A FIELD-BASED MODEL OF THE EFFECTS OF LANDCOVER CHANGES ON DAYTIME SUMMER TEMPERATURES IN THE NORTH CASCADES

Michael A. O’Neal, Lyndsey B. Roth, Brian Hanson, and Daniel J. LeathersDepartment of Geography

University of DelawareNewark, Delaware 19716

Abstract: Removal or reduction of forest landcover has been shown to increase dailyhigh temperatures during the summer in clearcut and young, regrowth forests relative tomature or old-growth forests. Although these temperature differences are easily observedin individual stands, extrapolating the net temperature effect to an actively logged terrainis made difficult by the heterogeneous mixture of stand heights and densities. To study theeffect of temperature produced by changes in forest landcover on a regional scale, 13temperature sensors were placed in structurally different stands in a 15 × 24 km area of theGifford Pinchot National Forest on the northwest side of Mount Adams, Washington.These sensors operated for 60 days, from July 20, 2008, through September 17, 2008, insnow-free conditions across a range of elevations. Using field data and a previously devel-oped proxy dataset for stand height and density, near-surface daytime air temperaturemodels were developed for the modern and pre-anthropogenic landscapes. Our modelresults indicate that a 0.7° C average daytime summer temperature difference between thepre-anthropogenic and modern landscapes may be attributed to 20th century reductions inforest cover. [Key words: landcover change, Cascades, climate, glaciers, surface temperature.]

INTRODUCTION

For nearly a century, numerous studies conducted in actively logged terrainshave found that midlatitude mature or old-growth forests experience lower dailymaximum near-surface temperatures during the summer months than adjacentclearcut and young-regrowth sites (Pearson, 1914; Fritts, 1961; Hornbeck, 1970;Raynor, 1971; McCaughey, 1985; Carlson and Groot, 1997; Barg and Edmonds,1999; Heithecker and Halpern, 2006; O'Neal et al., 2009). Although the net dailyor seasonal energy balance that results from reduced forest cover may remainunchanged, the difference between the minimum and maximum daily near-surfacetemperatures, and the averages of near-surface daytime and nighttime temperatures,typically increase in a clearcut forest relative to a forested site (e.g., Hornbeck1970). Such increases in extreme temperature conditions are known to result in aseries of cascading negative impacts on hydrological and biological systems withinor near these altered forested landscapes (Thompson et al., 1998; Stott and Marks,2000; Bourque and Pomeroy, 2001; Hashimoto and Suzuki, 2004).

Previous studies of the climatic effects resulting from midlatitude forest-coverchange typically focused on local areas, comparing adjacent stands on the order ofa few tens of hectares. The variation of near-surface temperatures among a few dif-ferent stands is often described as a function of the maturity of the forest in terms ofstand height and density at each location. However, the challenge in regional-scale

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Physical Geography, 2010, 31, 2, pp. 137–155. DOI: 10.2747/0272-3646.31.2.137Copyright © 2010 by Bellwether Publishing, Ltd. All rights reserved.

138 O’NEAL ET AL.

temperature modeling is that an actively logged terrain presents a patchwork land-scape of stands with different areal extents in various stages of growth, with eachstand having a unique surface energy balance and temperature.

O’Neal et al. (2009) produced a model of regional near-surface temperature foran actively logged terrain in the Cascade Range of western Washington usingremotely sensed imagery, digital elevation model data, and field sensor data from aclearcut and an adjacent mature forest. That model relied on the following assump-tions: (1) a proxy record of stand height and density accurately represents the arrayof natural vegetation among the forest endmember selections; (2) there is a domi-nant regional climate pattern that affects all their study sites and their modeldomain; and (3) the average environmental lapse rate of 7° C km–1 is appropriate forthe Cascade Range study area.

Our purpose in this study is to expand on O’Neal et al. (2009) by: (1) testingassumptions regarding the relationship between forest height and density and tem-perature used in previous studies; and (2) developing a refined regional temperaturemodel for a 15 × 24 km study area near Mount Adams, Washington, grounded innew field data. We first investigate the relationship between stand height and den-sity and temperature by collecting data from 13 different forest stands at differentelevations. Second, we assess the accuracy of the shade-based stand height anddensity record obtained from analysis of remotely sensed imagery by comparingthose results with field observations. Finally, we determine a field-based lapse ratethat can be combined with the aforementioned data to produce near-surfacetemperature models of the modern, actively logged study area and a modeled pre-anthropogenic forested landscape. We compare these models to estimate theeffects of logging on near-surface atmospheric temperatures.

BACKGROUND

Landcover Change in the Cascade Range of Western Washington

Forestry practices in western Washington have significantly reduced the amountof mature and old-growth forests in the region. Strittholt et al. (2006) estimated that,before European settlement, approximately two-thirds of the total land area in theregion was old-growth forest. However, due to recent intensive forestry practices,approximately 70 to 90% of the original conifer old-growth forests have been lost.At historic rates of reduction, most old-growth forests would have been eliminatedwithin the first few decades of the 21st century (Franklin et al., 1981). In 1994, theNorthwest Forest Plan (NWFP) was implemented for the Pacific Northwest becausedeclines in the amount of old-growth forests were exceptionally high in previousdecades. The plan provided management of late-successional and old-growth for-ests to preserve biodiversity. As a result, the annual increase of late-successionaland old-growth forest in 2004 was 1.9%, higher than expected (Thomas et al.,2006). Although the plan reduced the cutting of old-growth forests on federal landsby approximately 80%, historically and actively logged terrains often do not fullymature before harvesting and consist entirely of varying stages of regrowth(Wimberly et al., 2000).

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The Physics of a Changing Energy Balance as a Result of Forest Reduction

The surface energy balance where natural vegetation is removed and replacedwith industrial materials (i.e., concrete, asphalt, steel, and glass) is well studied inurban areas (Oke, 1988; Grimmond and Oke, 1995; Du et al., 2007). In a mannersimilar to urbanization, removal of natural vegetation from forested landscapesexposes soil, rock, or dry vegetation and alters the diurnal change in the surfaceradiation and energy budget while also altering the coupling between the surfaceand the atmosphere for energy and moisture exchanges.

Primary inputs to the surface are downward-directed shortwave and longwaveradiation. These are influenced by the sun and the atmosphere but not by surfaceconditions. Radiative outputs include reflected radiation, primarily consisting ofalbedo multiplying downward-directed shortwave, and longwave reradiation,which is mostly a function of surface temperature. Thus, surface conditions do notaffect the radiative inputs but strongly affect the radiative outflows. However, onaverage, radiative inputs to the surface are larger than the outputs, both globally(Kiehl and Trenberth, 1997) and locally within a forest (McCaughey, 1981), so addi-tional energy must be removed by nonradiative means.

The nonradiative coupling between the surface and the atmosphere has fivecomponents: latent heat flux, sensible heat flux, ground heat flux, net latent andsensible energy storage within the biomass, and net chemical energy storage due tophotosynthesis. In forested regions, ground heat flux and the storage terms sum toapproximately 5 to 10% of the radiative excess, so latent and sensible heat fluxesare the terms with most of the energy available to balance other changes in surfaceconditions (McCaughey, 1985).

Moisture availability accounts for much of the difference in nonradiative surfaceenergy between clearcut and mature-growth terrains. Hornbeck (1970) found thatin a West Virginia clearcut the amount of runoff increased, leaving less moistureavailable and thus less energy was used for evapotranspiration. Approximately one-fourth of the excess energy became upward thermal radiation, while the remainingthree-fourths were converted into sensible heat, increasing near-surface air temper-atures in the clearcut area. Although the clearcut will likely have a higher surfacealbedo, and hence less solar input, than the forest, a mature forest is an extremelyefficient mixing environment to transfer sensible and latent heat into the atmo-sphere (Klingaman et al., 2008). The daytime net radiation of a clearcut is signifi-cantly smaller than that of a forested site, while the nighttime net radiative loss of aclearcut is larger (Hornbeck, 1970; McCaughey, 1981). Despite the wide differ-ences in daytime energy balance conditions, the net radiation for a 24-hour periodmay be similar for both the clearcut and forested sites, with nighttime longwavereradiation balancing the system in both cases.

The finding that clearcut areas should have higher afternoon temperatures undersunny conditions than nearby forested areas subject to the same regional weather,as described in the citations at the beginning of this paper, is thus supported by con-siderations of the energy balance components involved. Our purpose in the remain-der of the paper is to quantify the regional effect on temperature caused by theinfluence of clearcutting on the local energy balance.

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STUDY AREA

Our study area is a 15 × 24 km area on the west and northwest flanks of MountAdams in the Gifford Pinchot National Forest, located in the Cascades mountainrange of southwestern Washington state (Fig. 1). This study area is located 55 kmeast of Mount Saint Helens and 73 km south of Mount Rainier; the latitude rangesfrom 46°11' N to 46°24' N and the longitude ranges from 121°32' W to 121°43' W.Elevation ranges from approximately 500 m to 2000 m. Field surveys of the standsused for this study area revealed that all are mixed conifer types with no singledominant overstory species. Common species found in the study domain includeDouglas fir (Pseudotsuga menziesii), Pacific silver fir (Abies amabilis), grand fir

Fig. 1. A. Cascade Mountain range of southwestern Washington state. B. The study area on thenorthwestern side of Mount Adams.

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(Abies grandis), and western and mountain hemlock (Tsuga heterophylla and Tsugamertensiana; Little, 1980). The area is an actively logged terrain with intermixedstands of varying ages, the oldest of which are dominated by trees estimated to beover 400 years in age (USDA, 1999).

FIELD METHODS

One of the primary goals of this study was to better understand temperaturedifferences among forests of different stand densities at different elevations. Toaccomplish this, we placed temperature sensors at 13 field sites, selected toaccount for six groups of different forest ages. Although there is no absolute rela-tionship between stand ages and densities, we identified prospective sites usingforest inventory data from the USDA (1999) in combination with visual assessmentof topographic maps and high-resolution aerial imagery from the 2006 NationalAgriculture Imagery Program (NAIP) dataset (USDA, 2006). Age groups weredefined by 25-year intervals ranging from 0 to 25 years up to 125 years and older.The different forest age groups were then stratified into three elevation ranges (400–800 m, 800–1200 m, and 1200–1600 m) with the intent of having both age andelevation diversity in site selection. Potential sites were selected within 1 km fromthe road network to provide reasonable access to the sensors, and a final restrictionon potential sites was attained by avoiding locations within 0.5 km of large riversand lakes to limit local moisture effects on the temperature data. Final selection ofsites could only be made during the actual field reconnaissance, leading to the 13sites that were established (Fig. 2).

At each site, temperature sensors (HOBO Pro v2 data loggers) were placed atapproximately 1 m above the surface and at least 50 m inside the forest/clearcutboundary to prevent complications by forest fringe effects with other local environ-ments. Sensors were placed in areas that received no direct sunlight (in the forest)or in radiation shields (in the clearcut or regrowth sites) to prevent direct heating ofthe sensor. Each sensor collected temperature data at 10-minute intervals for 60days from July 20, 2008, through September 17, 2008. Although this period trun-cates the summer record, it includes only snow-free summer days. Snow coveraffects surface albedo and radiation budgets far beyond the vegetation effects thatare being studied; avoiding such effects was essential.

Stand Density Data

Because of the patchwork of nonuniform stand heights and densities in the studyarea, a regional model of temperature based on interpolating between the 13 sen-sors to a spatial field would have limited value. Therefore, we combined our fielddata with analyses of remotely sensed imagery to derive a forest stand-height anddensity relationship that can be extrapolated to our entire study area. Fortunately,several studies have shown the utility of space-borne imagery in developing modelsof forest canopy cover and successional stages on a regional scale (Roy et al., 1996;Boyd et al., 2002; Sabol et al., 2002; Song et al. 2007). Thus, the temperaturemodels developed for this study rely on a stand height and density proxy record

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developed by O’Neal et al. (2009), which is based on the percentage of shadederived from spectral mixture analysis (SMA) applied to an ASTER satellite datasetfrom July 4, 2007 (for a detailed explanation of SMA see Adams et al., 1986). Sim-ilar SMA data have been shown to be useful for detecting stages of regrowth in avariety of different forested terrains (Hall et al., 1995; Shimabukuro et al., 1998;Souza and Barreto, 2000). In our application, SMA yields an image where old,dense forests appear dark and have a higher shade fraction value, and young,clearcut forests appear light and have a smaller shade fraction value.

Fig. 2. A 2006 NAIP orthophoto displaying the location of each sensor (represented by white circles).The white lines are contour lines at 500-meter increments.

EFFECTS OF LANDCOVER CHANGES ON DAYTIME SUMMER TEMPERATURES 143

To give a field-based comparison to the SMA-based model of stand height anddensity, we measured the diameter and number of trees within plots around each ofour 13 field sites. The plots varied in areal extent, between 600 and 1000 m2, dueto physical restrictions of topography and vegetation cover. Stand measurementswere normalized using the commonly applied stand density index (SDI) (Reineke,1933). The SDI metric accounts for the volume of each tree, resulting in a meandiameter that takes into account the larger volume of large-diameter trees, animportant aspect when considering the surface energy balance in mature forests(Curtis and Marshall, 2000).

Surface Temperature Model

The temperature model of the modern landscape, developed for this study, usesthe average daily high surface temperatures (12 noon to 6 p.m. PDT) collected bythe 13 sensors, elevation data from the U.S. Geological Survey National ElevationDataset, and the SMA-based proxy record of canopy structure and stand density.Temperature values for each point representing 225 m2 (the same area as the 15 ×15 m cells from the SMA) within the study area are calculated using a simple lineartemperature model. This model is based on two assumptions. The first is that thetemperature (T) at any given elevation in an alpine setting can be described as afunction of the environmental lapse rate:

T = T0 + Γ · (z – z0), (1)

where T0 is the temperature at an arbitrary reference elevation, z0 (1094 m for ourmodels), Γ is the lapse rate, and z is the land surface elevation for any given cell,obtained from a digital elevation model (DEM) for this study area. Equation 1 yieldsthe temperature for any given cell based on elevation and the environmental lapserate that was determined from a least squares fit of our field-based temperature data.

The second assumption is that canopy structure and stand density are the pri-mary forcings that determine the near-surface temperature within a stand at anygiven elevation. The temperature anomaly relative to a fully forested terrain for anystand at a given elevation can be expressed as:

Ta = (Di · ΔT), (2)

where Di represents the percentage shade (i.e., forest density) in a given cell, whichwas obtained from the SMA shade-fraction image, and ΔT is the maximum temper-ature range between a mature, dense forest, and a young clearcut. Thus, equation 2relates temperature to the amount of shade observed in satellite imagery (i.e., theshadiest areas at any given elevation are ΔT cooler than clearcuts at the same ele-vation). Combining equations (1) and (2) produces a near-surface temperature thatincludes effects of both the environmental lapse rate and stand density:

TD = T0 + Γ · (z – z0) – Di · ΔT. (3)

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To develop a pre-anthropogenic temperature model, we assume that the studyarea is dominated by old-growth forests. However, there are clearly portions of thestudy terrain that were not capable of producing significant vegetation prior toindustrial use of the area (i.e., river channels, lakes, ice, or rocky surfaces). We usethe normalized difference vegetation index (NDVI), which is the differencebetween the visible (red) and the near infrared bands, over their sum (Rouse et al.,1973), to determine the location of dense vegetation on the modern landscape. Forthe pre-anthropogenic model, the areas where there was little or no vegetationcover (NDVI < 0.2), were assigned values that matched the modern landscape, notmature forests.

RESULTS

The data from these 13 study sites show a strong regional correlation betweenboth daytime and nighttime temperatures (Fig. 3). Although there are differences in

Fig. 3. Temperature (° C) for 13 sites in Gifford Pinchot National Forest from July 20, 2008 to September17, 2008. The y-interscept, major tic mark for each sensor dataset is 10° C. Each minor tic on the y-axisrepresents a 10° C change in temperature.

EFFECTS OF LANDCOVER CHANGES ON DAYTIME SUMMER TEMPERATURES 145

the magnitudes of the daily highs and lows at each location, the similarity in thedaily trends among sites suggests the presence of a dominant regional forcing.

The shade fraction values (O’Neal et al., 2009), are stretched to range from 0 to1, representing <5% to >80% shade, respectively. The white areas in Figure 4 rep-resent regions of low shade (i.e., low forest density) with increasingly darker areasrepresenting relative increases in the amount of shade (i.e., high forest density). Fig-ure 5 displays the NDVI results with values that range from –0.52 (white) to 0.55(black). In Figure 5, the white areas indicate NDVI values less than or equal to 0.2,

Fig. 4. Shade fraction as calculated from the spectral mixture analysis of ASTER satellite dataset fromJuly 4, 2007. The white areas are values of 0, representing 0% shade, and the black areas are values of1, representing 100% shade. The grey areas represent values between 0 and 1.

146 O’NEAL ET AL.

and are considered nonvegetative regions. The shade fraction and NDVI valueswere used to produce a pre-anthropogenic shade fraction image (Fig. 6).

The SDI values obtained at our field sites range from 137 to 1903 (Table 1). Thestand density values were compared to the shade proxy via an exponential regres-sion, producing an R2 value of 0.90, indicating a strong relationship between theshade proxy record used in the model and field-based stand density measurements(Fig. 7).

Using the field-based temperature data and the SMA-based stand height anddensity data from our 13 field sites, both the lapse rate and the maximum range of

Fig. 5. NDVI values calculated for the study area using the ASTER satellite dataset from July 4, 2007.Low values (white) represent non-vegetative regions and higher values (grey to black) represent vegeta-tive regions.

EFFECTS OF LANDCOVER CHANGES ON DAYTIME SUMMER TEMPERATURES 147

temperatures between a clearcut and mature forest in Equation 3 can be deter-mined from a least-squares fit of the model output with our field-sensor data. Valuesof ΔT, and Γ in Equation (6) are 6.4° C and 4° C km–1, respectively. We subsequentlydetermined an average temperature for the entire study area using the mean of alldata points in our temperature model (Fig. 8). The modeled average daily high tem-perature for the 2007 landscape is 17.5° C, whereas the average daily high temper-ature of the pre-anthropogenic landscape is 16.8° C. Subtracting the two models(modern versus pre-anthropogenic) indicates that a possible 0.7° C temperaturechange can be attributed solely to forest landcover change.

Fig. 6. Pre-anthropogenic shade fraction. The white areas are values of 0, representing 0% shade,and the black areas are values of 1, representing 100% shade. The grey areas represent values between0 and 1.

148 O’NEAL ET AL.

Table 1. Locations,a Elevation, Aspect, and Stand Density Index for Each Site in Gifford Pinchot National Forestb

Sensor Latitude Longitude Elevation, m AspectStand density

index

1 46.36 –121.72 619 223 1095

2 46.36 –121.71 635 237 862

3 46.34 –121.71 699 79 1118

4 46.34 –121.66 758 210 156

5 46.36 –121.62 915 242 484

6 46.37 –121.63 947 224 1809

7 46.39 –121.54 963 292 338

8 46.35 –121.65 1093 228 341

9 46.27 –121.66 1235 121 1454

10 46.26 –121.64 1274 214 137

11 46.26 –121.64 1282 236 1903

12 46.29 –121.62 1293 39 1234

13 46.28 –121.60 1349 252 864

aLatitude and longitude in decimal degrees.bElevation and aspect represent average values of 10 m DEM data within 50 m of each sensor.

Fig. 7. Shade fraction (x-axis) vs. the stand density index (y-axis) at each study site location. Eachpoint is labeled by the site number from Figure 2.

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DISCUSSION

The strong correlation between the temperatures observed in our field data overthe 60-day period suggests the dominance of regional weather patterns in our data(Fig. 3). Thus, we suggest that landcover and elevation are the likely forces drivingtemperature differences among different stands. The strong explained variance of0.90 between the SDI values and the shade fraction in these data indicates thatshade fraction provides an accurate representation of stand height and density.However, the SDI equation was designed for pure stands and these results suggestthat this approximation is valid. Application of a more rigorous field analysis usinga more appropriate mixed-stand algorithm may produce more definitive results.

Because of the variety of elevations, and stand height and densities in which sen-sors were placed, our field data can be used to determine a best-fit lapse rate andthe temperature range between field-site endmembers when applied to Equation(3). The 6.4°C maximum range of average daily high temperatures between ayoung, clearcut forest, and a mature, old-growth forest is similar to other results col-lected near our study area (Barg and Edmonds, 1999; Heithecker and Halpern,2006; O'Neal et al. 2009). Our 4° C km–1 daytime-temperature lapse rate, deter-mined from our field data, is in agreement with the range of values determined byother researchers in the Cascades range (Lookingbill and Urban, 2003; Mote et al.,2009). This differs significantly from the roughly 7° C km–1 environmental lapse ratetypically used in alpine settings (Mccarthy et al., 1991; Reasoner and Jodry, 2000;Kitoh, 2002). Similar discrepancies have been clearly demonstrated in data fromMount Kilimanjaro, in which the lapse rate ranges from 1.4° C km–1 in the vicinityof the tree line to 10.3° C km–1 near the crater (Duane et al., 2008).

Our average temperature difference of 0.7° C between modern and pre-anthro-pogenic landscapes reinforces similar model estimates by Goldstein (2008) andO’Neal et al. (2009). Because of unresolved topographic shade in our imagery (i.e.,shadows on some steep slopes) that would result in the overrepresentation ofmature forests, our model underestimates landcover change, a result that producesa lower modeled temperature difference between modern and pre-anthropogeniclandscapes. Additionally, because our pre-anthropogenic model uses NDVI data toidentify candidate sites for historic old-growth stands, we are not able to differenti-ate between a natural bare-earth surface and the most recent clearcut. As a result,a few of the young, regrowth forests were not converted into mature, old growthforests, which may result in a temperature difference even greater than we suggest.

We attributed this temperature difference to a repartitioning of the latent andsensible heat flux by increased canopy structure and stand density in the pre-anthropogenic model. For the Pacific Northwest, this temperature rise is compara-ble to the roughly 0.7° C that may be expected from 20th century global warmingtrends (Meehl et al., 2004). As suggested in O’Neal et al. (2009), the local climaticresponse to pervasive logging coincidentally operates in the same timescale anddirection as recent global warming trends, and it is large enough to be part of theforcing for other physical and biological system responses. For example: (1) previ-ous studies have shown that when the temperature is too high, microorganismactivity decreases, resulting in a decrease of organic matter decomposition and soil

EFFECTS OF LANDCOVER CHANGES ON DAYTIME SUMMER TEMPERATURES 151

respiration rates (Hashimoto and Suzuki, 2004); (2) certain types of vegetation canonly germinate seeds in a narrow range of temperatures, while the increase intemperature may result in an increase for other competitive species (Yirdaw andLeinonen, 2002); and (3) glacier retreat may also be influenced because the temper-ature increases caused by clearcutting often happen in forests that are near glaciertermini.

Clearcutting in the Cascades region often happens in forests that are near glacialtermini, primarily for the simple fact of proximity. At higher latitudes in the PacificNorthwest, where glaciers have been maintained by high winter precipitation ratesdespite relatively high summer temperatures, glacier termini extend below thetreeline. The elevation for glacier termini gets lower northward along the BritishColumbia and Alaska coasts until the glaciers become largely tidewater (calvinginto the ocean), but forestry becomes less pervasive farther north, especially withrespect to clearcutting. This regional situation should apply in parts of Scandinaviaand South America, but probably not in the Alps, the Northern Rockies, or theHimalayas.

Regarding whether the temperature changes are sufficient for affecting glaciermass balance, consider that a 0.7° C change combined with a 4° km–1 lapse rate,as found in this study, imply a 175 m elevation change in the temperature field.Allowing for no change in precipitation, a 175 m change in equilibrium line alti-tude (ELA) would be implied, and with slopes in the range of 10% to 20%, animplied ELA position of 0.9 to 1.8 km results. Using a constant accumulation arearatio (AAR), the ELA position retreat is roughly half the terminus position retreat.

Several of the previous assumptions are merely first-order approximations, butthey lead to a potential glacial margin retreat of a few kilometers. Warming couldreduce the length of the snow season while simultaneously increasing potentialsnowfall, so there is no clear direction of error in that assumption. Our lapse rate ison the low end of estimates, but a more common 7°/km rate still leads to a 100 mchange in ELA. In the slope ranges of Cascade glaciers, our low end of 10% is morecommon than our high end of 20% for glaciers that are long enough to show thismuch change (i.e., high slope glaciers are probably already confined to highcirques where a >100 m change in ELA may cause them to disappear), so the higherestimates of retreat distance are more likely than the lower estimates. Absolutelyconstant AAR is unlikely, but changes will vary statistically in both directions.

The direct signal of global warming from greenhouse gases should be anincrease in nighttime low temperatures, caused by the increased atmospheric radi-ation blanket. Thus, much of the direct effect of global warming should have a rel-atively small effect on glacier melt. Nevertheless, a small but detectable signalindicates that glaciers have, on average, been retreating. Roe and O’Neal (2009)indicated that glacier retreat cannot be unequivocally attributed to climate fluctua-tions at the scale of a few decades but rather requires substantially longer, sustainedclimate changes to show an influence above and beyond the natural variability of aglacier system. Therefore, it is useful to determine another forcing that might con-tribute to glacier retreat on a regional scale, and this study, along with O’Neal et al.(2009), supports the idea that regional-scale forest reduction may produce a non-trivial effect on regional climate.

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Acknowledgments: We would like to thank Phillip Hendrickson, Ashley Satinsky, Monica Hanson,Katherine Skalak, David Mueller, Ryan Ippolito, and Justin Wagner for their help in collecting the fielddata used in our study. Also, we thank Claire O’Neal for providing constructive comments and help inthe editing of this manuscript.

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