INTERANNUAL TRENDS IN THE RADIATION CLIMATOLOGY OF THE CANADIAN HIGH ARCTIC

Scott Thomas Weston B.Sc., Simon Fraser University, 2004

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

In the Department of Geography

0 Scott Weston 2006

SIMON FRASER UNIVERSITY

Spring 2006

All rights reserved. This work may not be reproduced in whole or in part, by photocopy or other means, without permission of the author. APPROVAL

Name: Scott Thomas Weston

Degree: Master of Science

Title of Thesis: INTERANNUAL TRENDS IN THE RADIATION CLIMATOLOGY OF THE CANADIAN HIGH ARCTIC

Examining Committee:

Chair: Dr. S. Dragicevic Assistant Professor

Dr. 0. Hertman acting for the late Dr. W.G. Bailey Sessional Instructor Senior Supervisor Geography Dept., SFU Professor, Geography Dept.

Dr. A.M. Sawchuk, University College Professor Kwantlen University College

Dr. L.J.B. McArthur, Chief, Air Quality Research Branch, Experimental Studies Division, Meteorological Service of Canada, Environment Canada

Dr. I.G. McKendry, Professor Department of Geography and Atmospheric Sciences, University of British Columbia External Examiner

Date Approved: March 24, 2006 IEI SIMON FRASER 0@ UNlVERSlTYl ibrary

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Simon Fraser University Library Burnaby, BC, Canada ABSTRACT

Net and solar radiation trends over the past half-century at Alert and Resolute,

Nunavut are presented and analyzed. Substantive changes to the Canadian Arctic climate are observed over the data record. A significant decrease in solar radiation is observed at both sites. An even more significant overall increase in net radiation is measured, with those changes concentrated in the months of May and June. Exploration of annual and interannual albedo trends and cluster analysis of natural seasons both reflect this result in earlier snowmelt dates and longer snow-free periods. A persistently positive phase of the

Arctic Oscillation in the mid 1990's countered this overall trend and resulted in lower net radiation values. Evidence is presented showing that in addition to increased temperatures, changes in the winter longwave radiation regime have driven the observed net radiation trends.

Keywords:

Arctic, climate, net radiation, solar radiation, Arctic Oscillation lothe memy of Dr. !BillBaiky, an incredi6k teadier and incredi6k man, takenfrom us far too soon. Withuthis wntri6ution and support, this

work would not have 6een possi6le. ACKNOWLEDGEMENTS

This project would not have been possible without the support of a number of people. The greatest thanks go to my senior supervisor, the late Dr. Bill Bailey.

Although he passed away before the completion of the thesis, Bill's contributions were immeasurable. As senior supervisor, he oversaw virtually every aspect of designing and implementing the research, and provided invaluable edits, suggestions and advice. As the teacher of a number of my graduate classes, he also greatly improved my academic skills as a writer, teacher, programmer and public speaker.

I am also extremely grateful to Dr. Owen Hertzman for his academic contributions to the research and for his friendship over the past two years. Owen replaced Dr. Bailey as my senior supervisor and helped bring the project to completion.

His advice and support on both an academic and personal level, however, go far beyond his editing of the thesis and his organization of the defence.

I would also like to thank the rest of my committee for their assistance and support. Dr. Bruce MacArthur provided the data on which the research is based and numerous comments and suggestions, which helped immensely in improving the final product. Dr. Allan Sawchuk and Dr. Ian McKendry also read and edited the thesis, providing their valuable incite whenever necessary.

My lab partner and friend, James Morley, provided invaluable advice and support in the completion of my graduate classes and the proper writing and formatting of a thesis. I also greatly enjoyed his company in the climate lab; the second year of my degree was not the same without him.

I am thankful to the Natural Sciences and Engineering Research Council of

Canada (NSERC) for their funding of the research. I am also thankful to all the other graduate students, staff and professors in the Geography Department of Simon Fraser

University. I am especially thankful to Marcia Crease for her assistance in bringing this project to completion and to Kim Blais, Chris Bone, B-Jae Kelly, Ranae Kowalczuk,

Cyrille MCdard De Chardon, Tami Nicoll, Taskin Shirazi, Daniel Stevens, Tanya Turk and Erin Welk for their friendship.

I am eternally grateful to my parents, Bill and Susan, for their love and encouragement in all aspects of my life. Without their assistance, especially financially, it would not have been possible for me to pursue my university education, while maintaining my commitment to rugby. I am also extremely thankful to my twin brother

Sean for his support and friendship.

Finally, I would like to thank all my friends for making these last two years so memorable and for providing me with a release from my graduate work. This obviously includes all the men and women of the SFU Rugby Club that I have had the pleasure to play, practice and party with over the past two years. I would especially like to thank

Lindsey Allen, Marissa Huber, Amanda Miller, Yalena Sheldon, Melanie Skolovy and

Chris Vecchies for being such important people in my life for all or part of the past two years. TABLE OF CONTENTS

.. Approval ...... 11 ... Abstract ...... 111 Dedication ...... iv Acknowledgements ...... v .. Table of Contents ...... vii List of Figures ...... ix ... List of Tables ...... xlli Chapter 1. Introduction ...... 1 1.1. Study objectives ...... 2 1.2. Thesis organization ...... 3 1.3. Site descriptions ...... 4 Chapter 2. Solar and Net Radiation Climatology of the Canadian High Arctic ...... 8 2.1. Introduction ...... 8 2.2. Theory ...... 10 2.3. Methodology ...... -15 2.4. Results and discussion ...... 19 2.4.1. Interannual solar radiation trends ...... 19 2.4.2. Cloudless solar radiation trends ...... 22 2.4.3. Interannual net radiation trends ...... 23 2.4.4. Relationships between net radiation and other parameters ...... 25 2.4.5. The Arctic Oscillation ...... 29 2.5. Conclusions ...... 32 Chapter 3 .Annual and Interannual Surface Albedo Trends at Resolute Bay. Nunavut ...... SO 3.1. Introduction ...... 50 3.2. Theory ...... 1 3.3. Previous research ...... 53 3.4. Methodology ...... 58 3.5. Results and discussion ...... 60 3.5.1. Interannual trend in average annual albedo ...... 60 3.5.2. Annual albedo trends ...... 62

vii 3.5.3. Other interannual albedo trends ...... 64 3.5.4. Relationship between albedo and net radiation ...... 67 3.6. Conclusions ...... 70 Chapter 4 . Natural Arctic Seasons at Resolute Bay. Nunavut ...... 82 4.1. Introduction ...... 82 4.2. Theory ...... 83 4.3. Previous research ...... 85 4.4. Methodology ...... 89 4.5. Results and discussion ...... 92 4.5.1. Delineation of natural seasons ...... 92 4.5.2. Characteristics of natural seasons ...... 92 4.5.3. Interannual trends in the nature of Arctic seasons ...... 95 4.6. Conclusions ...... 96 Chapter 5 .Conclusion ...... 104 5.1. Introduction ...... 104 5.2. Solar and net radiation climatology of the Canadian high Arctic ...... 104 5.3. Surface albedo trends at Resolute Bay, Nunavut ...... 106 5.4. Natural seasons at Resolute Bay. Nunavut ...... 107 5.5. Future research ...... -108 Appendix 1: List of Symbols And Units ...... 110 Appendix 2: Statistical Terms...... 112 A2.1. Trend analysis ...... 112 A2.2. Correlation ...... 113 Appendix 3: Data Availability ...... 114 Appendix 4: Quality Control Procedures ...... 115 Appendix 5: Sensitivity Analysis ...... 119 A5.1. Solar Radiation ...... 119 A5.2. Net Radiation ...... 121 Appendix 6: Instrumentation Record ...... 123 Appendix 7: Natural Seasons...... 125 Appendix 8: Supplementary Graphs ...... 130 References ...... 137 LIST OF FIGURES

Figure 1.l. Map of the four study sites in the Canadian Arctic ...... 6 Figure 1.2. The instrument compound at Alert, Nunavut. 63 2004, Dr. L.J.B. MacArthur, by permission...... 6 Figure 1.3. The pyranometer (right) and pyradiometer(1eft) at Eureka, Nunavut. 63 204, Dr. L.J.B. MacArthur, by permission...... 7 Figure 2.1. Total annual solar radiation at Alert from 1965-2000. Incomplete years excluded. All data presented in Figure A8.5 ...... 39 Figure 2.2. Total annual solar radiation at Resolute Bay from 1958-2002. Incomplete years excluded. All data presented in Figure A8.6...... 39 Figure 2.3. Annual average clearness index at Alert from 1965-2001. Refer to Appendix 4 for further description of units. Incomplete years excluded. All solar radiation data presented in Figure A8.5...... 40 Figure 2.4. Annual average clearness index at Resolute Bay from 1958-2002. Refer to Appendix 4 for further description of units. Incomplete years excluded. All solar radiation data presented in Figure A8.6...... 40 Figure 2.5. Daily average clearness index for all cloudless days at Alert. Refer to Appendix 4 for further description of units...... 41 Figure 2.6. Daily average clearness index for all cloudless days at Resolute Bay. Refer to Appendix 4 for further description of units...... 41 Figure 2.7. Total annual net radiation at Alert from 1969-2000. Incomplete years excluded. All data presented in Figure A8.7 ...... 42 Figure 2.8. Total annual net radiation at Resolute Bay from 1964-2002. Incomplete years excluded. All data presented in Figure A8.8 ...... 42 Figure 2.9. May net radiation at Alert from 1969-2002...... 43 Figure 2.10. May net radiation at Resolute Bay from 1964-2003...... 43 Figure 2.1 1 . June net radiation at Alert from 1969-2002...... 44 Figure 2.12. June net radiation at Resolute Bay from 1964-2003...... 44 Figure 2.13. Average annual albedo from 1957-2002 at Resolute Bay. Incomplete years excluded. All data prior to application of quality assurance procedures is presented in Figure A8.9 ...... 45 Figure 2.14. Mean annual surface air temperature at Alert from 195 1-2002...... 45 Figure 2.15. Mean annual surface air temperature at Resolute Bay from 1948-2003...... 46 Figure 2.1 6. Total annual net longwave radiation at Resolute Bay from 1964-2002. Net longwave radiation is solved by residual from the radiation balance, therefore a large number of years are excluded due to incomplete albedlo, solar radiation and/or net radiation data ...... 46 Figure 2.1 7 . Relationship between annual totals of net radiation and net longwave radiation . Incomplete years in either dataset excluded ...... 47 Figure 2.1 8 . March net longwave radiation at Resolute Bay from 1964.2003 ...... 47 Figure 2.1 9 . Relationship between the annual total of net radiation and the March total of net longwave radiation ...... 48 Figure 2.20. Schematic showing longwave driving of increased annual net radiation when the Arctic Oscillation is negative ...... 48 Figure 2.21. Annual value of the Arctic Oscillation Index from 1950-2003. The index is normalized using 1950-2000 base period statistics . More information on the Arctic Oscillation and its calculation is presented in Chapter 2 ...... 49 Figure 2.22. Schematic showing longwave driving of decreased annual net radiation when the Arctic Oscillation is positive (i.e. during the early to mid 1 990' s)...... 49 Figure 3.1. Average annual albedo from 1957-2002. Incomplete years excluded . All data prior to application of quality assurance procedures is presented in Figure A8.9...... 73 Figure 3.2. Time-series of daily albedo values during 1991. Decimal month values represent the start of new months starting with 0 used for January 1" (4 represents May IS', 7 represents August I", etc.). Albedo time-series prior to application of quality assurance procedures is presented in Figure

Figure 3.3. Average albedo of the annual snow-covered period between March 8' and the beginning of snowmelt from 1958-2003...... 74 Figure 3.4. Average albedo for the month of March from 1958-2003...... 74 Figure 3.5. Average albedo for the month of April from 1958.2003 ...... 75 Figure 3.6. Date at the onset of snowmelt from 1958.2003 ...... 75 Figure 3.7. Date at the conclusion of snowmelt from 1958.2003 ...... 76 Figure 3.8. Average albedo for the month of May from 1 958.2003 ...... 76 Figure 3.9. Average albedo for the month of June from 1958.2003 ...... 77 Figure 3.10. Average albedo of the annual snow-free period from 1958.2002 ...... 77 Figure 3.1 1. Average albedo for the month of July from 1957-2002...... 78 Figure 3.12. Average albedo for the month of August from 1957.2002 ...... 78 Figure 3.1 3 . Length of snow-free period from 1958-2002...... 79 Figure 3.14. Date at the conclusion of the snow-free period from 1957-2002...... 79 Figure 3.1 5 . Average September albedo from 1957.2000 ...... 80 Figure 3.16. Time-series of daily average annual albedo and daily total net radiation for 1991...... 80 Figure 3.1 7 . Total annual net radiation from 1964-2002 and average annual albedo from 1957-2002. Incomplete years excluded . All data presented in Figures A8.8 and A8.9 ...... 81 Figure 3.1 8 . Relationship between total annual net radiation and average annual albedo . Incomplete years in either dataset excluded . Data from 1993 is indicated on the graph, as it is speculated that 1993 albedo could possibly be erroneous ...... 81 Figure 4.1. Three natural seasons delineated by k-means cluster analysis using data from Resolute Bay in 1971. Weekly values of solar radiation, net radiation, albedo and mean air temperature are provided. Albedo values have been multiplied by 100...... 99 Figure 4.2. Three natural seasons delineated by k-means cluster analysis using data from Resolute Bay in 1991. Weekly values of solar radiation, net radiation, albedo and mean air temperature are provided. Albedo values have been multiplied by 100...... 99 Figure 4.3. Three natural seasons delineated by k-means cluster analysis using data from Resolute Bay in 1985. Weekly values of solar radiation, net radiation, albedo and mean air temperature are provided. Albedo values have been multiplied by 100...... 100 Figure 4.4. Four natural seasons delineated by k-means cluster analysis using data from Resolute Bay in 1985 with a seasonal boundary now present during snowmelt. Weekly values of solar radiation, net radiation, albedo and mean air temperature are provided. Albedo values have been multiplied by 100...... 100 Figure 4.5. Number of weeks from March 8" until the onset of spring (the length of the first annual portion of the winter season) for years from 1958 to 2002 ...... 101 Figure 4.6. Length of the spring season for years from 1958 to 2002...... 101 Figure 4.7. Number of weeks from March 8'h until snowmelt, which marks the beginning of the summer season, for years from 1958 to 2002...... 102 Figure 4.8. Length of the summer season for years from 1958 to 2002 ...... 102 Figure 4.9. Number of weeks from March 8' until the onset of the winter season for years from 1958 to 2002...... 103 Figure A8.1. Total annual solar radiation at Eureka from 1964-1998. Incomplete years presented as dashes...... 131 Figure A8.2. Total annual net radiation at Eureka from 1970-1995. Incomplete years presented as dashes...... 131 Figure A8.3. Total annual solar radiation at Iqaluit from 1973-1998. Incomplete years presented as dashes...... 132 Figure A8.4. Total annual net radiation at Iqaluit from 1973- 1998. Incomplete years presented as dashes...... 132 Figure A8.5. Total annual solar radiation at Alert from 1964-2002. Incomplete years presented as dashes...... 133 Figure A8.6. Total annual solar radiation at Resolute Bay from 1957-2003. Incomplelte years presented as dashes ...... 133 Figure A8.7. Total annual net radiation at Alert from 1968-2002. Incomplete years presented as dashes...... 134 Figure A8.8. Total annual net radiation at Resolute Bay from 1963-2003. Incomplete years presented as dashes ...... 134 Figure A8.9. Annual average albedo from 1957-2003 prior to application of quality assurance procedures...... 135 Figure A8.10. Time-series of 1991 daily average albedo values at Resolute Bay prior to application of quality assurance procedures...... 135 Figure A8.11. Seasonal variation in clearness index at Alert. 3'd order polynomial trend line and corresponding equation used to minimize this seasonal variation are also shown ...... 136 Figure A8.12. Seasonal variation in clearness index at Resolute Bay. 3'd order polynomial trend line and corresponding equation used to minimize this seasonal variation are also shown...... 136

xii LIST OF TABLES

Table 2.1. Alert radiation summary. Highlighted cells indicate incomplete data...... 35 Table 2.2. Resolute Bay radiation summary (page 1). Highlighted cells indicate incomplete data...... 36 Table 2.3. Resolute Bay radiation summary (page 2). Highlighted cells indicate incomplete data...... 37 Table 2.4. Changes in monthly net radiation totals at both Alert and Resolute Bay ...... 38 Table 2.5. Correlations between monthly totals of net longwave radiation and annual totals of net radiation for Resolute Bay...... 38 Table 3.1 . Albedo data summary ...... 72 Table 4.1. Results of k-means cluster analysis (3 clusters) of weekly solar radiation, albedo and mean air temperature at Resolute Bay. The number 1 represents the winter season, the number 2 represents the spring season (and in some cases a short autumn season as well) and the number 3 represents the snow-free, summer season...... 98 Table 4.2. Re-analysis of data for the years 1985 and 1988 when a seasonal boundary was not delineated during the spring snowmelt. A cluster was added (number 4) in order to have this occur...... 98 Table A3.1. Available data and exclusion criteria for all datasets...... 1 14 Table A4.1. Quality control procedures applied to the data...... 115 Table A5.1 . Solar radiation sensitivity analysis results ...... 120 Table A5.2. Solar radiation sensitivity analysis summary...... 120 Table A5.3. Net radiation sensitivity analysis results...... 12 1 Table A6.1. Radiation instrument record at Alert, Eureka, Iqaluit and Resolute Bay...... 124 Table A7.1. Results of k-means cluster analysis (3 clusters) of weekly solar radiation, albedo and mean air temperature at Resolute Bay using only the 31 annual weeks with albedo data (March gthto October 71h)...... 126 Table A7.2. Results of k-means cluster analysis (starting with 3 clusters with clusters added until a seasonal boundary occurs during the Spring snowmelt) of weekly solar radiation, albedo and mean air temperature at Resolute Bay using only the 31 annual weeks with albedo data (March gthto October 7th)...... 127 Table A7.3. Results of k-means cluster analysis (3 clusters) of weekly net radiation at Resolute Bay...... 128 Table A7.4. Results of k-means cluster analysis (3 clusters) of weekly mean air temperature at Resolute Bay...... 129 CHAPTER 1. INTRODUCTION

Until quite recently, the Arctic region has been largely ignored by climate studies and the deployment of research stations and instrumentation. This is largely due to the inaccessibility and harsh weather of the region, which make the setup and maintenance of climate stations extremely difficult and costly. Scientists have long suspected that the

Arctic is the most sensitive area in the world to climate change (Arrhenius 1896). More recently, however, the importance of the Arctic to the study of climate change has been re-emphasized, and data collection has intensified (Comiso 2001; Houghton et al. 2001;

Dorn et al. 2003). The positive feedback resulting from snow and ice melt in the Arctic will accelerate any changes resulting from any global warming scenario (Comiso 2003;

Belchansky et al. 2004). The entire global climate is interconnected, so while enhanced in the Arctic, the rest of the global circulation pattern will also be affected.

The relatively recent exploration of the Arctic climate unfortunately provides only limited data for long-term analyses. Because radiation measurements are crucial to the understanding of an area's climate, there has been a limited number of radiation studies conducted in the Arctic. Although some meteorological measurements were taken earlier, the earliest radiation measurements were taken in the Canadian Arctic in the late

1950's. Although the Arctic climate is extremely variable over a number of timescales

(Soon 2005), thereby complicating any conclusions formed from this relatively small period of its climatic history, the past half-century does encompass most of the possible effect of anthropogenic climate change. The data record should be of sufficient duration to establish significant trends in the Arctic radiation regime.

Data from four climate stations in the Canadian Arctic that collect solar and net radiation measurements are employed herein (other stations that collected data were closed) to examine changes in the Arctic climate over the past half-century. Only two of these stations, however, contain sufficient data to produce adequate interannual trends.

The limited number of stations examined may make any regional conclusions difficult

(Woo and Young 1996); causes of phenomena observed in the radiation regimes of both stations are nonetheless hypothesized.

The Canadian Arctic radiation data presented are formed into interannual trends in solar and net radiation. Correlations are formed between those trends and other aspects of the Arctic climate such as air temperature. An in-depth analysis of the annual and interannual variations in albedo at a high Arctic site is then conducted. Finally, natural seasons are objectively delineated using elements of the radiation balance as input parameters.

1.1. Study objectives

The objectives of this thesis are four-fold:

1. To summarize interannual trends in solar and net radiation at two Canadian

high Arctic locations over the past half-century. Attention is given to the

overall changes in these parameters, and, when relevant, to the periods of the

year in which those changes primarily occurred. 2. To explore correlations between the observed radiation trends and other

pertinent climatological parameters and to proffer possible explanations for

the observed trends and relationships. Solar radiation trends are related to

changes in overall cloud cover and atmospheric constituents. Net radiation

trends are related to those of solar radiation, air temperature, surface albedo,

longwave radiation and the Arctic Oscillation index.

3. To perform a detailed study of albedo trends at Resolute Bay, Nunavut. The

nature of annual albedo trends and interannual trends in average albedo are

explored.

4. To delineate Arctic seasons using cluster analysis of pertinent climatological

parameters. The major inputs to the radiation balance (solar radiation, surface

albedo and air temperature) are employed.

1.2. Thesis organization

This thesis is divided into five chapters. Between an introductory (Chapter 1) and a concluding (Chapter 5) chapter there are three chapters containing the substantive content of the study. Chapter 2 describes the interannual trends of solar and net radiation at the Canadian Arctic sites of Alert and Resolute Bay, Nunavut. These trends are then compared to those of other relevant climatic parameters such as air temperature and the

Arctic Oscillation index. Theories are examined to explain the observed radiation trends

over the past half-century using evidence from the trends and relationships observed, and

information from previous Arctic studies. Chapter 3 examines in detail the annual and

interannual variations in surface albedo at Resolute Bay, Nunavut. Relationships between albedo and net radiation are also examined on both time scales. Chapter 4 employs cluster analysis of solar radiation, surface albedo and air temperature data to delineate natural seasons at Resolute Bay, Nunavut. Upon delineation of these seasons for each year in the dataset, variations in the lengths of the seasons are examined.

Overall conclusions of the research are presented in Chapter 5.

1.3. Site descriptions

All data presented in this thesis were collected at four Environment Canada climate stations on the Canadian Arctic archipelago in the territory of Nunavut

(Figure 1.1). Data from Eureka (80•‹00'N, 85O56' W) and Iqaluit (63O45' N, 68O33' W) are presented in the appendices, but discussion is limited to data from Alert (82'30' N,

6Z020' W) and Resolute Bay (74'42' N, 94O50' W) because the data records of the first two stations are relatively incomplete. Three of the four stations are located north of the

Arctic Circle and all are characterized by cold year-round temperatures and persistent

snow cover for over half the year. When not snow covered, surfaces are mainly treeless

rock and tundra. At each location, instruments are located at an Environment Canada

compound. The exact nature of the instruments installed at each site, most notably

pyranometers to measure solar radiation and pyrradiometers to measure net radiation, is

provided in Appendix 5. All data were collected under World Meteorological

Organization guidelines and quality checked by Environment Canada. Brief site

descriptions for all four sites follow (Site information is cited from Maxwell (1982) when

applicable). The Alert station is at an elevation of 63 metres above sea level on the

northeast shore of Ellesmere Island. The site is located on an uneven plateau

approximately five kilometres from the Arctic Ocean and two kilometres from the termination of Parr Inlet, an extension of Dumbell Bay. Hills eight kilometres southwest of the station rise to heights of 360 to 490 metres. At a distance between 16 and 24 kilometres they rise to heights of 700 metres. The United States Range is located 60 kilometres away to the west. Figure 1.2 shows the instrument compound at Alert. Eureka is located at an elevation of 10 metres above sea level on Slidre Fjord on west-central

Ellesmere Island. Slidre Fjord is a branch of Eureka Sound, which separates Ellesmere

Island from Axel Heiberg Island to the west. Mountain ranges on both islands keep the site relatively sheltered from the Arctic Ocean. Hills within 20 kilometres ranging up to

760 metres in elevation virtually surround the site. Figure 1.3 shows the radiation instruments employed at the Eureka site.

Resolute Bay is located at an elevation of 64 metres in a flat valley about three kilometres northwest of Resolute Bay on the southeast corner of Cornwallis Island. The site is exposed on the south to Barrow Strait and on the north to the interior hills of the island. Hills less than two kilometres northeast of the station rise to over 150 metres in elevation, as does a single hill on Cape Matyr, three kilometres to the southwest.

Iqaluit (formerly Frobisher Bay), the capital city of Nunavut, is located just south of the Arctic Circle and significantly further south than the three other stations. The site is located at an elevation of 21 metres on the southeast shore of Baffin Island on

Frobisher Bay. Hills ranging in height from 30 to 180 metres on Halls Peninsula and

Meta Incognito Peninsula rise to the northeast and southwest respectively. ARCTIC OCEAN Figure 1.3. The pyranometer (right) and pyradiomcter(1eft) at Eureka, Nunav~~t.'(1 2004, Dr. L.J.B. MacArth~lr.by permission. CHAPTER 2. SOLAR AND NET RADIATION CLIMATOLOGY OF THE CANADIAN HIGH ARCTIC

2.1. Introduction

Radiation studies provide a valuable summary of a region's energy availability and climate. A number of studies in Arctic regions measure various radiative components on a daily or seasonal time-scale, but little research has been done on an interannual basis. This is partially due to the limited data available from Arctic regions.

The dataset explored herein from the Canadian Arctic, despite its limitations, is thought to be the longest and most reliable Arctic radiation dataset in the world (L.J.B. McArthur

2005, personal communication).

A number of interannual studies have been conducted in Arctic regions on various climatic parameters, but very few include a radiative component. Numerous studies have noted the recent decrease in Arctic sea ice extent and thickness (e.g. Chapman and Walsh

1993; Wadhams 1995; Parkinson et al. 1999; Rothrock et al. 1999; Holloway and Sou

2002). Others have analyzed changes in interannual temperature trends in Arctic regions

(e.g. Rigor et al. 2000; Przybylak 2000; Polykov et al. 2003; Bengtsson 2004). Comiso

(200 1) shows trends in albedo, sea ice extent, air temperature and cloud cover using satellite data from 1987-1998. Serreze et al. (2000) summarize recent Arctic climate change in temperature, snow cover, precipitation, sea ice extent and vegetative growth.

A large, comprehensive study by Houghton et al. (2001) summarizes world climate change, including a focus on Arctic-specific effects such as loss of sea ice. This was followed by a very detailed and comprehensive study of Arctic climate change entitled the Arctic Climate Impact Assessment (McBean 2005). Existing temperature, precipitation, snow cover, sea ice cover and surface pressure data are presented and projected for the future based on hypothesized climate change scenarios. The above studies, although showing interannual variability in the Arctic climate are virtually void of radiative elements.

Most previous Arctic radiation studies are either outdated (and therefore do not include the effects of anthropogenic climate change) or cover very short time-scales.

Przybylak (2003) provides a summary of radiation studies that have been conducted in the Arctic worldwide. Maxwell (1980 and 1982) presents a very detailed two-volume summary of the climate of the Canadian Arctic Islands; his study, however, provides only average values and no long-term trends. The only interannual study referenced is a classic study conducted by Gavrilova (1963) that does summarize overall radiation trends over the global Arctic region. More recent studies are limited in their applicability by short time-scales or use of data from only specific portions of the year. For instance, a study by Liu et al. (1998) shows the variability in all components of the radiation budget in Koldeway, Spitsbergen, but only from 1992-1996 (obviously too short too establish any meaningful trends). Woo and Young (1996) show trends in summer (June-August) global solar radiation at the Canadian Arctic stations of Alert, Eureka, Isachsen, Mould

Bay and Resolute Bay from 1974-1993. A decrease of summer radiation input at

Resolute Bay is the only significant trend observed. The only similar study to the one conducted herein was done by Stanhill in 1995 on interannual net and solar radiation trends at a number of Arctic locations worldwide.

Averaging 389 total years of data from 1950-1994 from 22 Arctic sites resulted in a significant decrease of 0.36a.05 W m-' in solar radiation (p-value: 0.0001). Decreases were observed at Alert, Eureka and Resolute Bay as well as the entire Eastern Canadian

Arctic. 133 years of net radiation from 1964-1984 from 11 sites worldwide yielded an insignificant decrease of 0.12 W m-'. The same problems with missing data encountered in this study, were also encountered by Stanhill over his study period, as the same datasets were employed for both. Although comparable to the Canadian Arctic study presented herein, results from Stanhill's study, especially in net radiation, pre-date the majority of the recent observed radiative changes.

This chapter continues by introducing the radiation balance as it pertains to Arctic environments. This is followed by a description of the solar and net radiation data employed in the study and an examination of how those data were edited and analyzed to minimize the effects of missing data. This is followed by analysis of interannual trends in solar and net radiation at Alert and Resolute Bay. Relationships between those radiative trends and those of other climatic variables are then presented. Finally, the effects of the Arctic Oscillation, especially in the early-1990's, are explored with emphasis given to its effect on net radiation trends.

2.2. Theory

The radiation balance provides a summary of the individual radiative components of the climate system. Global solar radiation (KJ) is the driving force of the entire system and the most influential component of the radiation balance on an annual basis. A portion of global solar radiation is reflected in proportion to surface albedo (a)resulting in net solar radiation (K*)

where Kt represents reflected solar radiation.

The total energy emitted (E) by a body depends on its surface temperature (To), and emissivity (E), in accordance with the Stefan-Boltzmann Law

E = mTO4 (2.2) where o is the Stefan-Boltzmann constant (5.67 x W m-2K-~). The other major component of the radiation balance is net longwave radiation (L*) comprised of longer wavelength radiation emitted in accordance with the Stefan-Boltzmann Law by the

Earth's atmosphere and the Earth's surface and defined as

where LJ is atmospheric longwave radiation and Lt is terrestrial longwave radiation.

The variation in emissivity between Arctic surfaces and seasons is negligible and approaches unity, therefore the final term is also negligible and often excluded (Weller and Wendler 1990).

The surface radiation budget results from the sum of net solar radiation and net longwave radiation

Q*=K*+L* (2.4)

where Q* represents net all-wave radiation. Q* dictates the amount of energy available for surface processes. For a thorough understanding of high-latitude climates, a number of other points must be considered. All locations north of the Arctic Circle undergo Polar Night with its duration increasing towards the North Pole. During that period, the region receives no solar radiation and net radiation is comprised exclusively of net longwave radiation. In most Arctic areas, surface reflectance varies on an annual basis from high albedo snow and ice (-0.80) to relatively low albedo tundra (-0.20). This contributes to extremely low net radiation values when the surface is snow-covered and relatively higher ones in the snow-free summer period. The timing of snowmelt in the spring is especially critical to the Arctic climate (Maykut and Church 1973; Kukla and Robinson 1988; Stone et al.

2002). Earlier snowmelt allows additional absorption of radiation near its annual peak, thereby increasing annual totals of net radiation. More details on the annual Arctic climate are provided in Chapters 3 and 4.

The Arctic Oscillation (AO) is an interdecadal phenomenon of changing sea-level

pressure in the northernmost portions of the globe. Details of how the Arctic Oscillation

is defined can be found in Kutzbach (1970) and Thompson and Wallace (1998). It is

highly correlated with the North Atlantic Oscillation, but with key distinctions (Wallace

2000; Ambaum et al. 2001). It is characterized by either a positive or negative sea-level

pressure anomaly at the North Pole and a corresponding anomaly of the opposite sign

centered at about 37-45"N latitude. In the positive, or warm phase, characterized by low

pressure at the pole, warmer Atlantic water is allowed to encroach on Arctic waters, raise

their temperature and melt sea ice. In the cold or negative phase, a strong clockwise

current caused by surface winds keeps warm Atlantic water out of the Arctic. Although usually fluctuating from positive to negative, the Arctic Oscillation remained positive for most of the 1990's.

The Arctic Oscillation has been observed to highly correlate with the strength of the polar vortex (Wallace and Wallace 1998; Shindell et al. 1999; Ambaum et al. 2001 ;

Hu and Tung 2002); a persistent, large-scale cyclonic circulation centred over the North

Pole and extending from the middle Troposphere to the Stratosphere. The strength of the polar vortex is described by the difference in pressure between 50 hPa (about 20 kilometres in altitude) and the surface; it is manifested in the strength of the westerly polar jet, which also occurs at the 50 hPa pressure surface (Hartmann et al. 2000). The strength of the polar vortex is proportional to the temperature gradient between the North

Pole and the equator; both phenomena are strongest in the Arctic winter. The positive phase of the Arctic Oscillation is associated with both the strengthening of the subtropical and polar jets and of the polar vortex (Ambaum et al. 2001). This association is

supported by the break down of the Arctic Oscillation in March when the polar vortex

also begins to weaken (Schaefer et al. 2004). In addition, the strength of the polar vortex

was amplified in the late 1980's and early 1990's, coinciding with the persistently

positive phase of the Arctic Oscillation (Polyakov et al. 2004).

Mechanisms forcing changes in the meriodional temperature gradient, in the polar

vortex and ultimately in the Arctic Oscillation may include greenhouse gases, ozone

depletion and volcanics (Pawson and Naujokat 1999; Hartmann et al. 2000; Shindell

et al. 2001; Hu and Tung 2002). It is speculated, however, that the majority of recent

forcing has been attributable to increased concentration of greenhouse gases (Shindell

et al. 1998; Corti et al. 1999; Cowen 1999; Hartmann et al. 2000; Shindell et al. 2001 ; Hu and Tung 2002; Rind et al. 2005). Higher tropical temperatures generate a more vertically developed troposphere than cooler polar temperatures, causing the upper tropical troposphere to be at the same altitude as the lower polar stratosphere. There is usually a temperature gradient transporting heat towards the cooler poles; it is speculated that greenhouse gases enhance this equator-to-pole temperature gradient by heating the tropical troposphere and cooling the lower polar stratosphere (Cowen 1999; Shindell et al. 2001 ; Hu and Tung 2002). This phenomenon is amplified in the winter, when the poles are under polar night, and the tropical atmosphere is still heated (Hartmann et al.

2000).

A strong polar vortex decreases polar stratospheric temperatures by several degrees (Wallace and Wallace 1998; Cowen 1 999). Greenhouse gases, thought to be a major contributor to the strengthening of the polar vortex, warm the surface but cause the stratosphere to cool radiatively (Shindell et al. 1998). Lower stratospheric temperatures then cause increased ozone depletion, as dissociation of ozone requires stratospheric temperatures below 195 K and the presence of both sunlight and chlorine (Shindell et al.

1998); this phenomenon occurs virtually exclusively in the spring when all conditions can be met. Ozone loss further increases the strength of the polar vortex in a positive feedback by further cooling the lower stratosphere (Braathen et al. 2002; Shindell et al.

1998; Hirooka et al. 1999; Robock 2002a). A strong polar vortex then further decreases

ozone concentration by isolating the poles and suppressing transport of ozone from lower

latitudes (Hirooka et al. 1999; Hu and Tung 2002; Weatherhead et al. 2005).

Large tropical volcanic eruptions like those of El Chichon in 1982 and Mount

Pinatubo in 1991 also lead to a strengthening of the polar vortex and in turn to a positive phase of the Arctic Oscillation (Robock 2002b; Stenchikov et al. 2002; Stenchikov et al.

2004). Volcanic aerosols warm the tropical stratosphere, thereby enhancing the pole-to- equator temperature gradient and strengthening the polar vortex (Stenchikov et al. 1998;

Hartmann et al. 2000; Robock 2002a; Robock 2004; Stenchikov et al. 2004). As with all aerosols, this effect is seen mainly in the winter when the poles are under polar night and the tropical stratosphere continues to be heated radiatively by volcanic aerosols.

Volcanic aerosols also serve as surfaces for chemical reactions that destroy ozone and further increase the strength of the polar vortex (Stenchikov 2002; Robock 2002a;

Robock 2004; Weatherhead 2005). This manner of volcanic ozone depletion, however, has been a relatively recent phenomenon due to increased anthropogenic atmospheric loadings of chlorine (Robock 2002a).

The following results show the effects of these phenomena on the radiation budget of the Canadian Arctic. Hypotheses are presented given the data presented and the above theoretical framework concerning the Arctic climate. Focus is given to the portion of the data record during which the Arctic underwent a persistent positive or

warm phase of the Arctic Oscillation.

2.3. Methodology

The data for this research project was acquired from Environment Canada for the

four Canadian high Arctic climate stations at Alert (82'30' N, 62'20' W), Eureka,

(80'00' N, 85'56' W), Resolute Bay (74'42' N, 94'50' W) and Iqaluit (63'45' N,

68'33' W). All four stations are located on the Canadian Arctic archipelago in the

territory of Nunavut. More details on the locations of these stations are provided in Chapter 1. The data were all collected by World Meteorological Organization guidelines and quality checked by Environment Canada before their employment in this study.

The radiation records at Eureka and Iqaluit are incomplete and missing too much data to make accurate estimates of interannual trends at those locations. Data from these stations are nonetheless provided in Figures A8.1 -A8.4. Further discussion will be confined to the more complete datasets from Alert and Resolute Bay. Although the records at these two stations also contain missing data, there is sufficient information to produce reliable annual and interannual summaries of solar and net radiation. At

Resolute Bay, reflected radiation values were also measured, allowing for determination of changes in surface albedo at that location as well. A more detailed summary of radiation data availability at all four stations can be found in Appendix 3.

For both Alert and Resolute Bay, radiation data has been acquired on an hourly basis for the majority of the past half-century. This vast amount of data was summarized into daily, monthly and yearly totals to allow for comparison of annual and interannual trends. Precautions were taken to reduce the effect of missing data whenever possible. A summary of the procedures applied to each dataset is presented in Appendix 4.

Whenever an unacceptable amount of missing data was present, those days, months and years were flagged and not used for further analysis. The number of years excluded in each dataset, as well as the criteria for their exclusion, is also presented in Appendix 3.

Hourly solar radiation values for a daily period were averaged and subsequently multiplied by 24 in order to obtain daily totals. Monthly and yearly totals were calculated in a similar manner. This method is justified in the Arctic, as the solar elevation does not change greatly over the daylight period reducing the difference between values at solar noon and sunrise/sunset. In many years however, a disproportionate amount of data is missing from low radiation periods resulting in erroneous annual values for those years.

Procedures were therefore conducted to decrease the effect of seasonality on the solar radiation data, in order to produce a more viable interannual trend.

Daily clearness indices (CI) were calculated for the entire Alert and Resolute Bay solar radiation datasets. The clearness index is the ratio of solar radiation to extraterrestrial radiation. It does not vary nearly as greatly on a daily or annual basis as solar radiation and therefore provides a more accurate interannual comparison of solar radiation inputs to the surface, given the missing data present in the analyzed datasets

(which in most cases is seasonally biased, and not distributed randomly). Daily extraterrestrial radiation values were first calculated by summing minute totals; daily solar radiation values were then divided by these values to produce daily average clearness indices. When the clearness indices were plotted against day of year, a seasonal trend was still evident. A third order polynomial was fitted to the data and the percentage difference between the actual measured value and the daily value output from the polynomial equation was calculated for each day in the dataset. This minimized the seasonal effects of missing data in calculating interannual trends in solar radiation. More details on this procedure are provided in Appendix 4. A sensitivity analysis depicting the effects of missing data, and the degree to which this procedure minimized its effects is provided in Appendix 5.

Daily net radiation values were calculated by averaging hourly values and multiplying those averages by 24. This method can be employed for net radiation as well since solar radiation does not change greatly over the daylight period and longwave radiation is conservative. Monthly averages were then calculated from those daily values and subsequently multiplied by 28, 29, 30 or 31. Values of isolated missing or incomplete months during Polar Night were interpolated from the values of surrounding months. Monthly values during this period are small and conservative allowing them to be approximated quite accurately. With these values missing, calculations of annual totals are inflated, as they under-represent the low-radiation period of Polar Night.

Annual totals of net radiation were subsequently calculated by summing the monthly totals; any years with any missing or incomplete months of data were excluded from further analysis. A sensitivity analysis comparing calculation of annual total net radiation using daily averages and monthly totals is provided in Appendix 5.

For albedo calculations at Resolute Bay, hourly values of solar radiation were divided by hourly values of reflected solar radiation to calculate hourly albedo values.

As in Winther (1 993) and Lindsey and Rothrock (1 994), average daily albedo values were then calculated by averaging hourly albedo values for those hours when the solar zenith angle is less than 80 degrees. This method enabled equal representation of every hourly value throughout the diurnal albedo cycle. At Resolute Bay's latitude, this procedure results in 214 days of daily albedo values per year, from March 8 to October 7.

Whenever possible, missing values were interpolated to maximize the number of years retained for further analysis and to ensure every year has 21 4 days of albedo data. More detail on these procedures is provided in Appendix 4 and in Section 2.3.

Along with the radiation data at all four locations, other relevant climatological

data were also acquired and summarized to assist in analysis of the radiation data.

Meteorological data such as temperature, precipitation, cloud cover and snow cover contained minimal missing data for any of the stations and were available over a longer time period than the radiation data. These data were also summarized into daily, monthly and annual averages and totals. More details can be found in Appendices 3 and 4.

Although all data were acquired and analyzed, this meteorological data is only explicitly discussed and presented herein when relevant to the trends in solar and net radiation.

Arctic Oscillation data were acquired from the National Weather Service Climate

Prediction Center (NOAA 2005). The data is from 1950-2003 and has been normalized using base period statistics for that same time period. Values are given as monthly averages. For comparison with interannual trends in other data, annual averages are calculated by averaging monthly values. Since the Arctic Oscillation has the largest variability during the coldest winter months, the annual average mainly captures the characteristics of the cold season.

2.4. Results and discussion

2.4.1. Interannual solar radiation trends

As described above, annual average clearness indices were calculated from the

Resolute Bay and Alert data in order to create a more accurate representation of the

interannual variations in solar radiation at those locations. This does not however enable

description of the magnitude of annual solar radiation received at each site. The basic

quantitative description of the solar radiation at both sites is therefore given using only

the most reliable years in the dataset (The solar radiation trends, prior to the application

of any of these procedures, are provided in Figures A8.5 and A8.6). At Alert (Figure

2.1), the annual average is 2831.6 MJ m-* y-' with a maximum value of 3230.2 MJ m-* y-' measured in 198 1 and a minimum value of 249 1.6 MJ m-2y-' measured in 1991 (a range of 738.6 MJ m-2y-1).At Resolute Bay (Figure 2.2), the annual average is 3056.8

2 -1 MJ m-? y-l with a maximum value of 3363.3 MJ m- y measured in 1970 and a minimum value of 2648.7 MJ m-2y-l measured in 1994 (a range of 714.6 MJ m-? y-l).

The lower annual totals of solar radiation measured at Alert are expected due the

site's higher latitude compared with Resolute Bay's. The difference in their annual

averages, however, is less than lo%, only 225.2 MJ m-2 y-1 . The ranges of values around

their means over the interannual period studied herein is similar for both sites. The

highest and lowest values were not measured in the same years at both sites. Given the

missing data present, since clearness index has less variation on an annual basis, it will be

employed to assess the interannual variability in the solar radiation trend at both Alert

and Resolute Bay.

The overall clearness index (CI) trend at Alert shows a decrease of 2.25% of the

daily mean per decade (r': 0.1 64; p-value: 0.01 29) (Figure 2.3). The maximum values

are measured in 1965 (1 1 1 .O%) and 1985 (109.5%) with the minimum value measured in

1991 (85.2%). The annual variability is somewhat concave from 1965-1985 with an

intervening CI minimum in 1973; the average value during that period is 99.4% of the

daily mean. From 1985-1991, the majority of the decrease in CI over the interannual

period occurs, as the CI decreases by 24.3% in only 6 years (r2: 0.956; p-value: 0.0001).

Since 1 99 1, annual average clearness indices have been gradually increasing at a rate of

8.8% of the daily mean per decade (r2:0.321; p-value: 0.0691).

The annual average CI trend measured at Resolute Bay shows a decrease of

2.50% of the daily mean per decade (2:0.375; p-value: <0.0001) (Figure 2.4). The maximum value is measured in 1968 (106.7%) with the minimum value measured in

1994 (83.2%). The overall trend follows a relatively smooth curve, increasing until the late-1960's, subsequently decreasing till the mid-1990's and finally increasing again.

The majority of the overall decreasing trend occurs between 1968 and 1994 when it decreases by 23.5% of the daily mean in 26 years (r2: 0.598; p-value: ~0.0001).

The solar radiation trends at Alert and Resolute Bay do not correlate strongly (r- value: 0.354; r2: 0.1 25) indicating that they are probably largely dominated by local controls and not by regional controls affecting the entire Arctic. Both trends however show a significant and comparable overall decrease (1 1.26% at Alert and 12.52% at

Resolute Bay) with a recovery in the most recent decade. That decrease is, however, manifested differently at the two sites. At Alert, the majority of the decrease occurs in the 6-year period between 1985 and 1991, whereas at Resolute Bay it occurs in the

26-year period between 1968 and 1994.

These results are supported by existing studies of interannual solar radiation trends. Woo and Young (1996) show a decrease in solar radiation at Resolute Bay between 1974 and 1993. Stanhill (1 995) shows a significant decrease in solar radiation at

Alert, Resolute Bay and the entire Eastern Canadian Arctic between 1950 and 1994.

These results are not surprising, however, since the same datasets were undoubtedly used for all Canadian radiation studies. Similar results have also been measured elsewhere in the world. A study by Che et al. (2005) using data from 1961-2000 from 64 stations in

China also shows similar results. A 4.5 W m-' (or 1419.1 MJ~.~)per decade decrease in solar radiation is observed, reaching a minimum in 1989. Values subsequently rebounded as they have similarly in the Canadian Arctic. 2.4.2. Cloudless solar radiation trends

Neither the Alert nor Resolute Bay cloud cover nor cloud opacity datasets show significant correlation with their corresponding trends in annual average clearness index.

Clearness indices of all cloudless days were therefore extracted from the databases to determine if their trends corresponded with those of the annual totals. Cloudless days are those with zero documented observed cloud in all hours of the day. The methods described above were again used to modify the clearness index to limit seasonal variations and allow for a representation of the interannual trend using only the limited number of cloudless days of data found in the datasets. There is only one snow-free cloudless day in either dataset (June 1 6th,1984 at Resolute) minimizing any effects variations in surface albedo may have in this analysis.

Although one cannot directly compare the correlation between the resulting interannual cloudless day trend and the annual average clearness index, one can observe comparable patterns in both trends. Although, it was only possible to extract cloudless days at Alert from 1965-1991, due to the subsequent absence of cloud cover data, the trend does show a very similar concave trend to that observed in that of the average annual clearness index from 1965-1985 (Figure 2.5). The Resolute Bay cloudless sky data shows a similar interannual trend to its annual average clearness index counterpart over the entire period from 1958-2002 (Figure 2.6). Both show an increase until the late-

1960's followed by a relatively linear decrease until the mid-1 980's and finally a recovery in the most recent years.

The similarity between the cloudless sky and overall clearness index trends

suggest that variations in cloud cover have not been the largest contributors to the interannual variations observed at these Arctic locations. The magnitude of the changes is also too large to be accounted for by variations in extraterrestrial radiation (Stanhill,

1995). It is therefore speculated that these trends are the result of variations in atmospheric constituents. Stanhill (1995), Lohmann et al. (2004) and Che et al. (2005) speculate that recent fluctuations in solar radiation are due to changing concentrations of aerosols and greenhouse gases, although the recent rebound in solar radiation serves to further complicate that hypothesis. The exact cause of these trends remains unclear and needs to be further explored in the future.

2.4.3. Interannual net radiation trends

Annual net radiation totals were calculated from the data collected at Alert and

Resolute Bay. From these values, long-term trends in energy availability were determined. Only complete years of data are presented in these figures (the methodology used to exclude years due to insufficient data is discussed earlier in this chapter and in

Appendix 3). Plots containing all data for both the annual and monthly trends presented herein are provided in Figures A8.7 and A8.8).

The average annual total of net radiation at Alert is 429.8 MJ m-2y-' (Figure 2.7).

The highest values were measured in 1990 at 791.1 MJ m-2 y-l and in 1999 at 770.8

MJ m-2y-'. The minimum values were measured in 1975 at 193.2 MJ m 2 y-1 and in 1993

at 198.7 MJ m-2y-'. Overall, net radiation is increasing by 1 14.6 MJ m-' per decade

(r2: 0.335; p-value: 0.001 3). Most of that increase is concentrated between 1975 and

1990 where totals increase by 597.9 MJ m-2in only 15 years (r2: 0.708; p-value:

<0.0001). During the 1990's however, values decrease drastically by 592.4 MJ m-2from

1990 to 1993 and subsequently rebound to a value of 770.8 MJ m-' in 1999. Further south at Resolute Bay, the average annual total is 460.5 MJ m-2y-'

(Figure 2.8). The highest values were measured in the most recent years peaking at 875.4

MJ m-2 y-1 in 2002. The minimum value was measured in 1978 at 1 1 1.2 MJ m-2 y -1 .

Since 1964, net radiation is increasing by 82.2 MJ m-? per decade (r2: 0.289; p-value:

0.001 0). Values stay relatively consistent, or decrease slightly from 1964-1978. Net radiation totals then increase by 598.7 MJ m-2from 1 1 1.2 MJ m-2y-' in 1978 to 709.9

MJ-~m- 2 y-1 in 1983. They subsequently decrease to a minimum of 186.3 MJ m-2 y-1 in

1993. In the following decade however, totals increase relatively linearly by 689.1

MJ m-2in only nine years (r2: 0.879; p-value: 0.0006).

Although correlation using individual annual totals is not high between the two locations (r-value: 0.320; r2: 0.102), they do share a number of overall similarities. The average values of the two locations are similar with the more southerly station, as expected, having a slightly more positive net radiation balance. Both stations have seen a large, significant increase in net radiation over the past half-century. The increase observed at Alert however is 37.3 MJ m-2per decade higher than that observed at

Resolute Bay. This is reasonable however, considering most climate models show that any climate change effects will be greater the further north one travels. In both locations, a decrease in net radiation is observed in the early- 1990's with the decrease greater at the more northerly location. Since then, both locations have also seen a relatively linear increase in net radiation totals.

Interannual trends of monthly totals of net radiation give further insight into the increase in annual totals observed at both Alert and Resolute Bay. Net radiation values for all months are provided in Table 2.4. In the month of May (Figures 2.9 and 2.10) totals have increased by 37.2 MJ m-2per decade (r2:0.267; p-value: 0.0025) at Alert and by 16.6 MJ per decade at Resolute Bay (12: 0.1 02; p-value: 0.0535). In the month of

June, changes have been even greater (Figures 2.1 1 and 2.1 2). At Alert totals have increased by 97.1 MJ m-2per decade (r2: 0.666; p-value: <0.0001) while at Resolute Bay totals have increased by 32.5 MJ m-2per decade (r2: 0.256; p-value: 0.0023). The only other relatively strong, significant trend at either location is a decrease in net radiation in

March at Alert. At Resolute Bay, all months show an increasing trend in net radiation although in most cases that trend is not highly significant. At Alert, monthly totals have increased from March to September and have decreased in the winter months.

Since there have not been strong changes in other months, the annual change in net radiation is concentrated in the months of May and June when snowmelt occurs. In fact, June totals account for 81.2% and 39.5% of the annual change observed at Alert and

Resolute Bay, respectively. Exposure of more low-albedo tundra surface on an annual basis due to earlier snowmelt has undoubtedly led to this increase in net radiation.

Analysis of albedo data to determine if this is indeed the case is discussed briefly in this chapter and in greater detail in Chapter 3.

2.4.4. Relationships between net radiation and other parameters

The parameters that most influence net radiation totals are surface albedo, solar radiation and temperature. The longwave regime however is better represented by the difference in temperature between the ground and the atmosphere (usually the cloud base) than by surface air temperature alone. These parameters are compared to annual totals of net radiation in order to determine which ones have varied over the past half- century and dictated corresponding variations in net radiation. Average annual albedo values were calculated solely for Resolute Bay, as reflected radiation data was unavailable from any of the other stations (Figure 2.13). The albedo regime at Resolute Bay, as well as its relationship with net radiation on an annual and interannual basis, is described in much more detail in Chapter 3. Excluding an anomalous relationship observed in 1993, net radiation and albedo are strongly inversely correlated (r-value: -0.635; r2: 0.403). Annual totals of net radiation decrease by 248.3

MJ m-2 y-1 for every 0.1 unit increase in albedo.

In Chapter 3, it is proven that variations in annual average albedo are largely due to variations in the spring snowmelt date. An earlier snowmelt date allows for more radiation absorption by lower-albedo tundra at a period of time when solar radiation is at its peak. This in turns results in increased annual totals of net radiation. Although net radiation is directly affected by variations in albedo, another force is causing variations in the snowmelt date and in turn variations in albedo and net radiation.

Fluctuations in solar radiation input into the system could theoretically account for variations in net radiation if the other components of the surface radiation budget do not change. At both Resolute Bay and Alert, annual totals of solar and net radiation are inversely correlated (respective r-values: -0.338 and -0.235; r2: 0.1 14 and 0.055).

Although it is possible that the same mechanisms are causing increased values of net radiation and decreased values of solar radiation, these values suggest that the latter is not directly driving the former. Increased solar radiation values over the past half-century would have been necessary to explain the observed net radiation measurements.

It has long been speculated that global warming will be manifested in increased surface air temperatures. Mean annual air temperature at Alert (Figure 2.14) decreased until 1979 and has subsequently been increasing relatively steadily, by 0.66"C per decade

(r2:0.245; p-value: 0.0140). At Resolute Bay, temperatures remained relatively constant until 1989 (Figure 2.15). Since then temperatures have been increasing linearly by 1.3"C per decade (r2: 0.409; p-value: 0.01 03). There is, therefore, evidence that surface air temperatures have indeed been warming at these Arctic locations, especially in the past two decades. Similar increases in recent decades have been measured by a number of other studies (e.g. Przybylak 2000; Serreze et al. 2000; Houghton et al. 2001; Polyakov et al. 2003; McBean 2005). It is speculated by Polyakov et al. (2003) that these recent increases are driven largely by increases in greenhouse gas concentration.

Although air temperature is a component of net longwave radiation, the latter is calculated as the difference in radiation emitted from the surface and the atmosphere and is therefore not guaranteed to be affected a great deal by mean air temperature alone. Air temperature, however, also greatly affects the timing of snowmelt in the spring, which in turn has a profound effect on average annual albedo and annual total net radiation.

Because of this relationship, correlations between mean air temperature and net radiation are relatively strong at both Alert and Resolute Bay (respective r-values: 0.432 and 0.340; r2: 0.186 and 0.1 15).

Since there has been a decrease in solar radiation input accompanying an increase

in net radiation, it is also possible that increased totals of net longwave radiation are

causing increased energy input into snowmelt. This would enable earlier snowmelt and

in turn lead to decreased average annual albedo and increased total annual net radiation.

Net longwave radiation values are solved by residual from solar radiation, albedo and net

radiation data and are therefore only available for Resolute Bay. Although a limited number of years contain sufficiently complete data of the three input parameters, net longwave radiation has been increasing relatively steadily at a rate of 70.1 MJ m-2per decade (I? 0.21 2; p-value: 0.0180) (Figure 2.1 6). This rate of increase is similar to the increase in annual net radiation of 82.2 MJ rn-' per decade measured at the same location. The correlation between annual totals of net longwave radiation and net radiation is also a remarkably high 0.796 (r2: 0.633) (Figure 2.17). The net flow of longwave radiation between the surface and the atmosphere is therefore seen to be crucial to the Arctic climate.

Given the increase in surface air temperature observed at both Resolute Bay and

Alert, accompanied presumably by an increase in surface temperature, atmospheric longwave radiation must have increased at an even greater rate to account for the increase in net longwave radiation. Increases in atmospheric longwave radiation could be caused by increases in greenhouse gas concentration and/or aerosols, or variations in cloud cover. Since cloud cover did not affect trends in solar radiation and does not show any highly significant, conclusive trends in any months of the year, it is unlikely, but not impossible, that variations in cloud cover are the main contributor. It is likely therefore, that changes in atmospheric constituents (most likely greenhouse gases) are warming the atmosphere and driving the observed increases in net longwave radiation.

The net longwave radiation trend is broken into separate monthly trends and correlated with annual net radiation with the results shown in Table 2.5. Although there is also a strong relationship in August (r-value: 0.730; r2: 0.533), the strongest relationship between monthly net longwave radiation and total annual net radiation occurs in March (r-value: 0.773, r? 0.598) (Figures 2.1 8 - 2.19). A significant decrease in the early 1990's is observed in the March data, as it is in the annual totals of net radiation. Since the climate at Resolute Bay is driven largely by the timing of the snowmelt date, the data prior to its occurrence is especially important for determining why its timing differs. High correlations during the snow-free months are likely a result and not a cause of a differing snowmelt date.

It is speculated that variations in net longwave radiation during the late winter months (most notably March), when the surface is completely snow covered, are causing variations in net radiation and therefore in energy available for snowmelt. This energy and resultant snowmelt in turn causes decreased albedo in the subsequent months, which

is observed in the analysis of April albedo data in Chapter 3. Decreased albedo further

increases net radiation and leads to an earlier spring snowmelt. The timing of the spring

melt, which in turn largely dictates average annual albedo and total annual net radiation, could perhaps then be dictated almost exclusively by processes occurring in the winter

prior to any occurrence of snowmelt. Figure 2.20 shows a schematic of these

relationships. Possible mechanisms for these processes, in conjunction with variation in

the strength of the Arctic Oscillation, are explored in the next section.

2.4.5. The Arctic Oscillation

The Arctic Oscillation, as described earlier in the chapter, is a phenomenon of

changing sea-level pressure in the northernmost parts of the globe. Although the exact

mechanisms causing it to change phase are not completely understood, it does have a

profound influence on the Arctic climate (Black 2002), much like the El Nino - Southern

Oscillation (ENSO) does on the global climate. It is speculated by a number of authors,

however, that changes in greenhouse gas concentration have probably contributed to driving changes in the Arctic Oscillation (Shindell et al. 1998; Corti et al. 1999; Cowen

1999; Hartrnann et al. 2000; Shindell et al. 2001 ; Hu and Tung 2002; Rind et al. 2005).

The Arctic Oscillation remained almost exclusively in its negative phase, characterized by high atmospheric pressure at the North Pole from 1950 to 1988

(Figure 2.21). Although individual years show Arctic Oscillation index values greater than zero, most values are overwhelmingly below that value. From 1988 to 1994 however, the Arctic Oscillation index increases significantly above zero and remains so for the entire six years. With the Arctic Oscillation in its positive phase, Atlantic water encroaches on the Arctic and decreases the total amount of Arctic sea ice; Arctic waters were significantly warmer in the early-] 990's than in surrounding decades (Dickson et al.

2000; Overland et al. 2002). Despite increased sea ice melt, this period also coincides with a significant decrease in net radiation measured at both Alert and Resolute Bay. The reason for this relationship is undoubtedly complex with multiple feedbacks and requires further study. Similar to the trend in annual net radiation, the trend in the Arctic

Oscillation reverts to its negative phase in the mid-1990's.

As described earlier in the chapter, the positive phase of the Arctic Oscillation, which persisted in the early and mid-1990's, is linked to the strength of the polar vortex, stratospheric temperatures and rates of ozone depletion. A strong polar vortex is generated by a strong equator-to-pole temperature gradient arguably driven by greenhouse gases. Many studies show a strong polar vortex and accompanying cold stratospheric temperatures during the early 1990's (Braathen et al. 2002; Pawson and

Naujokat 1999; Comiso 2001 ; Overland and Wang 2005). Low stratospheric temperatures also contributed to increased ozone depletion over the same period (Manney et al. 1994; Braathen et al. 2002; Weatherhead et al. 2005). The strength of the polar vortex peaks in the winter of 1992-93. Many studies concur that aerosols from the eruption of Mount Pinatubo further increased the strength of the Arctic Oscillation and the polar vortex, with the effect peaking a year after the occurrence of the eruption itself

(Pawson and Naujokat 1999; Stenchikov et al. 2002; Stenchikov et al. 2004).

The ultimate cause of the observed decrease in net radiation is an increase in annual average albedo, in turn largely caused by changes in the snowmelt date (shown in

Chapter 3). Although this strong relationship between albedo and net radiation is intuitive, another force is causing variations in the snowmelt date and in turn variations in albedo and net radiation. Earlier in this chapter, it is shown that longwave radiation in the late winter months (particularly in March) may be crucial in determining energy available to snowmelt, spring surface albedo and the timing of the snowmelt date.

Decreased stratospheric temperatures from the enhanced polar vortex occurring during the positive phase of the Arctic Oscillation would lower the effective emission temperature of the atmosphere in clear portions of the sky. Assuming that the variation in surface temperatures is conservative, this would cause the observed deficit in longwave radiation. These effects are most significant in the late winter, when the polar vortex is at its strongest, coinciding with the significant decrease in longwave radiation observed in all late winter months (especially February and March). This significant decrease in longwave radiation in the late winter drives decreased annual net radiation values by curtailing and delaying snowmelt and raising the annual average albedo.

Figure 2.22 shows a schematic of these relationships; these are virtually the exact opposite effects of those depicted in Figure 2.20 when the Arctic Oscillation is negative. Since Alert is a more northerly site, it is located closer to the centre of the polar vortex and would therefore see further decreased atmospheric temperatures than Resolute

Bay. This is reflected in the annual net radiation totals, as Alert shows a more significant decrease during the positive phase of the Arctic Oscillation.

2.5. Conclusions

Although it is difficult to make long-term conclusions about the Arctic climate from a fifty-year sample, a great deal of change has indeed occurred in the radiative budgets of Alert and Resolute Bay over that time. These changes are undoubtedly at least partially anthropogenic in nature as technology and population have had an increasing effect on the Earth's ecosystem.

Both data from Alert and Resolute Bay show an overall decrease in solar radiation input over the past half-century. Data from Alert shows a decrease of 2.25% of the daily mean per decade (r2:0.1 64; p-value: 0.0129), and Resolute Bay shows a decrease of

2.50% of the daily mean per decade (r2: 0.375; p-value: <0.0001). Both sites however show a recent recovery over the past decade. Cloudless sky trends match overall trends suggesting that changes in cloud cover and cloud amount are not the major controls on these solar radiation trends. As is speculated by other authors (e.g. Stanhill 1995;

Lohmann et al. 2004; Che et al. 2005), it is most likely that changes in atmospheric constituents (aerosols and/or greenhouse gases) are the major cause. Further study in this area is definitely warranted.

At both sites, net radiation values have increased substantially over the past half- century. Overall, at Alert net radiation is increasing by 114.6 MJ rn-2 per decade (r2:0.335; p-value: 0.001 3) and at Resolute Bay net radiation is increasing by 82.2

MJ m-2per decade (r2: 0.289; p-value: 0.0010). At both sites, though more pronounced at the more northerly Alert site, values decreased and then increased again during a period in the early-1990's that corresponds with a positive phase of the Arctic Oscillation. With the exclusion of that period, the resulting overall increases are even more dramatic.

The majority of the increase in net radiation has occurred in the spring months of

May and June. In June alone, Alert totals have increased by 97.1 MJ m-2per decade (r2:

0.666; p-value: ~0.0001)while Resolute Bay totals have increased by 32.5 MJ m-"er decade (r2: 0.256; p-value: 0.0023). As supported by the albedo data studied in

Chapter 3, this is largely caused by a retreat in the spring snowmelt date.

Annual net radiation trends are highly dependent on annual average albedo

(r-value: -0.635; r2: 0.403 at Resolute Bay). This is expected since, in an Arctic environment, albedo is the most variable component of the radiation balance on an interannual basis. The timing of snowmelt in the spring, which largely dictates annual average albedo, is crucial to dictating the annual total of net radiation. There must be, however, another aspect of the Arctic climate which is causing changes in albedo and ultimately in net radiation.

A correlation between annual net radiation totals and net longwave radiation totals has been observed to be quite strong (r-value: 0.787; r2: 0.619 at Resolute Bay).

That relationship is especially strong in the month of March (r-values: 0.773; r2: 0.598).

Changes in the longwave radiation regime, driven by changes in the temperature gradient, in the early months of the year are therefore driving changes in energy availability, snowmelt rate and annual total net radiation. This change in the temperature gradient could be the result of changes in greenhouse gas concentration and/or atmospheric particulates or variations in cloud cover. Of these possibilities, closer examination of the data from the early-1990's suggest changes in greenhouse gas concentration are the most likely candidate.

Both Alert and Resolute Bay data show a noticeable decrease in annual net radiation in the early-1990's. This period coincides with a persistently positive phase of the Arctic Oscillation. Greenhouse gases, ozone depletion and volcanic aerosols are the commonly hypothesised causes for this shift in the Arctic Oscillation. Data from this study as well as information from previous studies suggest that greenhouse gases are the most likely cause of the strengthening of the equator-to-pole temperature gradient and the polar vortex. Decreased stratospheric temperatures resulting from an enhanced polar vortex, decrease longwave emission from the atmosphere under clear skies. With surface temperatures remaining nearly constant on an annual cycle, this results in a decrease in net longwave radiation. Less energy available for snowmelt in the late winter months, delays snowmelt and results in higher annual albedo and lower annual totals of net radiation.

A pronounced phase shift in the Arctic Oscillation in the early-] 990's caused a significant decrease in net radiation at both sites, especially at Alert. This curtailed a relatively linear increase in net radiation at both sites, which has continued after the

reversion of the Arctic Oscillation to its negative phase. Most of the increase in available energy has been caused by the retreat of the snowmelt date allowing more incoming

radiation to be absorbed by the resulting lower-albedo surface. Table 2.1. Alert radiation summary. Highlighted cells indicate incomplete data. Year I Solar I Clearness ( % of daily l~etradiation] May net radiation I June net radiation Mean air temperature ("(3

Table 2.3. Resolute Bay radiation summary (page 2). Highlighted cells indicate incomplete data. Table 2.5. Correlations between monthly totals of net longwave radiation and annual totals of net radiation for Resolute Bay. Month January 0.353 February 0.423 March 0.598 April 0.554 0.307 May 0.348 0.121 June 0.604 0.365 July 0.584 0.341 August 0.730 0.533 Seotember 0.548 0.300 October 0.286 0.082 November 0.155 0.024 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 2.1. Total annual solar radiation at Alert from 1965-2000. Incomplete years excluded. All data presented in Figure A8.5.

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 2.2. Total annual solar radiation at Resolute Bay from 1958-2002. Incomplete years excluded. All data presented in Figure A8.6. Year

Figure 2.3. Annual average clearness index at Alert from 1965-2001. Refer to Appendix 4 for further description of units. Incomplete years excluded. All solar radiation data presented in Figure A8.5.

Year

Figure 2.4. Annual average clearness index at Resolute Bay from 1958-2002. Refer to Appendix 4 for further description of units. Incomplete years excluded. All solar radiation data presented in Figure A8.6. Year

Figure 2.5. Daily average clearness index for all cloudless days at Alert. Refer to Appendix 4 for further description of units.

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 2.6. Daily average clearness index for all cloudless days at Resolute Bay. Refer to Appendix 4 for further description of units. 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 2.7. Total annual net radiation at Alert from 1969-2000. Incomplete years excluded. All data presented in Figure A8.7.

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 2.8. Total annual net radiation at Resolute Bay from 1964-2002. Incomplete years excluded. All data presented in Figure A8.8. Year

Figure 2.9. May net radiation at Alert from 1969-2002.

...... 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 2.10. May net radiation at Resolute Bay from 1964-2003. Year

Figure 2.1 1. June net radiation at Alert from 1969-2002.

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 2.12. June net radiation at Resolute Bay from 1964-2003. 1950 1960 1970 1980 1990 2000 201 0 Year

Figure 2.13. Average annual albedo from 1957-2002 at Resolute Bay. Incomplete years excluded. All data prior to application of quality assurance procedures is presented in Figure A8.9.

1945 1955 1965 1975 1985 1995 2005 Year

Figure 2.14. Mean annual surface air temperature at Alen from 195 1-2002. Year

Figure 2.15. Mean annual surface air temperature at Resolute Bay from 1948-2003.

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 2.1 6. Total annual net longwave radiation at Resolute Bay from 1964-2002. Net longwave radiation is solved by residual from the radiation balance, therefore a large number of years are excluded due to incomplete albedo, solar radiation and/or net radiation data. Net Longwave Radiation (MJ rn-'y-')

Figure 2.1 7. Relationship between annual totals of net radiation and net longwave radiation. Incomplete years in either dataset excluded.

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 2.18. March net longwave radiation at Resolute Bay from 1964-2003. 900 -

800 -

CI 7* 700 - 'Y 4 600: I Y c 500- .-0 *t CI .-Q u 400: ** !? CI g 300-

200 -

100 -

o,...... -230 -210 -190 -170 -150 -130 -110 -90 - 70 Net longwave radiation (MJ~-2month-')

Figure 2.19. Relationship between the annual total of net radiation and the March total of net longwave radiation.

Warming of Increase in LJ in the atmosphere early months of the Increase in L* and Ir (greenhouse gases or * year, exceeding Increase in Q* atmospheric dynamics) increase in LT I

Longer snow-free Earlier spring More energy available period snowmelt date @ for snowmelt

Lower average annual Higher annual totals of albedo net radiation

Figure 2.20. Schematic showing longwave driving of increased annual net radiation when the Arctic Oscillation is negative. 1945 1955 1965 1975 1985 1995 2005 Year

Figure 2.21. Annual value of the Arctic Oscillation Index from 1950-2003. The index is normalized using 1950-2000 base period statistics. More information on the Arctic Oscillation and its calculation is presented in Chapter 2.

Decrease m LJ under clear skies m the early Decrease m L* and

Shorter snow-free Later spmg snowmelt Less energy available for snowmelt

Higher average annual Lower annual totals of albedo net radation

Figure 2.22. Schematic showing longwave driving of decreased annual net radiation when the Arctic Oscillation is positive (i.e. during the early to mid 1990's). CHAPTER 3. ANNUAL AND INTERANNUAL SURFACE ALBEDO TRENDS AT RESOLUTE BAY, NUNAVUT

3.1. Introduction

The preceding chapter discusses the interannual solar and net radiation trends in the Canadian high Arctic. This chapter provides an analysis of surface albedo at Resolute

Bay on an annual and interannual basis. Detailed investigation of albedo at such a site provides valuable insight into the net radiation regime and climate of both Resolute Bay and the overall Arctic. Since the annual albedo cycle in Arctic environments is comprised of two distinct regimes (snow-covered and snow-free), its study becomes crucial to understanding the region's energy availability (Wendler and Eaton 1990).

Surface albedo studies are also vital for the understanding of such processes as surface energy budget, water availability and vegetation growth (Winther 2002). Changes in interannual albedo have also been used as early indicators of global warming in Arctic environments (Foster 1989; Robinson et al. 1992; Stone et al. 2002). Despite its importance, the study of albedo in the Arctic, especially on an interannual scale, has been limited.

Chapter 2 showed that annual net radiation totals have increased at Resolute Bay over the past half-century, in contrast to decreasing global solar radiation values.

Changes in surface albedo could therefore be the largest contributor to the observed

increase in available energy at the surface. This chapter examines the changes in the Arctic surface on an annual basis, and the effect those changes have on the net radiation regime.

The chapter begins by introducing the theory and previous research relating to albedo in Arctic environments. Focus is given to studies conducted in Arctic regions that examine annual and interannual trends in surface albedo. This is followed by a description of the data employed in the study and an examination of how those data were edited and analyzed to minimize any errors in the database and in the calculated albedo values. This is followed by detailed examination of annual and interannual albedo trends at Resolute Bay. Finally, relationships between net radiation and albedo are examined on an annual and interannual basis.

3.2. Theory

The radiation balance provides a summary of the individual radiative components of the climate system. Global solar radiation (KJ) is the driving force of the entire climate system and the most influential component of the surface radiation balance on an annual basis. A portion of global solar radiation is reflected in proportion to surface albedo (a) resulting in net solar radiation (K*)

where Kt represents reflected solar radiation. Albedo is a dimensionless value that ranges from zero (complete absorption) to one (complete reflectance).

The surface radiation budget results from the sum of net solar radiation and net longwave radiation (L*) where Q* represents net radiation. Q* dictates the amount of energy available for surface processes. Since net longwave radiation values tend to remain relatively constant on an annual basis, albedo is the second most important contributor to net radiation after solar radiation in Arctic regions (Przybylak 2003). On an interannual basis, albedo exercises the greatest influence on net radiation, as its annual averages are more variable than annual totals of solar radiation (Wendler and Eaton 1990).

The Arctic year is characterized by two very distinct regimes of surface reflectance. For approximately nine months of the year, the surface is virtually snow- covered. Since snow has a high albedo and reflects most incoming solar radiation, corresponding net radiation values are extremely low. Once snowmelt occurs, usually in

June, the surface is characterized by lower-albedo tundra for an average of three months, enabling the absorption of additional solar radiation.

The amount of solar radiation incident on the region peaks near snowmelt, which is accompanied by a large decrease in albedo. This makes the date of its occurrence critical for the distribution of energy in the Arctic region (Maykut and Church 1973;

Kukla and Robinson 1988; Stone et al. 2002). An early snowmelt enables greater absorption of solar radiation when it is near its highest value, which in turn produces higher annual totals of net radiation. At the conclusion of the snow-free period, values of solar radiation are extremely small, lessening the impact of changes in surface albedo.

The timing of the spring snowmelt is therefore vital in determining the annual Arctic climate; much more so than the date of snow re-establishment or even the length of the

snow-free period. 3.3. Previous research

A number of studies have been conducted in Arctic regions that examine the albedo of snow and ice as well as the albedo of the surfaces underlying them. This research covers a wide range of topics varying in its relevance to the study conducted herein. Very few studies currently exist, however, that describe the annual and interannual variations of albedo in Arctic locations. This is largely due to the lack of reflected radiation or albedo measurements taken at Arctic locations. The inaccessibility and inhospitality of the Arctic to both people and meteorological instruments have limited the number of radiation measurements conducted in those regions. Of the limited number of Arctic climate stations, fewer still measure reflected radiation, as it is not considered a high priority.

Studies of the albedo of sea ice covering the Arctic Ocean have been numerous

(e.g. Hanson 1961 ; Langleben 197 1 ; Grenfell and Perovich 1984; Robinson et al. 1992;

Curry et al. 1995; Perovich et al. 2002). The complex relationship between the albedo of snow cover, sea ice, and the underlying ocean during the snowmelt period is commonly explored. Robinson et al. (1 992) and Belchansky et al. (2004) describe the interannual variations in ice melt dates throughout the Arctic Ocean. Belchansky et al. (2004) finds a significant correlation between the timing of ice melt and the seasonal strength of the

Arctic Oscillation (AO). The amount of spatial variability observed across the Arctic, however, makes it difficult to compare these results with the site-specific land-based radiation observations studied.

The spectral distribution of snow albedo has been the subject of much examination (e.g. Kondratyev et al. 1954; Bryazgin and Koptev 1969; Grenfell and Maykut 1977; Grenfell et al. 1981 ; Wiscombe and Warren 1981). The study conducted herein does not separate albedo into its spectral components (for more details on the sensors employed in this study refer to Chapter 1 and Appendix 6). The effect of soot and various other absorptive impurities in decreasing the albedo of the Arctic snowpack has also been explored (e.g. Warren and Wiscombe 1980; Clarke and Noone 1985).

Clarke and Noone concluded that sooty snow possessed an albedo 5-10% lower than clean snow.

Although not examined herein, the diurnal variations in albedo in high-latitude, snow-covered environments have been investigated (e.g. Hubley 1955; Dirmhirn and

Eaton 1975; McGuffie and Henderson-Sellers 1985). The relationship between albedo and various parameters, most notably zenith angle and cloud cover, are explored. All three studies show higher albedo values with increased zenith angle under cloudless skies. Hubley (I 955) shows increased albedo and an extreme dampening of the zenith angle-albedo relationship under cloudy skies. Errors in albedo measurements at high zenith angles are investigated by Dubreuil and Woo (1984).

Seasonal studies in Arctic environments have often been focussed on a particular portion of the annual albedo cycle with data usually limited to a couple of years.

Research has been conducted during the snow-free period of the Arctic year when the exposed surface is characterized mainly by tundra. Pioneering studies conducted by

Jackson (1960) and Davies (1962) measured the albedo of various sub-Arctic surfaces on

Canada's Labrador-Ungava peninsula. McFadden and Ragotzkie (I 967) undertook a similar study using albedo data collected on reconnaissance flights from the north-central

United States to the Arctic Ocean over various surfaces, including tundra. More recently, Petzold and Rencz (1 975) collected albedo data in sub-Arctic Quebec and compared their results to previous studies. In their case, however, tundra was separated into eight distinct types based on the species and colour of lichen covering its surface.

Variations of albedo during the snowmelt period have been investigated over a variety of surfaces. Focus is usually given to the rate of albedo decrease in order to enable modelling of snow disappearance. U.S. Army Corps of Engineers (1956) and

O'Neill and Gray (1 973) studied the temporal variations of albedo during snowmelt of a deep mountain snowpack and a shallow prairie snow cover respectively. Winther (1993) measured snowmelt albedo of a shallow mountain snowpack in Norway. The observed rate of albedo decrease is then compared to those measured in previous studies. Time- series of snowmelt albedo were measured at Barrow, Alaska (71 "1 8' N, 156O47' W) from

1986 to 1989 by Dutton and Endres (1 991). Albedo measurements were taken during the

snowmelt period from 2000-2002, again at Barrow, Alaska, by Grenfell and Perovich

(2004). The changes in albedo during snowmelt of sea ice, lagoon ice, fresh ice and

tundra are measured and compared. Variations in spectral albedo and effects of

contaminant loading are explored as well. In all studies, the rate of snowmelt is observed

to accelerate tremendously when snow cover has sufficiently melted to expose the

underlying surface to radiative effects (Wiscombe and Warren 1981; Winther 1993). In

both studies conducted at Barrow, for example, albedo values decrease from greater than

0.8 to less than 0.2 in less than two weeks.

Maykut and Church (1973) explored the annual trend in albedo at Barrow, Alaska

from 1962-1966. Four seasons in the annual albedo regime were observed. A winter

stationary period is characterized by albedo values of 0.80 to 0.90, typical of fresh snow cover. A spring transitional period of 3-5 days then occurs during snowmelt when albedo values decrease from 0.75 to 0.1 0. This is followed by the snow-free period characterized by relatively constant albedo values. Finally, another transitional period occurs while snow-cover is re-established. Annual trends in longwave and net radiation are then explored and related to the seasonal variations in albedo. Net radiation is dominated by longwave radiation from October to April. Annual net radiation totals are then observed to be dictated almost solely by totals from the month of June, which are in turn determined by the change in albedo accompanied by snowmelt.

Foster (1 989) uses data from 12 weather stations in the Arctic to explore interannual variations in the date of snowmelt occurrence; the five European and Russian sources, however, cover a small temporal period and are not considered further. Data from the late 1950's to 1985 are examined for five Canadian stations and two Alaskan stations. A statistically significant earlier snowmelt date (no quantitative values are given) is observed at both the Alaskan stations and the Canadian stations since the early

1950's and late 1950's respectively. A similar study conducted by Stone et al. (2002) using data from Barrow, Alaska shows a retreat in the snowmelt date of 8 days since the mid-1 960's. Annual trends in albedo in 1994 and 1998 are presented and compared. The influence of both annual variations in albedo and interannual variations in the snowmelt date on the net radiation regime are explored. Finally, a multiple regression equation is derived to model the timing of snowmelt. Seventy percent of variation is found to be explained by trends in winter (October-February) snowfall, spring (March and April) temperatures and spring cloud cover. Only a small number of studies have been conducted which describe the interannual variability in albedo at an Arctic location. Winther et al. (2002) describe annual and interannual trends in albedo from 1981 -1997 at ~~-Alesund(78'55' N,

11'56' E), located on the islands of Svalbard, north of Scandinavia. No significant trends are observed in the timing of snowmelt, the rate of snowmelt, the length of the snow-free period or the timing of the re-establishment of snow cover at the conclusion of the snow- free period. Linkage between the North Atlantic Oscillation (NAO) and albedo trends is also explored, with no significant correlation detected.

Comiso (2001) uses data collected by the U.S. National Oceanic and Atmospheric

Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) to obtain average annual albedo, sea ice extent, cloud cover and surface temperature over the entire Arctic region from 1987-1998. Albedo values were calculated by masking the effects of clouds and thereby only employing cloud-free data. The study shows the overall temporal and spatial variability of the datasets and the correlations between them.

Overall, the study shows significant low-albedo anomalies in 1989-1991 and 1995 and high-albedo anomalies in 1992-1994 and 1997 over the entire Arctic. Although the results give a valuable summary of the overall changes in albedo over the entire region, the limited spatial resolution and significant spatial variability of the data limit the study's application to a particular Arctic location such as Resolute Bay or Alert.

The importance of albedo to the radiation budget in Arctic environments and the lack of existing interannual studies present a need for further research in the field. The study conducted herein examines the annual and interannual albedo trends observed in the Canadian high Arctic over the past half-century. The study conducted by Winther et al. (2002) describes similar trends measured at a comparable latitude, but in much less detail and over a shorter time period. Comiso (2001) shows that interannual variations in albedo differ significantly in Europe and North America, further validating the importance of a study conducted in the Canadian Arctic. Comiso presents annual average albedo values, again over a smaller temporal period, which are too spatially variable to compare with those collected at a specific location.

3.4. Methodology

Data from Environment Canada's weather station at Resolute Bay, Nunavut

(74'42' N, 94'50' W) is employed in this analysis. More detail on the location of the station is provided in Chapter 1. Similar analysis was not conducted for the other high

Arctic stations, as they are lacking the reflected radiation measurements necessary to calculate albedo values.

Hourly values of solar radiation were divided by hourly values of reflected solar radiation to calculate hourly albedo values. As in Winther (1 993) and Lindsey and

Rothrock (1 994), average daily albedo values were then calculated by averaging hourly albedo values for those hours when the solar zenith angle is less than 80 degrees. This method enabled equal representation of every hourly value throughout the diurnal albedo cycle. This is necessary in Arctic environments, as hourly albedo values often fluctuate significantly due to snowmelt over a daily period, with the hours corresponding to the highest radiation values not always representative of the average daily albedo. Values calculated for hours when the zenith angle is greater than 80 degrees are often erroneous, as instrument sensitivity is limited at low intensity levels and the error in cosine response

generally increases at large zenith angles. At high zenith angles, reflected radiation values can also be overestimated due to internal reflectance at low solar angles (Dubreuil and Woo 1984). Errors in albedo calculations are approximately 10% at 80 degrees and increase with higher zenith angles (Dubreuil and Woo 1984).

At Resolute Bay's latitude, this procedure results in 214 days of daily albedo values per year from March 8 to October 7. Impossible albedo values of zero, one and greater than one were removed from the resulting daily values. These erroneous values resulted from invalid measurements or missing data in either radiation dataset. Whenever possible, missing values were interpolated to maximize the number of years retained for further analysis and to ensure every year has 214 days of albedo data. Differing days of data would have resulted in inaccurate interannual comparisons, as missing values would have given disproportionate weighting to either the snow or tundra surface. When values were missing during key parts of the year (such as the snowmelt period), that year was excluded from further analysis. This resulted in the exclusion of 9 years of data from

1957-2003 (years in Table 3.1 with fewer than 214 days of data). For the calculation of average monthly albedo, those months with greater than 10 days of missing data were excluded from resulting interannual trends. More details on these procedures is provided

in Appendix 4.

In addition to average annual albedo, a number of other characteristics were also

acquired from every year of albedo data in order to evaluate their changes from 1957 to

2003 (Table 3.1). The dates marking the beginning of the major spring snowmelt,

commencement of the snow-free period and conclusion of the snow-free period were

determined from annual time-series. The date marking the beginning of snowmelt was

selected as the peak albedo value immediately preceding the largest annual decrease in albedo. All other dates were determined by the same method. By subtracting the appropriate dates, the length of the snow-free period was subsequently calculated.

Finally, average albedo values for the snow-covered period from March 8 to the beginning of snowmelt and for the snow-free period were both calculated. The average albedo for the snow-covered period following the snow-free period was not calculated, as the time period before polar sunset is short in duration and the albedo values fluctuate a great deal from year to year depending on the date that continuous snow cover is established (Maykut and Church 1973; Winther et al. 2002).

Annual and interannual net radiation values were calculated in a similar fashion.

Hourly values were summed into daily values and then plotted to create annual time- series. Daily values were subsequently used to create average daily values, which were then multiplied by the number of days in a given month to create monthly net radiation totals. Monthly totals were then summed to produce annual totals. Years containing any months of missing data were eliminated from subsequent plots of interannual net radiation. More details on these procedures are provided in Appendix 4 and Chapter 1.

3.5. Results and discussion

3.5.1. Interannual trend in average annual albedo

Once quality assurance procedures were applied to every year of albedo data, annual average albedo values were calculated. Prior to the application of these procedures, only 28 years of data were considered sufficiently complete to be compared on an interannual basis; these years contained an average of 187 days of data (with only 9 years containing a full 214 days of data). After the aforementioned procedures were applied, the number of 'complete' years increased to 38. The use of the same 214 days of data for every year also permits the production of a more meaningful interannual albedo trend (the interannual trend in annual albedo prior to application of these procedures is presented in Figure A8.9).

The time series of average annual albedo has an overall average value of 0.495 with a great deal of variation across the temporal period (Figure 3.1). The maximum value of 0.559 was measured in 1959 while the minimum value of 0.399 was measured in

1985. The overall trend is decreasing at a rate of 0.014 units per decade (r2:0.1 84, p-value: 0.0073). The trend would be much more significant if not for a large increase and subsequent decrease in albedo from 1988 to 1998, which coincides with the positive phase of the Arctic Oscillation (refer to Chapter 2). With the intervening years excluded, the rate of decrease increases to 0.023 units per decade (r2:0.449, p-value: ~0.0001).The most significant and largest decrease in albedo occurs between 1974 and 1985. In that period, the average albedo decreases by 0.101 units in only 1 1 years (r2:0.696, p-value:

0.0027); this amounts to a decrease of 0.096 units over a decade.

There are various components of the annual Arctic albedo cycle that could have varied to account for the observed changes in average annual values. These will be explored following examination of the nature of changing albedo values on an annual basis. The mechanisms causing perturbations in the overall Arctic climate including the interannual albedo trend, such as changes in temperature and variability in the Arctic

Oscillation, have been explored in Chapter 2. 3.5.2. Annual albedo trends

Although the time-series of albedo data for every year was created and analyzed, only one year of data is presented and thoroughly analyzed here, as all years demonstrate very similar trends. Although the dates of crucial changes in albedo, the average albedos of the snow-covered and snow-free periods and the number of small spring snowmelts and summer snowfalls differ, the overall pattern is repeated each year.

Data from 1991 is presented in Figure 3.2 as representative of the entire dataset

(1 991 data prior to application of quality assurance procedures is presented in Figure

A8.10). The average albedo for the annual period is 0.503; however, individual daily

values vary greatly around that mean. Albedo values change drastically between the

snow-covered and snow-free periods.

The period prior to snowmelt is characterized by daily albedo values ranging from

0.64 to 0.89 with an average value of 0.758, typical of a snow-covered surface (Angstrom

1925; Kukla and Robinson 1980; Wiscombe and Warren 1981). The albedo of a snow

surface is determined by snow grain size, solar zenith angle, the ratio of direct to diffuse

radiation and, for packs less than 20 centimetres, snow thickness (Wiscombe and Warren

1981). The largest influence on daily albedo values is grain size, which increases with

the age of the surface snow cover (Wiscombe and Warren 1981). As can be seen from

the plot, albedo values degrade as snow ages until a fresh snowfall increases them again.

Near the end of May, the last major snowfall of the year occurs. Soon after,

temperatures have increased sufficiently to begin melting the snow cover. When

snowmelt begins, it proceeds quite rapidly as positive feedback enables increased

radiation absorption and snowmelt with any decrease in albedo (Belchansky et al. 2004). When snow cover has thinned sufficiently to allow the underlying vegetative surface to influence the surface albedo, snowmelt proceeds even more rapidly (O'Neill and Gray

1973). Although in 1991, the snow does melt slightly faster than normal, this process does occur usually over a period of less than two weeks (Woo and Ohmura 1997;

Grenfell and Perovich 2004).

During the two-week snowmelt period, as also seen by Woo and Ohmura (1 997), daily average albedo values decrease from about 0.8 to 0.2. The exposed surface is then tundra, which absorbs much more radiation than a snow surface. From the beginning of

June to mid-September (a period of three and one-half months) albedo values stay remarkably consistent. The average albedo during this period is 0.241, similar to values found by Petzold and Rencz (1975) for lichen tundra and Monteith (1959) for any short vegetated surface. Values rarely decline below 0.2 and are only greater than 0.3 during summer snowfalls (when the albedo can be greater than 0.7 for a short time). These events, which usually occur a couple of times a year in Resolute Bay, as illustrated by albedo values that drastically increase for a period of a day or two. Usually the snow melts quickly and a characteristic tundra albedo value is restored. Such a summer snowfall event can be seen in the beginning of September 1991.

At some point in the year, in this case in mid-September, a relatively large snowfall occurs when temperatures remain too low to melt it completely. As temperatures continue to drop and snow continues to fall, the winter albedo value of approximately 0.75 is restored. If a limited amount of snow falls during this first snowfall event, the resulting thin snow cover will often begin to melt and yield slightly lower values for a short period (Maykut and Church 1973). Such an event occurs in late- September and early October (when the average albedo decreases to 0.52) before another snowfall event restores albedo values to 0.75. Once complete snow cover has been established, the ground remains snow-covered until temperatures rise above freezing the following year.

3.5.3. Other interannual albedo trends

With an understanding of the annual Arctic albedo cycle, and the trend in average annual albedo, a closer examination of the data can now be undertaken to determine what aspects of that annual cycle have changed over the past half-century. Interannual trends in various related parameters, including average monthly values, are investigated, including their correlation with the interannual trend in overall annual albedo.

Prior to snowmelt, the surface is a snow surface with high albedo. The average albedo of that snow surface varies throughout the temporal period. On average, the value is 0.715 reaching a maximum value of 0.793 in 1974, with a minimum value of 0.587 in

1985 (Figure 3.3). Overall the trend exhibits a decrease of 0.01 2 units per decade

(r2:0.108, p-value: 0.0388) with a more significant decreasing trend until 1985. From

1958-1985 the rate of decrease rises to 0.032 units per decade (r2: 0.280, p-value:

0.0046). The monthly values of March (Figure 3.4) and April (Figure 3.5) correspond to completely snow-covered surfaces, exhibiting average albedos of 0.727 and 0.725 respectively. Of the two months, only April shows a significant decrease in albedo of

0.01 6 units per decade (r2:0.175, p-value: 0.0065). Both the mean snow-covered albedo and average April albedo values correlate highly with average annual albedo (respective r-values: 0.796 and 0.808; r2: 0.634 and 0.653). The reflectance of the Arctic surface over the period examined in this study is, therefore, seen to be changing, due to aging and melting of the top layers of snow, even when the surface is still completely covered by thick snowpack. The fact that these variations correlate highly with the trend in annual average albedo suggests these changes to the nature of the snow surface are contributing to changes in the overall climate.

Both the date marking the onset of snowmelt (Figure 3.6) and the conclusion of snowmelt at the beginning of the snow-free period (Figure 3.7) have exhibited similar, highly significant trends. Both dates have retreated substantially with the rate of retreat most prominent prior to 1985. After 1985, the average dates have stayed relatively consistent with little variability. The beginning of snowmelt shows a retreat of 4.2 days per decade (r2: 0.230, p-value: 0.0015) with that rate being 7.3 days per decade prior to

1985 (r2: 0.296, p-value: 0.001 9). The date marking the conclusion of snowmelt shows a larger and more significant rate of retreat of 5.1 days per decade (r2: 0.331, p-value:

0.0001) with that rate being 8.7 days per decade prior to 1985 (r2: 0.455, p-value:

0.0001). A significant retreat in the snowmelt date has also been observed in studies by

Groisman et al. (1994), Kuang and Yung (2000), Dye (2002) and Bamzai (2003). The trends in the timing of both dates correlate highly with that of average annual albedo

(respective r-values: 0.754 and 0.761 ;r2: 0.569 and 0.579); the dependence of annual average albedo on the date of snowmelt is also noted by Robinson et al. (1992).

The intermediate average albedo values of the months of May (Figure 3.8) and

June (Figure 3.9) correspond with the snowmelt period. The average May albedo is

0.671 and the average June albedo is 0.384. Both trends show overall decreases, with the rate of decrease concentrated between 1974 and 1985. The overall May trend exhibits a decrease of 0.028 units per decade (r2: 0.158, p-value: 0.0123) and the June trend exhibits a decrease of 0.044 units per decade (?: 0.222, p-value: 0.0021). The albedo values of the spring months of May and June greatly influence the annual totals as correlations are high in both cases, especially in June (respective r-values: 0.795 and 0.844; r2: 0.632 and

0.712). An earlier snowmelt has obviously contributed to more exposure of the snow- free tundra surface, which in turn corresponds to a decrease in average annual albedo.

The snow-free period has an average albedo of 0.245 (Figure 3.10). No significant linear trend is observed in the data. The data instead exhibits a significant. curvilinear pattern characteristic of a second order polynomial (r2: 0.242, p-value:

0.0079). Monthly values of July (Figure 3.1 1) and August (Figure 3.12) are both characteristic of snow-free tundra surfaces. Their respective average values are 0.23 1 and 0.247. Neither month shows a significant linear trend, although August albedo values show a curvilinear trend similar to the overall snow-free albedo (r2: 0.200, p-value: 0.01 30). Snow-free values have stayed relatively consistent over the past half- century; these trends therefore do not highly correlate with that of average annual albedo

The length of the snow-free period (Figure 3.1 3) has increased by a substantive

6.6 days per decade over the temporal period, 37.2% of its average length of 88 days

(r2:0.301, p-value: 0.0004). Prior to 1988 that rate of increase changes to 9.2 days per decade (r2: 0.51 1, p-value: <0.0001). The date marking the conclusion of the snow-free period (Figure 3.1 4) has however not changed significantly over the past half-century

(?: 0.007, p-value: 0.5881). Albedo data from the month of September (Figure 3.15)

when snow cover is being re-established shows no significant linear trend either

(r2:0.002, p-value: 0.8089). As also observed by Bamzai (2003), the increase in length of the snow-free period is concentrated almost solely in a retreat in the snowmelt date in the spring (r-value: 0.821 ; r2: 0.674). The date marking its conclusion (r-value: 0.569; r2: 0.324) has not changed significantly.

3.5.4. Relationship between albedo and net radiation

Although the net radiation regime is greatly affected by solar radiation, herein the

relationship between albedo and net radiation on an annual and interannual basis is

examined. Whenever relevant to the discussion, however, relationships between solar

and net radiation values are briefly considered as well.

On an annual basis, every year in the dataset displays a similar relationship.

Therefore, 1991 is again used as being representative of the entire dataset (Figure 3.16).

Net radiation values remain low, and generally below zero, until mid-April. In mid-

April, net radiation values begin to increase due to increased incoming solar radiation,

despite high albedo values characteristic of complete snow cover. Even when there is

still complete snow cover, however, net radiation values still show corresponding, but

opposite changes to those observed in albedo. An overall albedo decrease from mid-

April to the beginning of snowmelt results in an increase in net radiation; this is caused

by increased incident radiation and warmer air temperatures melting and aging the snow

cover. Three significant snowfall events occur before the major snowmelt begins, and

corresponding increases in albedo are accompanied by decreases in net radiation. In fact,

the small-scale fluctuations in albedo and net radiation from mid-April to the onset of

snowmelt mirror each other quite explicitly. When the major yearly snowmelt event occurs in late May, albedo values drastically decrease from 0.8 to almost 0.2 over a period of only one week, and over that same period, daily net radiation values increase dramatically from 2 MJ m -2 d -1 to 15

MJ m-2 d -1 , due to the increased absorption of radiation accompanied by the decrease in albedo. The albedo and net radiation trends cross in the middle of the snowmelt period in

1991, as they do every year in the dataset.

Over the snow-free period, the trend in net radiation tracks the trend in solar radiation, as albedo values are essentially constant and low in magnitude and variability.

When the snow-free period ends, solar radiation values are already sufficiently small that net radiation values are small, and little change is observed when the albedo again drastically changes from about 0.2 to 0.8. The influence that the large change in albedo has on net radiation values is therefore observed to be vastly different at snowmelt than it is when snow cover is re-established.

Although solar radiation influences the value of net radiation as much or more than albedo on a daily basis, it is unlikely that it has fluctuated as greatly over the interannual period from 1958-2003 (the interannual trend in solar radiation at Resolute

Bay is examined in Chapter 2). It is therefore informative to compare the trend in albedo to that of net radiation in order to determine if fluctuations in surface conditions over that temporal period have indeed been the major driving force in determining changes in energy availability.

The two trends of albedo (Figure 3.1) and net radiation (Figure 3.1 7) from 1958 to

2003 show both significant similarities and differences. Net radiation does show an overall increase of 82.2 MJ m-' per decade (r2: 0.289, p-value: 0.0010) corresponding to a similar decrease in albedo over the dataset. The decrease in net radiation in the late-

1980's and early-] 990's is not nearly as pronounced as it is in the albedo dataset, but it is noticeable. An anomaly is observed in 1993, however, as a low total annual net radiation value coincides with a low average annual albedo. Comiso (2001) shows higher than average albedo values over the Canadian Arctic archipelago in 1993, however, indicating that this value may be erroneous.

Overall, total annual net radiation and average annual albedo are inversely correlated (r2:0.278, p-value: 0.001 9) (Figure 3.1 8). An increase in average annual albedo of 0.1 will result in a decrease in annual net radiation of 219.1 MJ m-? y-'. If the anomalous relationship observed in 1993 is excluded, the relationship is much stronger

(r': 0.403, p-value: 0.0001). The rate of decrease in net radiation also increases to 248.3

MJ m-' y-l per 0.1 unit increase in albedo.

It appears from the preceding analysis that albedo does in fact play a significant role in determining the interannual net radiation trend in Resolute Bay; this is not surprising due to the obvious links between albedo and net radiation on an annual basis as described. Changes in the nature of the Arctic surface have been most prominently caused by the retreat of the snowmelt date. The timing of its occurrence is critical in determining the annual totals of net radiation (Maykut and Church 1973; Kukla and

Robinson 1988; Stone et al. 2002), resulting in increased energy available to the Arctic region. It appears however that there are other factors that have contributed to variations in the interannual net radiation trend as well. Relationships with some of these other components, such as solar radiation, as well as mechanisms responsible for changes in surface albedo, were examined in Chapter 2. 3.6. Conclusions

The substantial difference in albedo between snow-covered and snow-free surfaces plays a critical role in the Arctic radiation balance. As solar radiation is near its peak at its occurrence, the timing of snowmelt is especially critical in determining both the annual average albedo and total net radiation. A decrease in average annual albedo of

0.014 units per decade (r2:0.1 84; p-value: 0.0073) has been observed over the past half- century. Excluding the decade from 1988-1998, which coincides with the positive phase of the Arctic Oscillation, that decrease rises to 0.023 units per decade (r2:0.449; p-value:

<0.0001). This change has in turn influenced the net radiation regime, and contributed to an increase in net radiation over the same temporal period. A 0.1 unit decrease in average annual albedo has coincided with a 248.3 MJ m-2 y -1 increase in total annual net radiation.

The change in average annual albedo has been related to a number of changes in the nature of the Arctic surface during its annual cycle. The albedo of the snow surface prior to snowmelt has decreased and the length of the snow-free period has increased.

Both of these have contributed significantly to the decrease in overall annual albedo.

Changes have been concentrated in the months of April, May and June. The observed increase in length of the snow-free period of 6.6 days per decade (r2:0.301 ; p-value:

0.0004) has been largely dictated by a retreat in the date snowmelt commences in the spring; the date of its conclusion has not changed significantly (R~:0.007; p-value:

0.5881). As the date of snowmelt is dictated largely by temperature, Foster (1989),

Robinson et al. (1 992) and Stone et al. (2002) conclude that its retreat could provide early evidence of global warming. Combined with the observed increase in net radiation, the large decrease in average annual albedo resulting from these changes has therefore resulted in a profound effect on the radiation budget and the overall climate of the

Canadian Arctic. Table 3.1. Albedo data summary

1. Note: All dates given in decimal months Year

Figure 3.1. Average annual albedo from 1957-2002. Incomplete years excluded. All data prior to application of quality assurance procedures is presented in Figure A8.9.

0 1 2 3 4 5 6 7 8 9 10 11 12 Decimal Months

Figure 3.2. Time-series of daily albedo values during 1991. Decimal month values represent the start of new months starting with 0 used for January lS'(4 represents May lSt,7 represents August lSt,etc.). Albedo time-series prior to application of quality assurance procedures is presented in Figure A8.10. 1950 1960 1980 1990 2000 2010 Year

Figure 3.3. Average albedo of the annual snow-covered period between March gth and the beginning of snowmelt from 1958-2003.

1960 1970 1980 1990 2000 2010 Year

Figure 3.4. Average albedo for the month of March from 1958-2003. Year

Figure 3.5. Average albedo for the month of April from 1958-2003.

1950 1970 1980 2000 2010 Year

Figure 3.6. Date at the onset of snowmelt from 1958-2003. Year

Figure 3.7. Date at the conclusion of snowmelt from 1958-2003.

0.85

0.75

0.65 U) I -c .: .: 0.55 c .-E D .;; 0.45 D d a 0.35

0.25

0.15 1950 1960 1970 1980 1990 2000 2010 Year

Figure 3.8. Average albedo for the month of May from 1958-2003. Year

Figure 3.9. Average albedo for the month of June from 1958-2003.

0.31 7

1950 1960 1970 1980 1990 2000 201 0 Year

Figure 3.10. Average albedo of the annual snow-free period from 1958-2002. Year

Figure 3.1 1. Average albedo for the month of July from 1957-2002.

1950 1960 1970 1980 1990 2000 2010 Year

Figure 3.12. Average albedo for the month of August from 1957-2002. Year

Figure 3.13. Length of snow-free period from 1958-2002.

1950 1960 1970 1980 1990 2000 2010 Year

Figure 3.1 4. Date at the conclusion of the snow-free period from 1957-2002. 1950 1960 1970 1980 1990 2000 Year

Figure 3.15. Average September albedo from 1957-2000.

--Net Radial~on +Albedo

0 1 2 3 4 5 6 7 8 9 10 11 12 Decimal Months

Figurc 3.16. Time-series of daily avcragc annual albedo and daily total net radiation for 1991 Year

Figure 3.17. Total annual net radiation from 1964-2002 and average annual albedo from 1957-2002. incomplete years excluded. All data presented in Figures A8.8 and A8.9

0.35 0.40 0.45 0.50 0.55 0.60 Albedo (dimensionless)

Figure 3.1 8. Relationship between total annual net radiation and average annual albedo. Incomplete years in either dataset excluded. Data from 1993 is indicated on the graph, as it is speculated that 1993 albedo could possibly be erroneous. CHAPTER 4. NATURAL ARCTIC SEASONS AT RESOLUTE BAY, NUNAVUT

4.1. Introduction

The preceding two chapters examine the interannual trends in radiation climatology in the Canadian high Arctic. The former reviews trends in solar and net radiation, while the latter is devoted to trends in albedo. This chapter uses radiation data to delineate natural seasons in the Canadian high Arctic using k-means cluster analysis.

Yasunari (1 986) showed that the seasonal scale can be an effective method to observe climate change signals, especially in mid-to-high latitudes. This process is therefore conducted for every year in the dataset in order to explore the variability in the lengths of the resulting seasons and the dates marking the transitions between them. As discussed in Chapter 3, the timing of the snowmelt date is of particular importance in dictating the annual Arctic climate.

A natural season is generally defined as a fraction of the calendar year characterized by relatively homogenous weather and climate (Alsop 1989). Traditionally either the astronomical or the meteorological definition of the seasons is employed

(Trenberth 1983). The transitions between the astronomical seasons are marked by the dates that the vertical ray of the sun at solar noon strikes certain positions on the Earth's surface. New seasons begin when the sun's rays strike their northernmost point (June solstice) and their southernmost point (December solstice) and as they strike the equator (vernal and autumnal equinoxes). The meteorological definition splits the year into four three-month sections. Winter is comprised of December, January and February with each three successive months making up each of the remaining three seasons. Despite the widespread use of these definitions, meteorological phenomena are, in reality, much too variable to be dictated by four constant dates throughout the globe (Allen 1940). The length of the seasons at any given location also varies significantly on a year-to-year basis due to variations in the local climate.

A number of studies have been conducted in various locations to delineate natural seasons based on numerous different meteorological input parameters. Such studies have, however, yet to be conducted in Arctic environments. High-latitude regions have unique climates and are particularly incompatible with traditional definitions of the seasons. In this study the three most significant components of the radiation balance, solar radiation, albedo and temperature, are used to create more meaningful natural seasons based on climatic characteristics.

4.2. Theory

The radiation balance provides a summary of the individual radiative components of the climate system. Global solar radiation (KJ) is the driving force of the entire system and the most influential component of the radiation balance on an annual basis. A portion of global solar radiation is reflected in proportion to surface albedo (a) resulting in net solar radiation (K*)

K*=KJ-K?=K\~(~-~) where K 1represents reflected solar radiation. The total energy emitted (E) by a body depends on its surface temperature (To), and emissivity (E), in accordance with the Stefan-Boltzmann Law

E = €flO4 (4.2) where o is the Stefan-Boltzmann constant (5.67 x Wm-2 K -4 ). The other major component of the radiation balance is net longwave radiation (L*) comprised of longer wavelength radiation emitted in accordance with the Stefan-Boltzmann Law by the

Earth's atmosphere and the Earth's surface and defined as

where LL is atmospheric longwave radiation and Lf is terrestrial longwave radiation.

The variation in emissivity between Arctic surfaces and seasons is negligible and approaches unity, therefore the final term is also negligible and often excluded (Weller and Wendler 1990).

The surface radiation budget results from the sum of net solar radiation and net longwave radiation

where Q* represents net all-wave radiation. Q* dictates the amount of energy available for surface processes. At night, solar radiation is nonexistent and Q* is comprised exclusively of net longwave radiation.

A number of aspects of high-latitude climates must be considered for a thorough understanding of their annual variability. At its latitude of 74'42' N, Resolute Bay undergoes Polar Night from October 201h to February 241h, a period of 127 days. During that period, the region receives no solar radiation and net radiation is comprised exclusively of net longwave radiation.

The Arctic year is also characterized by two very distinct surface types. For nine months of the year, including Polar Night in its entirety, the surface is virtually snow- covered. Snow-covered days with available measurements have an average albedo of

0.714. Since most incoming solar radiation is reflected, net radiation values are extremely low. Once snowmelt occurs, usually in June, the surface is characterized by tundra for an average of three months. The average albedo of snow-free days in the dataset is a much lower 0.245, enabling the absorption of additional solar radiation.

The amount of solar radiation incident on the region peaks near snowmelt, making the date of its occurrence critical for the distribution of energy in the Arctic region (Maykut and Church 1973; Kukla and Robinson 1988; Stone et al. 2002). An early snowmelt enables greater absorption of solar radiation when it is near its highest value, and in turn produces higher annual totals of net radiation. At the conclusion of the snow-free period, values of solar radiation are extremely small, lessening the impact of changes in surface albedo.

4.3. Previous research

A number of studies have previously been conducted that attempt to delineate more meaningful seasons than the astronomical and meteorological seasons generally employed. The geographical locations, climatological parameters and statistical methods employed in these studies differ widely. Such research usually covers a small geographic area however, as the variability of climatic events over the planet make the creation of global seasons inadvisable (Allen 1940). A global study has, however, recently been conducted by the National Oceanic and Atmospheric Administration (Mapes et al. 2005), which utilizes over a million total datasets from numerous worldwide locations. Fourier and wavelet analysis are used to demarcate seasons. Although the study does attempt to broadly generalize global seasons, even its results focus on localized seasons such as the

Asian monsoon and the North American January thaw.

The most common parameter used to delineate seasons is the frequency of dominant synoptic air mass types. This method was pioneered by Lamb (1 950) using data collected in the British Isles from 1898-1947. Five natural seasons were observed upon analysis of the beginning, end and duration of governing synoptic systems. Barry and Perry (1973) review previous studies of natural seasons using synoptic classification.

Focus is given to the work of Lamb (1 950) and Baur (I 958). Baur (1 958) shows eight seasons in Central Europe using data collected in Germany. Studies conducted in Japan by Sakata (1 950) and Yoshino (1 968), in North America by Bradka (1 966) and in Europe by Bryson and Lahey (I 958) are also considered.

A number of ensuing studies also use synoptic systems as the means of classifying the calendar year into natural seasons. Kalnicky (1 987) uses principal component analysis to show synoptic seasons and discontinuities (such as Indian

Summers) in the Northern Hemisphere from 1899-1969. Cheng (1 997) employs discriminant function analysis to assign daily values for 14 stations on the American eastern seaboard into 18 air mass types. The frequency of these synoptic air masses is then used to delineate natural seasons over the region. Wos (I 980) delineates natural seasons in northwest Poland using the frequency of dominant weather types. The Wroclaw dendrite, a method of cluster analysis, is used to demarcate the seasons.

Finally, Alpert (2004) classifies 53 years of daily data from the Eastern Mediterranean into four natural seasons. Frequency distributions are used to determine the dominant air mass types throughout the year.

In addition to synoptic air masses, temperature is also commonly used to delineate natural seasons. Trenberth (1 983) uses surface temperature data to split the calendar year into four equal-length seasons. The method, although generalized over the entire globe, functions reasonably well at mid-latitudes, but is shown to fail in high-latitude environments. Based on Trenberth's work, a study was conducted by Ovadiah (1 997) focussing on natural seasons in Israel. Jaagus et al. (2003) use daily mean temperature data from 1881-1 995 to delineate natural seasons on the East European Plain. A study by

Alsop (1 989) delineates natural seasons in Western Oregon and Washington using cluster analysis of average weekly maximum, minimum and mean air temperatures. Bednorz et al. (2003) employ cluster analysis to define thermal seasons using 15 thermal characteristics measured at each of six Polish locations from 1966-1990. Twelve seasons were created in the majority of years (the exceptions had 13 seasons) within six fundamental thermal seasons.

In addition to synoptic-based studies conducted by Sakata (1 950) and Yoshino

(1 968), numerous other studies have been conducted in Japan employing a multitude of different climatological parameters (e.g. Maejima 1967; Kawamura 1973; Yamakawa,

1988; Matsumoto 1992 and Inoue et al. 2003). Maejima (1 967), Yamakawa (1 988) and

Inoue et al. (2003) all split the Japanese year into six natural seasons. Maejima (1 967) uses sunshine duration, cloud amount and precipitation data to demarcate the seasons. Yamakawa (1 988) uses surface pressure patterns in East Asia from 1941-1 985. Inoue et al. (2003) use sunshine duration data to delineate objective natural seasons and explore the interannual trends in the lengths of those seasons from 1951 -2003. Seasonal transitions are marked by the periods of greatest rate of change of sunshine duration.

Recently, biological measures have been used to show changes in the nature of the seasons. Robertson's (1968) landmark study laid the framework for such analysis by introducing the concept of biometeorological time, which links surface temperatures with the timing of budburst in the spring. Studies by Menzel and Fabian (1999) and

D'Odorico et al. (2002) have used phenological indices such as the dates of leaf unfolding, flower blooming and pollination of certain plant species to show changes in the timing of the spring season in Europe. Jaagus and Ahas (2000) compare the lengths of the climatic and phenological seasons in Estonia from 1946-1998. Ferguson and Elkie

(2004) use polynomial regression of movement patterns of woodland caribou to demarcate five natural seasons in northern Ontario.

Studies on natural seasons applicable to high-latitude environments are almost nonexistent. Furthermore, peculiarities of the Arctic climate prevent other methods from being applied accurately to high-latitudes. A study currently being conducted at

Pennsylvania State University by Ivanova, et al. (2004) is using surface temperatures to

delineate natural Arctic seasons. In addition to temperature, the study connected herein employs albedo and solar radiation measurements. A more complete view of the

seasonal changes to the radiation balance and overall climate can, therefore, be observed

in the subsequently created seasons. 4.4. Methodology

Data from the Meteorological Service of Canada weather station at Resolute Bay,

Nunavut (74'42' N, 94'50' W) is employed in this analysis. More detail on the location of the station is provided in Chapter 1. Similar analysis was not conducted for the other high Arctic stations, as they lack reflected radiation measurements necessary to properly conduct the analysis.

K-means cluster analysis was used in this study to objectively define natural

Arctic seasons based on climatological parameters. All analysis is conducted using

S-Plus 6.2 (Insightful 2003). Cluster analysis searches for groups within a dataset that maximize the similarity within those groups while minimizing the similarity between them. The k-means algorithm, one of the most common methods of clustering, assigns data to one of k groups. Group membership is determined by assigning centroids to each group and assigning each individual observation to the group with the closest centroid.

Cluster analysis was also employed by Alsop (1989) and Bednorz et al. (2003) to delineate natural seasons in similar studies. The CLUSTER program (Keniston 1978) used by Alsop (1989) pre-dated the innovations in statistical software now available.

Alsop may, therefore, have differed slightly in its method of clustering. Bednorz et al.

(2003) illustrate the applicability of three clustering methods, including the k-means algorithm, in defining natural seasons.

The three most significant components of the radiation balance solar radiation,

albedo and mean air temperature, are used as input parameters for the cluster analysis.

Solar radiation and surface albedo are the only significant influences on the shortwave

regime. These parameters are more important in high-latitude environments than in lower latitude ones, as both vary more on an annual basis. The range of surface emissivities between surfaces and seasons in the Arctic is negligible (Weller and Wendler

1990). Therefore, temperature is the largest influence on the entire longwave regime on an annual basis. The use of the aforementioned input parameters creates much more meaningful, contiguous seasons than mean air temperature or net radiation alone. Cluster analysis results from these two solitary input parameters are provided in Appendix 7.

Net radiation values, although representative of annual climate variability, are excluded from the analysis as they are calculated from the other input parameters and are, therefore, not statistically independent. Bednorz et al. (2003) employ day of year as an input parameter in order to differentiate between the similar conditions in the Polish transitional seasons. This is not necessary when studying Arctic environments due to the vast difference in solar radiation, net radiation and mean temperature values between the spring and autumn seasons.

Mean air temperature data are provided as daily values. Daily solar radiation values are calculated by summing hourly values. Daily albedo values are calculated by averaging hourly albedo values for those hours when the solar zenith angle is below 80 degrees. At Resolute Bay's latitude, this results in 214 days of daily albedo values per year from March gth to October 7th. More details on this procedure are provided in

Chapter 3.

All years with sufficient data present in all three datasets are used for the analysis in order to produce the most reliable interannual trends. Any years that contained a significant amount of successive missing data (at least five successive days) in any of the three datasets are excluded. This leads to the exclusion of 14 of the 47 years of data available from 1957 to 2003.

Weekly values are employed in this study, as they are in Alsop (1989), in order to minimize the effects of dynamic fluctuations in climate on a daily basis. Climatic events must, therefore, persist for a number of days to be expressed in the subsequent weekly values. Weekly values were calculated by summing solar radiation values and averaging albedo and mean air temperature values. Reflectivity values are used to describe the surface reflectance of the snow surface during Polar Night, enabling the use of all 52 weeks of data per year. The reflectivity of the snow-covered winter surface is randomly assigned a value between 0.7 and 0.8.

Due to the large difference in range between weekly values of all three parameters, the values need to be normalized to be properly used in k-means cluster analysis (Insightful 2003). Otherwise, the much larger variations in weekly total solar radiation would be emphasized much more heavily than the relatively minute variations in weekly average albedo. Each year of data for all three variables was therefore normalized to a standard scale from zero to 100

The choice of how many groups to employ in the cluster analysis is problematic.

Intuitively, the Canadian Arctic undergoes merely two seasons, a snow-covered season and a snow-free season. This study, however, wishes to delineate those seasons, but also consistent, objective transitional seasons as well. Cluster analyses are conducted with two, three, four and five groups using a selection of the most reliable years in the dataset to determine the ideal number of clusters to use.

4.5. Results and discussion

4.5.1. Delineation of natural seasons

The association of snowmelt with a seasonal boundary is emphasized by Lamb

(1958) and Bryson and Lahey (1958). The significant change in net radiation and overall climate caused by the large decrease in albedo during spring snowmelt in the Resolute

Bay data results in an obvious transitional period as well. Despite the two intuitive Arctic seasons, when only two clusters are used, the beginning of the snow-free period is not consistently demarcated as a seasonal boundary. Three clusters delineated a seasonal boundary at snowmelt in all but two cases (the years 1985 and 1988), and was therefore chosen as the ideal representation of this particular environment. Results of the k-means cluster analysis are provided in Table 4.1.

In the two exceptions, a gradual change in spring albedo combined with a particularly cloudy late snow-free period delineated a seasonal boundary in the middle of the summer instead. For those two years, a fourth cluster is added in order to allow

meaningful comparison with the seasons demarcated in all the other years. These results

are shown in Table 4.2. The fourth season is added to the third, as one contiguous snow- free season, for comparative purposes.

4.5.2. Characteristics of natural seasons

Plots showing the delineation of seasons in two years, 1971 and 1991, are

provided in Figures 4.1 and 4.2 respectively. The delineation of seasons in 1985 with both three and four clusters is provided in Figures 4.3 and 4.4 respectively. Trends in solar radiation, net radiation, mean air temperature and surface albedo are included to enable better understanding of the climatological parameters characteristic of each season. The first resulting season (I) is a completely snow-covered, cold winter, which includes polar night in its entirety. This will henceforth be referred to as winter. The second (2) is a transitional spring season, which ends during snowmelt. This season is henceforth referred to as spring. The third (3) is the snow-free period, which ends with the re-establishment of snow cover and the onset of the next winter season. This season

is referred to as summer. A consistent autumn season was not observed, as values of

solar radiation are extremely low when snow cover is re-established and the climate

rapidly shifts to the characteristics of the winter climate. In six cases, however, there

are is at least a week of transitional season between the summer and winter seasons,

sharing the characteristics of the spring season. This period is usually quite short and is

usually due to a gradual reversion of surface albedo from a snow-free value to one of a

thick snow cover. For further analysis, these weeks are added to the winter season

following the snow-free period.

The average winter season lasts 29.8 weeks, with 14.5 weeks of that total

occurring prior to the onset of spring and 15.3 weeks after the conclusion of the snow-

free period. The winter season is characterized by no or limited amounts of solar

radiation and a highly reflective, completely snow-covered surface. Solar radiation

values begin to increase late during the end of winter as the sun rises higher into the sky

on a daily basis. Mean daily air temperatures are consistently well below freezing. Weekly values of net radiation remain below zero during the entire winter season and, due to small values of solar radiation, are largely dictated by the longwave regime.

On April 1 2Ih, the average Arctic year changes seasons from winter to spring.

The spring season lasts an average of 7.8 weeks, and concludes with the melting of snow cover and exposure of the underlying low-albedo tundra surface. Both values of solar radiation and mean air temperature increase drastically during this transitional period, with both reaching their maximum values near its conclusion. Values of surface albedo remain high, characteristic of still complete snow cover. Periods of snowmelt and snow aging, accompanying higher air temperatures and increased amounts of direct solar radiation, ensure values are somewhat lower than those during the winter. Values of net radiation increase somewhat with increasing values of solar radiation, but remain quite low throughout the spring due to the high reflectance of the snow surface.

In an average year, the large snowmelt event that ushers in the snow-free period and the summer season occurs June 9; the summer season lasts an average of 14.4 weeks.

Weekly mean air temperatures remain at or near their peak of about 5•‹C during the entire summer season. Albedo values remain low as the surface is characterized by low albedo tundra. Short summer snowfalls raise some albedo values slightly, but weekly values are very rarely above 0.30. Solar radiation values generally peak near snowmelt and decrease during most of the summer season. When the snow-free period ends with the re-establishment of complete snow cover (on September 17 on average), solar radiation values are extremely low and the next winter season begins. With the value of surface albedo low and relatively consistent, the net radiation trend follows the trend in solar radiation. It peaks near snowmelt at the end of the spring and decreases to approximately zero by the end of the snow-free period.

4.5.3. Interannual trends in the nature of Arctic seasons

Once cluster analysis had been conducted on every year in the dataset included in the study, interannual trends can be determined in season lengths and in the dates marking the transitions between those seasons. This gives an indication of the changing nature of the Arctic climate over the past half-century as it is reflected in the natural seasons of the region.

Since the start date is the same in each year considered, the length of the last section of winter and the date marking the transition from winter to spring both show the same trend. The length of this section of winter and the date marking its conclusion are both retreating by 0.242 weeks per decade (r2: 0.1 14, p-value: 0.0549) (Figure 4.5). The length of the spring season has decreased by 0.267 weeks per decade (r2:0.033, p-value:

0.3090) (Figure 4.6), although that decrease is not statistically significant.

The date marking the conclusion of spring and the onset of summer has changed drastically over the entire dataset. The date has retreated relatively linearly by 0.857 weeks per decade (3: 0.352, p-value: 0.0003) (Figure 4.7). Studies by Groisman et al.

(1994), Kuang and Yung (2000), Dye (2002), Bamzai (2003) have also shown significant retreats of the snowmelt date in the Northern hemisphere.

The length of the summer snow-free period has also shown a highly significant increase of 0.883 weeks per decade (3: 0.243, p-value: 0.0036) (Figure 4.8). This coincides with the substantive decrease in average annual albedo observed in Chapter 3. The date marking the end of the snow-free period is occurring slightly later, although that decrease is statistically insignificant (r2: 0.0004, p-value: 0.9078) (Figure 4.9). The majority of the lengthening of the snow-free period is due to the retreat of the spring snowmelt date.

Changes in the nature of the seasons in Resolute Bay have manifested themselves most drastically in the retreat of the snowmelt date. Earlier snowmelts have a large impact on the region's climate, as it is near this date, that solar radiation reaches its annual maximum (Maykut and Church 1973; Kukla and Robinson 1988; Stone et al.

2002). Earlier snowmelt enables more absorption of that radiation at an earlier date, and enables more energy to be available to the ecosystem. The length of the snow-free period has experienced a much smaller change, according to this analysis. The date marking the end of the snow-free period has much less impact on the overall climate, however, as radiation levels are sufficiently low that little effect is observed even with drastic changes in surface albedo.

4.6. Conclusions

Natural seasons are delineated using climatological data from Resolute Bay, which are much more applicable to the Arctic environment than the astronomical and meteorological seasons traditionally employed. The majority of years in the dataset show three distinct seasons based on input parameters from the radiation balance. The winter season, which lasts for over half the year, is characterized by low temperatures, low values of solar and net radiation, and a high-albedo snow surface. The winter is followed by a transitional spring season marked by increasing temperatures and increasing values of solar radiation. Net radiation remains relatively low due to the high albedo of the still completely snow-covered surface. When snowmelt occurs, usually in June, the snow- free summer period begins. The summer is characterized by high temperatures, high

values of net and solar radiation and a low-albedo tundra surface. When snow-cover is

re-established at the conclusion of the snow-free period, solar radiation is sufficiently low that no autumn season is experienced, with the region transitioning immediately into

winter.

Significant changes have occurred in the partitioning of the Resolute Bay year

into natural seasons over the past half-century. The end of the winter season and the

spring season have decreased in duration by 0.242 weeks per decade (r2: 0.1 14, p-value:

0.0549) and 0.267 weeks per decade (r2: 0.033, p-value: 0.3090) respectively, which has

caused the date marking snowmelt and the beginning of the snow-free period to retreat by

0.857 weeks per decade (r2: 0.352, p-value: 0.0003). Mainly due to the retreat in the

spring snowmelt date, the length of the snow-free period has also increased substantially,

by 0.883 weeks per decade (r2: 0.243, p-value: 0.0036). The changes to the Arctic

climate have been drastic, since solar radiation is at its peak at the beginning of the snow-

free period. A retreat in the snowmelt date allows absorption of much more solar energy,

while a change in the date marking the conclusion of the summer has little effect on the

climate, as solar radiation values at that time are relatively insignificant. Table 4.1. Results of k-means cluster analysis (3 clusters) of weekly solar radiation, albedo and mean air temperature at Resolute Bay. The number 1 represents the winter season, the number 2 represents the spring season (and in some cases a short autumn season as well) and the number 3 represents the snow-free, summer season.

Table 4.2. Re-analysis of data for the years 1985 and 1988 when a seasonal boundary was not delineated during the spring snowmelt. A cluster was added (number 4) in order to have this occur. Week Year 1111111111222222222233333333334444444444555 123456789~1~3456789~123456789~123456789~~234~6,7~9~~~ I985 11111111111112222222333333334444444444111l1111,111111 1998 111111l111111222233333333333334444444442222222~11 I Solar Radiation ---- ktRadiation . ----Albedo - ---A7-- banTenperature

:\, AM-"' \ ,'

25 28 31 34 37 40 43 46 49 52 Week Figure 4.1. Three natural seasons delineated by k-means cluster analysis using data from Resolute Bay in 1971. Weekly values of solar radiation, net radiation, albedo and mean air temperature are provided. Albedo values have been multiplied by 100.

Solar Radiation ktRadiation - - - -Albedo banTenperatu

TI al P a 150 TI C B

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 Week

Figure 4.2. Three natural seasons delineated by k-means cluster analysis using data from Resolute Bay in 1991. Weekly values of solar radiation, net radiation, albedo and mean air temperature are provided. Albedo values have been multiplied by 100. 1 4 7 10 13 16 19 22 25 28 31 34 37 Week Figure 4.3. Three natural seasons delineated by k-means cluster analysis using data from Resolute Bay in 1985. Weekly values of solar radiation, net radiation, albedo and mean air temperature are provided. Albedo values have been multiplied by 100.

Solar Radiation ktRadiation - - - -Albedo . . - - - - - Mean Tenperature

22 25 28 31 34 37 40 43 46 49 52 Week

Figure 4.4. Four natural seasons delineated by k-means cluster analysis using data from Resolute Bay in 1985 with a seasonal boundary now present during snowmelt. Weekly values of solar radiation, net radiation, albedo and mean air temperature are provided. Albedo values have been multiplied by 100. 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 4.5. Number of weeks from March 8" until the onset of spring (the length of the first annual portion of the winter season) for years from 1958 to 2002.

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 4.6. Length of the spring season for years from 1958 to 2002. Year

Figure 4.7. Number of weeks from March 8' until snowmelt., which marks the beginning of the summer season, for years from 1958 to 2002.

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 4.8. Length of the summer season for years from 1958 to 2002. 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure 4.9. Number of weeks from March 8thuntil the onset of the winter season for years from 1958 to 2002. CHAPTER 5. CONCLUSION

5.1. Introduction

This study examines the radiation climatology of the Canadian high Arctic over the majority of the past half-century. Data from the sites of Alert and Resolute Bay,

Nunavut were specifically examined. Interannual trends in solar and net radiation were presented for both sites. Hypotheses were then proffered to explain those trends using other supporting data such as air temperature, albedo and the Arctic Oscillation index. A comprehensive study of the interannual and annual trends in albedo at Resolute Bay was then conducted. Finally, natural seasons at Resolute Bay were delineated using cluster analysis of radiation budget parameters. Very little research has been done on radiation climatology in the Arctic, especially on an interannual basis. Therefore, the results of this study have provided much needed insight into a little known topic.

5.2. Solar and net radiation climatology of the Canadian high Arctic

Solar and net radiation data are analyzed from the past half-century from both

Alert and Resolute Bay. Although, this is a relatively small period in climate terms, it does encompass some dramatic changes in radiation climatology. This is partially because this period includes very little effect of any anthropogenic climate change.

The data analyzed herein shows an overall decrease in solar radiation at both

Alert and Resolute Bay. Alert shows a decrease of 2.25% of the daily mean per decade

(r2:0.1 64; p-value: 0.01 29) and Resolute Bay shows a decrease of 2.50% of the daily mean per decade (r2: 0.375; p-value: <0.0001). Cloudless sky trends match these overall trends suggesting that variations in cloud cover are not forcing these changes in solar radiation. It is more likely, therefore, that changes in atmospheric constituents, such as greenhouse gases (e.g. Stanhill 1995; Lohmann et al. 2004; Che et al. 2005) or atmospheric dynamics are significant contributors

Annual net radiation values have increased at both sites. Overall, at Alert, net radiation is increasing by 114.6 MJ m-' per decade (r2:0.335; p-value: 0.0013) and at

Resolute Bay net radiation is increasing by 82.2 MJ m-? per decade (r2:0.289; p-value:

0.0010). When the early 1990's, when the Arctic was influenced by a persistently positive phase of the Arctic Oscillation, are excluded, the resulting increases are even more dramatic. The majority of changes have occurred in the spring months of May and

June. This suggests that most of the change in net radiation is caused by change in albedo, which in turn is caused by changes in the snowmelt date.

The correlation between annual totals of net radiation and net longwave radiation is highly significant (r-value: 0.787; r2: 0.619 at Resolute Bay) with that relationship especially strong in the month of March. Variations in longwave radiation, driven by changes in the surface-to-atmosphere temperature gradient, are dictating the amount of energy available for snowmelt. This, in turn, feeds back to the net radiation available to the system through change in albedo, and ultimately determines the date of the major

spring snowmelt. To account for the overall increase in longwave radiation, despite a recent increase in surface temperature, atmospheric temperatures must be increasing at a greater rate. It is likely that this increase in atmospheric temperatures is due either to changes in atmospheric dynamics or to an increase in the local or regional concentration of atmospheric particulates and/or greenhouse gases.

Both Alert and Resolute Bay data show a noticeable decrease in annual net radiation in the early-1990's coinciding with a persistently positive phase of the Arctic

Oscillation. Greenhouse gases, ozone depletion and volcanic aerosols are the commonly hypothesised causes for shifts in the phase of the Arctic Oscillation. All three are observed to cause a strengthening in the polar vortex and a decrease in stratospheric temperatures. Most studies show that recent forcing is largely due to changes in greenhouse gas concentration (Shindell et al. 1998; Corti et al. 1999; Cowen 1999;

Hartmann et al. 2000; Shindell et al. 2001 ; Hu and Tung 2002; Rind et al. 2005). The resulting decrease in net longwave radiation is likely driven by a decrease in atmospheric longwave radiation due to colder stratospheric temperatures.

5.3. Surface albedo trends at Resolute Bay, Nunavut

The substantial difference in albedo between snow-covered and snow-free surfaces plays a critical role in the Arctic radiation balance. As solar radiation is near its peak at its occurrence, the timing of snowmelt is especially critical in determining the annual average albedo. A decrease in average annual albedo of 0.01 4 units per decade

(r2:0.1 84; p-value: 0.0073) has been observed over the past-century. Excluding the decade from 1988-1998, which coincides with the positive phase of the Arctic

Oscillation, that decrease rises to 0.023 units per decade (r': 0.449; p-value: <0.0001).

This change has in turn influenced the net radiation regime, and contributed to an

increase in net radiation over the same temporal period. Changes in the albedo have been concentrated in the months of April, May and

June when spring snowmelt usually occurs. The observed increase in length of the snow- free period of 6.6 days per decade (r2:0.301 ; p-value: 0.0004) has been largely dictated by a retreat in the date it commences in the spring; the date of its conclusion has not changed significantly (r2: 0.007; p-value: 0.5881). As the date of snowmelt is dictated largely by temperature, Foster (1989), Robinson et al. (1992) and Stone et al. (2002) conclude that its retreat could provide early evidence of global warming. The large decrease in average annual albedo has resulted in a significant increase in net radiation and has had a profound effect on the climate of the Canadian Arctic.

5.4. Natural seasons at Resolute Bay, Nunavut

Natural seasons are delineated for Resolute Bay, Nunavut using cluster analysis of solar radiation, albedo and air temperature values. These seasons are much more applicable to the unique Arctic environment than the astronomical and meteorological seasons traditionally employed. In all but two cases (1985 and 1998), three distinct seasons are delineated. The First season is a low radiation, low temperature, completely snow-covered winter, which lasts over half the calendar year. The second is a transitional spring season with still complete snow cover, but increasing values of radiation and temperature. Finally, the third season is a snow-free summer season with the annual peak values of radiation and temperature.

Significant changes have occurred in the partitioning of the Resolute Bay year into natural seasons over the past half-century. The snow-covered period prior to the spring snowmelt has decreased in duration substantially causing the key date marking snowmelt and the beginning of the snow-free period to retreat by 0.857 weeks per decade (r2: 0.352, p-value: 0.0003). The length of the snow-free period has also increased by

0.883 weeks per decade (r2: 0.243, p-value: 0.0036) largely due to the retreat in the snowmelt date. This has however led to drastic changes in the Arctic climate since solar radiation is at its peak at the beginning of the snow-free period. A retreat in the snowmelt date therefore allows absorption of much more solar energy, while a change in the date marking the conclusion of the summer has little effect on the climate.

5.5. Future research

This study explores radiation and climate trends over the past half-century at two sites in the Canadian high Arctic. Albedo data and longwave radiation data are limited solely to Resolute Bay as reflected radiation measurements have not been taken at Alert.

Making conclusions from two discrete (or in some cases one) sites in the Arctic is tenuous at best. It would be greatly beneficial to re-produce the analyses conducted herein at other Arctic sites worldwide. Obviously, data availability is problematic as other Canadian radiation data does not exist, and it is quite possible that other worldwide databases are even more sparse.

The hypotheses proffered herein for the trends in net radiation and albedo, as well as for the shift in phase in the Arctic Oscillation, are largely speculated from supporting data from this study and many others. It would be greatly beneficial to have an interannual record of greenhouse gas concentrations in the Arctic and worldwide to determine if their variability is indeed the largest forcing of changes in Arctic climate.

Unfortunately, although that data is now being measured, the time-series is not very long

(L.J.B. McArthur 2005, personal communication). Changes in atmospheric dynamics resulting from climate change add more complexity to the problem. When substantive proof of the mechanisms behind the phase changes in the Arctic Oscillation and of variations in solar and net radiation is found, it will provide a much greater understanding of the Arctic climate and of climate change in general. APPENDIX 1: LIST OF' SYMBOLS AND UNITS

Roman Upper Case

energy emitted from a blackbody (W m-2,MJ m-*)

global solar radiation (W m-2,MJ m-2)

reflected solar radiation (W m-', MJ m-*)

net solar radiation (W m-*, MJ m-*)

atmospheric longwave radiation (W m-2,MJ m-2)

terrestrial longwave radiation (W m-*, MJ m-2)

net longwave radiation (W m-2,MJ mb2)

net radiation (W m'2, MJ m-')

surface temperature (OC, OK)

Roman Lower Case

P population correlation coefficient (dimensionless) r correlation coefficient (dimensionless) r2 coefficient of determination (dimensionless) x individual observation value (units pertain to data employed) x,,, maximum value of x (units pertain to data employed)

110 Xmin minimum value of x (units pertain to data employed)

Xstmdard standardized value of x scaled from 0 to 100 (units pertain to data employed)

Greek a surface albedo (dimensionless)

E emissivity (dimensionless) ts Stefan-Boltzmann constant (5.67 x 1 w~-'K-~) APPENDIX 2: STATISTICAL TERMS

A2.1. Trend analysis

The majority of the plots displayed herein are time-series, which depict the variation in the magnitude of a given variable over time. Equations within the text denote the slopes of the best-fit lines through the data between two specified years. These lines are linear unless otherwise noted. The slope values are accompanied by statistics indicating the strength of the relationship. r2: Coefficient of determination

The coefficient of determination, denoted by r2, represents the amount of observed variation in y that is explained by the regression. The higher the value of r2, the more successful the regression is in explaining the variation in y. If for instance a plot of net radiation versus time has an r2 value of 0.70, then 70% of the variation in net radiation is explained by the best-fit linear trend (either increasing or decreasing) between the two specified years. p-value: Population correlation coefficient

The population correlation coefficient, or p-value, represents the significance of the observed trend. It shows whether the slope of the observed linear trend is statistically different from zero given a desired confidence level. The slope is significantly different from zero if the p-value is less than one minus the desired confidence level (in percent).

For instance, if a confidence level of 95% is desired, the p-value must be below 0.05 (1 - 0.95) to ensure that there is a statistically significant slope. The p-value does not give any indication of the accuracy of the slope's magnitude, only whether it is statistically significantly greater than zero.

A2.2. Correlation

When the relationship between two different variables is examined (net and solar radiation for instance), slightly different statistical methods are used. In this case it is not important whether the slope of the relationship is statistically different from zero, but how close the relationship is to one-to-one (or perfect correlation). Although r2 values are given for completeness sake, correlation coefficient values best represent this relationship. r: Correlation coefficient

The correlation coefficient, represented by r, is a measure of how strongly related the two variables x and y are in a given sample. Although this does not necessarily indicate a cause-and-effect relationship between two variables, an r-value close to one does suggest a linkage between the two. For linear regressions, the r-value is the square root of r2. APPENDIX 3: DATA AVAILABILITY

Table A3.1. Available data and exclusion criteria for all datasets.

% of hours < 90%

Snow thickness % of days <95%

1. Note: All ratios are dimensionless APPENDIX 4: QUALITY CONTROL PROCEDURES

Due to the presence of a significant number of missing values, especially in the radiation datasets, various procedures are applied to ensure minimum contamination of

output results. The steps applied to each dataset are represented in Table A4.1.

Descriptions of those steps follow the table.

Table A4.1. Quality control procedures applied to the data. 1. Daily sum or average is calculated from hourly values. For sums, the average hourly

value is calculated and multiplied by 24 to get the daily total. Obviously, this step is

not applied to data such as temperature given as daily averages or totals.

2. Albedo values when the solar zenith angle is greater than 80" are removed from the

dataset to ensure increased accuracy in calculation of daily average albedos. This

results in yearly data being truncated before March gthand after October 7th(resulting

in 21 4 days of data for each year). This removes periods of the day and year when

zenith angles and radiation values are low, and small errors in radiation values can

lead to erroneous albedo values.

3. Daily average clearness index and diffuse ratios calculated by dividing daily averages

or totals of appropriate parameters. For calculation of the clearness index, daily

extraterrestrial radiation values are first calculated by summing minute totals.

4. Daily average albedos are calculated by averaging hourly values to ensure

representation of varying snow albedo throughout the day. In most locations, albedo

values do not change over a daily period, and the daily average value is calculated

from daily radiation totals to over-represent the most accurate albedo value at midday

when radiation values are at their maximum (other values are usually higher due to

the zenith angle-albedo relationship). The elimination of values at zenith angles over

80•‹,the presence of a large amount of cloud throughout the study period and 24-hour

daylight during the summer (when albedo values vary little) all dampen the albedo-

zenith angle relationship and allow this method to be applicable in this situation.

5. For clearness index calculations, all daily values in the entire dataset are plotted to

determine annual variation (Figures A8.11 and A8.12). A third order polynomial is then fitted to the data. The percentage difference between the actual value and the

daily value output from the polynomial equation is calculated for each day in the

dataset. This minimizes the seasonal effects of missing data, allowing a much more

accurate depiction of the changing trend in solar radiation. A sensitivity analysis

depicting the effects of missing data is presented in Appendix 5.

6. Data values of 0, 1 and greater than 1 are excluded as impossible albedo values.

7. Whenever possible missing daily values are interpolated from surrounding values to

ensure an equal 214 days in each annual albedo dataset.

8. Yearly sum or average is calculated from daily values. For sums, the average daily

value is calculated and multiplied by 365 or 366.

9. Monthly sum or average is calculated from daily values. For sums, the average daily

value is multiplied by 28, 29, 30 or 3 1.

10. Values of isolated missing or incomplete months during Polar Night are interpolated

from those of surrounding complete months. With these values missing, calculations

of annual totals are inflated, as they under-represent Polar Night. Monthly values

during this period are conservative allowing these values to be approximated quite

accurate]y.

11. Yearly totals are calculated by summing monthly totals. A sensitivity analysis

depicting the effects of missing data is presented in Appendix 5.

12. Years are excluded from further analysis with unacceptable levels of missing data.

Refer to Appendix 3 for criteria of exclusion for each dataset.

13. Months excluded with less than 20 days of available data. 14. Plots produced of interannual trends of yearly totals or averages both with all data and

with incomplete years excluded.

15. Plots produced of interannual trends of all months both with all data and with

incomplete years excluded. APPENDIX 5: SENSITIVITY ANALYSIS

A5.1. Solar Radiation

A sensitivity analysis was conducted on the Alert solar radiation data to determine the effects of missing data on the annual totals and the relative success of various quality assurance procedures in minimizing those effects. In many years, a disproportionate amount of data is missing from the low radiation periods nearer to polar night. This causes an overestimation of the annual total when calculating the value from daily averages. Procedures have however been applied to the data to minimize the seasonal effects of missing data; these procedures are detailed in Appendix 4. A sensitivity analysis has been conducted simulating the least complete year retained in the dataset (2001, which is missing 45 days of data). The two most complete years in the dataset (1 991 and 1992) both had 45 days removed using two different methods. First,

45 days were removed at random, and secondly the same 45 days were removed as are missing in the 2001 data (July 1-2, August 16-September 16, October 1-1 1). Table A5.1. Solar radiation sensitivity analysis results. Parameter Year 1 Amount of Missing 1 Values Difference Between Difference as Estimated and Actual Percentage of IValues Range None 2490.2 1991 45 Random Davs 2626.1 Solar Radiation I 45 Selected Days 1 2787.5 (MJ~.~~.') None 1 2518.0 1992 45 Random Days 1 2523.0 I 45 Selected Davs 1 2662.2 None 0.51 1 1991 45 Random Days 0.5 14 0.003 5.9 Clearness index 45 Selected Days 0.534 0.023 45.1 (dimensionless) None 0.475 1992 45 Random Days 0.476 0.001 2.0 45 Selected Days 0.502 0.027 52.9

% of mean I 45 Selected Davs 1 81.6 1 3.62 I 14.1 clearness index None 92.0 1992 45 Random Days 91.2 0.82 3.2 45 Selected Days 93.6 1.58 6.1

rable A5.2. Solar radiation sensitivity analysis summary. Parameter I Average Difference as Percentage of Range Random Days Selected Days Overall I Removed I Removed I Solar Radiation 9.5 29.9 19.7 Clearness index 3.9 49.0 26.5 % of mean clearness index 3.4 10.1 6.8

The use of clearness index is sufficient when days are missing at random.

This is not the case however for the majority of the years in the solar radiation dataset, as

there is the amount of missing data is usually biased towards the low-radiation periods at

the beginning and end of the year. When this is the case, the final procedure further

decreases the effect of seasonal bias and significantly reduces the uncertainty of the

results. A5.2. Net Radiation

A sensitivity analysis was conducted on the Alert net radiation dataset to determine the effects of missing data and the accuracy of summing monthly totals to determine annual totals. This method enabled interpolation of missing monthly values during Polar Night when values are relatively conservative, thereby allowing the inclusion of extra years in further analysis (two years at Alert and three at Resolute Bay).

A sensitivity analysis was conducted simulating the least complete year retained in the dataset (1 986, which is missing 53 days of data). Since missing is data is distributed relatively randomly in the net radiation database, 53 days were removed randomly from the two most complete years in the dataset (1971 and 1991). Yearly totals were then calculated using two methods. First, the daily average was multiplied by the number of days in the year and secondly the monthly totals were summed.

Table A5.3. Net radiation sensitivity analysis results. Year Missing data Summation method Yearly total Percentage of (MJ~'~~-') actual value 1971 None 337.0 Daily average * 365 330.7 98.1 53 Random Days Sum of monthly totals 320.5 95.1 1991 None 588.1 Daily average * 365 553.1 94.0 53 Random Days - Sum of monthly totals 578.8 98.4

With 53 random days removed from the data, the annual total is quite accurate using both summation methods. Both methods would therefore be equally applicable for the purposes of this study. In certain years, however, incomplete or missing months during polar night are present. The summation of monthly totals greatly improves results in these cases, because the values of the missing months can be interpolated from surrounding ones, as Arctic winter net radiation values are small and conservative. The daily average would under represent the low radiation Polar Night period in such years and yield a disproportionately high annual total. Using the summation of monthly totals, therefore, allowed the inclusion of a couple of extra years in the study. APPENDIX 6: INSTRUMENTATION RECORD

The following provides a summary of radiation instrumentation employed at the four sites over the study period, as provided by Environment Canada. All measurement records switch from instruments configured using the IPS (International Pyrheliometric

Scale) radiation standard to those using the WRR (World Radiometric Reference) standard (WRR = 1.022 * IPS). This correction has not been applied, but this added uncertainty is not thought to have a significant effect on the data or the results. Table A6.1. Radiation instrument record at Alert, Eureka, Iqaluit and Resolute Bay. Isensor Type I Number I Brand I Standard l~ate I

,Installed: March loth, 1981 708 WRR I~e~orted: June 3rd, 1989 Iqaluit UA 10033 Epply 11 IPS Reported: May 31st, 1978 Solar 12249 Epply I1 IPS Reported: June 7th, 1982 10103 Epply 11 WRR Installed March 20th, 1986 526 CSlRO IPS Reported: May 3 1 st, 1978 Net 863 CSlRO IPS Reported: June 7th, 1982 135 1 CSIRO WRR Installed July 24th, 1984 Resolute A 1 1668 Epply IPS Reported: August 2nd, 1976 Solar 10034 Epply IPS Installed: March loth, 1980 11673 Epply WRR Installed: August 18th, 1990 71 1174 IPS Reported: August 2nd, 1976 Diffuse Kipp 78491 5 Kipp WRR Reported: August 5th, 1987 700823 Kipp IPS Reported: August 12th, 1976 Retlec ted 700770 Kipp IPS Installed: March 6th, 1980 784909 Kipp WRR Installed: July 17th, 1990 707 CSlRO IPS Reported: August 2nd, 1976 Net 493 CSlRO IPS Installed: June 4th, 198 1 1358 CSlRO WRR Installed: May 5th, 1989 APPENDIX 7: NATURAL SEASONS

The same methods used to delineate seasons by cluster analysis were also applied using other input data. A three-cluster analysis was conducted using only the 31 weeks of the year with albedo data (March gthto October 7'4. These results are presented in

Table A7.1. A number of resulting years did not have seasonal boundaries during the

Spring snowmelt; seasons were added to those years until this became the case. These results are presented in Table A7.2. Cluster analyses using solely net radiation and mean air temperature are presented in Tables A7.3 and A7.4 respectively. In all cases, the winter season is represented by the number I, the spring season by the number 2, and the summer season by the number 3. In Table A7.2, the number 4 represents a second component of the snow-free period and number 5 represents a transitional autumn season. Table A7.1. Results of k-means cluster analysis (3 clusters) of weekly solar radiation, albedo and mean air temperature at Resolute Bay using only the 31 annual weeks with albedo data (March 8" to October 71h). Table A7.2. Results of k-means cluster analysis (starting with 3 clusters with clusters addad until a seasonal boundary occurs during the Spring snowmelt) of weekly solar radiation, albedo and mean air temperature at Resolute Bay using only the 3 1 annual weeks with albedo data (March gthto October 7").

APPENDIX 8: SUPPLEMENTARY GRAPHS

The following graphs provide additional, supporting information for those graphs previously presented in the thesis. This includes solar and net radiation data from

Eureka and Iqaluit as well as trends in solar radiation, net radiation and albedo at Alert and Resolute Bay prior to the application of quality assurance procedures. 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure A8.1. Total annual solar radiation at Eureka from 1964-1998. Incomplete years presented as dashes.

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure A8.2. Total annual net radiation at Eureka from 1970-1995. Incomplete years presented as dashes. 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure A8.3. Total annual solar radiation at Iqaluit from 1973-1998. Incomplete years presented as dashes.

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure A8.4. Total annual net radiation at Iqaluit from 1973-1998. Incomplete years presented as dashes. .... I ..a. I .... I ....I...... l.... r .... I ....I.... 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure A8.5. Total annual solar radiation at Alert from 1964-2002. Incomplete years presented as dashes.

. . . . , . . . . , . . . 7.. . . .,.. . . 1.. . . I.. . . I.. . . , . . . . , . . . . 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure A8.6. Total annual solar radiation at Resolute Bay from 1957-2003. Incomplete years presented as dashes. Year

Figure A8.7. Total annual net radiation at Alert from 1968-2002. Incomplete years presented as dashes.

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Figure A8.8. Total annual net radiation at Resolute Bay from 1963-2003. Incomplete years presented as dashes. Year

Figure A8.9. Annual average albedo from 1957-2003 prior to application of quality assurance procedures.

0 1 2 3 4 5 6 7 8 9 10 11 12 Decimal Months

Figure A8.10. Time-series of 1991 daily average albedo values at Resolute Bay prior to application of quality assurance procedures. Decimal month

Figure A8.1 1. Seasonal variation in clearness index at Ah. 3rd order polynomial trend line and corresponding equation used to minimize this seasonal variation are also shown.

0 2 4 6 8 10 12 Decimal month

Figure A8.12. Seasonal variation in clearness index at Resolute Bay. 3rdorder polynomial trend line and corresponding equation uscd to minimize this seasonal variation are also shown. REFERENCES

Allen, R. A., R. Fletcher, J. Holmboe, J. Namias, and H. C. Willett, 1940: Report on an experiment in five-day weather forecasting. Massachusetts Instiute of Technology, Woods Hole Oceanography Institute, 8 (3), 94 pp. Alpert, P., I. Osetinsky, B. Ziv, and H. Shafir, 2004: A new seasons definition based on classified daily synoptic systems: an example for the eastern Mediterranean. Int. J. Climatol., 24, 1013-1021. Alsop, T. J., 1989: The natural seasons of western Oregon and Washington. J. Climate, 2, 888-896. Ambaum, M. H. P., B. J. Hoskins, and D. B. Stephenson, 2001: Arctic Oscillation or North Atlantic Oscillation. J. Climate, 14, 3495-3507. Arrhenius, S. A., 1896: On the influence of carbonic acid in the air upon the temperature of the ground. Phil. Mag. S., 41, 237-276. Bamzai, A. S., 2003: Relationship between snow cover variability and Arctic Oscillation index on a hierarchy of time scales. Int. J. Climatol., 23, 13 1-142. Barry, R. G., and A. H. Perry, 1973: Synoptic Climatology. Methuen & Co. Ltd., 555 pp. Baur, F., 1958: Physikalische-statistiche Regeln als Grundlagen fur Wetter und Witterungsvorhersagen. Vol. 2, Verlagsgesell-schaft, 152 pp. Bednorz, E., A. Bokwa, M. Zelazny, B. Kicinska, P. Lewik, M. Nowosad, Z. Ustrnul, L. Kolendowicz, and Z. Bielac-Bokowska, 2003: Regionalisations, typologies and climatic seasons with application of cluster analysis. Przegl. Geojiz., 48, 11 -32 (in Polish). Belchansky, G. I., D. C. Douglas, and N. G. Platonov, 2004: Duration of the Arctic sea ice melt season: regional and interannual variability, 1979-2001. J. Climate, 17, 67-80. Bengtsson, L., V. A. Semenov, and 0.M. Johannessen, 2004: The early twentieth- century warming in the Arctic - a possible mechanism. J. Climate, 17,4045-4057. Black, R. X., 2002: Stratospheric forcing of surface climate in the Arctic Oscillation. J. Climate, 15, 268-277. Braathen, G. O., B. M. Sinnhuber, and M. Muller, 2002: Ozone loss in the Arctic from 1988-89 to present. Proceedings of Sixth European Symposium on Stratospheric Ozone, Goteberg, Sweden, European Comission Environment and Sustainability Programme, 20. Bradka, J., 1966: Natural seasons in the Northern Hemisphere. Geofys. Sbomik, 14, 597- 648. Bryazgin, N., and A. Koptev, 1969: Spectral albedo of snow-ice cover. Prob. Arct. Antarc., 29,445-463. Bryson, R. A., and J. F. Lahey, 1958: The March of the Seasons. Dept. of Meteor., U. of Wisconsin, 41 pp. Chapman, W. L., and J. E. Walsh, 1993: Recent variations of sea ice and air temperature in high latitudes. Bull. Amer. Meteor. Soc., 74, 33-47. Che, H. Z., G. Y. Shi, X. Y. Zhang, R. Arimoto, J. Q. Zhao, L. Xu, B. Wang, and Z. H. Chen, 2005: Analysis of 40 years of solar radiation data from China, 1961-2000. Geophvs. Res. Lett., 32, L06803. Cheng, S., and L. S. Kalkstein, 1997: Determination of climatological seasons for the East Coast of the US using an air mass-based classification. Climate Res., 8, 107- 116. Clarke, A. D., and K. J. Noone, 1985: Soot in the Arctic snowpack: a cause for perturbations in radiative transfer. Atmos. Environ., 19, 2045-2053. Comiso, J. C., 2001 : Satellite-observed variability and trend in sea-ice extent, surface temperature, albedo and clouds in the Arctic. Ann. Glaciol., 33,457-473. Comiso, J. C., 2003: Warming trends in the Arctic from clear sky satellite observations. J. Climate, 16, 3498-35 10. Corti, S., F. Molteni, and T. N. Palmer, 1999: Signature of recent climate change in frequencies of natural atmospheric circulation regimes. Nature, 398, 799-802. Cowen, R. C., 1999: New suspect in trend toward warmer winters: wind. Christian Science Monitor, 92 (1 9), 2. Curry, J. A., J. L. Schramm, and E. E. Ebert, 1995: On the sea ice albedo climate feedback mechanism. J. Climate, 8,240-247. Davies, J. A., 1 962: Albedo measurements over sub-arctic surfaces. McGill Sub-Arctic Research Papers, 13, 1-86. Dickson, R. R., T. J. Osborn, J. W. Hurrell, J. Meincke, J. Blindheim, B. Adlandsvik, T. Vinje, G. V. Alekseev, and W. Maslowski, 2000: The Arctic Ocean response to the North Atlantic Oscillation. J. Climate, 13, 267 1-2696. Dirmhirn, I., and F. D. Eaton, 1975: Some characteristics of the albedo of snow. J. Appl. Meteor., 14, 375-379. D'Odorico, P., J. Yoo, and S. Jaeger, 2002: Changing seasons: an effect of the North Atlantic Oscillation? J. Climate, 15,435-445. Dorn, W., K. Dethloff, A. Rinke, and E. Roeckner, 2003: Competition of NAO regime changes and increasing greenhouse gases and aerosols with respect to Arctic climate projections. Climate Dyiz., 21,447-458. Dubreuil, M. A., and M. K. Woo, 1984: Problems of determining snow albedo for the high Arctic. Atmos.-Ocean, 22, 379-386. Dutton, E. G., and D. J. Endres, 1991: Date of snowmelt at Barrow, Alaska, U.S.A. Arct. Alp. Res., 23, 1 15-1 19. Dye, D. G., 2002: Variability and trends in the annual snow-cover cycle in Northern Hemisphere land areas. Hydrol. Proc., 16,3065-3077. Ferguson, S. H., and P. C. Elkie, 2004: Seasonal movement patterns of woodland caribou. J. Zool., 262, 125-134. Foster, J. L., 1989: The significance of the date of snow disappearance on the Arctic tundra as a possible indicator of climate change. Arct. Alp. Res., 21, 60-70. Gavrilova, M. K., 1963: Radiation Climate of the Arctic. Gidrometeoizdat, 225 pp. Grenfell, T. C., and G. A. Maykut, 1977: The optical properties of ice and snow in the Arctic basin. J. Glaciol., 18,445-463. Grenfell, T. C., and D. K. Perovich, 1984: Spectral albedos of sea ice and incident solar irradiance in the southern Beaufort Sea. J. Geophys. Res., 89 (C3), 3573-3580. Grenfell, T. C., and D. K. Perovich, 2004: Seasonal and spatial evolution of albedo in a snow-ice-land-ocean environment. J. Geophys. Res., 109 (Cl ), C01001. Grenfell, T. C., D. K. Perovich, and J. A. Ogren, 1981 : Spectral albedos of alpine snowpack. Cold Reg. Sci. Tech., 4, 121-1 27. Groisman, P. Y., T. Karl, R. W. Knight, and G. L. Stenchikov, 1994: Changes of snow cover, temperature and radiative heat balance over the Northern Hemisphere. J. Climate, 7, 1 633- 1 656. Hanson, K. J., 1961: The albedo of sea ice and ice islands in the Arctic Ocean basin. Arctic, 14, 188-196. Hartmann, D. L., J. M. Wallace, V. Limpasuvan, D. W. J. Thompson, and J. R. Holton, 2000: Can ozone depletion and global warming interact to produce rapid climate change? Proc. Natl. Acad. Sci., 97, 1412-1417. Hirooka, T., M. Yoshikawa, S. Miyahara, and T. Kayahara, 1999: Radiative and dynamic impacts of Arctic and Antarctic ozone holes: General circulation model experiments. Adv. Space Res., 24, 1637-1640. Holloway, G., and T. Sou, 2002: Has Arctic sea ice rapidly thinned? J. Climate, 15, 1691-1701. Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, van der Linden, P.J., X. Dai, K. Maskell, and C. A. Johnson Eds., 2001: Climate Change 2001: The Scientzjk Basis. Cambridge University Press, 88 1 pp. Hu, Y., and K. K. Tung, 2002: Interannual and decadal variations of planetary wave activity, stratospheric cooling, and Northern Hemisphere Annular mode. J. Climate, 15, 1659- 1 673. Hubley, R. C., 1955: Measurements of diurnal variations in snow albedo on Lemon Creek Glacier, Alaska. J. Glaciol., 2,560-563. Inoue, T., and J. Matsumoto, 2003: Seasonal and secular variations of sunshine duration and natural seasons in Japan. Int. J. Climatol., 23, 1219- 1234. Ivanova, K., J. Y. Harrington, J. Verlinde, E. E. Clothiaux, and C. P. Bahrmann, 2004: Objective criterion to distinguish seasons in Arctic climate. Proceedings of the 14th Atmospheric Radiation Measurement Science Team Meeting, Alburquerque, NM. Jaagus, J., and R. Ahas, 2000: Space-time variations of climatic seasons and their correlation with the phenological development of nature in Estonia. Climate Res., 15,207-219. Jaagus, J., I. Truu, R. Ahas, and A. Aasa, 2003: Spatial and temporal variability of climatic seasons on the East European Plain in relation to large-scale atmospheric circulation. Climate Res., 23, 1 1 1-1 29. Jackson, C. I., 1960: Estimates of total radiation and albedo in sub-arctic Canada. Arch. Meteor. Geophys. Bioklimatol., 10, 193-199. Kalnicky, R. A., 1987: Seasons, singularities, and climate changes over the midlatitudes of the Northern Hemisphere. J. Climate Appl. Meteor., 26, 1496-15 10. Kawamura, T., 1973: Natural seasons in monsoon Asia. Water Resources of Monsoon Asia, M. M. Yoshino, ed., Kokonshoin, 227-244 (in Japanese). Keniston, J. A., 1978: Program CLUSTER: an aid to numerical classification using the CDC cyber computer. Pliuronectic Project Tech. Rep. No. 2. Oregon State University. Kindem, I. T., and B. Christiansen, 2001 : Tropospheric response to stratospheric ozone loss. Geophys. Res. Lett., 28, 1547-1550. Kondratyev, K. Y., Z. F. Miranova, and A. N. Otto, 1964: Spectral albedo of natural surfaces. Pure Appl. Geophys., 59,207-21 6. Kuang, Z., and Y. L. Yung, 2000: Observed albedo decrease related to the spring snow retreat. Geophys. Res. Lett., 27, 1299-1302. Kukla, G., and D. A. Robinson, 1980: Annual cycle of surface albedo. Mon. Wea. Rev., 10,56-68. Kukla, G., and D. A. Robinson, 1988: Variability of summer cloudiness in the Arctic basin. Meteor. Atmos. Phys., 39,42-50. Kutzbach, J. E., 1970: Large-scale features of monthly mean Northern Hemisphere anomaly maps of sea-level pressure. Mon. Wea. Rev., 98,708-71 6. Lamb, H. H., 1950: Types and spells of weather around the year in the British Isles. Quart. J. Roy. Meteor. Soc., 76, 393-438. Lamb, H. H., 1958: Two-way relationships between the snow or ice-limit and 1000-500 mb thickness in the overlying atmosphere. Quart. J. Roy. Meteor. Soc., 81, 172- 189. Langleben, M. P., 1971 : Albedo of melting sea ice in the southern Beaufort Sea. J. Glacial., 10, 101- 104. Lindsey, R. W., and D. A. Rothrock, 1994: Arctic sea ice albedo from AVHRR. J. Climate, 7, 1737-1749. Liu, Q., G. Konig-Langlo, and C. Simmer, 1998: Surface radiation measurements from polar stations. Phys. Chem. Earth, 23, 593-598. Lohmann, G., N. Rimbu, and M. Dima, 2004: Climate signature of solar irradiance variations: Analysis of long-term instrumental, historical, and proxy data. Int. J. Climatol., 24, 1045-1056. Maejima, I., 1967: Natural seasons and weather singularities in Japan. Geographical Reports of Tokyo Metropolitan University. No. 2, 77 pp. Manney, G. L., L. Froidevaux, J. W. Waters, R. W. Zurek, W. G. Read, L. S. Elson, J. B. Kumer, J. L. Mergenthaler, A. E. Roche, A. O'Neill, R. S. Harwood, I. MacKenzie, and R. Swinbank, 1994: Chemical depletion of ozone in the Arctic lower stratosphere during winter 1992-93. Nature, 370,429-434. Mapes, B. E., N. Buenning, I. S. Kang, G. N. Kiladis, D. M. Schultz, and K. M. Weickmann, 2005: Strides, steps and stumbles in the march of the seasons. Bull. Amer. Meteor. Soc. (Submitted). Matsumoto, J., 1992: The seasonal changes in Asian and Australian monsoon regions. J. Meteor. Soc. Japan, 70, 257-273. Maxwell, J. B., 1 980: The Climate of the Canadian Arctic Islands and Adjacent Waters. Vol. 1, Environment Canada, 532 pp. Maxwell, J. B., 1982: The Climate of the Canadian Arctic Islands and Adjacent Waters. Vol. 2, Environment Canada, 587 pp. Maykut, G. A., and P. E. Church, 1973: Radiation climate of Barrow, Alaska, 1962-66. J. Appl. Meteor., 12,620-628.

McBean, G., 2005: Arctic Climate - Past and Present. Arctic Climate Impact Assessment Report, Cambridge University Press, 21 -60. McFadden, J. D., and R. A. Ragotzkie, 1967: Climatological significance of albedo in central Canada. J. Geophys. Res., 72 (4), 1 135-1 143.

McGuffie, K., and A. Henderson-Sellers, 1985: The diurnal hysteresis of snow albedo. J. Glacial., 31, 188-189. Menzel, A., and P. Fabian, 1999: Growing season extended in Europe. Nature, 397,659. Monteith, J. L., 1959: The reflection of short-wave radiation by vegetation. Quart. J. Roy. Meteor. Soc., 85, 386-392. NOAA, cited 2005: Daily Arctic Oscillation index. [available online at http://www.cpc. ncep.noaa.gov/products/precip/CWlink/daily~ao~index/ao~index.html.1. O'Neill, A. D. J., and D. M. Gray, 1973: Spatial and temporal variations of the albedo of prairie snowpack. The Role of Snow and Ice in Hydrology, Banff, AB, Unesco- WMO-IAHS, 176-186. Ovadiah, E., and Y. Goldreich, 1997: Year division in Israel according to temperature data. 6th Conference of Judea and Samaria Researches, The Judea and Samaria College, Ariel, Israel, 293-297 (in Hebrew). Overland, J. E., and M. Wang, 2005: The Arctic climate paradox: The recent decrease of the Arctic Oscillation. Geophys. Res. Lett., 32, LO6701 . Overland, J. E., M. Wang, and N. A. Bond, 2002: Recent temperature changes in the western Arctic during Spring. J. Climate, 15, 1702-171 6. Parkinson, C. L., D. J. Cavalieri, P. Gloersen, H. J. Zwally, and J. C. Comiso, 1999: Arctic Sea ice extents, areas, and trends, 1978-1996. J. Geophys. Res., 104, 20837-20856. Pawson, S., and B. Naujokat, 1999: The cold winters of the middle 1990s in the northern lower stratosphere. J. Geophys. Res., 104 (Dl 2), 14209-14222. Perovich, D. K., T. C. Grenfell, B. Light, and P. V. Hobbs, 2002: Seasonal evolution of Arctic sea-ice albedo. J. Geophys. Res., 107 (CIO), 8044. Petzold, D. E., and A. N. Rencz, 1975: The albedo of selected subarctic surfaces. Arct. Alp. Res., 7, 393-398. Polyakov, I. V., R. V. Bekryaev, G. V. Alekseev, U. S. Bhatt, R. L. Colony, M. A. Johnson, A. P. Maskshtas, and D. Walsh, 2003: Variability and trends of air temperature and pressure in the maritime Arctic, 1875-2000. J. Climate, 16, 2067- 2077. Polyakov, I. V., G. V. Alekseev, L. A. Timokhov, U. S. Bhatt, R. L. Colony, H. L. Simmons, D. Walsh, J. E. Walsh, and V. F. Zakharov, 2004: Variability of the intermediate Atlantic water of the Arctic Ocean over the last 100 years. J. Climate, 17,4485-4497. Przybylak, R., 2000: Temporal and spatial variation of surface air temperature over the period of instrumental observations in the Arctic. Int. J. Climatol., 20, 587-61 4. Przybylak, R., 2003: The Climate of the Arctic. Kluwer Academic Publishers, 270 pp. Rigor, I. G., R. L. Colony, and S. Martin, 2000: Variations in surface air temperature observations in the Arctic, 1979-97. J. Climate, 13, 896-914. Rind, D., J. Perlwitz, and P. Lonergan, 2005: AOINAO response to climate change: 1. Respective influences of stratospheric and tropospheric climate changes. J. Geophys. Rex, 110, Dl 2 107. Robertson, G. W., 1968: A biometeorological time scale for a cereal crop involving day and night temperatures and photoperiod. Irtt. J. Biometeor., 12, 191-223. Robinson, D. A., M. C. Serreze, R. G. Barry, G. Scharfen, and G. Kukla, 1992: Large- scale patterns and variability of snowmelt and parameterized surface albedo in the Arctic basin. J. Climate, 5, 1 109-1 1 19. Robock, A., 2002a: Pinatubo eruption: The climatic aftermath. Science, 295, 1242-1244. Robock, A., 2002b: Volcanic Eruption, El Chichon. Encyclopedia of Global Environmental Change, Volume I, The Earth System: Physical and Chemical Dimensions of Global Environmental Change, M. C. MacCracker and J. S. Perry, eds., John Wiley & Sons, 736. Robock, A., 2004: Climatic Impact of Volcanic Emissions. State of the Planet, S. Sparks and C. Hawkesworth, eds., American Geophysical Union, 125-1 34. Rothrock, D. A., Y. Yu, and G. A. Maykut, 1999: Thinning of the Arctic sea-ice cover. Geophys. Res. Lett., 26, 3469-3472. Sakata, K., 1950: A new classification of season. J. Meteor. Res., 2, 1 82- 196 (in Japanese). Schaefer, K., A. S. Denning, and 0. Leonard, 2004: The winter Arctic Oscillation and the timing of snowmelt in Europe. Geophys. Res. Lett., 31, L22205. Serreze, M. C., J. E. Walsh, F. S. Chapin 111, T. Osterkamp, M. Dyurgerov, V. Romanovsky, W. C. Oechel, J. Morison, T. Zhang, and R. G. Barry, 2000: Observational evidence of recent change in the northern high-latitude environment. Climatic Change, 46, 159-207. Shindell, D. T., D. Rind, and P. Lonergan, 1998: Increased polar stratospheric ozone losses and delayed eventual recovery owing to increasing greenhouse-gas concentrations. Nature, 392,589-592. Shindell, D. T., R. L. Miller, G. A. Schmidt, and L. Pandolfo, 1999: Simulation of recent northern winter climate trends by greenhouse-gas forcing. Nature, 399,452-455. Shindell, D. T., G. A. Schmidt, R. L. Miller, and D. Rind, 2001: Northern Hemisphere winter climate response to greenhouse gas, ozone, solar, and volcanic forcing. J. Geophys. Res., 106 (D7), 7 193-7210. Soon, W., 2005: Variable solar irradiance as a plausible agent for multidecal variations in the Arctic-wide surface air temperature record of the past 130 years. Geophys. Res. Lett., 32, Ll67 12. S-Plus 6.2for Windows. 2003. Insightful Corporation, Seattle, WA. Stanhill, G., 1995: Solar irradiance, air pollution and temperature changes in the Arctic. Phil. Trans. R. Soc. Lond. A., 352, 247-258. Stenchikov, G. L., I. Kirchner, A. Robock, H. F. Graf, J. C. Antuna, R. G. Grainger, A. Lambert, and L. Thomason, 1998: Radiative forcing from the 1991 Mount Pinatubo volcanic eruption. J. Geophys. Res., 103, 13837-1 3857. Stenchikov, G. L., A. Robock, V. Ramaswamy, M. D. Schwarzkopf, K. Hamilton, and S. Ramachandran, 2002: Arctic Oscillation response to the 1991 Mount Pinatubo eruption: Effects of volcanic aerosols and ozone depletion. J. Geophys. Res., 107 (D24), 4803. Stenchikov, G. L., K. Hamilton, A. Robock, V. Ramaswamy, and M. D. Schwarzkopf, 2004: Arctic oscillation response to the 1991 Pinatubo eruption in the SKYHI general circulation model with a realistic quasi-biennial oscillation. J. Geophys. Res., 109, DO3 1 1 2. Stone, R. S., E. G. Dutton, J. M. Harris, and D. Longenecker, 2002: Earlier spring snowmelt in Northern Alaska as an indicator of climate change. J. Geophys. Res., 107 (D1 O), 4089. Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic oscillation signature in wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 1 297- 1 300. Trenberth, K. E., 1983: What are the seasons? Bull. Amer. Meteor. Soc., 64, 1 276- 1 282. United States Army Corps of Engineers, 1956: Snow Hydrology: Summary Report of Snow Investigations. North Pacific Division, 141- 19 1. Wadhams, P., 1995: Arctic sea ice extent and thickness. Phil. Trans. R. Soc. Lond. A., 352, 301 -319. Wallace, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 1 297- 1 300. Wallace, J. M., 2000: North Atlantic oscillation/annular mode: two paradigms-one phenomenon. Quart. J. Roy. Meteor. Soc., 126,791-805. Warren, S. G., and W. J. Wiscombe, 1980: A model for the spectral albedo of snow. 11: Snow containing atmospheric aerosols. J. Atmos. Sci., 37, 2734-2745. Weatherhead, B., A. Tanskanen, and A. Stevermer, 2005: Ozone and Ultraviolet Radiation. Arctic Climate Impact Assessment Report, Cambridge University Press, 1 5 1 - 1 82. Weller, G., and G. Wendler, 1990: Energy budgets over various types of terrain in polar regions. Ann. Glacial., 14, 3 1 1-314. Wendler, G., and F. D. Eaton, 1990: Surface radiation budget at Barrow, Alaska. Theor. Appl. Climatol., 41, 107-1 15. Winther, J. G., 1993: Short- and long-term variability of snow albedo. Nordic Hydrol., 24, 199-212. Winther, J. G., F. Godtliebsen, S. Gerland, and P. E. Isachsen, 2002: Surface albedo in Ny-Alesund, Svalbard: variability and trends during 1981-1997. Glob. Planet. Change, 32, 127-139. Wiscombe, W. J., and S. G. Warren, 1981: A model for the spectral albedo of snow. I: Pure snow. J. Atmos. Sci., 37, 2712-2733. Woo, M. K., and A. Ohmura, 1997: The Arctic islands. The Surface Climates of Canada, W. G. Bailey, T. R. Oke and W. R. Rouse, eds., McGill-Queen's University Press, 172-197. Woo, M. K., and K. L. Young, 1996: Summer solar radiation in the Canadian high Arctic. Arctic, 49, 170-180. Wos, A., 1980: Climatic seasons of the year in NW Poland. Quaest. Geogr., 6,99-113. Yamakawa, S., 1988: Climatic variations in recent years from the viewpoint of seasonal transition of prevailing pressure pattern over East Asia. Geogr. Rev. Japan, 61, 38 1 -403 (in Japanese). Yasunari, T., 1986: Recent observed interdecadal climate changes in the Northern Hemisphere. Periodicity and Regionality of Climate Change, T. Kawamura, ed., Kokonshoin, 27-40 (in Japanese). Yoshino, M. M., 1968: Pressure pattern calender of east Asia. Meteor. Rundsch., 21, 162- 169 (in Japanese).