NOAA Technical Memorandum NMFS-AFSC-166


Time Series Analyses of Physical 

Environmental Data Records From 

Auke Bay, Alaska 

by 

B. L. Wing, M. M. Masuda, and S. G. Taylor 

U.S. DEPARTMENT OF COMMERCE 

National Oceanic and Atmospheric Administration


National Marine Fisheries Service


Alaska Fisheries Science Center


October 2006 
NOAA Technical Memorandum NMFS 

The National Marine Fisheries Service's Alaska Fisheries Science Center 

uses the NOAA Technical Memorandum series to issue informal scientific and 

technical publications when complete formal review and editorial processing 

are not appropriate or feasible. Documents within this series reflect sound 

professional work and may be referenced in the formal scientific and technical 

literature. 

The NMFS-AFSC Technical Memorandum series of the Alaska Fisheries 

Science Center continues the NMFS-F/NWC series established in 1970 by the 

Northwest Fisheries Center. The NMFS-NWFSC series is currently used by 

the Northwest Fisheries Science Center. 

This document should be cited as follows: 

Wing, B. L, M. M. Masuda, and S. G. Taylor. 2006. Time series 

analyses of physical environmental data records from Auke Bay, 

Alaska. U.S. Dep. Commer., NOAA Tech. Memo. NMFS-AFSC-166, 

75 p. 

Reference in this document to trade names does not imply endorsement by 

the National Marine Fisheries Service, NOAA. 
NOAA Technical Memorandum NMFS-AFSC-166


Time Series Analyses of Physical


Environmental Data Records From


Auke Bay, Alaska


by 

B. L. Wing, M. M. Masuda, and S. G. Taylor 

Auke Bay Laboratory


Alaska Fisheries Science Center


11305 Glacier Highway


Juneau, AK 99801-8626


www.afsc.noaa.gov


U.S. DEPARTMENT OF COMMERCE 

Carlos M. Gutierrez, Secretary 

National Oceanic and Atmospheric Administration 

Vice Admiral Conrad C. Lautenbacher, Jr., U.S. Navy (ret.), Under Secretary and Administrator 

National Marine Fisheries Service 

William T. Hogarth, Assistant Administrator for Fisheries 

October 2006 
This document is available to the public through: 

National Technical Information Service 

U.S. Department of Commerce 

5285 Port Royal Road 

Springfield, VA 22161 

www.ntis.gov 
Notice to Users of this Document 

This document is being made available in .PDF format for the convenience of users; however, the 

accuracy and correctness of the document can only be certified as was presented in the original hard 

copy format. 

iii 

ABSTRACT

 Physical environmental data from Auke Bay, Alaska, collected by staff of the 

National Marine Fisheries Service’s Auke Bay Laboratory (ABL) during 1959-2004 near 

Auke Bay, Alaska, are summarized in tabular and graphic formats. Data for the 1959-

1993 period were summarized in 1998 by ABL scientists Bruce L. Wing and Jerome J. 

Pella. This report updates the records and analyses through December 2004. Data 

include air temperatures, precipitation (rainfall and melted snowfall), water temperature 

for Auke Creek, annual dates of freeze-up and ice-out for Auke Lake, and Auke Bay sea 

surface temperature. Statistical time series methods, including spectral analysis for the 

underlying cycles and univariate modeling for temporal dependence, were used to 

describe the monthly records for precipitation, snowfall, average daily high, low, and 

midrange air temperatures, and sea surface temperature. 

Significant trends observed were an increase of average daily high, daily low and 

daily mid-range air temperatures, a decrease in annual snowfall, an increase of average 

daily sea surface temperature, and an increase in Auke Creek stream temperature. The 

observed trends of an increase in total precipitation and decrease in duration of ice cover 

on Auke Lake associated with earlier dates of ice-out on Auke Lake were not significant. 

v 

CONTENTS 

Abstract ............................................................................................................................ iii

Introduction ........................................................................................................................ 1

Methods .............................................................................................................................. 1

Meteorological Records ......................................................................................... 1

Air Temperatures ....................................................................................... 2

Precipitation ............................................................................................... 2

Auke Bay Seawater Temperatures ......................................................................... 2

Auke Bay Laboratory ................................................................................. 2

Auke Bay Monitor Station ......................................................................... 3

Auke Lake Watershed Temperatures ..................................................................... 3

Auke Creek ................................................................................................ 3

Auke Creek Hatchery ................................................................................. 3

Auke Lake Freeze-up and Ice-Out ............................................................. 4

Time Series Analysis ............................................................................................. 4

Results and Discussion ...................................................................................................... 6

Meteorological Records ......................................................................................... 6

Air Temperatures ....................................................................................... 6

Precipitation and Snowfall ......................................................................... 9

Auke Bay Saltwater Temperatures ....................................................................... 11

Auke Bay Monitor Station ....................................................................... 11

Auke Bay Laboratory ............................................................................... 11

Auke Lake Watershed Temperatures ................................................................... 13

Auke Creek Temperatures ....................................................................... 13

Auke Lake Freeze-up and Ice-Out ........................................................... 13

Summary .......................................................................................................................... 14

Acknowledgments ............................................................................................................ 14

Citations ........................................................................................................................... 15

Tables ............................................................................................................................... 17

Figures .............................................................................................................................. 45
vi 

TABLES 

Table 1.-- Monthly averages of daily high air temperature (oC) (MADHTs) at Auke Bay, 

Alaska, 1963-2004. 

Table 2.-- Monthly averages of daily low air temperature (oC) (MADLTs) at Auke Bay, 

Alaska, 1963-2004. 

Table 3.-- Monthly averages of daily midrange air temperature (oC) (MADMTs) at Auke 

Bay, Alaska, 1963-2004. 

Table 4.-- Annual means, extremes, percentage of total variation (%) in weather variable 

due to an annual cycle and its statistical significance (P), and slope of long-term 

trend and its statistical significance (P), for monthly values of average daily high 

(MADHT), low (MADLT), and midrange (MADMT) air temperatures, 

precipitation, snowfall, and average daily sea surface temperature (MADSST). 

Annual mean and extremes of Auke Creek stream temperature, Auke Lake ice-out 

date, and Auke Lake ice cover duration. Slope of long-term trend and its 

statistical significance (P) for annual Auke Creek stream temperature, Auke Lake 

ice cover duration, and the Auke Lake break up date. 

Table 5.-- Conditional least-squares estimates of the moving-average and autoregressive 

parameters necessary to describe interannual month differences in average daily 

high air temperature series (MADHTs), and statistics of the fit. 

Table 6.-- Conditional least-squares estimates of the moving-average and autoregressive 

parameters necessary to describe interannual month differences in average daily 

low air temperature series (MADLTs), and statistics of the fit. 

Table 7.-- Conditional least-squares estimates of the moving-average and autoregressive 

parameters necessary to describe interannual month differences in average daily 

midrange air temperature series (MADMTs), and statistics of the fit. 

Table 8.-- Monthly precipitation (cm) at Auke Bay, Alaska, 1963-2004. 

Table 9.-- Monthly snowfall (cm) at Auke Bay, Alaska, 1963-2004. 

Table 10.--Seasonal snowfall (cm) at Auke Bay, Alaska, 1963-2004. 

Table 11.--Conditional least-squares estimates of the moving-average and autoregressive 

parameters necessary to describe the interannual month differences in 

precipitation series, and statistics of the fit. 

Table 12.--Conditional least-squares estimates of the moving-average and autoregressive 

parameters necessary to describe the interannual month differences in snowfall 

series, and statistics of the fit. 

Table 13.--Monthly averages of daily afternoon observations of sea surface temperature (oC) 

(MADSSTs) at Auke Bay Laboratory, 1975-2004. 

Table 14.--Conditional least-squares estimates of the moving-average and autoregressive 

parameters necessary to describe interannual month differences in monthly 

average daily sea surface temperature series (MADSSTs), and statistics of the fit. 

Table 15.--Average monthly Auke Creek temperatures (oC), 1962-2005. 

Table 16.--Auke Lake ice-out dates, 1960-2005. 

Table 17.--Duration of Auke Lake ice cover, 1992-2006. 
vii 

FIGURES 

Figure 1.-- Auke Bay, Alaska, and vicinity. 

Figure 2a.-- Monthly averages of daily high temperature (MADHTs) at Auke Bay 

Laboratory, February 1963 through December 2004. 

Figure 2b.-- Monthly averages of daily low temperature (MADLTs) at Auke Bay 

Laboratory, February 1963 through December 2004. 

Figure 2c.-- Monthly averages of daily midrange temperature (MADMTs) at Auke Bay 

Laboratory, February 1963 through December 2004. 

Figure 3.-- Statistics of annual temperature cycle at the Auke Bay Laboratory (1963-2004) 

including daily extremes (lowest low and highest high []) and monthly 

extrema (high ["], low [!] and series averages [Ë]) for monthly average daily 

high (MADHT), low (MADLT), and midrange (MADMT) air temperature 

series. 

Figure 4.-- Annual average of monthly average high (MADHTs[#]), midrange 

(MADMTs[Ë]), and low (MADLTs[!]) daily air temperatures at Auke Bay 

Laboratory, 1963-2004. Missing monthly values were estimated by cubic 

spline interpolation. Solid horizontal lines are the overall annual means. 

Dashed lines are the trends. 

Figure 5.-- Sample autocorrelation function (SACF) of interannual month differences in 

logarithm-transformed monthly average daily high air temperatures 

(MADHTs) at Auke Bay. The interval covering two standard errors is 

indicated. 

Figure 6.-- Sample partial autocorrelation function (SPACF) of interannual month 

differences in logarithm-transformed monthly average daily high air 

temperatures (MADHTs) at Auke Bay. The interval covering two standard 

errors is indicated. 

Figure 7.-- Sample autocorrelation function (SACF) of interannual month differences in 

logarithm-transformed monthly average daily low air temperatures (MADLTs) 

at Auke Bay. The interval covering two standard errors is indicated. 

Figure 8.-- Sample partial autocorrelation function (SPACF) of interannual month 

differences in logarithm-transformed monthly average of daily low air 

temperatures (MADLTs) at Auke Bay. The interval covering two standard 

errors is indicated. 

Figure 9.-- Sample autocorrelation function (SACF) of interannual month differences in 

logarithm-transformed monthly average daily midrange air temperatures 

(MADMTs) at Auke Bay. The interval covering two standard errors is 

indicated. 

Figure 10.-- Sample partial autocorrelation function (SPACF) of interannual month 

differences in logarithm-transformed monthly average daily midrange air 

temperatures (MADMTs) at Auke Bay. The interval covering two standard 

errors is indicated. 

Figure 11a.-- Total monthly precipitation at the Auke Bay Laboratory, February 1963 

through December 2004. 
viii 

Figure 11b.-- Monthly snowfall at the Auke Bay Laboratory, February 1963 through 

December 2004. 

Figure 12.-- Statistics of annual precipitation cycle at Auke Bay Laboratory (1963-2004) 

from monthly precipitation (cm) (maximum["], mean [Ë], and minimum [!]) 

for total precipitation, calendar-year snowfall (January-December), and 

seasonal snowfall (July-June). 

Figure 13.-- Annual total precipitation (cm) at Auke Bay, 1964-2004. Missing monthly 

values were estimated by cubic spline interpolation. Slope of the trend line was 

not significant (P = 0.20). Solid horizontal line is the overall mean, and the 

dashed line is the trend. 

Figure 14.-- Sample autocorrelation function (SACF) of interannual month differences in 

logarithm-transformed precipitation at Auke Bay. The interval covering two 

standard errors is indicated. 

Figure 15.-- Sample partial autocorrelation function (SPACF) of interannual month 

differences in logarithm-transformed precipitation at Auke Bay. The interval 

covering two standard errors is indicated. 

Figure 16.-- Annual total snowfall (cm) at Auke Bay, 1964-2004. Missing monthly values 

were estimated by cubic spline interpolation. Solid line is the overall annual 

mean, and the dashed line is the trend. 

Figure 17.-- Sample autocorrelation function (SACF) of interannual month differences in 

logarithm-transformed snowfall at Auke Bay. The interval covering two 

standard errors is indicated. 

Figure 18.-- Sample partial autocorrelation function (SPACF) of interannual month 

differences in logarithm-transformed snowfall at Auke Bay. The interval 

covering two standard errors is indicated. 

Figure 19.-- Monthly averages of daily sea surface temperatures (MADSSTs) at Auke Bay 

Laboratory, February 1975 through December 2004. 

Figure 20.-- Annual average of monthly average daily sea surface temperatures 

(MADSSTs) at Auke Bay, Alaska, 1976-2004. The solid line is the overall 

annual mean, and the dashed line is the trend. 

Figure 21.-- Sample autocorrelation function (SACF) of interannual month differences in 

logarithm-transformed monthly average daily sea surface temperatures 

(MADSSTs) at Auke Bay. The interval covering two standard errors is 

indicated. 

Figure 22.-- Sample partial autocorrelation function (SPACF) of interannual month 

differences in logarithm-transformed monthly average daily sea surface 

temperatures (MADSSTs) at Auke Bay. The interval covering two standard 

errors is indicated. 

Figure 23.-- Average of daily Auke Creek stream temperatures (oC), 1962-2005 (Taylor 

2006). 

Figure 24.-- Average annual temperature of Auke Creek and the trend line over all years. 

Figure 25.-- Winter ice cover duration for Auke Lake and the trend line for 1992-2005. 

Figure 26.-- Ice breakup dates of Auke Lake and the trend line for 1960-2005. 
INTRODUCTION 

Auke Bay is a small (11 km2) embayment in the fjord system of southeastern Alaska, 

located approximately 130 km inland from the open ocean, 16 km northwest of Juneau, Alaska, 

and 5 km west of the Juneau International Airport (Fig. 1). Meteorological data for the latter two 

locales extend back to the 1880s and 1940, respectively (Lomire 1979). Auke Bay is subject to a 

northern maritime climate, having moderate temperatures, high precipitation, and predominantly 

southeasterly winds. Average monthly insolation varies greatly with day length and cloud cover 

density, ranging from 20 g cal cm-2 day

-1 in midwinter to 340 g cal cm-2 day-1 in midsummer 

(Bruce et al. 1977). The diel photoperiod ranges from 6.4 hours at the winter solstice to 

18.3 hours at the summer solstice. The mountainous terrain of southeastern Alaska causes 

considerable local weather variation over short distances, as is evident in comparing Auke Bay 

weather records to those of nearby Juneau, Mendenhall Glacier, and the Juneau International 

Airport. Auke Bay is protected by surrounding hills from the prevailing southeasterly winds 

year-round and in the winter from severe north winds associated with periods of strong 

atmospheric high pressure in the interior of northwestern Canada. 

Auke Bay and vicinity have been the locale of biological research since the 1950s. The 

National Marine Fisheries Service and the University of Alaska both have research laboratories 

at Auke Bay. The U.S. Fish and Wildlife Service, U.S. Forest Service, U.S. Geological Survey, 

and the Alaska Department of Fish and Game have used the area for experimental and 

observational studies. Additionally, the U.S. Environmental Protection Agency, U.S. Army 

Corps of Engineers, and the Alaska Department of Environmental Conservation have studied the 

area for environmental impact assessments for the Alaska Marine Highway System ferry 

terminal, small boat harbor breakwater, fish processing plant, sewage outfall, and related 

shoreline developments. 

This report presents tabular and graphic summaries and time series analyses of the 

physical data gathered by scientists from the National Marine Fisheries Service’s Auke Bay 

Laboratory (ABL) since 1959. These summaries supplement and update previously published 

data from Auke Bay by Bruce et al. (1977), Coyle and Shirley (1990), and Wing and Pella 

(1998). In this report, we present new data for air temperatures and precipitation, Auke Creek 

stream temperature, and dates of Auke Lake freeze-up and ice-out, and Auke Bay sea surface 

temperature (SST). 

 METHODS 

 Meteorological Records 

Weather observations have been taken daily since February 1963 at ABL (lat. 

58E22.88'N, long. 134E38.67'W), as part of the National Weather Service Cooperative Observer 

Program. Daily air temperatures and precipitation were recorded at approximately 1630 hours 

local time according to procedures in the Weather Bureau Observing Handbook (ESSA 1970). 

Although daily climate records have been obtained, the series began before general availability 
of computers and only the monthly summaries have been transcribed to computer-readable

media. Extreme daily high and low temperatures were also recorded. 

Air Temperatures

Maximum, minimum, and current air temperatures were measured by liquid-in-glass 

thermometers from February 1963 through July 1988. Beginning in August 1988, air temperature 

observations were taken with an electronic maximum/minimum system. The liquid-in-glass 

thermometers serve as backup during electric power outages. From October 1979 through June 

1998, a battery-powered hygrothermograph served as a secondary backup to the standard system 

for periods when an observer was not available. For purposes of this report the monthly average 

temperatures have been converted to the Celsius scale. 

Precipitation

Daily precipitation (rainfall) observations from a standard 8-inch (20.3 cm) nonrecording

gauge are recorded to the nearest 0.01 inch. Precipitation includes the water equivalent of any

snowfall or ice pellets. Snowfall and snow on the ground are measured with standard 12-inch and 

36-inch rulers. Snowfall is recorded to the nearest 0.1 inch and snow on the ground to the nearest 

whole inch. For this report, average precipitation records were converted to centimeters. Rainfall 

and snowfall are reported as the monthly accumulation. Snow-on-the-ground data have not been 

analyzed for this report.

Auke Bay Seawater Temperatures

Auke Bay Laboratory

Daily sea surface temperatures (SSTs) for the Auke Bay Laboratory from January 1959 

through April 1963 were taken on the beach at a private residence adjacent to the laboratory.

These temperatures were normally taken between 0700 and 0800 hours with a mercury-in-glass 

thermometer at the water=s edge. From May 1963 through 1969, the temperatures were taken off 

the laboratory float (10-20 m beyond the zero tide line) and on a less regular schedule according

to specific project needs. In 1975, daily SST observations were reinstituted at the ABL float. To 

ensure continuity of the data, SSTs are taken at the end of the normal work day (1630 hours) and 

included as part of the daily weather observations. Sea surface temperatures were taken with a 

mercury thermometer until 2003 when it was replaced with an electronic YSI Model 30 Salinity, 

Conductivity, and Temperature System. A Vemco Minilog-T is used as a backup when an 

observer is not available. The temperature and salinity observations are taken at a standard 

12 inch (30 cm) depth. 

The large gaps in data records in the 1960s and 1970s resulted from changing priorities, 

personnel, and programs.

2
Auke Bay Monitor Station 

The Auke Bay Monitor (ABM) oceanographic station (lat. 58E21.97'N, long. 

134E40.00'W) has been a reference for research projects in Auke Bay since 1960. Located near 

the middle of Auke Bay, it is considered representative of much of the bay (Bruce et al. 1977, 

Coyle and Shirley 1990). Oceanographic data are collected there on varying and irregular 

schedules according to the needs of specific projects. 

Auke Lake Watershed Temperatures

Auke Bay receives most of its freshwater input from the Mendenhall River and three 

small watersheds drained by Auke Creek, Auke Nu Creek, and Waydelich Creek (Fig. 1). 

Several small permanent streams and intermittent streams contribute to the runoff into Auke Bay.

Auke Creek

Auke Creek, the outflow from Auke Lake to Auke Bay, has a 10 km2 (4.0 mi2) 

watershed. Surface water from Auke Lake to Auke Creek passes through a shallow lagoon. The 

narrow entrance to the lagoon is often less than 50 cm deep during extended low runoff periods. 

The exit from the lagoon passes through a narrow channel over a bedrock sill. Water depth at the 

sill is often less than 20 cm. The stream is short (0.65 km) and has a steep gradient (average 

26 m km-1). The upper portion of the stream was modified in 1963 to increase salmon spawning

area, but pink salmon do not consistently spawn in the modified area. 

Auke Creek temperatures and stream flow were recorded continuously by a stream gauge 

at the upper end of the creek from October 1962 through September 1975; thereafter, the stream 

temperatures were recorded at the Auke Creek Hatchery weir at the lower end of the stream. 

Stream temperatures at the weir were monitored each morning with liquid-in-glass 

thermometers. Since 2000 the stream temperatures have been recorded every 4 hours by a 

Vemco Minilog-T. 

Auke Creek Hatchery

Until 1990, temperatures of inflow water to the Auke Creek Hatchery were recorded each 

morning by liquid-in-glass thermometers at the hatchery trough. The water was drawn from 6 m 

(20 ft) depth in the lake and flowed through an insulated 35-cm diameter pipe to the hatchery. 

Flow rate into the hatchery was approximately 0.06 m3s

-1 (0.5 cfs). Although some heat 

exchange occurred during the flow through the pipe, it was considered minimal. Auke Creek 

Hatchery temperatures were probably representative of Auke Lake temperatures at 6 m depth 

through the spring of 1990, when the ABL ceased using water from the same pipe for domestic 

and experimental use. The Auke Creek Hatchery continues to use water drawn from the lake, but 

temperatures in the hatchery are now monitored with liquid-in-glass thermometers once a day,

depending on individual project requirements. 

3
Auke Lake Freeze-up and Ice-Out

Specific records of freeze-up or ice-over (date of first complete ice cover) were not

routinely maintained until 1992. Several individuals kept private records of the last day of the 

year that float planes could land and take off from Auke Lake.1 

Ice-out (last day of ice on the lake) was followed more closely than freeze-up because it

is associated with the emigration of fish from Auke Lake. The dates of ice-out from 1960 

through 2005 were taken from field notes of ABL fishery biologists. 

Time Series Analysis 

Weather variables with the longest periods of observations from the Auke Bay climate 

data were examined by statistical time series methods using the SAS SPECTRA (spectral 

decomposition), AUTOREG (autoregressive error models), and ARIMA (autoregressive 

integrated moving-average models) procedures (SAS Institute, Inc. 1993). Those variables 

available and analyzed were monthly consolidations of daily records—averages or totals 

(depending on the variable)—for air temperatures, precipitation (rainfall and melted snowfall), 

and snowfall from February 1963 to December 2004 inclusive (42 years of nearly continuous 

monthly records for each variable with no series missing more than 5 of the 504 months), and 

SST from February 1975 to December 2004 inclusive (30 years of continuous monthly records). 

Over the span of observation (say T months), the monthly values for any variable or its transform 

will be denoted as zt; fitted values from ARIMA modeling, by zˆ ;t and the residual from the fit, 

by aˆ z zˆ , t 1, 2, , T. t = t − t = K The forecast value for 1 month beyond the span T will be denoted

as . ~z

SPECTRA was used to decompose the total variation in a time series into cyclical

components of time (months). That proportion of the total variation due to the annual cycle was 

extracted and tested for statistical significance using an F-test for a known period (Fuller 1976). 

Missing values (except at the beginning of the series) were estimated by cubic spline

interpolation using the SAS EXPAND procedure before analysis of untransformed variables by

SPECTRA. 

AUTOREG was used to fit and test statistical significance of linear trend models to 

annual totals (precipitation and snowfall) or averages (temperatures). The procedure allowed for 

estimating autocorrelation structure in residuals about the line. The Durbin-Watson statistic was 

used to test for serial correlation of residuals. These regression analyses omitted the beginning

year of each series (1963 for precipitation, snowfall, and air temperatures; 1975 for SST) because

the January observation was missing. 

Standard Box-Jenkins methods of time series modeling (Box and Jenkins 1976; Pankratz

1983) were used to further describe the time series using the SAS ARIMA Procedure (SAS 

Institute, Inc. 1993): 1) examination of the sample autocorrelation function (SACF) and sample 

partial autocorrelation function (SPACF) to identify candidate ARIMA models; 2) estimation of

parameters of the model(s) chosen; and 3) checking for adequacy of the model(s) by testing

1

Lief Lie, National Weather Service, NOAA, Juneau, Alaska. Pers. Comm. 3 Dec. 1992. 

4
whether the time series of residuals were distinguishable from white noise using the SACF and 

SPACF of residuals and the Ljung-Box (1978) statistic. The conditional least-squares method 

was used for parameter estimation. When residual analyses indicated a proposed model was 

inadequate, the model was revised, parameters re-estimated, and adequacy rechecked. 

 Statistics were computed up to a maximum lag of 120 months for precipitation and 

temperature series, up to a maximum lag of 70 months for snowfall (5 months, May through 

September, with no or very low snowfall were omitted from the series), and up to a maximum 

lag of 84 months for SST, thereby meeting the usual recommendation not to exceed a lag equal

to one-fourth of the total observations. 

In a preliminary examination, the SACF and SPACF for monthly time series of each 

weather variable were computed using interannual month differences ( t = t − t−12 w z z ), intraannual month differences ( = − −1 ′

t t t w z z ), or the combined differences 

( = − −12 − −1 + −13 ′′ t t t t w z z z z ) for untransformed and logarithm-transformed observations. So that 

the logarithm transformation (which can be applied only to positive values) could be used, 

monthly snowfall measurements (cm) were increased by 1 cm and monthly temperature 

measurements (°C) except for SST were increased by 20°C. Interannual month differences of 

logarithm-transformed observations were chosen for analyzing all the time series because

reasonably simple SACFs and SPACFs that were helpful for modeling (Box and Jenkins 1976) 

generally resulted. 

After the models were chosen by standard Box-Jenkins methods, their forecast accuracy

was compared with that from simpler models in which one or more of the coefficients of the

chosen models were omitted. Forecast performance for each weather variable during the last 

5 years was the basis of the comparison. For each test year and weather variable, the chosen 

models, as well as the simpler models, were fitted by conditional least squares to data available 

at the beginning of the test year. The parameter estimates and the weather variable values

preceding the test year were used to forecast the weather variable for each of the 12 months of 

the test year. These forecasts were compared with the actual values of the weather variable; the 

mean square of forecast errors (average squared errors) was used as a summary statistic. 

The autocorrelation coefficients of the SACF are denoted as { rk , k = 1, 2,K, K }, and the 

partial autocorrelation coefficients of the SPACF are denoted as {φ

ˆ

kk , k = 1, 2,K, K }. Precision 

of autocorrelation coefficients was determined from the approximation for standard error (SE) 

Bartlett (1946), 

SE( ) 1 2 . 1/ 2

1/ 2

1

1

2 − −

= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

r = + ∑r n

k

j

k j (1)

Precision of partial autocorrelation coefficients was approximated by (Box and Jenkins 1976) 

SE ( ) . ˆ −1/ 2 φkk = n (2)

5
In Equations 1 and 2, n is the number of values in the time series, reduced for missing values and 

losses during differencing. 

Forecast equations for weather variables were derived using the difference equations 

approach (Box and Jenkins 1976). The 1-month-ahead forecast equations were included to 

clarify important temporal relationships within time series. Although these equations could be 

used in forecasting 1-month ahead and could be modified easily to forecast an arbitrary number 

of months ahead (see Chapter 10 of Pankratz 1983), the time elapsed after the final information 

included requires updating with the most recent observations available from the U.S. National 

Weather Service, Western Regional Climate Center. Also, the logarithm transformation requires 

special methods to translate forecasts to original measurement scales (see Section 10.3 of 

Pankratz 1983). 

RESULTS AND DISCUSSION

Meteorological Records

Daily meteorological records were nearly complete from February 1963 through 

December 2004. The accompanying tables and figures summarize the monthly means, maxima, 

and minima. 

Air Temperatures

The monthly averages of daily high temperature (MADHTs) (Figs. 2a, 3; Table 1), daily

low temperature (MADLTs) (Figs. 2b, 3; Table 2), and daily midrange temperature (MADMTs); 

that is, the temperature midway between the daily high and low for the 24 hours preceding the

observation (Figs. 2c, 3; Table 3), exhibited less variation when superimposed on annual cycling. 

The annual cycles were discerned easily (P < 0.001) and accounted for more than 85% of the 

total variation in each temperature series (Table 4). January was the coldest month (-3.41EC) and 

July was the warmest month (14.30EC) by average MADMT (Table 3). Annual variation was 

evident from 1-month shifts of monthly extremes such that in exceptionally cold winters, 

December or February was colder than January; in exceptionally warm summers, August was 

warmer than July. Daily extremes showed that frost occurred in all months except August, and 

that temperatures occasionally approached or fell below -20EC from December through March 

(Table 2). Above freezing temperatures occurred in all months. Maximum daily air temperatures 

approaching or exceeding 30EC were recorded in June, July, and August (Table 1). 

Annual averages of the three temperature series (MADHT, MADLT, and MADMT) 

between 1964 and 2004 (missing values were estimated by cubic spline interpolation) contained 

an irregular upward trend (Table 4; Fig. 4). Annual average MADHT ranged from a low of 

7.43EC in 1972 to a high of 11.01EC in 1993 (Table 4; Fig. 4) with a series average of 9.11EC 

(SD = 0.79EC). Annual average MADLT ranged from a low of 0.33EC in 1972 to a high of 

4.11EC in 2004 (Table 4; Fig. 4) with a series average of 2.34EC (SD = 0.98EC). Annual average 

6
MADMT temperatures ranged from a low of 3.88EC in 1972 to a high of 7.57EC in 2004 (Table 

4; Fig. 4) with a series average of 5.76EC (SD = 0.86EC). Slopes of the trend lines (Table 4; Fig.

4) for MADHT, MADLT, and MADMT showed that temperatures increased over the 41 years 

fitted (1964–2004) at rates of 0.037 °C yr

-1 (SE = 0.009, P < 0.001), 0.058 °C yr-1 (SE = 0.009, 

P < 0.001), and 0.049°C yr

-1 (SE = 0.009, P < 0.001), respectively. The Durbin-Watson statistics 

equaled 1.977, 1.728, and 1.917 for the three series, respectively, and provided no evidence of 

lag-1 autocorrelation in residuals from the fitted lines. 

Interannual month differences in the logarithm-transformed high, low, and midrange 

temperature series produced SACFs and SPACFs indicating presence of an annual component 

composed of pure or mixed moving-average and autoregressive processes. For the MADHTs, the 

SACF (Fig. 5) had a large negative spike (r12 = -0.511, SE = 0.049) at a lag of 12 months and no 

further significant values at lag multiples of 12 months. The SPACF (Fig. 6) had a slowly

decaying (more slowly than exponential decay) series of spikes at lag multiples of 12 months. 

For the MADLTs, the SACF (Fig. 7) had an apparent oscillating series of spikes with significant 

spikes at lags of 12, 36, and 96 months: r12 = -0.474, SE = 0.049; r36 = 0.125, SE = 0.060; r96 = 

-0.044, SE = 0.064. The SPACF (Fig. 8) for the MADLTs had a slowly decaying (more slowly

than exponential decay) series of spikes at lag multiples of 12 months. For MADMTs, the SACF

(Fig. 9) had a large negative spike at lag of 12 months (r12 = -0.495, SE = 0.049) and no further 

significant spikes at lag multiples of 12 months. The SPACF (Fig. 10) had slowly decaying

spikes at multiples of 12 months. 

Intra-annual variation for the high, low, and midrange temperature series included a firstorder autoregressive process. The SACFs had a significant coefficient at lag of 1 month, and the 

SPACFs had significant coefficients at lag of 1 month, followed by a cutoff at higher lags. 

An initial model consisting of a first-order moving-average process for interannual 

variation and a first-order autoregressive process for intra-annual variation was fitted to each

temperature series and tested for adequacy. Generally, the SACF and SPACF of the residuals

indicated the initial models that provided for interannual and intra-annual variation were 

adequate. 

Statistics of the model fitted to the transformed high temperature series (MADHTs) 

(Table 5) showed all coefficients (excluding the constant) were significant and weakly

correlated. The 1-month ahead forecast for transformed high temperature (MADHT) in January

2005 (Table 5) included linear terms of the transformed MADHT for the previous January

(2004), the difference in transformed December MADHTs of the two previous years (2003 and 

2004), and the residual for the previous January (2004). A general forecast equation for any

month would show that events 1, 12, and 13 months previous to that month were of value for 

predicting MADHTs. Little or no additional information occurred in the residuals as evidenced 

by their SACF and SPACF (not shown). Two autocorrelation coefficients (at lags of 47 and 

95 months) for residuals from this final high temperature model were statistically significant (six

were expected by chance); and five partial autocorrelation coefficients (at lags of 26, 47, 95, 114, 

and 120 months) for the residuals were significant (six were expected by chance). The LjungBox test supported the conclusion that the residuals were indistinguishable from white noise for 

lags up to 120 months (Table 5). 

For MADLTs, an interannual autoregressive term at a lag of 36 months was added to the 

initial model. Statistics of the fit (Table 6) showed all coefficients (excluding the constant) were

significant and all but one pair were weakly correlated. The 1-month ahead forecast for 

transformed low temperature (MADLT) in January 2005 (Table 6) included linear terms of the 

7
transformed MADLT for the previous January (2004), the difference in transformed December 

MADLTs of the two previous years (2003 and 2004), the difference in transformed January

MADLTs of 3 and 4 years previous (2001 and 2002), and the residual for the previous January

(2004). A general forecast equation for any month would show events 1, 12, 13, 36, and 

48 months earlier than that month were of value in predicting MADLTs. Little or no additional 

information occurred in the residuals as evidenced by their SACF and SPACF (not shown). One

autocorrelation coefficient (at lag of 95 months) for residuals from this final low temperatures 

model was statistically significant (six were expected by chance), and four partial autocorrelation 

coefficients (at lags of 60, 85, 95, and 120 months) for the residuals were significant (six were 

expected by chance). The Ljung-Box test supported the conclusion that the residuals were

indistinguishable from white noise for lags up to 120 months (Table 6). 

Statistics of the model fit to the transformed midrange temperature series (MADMTs)

(Table 7) showed all coefficients (excluding the constant) were significant and all were weakly

correlated. Just as was the case for the high temperature series (MADHTs), the 1-month ahead 

forecast for the transformed midrange temperature (MADMT) in January 2005 (Table 7) 

included linear terms of the transformed MADMT for the previous January (2004), the 

difference in transformed December MADMTs of the two previous years (2003 and 2004), and 

the residual for the previous January (2004). A general forecast equation for any month would 

show that events 1, 12, and 13 months prior to that month were of value to predicting MADMTs. 

Little or no additional information occurred in the residuals as evidenced by their SACF and 

SPACF (not shown). One autocorrelation coefficient (at lag of 95 months) for residuals from this 

final midrange temperature model was statistically significant (six were expected by chance), 

and six partial autocorrelation coefficients (at lags of 10, 26, 60, 85, 95, and 

120 months) for the residuals were significant (six were expected by chance). The Ljung-Box

test supported the conclusion that the residuals were indistinguishable from white noise for lags

up to 120 months (Table 7). 

The SACFs and SPACFs for residuals of the final models for the three temperature series 

indicated possible need of additional terms with high lags. The SACFs for MADHTs, MADLTs, 

and MADMTs contained significant coefficients at lags of 47 and 95 months, 95 months, and 

95 months, respectively. Also, the corresponding SPACFs contained significant coefficients at 

the following lags: 26, 47, 95, 114, and 120 months; 60, 85, 95, and 120 months; and 10, 26, 60, 

85, 95, and 120 months. All functions shared a significant coefficient at lag of 95 months 

(7 years and 11 months). When the final models were augmented by an autoregressive term with

lag of 95 months, this fitted coefficient was statistically significant (P < 0.01) for all three series. 

The final models for the three temperature series shared first-order moving-average terms 

for interannual variation and first-order autoregressive terms for intra-annual variation. In

addition, the low temperature series had an autoregressive coefficient at lag of 36 months. 

Test-year forecasts from the final models and simpler models supported certain of the 

simpler models. For MADHTs, mean square of forecast errors by the final model (parameterized 

by μ, θ12, and φ1) was smallest in two test years as well as overall (5 years combined), and that of

the simpler model (parameterized by μ and θ12) was smallest in three test years. For MADLTs, 

the mean square of forecast errors by the final model (parameterized by μ, θ12, φ1, and φ36) was 

smallest in two test years; that of the next simpler model (parameterized by μ, θ12, and φ1) was 

smallest in two test years as well as overall (5 years combined); and that of the simplest model 

(parameterized by μ and θ12) was smallest in one test year. For MADMTs, the mean square of 

forecast errors by the final model (parameterized by μ, θ12, and φ1) was smallest in three test 

8
years as well as overall (5 years combined), and that of the simpler model (parameterized by μ

and θ12) was smallest in two test years. For high and midrange daily temperatures, the final

model surpassed the simpler models in overall (5 years combined) forecast accuracy and was 

matched in certain years for best forecast accuracy by one of the simpler models. For low daily

temperature, the final model was surpassed in overall (5 years combined) forecast accuracy and 

matched in certain years for best forecast accuracy by one of the simpler models.

Precipitation and Snowfall

Precipitation was recorded as the monthly sum of amounts of liquid water collected daily

(rainfall and melted snowfall). Precipitation (Fig. 11a; Table 8) and snowfall (Fig. 11b; Table 9) 

were computed for each month of the calendar year (January through December). In addition, 

snowfall was computed for the snow year of July through June (Table 10). Although this snow 

year differs from the U. S. Geological Survey hydrological year of October through September, 

it placed the rare September snowfalls at the beginning of the oncoming winter rather than at the 

end of the passing summer. Mean monthly precipitation ranged from 7.33 cm in April to 

22.44 cm in September (Fig. 12). The mean monthly extremes were 0.18 cm in February 1989 

and 41.81 cm in September 1991 (Table 8). The annual cycle was discerned easily (P < 0.001) 

but accounted for only about 36% of the total variation (Table 4). Snowfall occurred from 

September through May (Fig. 12), although May, September, and October normally received 

only trace amounts; trace amounts of snowfall were treated as zeroes in analyses. Mean monthly

snowfall for November through April was 37 cm. January had the highest average snowfall 

(mean of 68 cm with a range of 5 cm in 1987 to 180 cm in 1982) (Table 9). The annual cycle 

was evident (P < 0.001) but accounted for only about 42% of total variation. 

Annual precipitation between 1964 and 2004 (Table 8) ranged from a low of 116.69 cm 

in 1995 to a high of 215.39 cm in 1991. Average annual precipitation was 159.19 cm (SD = 

20.08 cm). The annual series contained an irregular upward trend, interrupted about the time of 

the 1982-1983 El Niño and in 1995 (Fig. 13). Slope of the trend line (Table 4) showed that 

precipitation increased over the 41 years (1964-2004) fitted but not at a significant rate 

(0.341 cm yr

-1, SE = 0.264, P = 0.20). The Durbin-Watson statistic was 1.957 and provided no 

evidence of lag-1 autocorrelation in residuals from the fitted line.

Interannual month differences in logarithm-transformed precipitation values produced 

simple SACF and SPACF (Figs. 14, 15). An annual component of the transformed precipitation 

series was included as a first-order moving-average process: the SACF had a highly significant

spike (r12 = -0.453, SE = 0.046) at lag equal to 12 months (and no further significant 

autocorrelation coefficients at higher lag multiples of 12 months), and the SPACF generally

decayed toward zero at lag multiples of 12 months. The simple first-order annual movingaverage model was fitted to the series of interannual month differences in annual precipitation. 

The SACF and SPACF of the residuals (not shown) indicated an autoregressive coefficient of lag

equal to 48 months was needed: both functions contained significant coefficients at that lag.

A revised model with a moving-average parameter of lag equal to 12 months and an 

autoregressive parameter of lag equal to 48 months was fitted. Both the annual moving-average

parameter, θ12 (P < 0.001), and autoregressive parameter, φ48 (P = 0.002), were detected 

(Table 11), but the estimate for μ did not differ significantly from zero. The parameter estimates 

were weakly correlated. The one-month ahead forecast for transformed precipitation in January

9
2005 (Table 11) included linear terms of the transformed precipitation during the previous 

January (2004), the difference in transformed January precipitation 4 and 5 years earlier (2000 

and 2001), and the residual for the previous January (2004). A general forecast equation for any

month would show events 12, 48, and 60 months in advance of the current month were of value 

to predicting precipitation. Contrary to the temperature series, precipitation of the previous 

month was not useful in prediction. Apparently little or no additional information occurred in the

residuals as evidenced by their SACF and SPACF (not shown). Two autocorrelation coefficients

(at lags of 90 and 96 months) for residuals from this final precipitation model were statistically

significant (six were expected by chance), and four partial autocorrelation coefficients (at lags of 

8, 90, 101, and 119) for the residuals were significant (six were expected by chance). The LjungBox test supported the conclusion that the residuals were indistinguishable from white noise for 

lags up to 120 months (Table 11). 

Test-year forecasts from the final model (parameterized by μ, θ12, and φ48) and a simpler 

model (parameterized by μ and θ12) supported the final model: the mean square of forecast errors 

of the final model was smaller in three of the five test years, and the overall (5 years combined)

mean square of forecast errors of the final model was smaller as well.

Annual snowfall (calendar year) between 1964 and 2004 (Table 9) ranged from a low of 

36.59 cm (2000) to a high of 442.47 cm (1994), with an average of 219.24 cm (SD = 103.69 cm). 

The series contained an irregular downward trend until the late 1980s, whereafter snowfall rose

above the long-term mean in 1989, 1990, 1991, and 1994 (Fig. 16). The series fell below the 

long-term mean for 2 years preceding 1994 and thereafter, except in 1999 when the snowfall was 

slightly above the long-term mean (Fig. 16). Slope of the trend line (Table 4; Fig. 16) indicated 

that snowfall decreased over the 41 years fitted (1964-2004) at a rate of 3.582 cm yr

-1 (SE = 

1.263, P < 0.01). The Durbin-Watson statistic was 1.726 and provided no evidence of lag-1 

autocorrelation in residuals from the fitted line. Differences in annual snowfall in successive 

years reached more than 250 cm three times (1970-71, 1993-94, and 1994-95) and more than 

100 cm fifteen times (Table 9). Snowfall during the snow year (July through June, but essentially

late September through early May) ranged from a low of 51.57 cm (2000-01) to a high of

421.64 cm (1975-76), with an average of 222.05 cm (SD = 95.00 cm) (Table 10). 

No snow fell during June, July, and August, and average snowfall for May and 

September was less than 0.1 cm. Therefore, these 5 months were omitted from the time series for

ARIMA modeling, and snowfall for the remaining 7 months was analyzed. Interannual 

(7 months) differences in logarithm-transformed snowfall produced simple SACF (Fig. 17) and 

SPACF (Fig. 18). The functions provided clear indication of an interannual moving-average 

process of order 1: the SACF had a highly significant spike at lag of 7 months (r7 = -0.494, SE = 

0.062), and the SPACF decayed regularly at lag multiples of 7 months. Intra-annual variation for 

the snowfall series included a first-order autoregressive process. The SACF and SPACF spikes at

lag of 2 months (r2 = 0.182, SE = 0.060; 22

ˆ

φ = 0.180, SE = 0.059) exceeded three standard errors, 

and were further remarkable for absence of adjoining significant values. Statistics of the fit of an

annual, first-order, moving average process and an intra-annual autoregressive process with a lag

of 2 months (Table 12) indicated the annual moving-average parameter, θ7, was highly

significant (P < 0.001) and its estimate was only weakly correlated with that of the other 

parameters; that is, the constant, μ, and the autoregressive coefficient at lag of 2 months, φ2. The 

estimates of the intra-annual autoregressive parameter, φ2, (P = 0.03) and the constant, μ, 

(P < 0.05) were also significant. A general forecast equation for any month (Table 12) would 

show that snowfall 2, 7 (= 1 year), and 9 (= 1 year and 2 months) months in advance of that 

10
month was of value to predicting snowfall. Little or no additional information occurred in the

residuals as evidenced by their SACF and SPACF (not shown). None of the residual 

autocorrelations to lag of 70 months (10 years) was significant at α = 0.05. The Ljung-Box test 

confirmed that the residuals were indistinguishable from white noise for lags up to 66 months 

(> 9 years) (Table 12). 

Test-year forecasts from the final model (parameterized by μ, θ7, and φ2) and a simpler 

model (parameterized by μ and θ7) were inconclusive: the mean square of forecast errors for the 

simpler model was smallest in 3 of the 5 test years and overall (5 years combined) and that of the

final model was smallest in 2 test years. The SACF of the simpler model’s residuals, however, 

had a significant spike (r2 = 0.127, SE = 0.060) at lag equal to 2 months, and the SPACF had a 

nearly significant spike ( 22

ˆ

φ = 0.117, SE = 0.060) at the same lag. The final model that included 

an autoregressive parameter, φ2, showed no additional information in the residuals as evidenced 

by their SACF and SPACF (not shown). 

Auke Bay Saltwater Temperatures 

Auke Bay Monitor Station 

Although the records for SST extend back to 1959, the observations were not taken on a 

regular schedule nor were the methods of observation constant (Wing and Pella 1998; Table 16). 

The irregular nature of the data did not permit clear interpretation of interannual variation; 

therefore, only the means and extremes for the annual surface temperature cycle at Auke Bay

Monitor Station were presented. This irregular schedule of observations has continued and 

currently monthly observations are made only from May through August. 

Auke Bay Laboratory

Sea surface temperatures in Auke Bay are affected by several factors, most important of 

which are daily solar heating, the annual cycle of cooling and heating, exchange of water with

adjacent channels, exposure to wind mixing, proximity to streams and surface freshwater 

sources, and rain and snowfall conditions. Because the ABL is located in the northeastern corner 

of Auke Bay and is well protected from the prevailing southeasterly winds, surface waters at the

ABL float are poorly mixed by the winds, strongly influenced by the outflow of Auke Creek, and 

subject to accumulation of a freshwater lens during heavy rains and snowfall. Surface 

temperatures at ABL tend to be lower in the winter and higher in the summer than those in either

the middle of Auke Bay (see Auke Bay Monitor Station above) or the more exposed waters of 

Lynn Canal and Stephens Passage (Williamson 1965, Jones 1978). Sea surface temperatures for 

1959-69 were taken in the morning, whereas temperatures were recorded in the afternoon from 

1975 to present. Sea surface temperatures for 1959-69 are reported in Wing and Pella (1998) and 

are not mentioned further here. 

 According to Hagen

2

, oscillations in the daily recorded Auke Bay SSTs and in a 4-day

running average of these values did not appear to be related to tidal cycles. The oscillations of 

2

 Hagen, P.T. 1988. Time series analysis of sea surface temperatures in Auke Bay, Alaska. Juneau Center for 

Fisheries and Ocean Sciences, University of Alaska-Fairbanks. 29 p., unpublished. 

11
the averaged data could be artifacts of smoothing called the Slutzky-Yule effect (Kendall and 

Stuart 1966). 

Monthly averages of daily sea surface temperatures (MADSSTs) fluctuated very

regularly (Fig. 19), with 92.5% of the variation explained by the annual cycle (P < 0.001, Table 

4). MADSST ranged from a minimum of 2°C in February to a maximum of 16°C in August 

(Table 13). Annual average of MADSSTs between 1976 and 2004 ranged from a low of 7.0°C in 

1976 to a high of 8.9°C in 1993 (Fig. 20). Average annual MADSSTs was 8.2°C (SD = 0.43°C). 

The annual series began with lowest annual temperature in 1976 and thereafter fluctuated with 

increasing variation (Fig. 20). The slope of the trend line suggested SST increased over the 

29 years fitted at a rate of 0.024°C year

-1 (SE = 0.009, P = 0.01). The Durbin-Watson statistic 

was 1.754 and provided no evidence of lag-1 autocorrelation in residuals from the fitted line. 

Interannual month differences in logarithm-transformed MADSST produced simple 

SACF and SPACF (Figs. 21, 22). The annual component of the transformed MADSST series 

was a first-order moving-average process: the SACF had a highly significant spike (r12 = -0.386, 

SE = 0.077) at lag equal to 12 months (and no further significant autocorrelation coefficients at 

higher lag multiples of 12 months), and the SPACF decayed toward zero at lags of 12, 24, and 

35 months. The intra-annual component was a first-order autoregressive process; the SACF

appeared to decay exponentially from lags of 1 through 5 months (those for lags of 1 and 

2 months were highly significant), and the SPACF had a spike at lag of 1 month [ 11

ˆφ = 0.584, 

SE = 0.055]. In addition, the SACF had two additional significant coefficients at lags equal to 11 

and 13 months, and the SPACF had seven additional significant coefficients at lags of 11, 13, 23, 

25, 37, 49, and 52 months; roughly four significant coefficients were expected from sampling

variation for either case if all remaining actual coefficients were zero. The simple model with 

first-order annual moving-average and first-order intra-annual autoregressive terms was fit to the 

series of interannual month differences in MADSST. Statistics of the estimation (Table 14) 

indicated that all coefficients excluding the constant were highly significant (P < 0.001). All 

parameter estimates were weakly correlated. The 1-month-ahead forecast for transformed SST in

January 2005 (Table 14) included linear terms of transformed SST during the previous January

(2004), the difference in transformed December SST of the two previous years (2003 and 2004), 

and the residual for the previous January (2004). A general forecast equation for any month 

would show that events 1, 12, and 13 months prior were of value in predicting SST. Apparently, 

little or no additional information occurred in the residuals, as evidenced by their SACF and 

SPACF (not shown). Two autocorrelation coefficients (at lags of 10 and 39 months) for residuals 

from this final model were statistically significant (four were expected by chance), and two 

partial autocorrelation coefficients for residuals at the same lags were significant. The Ljung-Box

test (Table 14) supported the conclusion that the residuals were indistinguishable from white 

noise for lags up to 84 months. 

Mean square of forecast errors for the final model (parameterized by μ, θ12, and φ1) was 

smallest in three of the five test years as well as overall (5 years combined), and that of the 

simpler model (parameterized by μ and θ12) was smallest in two test years.

The trend of increase in SST at Auke Bay (+0.024oC yr

-1) is comparable to the 

+0.03oC yr

-1 increase observed for the integrated 0-100 m by Royer and Grosch (in press) at 

GAK-1 in the northern Gulf of Alaska but lower than the +0.04oC yr

-1 observed by Nixon et al. 

(2004) for Woods Hole, MA. Auke Bay is 130 km inland from the open ocean and influenced by

local terrestrial climate conditions. The GAK-1 station is exposed to the open ocean circulation 

of the Gulf of Alaska with less terrestrial effect than Auke Bay. Woods Hole, although in a well 

12
protected harbor, is close to the open coast and influenced by both terrestrial climate and oceanic 

conditions. Although each of the three studies uses different methods, the close similarity of

results suggest the warming trends have been hemispheric for the last 40 years and are not 

localized phenomena. 

Auke Lake Watershed Temperatures

Auke Creek Temperatures 

Auke Creek stream temperatures are strongly influenced by seasonal snow melt and 

runoff. Observed stream temperatures have ranged from a minimum -0.2oC (multiple records) to 

a maximum 21.8oC (21 August 1977). Temperatures are at the annual minimums from late 

December through March when stream flows are low and Auke Lake is typically ice covered 

(Table 15; Fig. 23). Annual average Auke Creek stream temperatures between 1971 and 2005, 

including 1968 (intervening years with missing monthly temperatures were excluded), contained 

an upward trend (Fig. 24, Table 4) due to the increasing trend of summer temperatures (Taylor 

2006). Annual average stream temperatures ranged from a low of 7.3oC in 1972 to a high of 

8.9oC in 1998 (Table 4). The slope of the trend line (Table 4) showed that stream temperatures 

increased over the 36 years fitted (1968, 1971-2005) at a rate of 0.055oC yr

-1 (SE = 0.009, 

P < 0.001). The Durbin-Watson statistic equaled 1.713 and provided no evidence of lag-1 

autocorrelation in residuals for the fitted line. Maximum stream temperatures occur from June 

through August during periods of low runoff and when the surface waters of Auke Lake are 

warmed by incident sunlight. During exceptionally low runoff conditions, surface flows from 

Auke Lake may cease and a minimum flow is maintained in the creek by groundwater seepage 

from the surrounding hill side. Under these conditions the stream temperatures at the weir near 

the mouth of the creek are lower than the surface temperatures of Auke Lake. 

Auke Lake Freeze-Up and Ice-Out

Dates of freeze-up and ice-out are indicators of heat loss and accumulation in Auke Lake 

and of autumnal and vernal turnover. Freeze-up (complete ice cover) does not occur until after 

turnover in November when surface waters cool to 0EC. Frequently, ice forms intermittently

around the lake margins for several days to 2 weeks before the lake ices over completely. 

November 25 was the average last day of float plane operation in Auke Lake and was usually

2 weeks before the lake froze over completely.

1 Freeze-up appears to have occurred earlier in the 

past decades than in the 1990s and 2000s. 

The duration of Auke Lake ice cover between the 1992-1993 winter and the 2004-2005 

winter contained a downward trend (Fig. 25). The slope of the trend line (Table 4) showed that 

the duration of ice cover declined over the 13 winters but not at a significant rate 

(-3.615 days yr

-1, SE = 1.947, P = 0.09). The Durbin-Watson statistic was 2.481 and provided no 

evidence of lag-1 autocorrelation in residuals from the fitted line.

The ice-out-date between 1967 and 2005, including 1960 and 1963, contained a trend 

toward earlier dates (Fig. 26). The slope of the trend line (Table 4) showed that the ice-out date

was earlier over the 41 years but not at a significant rate (-0.385 days yr

-1, SE = 0.242, P = 0.12). 

The Durbin-Watson statistic was 2.326 and provided no evidence of lag-1 autocorrelation in 

residuals from the fitted line. 

13
Freeze-up occurred as early as 15 November in 1963 to as late as 4 January in 2005 

(Table 17). Several years the freeze-up has not happened in the fall but was delayed until early

winter shortly after the winter solstice (Table 17), resulting in shorter duration of the ice cover

(Fig. 25). 

Ice-out is associated with the vernal turnover and often is quite rapid. Auke Lake may go 

from less than 10% open water to over 95% open water within 3 or 4 days. For our purposes, we 

have determined ice-out as the day the lake surface was 90% open water. Ice-out (Table 16) on 

Auke Lake normally occurred in late April or early May: 18 April was the average date of ice out 

for the years of available data. Earliest date of ice-out was 1 February 1977 and latest ice-out 

date was 20 May 1972. On several occasions the ice cover on Auke Lake disappeared in early

January or February and then re-froze over again until April. 

SUMMARY 

Time series analyses of the physical environmental records available for Auke Bay reveal

six significant trends. Air temperatures (daily high, midrange and low temperatures), SSTs, and 

annual stream temperatures have increased, and snowfall has decreased. These significant trends 

are all related to the rise of mean air temperatures. The non-significant trends for increased total 

precipitation, decreased duration of ice cover on Auke Lake, and earlier date of ice-out on Auke 

Lake may or may not be related to the rising air temperatures. 

 ACKNOWLEDGMENTS

Many people assisted in gathering data for this report. Special appreciation is due to the

late John Hagmeir, who recorded the daily sea surface temperature at Auke Bay from 1959 

through 1962, and to J. Douglas Jones, who reinitiated the observation in 1975. Richard S. 

Williamson maintained the sea surface temperatures from 1976 through 1979. Roy May, from 

1963 through 1965, and Gerald Olson, from 1966 through 1979, were the volunteers for the U.S. 

National Weather Service Cooperative Observing Program. Bruce L. Wing has been the primary

weather and sea surface temperature observer since 1979. S. G. AJerry@ Taylor has been 

responsible for obtaining the Auke Creek Hatchery and stream temperatures since the fall of 

1971. The dedication of these and other volunteers ensured the remarkable completeness of these 

time-series data. 

14
CITATIONS 

Bartlett, M. S. 1946. On the theoretical specification of sampling properties of autocorrelated 

time series. J. Royal Stat. Soc. 8 (suppl.): 27-41. 

Box, G. E. P., and G. M. Jenkins. 1976. Time series analysis: forecasting and control. Rev. Ed. 

Holden-Day, San Francisco, 575 p. 

Bruce, H. E., D. R. McLain, and B. L. Wing. 1977. Annual physical and chemical oceanographic 

cycles of Auke Bay, southeastern Alaska. U.S. Dep. Commer., NOAA Tech. Rep. NMFS

SSRF-712, 11 p. 

Coyle, K. O., and T. C. Shirley. 1990. A review of fisheries and oceanographic research in Auke

Bay, Alaska and vicinity 1966-1985, p. 3-60. In D. A. Zieman and K. W. Fulton-Bennett 

(editors), APPRISE - Internannual variability and fisheries recruitment. The Oceanic 

Institute, Honolulu, HI. 

Environmental Science Services Administration (ESSA). 1970. Weather Bureau Observing

Handbook No. 2. Substation Observations. U.S. Dep. Commer., Environ. Sci. Serv. 

Admin., Silver Spring, MD, 77 p. 

Fuller, W. A. 1976. Introduction to statistical time series. John Wiley & Sons, New York, 470 p. 

Jones, J. D. 1978. Southeastern Alaska sea surface temperatures, 1964-1974. U. S. Dep. 

Commer., NWAFC Processed Rep., 67 p. Auke Bay Laboratory, Natl. Mar. Fish. Serv., 

NOAA, 11305 Glacier Hwy., Juneau, AK 99801-8626. 

Kendall, M. G., and A. Stuart. 1996. The advanced theory of statistics. Vol. 3. Griffin, London, 

557 p. 

Ljung, G. M., and G. E. P. Box. 1978. On a measure of lack of fit in time series models. 

Biometrika 65: 297-303. 

Lomire, R. 1979. Climatological data for Juneau, Alaska. U. S. Dep. Commer., NOAA NWS 

Alaska Region Tech. Info. Paper No. 2, 26 p. 

Nixon, S., S. Granger, A. Buckley, M. Lamont, and B. Rowell. 2004. A one hundred and 

seventeen year coastal water temperature record from Woods Hole, Massachusetts. 

Estuaries 27(3): 397-404.

Pankratz, A. 1983. Forecasting with univariate Box-Jenkins models. Concepts and cases. Wiley

& Sons, New York, 562 p. 

Royer, T. C., and C. E. Grosch. In Press. Ocean warming and freshening in the northern Gulf of

Alaska. Geophys. Res. Letters 

15
SAS Institute, Inc. 1993. SAS/ETS® User’s Guide. Vers. 6, 2nd ed., 1,022 p. 

Taylor, S. G. 2006. Auke Creek Weir 2005 Annual Report: operations, fish counts, and historical 

summaries. Unpublished Report. 24 p. National Marine Fisheries Service, Auke Bay

Laboratory, 11305 Glacier Highway, Juneau, Alaska 99801-8626. 

Williamson, R. S. 1965. Southeastern Alaska sea surface temperatures, 1959-1963. U. S. Fish. 

Wildl. Serv. Data Rep. 8, 41 p. 

Wing, B. L., and J. J. Pella. 1998. Time series analyses of climatological records from Auke Bay, 

Alaska. U.S. Dep. Commer., NOAA Tech. Memo. NMFS-AFSC-91, 90 p. 

16
TABLES 

17

Table 1.-- Monthly averages of daily high air temperature (EC) (MADHTs) at Auke Bay, Alaska, 1963-2004. 

1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975

Jan 0.84 -1.85 -6.83 -1.90 -2.07 -8.40 -1.96 -6.10 -4.48 -2.74 -4.48 -1.79Feb 3.36 4.03 0.06 2.18 2.69 1.96 1.18 5.21 2.02 -1.85 0.73 2.30 -0.28Mar 3.98 2.46 6.22 4.09 1.96 5.54 4.09 6.22 3.36 1.34 4.48 3.42 4.48Apr 9.30 7.95 9.13 8.79 9.80 7.45 9.58 8.40 8.23 5.94 8.85 8.18 8.06May 16.35 12.82 10.98 10.64 14.62 15.62 15.85 12.71 11.03 11.82 12.54 13.89 13.16Jun 14.73 17.58 16.13 19.82 17.53 21.11 15.40 18.54 15.01 15.85 15.23 14.73Jul 18.14 17.25 19.82 20.78 17.70 19.88 16.07 16.35 20.78 21.17 16.35 17.53 17.75Aug 19.54 16.41 20.05 17.02 19.82 14.06 15.29 17.86 17.02 15.46 18.26 17.25Sep 14.22 14.34 15.34 13.38 13.89 12.10 15.79 11.98 12.66 13.10 13.89 14.78 13.10Oct 9.86 9.18 9.07 7.00 9.24 7.95 10.42 8.51 6.94 7.50 7.56 8.12 8.74Nov 0.45 2.24 2.74 1.12 4.31 3.86 2.46 2.46 3.98 -1.12 4.59 0.78Dec 3.02 -5.25 0.28 -0.34 -2.80 4.14 -2.35 -1.18 -1.40 0.90 3.25 -1.51

1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988

Jan 0.56 2.91 -0.73 -3.14 -3.86 5.49 -6.66 0.78 1.68 4.14 2.97 2.56 -0.65Feb 0.62 6.38 3.81 -5.82 4.37 2.80 -1.68 2.80 3.92 -0.34 1.85 3.11 2.70Mar 3.58 5.38 5.32 6.44 4.09 7.56 4.31 6.50 6.94 4.70 4.87 4.72 6.22Apr 9.63 9.18 11.54 11.76 9.46 8.01 6.78 11.14 11.52 7.00 8.06 9.18 9.90May 11.98 14.56 12.88 12.77 15.06 16.13 11.37 14.67 15.74 11.76 13.44 13.33 14.33Jun 15.34 17.47 17.86 15.62 18.03 17.19 19.66 18.65 16.24 15.90 17.36 14.95 17.92Jul 19.04 18.93 17.08 18.42 16.69 17.42 19.38 19.60 17.53 18.42 18.48 20.29 16.78Aug 18.03 20.55 19.77 21.39 17.92 17.70 17.25 16.07 18.87 16.46 16.00 19.19 15.99Sep 14.22 15.23 14.06 14.73 13.33 13.50 13.61 13.05 14.56 13.55 15.62 12.21 13.28Oct 8.57 9.07 9.46 10.64 9.41 8.57 7.28 7.73 7.78 6.50 9.45 8.27 9.03Nov 6.55 2.13 2.13 4.93 5.43 4.59 1.29 2.35 1.96 -2.07 1.40 5.25 3.94Dec 3.14 -3.64 1.18 -0.06 -3.25 -0.11 1.74 -3.53 -1.51 2.74 3.42 2.74 1.50

19
Table 1.-- Cont. 

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Jan -0.96 -1.99 -1.68 3.33 -1.15 -0.94 0.13 -5.46 -1.01 -2.08 -0.76Feb 1.01 -1.06 3.68 2.96 2.96 -4.38 1.80 1.66 3.28 5.78 1.58Mar 3.43 5.92 4.03 5.96 5.58 5.24 3.09 4.89 3.04 6.23 4.72Apr 12.25 11.41 9.69 9.59 12.47 11.46 12.36 11.22 10.53 10.26 8.57May 14.02 14.77 13.97 12.72 17.74 13.97 15.92 16.00 16.78 15.53 11.89Jun 18.55 17.38 17.43 19.45 19.21 18.14 18.05 16.76 19.48 19.19 18.22Jul 21.35 20.19 15.93 18.10 21.31 19.49 18.82 20.57 18.80 19.19 19.24Aug 19.84 19.47 16.86 18.07 20.01 21.22 17.68 16.46 19.58 17.05 17.99Sep 15.08 13.54 12.61 11.31 15.64 14.11 16.45 13.74 16.28 13.90 12.17Oct 8.51 7.62 7.99 6.85 10.26 8.46 8.98 6.85 8.40 9.28 8.02Nov 1.72 -1.10 3.63 4.72 4.61 0.62 2.59 1.85 4.78 3.51 3.86Dec 3.92 -2.46 2.02 -1.55 3.42 -0.22 -1.71 -1.10 3.95 -0.34 3.83

2000 2001 2002 2003 2004 Min.1 Mean1 Max.1 Extreme1 

Jan -0.92 4.45 1.34 2.18 -1.25 -8.40 -1.04 5.49 12.88Feb 3.30 2.06 1.96 2.94 5.02 -5.82 1.97 6.38 14.56Mar 5.49 4.32 3.02 3.77 5.33 1.34 4.67 7.56 12.88Apr 8.79 10.25 8.94 12.00 10.30 5.94 9.59 12.47 21.67May 14.38 12.08 13.44 14.97 17.60 10.64 13.95 17.74 25.56Jun 17.36 18.03 17.58 17.30 21.34 14.73 17.50 21.34 29.68Jul 17.27 16.86 16.77 19.20 20.14 15.93 18.59 21.35 31.36Aug 17.23 18.86 16.11 18.10 21.59 14.06 18.03 21.59 28.34Sep 13.18 13.59 13.82 12.66 13.20 11.31 13.83 16.45 22.78Oct 8.29 7.91 9.41 10.11 8.71 6.50 8.51 10.64 17.22Nov 4.78 2.84 6.54 2.35 4.76 -2.07 2.92 6.55 12.32Dec 1.48 0.07 2.39 2.04 2.59 -5.25 0.47 4.14 9.521Min. is the lowest MADHT, Mean is the average of the MADHTs, Max. is the highest MADHT, and Extreme is the highest daily high temperature observed.

20
Table 2.--Monthly averages of daily low air temperature (EC) (MADLTs) at Auke Bay, Alaska, 1963-2004. 

1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975

Jan -3.64 -7.84 -13.38 -7.78 -8.62 -16.30 -7.39 -11.82 -11.98 -8.51 -10.58 -6.05Feb -2.69 0.00 -6.50 -4.59 -2.41 -5.04 -6.94 -0.45 -3.14 -9.41 -6.16 -2.91 -7.39Mar -3.08 -4.59 -2.35 -3.08 -6.89 -0.73 -2.18 0.17 -3.86 -5.21 -1.06 -7.34 -3.19Apr -1.85 0.17 -0.56 -0.73 -1.29 0.22 0.67 0.90 -0.34 -2.41 0.90 0.28 -0.11May 3.86 2.52 2.52 3.19 3.86 4.59 4.76 4.20 2.13 2.24 3.92 3.19 3.58Jun 6.78 8.55 5.82 7.56 9.02 7.90 9.63 7.67 7.45 6.27 6.83 6.27 7.00Jul 9.46 9.18 9.86 10.36 9.46 9.74 9.41 8.85 10.42 9.80 9.13 7.95 10.02Aug 9.69 8.79 9.52 8.96 9.91 9.41 7.28 8.68 10.08 9.91 7.90 8.51 8.62Sep 9.24 6.16 7.56 7.73 8.12 6.83 7.73 6.10 6.66 5.60 5.94 6.72 8.18Oct 4.37 3.98 3.98 2.07 3.86 1.96 3.64 3.02 1.85 5.66 2.97 3.42 2.58Nov -4.54 -1.74 -2.91 -4.65 0.00 -1.40 -3.02 -1.74 -0.45 -7.78 0.67 -3.19Dec -2.52 -12.04 -3.98 -5.15 -8.29 -0.06 -6.89 -7.84 -6.05 -3.19 0.73 -6.05

1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988

Jan -3.81 0.28 -5.77 -6.44 -8.90 1.06 -12.32 -3.42 -1.90 0.50 -0.45 -1.32 -4.59Feb -6.10 2.97 -2.91 -13.66 -1.62 -1.68 -8.01 -1.46 0.06 -4.93 -4.70 -0.86 -2.49Mar -2.24 0.00 -1.46 0.11 -1.46 0.90 -2.58 -2.13 1.62 -1.62 -0.34 -3.79 0.29Apr 0.73 2.46 0.84 -0.17 2.13 0.56 -1.01 1.40 2.07 -0.28 -0.67 1.89 1.64May 3.92 4.14 4.82 5.26 5.71 6.66 3.25 5.94 4.82 3.75 4.14 5.43 4.77Jun 7.06 8.68 8.57 8.23 8.85 8.57 8.79 9.18 9.41 5.77 7.62 7.24 7.95Jul 8.96 10.58 9.63 10.64 10.25 10.70 10.25 10.19 9.63 5.43 9.80 9.86 9.09Aug 9.74 11.14 10.08 10.81 8.96 10.42 9.02 9.86 9.35 8.06 8.98 9.18 9.16Sep 7.17 7.56 7.45 7.06 7.17 7.17 7.84 5.77 6.27 5.15 5.86 7.74 6.54Oct 4.03 3.64 5.77 5.77 5.10 3.81 3.47 3.75 1.85 2.30 5.35 4.35 5.24Nov 2.86 -2.41 -2.30 1.40 2.58 1.01 -2.46 -1.68 -2.35 -7.78 -2.54 1.96 -0.13Dec 0.06 -8.12 -2.63 -4.26 -8.23 -2.24 -1.79 -8.12 -6.38 -1.12 -0.06 -0.81 -2.17

21
Table 2.-- Cont. 

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Jan -4.72 -5.44 -6.56 0.03 -6.47 -4.33 -4.33 -11.17 -6.13 -6.22 -4.97Feb -7.84 -6.82 -0.38 -2.24 -4.14 -11.06 -4.60 -4.17 0.30 0.12 -3.92Mar -5.45 -0.65 -2.51 -0.27 -1.39 0.00 -3.86 -3.04 -3.34 -1.70 -1.97Apr 0.62 2.00 0.77 1.89 2.17 2.24 1.75 0.35 1.10 2.90 1.08May 5.32 6.53 4.68 4.35 6.78 5.57 6.36 4.41 5.96 6.72 3.92Jun 9.11 9.65 8.57 8.44 9.74 9.30 8.29 8.85 9.48 9.45 8.16Jul 11.83 11.20 8.38 10.73 11.47 10.53 10.82 10.24 11.17 10.67 10.37Aug 10.79 10.91 10.01 9.54 10.37 11.54 9.46 9.93 11.44 9.25 9.45Sep 8.44 8.70 7.65 5.25 8.23 7.50 9.28 6.33 8.16 6.78 7.95Oct 3.29 2.69 2.22 1.70 5.87 4.14 4.50 2.49 2.86 4.03 4.68Nov -1.66 -5.54 0.25 1.23 0.43 -3.19 -2.50 -2.82 0.97 -0.67 1.12Dec 0.11 -7.36 -0.96 -6.03 0.31 -4.54 -5.67 -5.19 0.47 -3.99 0.31

2000 2001 2002 2003 2004 Min.1 Mean1 Max.1 Extreme1 

Jan -4.95 0.74 -2.39 -3.00 -6.14 -16.30 -5.78 1.06 -24.60Feb -2.22 -3.98 -2.08 -2.00 0.17 -13.66 -3.76 2.97 -26.30Mar 0.27 -1.61 -5.60 -3.47 -0.56 -7.34 -2.17 1.62 -19.60Apr 1.40 1.34 -2.06 1.01 2.39 -2.41 0.68 2.90 -11.20May 4.64 4.14 4.88 4.86 7.08 2.13 4.60 7.08 -2.80Jun 8.57 9.11 9.30 9.00 11.18 5.77 8.31 11.18 0.00Jul 10.26 10.15 9.92 11.65 11.98 5.43 10.00 11.98 -1.12Aug 9.81 10.95 10.24 10.06 11.69 7.28 9.70 11.69 2.80Sep 7.07 8.05 8.34 7.43 7.38 5.15 7.23 9.28 -2.80Oct 3.86 3.65 5.54 4.72 3.83 1.70 3.76 5.87 -10.60Nov 1.85 2.27 3.01 -2.06 1.25 -7.78 -1.19 3.01 -20.20Dec -1.83 -3.61 -1.10 -1.71 -0.93 -12.04 -3.63 0.73 -25.801Min. is the lowest MADLT, Mean is the average of the MADLTs, Max. is the highest MADLT, and Extreme is the lowest daily low temperature observed. 

22
Table 3.--Monthly averages of daily midrange air temperature (EC) (MADMTs) at Auke Bay, Alaska, 1963-2004. 

1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975

Jan -1.40 -4.84 -10.11 -4.84 -5.35 -12.35 -4.68 -8.96 -8.23 -5.63 -7.53 -3.92Feb 0.34 2.03 -3.22 -1.20 0.14 -1.54 -2.88 2.38 -0.56 -5.63 -2.72 -0.31 -3.84Mar 0.45 -1.90 1.93 0.50 -2.46 2.41 0.95 3.19 -0.25 -1.93 1.71 -1.96 0.64Apr 3.72 4.09 4.28 4.03 4.26 3.84 5.12 4.65 3.95 1.76 4.87 4.23 3.98May 10.11 7.72 6.75 6.92 9.24 10.11 10.30 8.46 6.58 7.03 8.23 8.54 8.37Jun 10.75 13.13 10.98 14.42 12.71 15.37 11.54 12.99 10.64 11.34 10.75 10.86Jul 13.80 13.29 14.84 15.57 13.58 14.81 12.74 12.60 15.60 15.48 12.74 12.74 13.89Aug 14.62 12.67 14.78 13.47 14.62 10.67 11.98 13.97 13.47 11.68 13.38 12.94Sep 11.73 10.31 11.45 10.56 11.00 9.46 11.76 9.04 9.66 9.35 9.91 10.75 10.64Oct 7.11 6.61 6.52 4.54 6.55 4.96 7.03 5.77 4.40 6.58 5.26 5.77 5.66Nov -2.04 0.26 -0.08 -1.76 2.16 1.23 -0.28 0.36 1.76 -4.45 2.63 -1.20Dec 0.25 -8.67 -1.85 -2.74 -5.54 2.04 -4.62 -4.51 -3.72 -1.15 1.99 -3.78

1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988

Jan -1.62 1.60 -3.25 -4.79 -6.38 3.28 -9.49 -1.32 -0.11 2.32 1.26 0.62 -2.62Feb -2.74 4.68 0.45 -9.74 1.37 0.56 -4.84 0.67 1.99 -2.63 -1.43 1.12 0.11Mar 0.67 2.69 1.93 3.28 1.32 4.23 0.87 2.18 4.28 1.54 2.27 0.46 3.26Apr 5.18 5.82 6.19 5.80 5.80 4.28 2.88 6.27 6.80 3.36 3.70 5.54 5.77May 7.95 9.35 8.85 9.02 10.39 11.40 7.31 10.30 10.28 7.76 8.79 9.38 9.55Jun 11.20 13.08 13.22 11.93 13.44 12.88 14.22 13.92 12.82 10.84 12.49 11.10 12.94Jul 14.00 14.76 13.36 14.53 13.47 14.06 14.81 14.90 13.58 11.93 14.14 15.08 12.94Aug 13.89 15.85 14.92 16.10 13.44 14.06 13.13 12.96 14.11 12.26 12.49 14.19 12.57Sep 10.70 11.40 10.75 10.89 10.25 10.33 10.72 9.41 10.42 9.35 10.74 9.98 9.96Oct 6.30 6.36 7.62 8.20 7.25 6.19 5.38 5.74 4.82 4.40 7.40 6.31 7.13Nov 4.70 -0.14 -0.08 3.16 4.00 2.80 -0.59 0.34 -0.20 -4.93 -0.57 3.60 1.90Dec 1.60 -5.88 -0.73 -2.16 -5.74 -1.18 -0.03 -5.82 -3.95 0.81 1.68 0.97 -0.33

23
Table 3.-- Cont. 

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Jan -2.84 -3.71 -4.12 1.68 -3.81 -2.64 -2.11 -8.31 -3.57 -4.14 -2.86 -2.93 2.59Feb -3.41 -3.94 1.65 0.36 -0.59 -7.72 -1.37 -1.25 1.79 2.68 -1.17 0.45 -0.96Mar -1.01 2.64 0.76 2.85 2.09 2.62 -0.39 0.93 -0.15 2.27 1.37 2.88 1.36Apr 6.43 6.70 5.23 5.74 7.32 6.85 7.06 5.78 5.81 6.58 4.83 5.10 5.80May 9.67 10.65 9.32 8.53 12.26 9.77 11.70 10.21 11.37 11.13 7.92 9.51 8.11Jun 13.83 13.52 13.00 13.94 14.48 13.72 13.17 12.81 14.48 14.32 13.19 13.12 12.63Jul 16.59 15.70 12.16 14.41 16.39 15.01 14.82 15.41 14.99 14.93 14.88 13.76 13.50Aug 15.31 15.19 13.48 13.80 15.19 16.39 13.57 13.19 15.75 13.15 13.72 13.52 14.90Sep 11.76 11.12 10.13 8.28 11.94 10.78 12.86 10.04 12.14 10.34 10.06 9.18 10.82Oct 5.90 5.16 5.10 4.27 8.06 6.29 7.24 4.67 5.63 6.66 6.35 6.10 5.78Nov 0.03 -3.32 1.94 2.98 2.52 -1.85 0.04 -0.49 2.87 1.42 2.49 3.31 0.85Dec 2.01 -4.91 0.53 -3.79 1.86 -2.41 -3.70 -3.14 2.21 -2.17 2.04 -0.17 -1.38

2002 2003 2004 Min.1 Mean1 Max.1 

Jan -0.53 -0.41 -3.70 -12.35 -3.41 3.28Feb -0.06 0.47 2.60 -9.74 -0.90 4.68Mar 1.01 0.15 3.29 -2.46 1.31 4.28Apr 6.68 6.50 6.35 1.76 5.21 7.32May 11.05 9.92 12.34 6.58 9.34 12.34Jun 13.44 13.15 16.26 10.64 12.89 16.26Jul 13.34 15.43 16.06 11.93 14.30 16.59Aug 13.18 14.08 16.64 10.67 13.88 16.64Sep 11.08 10.04 11.50 8.28 10.54 12.86Oct 7.48 7.41 6.27 4.27 6.15 8.20Nov 4.77 0.15 2.42 -4.93 0.80 4.77Dec 0.69 0.17 0.83 -8.67 -1.57 2.211Min. is the lowest MADMT, Mean is the average of the MADMTs, and Max. is the highest MADMT.

24
Table 4.--Annual means, extremes, percent

a

g

e of t

otal variation (

%) in w

e

ather variable due to a

n

a

nnual c

y

cle

and its statistical

sig

nificance (P), and slope of long-term trend and its statistical significance (P), for monthl

y v

alues of aver

a

ge d

ail

y hi

g

h

(MADHT), low (MADLT), and midran

g

e (MADMT) air temperatures, precipitation, snowfall, and avera

ge dail

y sea surfa

c

e

temperature (MADSST). Annual mean and extremes of Auke Cre

ek stre

a

m temperature, Auke 

L

ake ice-out dat

e, and Auk

e

L

a

k

e ice cover duration. Slope of long-t

erm trend and its statistical si

g

nificance (P) for annual Auke Creek stream

te

m

perature, Auke 

L

ake ice cov

er dur

ation, and the Auke 

L

a

k

e break up date. 

Variation due 

to 

annual c

ycle 

Trend 

Variable 

 Mean 

annual 

Minimum 

annual 

Ma

ximum 

annual % P slope P 

MADHT 9.11EC 7.43EC 11.01EC 92.5 < 0.001 0.037EC yr-1 < 0.001 MAD

LT 2.34EC 0.33EC 4.11EC 85.4 < 0.001 0.058EC yr-1 < 0.001 

MADMT 5.76EC 3.88EC 7.57EC 90.2 < 0.001 0.049EC yr-1 < 0.001 Precipitation 159.19 cm 116.69 cm 215.39 cm 35.8 < 0.001 0.341 cm 

yr-1 0.20 

Snowfall 219.24 cm 36.59 cm 442.47 cm 41.5 < 0.001 -3.582 cm 

yr-1 < 0.01 

MA

DSST 8.2EC 7.0EC 8.9EC 92.5 < 0.001 0.024EC yr-1 0.01 

Auke Creek T

emperature 7.3oC 5.5oC 8.9oC 0.055oC 

yr-1 < 0.001 

Auke 

L

ake Ice Out Date 10 Apr 01 F

eb 05 Ma

y -0.385 da 

yr-1 0.12 

Auke 

L

ake Ice Cover 

Duration 

108.6 da

ys 78 da

ys 157 da

ys -3.615 da 

yr-1 0.09 

25
Table 5.-- Conditional least-squares estimates of the moving-average and autoregressive 

parameters necessary to describe interannual month differences in average daily high

air temperature series (MADHTs), and statistics of the fit. 

( ) , where ln(MADHT 20 C), 1, 2, , 492. 1

1 Model : 1 1

1

12

12 12 = + = K −

− − = + a z t

B

B

B zt t t t E φ

θ

μ

Parameter Estimate SE t statistic Probability Lag

μ 0.0015387 0.0010763 1.43 0.15 0 

θ12 0.78760 0.02848 27.65 < 0.001 12 

φ1 0.12777 0.04557 2.80 < 0.01 1 

Correlations of Estimates

Parameter μ θ12 φ1

μ 1 -0.021 -0.002 

θ12 -0.021 1 -0.080 

φ1 -0.002 -0.080 1 

Ljung-Box autocorrelation check of residuals 

To Lag Chi Square Degrees of Freedom Probability

 6 4.83 4 0.305 

 12 9.68 10 0.469 

 18 13.87 16 0.609 

 24 16.21 22 0.805 

 30 22.43 28 0.761 

 36 26.89 34 0.802 

 42 31.67 40 0.824 

 48 41.46 46 0.663 

 54 43.58 52 0.791 

 60 47.23 58 0.843 

 66 48.91 64 0.919 

 72 50.28 70 0.964 

 78 54.00 76 0.974 

 84 56.24 82 0.987 

 90 59.60 88 0.991 

 96 70.88 94 0.964 

102 72.29 100 0.983 

108 73.45 106 0.993 

114 76.06 112 0.996 

120 80.50 118 0.997 

One-month-ahead forecast equation for transformed temperature of January 2005 from 

December 2004 (T = 492): 

0.001342 0.12777( ) 0.78760 ˆ . ~ = + T −11 + T − T −12 − aT −11 z z z z

26
Table 6.-- Conditional least-squares estimates of the moving-average and autoregressive 

parameters necessary to describe interannual month differences in average daily low 

air temperature series (MADLTs), and statistics of the fit.

( ) , where ln(MADLT 20 C), 1, 2, , 492. 1

1 Model : 1 36

36

1

1

12

12 12 = + = K − −

− − = + a z t

B B

B

B zt t t t E φ φ

θ

μ

Parameter Estimate SE t statistic Probability Lag

μ 0.0031713 0.0023058 1.38 0.17 0 

θ12 0.77006 0.03211 23.98 < 0.001 12 

φ1 0.10420 0.04574 2.28 0.02 1 

φ36 0.14894 0.05019 2.97 < 0.01 36 

Correlations of Estimates

Parameter μ θ12 φ1 φ36

μ 1 -0.018 -0.003 -0.002 

θ12 -0.018 1 -0.070 0.407 

φ1 -0.003 -0.070 1 -0.064 

φ36 -0.002 0.407 -0.064 1 

Ljung-Box autocorrelation check of residuals 

To Lag Chi Square Degrees of Freedom Probability

 6 1.84 3 0.606 

 12 6.62 9 0.677 

 18 11.12 15 0.744 

 24 15.46 21 0.799 

 30 19.53 27 0.850 

 36 22.74 33 0.910 

 42 26.82 39 0.930 

 48 32.41 45 0.920 

 54 33.77 51 0.970 

 60 40.12 57 0.956 

 66 41.46 63 0.984 

 72 43.10 69 0.994 

 78 48.14 75 0.993 

 84 52.48 81 0.994 

 90 58.40 87 0.992 

 96 70.36 93 0.962 

102 72.00 99 0.981 

108 73.99 105 0.991 

114 75.50 111 0.996 

120 82.90 117 0.993 

One-month-ahead forecast equation for transformed temperature of January 2005 from 

December 2004 (T = 492): 

0.002369 0.10420( ) 0.14894( ) 0.77006 ˆ . ~ = + T −11 + T − T −12 + T −35 − T −47 − aT −11 z z z z z z

27
Table 7.-- Conditional least-squares estimates of the moving-average and autoregressive 

parameters necessary to describe interannual month differences in average daily

midrange air temperature series (MADMTs), and statistics of the fit. 

( ) , where ln(MADMT 20 C), 1, 2, , 492. 1

1 Model : 1 1

1

12

12 12 = + = K −

− − = + a z t

B

B

B zt t t t E φ

θ

μ

Parameter Estimate SE t statistic Probability Lag

μ 0.0023811 0.0015702 1.52 0.13 0 

θ12 0.75144 0.03035 24.76 < 0.001 12 

φ1 0.12007 0.04557 2.63 < 0.01 1 

Correlations of Estimates

Parameter μ θ12 φ1

μ 1 -0.024 -0.002 

θ12 -0.024 1 -0.069 

φ1 -0.002 -0.069 1 

Ljung-Box autocorrelation check of residuals 

To Lag Chi Square Degrees of Freedom Probability

 6 3.16 4 0.531 

 12 10.97 10 0.360 

 18 14.17 16 0.586 

 24 16.85 22 0.772 

 30 22.38 28 0.763 

 36 29.03 34 0.710 

 42 33.47 40 0.758 

 48 41.85 46 0.647 

 54 42.74 52 0.817 

 60 49.07 58 0.792 

 66 50.07 64 0.899 

 72 50.81 70 0.959 

 78 53.65 76 0.976 

 84 55.76 82 0.988 

 90 61.47 88 0.986 

 96 74.21 94 0.935 

102 76.22 100 0.963 

108 78.07 106 0.981 

114 79.46 112 0.991 

120 84.28 118 0.992 

One-month-ahead forecast equation for transformed temperature of January 2005 from 

December 2004 (T = 492): 

0.002095 0.12007( ) 0.75144 ˆ . ~ = + T −11 + T − T −12 − aT −11 z z z z

28
Table 8.--Monthly precipitation (cm) at Auke Bay, Alaska, 1963-2004. 

1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975

Jan 7.04 18.77 9.96 11.40 6.73 4.85 7.85 14.83 15.14 12.01 9.63 13.28Feb 16.05 16.18 11.81 7.47 10.72 8.89 3.10 7.09 10.77 7.90 8.86 18.08 7.90Mar 9.17 12.24 4.29 12.47 3.56 7.54 8.26 10.41 9.78 12.75 7.47 3.78 8.74Apr 6.32 10.26 7.54 7.92 3.45 6.96 4.34 10.03 5.77 8.46 8.15 9.55 8.18May 4.75 12.22 7.90 14.50 11.61 6.20 8.86 10.36 11.23 14.88 13.84 5.87 12.34Jun 14.10 10.29 10.67 6.43 9.12 7.59 8.71 10.74 6.15 12.50 8.59 20.57 13.51Jul 15.75 23.27 6.43 10.59 14.17 15.47 30.30 13.56 7.57 4.78 13.82 12.57 15.80Aug 3.45 14.12 16.54 25.15 20.75 7.32 23.27 26.67 20.45 27.84 23.55 18.01 10.57Sep 22.48 8.79 8.46 21.39 26.70 30.76 16.38 33.99 16.99 21.31 13.79 20.55 25.58Oct 18.34 22.15 22.05 17.88 14.50 13.41 11.30 18.47 23.83 24.49 15.90 39.75 10.87Nov 9.04 12.47 3.89 11.48 14.76 23.47 7.06 10.85 8.61 5.08 19.02 9.37Dec 10.46 10.41 10.11 9.91 5.59 10.74 5.97 11.15 9.88 8.89 15.60 16.28Annual 159.44 128.45 155.14 131.22 153.59 162.20 149.35 168.53 139.95 192.99 152.43

1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988

Jan 21.67 14.40 4.95 8.36 10.59 13.49 15.24 14.20 18.75 23.83 14.53 9.40 7.67Feb 13.79 12.07 3.20 5.03 5.49 9.37 5.41 6.81 9.02 19.53 6.96 8.33 18.54Mar 10.46 14.33 6.35 10.87 8.13 3.76 9.22 1.80 8.18 9.25 17.35 5.41 11.48Apr 5.31 13.51 6.05 3.61 10.24 6.53 7.65 8.33 4.29 8.84 9.37 6.68 8.00May 11.48 4.17 9.37 7.62 6.76 9.47 12.45 16.79 5.64 10.80 10.13 9.37 13.64Jun 9.50 11.07 13.34 13.84 15.19 9.73 4.27 11.79 11.86 8.81 9.40 17.83 7.49Jul 9.55 11.81 15.01 20.96 16.61 12.17 5.41 12.98 14.00 11.81 10.36 8.38 16.33Aug 12.19 9.47 13.84 8.20 17.60 17.65 14.55 24.08 14.15 11.79 26.62 15.77 17.78Sep 24.64 18.36 9.32 22.94 22.48 32.49 20.88 19.33 10.54 17.35 9.02 27.00 16.81Oct 17.15 21.03 39.47 27.71 32.11 20.78 23.04 16.21 15.70 18.21 36.42 30.56 30.89Nov 14.76 13.21 14.30 16.00 17.91 20.78 7.49 4.55 8.18 4.50 17.07 21.26 20.37Dec 18.69 7.16 13.21 17.35 11.79 6.99 5.00 3.61 12.75 22.45 14.58 11.81 11.46Annual 169.19 150.60 148.41 162.48 174.88 163.20 130.61 140.46 133.05 167.16 181.81 171.81 180.47

29
Table 8.-- Cont. 

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Jan 16.00 10.36 11.61 15.14 14.91 20.37 3.66 5.13 4.17 5.11 19.00 5.56 12.90Feb 0.18 10.49 18.54 12.27 14.22 6.10 5.18 16.66 19.00 2.51 6.05 3.15 9.60Mar 5.92 11.68 7.67 10.24 6.40 13.23 5.36 8.23 4.88 8.46 5.54 11.63 8.66Apr 4.75 4.55 8.36 7.11 5.18 7.32 5.51 5.54 9.55 8.86 14.02 14.10 6.15May 14.20 5.79 11.33 18.90 5.05 10.95 10.01 4.57 8.08 5.56 14.83 10.06 12.83Jun 6.45 11.53 8.53 8.99 9.40 6.43 9.47 17.78 11.15 6.50 7.95 16.84 6.17Jul 11.61 13.61 13.94 13.97 5.54 14.25 12.95 9.45 24.59 17.40 13.46 16.99 16.56Aug 9.12 17.25 25.32 15.06 8.43 7.54 13.21 21.87 10.77 19.46 21.77 16.59 10.85Sep 20.70 35.41 41.81 23.98 27.10 30.91 20.73 30.07 22.15 19.66 29.90 24.71 22.23Oct 20.47 20.40 22.94 14.50 21.69 26.64 13.59 14.73 18.47 27.69 26.37 20.57 20.27Nov 22.40 15.65 21.11 16.69 26.29 19.41 7.09 7.75 11.23 5.69 12.83 14.02 8.36Dec 20.90 18.62 24.23 13.11 15.04 9.93 9.93 5.82 26.42 11.76 27.10 8.10 9.60Annual 152.70 175.34 215.39 169.95 159.26 173.08 116.69 147.60 170.43 138.66 198.81 162.33 144.17

2002 2003 2004 Min.1 Mean1 Max.1 

Jan 8.03 14.17 15.65 3.66 11.96 23.83Feb 12.50 5.51 11.00 0.18 9.79 19.53Mar 4.47 9.98 10.62 1.80 8.57 17.35Apr 2.29 1.96 7.14 1.96 7.33 14.10May 8.41 6.81 3.68 3.68 9.84 18.90Jun 14.53 12.62 3.53 3.53 10.50 20.57Jul 15.52 11.40 10.52 4.78 13.60 30.30Aug 28.88 13.51 7.77 3.45 16.40 28.88Sep 16.94 33.38 24.31 8.46 22.44 41.81Oct 29.95 12.67 15.82 10.87 21.64 39.75Nov 21.56 14.38 18.06 3.89 13.61 26.29Dec 14.33 11.79 22.02 3.61 12.94 27.10Annual 177.39 148.18 150.11 116.69 159.19 215.391Min. is the lowest monthly precipitation, Mean is the average of the monthly precipitation, and Max. is the highest monthly precipitation.

30
Table 9.--Monthly snowfall (cm) at Auke Bay, Alaska, 1963-2004. 

1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975

Jan 40.64 94.23 128.02 120.40 83.06 52.07 39.37 118.62 101.35 125.98 106.43 96.27Feb 53.85 27.69 137.41 30.48 74.93 34.29 55.12 10.16 43.18 93.98 48.77 81.28 36.83Mar 35.31 62.99 11.43 110.49 16.76 16.51 40.64 2.54 132.59 56.39 19.81 36.83 48.26Apr 82.55 6.86 6.35 5.08 trace 5.08 0.00 7.62 1.27 17.78 trace trace 12.70May 0.00 3.81 trace 0.00 0.00 trace 0.00 trace 0.00 trace 0.00 0.00 0.00Jun 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Jul 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Aug 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Sep 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.27 0.00Oct 0.00 0.00 trace 5.08 0.00 trace 0.00 1.27 6.35 2.54 0.00 trace 2.54Nov 22.86 trace 26.67 44.45 10.16 38.10 58.17 48.26 8.89 46.23 12.70 80.01Dec 39.12 81.28 35.56 36.83 68.83 4.57 55.37 88.39 77.98 50.80 23.88 116.84Annual 223.27 311.66 360.43 217.93 190.50 174.50 438.66 358.90 291.59 262.38 393.45

1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988

Jan 92.71 5.08 20.32 46.99 100.58 7.87 180.34 92.96 108.71 16.26 33.27 5.08 10.16Feb 78.74 trace 3.81 71.12 11.18 48.51 48.51 41.66 7.37 88.39 22.10 2.79 30.23Mar 50.80 12.70 3.81 19.05 4.57 2.29 21.34 3.81 1.27 23.11 59.94 17.78 0.25Apr trace 1.27 2.54 1.27 trace 5.84 4.57 1.27 3.81 2.03 11.18 0.51 0.76May 0.00 0.00 0.00 0.00 trace 0.00 trace 0.00 trace 0.00 trace trace 0.00Jun 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Jul 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Aug 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Sep 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Oct 0.00 0.00 0.00 0.00 0.00 0.00 3.56 0.00 0.00 2.54 0.00 0.51 0.00Nov 0.00 88.90 30.99 8.13 trace 10.67 4.57 17.27 13.97 17.78 63.25 9.14 12.95Dec 65.53 53.34 29.21 110.74 137.67 19.05 22.61 29.72 66.55 14.22 4.83 14.73 28.70Annual 287.78 161.29 90.68 257.30 254.00 94.23 285.50 186.69 201.68 164.34 194.56 50.55 83.06

31
Table 9.-- Cont. 

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Jan 118.62 90.68 69.09 37.85 48.01 160.27 17.78 47.24 33.27 39.62 104.65 26.67 5.08Feb 0.51 91.19 38.61 18.54 65.53 72.14 25.15 75.95 41.40 0.76 67.56 3.56 38.61Mar 56.64 4.57 23.11 2.03 7.87 43.69 54.36 12.70 45.72 7.87 4.32 0.00 1.52Apr trace 0.00 0.76 trace 0.00 0.51 0.00 0.00 0.00 0.00 3.05 0.00 0.00May 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Jun 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Jul 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Aug 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Sep 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Oct trace trace 16.76 5.08 0.00 0.00 0.00 1.27 2.29 0.00 0.00 2.54 10.16Nov 85.09 112.27 25.91 10.92 3.81 111.00 7.87 6.35 2.54 12.45 16.76 0.01 7.11Dec 22.86 95.00 151.64 59.94 23.88 54.86 61.47 7.37 32.26 40.13 43.18 3.81 66.55Annual 283.72 393.70 325.88 134.37 149.10 442.47 166.62 150.88 157.48 100.84 239.52 36.59 129.03

2002 2003 2004 Min.1 Mean1 Max.1 

Jan 51.82 47.75 71.63 5.08 68.21 180.34Feb 69.60 44.45 7.62 0.00 43.89 137.41Mar 4.32 42.67 40.64 0.00 27.70 132.59Apr 0.25 0.25 0.51 0.00 4.42 82.55May 0.00 0.00 0.00 0.00 0.09 3.81Jun 0.00 0.00 0.00 0.00 0.00 0.00Jul 0.00 0.00 0.00 0.00 0.00 0.00Aug 0.00 0.00 0.00 0.00 0.00 0.00Sep 0.00 0.00 0.00 0.00 0.03 1.27Oct 0.00 0.00 0.01 0.00 1.49 16.76Nov 0.00 51.56 1.52 0.00 27.54 112.27Dec 41.40 27.43 21.08 3.81 49.49 151.64Annual 167.39 214.12 143.01 36.59 219.24 442.471Min. is the lowest monthly snowfall, Mean is the average of the monthly snowfall, and Max. is the highest monthly snowfall. 

32
Table 10.-- Seasonal snowfall (cm) at Auke Bay, Alaska, 1963-2004. 

 62-63 63-64 64-65 65-66 66-67 67-68 68-69 69-70 70-71 71-72 72-73 73-74 74-75Jul 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Aug 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Sep 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.27Oct 0.00 0.00 trace 5.08 0.00 trace 0.00 1.27 6.35 2.54 0.00 TraceNov 22.86 trace 26.67 44.45 10.16 38.10 58.17 48.26 8.89 46.23 12.70Dec 39.12 81.28 35.56 36.83 68.83 4.57 55.37 88.39 77.98 50.80 23.88Jan 40.64 94.23 128.02 120.40 83.06 52.07 39.37 118.62 101.35 125.98 106.43 96.27Feb 53.85 27.69 137.41 30.48 74.93 34.29 55.12 10.16 43.18 93.98 48.77 81.28 36.83Mar 35.31 62.99 11.43 110.49 16.76 16.51 40.64 2.54 132.59 56.39 19.81 36.83 48.26Apr 82.55 6.86 6.35 5.08 trace 5.08 0.00 7.62 1.27 17.78 trace trace 12.70May 0.00 3.81 trace 0.00 0.00 Trace 0.00 trace 0.00 trace 0.00 0.00 0.00Jun 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Total 203.96 330.71 336.30 298.45 226.82 102.36 410.46 412.50 283.97 321.56 231.90 75-76 76-77 77-78 78-79 79-80 80-81 81-82 82-83 83-84 84-85 85-86 86-87 87-88Jul 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Aug 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Sep 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Oct 2.54 0.00 0.00 0.00 0.00 0.00 0.00 3.56 0.00 0.00 2.54 0.00 0.51Nov 80.01 0.00 88.90 30.99 8.13 Trace 10.67 4.57 17.27 13.97 17.78 63.25 9.14Dec 116.84 65.53 53.34 29.21 110.74 137.67 19.05 22.61 29.72 66.55 14.22 4.83 14.73Jan 92.71 5.08 20.32 46.99 100.58 7.87 180.34 92.96 108.71 16.26 33.27 5.08 10.16Feb 78.74 trace 3.81 71.12 11.18 48.51 48.51 41.66 7.37 88.39 22.10 2.79 30.23Mar 50.80 12.70 3.81 19.05 4.57 2.29 21.34 3.81 1.27 23.11 59.94 17.78 0.25Apr trace 1.27 2.54 1.27 trace 5.84 4.57 1.27 3.81 2.03 11.18 0.51 0.76May 0.00 0.00 0.00 0.00 trace 0.00 trace 0.00 trace 0.00 trace trace 0.00Jun 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Total 421.64 84.58 172.72 198.63 235.20 202.18 284.48 170.43 168.15 210.31 161.04 94.23 65.79

33
Table 10.-- Cont. 

88-89 89-90 90-91 91-92 92-93 93-94 94-95 95-96 96-97 97-98 98-99 99-00 00-01

Jul 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Aug 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Sep 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Oct 0.00 trace trace 16.76 5.08 0.00 0.00 0.00 1.27 2.29 0.00 0.00 2.54Nov 12.95 85.09 112.27 25.91 10.92 3.81 111.00 7.87 6.35 2.54 12.45 16.76 0.01Dec 28.70 22.86 95.00 151.64 59.94 23.88 54.86 61.47 7.37 32.26 40.13 43.18 3.81Jan 118.62 90.68 69.09 37.85 48.01 160.27 17.78 47.24 33.27 39.62 104.65 26.67 5.08Feb 0.51 91.19 38.61 18.54 65.53 72.14 25.15 75.95 41.40 0.76 67.56 3.56 38.61Mar 56.64 4.57 23.11 2.03 7.87 43.69 54.36 12.70 45.72 7.87 4.32 0.00 1.52Apr trace 0.00 0.76 trace 0.00 0.51 0.00 0.00 0.00 0.00 3.05 0.00 TraceMay 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 TraceJun 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Total 217.42 294.39 338.84 252.73 197.36 304.29 263.14 205.23 135.38 85.34 232.16 90.17 51.57

01-02 02-03 03-04 04-05 Min.1 Mean1 Max.1 

Jul 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Aug 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sep 0.00 0.00 0.00 0.00 0.00 0.03 1.27 Oct 10.16 0.00 0.00 0.01 0.00 1.49 16.76 Nov 7.11 0.00 51.56 1.52 0.00 27.54 112.27 Dec 66.55 41.40 27.43 21.08 3.81 49.49 151.64 Jan 51.82 47.75 71.63 5.08 68.21 180.34 Feb 69.60 44.45 7.62 0.00 43.89 137.41 Mar 4.32 42.67 40.64 0.00 27.70 132.59 Apr 0.25 0.25 0.51 0.00 4.42 82.55 May 0.00 0.00 0.00 0.00 0.09 3.81 Jun 0.00 0.00 0.00 0.00 0.00 0.00 Total 209.80 176.53 199.39 51.57 222.05 421.64 1Min. is the lowest seasonal monthly snowfall, Mean is the average of the seasonal monthly snowfall, and Max. is the highest seasonal 

monthly snowfall. 

34
Table 11.-- Conditional least-squares estimates of the moving-average and autoregressive 

parameters necessary to describe the interannual month differences in 

precipitation series, and statistics of the fit. 

( ) , where ln(precipitation ), 1, 2, , 492

1

1 Model : 1 48

48

12

12 12 = = K −

− − = + a z t

B

B B zt t t t φ

θ

μ . 

Parameter Estimate SE t statistic Probability Lag

μ -0.0010378 0.0034992 -0.30 0.77 0 

θ12 0.84543 0.02590 32.64 < 0.001 12 

φ 48 -0.15140 0.04968 -3.05 0.002 48 

Correlations of Estimates

Parameter μ θ12 φ 48

μ 1 -0.032 -0.007 

θ12 -0.032 1 0.292 

φ 48 -0.007 0.292 1 

Ljung-Box autocorrelation check of residuals 

To Lag Chi Square Degrees of Freedom Probability

 6 5.18 4 0.270 

 12 10.54 10 0.394 

 18 14.98 16 0.526 

 24 20.61 22 0.545 

 30 22.70 28 0.748 

 36 27.89 34 0.761 

 42 29.86 40 0.879 

 48 32.35 46 0.936 

 54 34.89 52 0.967 

 60 35.63 58 0.991 

 66 36.89 64 0.997 

 72 42.26 70 0.997 

 78 44.42 76 0.999 

 84 59.22 82 0.973 

 90 67.80 88 0.946 

 96 75.90 94 0.914 

102 89.42 100 0.767 

108 92.52 106 0.822 

114 97.69 112 0.830 

120 106.63 118 0.765 

One-month-ahead forecast equation for transformed precipitation of January 2005 from 

December 2004 (T = 492): 

0.001195 0.15140( ) 0.84543ˆ . ~ = − + T −11 − T −47 − T −59 − aT −11 z z z z

35
Table 12.--Conditional least-squares estimates of the moving-average and autoregressive 

parameters necessary to describe the interannual month differences in snowfall series, 

and statistics of the fit.1

( ) , where ln(snowfall 1 cm), 1, 2, , 287. 1

1 Model : 1 2

2

7

7 7 = + = K −

− − = + a z t

B

B

B zt t t t φ

θ

μ

Parameter Estimate SE t statistic Probability Lag

μ -0.01804 0.0090068 -2.00 < 0.05 0 

θ 7 0.90300 0.02664 33.89 < 0.001 7 

φ 2 0.13033 0.06000 2.17 0.03 2 

Correlations of Estimates

Parameter μ θ 7 φ 2

μ 1 -0.018 0.014 

θ 7 -0.018 1 -0.082 

φ 2 0.014 -0.082 1 

Ljung-Box autocorrelation check of residuals 

To Lag Chi Square Degrees of Freedom Probability

 6 3.23 4 0.520 

 12 7.88 10 0.641 

 18 12.33 16 0.721 

 24 16.77 22 0.776 

 30 18.93 28 0.900 

 36 27.22 34 0.789 

 42 36.18 40 0.643 

 48 38.35 46 0.781 

 54 41.38 52 0.854 

 60 48.08 58 0.820 

 66 56.27 64 0.743 

One-month-ahead forecast equation for transformed snowfall of January 2005 from December 

2004 (T = 287): 

0.015689 0.13033( ) 0.90300 ˆ . ~ = − + T −6 + T −1 − T −8 − aT −6 z z z z

1

Time variable t values and calendar months: 1 = October, 2 = November, 3 = December, 4 = January, 5 = 

February, 6 = March, and 7 = April.

36
Table 13.--Monthly averages of daily afternoon observations of sea surface temperature (EC) (MADSSTs) at Auke Bay Laboratory, 

1975-2004. 

1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987

Jan 2.56 4.04 3.31 3.56 3.63 4.15 3.66 3.57 3.50 3.70 3.10 3.65Feb 2.53 2.09 4.78 3.10 2.36 3.15 3.71 2.56 3.36 2.99 2.69 3.12 3.71Mar 2.77 2.32 4.70 3.29 3.45 3.74 4.65 3.12 4.04 4.43 3.48 3.68 4.48Apr 4.34 4.42 5.68 5.54 5.98 5.63 5.98 4.88 6.96 7.10 5.02 4.97 6.30May 7.94 6.86 9.18 8.30 9.21 10.16 11.25 7.91 10.63 10.52 8.70 9.44 9.40Jun 11.31 11.28 12.96 12.37 11.55 14.36 14.01 14.54 14.08 13.06 12.13 13.25 11.85Jul 13.28 12.54 13.95 13.11 13.22 14.09 13.74 15.27 15.36 14.35 14.65 15.12 15.93Aug 12.26 13.32 14.36 14.04 15.18 13.16 13.55 14.16 12.34 14.36 13.77 12.88 15.25Sep 9.95 10.47 11.26 11.13 11.21 11.16 10.50 11.59 10.57 11.44 10.94 11.32 10.33Oct 7.45 7.31 7.27 7.41 8.00 7.41 8.06 7.56 7.60 8.24 7.54 7.40 7.61Nov 4.32 5.71 5.47 5.64 6.13 6.20 6.19 5.26 6.00 5.24 4.60 4.43 5.97Dec 3.41 4.58 4.40 4.38 5.02 4.65 5.09 4.30 4.98 4.08 3.32 3.86 4.98

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Jan 4.56 3.73 3.03 2.58 3.96 3.74 4.20 3.30 3.00 2.90 4.10 4.10 3.90Feb 4.47 3.24 3.05 2.80 4.29 2.68 3.30 3.49 2.40 2.90 4.30 2.50 3.43Mar 4.59 3.80 3.60 3.40 4.77 3.82 3.80 3.50 2.80 3.00 5.10 4.00 3.67Apr 6.46 7.64 6.83 6.27 6.78 7.93 6.80 7.00 5.70 6.30 7.30 6.00 6.30May 10.67 10.41 10.98 10.83 9.65 12.85 8.90 12.60 10.80 12.00 11.40 9.10 10.35Jun 12.81 14.33 13.88 13.86 14.35 15.00 13.60 13.70 13.80 13.90 15.20 13.70 13.27Jul 13.66 16.03 15.20 14.46 14.93 15.74 15.20 15.10 15.08 14.70 15.80 14.90 14.25Aug 13.05 15.62 14.77 13.07 13.65 15.15 16.30 13.70 13.20 15.00 13.90 15.00 13.07Sep 10.72 12.28 11.23 10.16 10.07 11.53 11.30 11.54 10.80 12.10 10.90 10.30 10.52Oct 8.03 8.28 7.54 7.68 6.75 8.40 7.90 8.60 7.50 7.70 8.20 7.90 7.72Nov 6.30 4.93 5.12 5.41 5.47 5.96 5.50 5.20 5.20 5.70 5.90 5.70 5.79Dec 4.42 4.16 3.27 3.89 4.43 4.38 4.10 3.70 3.80 4.40 5.00 4.60 4.81

37
Table 13.-- Cont. 

2001 2002 2003 2004 Min.1 Mean1 Max.1 

Jan 4.56 3.53 4.25 3.72 2.56 3.64 4.56 Feb 4.21 4.08 4.68 3.61 2.09 3.32 4.78 Mar 4.60 3.76 5.07 3.87 2.32 3.84 5.10 Apr 6.96 7.40 7.25 6.24 4.34 6.27 7.93 May 8.90 8.72 10.55 12.50 6.86 10.02 12.85 Jun 13.80 13.57 13.64 15.50 11.28 13.49 15.50 Jul 13.73 11.66 14.20 16.10 11.66 14.51 16.10 Aug 15.03 11.57 13.88 15.94 11.57 14.02 16.30 Sep 9.96 9.65 10.93 11.11 9.65 10.90 12.28 Oct 7.40 7.61 8.50 7.58 6.75 7.74 8.60 Nov 5.50 6.83 5.67 5.14 4.32 5.55 6.83 Dec 4.03 4.55 4.40 4.69 3.27 4.32 5.09 1Min. is the lowest MADSST, Mean is the average of the MADSSTs, and Max. is the highest MADSST.

38
Table 14.--Conditional least-squares estimates of the moving-average and autoregressive parameters necessary

to describe interannual month differences in monthly average daily sea surface temperature series 

(MADSSTs), and statistics of the fit. 

( ) , where ln(MADSST ), 1, 2, , 348. 1

1 Model : 1 1

1

12

12 12 = = K −

− − = + a z t

B

B

B zt t t t φ

θ

μ

Parameter Estimate SE t statistic Probability Lag

μ 0.0076577 0.0044221 1.73 0.08 0 

θ12 0.74448 0.03722 20.00 < 0.001 12 

φ1 0.61631 0.04319 14.27 < 0.001 1 

Correlations of Estimates

Parameter μ θ12 φ1

μ 1 0.050 0.024 

θ12 0.050 1 0.031 

φ1 0.024 0.031 1 

Ljung-Box autocorrelation check of residuals 

To Lag Chi Square Degrees of Freedom Probability

 6 3.07 4 0.546 

 12 8.58 10 0.573 

 18 10.41 16 0.844 

 24 12.52 22 0.946 

 30 16.07 28 0.965 

 36 17.91 34 0.989 

 42 27.03 40 0.942 

 48 31.17 46 0.954 

 54 37.87 52 0.929 

 60 40.89 58 0.957 

 66 52.11 64 0.856 

 72 57.11 70 0.866 

 78 60.60 76 0.902 

 84 67.13 82 0.882 

One-month-ahead forecast equation for transformed temperature of January 2005 from December 2004 

(T = 348): 

0.002938 0.61631( ) 0.74448 ˆ . ~ = + T −11 + T − T −12 − aT −11 z z z z

39
Table 15.--Average monthly Auke Creek temperatures (oC), 1962- 2005. 

1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977

Jan. 0.8 1.1 0.8 0.8 0.9 1.0 1.1 1.1 0.5 0.3 1.2 0.8 1.7Feb. 1.0 1.0 0.2 1.7 0.3 0.9 0.4 1.0 0.6 0.6 2.7Mar. 1.1 0.9 1.0 3.6 1.2 0.7 0.9 0.4 1.1 0.8 2.8Apr. 3.9 3.5 1.8 2.3 3.8 5.5 1.7 1.1 3.1 2.3 2.3 2.5 4.7May 6.7 6.1 4.5 5.3 8.9 9.0 4.6 3.4 7.2 5.9 6.8 6.6 10.0Jun. 12.1 12.1 13.3 15.9 17.2 10.8 12.7 9.2 12.8 11.9 12.1 11.5 13.8Jul. 16.4 17.2 15.4 17.1 13.6 11.7 16.6 16.5 15.0 15.2 15.9 14.2 18.0Aug. 15.9 14.9 13.7 13.7 12.5 16.8 13.5 13.1 14.9 13.9 15.3 17.4Sep. 11.9 10.8 11.0 11.1 12.0 12.9 9.9 10.8 12.9 11.4 11.9 12.2Oct. 8.1 8.0 7.3 8.4 7.5 7.7 7.0 5.1 7.3 7.0 7.9 8.2 7.7Nov. 5.7 3.9 3.9 3.7 3.5 5.1 3.3 3.2 2.1 5.1 2.6 5.3 3.4Dec. 3.0 1.8 1.3 1.7 1.7 1.4 1.6 1.1 0.9 1.1 2.8 0.8 2.8 0.7average 6.4 7.5 6.5 5.5 6.2 6.5 6.4 6.2 6.3

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

Jan. 0.8 0.3 0.8 1.7 0.5 1.1 1.2 1.7 1.3 1.9 1.4 1.1 1.1 0.8 1.7 0.9Feb. 1.4 0.0 1.1 1.8 0.5 0.6 1.4 0.6 1.5 2.1 1.1 0.5 0.6 1.3 1.5 1.0Mar. 2.2 1.1 1.7 2.7 1.1 2.3 2.9 1.0 1.9 2.2 2.8 0.4 1.8 1.3 2.6 1.2Apr. 4.9 3.6 4.1 4.9 2.4 4.9 6.0 2.4 3.0 4.6 5.1 3.0 4.9 2.2 5.4 3.6May 7.6 9.3 9.6 12.6 5.4 10.3 11.9 7.0 8.7 9.5 9.2 9.3 11.0 8.4 9.3 12.2Jun. 12.6 14.1 16.6 15.0 13.7 15.2 13.6 10.9 13.2 12.4 13.2 15.0 15.1 13.5 15.0 16.7Jul. 13.9 15.0 15.1 15.0 16.2 16.3 14.6 14.9 15.7 16.5 13.7 18.2 17.5 16.3 16.4 16.7Aug. 15.3 16.6 14.6 14.9 15.2 14.0 14.8 14.5 13.8 16.2 13.5 16.7 16.5 14.4 15.1 16.5Sep. 12.2 12.9 11.5 11.2 12.1 10.8 12.0 11.3 11.9 11.4 11.4 13.6 12.6 11.0 11.2 12.6

40
Table 15.-- Cont.

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

Oct. 8.2 8.7 8.2 7.9 7.6 7.5 8.2 7.0 8.6 7.9 8.7 8.4 8.1 7.4 7.0 8.6Nov. 3.7 5.1 5.8 5.0 3.8 3.7 2.9 2.1 3.7 5.0 5.3 4.1 3.5 3.5 4.5 5.3Dec. 1.4 2.2 1.5 1.8 1.8 0.5 1.0 1.0 2.3 3.1 2.6 2.3 1.2 2.1 1.6 2.4average 6.6 7.2 7.4 7.9 6.7 7.3 7.6 6.2 7.2 7.8 7.3 7.8 7.9 6.9 7.6 8.2

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average 

Jan. 1.8 1.0 1.2 1.1 2.1 1.6 1.8 2.0 1.5 2.0 1.2 1.4 1.2Feb. 1.1 1.4 1.2 2.0 2.9 1.0 2.0 1.4 1.1 1.8 1.5 1.5 1.2Mar. 1.4 1.0 1.6 1.6 3.8 1.1 3.2 2.4 1.6 2.5 2.5 2.4 1.8Apr. 4.2 3.2 3.9 4.2 6.6 2.5 4.9 5.4 3.3 5.5 5.0 6.5 3.8May 9.9 12.5 10.1 11.6 12.9 7.1 10.2 8.7 8.0 11.6 13.5 14.5 8.9Jun. 14.8 15.1 13.6 16.4 17.4 13.5 15.1 14.8 13.9 15.1 16.7 17.4 14.0Jul. 15.9 17.1 15.1 17.0 17.7 15.9 15.9 15.4 15.0 15.9 17.6 17.1 15.8Aug. 16.5 14.9 14.2 16.9 15.4 16.0 15.0 16.3 13.8 15.5 18.4 16.6 15.1Sep. 11.7 13.4 11.6 13.9 11.6 11.3 11.5 11.7 11.7 11.1 12.8 12.3 11.8Oct. 8.3 8.7 7.5 8.5 8.7 8.0 8.1 8.1 8.7 8.4 8.2 8.0 7.9Nov. 4.0 3.6 4.1 5.6 4.6 5.1 5.3 4.2 6.2 3.8 4.4 4.5 4.2Dec. 1.2 1.2 2.1 3.7 2.3 2.7 2.5 1.4 3.7 1.4 2.4 2.0 1.9average 7.6 7.8 7.2 8.6 8.9 7.2 8.0 7.7 7.4 7.9 8.7 8.7 7.3

41
Table 16.--Auke Lake ice-out dates, 1960-2005. 

1960s 1970s 1980s 1990s 2000s 

1960 Apr 26 1970 Mar 24 1980 Apr 19 1990 Apr 08 2000 Apr 3 1961 1971 May 13 1981 Mar 26 1991 Apr 29 2001 Apr 6 1962 Apr late 1972 May 20 1982 May 14 1992 Mar 18 2002 Apr 28 1963 Apr 29 1973 Apr 30 1983 Apr 18 1993 Apr 23 2003 Apr 13 1964 1974 May 07 1984 Mar 29 1994 Apr 11 2004 Apr 6 1965 1975 May 08 1985 Apr 26 1995 Apr 25 2005 Apr 10 1966 1976 Apr 28 1986 Apr 28 1996 Apr 21 1967 May 11 1977 Feb 01 1987 Mar 30 1997 Apr 25 1968 Apr 23 1978 Apr 20 1988 Apr 15 1998 Mar 31 1969 Apr 30 1979 Apr 24 1989 Apr 28 1999 May 5 

42
Table 17.--Duration of Auke Lake ice cover, 1992-2006. 

Winter Ice over Ice out Duration 

(days) 

1992-1993 18 Dec 23 Apr 127 

1993-1994 02 Jan 11 Apr 99 

1994-1995 23 Nov 25 Apr 154 

1995-1996 20 Nov 21 Apr 153 

1996-1997 20 Nov 25 Apr 156 

1997-1998 01 Jan 31 Mar 89 

1998-1999 17 Dec 05 May 157 

1999-2000 17 Dec 06 Jan 13 

1999-2000 08 Jan 03 Apr 85 

2000-2001 14 Dec 23 Jan 40 

2000-2001 15 Feb 06 Apr 50 

2001-2002 27 Nov 20 Apr 113 

2002-2003 29 Dec 06 Jan 8 

2002-2003 10 Jan 13 Apr 70 

2003-2004 26 Dec 06 Apr 102 

2004-2005 27 Dec 10 Apr 106 

2005-2006 04 Jan 14 Apr 101 

Note: An early January breakup was followed by refreezing in 2000, 2001 and 2003. 

43

FIGURES 

45

Figure 1. -- Auke Bay, Alaska, and vicinity. 

47
Figure 2a.-- Monthly averages of daily high temperature (MADHTs) at Auke Bay Laboratory, 

February 1963 through December 2004. 

48
Figure 2b.-- Monthly averages of daily low temperature (MADLTs) at Auke Bay Laboratory,

February 1963 through December 2004. 

49
Figure 2c.-- Monthly averages of daily midrange temperature (MADMTs) at Auke Bay

Laboratory, February 1963 through December 2004. 

50
Figure 3.-- Statistics of annual temperature cycle at the Auke Bay Laboratory (1963-2004) 

including daily extremes (lowest low and highest high [◊]) and monthly extrema 

(high [○], low [●] and series averages [♦]) for monthly average daily high 

(MADHT), low (MADLT), and midrange (MADMT) air temperature series. 

51
Figure 4.-- Annual average of monthly average high (MADHTs[■]), midrange (MADMTs[♦]),

and low (MADLTs[●]) daily air temperatures at Auke Bay Laboratory, 1963-2004. 

Missing monthly values were estimated by cubic spline interpolation. Solid horizontal 

lines are the overall annual means. Dashed lines are the trends. 

52
Figure 5.-- Sample autocorrelation function (SACF) of interannual month differences in 

logarithm-transformed monthly average daily high air temperatures (MADHTs) at

Auke Bay. The interval covering two standard errors is indicated. 

53
Figure 6.-- Sample partial autocorrelation function (SPACF) of interannual month differences 

in logarithm-transformed monthly average daily high air temperatures (MADHTs) 

at Auke Bay. The interval covering two standard errors is indicated. 

54
Figure 7.-- Sample autocorrelation function (SACF) of interannual month differences in 

logarithm-transformed monthly average daily low air temperatures (MADLTs) at

Auke Bay. The interval covering two standard errors is indicated. 

55
Figure 8.-- Sample partial autocorrelation function (SPACF) of interannual month differences in 

logarithm-transformed monthly average of daily low air temperatures (MADLTs) at

Auke Bay. The interval covering two standard errors is indicated. 

56
Figure 9.-- Sample autocorrelation function (SACF) of interannual month differences in 

logarithm-transformed monthly average daily midrange air temperatures (MADMTs) 

at Auke Bay. The interval covering two standard errors is indicated. 

57
Figure 10.-- Sample partial autocorrelation function (SPACF) of interannual month differences 

in logarithm-transformed monthly average daily midrange air temperatures 

(MADMTs) at Auke Bay. The interval covering two standard errors is indicated. 

58
Figure 11a.--Total monthly precipitation at the Auke Bay Laboratory, February 1963 through 

December 2004. 

59
Figure 11b.--Monthly snowfall at the Auke Bay Laboratory, February 1963 through December 

2004. 

60
Figure 12.-- Statistics of annual precipitation cycle at Auke Bay Laboratory (1963-2004) from 

monthly precipitation (cm) (maximum[○], mean [♦], and minimum [●]) for total 

precipitation, calendar-year snowfall (January-December), and seasonal snowfall 

(July-June). 

61
Figure 13.-- Annual total precipitation (cm) at Auke Bay, 1964-2004. Missing monthly values 

were estimated by cubic spline interpolation. Slope of the trend line was not 

significant (P = 0.20). Solid horizontal line is the overall mean, and the dashed line

is the trend. 

62
Figure 14.-- Sample autocorrelation function (SACF) of interannual month differences in 

logarithm-transformed precipitation at Auke Bay. The interval covering two 

standard errors is indicated. 

63
Figure 15.-- Sample partial autocorrelation function (SPACF) of interannual month differences 

in logarithm-transformed precipitation at Auke Bay. The interval covering two 

standard errors is indicated. 

64
Figure 16.-- Annual total snowfall (cm) at Auke Bay, 1964-2004. Missing monthly values were

estimated by cubic spline interpolation. Solid line is the overall annual mean, and 

the dashed line is the trend. 

65
Figure 17.-- Sample autocorrelation function (SACF) of interannual month differences in 

logarithm-transformed snowfall at Auke Bay. The interval covering two standard 

errors is indicated. 

66
Figure 18.-- Sample partial autocorrelation function (SPACF) of interannual month differences 

in logarithm-transformed snowfall at Auke Bay. The interval covering two standard

errors is indicated. 

67
Figure 19.-- Monthly averages of daily sea surface temperatures (MADSSTs) at Auke Bay

Laboratory, February 1975 through December 2004. 

68
Figure 20.-- Annual average of monthly average daily sea surface temperatures (MADSSTs) at 

Auke Bay, Alaska, 1976-2004. The solid line is the overall annual mean, and the 

dashed line is the trend. 

69
Figure 21.-- Sample autocorrelation function (SACF) of interannual month differences in 

logarithm-transformed monthly average daily sea surface temperatures (MADSSTs) 

at Auke Bay. The interval covering two standard errors is indicated. 

70
Figure 22.-- Sample partial autocorrelation function (SPACF) of interannual month differences 

in logarithm-transformed monthly average daily sea surface temperatures 

(MADSSTs) at Auke Bay. The interval covering two standard errors is indicated. 

71
0

5

10

15

20

1-Ja

n

11-Ja

n

21-Ja

n

31-Ja

n

10-Feb20-Feb

1-

Mar

11-Mar21-Mar31-Mar

10-Apr20-Apr30-Apr

10-May20-May30-May

9-Jun

19-Jun29-Jun

9-Jul

19-Jul29-Jul

8-

Aug

18-Aug28-Aug

7-Sep

17-Sep27-Sep

7-

Oct

17-Oct27-Oct

6-

Nov

16-Nov26-Nov

6-

Dec

16-Dec26-Dec

Tem

perature (

Celsius)

Auke Creek

Auke Creek 2005 average:1962-2004

lake ice-out

April 11, 2005

Figure 23.--Average of daily Auke Creek stream temperatures (oC), 1962-2005 (Taylor 2006). 

72
Figure 24.--Average annual temperature of Auke Creek and the trend line over all years. 

73
Figure 25.-- Winter ice cover duration for Auke Lake and the trend line for 1992-2005. 

74
Figure 26.-- Ice breakup dates of Auke Lake and the trend line for 1960-2005. 

75

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