Summary

Observed Climate–Snowpack Relationships in California and their Implications for the Future

SARAH KAPNICK AND ALEX HALL

University of California, Los Angeles, Los Angeles, California

(Manuscript received 27 October 2008, in final form 11 December 2009)

ABSTRACT

A study of the California Sierra Nevada snowpack has been conducted using snow station observations and

reanalysis surface temperature data. Monthly snow water equivalent (SWE) measurements were combined

from two datasets to provide sufficient data from 1930 to 2008. The monthly snapshots are used to calculate

peak snow mass timing for each snow season. Since 1930, there has been an overall trend toward earlier snow

mass peak timing by 0.6 days per decade. The trend toward earlier timing also occurs at nearly all individual

stations. Even stations showing an increase in 1 April SWE exhibit the trend toward earlier timing, indicating

that enhanced melting is occurring at nearly all stations. Analysis of individual years and stations reveals that

warm daily maximum temperatures averaged over March and April are associated with earlier snow mass

peak timing for all spatial and temporal scales included in the dataset. The influence is particularly pro-

nounced for low accumulation years indicating the potential importance of albedo feedback for the melting of

shallow snow. The robustness of the early spring temperature influence on peak timing suggests the trend

toward earlier peak timing is attributable to the simultaneous warming trend (0.18C decade 21

since 1930, with

an acceleration in warming in later time periods). Given future scenarios of warming in California, one can

expect acceleration in the trend toward earlier peak timing; this will reduce the warm season storage capacity

of the California snowpack.

1. Introduction

The California water supply is determined by cold

season precipitation (rain in low elevations and snow in

high elevations) and the capacity of natural and man-

made reservoirs. Most manmade reservoirs were built in

the early twentieth century for two purposes: 1) the

storage and disbursement of cold season rains and 2) the

storage and disbursement of runoff from spring snow-

melt. Reservoirs were designed to store only a fraction

of the state’s total yearly precipitation, under the as-

sumption that a sufficient delay between winter rains

and spring snowmelt runoff would always exist, with

snowmelt occurring at roughly the same time every year.

The annual mountain snowpack thus provides natural

storage for the water supply until the onset of snowmelt.

Changes in the amount of precipitation, percentage of

precipitation falling as rain instead of snow, and onset of

snowmelt can therefore affect the state’s water supply.

During anomalously high rain or snowmelt events, res-

ervoirs must not only store water, but also discharge

excess water to avoid flooding. Water must sometimes

even be discharged in anticipation of large events to re-

duce flood risk. The dual functions of storage and flood

management require reservoir managers to carefully

balance factors such as precipitation, snowmelt timing,

reservoir storage capacity, and demand. Even if future

climatological precipitation remains unchanged, shifts

in snowmelt timing can affect California’s water supply

during the warm season because of reservoir storage ca-

pacity constraints. To understand changes in snowmelt

water supply as a result of climate change, it is therefore

important to understand changes in the timing of snow-

melt in addition to total spring snowpack amounts.

Snowpack measurements are essential for predicting

timing and amount of warm season snowmelt runoff. For

this reason, a network of stations in the western United

States dating back to the 1930s tracks water content of

snow (also known as snow water equivalent; SWE). Mea-

surements are taken manually around the first of the

month at each station according to a prescribed monthly

Corresponding author address: Sarah Kapnick, University of

California, Los Angeles, P.O. Box 951565, Los Angeles, CA 90095-

1565.

E-mail: [email protected]

3446 J O U R N A L O F C L I M A T E VOLUME 23

DOI: 10.1175/2010JCLI2903.1

� 2010 American Meteorological Society

schedule. Because of the desire to track peak SWE—

thought to occur in early April—many more records are

available on or around 1 April (Serreze et al. 1999). Pre-

vious snowpack studies have focused on this well-sampled

1 April SWE dataset (Barnett et al. 2008; Mote 2006; Mote

et al. 2005; Cayan 1996) to assess the climatology and

variability of snowpack in the western United States.

These studies are important for understanding total melt-

water available during the warm months, but do not

directly address the timing of the transition from accu-

mulation to melt from snow observations.

Ideally, high-temporal resolution data would be avail-

able to study the evolution of the snowpack over the

course of the season, particularly the exact date and

amount of maximum SWE and subsequent melt rates.

Stations have been built in California since the 1970s

to measure daily SWE automatically, but do not begin

early enough for long-term variability analysis. Previous

observational studies have instead utilized streamflow

data, presumably snowmelt-dominated, as a proxy for

snowmelt timing. They show there has been a trend in

streamflow discharge toward earlier in the spring using

a variety of streamflow metrics (Regonda et al. 2005;

Stewart et al. 2005; Cayan et al. 2001). Daily SWE data

from 1992 to 2002 has also been combined with long-

term historic streamflow data to study the onset of

spring in the Sierra Nevada (Lundquist et al. 2004);

however, because of the shortness of the SWE time

series, streamflow measurements must still be relied

upon to measure long-term variability in snowmelt.

Unfortunately, this indirect variable is not a perfect mea-

sure of snowmelt, as it can be influenced by other factors

such as precipitation, temperature, lithology, soil compo-

sition, vegetation (Aguado et al. 1992), and presnowmelt

soil moisture.

Modeling studies to produce SWE, temperature, and

precipitation values have also been conducted to assess

changes in the snowpack of the Western United States.

Hamlet et al. (2005) created a snowpack simulation from

1915 to 2003 using the variable infiltration capacity hy-

drologic model as a surrogate for historical SWE ob-

servations and found that downward trends in 1 April

SWE and trends toward earlier peak snow accumulation

were due to warming over the period. Pierce et al. (2008)

produced simulations for 1600 years using two global

circulation models, yielding evidence that SWE as a per-

centage of precipitation has had a negative trend because

of anthropogenic forcing that will continue in the future.

These modeling studies show that the Sierra snowpack

has been declining and project a continuance of a nega-

tive trend in SWE in the future.

To study changes in the California snowpack directly

and provide a purely empirical sensitivity for future

projections, the present study focuses on observations of

monthly SWE. A dataset has been compiled from two

different sources to provide sufficient stations with SWE

measurements from mid-January through mid-May over

a long enough time period to do robust trend and sen-

sitivity analysis. The monthly data is used to infer peak

snow mass timing from February to May. Over this re-

cord stretching roughly from 1930 to the present, the

peak timing exhibits a trend toward earlier in the season.

Much of this trend can be explained by the sensitivity

of snow mass peak timing to early spring temperature.

Given future warming scenarios in the California Sierra

Nevada, we conclude the trend in earlier peak timing

will continue.

2. Data

A snow station dataset was compiled from two exist-

ing datasets for the state of California: the National

Resources Conservation Service (NRCS) and Water and

Climate Center (www.wcc.nrcs.usda.gov/snowcourse/) and

the California Department of Water Resources (http://

cdec.water.ca.gov/misc/SnowCourses.html). A total of

154 stations across California with recorded SWE data

from mid-January to mid-May with at least 30 years of

data from 1930 to 2008 are used (see Fig. 1). These

stations range in their years of available data. We show

that these temporal gaps have a negligible impact on our

analysis in the appendix. It has been noted that the exact

timing of historical monthly snow course measurements

can vary, with some measurements being taken within

a few days of the first-of-the-month measurement date

(Cayan 1996). In these measurements, there may also be

a systematic shift in the actual date of measurement

toward later (Mote et al. 2005). To circumvent these

issues we only selected stations with exact measurement

dates corresponding to raw SWE data for our analysis.

The correlations shown in this paper are noticeably re-

duced when SWE values are assumed to be first-of-the-

month values; such employment of SWE measurements

may therefore lead to a nonnegligible source of random

error in the other studies. Subsequent sections will de-

scribe criteria used to produce subsets of data for analysis.

Temperature data are also used to diagnose snow

accumulation and melt processes. Maximum and mini-

mum daily temperature data from 1930 to 2003 were

obtained from the Surface Water Modeling group at the

University of Washington from their Web site (www.hydro.

washington.edu/Lettenmaier/Data/gridded/) the develop-

ment of which is described by Hamlet and Lettenmaier

(2005). This dataset was chosen for its long temporal

coverage (1915–2003) and high spatial resolution (1/ 88)

relative to other pre-satellite-era temperature products.

1 JULY 2010 K A P N I C K A N D H A L L 3447

This product has also been used in previous snowpack

studies (Mote et al. 2005; Hamlet et al. 2005).

3. Methods and results

a. Calculation of snow mass peak

To assess interannual variations in California snow-

pack evolution, a metric was developed quantifying sys-

tematic changes in snow accumulation and melt timing.

In particular, we focused on the timing of peak snow

mass. We created a measure of this quantity relying on

SWE measurements taken around the first-of-the-month

from February to May. We used these monthly snap-

shots rather than daily SWE data because the daily data

are only robustly available from 1980 to the present, too

short a time series to calculate long-term trends in max-

imum SWE timing.

The peak snow mass timing is defined for any given

year as the temporal centroid date, also known as the center

of mass, of SWE values (SWE centroid date; SCD) from

approximately 1 February to 1 May for stations with

complete data over this four-month time period. The

SCD is given by the equation:

SCD 5 � t

i SWE

i

�SWE i

. (1)

Each individual measurement during the season is dis-

tinguished by i. The SWE measurements are given by

SWEi in centimeters. The value ti is the exact date of the

measurement in Julian days and falls within two weeks

of the first of the month for February, March, April, and

May. The SCD metric is similar to that used in previous

studies of streamflow peak timing (Stewart et al. 2004,

2005). Figure 2a provides a visualization of this calcu-

lation for a location and a year when daily data are also

available. As is clear from the figure, SCD captures the

gross timing of snow processes. For the peak to shift earlier

FIG. 1. Location of 154 snow stations with usable data in California. Open circles denote

NRCS Water and Climate Center stations and closed circles denote California Department of

Water Resources stations. Stations are colored by elevation in meters.

3448 J O U R N A L O F C L I M A T E VOLUME 23

(later), the percentage of snow accumulation later in the

season must decrease (increase), or there must be an in-

crease (decrease) in the percentage of snow melting later

in the season. Thus it corresponds roughly with the peak

in snow mass.

The SCD metric provides a more accurate represen-

tation of the timing of snow accumulation and melt than

the date of absolute maximum SWE value given in the

four approximate first-of-the-month point measurements.

It allows for the snow mass peak timing to shift on the

order of days instead of being constrained to shifts in

monthly increments. Long-term variability in snow mass

peak timing can be studied on submonthly time scales

despite the lack of daily data. As we show below, the

changes in peak snow mass timing in the California Sierra

Nevada are on the order of days, confirming the need for

a metric with this property.

For stations where daily data are available within

close proximity to long-term monthly stations, SCD was

calculated on a daily and monthly basis to assess the

accuracy of using historical monthly SWE values. Daily

SWE values from 15 January to 15 May from 13 stations

were used to calculate a daily SCD, while first-of-the-

month measurements taken from the daily stations were

used to calculate a monthly SCD. These stations were

chosen to correspond to those used in subsequent long-

term monthly trend analysis. For each station, years

missing 10 or more days from January to May were ex-

cluded; this criterion was similarly employed by Knowles

et al. (2006) and developed by Huntington et al. (2004).

The SCD values calculated from monthly and daily data

was found to be extremely highly correlated (r 5 0.98,

p , 0.01), giving confidence that the temporal resolution

of monthly snapshots is high enough to provide accurate

information about snowpack timing.

b. Trends in peak snow mass timing

Examination of SCD from 1930 to 2008 yields evi-

dence that it is trending earlier. When stations with data

for at least 75% of these years are included, SCD is

found to occur earlier at a rate of 0.6 days per decade

(Fig. 3; this is similar to figures showing trends in earlier

spring timing in Cayan et al. (2001)). This trendline has

a slope significantly different than zero (using the Stu-

dent’s t test, p , 0.01). When stations with fewer yearly

SCD values are also included, or when the starting year

of the trendline is set later to include more stations,

statistically significant nonzero trendlines of earlier peak

timing are still found (Table 1). In most cases, the trend

toward earlier peak timing is enhanced (i.e., the trend

becomes more negative). There is a similar enhancement

in the averaged March and April maximum daily tem-

perature warming trend from 1930 to 1970 as successively

FIG. 2. SCD monthly calculation example for (a) one station in 1996 and (b) a comparison of the monthly vs daily

calculations of SCD for 13 stations. In (a), the solid black line denotes daily 1996 SWE values at the SNOTEL Adin

Mountain station from 1 January to 31 May. The gray bars help illustrate how four measurements of SWE values are

used to calculate the SCD over the time period shown. The gray hatch on each bar denotes the first of February,

March, April, and May. The black dashed line at Julian day 72 denotes the SCD found by using the monthly SCD

values. In (b), 13 stations with daily data were used to calculate SCD using the daily and monthly methods. The

stations were chosen by their proximity to stations used in the long trend analysis shown in Fig. 3. The monthly

approximation of SCD is well correlated with the daily calculation of SCD (r 5 0.98, p , 0.01). There are 336 data

points for 13 stations from 1970 to 2008.

1 JULY 2010 K A P N I C K A N D H A L L 3449

later periods of the time series are isolated (Table 1,

last row). We discuss the potential causal link between

the warming and SCD trends in the discussion.

Almost all individual station SCD trends are also neg-

ative (Fig. 4), suggesting a consistent signal from catch-

ment to catchment. In addition, Fig. 4 compares station

trends in SCD to trends in the highly studied 1 April

SWE record. Note that measurements are taken within

two weeks of 1 April for the 1 April SWE record. Mote

et al. (2005) noted that the fluctuation in measurement

date may affect 1 April SWE trends, but concluded that

climatic factors likely have a dominant effect on the trend.

The majority of stations exhibit a negative trend in both

SCD and 1 April SWE. A negative SCD trend is as-

sociated with snow melting earlier, which also results in

trends toward lower SWE. Some of the change in SCD

may also be driven by a shift toward more rain and less

snowfall as described by Knowles et al. (2006); our

analysis shows that only the lowest elevation stations

have exhibited statistically significant trends in both

metrics.

Figure 4 resolves the apparent inconsistency between

increasing 1 April SWE at some locations and a warming

climate. All the points with positive trends in 1 April

SWE have negative trends in SCD. These points all have

positive trends in SWE from February to May (not

shown). Enhanced melting at these locations must there-

fore be compensating for the increased accumulation to

create the negative trend in SCD. The link between

1 April SWE values and melt during previous months

has been observed in some daily Snowpack Telemetry

(SNOTEL) stations in the California Sierra Nevada;

1 April SWE was shown to be highly anticorrelated with

daily melt events from the previous months, implying

changes in 1 April SWE have been due at least in part

to melt events (Mote et al. 2005).

TABLE 1. Trend in peak timing (days decade 21

) for collective

stations and temperature (8C decade 21

) found in the grid cells

covering the snow stations from start date (denoted in columns) to

2008. Trend in peak timing is given for three different cases: all

stations with available SCD data, stations with data for only 50% of

available years, and stations with data for only 75% of available

years. The SCD trend corresponding to Fig. 3 is given by the bolded

cell. Trend in average monthly maximum daily temperature at el-

evations above 1700 m (elevation minimum for snow stations used

in the bulk of this analysis) is given from start date to 2003 (due to

the limitation of the available temperature dataset) for the months

of January, February, March, and April with the last row providing

the averaged March and April temperature trend.

Case 1930 1940 1950 1960 1970

All Stations 20.7 20.8 20.7 20.7 20.4

50% of years 21.0 21.1 20.7 20.7 20.7

75% of years 20.6 20.9 20.8 21.0 20.5

January temperature 0.3 0.3 0.5 0.5 0.7

February temperature 0.2 0.2 0.1 0.0 20.1

March temperature 0.2 0.3 0.4 0.5 0.7

April temperature 0.0 0.1 0.1 0.3 0.5

May temperature 0.1 0.2 0.3 0.3 0.2

Averaged March

and April temperature

0.1 0.2 0.3 0.4 0.6

FIG. 3. SCD for 22 stations with annual data available for at least 75% of the record from 1930

to 2008. There are 1482 data points for the time period. The dashed line denotes the mean SCD

(Julian day 76) and the solid line denotes the linear trendline for the time series.

3450 J O U R N A L O F C L I M A T E VOLUME 23

c. Distribution of peak snow mass timing versus 1 April SWE

Variability in 1 April SWE is evaluated against vari-

ability in the SCD metric to explore relationships be-

tween SCD and the SWE variable used to predict water

supply. Figure 5 shows a scatterplot of 1 April SWE

versus SCD values. Each point represents a snow station

during one snow season. Snow stations with a minimum

of 75% of SCD values over the period from 1950 to 2003

were used for this analysis. This time period was selected

to coincide with the available temperature record and

increase the number of snow stations available for tem-

perature sensitivity studies in section 3d. (A nearly iden-

tical distribution is found if the start date is changed to

1930.) For the given subset of snow stations, SCD occurs

over a wide range, with the average SCD occurring on

Julian day 73 (mid March). The average 1 April SWE

value is 74 cm.

The striking bell-shaped distribution of the 1 April

SWE versus SCD scatterplot arises because of differing

behavior of SCD for large and small seasonal snow

accumulation. When 1 April SWE is large (roughly

$100 cm), the SCD tends to occur in a narrow band

between Julian day 70 and 90, with most points (96%)

above the mean of 73. This corresponds to a time period

mainly falling between the middle of the second and

third bar of Fig. 2, or the calendar month of March. There

are three main reasons for this behavior: 1) To attain

such high 1 April SWE values, relatively consistent storm

activity and steady accumulation is necessary from

February to March. 2) The large accumulation then in-

creases the effective thermal inertia of the snowpack,

delaying the onset of melting. 3) This large accumulation

then only melts once the seasonal warming becomes

great enough to initiate the melting process. These three

processes make for a late SCD, with little variation from

season to season.

When 1 April SWE is small (less than roughly 100 cm)

however, the SCD falls over a large range between

Julian day 21 and 114. This range is more than 4 times

that of the high seasonal accumulation and covers the

middle of the first bar to the middle of the fourth bar

in Fig. 2, or from the last day in January to the end

of April. The significantly greater range in SCD values

is due to two factors: 1) Low accumulation is the re-

sult of a relatively small number of storms with highly

variable timing. 2) Melting in shallow snow is more

sensitive to temperatures above freezing because of

the smaller thermal inertia of shallow snow and its

greater susceptibility to albedo feedback. This results

in earlier (later) snowmelt when temperatures are warm

(cold).

To explore the sensitivity of SCD to temperature fur-

ther, the colorbar given in Fig. 5 distinguishes the distri-

bution of SCD values by local averaged March and April

(MA) maximum daily temperature. The local average

maximum daily temperature for each station is calcu-

lated by taking the local gridcell maximum daily tem-

perature, averaging it over two months, and adjusting it

for the elevation of each station assuming a constant

lapse rate of 6.58C km21. When the distribution of SCD

versus 1 April SWE is distinguished by temperature,

SCD has very little systematic association with either

January or February temperatures, but is closely linked

to early spring temperature (colorbar in Fig. 5). The

correlations of observed SCD and temperatures for dif-

ferent months are shown in the first row of Table 2, and

clearly quantify the influence of late accumulation season

temperature on SCD. Lower MA temperatures appear

to shift SCD into the later half of the season. The most

likely reason for this connection is that snowmelt dur-

ing March and April is reduced (increased) by anoma-

lously cold (warm) March and April temperatures, thus

moving SCD to the later (earlier) portion of the season.

The sensitivity to MA temperature is particularly pro-

nounced for years when 1 April SWE is low (r 5 20.61,

p , 0.01 when 1 April SWE is less than 100 cm versus

r 5 20.47, p , 0.01 when it is above this threshold),

providing direct evidence of the greater susceptibility of

shallow snow to fluctuations in temperature and poten-

tially albedo feedback.

FIG. 4. Trend in SCD vs trend in 1 April SWE for 22 stations with

at least 75% of years available from 1930 to 2008. Stations are the

same ones used for Fig. 3. Dashed lines denote trends of zero.

Stations are colored by elevation in meters. Circled stations have

statistically significant trends (at p , 0.05) in 1 April SWE and

SCD. Stations with a cross (x) have statistically significant trends

(at p , 0.05) in 1 April SWE or SCD.

1 JULY 2010 K A P N I C K A N D H A L L 3451

d. Relationships between peak snow mass timing and temperature

Figure 5 provides visual evidence that air temperature,

the primary thermodynamic control of melt, is poten-

tially a major variable affecting SCD. Figure 6a pro-

vides a statistical measure of the link between the MA

maximum daily temperature and SCD for the direct

observations of these variables. Temperature is found

to shift SCD earlier in the season by 2.5 days per degree

and is significantly anticorrelated (r 5 20.62, p , 0.01)

with SCD. As noted in section 3b, the trend toward ear-

lier SCD coincides with a trend toward warmer MA

temperature. The anticorrelation between SCD and tem-

perature seen in Fig. 6a could result from these two trends.

However, when the SCD and temperature time series

are detrended, the anticorrelation remains (r 5 20.47,

p , 0.01). This suggests the link between MA temper-

atures and SCD is robust for temporal variability as well

as trends in SCD, a point we return to in the discussion.

Figures 6b,c reveal the SCD–temperature relationship

when controlled for spatial and temporal variability.

In Fig. 6b, the temporal SCD and maximum daily tem-

perature anomalies (defined as the observation value

minus the mean value at each station) are compared.

Here we eliminate any systematic relationship between

SCD and temperature in Fig. 6a arising from the fact

that the stations are at different locations and therefore

have different climatological temperatures. Conversely,

in Fig. 6c, temporal variability is eliminated by com-

paring station mean SCD values against station mean

maximum daily temperatures. Thus, each point on the

graph is an individual station. A negative relationship

between SCD and temperature remains when spatial

and temporal variability are each isolated in turn. More-

over, Table 2 shows that MA temperatures have the

greatest overall relationship with SCD from January to

May for direct observations, anomalies, and mean sta-

tion values. This analysis was also conducted using av-

eraged daily minimum temperatures; negative correlations

FIG. 5. Scatterplot of SCD vs 1 April SWE value for 70 stations with at least 75% of years

available from 1950 to 2003; colored by the local averaged March and April daily maximum

temperature. Temperature data are from the Hamlet and Lettenmaier (2005) dataset (avail-

able from 1915 to 2003) and have been adjusted for station elevation assuming a constant lapse

rate of 6.58C km 21

. If the graph is confined to stations with at least 75% of years available from

1930 to 2003, a similar distribution is found. The average SCD for the dataset is Julian day 73,

and is given by the dashed black line.

TABLE 2. Correlation of temperature vs SCD for 70 stations from 1950 to 2003 for January–May and for direct observations, anomalies,

and mean values of these variables. This table provides a sensitivity analysis of the correlations found in Fig. 6 (the last column, bolded) for

different months.

Sensitivity January February March April May Averaged March and April

Direct observation 20.21 20.21 20.56 20.52 20.28 20.62

Anomaly 0.04 0.02 20.52 20.49 20.10 20.65 Station mean 20.55 20.60 20.65 20.60 20.60 20.63

3452 J O U R N A L O F C L I M A T E VOLUME 23

between SCD and temperature remained, but were

lower than shown in Fig. 6 and Table 2. This is likely due

to maximum temperature being more closely associated

with snowmelt given that average minimum tempera-

tures are predominantly below freezing. This analysis

provides evidence of the predictive value of MA maxi-

mum temperature for both spatial and temporal vari-

ability in SCD.

4. Summary

In this study, a metric is developed to calculate peak

snow mass timing in the California Sierra Nevada using

monthly SWE data from 1930 to 2008. Robust statisti-

cal analysis is conducted to assess the variability in the

timing of peak snow mass. From 1930 to present, the

peak timing of the entire dataset exhibits a trend toward

earlier in the season of 0.6 days decade21. On an in-

dividual station basis, most stations show earlier SCD

and reduced 1 April SWE, and the only stations with

statistically significant trends in both SCD and 1 April

SWE exhibit negative trends in both variables. The trends

in SCD complicate interpretations of 1 April SWE as

a metric of Sierra Nevada snowpack trends as nearly all

stations exhibit negative trends in SCD indicating that

enhanced melting is occurring even when 1 April SWE

may be increasing. The influence of MA temperature on

SCD is almost certainly due to the effect of early spring

temperature on snowmelt. This relationship is particu-

larly pronounced for low accumulation years, indicating

the lower thermal inertia of shallow snow and potential

enhancement of snowmelt due to albedo feedback as bare

ground and vegetation is exposed. The robustness in

the sensitivity of SCD to MA temperature for all spatial

and temporal scales inherent in the dataset indicates

the SCD trend can be attributed to the MA warming

trend.

The trend in snow mass peak timing found in this study

is less than those of snowmelt-dominated streamflow

found in some previous studies (Regonda et al. 2005;

Stewart et al. 2005; Cayan et al. 2001), which provide

changes in the date of peak runoff on the order of a few

days per decade. The differences in the trends in these

two metrics may be accounted for by the fact that a shift

in the timing of streamflow runoff is not necessarily ac-

companied by an equal shift in peak snow mass. In fact,

FIG. 6. Scatterplot of averaged March and April daily maximum

temperature vs SCD for 70 stations from 1950 to 2003 for: (a) ob-

servations of SCD and local temperature, (b) anomalies, and (c)

mean values. The dashed black line denotes the linear trendline on

each graph. The two variables are strongly anticorrelated for all

plots: (a) r 5 20.62, (b) r 5 20.65, and (c) r 5 20.63, with p , 0.01

for all graphs. If the correlation is calculated for the detrended

direct observations and detrended anomalies, the anticorrelations

are slightly lower (r 5 20.47 in each case), but still material.

1 JULY 2010 K A P N I C K A N D H A L L 3453

if the shift in SCD is due to earlier snowmelt, the snow-

melt acceleration would probably have to be much more

rapid than the SCD shift. This is because of the steadi-

ness of the weights of the accumulation months (i.e.,

measurements around 1 February and 1 March) in the

SCD calculation. The involvement of four months of data

in the SCD calculation introduces more ‘‘inertia’’ into

this quantity than snowmelt runoff.

5. Discussion

Taken together, this study and previous studies paint a

picture of a California Sierra Nevada snowpack respond-

ing rapidly to the changing climate of the past few de-

cades. These trends are likely to continue and may be

accelerating. Extrapolating the current trend in MA

temperatures, peak snow mass timing should continue to

occur earlier. Projections of temperature in California

in the coming decades show that the trend in annual

temperature may accelerate, with surface temperatures

increasing by 28 to 78C by 2100 (Cayan et al. 2008).

Assuming a similar distribution change in temperature

in March and April, we can calculate a projection of the

shift in SCD by the end of the century. Using the re-

lationship between temperature and SCD anomalies in

Fig. 6b, this implies a shift in the SCD from current mean

values by 6 to 21 days earlier by the end of the century,

with potentially much larger shifts in snowmelt runoff

timing.

These extrapolations into the future may be too con-

servative because the trends in SCD found in this study

are probably low estimates of future changes in peak

snow mass timing. In addition to acceleration of climate

change itself, snow season temperatures will begin to

rise above the freezing point with increasing frequency

as the climate continues to warm, leading to more pre-

cipitation falling as rain instead of snow. On average,

this threshold has not yet been reached for the given

stations during the months of March and April. During

March and April there were only two instances (less than

0.1%) of data points shown in Fig. 5 having average daily

minimum temperatures above 08C. Most stations gener-

ally exhibit net snow accumulation during this time pe-

riod. As minimum temperatures begin to rise above the

critical threshold of 08C much more often, melt rates will

continue to increase, but precipitation will also shift from

being dominated by snow to rain, which will eventually

result in net melt rather than net accumulation in early

spring. At present, January and February have both

average daily maximum and minimum temperatures

below freezing. If these winter months begin to expe-

rience temperatures above freezing, less accumulation

may occur during this time, making the snowpack more

susceptible to temperature fluctuations later in the sea-

son. Stations at lower elevations will be the first to ex-

hibit changes in accumulation dynamics. This has been

shown in Knowles et al. (2006), where sites at elevations

below the majority of those in this study (below 1900 m)

have exhibited a shift in cold season precipitation from

snow to rain. The shift to rain will also impact SWE

values in the latest part of the season first, which will

contribute to the advance of SCD. The calculated sensi-

tivity of SCD to late season temperatures does not reflect

this mechanism yet, and therefore is probably a lower

bound.

Given the importance of high-resolution snowpack

predictions, continued research on the California Sierra

Nevada snowpack is critical to understanding the state’s

future water supply. Continuation of SWE measure-

ments is necessary to monitor and predict changes in the

water supply from the Sierra Nevada snowpack. Re-

gional modeling studies of the Sierra Nevada would also

be helpful to determine the mechanisms affecting accu-

mulation and melt events and to identify regions where

precipitation will shift from being snow-dominated to

rain-dominated. Snowmelt runoff will be affected by

changes in snowfall amounts and snowmelt timing. An

understanding of the mechanisms affecting these vari-

ables will help predict the future of the California water

supply.

Acknowledgments. Sarah Kapnick is supported by an

NASA Earth and Space Science Fellowship (#07-Earth07F-

0232) and a JPL fellowship (Earth Climate Science:

Collaborative Investigations with UCLA Graduate Stu-

dents: 1313181). Alex Hall is supported by UC Water

Resources Grant WR1024. The writers would also like

to gratefully acknowledge funding provided by JPL

(#1312546) under a project entitled: ‘‘Evaluating key

uncertainties in IPCC Climate Change projection of

California snowpack: Topography, snow, physics and

aerosol deposition.’’

APPENDIX

Test of Snow Station Coherence

If stations exhibit different accumulation patterns—

entirely possible given their broad geographical and al-

titudinal distribution—they must be treated as subgroups

of data rather than as a single system to avoid over-

generalization of the behavior of the snowpack. Under-

standing the spatial variability of SWE is therefore a

necessary step in our study. To achieve this, we calculate

the spatial coherence of the monthly SWE values. The

3454 J O U R N A L O F C L I M A T E VOLUME 23

subset of stations for each month (taken within two weeks

of the first of the month in February, March, April, and

May) with yearly values available for 50% of the time

period from 1930 to 2008 is used for this analysis. For

each month, the time series of SWE values averaged over

the state are calculated and then correlated with each

individual station time series. The results are then plotted

in Fig. A1.

FIG. A1. Correlation plot to show spatial station coherence. Correlations are calculated on a first-of-the-month

basis for (a) February, (b) March, (c) April, and (d) May. For the given month, correlations were calculated on

a station-by-station basis between individual station SWE values against the mean SWE value for the set stations

shown. Only stations with annual data for at least 50% of the record between 1930 and 2008 are used. There were 119,

120, 139, and 102 stations, respectively, in each month.

1 JULY 2010 K A P N I C K A N D H A L L 3455

We find that snow stations are generally highly spa-

tially correlated with the mean SWE value for the snow-

pack, especially those stations below 408N. For example,

in the month of April, 110 stations have correlations to

the mean SWE value above 0.80 with p , 0.01. This

pattern persists from February to May, implying SWE

anomalies are fairly uniform across the state for all

months. These correlations may also be lower than if the

analysis was conducted with actual first-of-the-month

SWE values given that measurements are taken within

two weeks of the first of the month. This measurement

practice may introduce some error in these correlation

calculations. However, the overall spatial coherence of

snowpack variability demonstrates that minor tempo-

ral gaps at individual stations will not materially affect

analysis of the climatology and variability of the overall

California snowpack when the dataset is taken as a co-

herent group. This finding is especially important for the

trend analysis found in section 3b and shown in Fig. 3 as

some stations do not have data over the entire time

period of interest (1930 to 2008). Sensitivity analysis of

calculated trends is also provided when stations with

different temporal records are used (Table 1).

It should be noted that the station below 408N with the

lowest correlation to the mean SWE time series is also

the station with the lowest elevation. This location likely

has different meteorological conditions, temperature pat-

terns, and may experience snowmelt earlier and more

frequently throughout the snow accumulation season

than points at higher elevation. Because of its aberrant

behavior, this station is left out of other analysis con-

ducted in this study.

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3456 J O U R N A L O F C L I M A T E VOLUME 23

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