snow cover that is consistent with ob-servations, although springtime trends in most climate real-izations are weaker than observed. In the coupled ensemble,a tendency towards excessive warming in wintertime leadsto a strong wintertime snow cover loss that is not found inobservations. The wintertime warming bias and snow coverreduction trends are reduced in the uncoupled ensemble withobserved SSTs. Natural climate variability generates widelydifferent regional patterns of snow trends across realizations;these patterns are related in an intuitive way to tempera-

L. R. MudrykDepartment of Physics, University of Toronto, 60 St. George Street,Toronto, Ontario, Canada, M5S 1A7. Tel.: +14169787796Fax: +14169788905E-mail: mudryk@atmosp.physics.utoronto.ca

P. J. KushnerDepartment of Physics, University of Toronto, 60 St. George Street,Toronto, Ontario, Canada, M5S 1A7.

C. Derksen Environment Canada, Downsview, Ontario, Canada.

ture, precipitation and circulation trends in individual real-izations. In particular, regional snow loss over North Amer-ica in individual realizations is strongly influenced by NorthPacific SST trends (manifested as Pacific Decadal Oscilla-tion variability) and by sea level pressure trends in the NorthPacific/North Atlantic sectors.

Keywords Climate Change · Snow · Climate Models ·Pacific Decadal Oscillation

1 Introduction

Seasonal snow cover is a key element of the Northern Hemi-sphere’s energy and water balance and contributes via albedofeedbacks to global climate sensitivity. In response to green-house gas forcing, Northern Hemisphere snow cover extent(SCE) is projected to decrease while snowfall is projected todecrease at midlatitudes (due to precipitation phase relation-ships with increasing temperatures) but increase at high lati-tudes (due to projected changes in atmospheric water vapour,Groisman et al. 1994; Raisanen 2008). Reliable decadal tomultidecadal prediction of seasonal snow cover on regionaland larger scales is predicated on the ability to properly sim-ulate historical changes and separate forced signals fromnatural climate variability. In this context, we examine twolarge-ensemble hindcasts of a global climate model (GCM),the National Center for Atmospheric Research’s Commu-nity Earth System Model (CCSM4), in comparison with ob-servations. Each ensemble member represents an indepen-dent climate realization that can be compared to the ob-served climate record (which represents, in a statistical sense,only one of many possible climate realizations). The designof this experiment is similar to work by Deser et al. (2012),who quantified the role of natural climate variability in fu-ture projections of temperature trends.

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Observed trends of snow-related quantities show largevariability in sign and magnitude that depends on the snowcover variable, region of focus, season, and time period (seeFig. 1 in Brown and Mote 2009, for example). Seasonalsnow cover duration is expected to be one of the variablesmost responsive to climate change (Brown and Mote 2009);and, while little change in the onset date of snow in bo-real autumn has been observed (hereafter all seasonal refer-ences are with respect to boreal climate), there is a markedtrend towards earlier springtime snow-off dates in the obser-vations (Derksen and Brown 2012a). This trend is echoedin the trends of satellite derived springtime SCE (Dery andBrown 2007), which are occurring in parallel with NH landsurface warming. For the period 1979–2011, Derksen andBrown (2012b) report decreasing NH June SCE of ∼ -18%decade−1, driven by particularly strong SCE reductions overthe past decade. Trends in snow water equivalent (SWE) arecomplicated by their dependence on temperature and pre-cipitation trends. For example, Bulygina et al. (2010) reportincreased SWE over Eurasian and northern Russia over theperiod 1966-2009 while Atkinson et al. (2006) report de-creased SWE over northern Canada during the period 1966-1996, despite both regions showing increased cold seasonprecipitation (Callaghan et al. 2010; Trenberth et al. 2007;Min et al. 2008).

It is challenging to separate anthropogenically forcedtrends in seasonal snow cover from trends reflecting nat-ural climate variability. For example, it is not clear if therecent dramatic SCE reduction trends reported by Derksenand Brown (2012b) reflect anthropogenic forcing or natu-ral climate variability. Natural modes of climate variability,like the Pacific North American (PNA) and Pacific DecadalOscillation (PDO) patterns, can influence North Americansnow cover — positive phases of the PNA and PDO areassociated with reduced snow accumulation and a shortersnow cover season (Derksen et al. 2007; Gutzler and Rosen1992; Brown and Goodison 1996). Furthermore, Eurasiansnow accumulation correlates positively with cyclone fre-quency and negatively with anticyclones (blocks); trends inthese phenomena explain portions of the variability and trendsin Eurasian snow depth since the 1950s (Popova 2004, 2007).The challenge of determining forced circulation trends thatinfluence snow cover compounds the challenge of determin-ing the influence of the forced component of temperatureand precipitation trends.

In this study we compare an ensemble of independentbut identically forced realizations of model simulations tothe single observed realization of climate. This allows usto assess what aspects of recent changes in seasonal snowcover are captured in the simulations while accounting forclimate variability. We analyze snow cover climatology, vari-ability, and trends in two different configurations of CCSM4.The first configuration is the coupled version of CCSM4 in

which the atmosphere is coupled to dynamical ocean and seaice components. The second configuration prescribes ob-served sea surface temperatures (SST) and sea ice. The dif-ferences between these two versions allow us to isolate theinfluence of SST and sea ice trends on the resulting sim-ulated trend patterns. While we expect the direct influenceof such differing SST/sea ice trends to dominant the differ-ences between the coupled and uncoupled simulations, wenote that ocean-atmosphere coupling may also contribute.To the extent that the model resembles the real world, eachindividual realization represents a potential historical evolu-tion of the climate, with individual realizations demonstrat-ing the range of trends possible as a result of natural vari-ability.

We organize the remainder of this paper as follows. InSection 2 we describe the experimental setup of the mod-els, the observational data used, and analysis techniques. InSection 3 we compare results from both experimental setupsagainst one another and observations. We conclude in Sec-tion 4.

2 Data and Methods

2.1 Model configurations

We use Version 1.0.2 of the National Center for AtmosphericResearch (NCAR) Community Earth System Model (CESM)in two configurations. The first configuration is the standardCommunity Climate System 4 (CCSM4) with the Commu-nity Atmosphere Model 4 (CAM4), the Community LandModel 4 (CLM4), the Community Ice Code 4(CICE4) andthe Parallel Ocean Program 2 (POP2) (see Gent et al. 2011,and http://www.cesm.ucar.edu/). The atmosphere andland model are run on a nominally two-degree finite volumegrid provided with the tag f19 in the Version 1.0.2 code re-lease; the ocean and ice models are run on the nominally1 degree Greenland dipole grid (g16). The two-degree at-mosphere/land grid resolution is coarser than the standardnominally 1 degree resolution of CCSM4 used in Gent etal. 2011 and Lawrence et al. 2012; it is chosen to reducecomputational cost of carrying out the large ensemble withavailable resources. This configuration will be referred toas coupled, where it is understood that the atmosphere iscoupled to a dynamical ocean and sea ice model. The sec-ond configuration uses the same atmosphere and land mod-els, however global sea ice concentration and sea surfacetemperatures (SSTs) are prescribed using the AtmosphericModel Intercomparison Project (AMIP) observed SST andsea ice data provided by NCAR (Hurrell et al. 2008). Thisprescribed SST and sea ice configuration will be referred toas uncoupled.

For coupled and uncoupled configurations we performforty historical climate realizations forced with identical time-

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dependent historical greenhouse gases, ozone, aerosols, vol-canic emissions and solar variability. The data for these pre-scriptions are the standard data sets provided by NCAR andare consistent with the historical radiative forcing CMIP5protocol (Taylor et al. 2012), apart from an adjustment wemake to merge the forcing time series at 2005 with thoseof the 4.5W Representative Concentration Pathway (rcp45)in order to extend the runs from January 2006 to Decem-ber 2010. In the case of the uncoupled experiment, at thetime of running these simulations SST and sea ice forcingdata were only available to December 2008, which limitsour simulations to that point.

All realizations in the coupled experiment are derivedfrom a January 1955 climate state taken from a ‘parent-run’ branched in 1850 from a pre-industrial control and thenrun forward from January 1850 using historical forc-ing. Starting January 1955, slightly distinct climate statesare created by altering the least-significant bit of a single at-mospheric variable at a single grid location. The effects ofthis perturbation propagate rapidly throughout the simulatedclimate. Based on results by Branstator and Teng (2010) weestimate that the realizations should be statistically indepen-dent within a decade (often much faster based on estimatesof the Lyaponov timescale). Comparisons of long time scaleclimate system indices such as the Atlantic Mean Overturn-ing Circulation, El Nino Southern Oscillation and PacificDecadal Oscillation as well as global mean surface tempera-ture time series confirm this assumption. We discard the first26 years of the simulation and analyze output from 1981-2010 which ensures that the realizations are statistically in-dependent apart from the common forcing and that the anal-ysis period overlaps with relatively high quality snow obser-vations from the satellite era (1967 onwards for SCE; 1979onwards for SWE).

Independent realizations for the uncoupled experimentwere created in a similar manner, except that the parent runwas initialized starting in 1950 from an unequilibrated state.A five year lead time is sufficient for land and atmosphericprocesses to equilibrate since they have much shorter intrin-sic time scales than the ocean and sea ice components of theclimate system.

2.2 Observational and Model Data

The fractional grid area covered by snow (snow cover frac-tion or SCF) and SCE data are from the NOAA snow chartclimate data record (CDR, Brown and Robinson 2011), atime series derived primarily by optical satellite imagery andhoused at the Rutgers University Global Snow Lab (http://climate.rutgers.edu/snowcover/). Brown and Derk-sen (2013) have shown that there is an October snow coverbias over a portion of the record that is not seen in other datasources. The bias leads to spurious trends in the Eurasian

region if not accounted for. During October we supplementthe NOAA CDR with bias-adjusted Eurasian data used inthe above publication and in situ Russian data (courtesy ofR. Brown and P. Groisman), both available over the period1982–2010.

During October we supplement the NOAA CDR withbias-adjusted Eurasian data used in the above publicationand in situ Russian data (?), both available over the pe-riod19822010.

SWE data are from the European Space Agency Glob-Snow project (version 1.3), derived through a combinationof satellite passive microwave data, forward snow emissionmodel simulations, and climate station observations for non-alpine regions of the northern hemisphere (Takala et al. 2011).Due to inconsistencies in North American climate stationdata in 1981, we limit the SWE data to 1982-2010.

Observed temperature records are from the Universityof East Anglia Climate Research Unit (UEA-CRU) versionCRUTEM4 (Jones et al. 2012).

Model data available for each grid cell consists of SCF,SWE, surface air temperature/sea surface temperature, sealevel pressure, and snow water precipitation defined in amanner consistent with the observational products but on themodel grid.

2.3 Analysis Methods

Time series analysis is carried out over three land masses: allNorthern Hemisphere land mass north of 30N (referred to asNH), the North American continent north of 30N (NA), andthe Eurasian continent north of 30N (EUR). We exclude theGreenland land mass from all three definitions. SCE time se-ries are derived by summing the total land area under snowover NH, NA, and EUR. Land surface temperature time se-ries are calculated as area weighted means over the threeregions. Snow water mass (SWM) time series (or equiva-lently snow water volume) are derived by multiplying thegrid box SWE by grid box area and summing the result overNH, NA, and EUR. We convert volume to mass units usingthe density of water. The SWM calculations neglect snowon land ice sheets in the Canadian Archipelago as well asthe Russian Arctic islands. We also use the same elevationmask applied in the GlobSnow data processing chain to re-move alpine regions, for which SWE retrievals are highlyuncertain in the GlobSnow product.

We use these time series to calculate climatologies, anoma-lies, standard deviations and linear trends. We treat each en-semble member as a possible climate realization that canbe compared to the observed climate. Accordingly, we cal-culate interannual variability and statistical significance oftrends independently for individual realizations just as onedoes with the observational record. We then examine the

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Fig. 1: (a) Annual cycle of snow cover extent (SCE) for NH (black), NA (red), and EUR (blue) regions for NOAA snow chart CDR (squares),ensemble mean coupled experiment (solid), and ensemble mean uncoupled experiment (dotted). (b) As (a) for observed interannual variabilityand ensemble mean of interannual variability of individual realizations of the simulations. (c)–(d) As (a)–(b) for GlobSnow and simulated SWE.Individual time series are detrended prior to calculating interannual variability.

mean and variability of the these statistics in the ensemble toassess whether observations and simulations are consistent.

Finally, we also present maps of trends in SCF and SWEin each grid cell, and of trends in model SLP and surfaceair temperature. These trends are either for individual real-izations or are ensemble averaged, as described in the text.Stippling is superimposed on these maps to mark regionswith trends significantly different from zero based on a two-sided t-test with a 95% confidence level. In determining thetest statistic, we use variance sampled from the detrendedanomalies of the ensemble mean, particular realization, orselected subset of realizations, as appropriate for the trendsbeing displayed.

3 Results

3.1 Observed and simulated seasonal snow cover

The simulated annual cycle of SCE in the coupled anduncoupled experiments is realistic for the Northern Hemi-sphere regions NH, NA, and EUR (Fig. 1a). Small biaseswith respect to observations and differences between thecoupled and uncoupled experiments are consistent withbiases in land surface temperature (not shown). Lawrenceet al. (2011) find that SCE in CCSM4 is greatly improved

compared to previous CCSM generations. We find that theSCE simulation is not greatly affected at the continental scaleby using our two degree version of the model instead of thestandard one degree version. The models’ interannual vari-ability in SCE (that is, the ensemble average of the inter-annual variability of detrended time series from individualrealizations) is generally less than or similar to the observed(Fig. 1b) throughout the year. The coupled experiment’sinterannual variability in NA is somewhat larger than theuncoupled experiment’s but this effect is not evident forthe more continental climate of EUR. Both model ensem-bles underestimate SCE variability in the Subartic and Arc-tic during the transitional months of June (month of maxi-mum observed seasonal SCE decline) and October (monthof maximum seasonal SCE increase). This discrepancy is re-lated to the inability of climate models to adequately capturethe magnitude of variability during the high latitude snowmelt and snow onset periods (Derksen and Brown 2012b).

The SWE climatology simulated in the coupled anduncoupled experiments is reasonable, however there is apositive bias in the spring season (AMJ) SWE over both con-tinents (Fig. 1c). In addition, the seasonal maximum in SWEis shifted towards March in the models for both continentsinstead of February in the GlobSnow data. A similar differ-ence is apparent with respect to variability, where the obser-

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Fig. 2: Annual cycle of SCE trends for NOAA snow chart CDR (red),coupled (black) experiment, and uncoupled (blue) experiment, forNH (top), NA (middle), and EUR (bottom). For the simulations, theextent of the vertical lines represents the range of trends for the 40ensemble members, boxes span the interquartile range (25th and 75thpercentiles), and dashes mark the median trends. Individual realiza-tions with a significant trend (based on 95% confidence intervals usingthe interannual variability of that realization) are marked with a shadedhorizontal bar. Signficant observed trends are indicated with a solid in-stead of an open red square. Encircled observational trends from Octo-ber are prominently biased and discussed further in the text and Fig. 5.

vational peak in North American variability occurs in Aprilin the models rather than March. The simulations underesti-mate interannual variability in SWE in October-November-December for EUR and NH.

Generally, the CCSM4 model appears to adequately cap-ture the principal features of seasonal snow cover for ourpurposes, especially compared to previous generations ofclimate models (e.g. Frei and Robinson 1998; Frei et al.2003). Observational uncertainty must also be accounted forwhen considering the assessment of the model simulations.In general, uncertainty associated with SCE is less than withSWE. The SCE datasets and the errors therein are now rea-sonably well understood because of the evaluation of mul-tiple datasets (Brown et al. 2007, 2010; Brown and Robin-son 2011). In fact, recently highlighted biases in the NOAAsnow cover CDR (Brown and Derksen 2013) have been dis-covered because of the comparatively better understandingof the SCE data sets and comparisons among them. Un-certainty in SWE observations are comparatively less con-

Fig. 3: Fraction of ensemble members with significant SCE reductiontrends in NH (black), NA (red), and EUR (blue) SCE, for coupled(solid) experiment and uncoupled (dotted) experiment.

strained because of both the difficulties associated with de-riving this variable (unlike binary SCE products, SWE un-certainties can accumulate through the snow season) and rel-atively fewer inter-dataset comparisons (Clifford, 2010).

Fig. 4: As Fig. 2, but for simulated and CRU land surface temperature.

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Fig. 5: October SCE trends for three differing geographical regions.Simulated trends from the coupled (black) and uncoupled (blue) ex-periments and trends from unadjusted NOAA CDR (red squares) as inFig. 2 for NH and EUR regions. Also shown are trends over the re-duced portion of Eurasia analyzed in the Brown and Derksen (2013)study based on: unadjusted NOAA time series (red square), adjustedNOAA time series (red diamond), and in situ observations (green dia-mond). Significant observed trends are indicated with filled symbols.

3.2 Trends in snow cover extent and fraction

The annual cycle of SCE trends for NH, NA, and EUR areshown for the observations (squares) and simulations (boxplots) in Fig. 2. In the box plots the full range and statisticalsignificance of trends in individual realizations are shownfor the coupled (grey) and uncoupled (blue) model ex-periments (see caption for details). For observed SCE, sta-tistically significant reductions are seen in AMJJA for NH,MJJ for NA, and MAMJJA for EUR. These observationaltrends have been documented in previous studies (for in-stance, Brown and Robinson 2011; Derksen and Brown 2012b).

In the coupled ensemble (black box plots), the vast ma-jority of the individual realizations have snow reduction trendsthroughout the entire year and across all three regions. (Theblue box plots represent the uncoupled simulations andwill be discussed below.) In particular, wintertime reductionof SCE is strong in many of the realizations, which con-trasts with the observations. This point is further quantifiedin Fig. 3, which shows the fraction of realizations with sig-nificant reduction trends in the coupled simulation as solidlines. It is seen that for every month at least 40% of theensemble produces SCE time series with significant trendsfor NH; this fraction is reduced for the individual continen-

Fig. 6: (Top) Ensemble mean trend in snow cover fraction for the coupled experiment. (Middle) As top for uncoupled experiment. (Bottom)As top for NOAA snow chart CDR. Stippling indicates trend significance at the 95% confidence level.

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tal regions, but the tendency towards snow reduction in allmonths and all regions is quite strong in the simulations.There are no months or regions with significant positive SCEtrends in the coupled ensemble.

The tendency of the model to show too much loss of SCEin winter probably arises because the model is systemati-cally biased towards excessive wintertime warming. Indirectevidence of this is shown in Fig. 4, which plots simulatedand CRU observed land surface temperature trends in thesame format as Fig. 2. For NH, NA, and EUR, the coupledensemble shows systematically more warming than observed,and this warming is likely connected to the SCE reductionssimulated in these periods. Additional indirect evidence isshown in the uncoupled simulation results in Figs. 2, 3, and4. In these simulations, the wintertime warming is reduced,along with the number and fraction of realizations with sig-nificant SCE reductions. Generally speaking, the simulationof SCE trends in simulations driven by observed SSTs arecloser to the observed record. We also note that NA trendsare more sensitive to ocean SSTs than EUR trends; this dif-ference reflects the relatively strong continentality of EURclimate (see discussion of Fig. 8).

The large ensemble dataset provides useful insight inthis analysis. For example, while the SCE and temperaturetrends are systematically different between coupled anduncoupled experiments (Figs. 2, 4), the range of trends inthe coupled and uncoupled simulations is for the mostpart similar (as seen by comparing interquartile ranges andthe extent of the vertical lines). This result implies that nat-ural variability in the coupled land-atmosphere system candrive much of the variability in the SCE and temperaturetrends, even in the absence of additional coupling to theoceans. The ensembles also reveal regions and seasons whereclear discrepancies between the simulations and the obser-vations are seen. We have discussed the case of wintertimewarming and snow loss. There are also the significant pos-itive trends in observed SCE seen during October for NHand EUR; corresponding significant positive trends in SCEare not found in any of the coupled or uncoupled realizations(Fig. 2, shaded bars). These trends are consistent with anal-ysis of this data product by Cohen et al. (2012) and Liu et al.(2012); however, they are challenging to reconcile with fallseason surface temperature warming trends across the re-gion (Fig. 4 and numerous other studies). Brown and Derk-sen (2013) have examined the October Eurasian trend in theNOAA SCE CDR and have determined that it is primar-ily driven by an internal bias in the amount of snow covercharted over the 1982–2005 period. They further show thatthe trend is inconsistent with trends in other independentdata sources including surface observations, reanalyses, andsatellite passive microwave retrievals. The authors suggestthat an increasing ability to detect small amounts of snow(due to increasing observational frequency and resolution)

may be responsible for the trend seen in the NOAA CDR.Such an effect would be especially prominent during theshoulder seasons and lead to an increased potential to de-tect snow onset during the fall and an increased potential todetect snow disappearance in the spring.

Fig. 5 summarizes how Brown and Derksen’s analysisaffects the agreement with the October SCE trends in thesimulations. Examining trends in the original NOAA CDRover the three different regions described in the figure cap-tion indicates that the trend in a geographically reduced anal-ysis region where independent Eurasian ground observationsare available is representative of the trend in the entire Eurasianregion, which itself dominantes the NH trend. This inferencesuggests that a large bias in the Eurasian trend would alsoprominently bias the NH trends. Adjusting the NOAA prod-uct time series so that its trend is consistent with groundobservations leads to a reversal in both the trend sign andsignificance and brings the magnitude into agreement withboth model results.

Finally, we note that during late springtime (May, June,July) the magnitude of the observed SCE trends over NHand EUR are also not captured by any of the simulations.Issues with model physics such as snow-melt sensitivity andalbedo feedbacks may be contributing to the discrepancy.

Spatial maps of observed trends in snow cover fraction(SCF) show a great deal of spatial and seasonal variability(Fig. 6, bottom row). Widespread reduction in spring (AMJ)SCF is seen over both continents, but in winter (JFM) SCFdecreases over western and northern EUR and southern NA,and increases over eastern EUR and northern NA. There arealso mixed trends during the fall period (because of the biasin October snow cover, we only plot a November/Decemberaverage for the observed SCF). In the ensemble mean ofthe simulations, there is a systematic tendency towards SCFloss in all seasons and regions, reflecting the broad conti-nental trends shown in Fig. 2. This tendency towards hemi-spherically coherent and regionally consistent snow loss issomewhat reduced in the uncoupled simulations, particu-larly in AMJ, resulting from reduced warming and increasedsnow precipitation over northwestern NA (see discussion ofFig. 12).

The ensemble means of both the coupled and uncoupledsimulations show less spatial structure than the observedtrend pattern, as expected from the smoothing arising fromaveraging over a large number of realizations. Nonethelessthe ensemble mean is not always representative of individ-ual realizations, some of which show trend patterns of sim-ilar magnitude and spatial scale to the observed record. Forexample, JFM SCF trends from two coupled realizationsthat were selected because they have highly contrasting SCFtrend patterns are shown in Fig. 7. The SCF trends in thesetwo realizations are consistent with each realization’s sur-face temperature and sea-level pressure trends. For example,

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a trend towards an intensified Aleutian low is coherent witha trend towards advective warming and SCF reduction overNA (Fig. 7, top row); the opposite holds for the other realiza-tion (Fig. 7, bottom row). Similar pairs of contrasting trendpatterns can also be found in the uncoupled ensemble. De-spite these contrasting patterns, the typical simulation showsmore warming and snow loss than observed, which is a qual-itatively unrealistic feature.

Since excessive wintertime warming in the coupled ex-periment is reduced in the uncoupled experiment, it is worth-while to examine which aspects of the SSTs explain regionaldifferences. Fig. 8a–b shows that the coupled ensembleis characterized by excessive JFM North Pacific warmingcompared to the observed historical SST trends. We recallthat these observed SST trends are present in the data setused to force the uncoupled experiment. In addition, thezonal gradient of warming in the Pacific is much weaker inthe ensemble mean of the coupled realizations (Fig. 8a),and in the individual coupled realizations (not shown), thanin the observations as represented by the uncoupled exper-iment SSTs (Fig. 8b). Associated with the coupled ensem-ble’s strong ocean warming and weak North Pacific dipole isa relatively uniform and large magnitude warming over NA

(see Shin and Sardeshmukh 2011). The pattern of snow lossin these two ensembles (Fig. 8d–e) reflects this difference intemperature trends.

Because natural climate variability gives rise to a rangeof spatial patterns of the trends in the coupled simulations,it is instructive to pick a subset of coupled simulationswhose SST trends most closely resemble the observed SSTsthat are driving the uncoupled simulations. This selectionis carried out with reference to the Taylor diagram (Taylor2001) shown in Fig. 8g (see caption for details). The Taylordiagram shows that 1) spatial gradients in individual realiza-tion SST trends are weaker than observed and 2) individualcoupled realizations are generally weakly spatially corre-lated with the observations. But there are a set of fifteen re-alizations (green points) whose spatial correlation with theobserved SST trends is generally stronger. The compositemean surface temperature (Fig. 8c) and SCF (Fig. 8f) of thisfifteen-member subset show reduced warming and/or cool-ing over northwestern NA, with a coincident pattern of in-creasing SCF; trends in Eurasia are relatively unaffected. Asecond composite of coupled realizations that shows stronganticorrelations with the historical SST trends (not shown)produces an oppositely signed dipole pattern in the North

Fig. 7: JFM trends in snow cover fraction (left), surface temperature (middle) and sea-level pressure (right) for two selected coupled realizations.Stippling indicates trend significance at the 95% confidence level.

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Pacific (as expected) associated with increased warming innorthwestern NA and strong, negative trends in SCF in thesame region. Results (not shown) are similar for the spring(AMJ) season and for the annual average.

Overall, the relatively weak direct correspondence be-tween observed NA SCE trends and those in the coupled

model appears strongly influenced by the simulated NorthPacific temperature trends. However, we note that the tropi-cal Pacific represents another possible source of the discrep-ancy. The tropical Pacific temperature trends seen in Fig. 8anot only have more warming than the historical trends inFig. 8b, but also a different pattern. The pattern of tropicalPacific warming has been shown to influence land surfacetemperature and precipitation patterns as identified by Shinand Sardeshmukh (2011).

In addition to SST variability, Fig. 7 suggests that decadalvariability in sea level pressure (SLP) may also play an im-portant role, driving trends in surface temperature and sub-sequently snow cover. In Fig. 9 we perform a similar anal-

ysis to that shown in Fig. 8, selecting realizations on thebasis of their pattern correlation with observed SLP (fromthe NCEP reanalysis product, Kalnay et al. 1996). As forSSTs, the greatest SCE trend sensitivity is seen over NA sowe focus on that region. We perform this analysis on theuncoupled ensemble. Selecting based on how well modelSLP correlates with observed trends in the North Pacific(Fig. 9c) yields a pattern similar to the ensemble mean (Fig. 9b),but with enhanced snow cover increases in northwestern NAcorresponding to enhanced cooling in the same region (notshown). By contrast, selecting realizations based on patterncorrelation of North Atlantic SLP trends yields increasingSCE trends in northeastern NA. Both patterns share the roughlydipole structure of the observations: positive SCE trends atmore northern latitudes and negative SCE trends at moresouthern latitudes. However, the region of NA with snowcover increases is located further north in the models thanin the observations (Fig. 9a). We note here that the observedSLP trends in the North Pacific and North Atlantic are not

Fig. 8: JFM trends in SSTs superimposed with land surface temperature trends for (a) coupled ensemble, (b) uncoupled ensemble, (c) and 15member subset of the coupled ensemble defined using panel (g). Panels (d)–(f) are as in (a)–(c) but for SCF. The subset in panels (c) and (f) isselected with reference to panel (g). Panel (g) is a Taylor diagram showing pattern correlation (azimuth) and root mean square difference (radius)of the SST trends in each coupled realization relative to the trend in the observed historical SSTs driving the uncoupled ensemble for the region50E-275E,0N-40N. The fifteen realizations used for panels c) and f) are represented by green dots and have SSTs that have pattern correlationwith historical SST trends greater then 0.4. Stippling indicates trend significance at the 95% confidence level.

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Fig. 9: JFM trends in SCFfor (a) NOAA snow chartCDR, (b) uncoupled ensemblemean and (c)–(d) subsets of theuncoupled ensemble selectedon the basis that the spatial pat-tern of a particular realization’sSLP trend is correlated with theobserved SLP trend with a co-efficient greater than 0.4. Theregions used for the correlationare the (c) North Pacific sector,137E–237E, and the (d) NorthAtlantic sector, 75W–25E, bothbetween 39N–77N. The num-ber of realizations that con-tribute to each trend are dis-played in brackets. Stippling isused to indicate trend signifi-cance. Stippling indicates trendsignificance at the 95% confi-dence level.

significant. Nonetheless the North Pacific trends tend to bereproduced in the uncoupled model and in the coupled re-alizations with SST trends similar to the observations andboth circulation trends appear to consistently influence NAtemperature and SCE trends.

3.3 Snow water Equivalent Trends

Unlike snow cover extent or fraction, snow water equivalent(SWE) or its spatial integral, snow water mass (SWM), rep-resents a measure of accumulated snowfall and is thus con-trolled by temperature and precipitation trends. In generalobserved SWM trends (Fig. 10) are negative for NH and NAin winter (consistent with Takala et al. 2011) but are not sig-nificant for EUR. Model trends during JFMA show a qual-itatively similar tendency towards SWM reduction over NAin the coupled simulations but not in the uncoupled sim-ulations, reflecting strong thermal control by SSTs in thatregion for the simulations. In EUR, observed, coupled, anduncoupled wintertime trends are mixed. As a consequenceof different trends in these regions, NH wintertime trendsare also mixed.

We examine the spatial distribution of the SWE trendsin Fig. 11. The Eurasian trends are very consistent betweenthe coupled and uncoupled ensembles and show reason-able agreement with the GlobSnow trends over this regionas well. In particular, all data sets indicate generally decreas-ing trends over Europe and western Siberia and increasingor weak trends over eastern Siberia for all snow seasons.This result is consistent with the continental SWM trendspresented in Fig. 10c. The spatial patterns are also consis-tent with results by Ghatak et al. (2012), linking the patternof Eurasian SWE, surface temperature and snowfall trendsto circulation changes initiated by September sea ice loss.The trends over NA differ more. The coupled model showsdecreasing SWE over Alaska and northern Canada duringspring and fall, in agreement with trends from the GlobSnowproduct. However, during winter there is increasing SWEover the Canadian archipelago and decreasing SWE acrosssouthern Canada and the northern US, in direct oppositionto the strong significant decreasing trends seen in the Glob-Snow product. The wintertime trends in the uncoupled en-semble mean are similar to the coupled experiment butshow strong increases in SWE during the springtime. Thediscrepancy in location of the decreasing SWE suggests that

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Fig. 11: (Top) Ensemble mean trends in SWE for the coupled experiment. (Middle) As top for the uncoupled experiment. (Bottom) As top forGlobSnow observations. Stippling indicates trend significance at the 95% confidence level.

the agreement between observed and simulated trends of to-tal NA snow mass during JFMA may be for the wrong rea-sons. The region of decreasing SWE for both ensembles inFig. 11 reflects the same region of decreasing SCF in Fig. 6and occurs at latitudes far enough south that surface tem-perature changes may driving trends in both snow variables.By contrast, in the observations NA SWM and SCE evolvedifferently with significant wintertime reduction of SWM(Fig. 10) and weakly positive SCE trends (Fig. 2). Compar-ing their spatial patterns in Figs. 6 and 11, we can see thatthe region of SWE reduction occurs further north where themajority of the climatological snow mass exists, whereas thewintertime trends in SCE occur at lower latitudes of morelimited snow depth.

The differences in North American SWE trends betweenthe model experiment and the GlobSnow observations notedabove are likely forced by snow precipitation trends present

in the models; however, these precipitation trends may bethemselves forced by temperature changes depending on theirlocation. Examining the model snow water precipitation trendsin Fig. 12 shows that they are highly correlated with the re-sulting SWE trends. In general these trends resemble thoseof liquid precipitation and consist of increased high lati-tude precipitation and decreased mid latitude precipitation.As expected, the resulting ensemble mean trend pattern insnow water precipitation has more zonal structure in theuncoupled experiment than in the coupled experiment.During AMJ (and JFM to a lesser extent) snow precipita-tion over northwestern NA has a strong influence on theuncoupled model trends in SWE (Fig. 11) as well as SCF(Fig. 6) in regions that would otherwise be susceptible totemperature-induced melting as seen in the coupled exper-iment. For both seasons there is an associated cooling trendover the same region that both drives the increased snow-

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Fig. 12: (Top) Ensemble mean trends in average monthly precipitated snow-water for the coupled experiment. (Bottom) As top for theuncoupled experiment. Stippling indicates trend significance at the 95% confidence level.

fall and reduces snow melt. Likewise, during the winter wesee that the pattern of low latitude drying and high latitudewetting is echoed in the simulated SWE trends of both ex-periments. However, over the Arctic ocean and surroundingland, the increase in snow precipitation is associated withwarming trends and therefore is forcing changes in SWE viaprecipitaiton only. The change in snow precipitation changeover the central Arctic is typical of future projections ofclimate models (Meehl et al. 2007). We reiterate here thatthe change in North American precipitation patterns seen inFig. 12 for the uncoupled experiment is a result of the SSTtrends used to force the atmosphere. A similar composite ofsnow precipitation to that used in Fig. 8 shows that it is pos-sible to obtain similar trend patterns of snow precipitationin the coupled experiment by selecting based on the SSTtrends.

4 Conclusion

We have analyzed Northern Hemisphere snow trends in theNational Center for Atmospheric Research’s Community EarthSystem Model with a focus on comparison to observationsand an attempt to account for the impacts of natural vari-ability using a large ensemble methodology. We generatedtwo 40-member ensembles of the model driven by histori-cal radiative forcings over the period 1981–2010, one cou-

pled to a dynamical ocean (coupled) and the other drivenby observed sea surface temperatures (uncoupled). Differ-ences between these two ensembles demonstrate that thetrends in seasonal snow cover parameters like snow coverextent/snow cover fraction (SCE/SCF) and snow water equiv-alent/snow water mass (SWE/SWM) over North Americaare strongly influenced by historical sea surface temperaturetrends in the North Pacific Ocean and by sea level pressuretrends in the North Pacific/North Atlantic sectors. The for-mer finding is consistent with previous results using realiza-tions from multiple GCMs (Shin and Sardeshmukh 2011).

Climatology and variability of simulated SCE comparewell with observations (Fig. 1). However, ensemble meantrends in snow cover extent from both experiments showunrealistically weak seasonality with overly strong negativeSCE trends during winter and overly weak trends during thespring (Fig. 2). While we cannot rule out natural variabilityaffecting the magnitudes of snow reduction in the observa-tions, the snow reduction biases reported in the coupled

model are consistent with overly strong and seasonally con-sistent warming trends (Fig. 4). Additional evidence is seenin the uncoupled simulations which have less wintertimewarming and a corresponding reduction in both magnitudeand significance of winter season SCE loss (Figs. 2–4).

The model climatology and variability for SWE are rea-sonable across the non-alpine regions available for compar-ison with the GlobSnow data record, but do illustrate a pos-

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itive bias during spring (Fig. 1). Both model configurationsagree on the magnitude of Eurasian trends throughout theyear and the magnitude and seasonal cycle are broadly con-sistent with the observed values; however, North Americantrends differ in both respects between each model config-uration and the observations (Fig. 10). Examining the spa-tial distribution of trends reinforces these conclusions: bothmodels show a pronounced east-west dipole over Europeand Siberia consistent with observations but fail to agreeover North America (Fig. 11).

For both SCE and SWE the differences between thecoupled and uncoupled ensemble mean trends are con-nected to differing mean temperature and precipitation trends.Within each ensemble natural climate variability generateswidely different spatial patterns of snow trends, but the SCEand SWE patterns in each realization can be related in anintuitive way to the corresponding temperature, precipita-tion and circulation trends of the given realization (Figs. 7and 11–12). These land surface temperature and precipita-tion trends are in turn related to the patterns of SST trends(Fig. 8) and SLP trends (Fig. 9) in the model. Thus, whilethe coupled model ensemble mean SST trends lead to astrong snow reduction signal over North America on aver-age, a subset of realizations from that experiment that show

Fig. 10: As Figs. 2 and 4 but for SWM. Observational trends fromthe European Space Agency’s GlobSnow product. Note that GlobSnowdata are not available or of limited frequency and quality from Junethrough September.

better correlation with the historical SSTs and also showsimilar trends in SCF and SWE as the uncoupled ensemblemean. In the uncoupled model, whose SST trends correlateperfectly with the historical trends, it is possible to discernhow intra-ensemble variability in regional SLP trends altersthe patterns of SCE trends, especially over North America.The influences of these trends appear to be consistent de-spite the fact that they are relatively weak with respect totheir variability.

These climate system connections demonstrated withinmodel realizations also aid the interpretation of the obser-vational record. The strong influence of North Pacific SSTson the model’s snow trends suggests that the pattern of ob-served SST trends over the last three decades may have alsoinfluenced observed snow trends. A simple extrapolation ofthe same effect seen within the model would indicate thatthe amount of snow loss observed over northwestern NA forthe past thirty years could have been intensified in a worldwith differing SST trends. On centennial time scales the sig-nal of anthropogenically forced climate change will even-tually overwhelm even regional scale natural variability, in-cluding that linked to decadal time scale SST and SLP fluc-tuations. However, the strong influence of decadal trends inSSTs demonstrates the importance of properly simulatingnot only the magnitudes but also the patterns of SST trendsin climate projection experiments as well as decadal predic-tion experiments on time scales of up to 30 years.

Acknowledgements We acknowledge assistance through legacies ofthe fourth International Polar Year. We also acknowledge funding fromthe Natural Sciences and Engineering Research Council of Canada’sClimate Change and Atmospheric Research initiative via the CanadianSea Ice and Snow Evolution Network. In situ Russian observations pro-vided coutesy of R. Brown and P. Groisman. Computations were per-formed at the SciNet HPC Consortium funded by: the Canada Founda-tion for Innovation under the auspices of Compute Canada; the Gov-ernment of Ontario; Ontario Research FundResearch Excellence; andthe University of Toronto. We thank Clara Deser and an anonymous re-viewer for comments which helped to greatly improve the manuscript.

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