______________

OCS Study

BOEM 2013-0119

Beaufort and Chukchi Seas

Mesoscale Meteorology Modeling 

Study

Final Report

U.S. Department of the Interior

Bureau of Ocean Energy Management

Alaska OCS Region

www.BOEM.gov 

OCS Study

BOEM 2013-0119

Beaufort and Chukchi Seas 

Mesoscale Meteorology Modeling 

Study

Final Report

Authors

Xiangdong Zhang and Jeremy Krieger

University of Alaska Fairbanks

Jing Zhang, Fuhong Liu, Steve Stegall, and Wei Tao

North Carolina A&T State University

Martha Shulski, Jinsheng You, William Baule, and Brittany Potter

University of Nebraska–Lincoln

Prepared under BOEM Contract

M06PC00018

by

University of Alaska Systems

P.O. Box 757880

Fairbanks, AK 99775-7880

Published by

U.S. Department of the Interior

Bureau of Ocean Energy Management Alaska OCS Region

 
DISCLAIMER

This report was prepared under contract between the Bureau of Ocean Energy Management 

(BOEM) and the University of Alaska Systems. This report has been technically reviewed by 

BOEM, and it has been approved for publication. Approval does not signify that the contents 

necessarily reflect the views and policies of BOEM, nor does mention of trade names or 

commercial products constitute endorsement or recommendation for use. It is, however, exempt 

from review and compliance with BOEM editorial standards.

REPORT AVAILABILITY

The report may be downloaded from the boem.gov website through the Environmental Studies 

Program Information System (ESPIS). You will be able to obtain this report also from the 

National Technical Information Service in the near future. Here are the addresses. You may also 

inspect copies at selected Federal Depository Libraries.

U.S. Department of the Interior U.S. Department of Commerce

Bureau of Ocean Energy Management National Technical Information Service

 Alaska OCS Region 5285 Port Royal Road

3801 Centerpoint Drive Springfield, Virginia 22161

Suite 500 Phone: (703) 605-6040

Anchorage, Alaska Fax: (703) 605-6900

99503-5823

Telephone requests may be placed at Email: bookstore@ntis.gov

(907) 334-5208

CITATION

Zhang, X., J. Zhang, J. Krieger, M. Shulski, F. Liu, S. Stegall, W. Tao, J. You, W. Baule, and B. 

Potter, 2013: Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study, Final 

Report. U.S. Dept. of the Interior, Bureau of Ocean Energy Management, Alaska OCS 

Region, Anchorage, AK. OCS Study BOEM 2013-0119. 204 pp.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

Acknowledgments

This study was funded by the U.S. Department of the Interior, Bureau of Ocean Energy 

Management, Alaska Outer Continental Shelf Region, Anchorage, Alaska, under 

Contract No. M06PC00018 as part of the BOEM Alaska Environmental Studies Program. 

The Arctic Region Supercomputing Center at the University of Alaska Fairbanks (UAF) 

provided significant computing and storage resources, without which this project would 

not have been possible.

Special thanks go to Robert Pickart of the Woods Hole Oceanographic Institution 

(WHOI) for his great help in deploying our meteorological buoy in the Chukchi–Beaufort 

Seas in 2009 and 2011, which facilitated the collection of high-quality offshore data. We 

also thank Jennifer Hutchings of the International Arctic Research Center, Jun Inoue of 

the Japan Agency for Marine-Earth Science and Technology, Robert Raye of the Shell 

Oil Company, Frank Urban of the United States Geological Survey, Herman Wong of the 

U.S. Environmental Protection Agency (EPA), and Chris Yetsko of the ConocoPhillips 

Company for providing observational data that greatly benefited this study.

The project team is grateful for the efforts of Deanna Huff of the Alaska Department of 

Environmental Conservation, Walter Johnson and his group at BOEM, Robert Pickart 

and his group at WHOI, Tom Weingartner and his group at UAF, and Herman Wong of 

the EPA for graciously examining and testing our interim products in their applications, 

and for their feedback which assisted in optimizing the final reanalysis product.

Most of all, the team members would like to express our sincere appreciation to our 

project manager at BOEM, Warren Horowitz, who offered us invaluable assistance over 

the years in all facets of the project, and without whom the resultant products would have 

been greatly diminished. In addition, we thank Ron Lai, Heather Crowley, and Caryn 

Smith of BOEM, who provided excellent recommendations and feedback as to the course 

of our research, as well as the members of the project’s Scientific Review Board––Keith 

Hines, Peter Olsson, Igor Polyakov, and Tom Weingartner––whose guidance throughout 

the course of this study has served to greatly enhance the quality and success of our 

research efforts.

 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

TABLE OF CONTENTS

List of Figures............................................................................................................................. i

List of Tables............................................................................................................................ xii

Partial List of Acronyms Used in This Report .................................................................... xiv

Abstract................................................................................................................................... xvi

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

2 Development and Analysis of the Chukchi–Beaufort Region Meteorological 

Database............................................................................................................................. 3

2.1 Introduction ......................................................................................................................3

2.2 Meteorological Observing Stations.................................................................................7

2.2.1 Land-Based Measurements .........................................................................................7

2.2.2 Offshore Measurements ............................................................................................13

2.3 Quality Control of the Meteorological Database.........................................................15

2.3.1 Introduction to Observational Quality Control .........................................................16

2.3.2 Enhancement to Basic Quality Control Procedures..................................................19

2.3.3 Quality Control Flags................................................................................................21

2.3.4 Results and Summary................................................................................................22

2.4 Analysis of the Meteorological Database .....................................................................29

2.4.1 Climatology of the Long-Term Observations...........................................................29

Temperature Patterns.......................................................................................................................29

Winds...............................................................................................................................................31

Pressure............................................................................................................................................35

2.4.2 Short-Term Observations..........................................................................................38

Offshore Locations ..........................................................................................................................38

Near-Shore Observations.................................................................................................................39

2.4.3 Inter-Network Comparison at Barrow.......................................................................42

2.4.4 Upper-Air Observations............................................................................................42

2.4.5 Climate Trends..........................................................................................................44

2.5 Summary .........................................................................................................................45

3 Development of a Physically Optimized Modeling System for the Chukchi–

Beaufort High-Resolution Atmospheric Reanalysis (CBHAR).................................. 46

3.1 Introduction ....................................................................................................................46

3.2 Optimization of Model Physical Parameterization Configuration............................48

3.2.1 Sensitivity Tests: September 2004 ............................................................................49

Sensitivity Test Design ....................................................................................................................49

Sensitivity Test Results....................................................................................................................52

Radiation.....................................................................................................................................52

Microphysics...............................................................................................................................57

Interactions between Radiation and Microphysics.....................................................................59

Planetary Boundary Layer (PBL) ...............................................................................................61

3.2.2 Sensitivity Tests Covering the SHEBA Period .........................................................66

3.2.3 Sensitivity Tests Covering August 2009...................................................................70

3.3 Improvements to Model Physical Parameterizations .................................................71

3.3.1 Coupling of Thermodynamic Sea Ice........................................................................71

3.3.2 Adoption of Polar WRF Improvements ....................................................................75

3.4 Optimization of Data Assimilation Configuration ......................................................77

3.4.1 Data Assimilation Sensitivity Test Design................................................................77
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

3.4.2 Sensitivity of WRFDA to Model Background Error ................................................79

3.4.3 Performance Analysis of WRFDA Assimilation of Multiple Datasets.....................83

3.4.4 Optimization of Analysis Nudging Configuration ....................................................90

3.5 Model Data Selections....................................................................................................93

3.5.1 Large-Scale Forcing Data..........................................................................................93

3.5.2 WRF Land-Use Dataset.............................................................................................95

3.6 Final Model Configuration and Production of CBHAR.............................................96

3.6.1 Initial and Boundary Conditions ...............................................................................96

3.6.2 Final Model Configuration........................................................................................97

3.6.3 Production of CBHAR ..............................................................................................98

4 Performance Evaluation of the Chukchi-Beaufort High-Resolution Atmospheric 

Reanalysis (CBHAR)...................................................................................................... 98

4.1 Introduction ....................................................................................................................98

4.2 Verification against Surface Station Observations ...................................................100

4.3 Verification against QuikSCAT Ocean-Surface Winds ...........................................103

4.4 Seasonal and Diurnal Error Analyses........................................................................105

4.5 Verification against Radiosondes................................................................................112

5 Surface Climate in the Chukchi–Beaufort High-Resolution Atmospheric 

Reanalysis (CBHAR).................................................................................................... 115

5.1 Large-Scale Surface Wind Field Analysis..................................................................115

5.1.1 Analysis Approach for the Basin-Scale Surface Wind Field ..................................115

5.1.2 SLP and Surface Wind Speed Climatology ............................................................116

5.1.3 Surface Wind Direction Climatology......................................................................126

5.1.4 Variability and Changes in Surface Wind Speed and Direction .............................129

5.1.5 Spatial Structure of Changes in Surface Wind........................................................141

5.1.6 Trends in Sea Ice, 2-m Air Temperature, and SST .................................................142

5.1.7 Possible Relationships between Wind Speed, Sea Ice, SST, and Boundary Layer 

Stability ................................................................................................................................150

5.1.8 Summary .................................................................................................................154

5.2 Mesoscale Wind Fields in CBHAR .............................................................................155

5.2.1 Introduction .............................................................................................................155

5.2.2 Sea Breeze and Up/Downslope Wind .....................................................................157

5.2.3 Cold-Air Damming .................................................................................................167

5.2.4 Summary .................................................................................................................171

5.3 Climatology of Storms and Their Impacts on Surface Winds in CBHAR .............172

5.3.1 Introduction .............................................................................................................172

5.3.2 Storm Identification and Tracking Algorithm.........................................................175

5.3.3 Climatology and Variability of Storm Activity.......................................................176

5.3.4 Impacts of Storms on Surface Wind........................................................................178

5.3.5 More Insight into the Impacts of Storms on Surface Winds: Case Studies ............180

6 Summary ................................................................................................................. 182

7 Recommendations................................................................................................... 190

8 References ............................................................................................................... 192

Appendix A: Structure of the Chukchi–Beaufort Region Meteorological Database

......................................................................................................................................... 201

Appendix B: Statistical Evaluation Metrics............................................................... 203
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List of Figures

Figure 1.1.1. Potential locations of offshore oil development in the Chukchi–Beaufort 

Seas as indicated by the red shaded areas offshore (Source: BOEM leasing data 

provided by Warren Horowitz) ...................................................................................1

Figure 1.1.2. Study area, encompassing the Chukchi and Beaufort Seas, Chukotka 

Mountains, Arctic (North) Slope, Brooks Range, and parts of Interior Alaska and the 

Canadian Yukon. The mean climatological extent of 80% sea ice concentration is 

highlighted by the dashed line; topographic height (m) over land is shaded. .............2

Figure 2.2.1. Locations of the 103 stations obtained from the National Climatic Data 

Center (NCDC). Stations denoted with a white circle indicate the long-term NCDC 

stations used for the climatology.................................................................................8

Figure 2.2.2. Locations of the 49 stations with daily snowfall and snow depth information 

obtained from the NCDC.............................................................................................8

Figure 2.2.3. Locations of the 32 stations in the Remote Automated Weather Station 

(RAWS) network.........................................................................................................9

Figure 2.2.4. Locations of the 6 stations in the Bureau of Ocean Energy Management 

(BOEM) network.......................................................................................................10

Figure 2.2.5. Locations of the 30 stations in the Water and Environmental Research 

Center (WERC) network ...........................................................................................11

Figure 2.2.6. Locations of the 4 stations in the Atmospheric Radiation Measurement 

(ARM) network and the Coastal-Marine Automated Network (C-MAN)................12

Figure 2.2.7. Locations of the 10 stations in the United States Geological Survey (USGS) 

network ......................................................................................................................13

Figure 2.2.8. Locations of the well sites and buoys operating in the Beaufort and Chukchi 

Seas, along with the ice camp SEDNA .....................................................................14

Figure 2.3.1. Example of the maximum and minimum daily temperatures created from 

high-resolution time series, along with the threshold limits used in the threshold test 

for Betty Pingo in April 2000....................................................................................19

Figure 2.3.2. Example of hourly temperature change (°C) during a one-month period, 

along with the limits used in the step change method for Betty Pingo in April 2000

...................................................................................................................................20

Figure 2.3.3. Example of a strong correlation in the hourly air temperatures (°C) between 

Betty Pingo and West Dock for May 2000 ...............................................................26
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Figure 2.3.4. Total percentage of flagged data for the three automated QC checks as 

delineated by the major observational networks. Percentage of data that were not 

able to undergo the step change and persistence tests due to missing observations is 

also given...................................................................................................................28

Figure 2.3.5. Total percentage of flagged data as categorized by variable and QC test ...28

Figure 2.4.1. Mean monthly temperatures (°C) for the long-term stations in the region..29

Figure 2.4.2. Frequency distribution of observed temperatures (°C) for the long-term 

stations.......................................................................................................................30

Figure 2.4.3. Monthly maximum and minimum temperatures (°C) for the long-term 

stations.......................................................................................................................31

Figure 2.4.4. February wind rose climatology for Barrow (1979–2009)..........................32

Figure 2.4.5. August wind rose climatology for Barrow (1979–2009).............................32

Figure 2.4.6. Mean monthly wind speed (m s-1) by month for the long-term stations......33

Figure 2.4.7. Maximum observed hourly wind speed (m s-1

) by month for the long-term 

stations.......................................................................................................................34

Figure 2.4.8. Standard deviation of hourly wind speed observations (m s-1) by month for 

the long-term stations................................................................................................34

Figure 2.4.9. Mean monthly sea level pressure (hPa) by month for the long-term 

observing stations......................................................................................................36

Figure 2.4.10. Observed maximum sea level pressure (hPa) by month for the long-term 

stations.......................................................................................................................37

Figure 2.4.11. Minimum observed sea level pressure (hPa) by month for the long-term 

stations.......................................................................................................................37

Figure 2.4.12. Mean monthly temperature (°C) for coastal stations in the WERC network

...................................................................................................................................40

Figure 2.4.13. Mean monthly temperature (°C) for near-coastal stations in the WERC 

network. .....................................................................................................................40

Figure 2.4.14. Mean monthly temperature (°C) for inland stations in the WERC network

...................................................................................................................................41

Figure 2.4.15. Mean monthly temperature (°C) for Brooks Range stations in the WERC 

network ......................................................................................................................41
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Figure 2.4.16. Monthly mode of the wind direction (degrees) for the 850-hPa level at 

locations where radiosonde data are available ..........................................................43

Figure 2.4.17. May to September wind direction (degrees) mode at the surface and 850-

hPa level for Barrow and Barter Island .....................................................................43

Figure 3.2.1. Distribution of the 96 observational stations over the CBHAR modeling 

domain used for model verification of the physical parameterization sensitivity 

simulations. Two ARM stations are indicated by red dots, 21 RAWS stations by 

magenta dots, and other networks by green dots.......................................................52

Figure 3.2.2. Downward longwave irradiance bias (W m-2, blue) and correlation (red) in 

the radiation sensitivity tests, as verified against observations from 2 ARM stations 

during September 2004..............................................................................................55

Figure 3.2.3. Modeled (lines) and observed (dots) downward longwave irradiance (W 

m-2

) in the radiation sensitivity tests, averaged over 2 ARM stations during 

September 2004 .........................................................................................................55

Figure 3.2.4. Downward shortwave irradiance bias (W m-2, blue) and correlation (red) in 

the radiation sensitivity tests, as verified against observations from 23 ARM and 

RAWS stations during September 2004....................................................................56

Figure 3.2.5. Daily mean downward shortwave irradiance (W m-2) in the radiation 

sensitivity tests, averaged over (a) 2 ARM and (b) 21 RAWS stations during 

September 2004 .........................................................................................................56

Figure 3.2.6. Daily mean 2-m temperature (°C) in the radiation sensitivity tests, averaged 

over 96 stations during September 2004 ...................................................................57

Figure 3.2.7. Downward longwave irradiance bias (W m-2, blue) and correlation (red) in 

the microphysics sensitivity tests, as verified against observations from 2 ARM 

stations during September 2004 ................................................................................58

Figure 3.2.8. Daily mean shortwave irradiance (W m-2

) in the microphysics sensitivity 

tests, averaged over 23 ARM and RAWS stations during September 2004 .............58

Figure 3.2.9. Daily mean 2-m temperature (°C) in the microphysics sensitivity tests, 

averaged over 96 stations during September 2004....................................................59

Figure 3.2.10. Downward shortwave irradiance bias (W m-2, blue) and correlation (red) in 

the radiation-microphysics sensitivity tests, as verified against observations from 23 

ARM and RAWS stations during September 2004...................................................60

Figure 3.2.11. Daily mean downward shortwave irradiance (W m-2) in the radiationmicrophysics sensitivity tests, averaged over 23 ARM and RAWS stations during 

September 2004 .........................................................................................................60
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Figure 3.2.12. Downward longwave irradiance bias (W m-2, blue) and correlation (red) in 

the radiation-microphysics sensitivity tests, as verified against observations from 2 

ARM stations during September 2004 ......................................................................61

Figure 3.2.13. Downward longwave irradiance bias (W m-2, blue) and correlation (red) in 

the PBL sensitivity tests, as verified against observations from 2 ARM stations 

during September 2004..............................................................................................62

Figure 3.2.14. Downward shortwave irradiance bias (W m-2, blue) and correlation (red) in 

the PBL sensitivity tests, as verified against observations from 23 ARM and RAWS 

stations during September 2004 ................................................................................63

Figure 3.2.15. Daily mean 2-m temperature (°C) in the PBL sensitivity tests, averaged 

over 96 stations during September 2004 ...................................................................63

Figure 3.2.16. RMSE of 2-m temperature (°C) in the PBL sensitivity tests, as verified 

against observations from 96 stations during September 2004 .................................64

Figure 3.2.17. Time series comparison of 10-m wind speeds (m s-1) in the PBL sensitivity 

tests, averaged over 96 stations during September 2004...........................................65

Figure 3.2.18. 10-m wind speed bias (m s-1, blue) and correlation (red) in the PBL 

sensitivity tests, as verified against observations from 96 stations during September 

2004 ...........................................................................................................................65

Figure 3.2.19. 10-m wind direction bias (degrees) in the PBL sensitivity tests, as verified 

against observations from 96 stations during September 2004 .................................66

Figure 3.2.20. 10-m wind vector RMSE (m s-1, blue) and correlation (red) in the PBL 

sensitivity tests, as verified against observations from 96 stations during September 

2004 ...........................................................................................................................66

Figure 3.2.21. Sensitivity simulation domain encompassing the SHEBA locations for 

tests during December 1997 (white track; the star indicates the SHEBA location at 

the beginning of the month) and July 1998 (orange track) .......................................68

Figure 3.3.1. Comparison of surface albedo between SHEBA observations (black and 

grey) and control (blue) and sea ice coupled (red) simulations during July 1998.....74

Figure 3.3.2. Sea ice skin temperature (°C) time series for SHEBA observations (black) 

and control (blue) and coupled sea ice (red) simulations during December 1997 ....75

Figure 3.4.1. Distribution of the 122 observational stations used in WRFDA assimilation 

sensitivity tests and verification. In situ surface stations are indicated by green dots, 

and radiosonde stations by red diamonds..................................................................79
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Figure 3.4.2. RMSE of (a) 2-m temperature (°C) and (b) 10-m wind vector (m s-1) as 

verified against 56 coastal stations over the 48-hour cold-start model cycle for BE 

experiments in July 2009...........................................................................................82

Figure 3.4.3. Monthly-averaged profiles of RMSE, averaged over all radiosonde stations 

for (a) temperature (°C) and (b) wind vector (m s-1) for BE experiments in July 2009

...................................................................................................................................83

Figure 3.4.4. (a) Snapshot of QuikSCAT surface wind speed (m s-1) (color) in the model 

domain at 0551 UTC 18 July 2009; (b) QuikSCAT surface wind speed (m s-1) versus 

BOEM buoy (71.29°N, 152.14°W) observed wind speed (m s-1

) in August–

September 2009. The red dot in (a) represents the BOEM buoy location.................86

Figure 3.4.5. Mean RMSE profiles of MODIS-retrieved temperature (°C) with different 

QC flags in 2009, as verified against all radiosonde stations in the study area ........87

Figure 3.4.6. Monthly-averaged profiles of temperature bias (°C) at Barrow in July 2009 

for experiments CTRL, MODIS, and MODIS-QA, as verified against radiosonde 

data at various vertical levels ....................................................................................89

Figure 3.4.7. RMSE of modeled wind vectors (m s-1) from control (ctrl) and various 

nudging simulations, as verified against surface observations during February (top) 

and August (bottom) 2009.........................................................................................92

Figure 3.4.8. RMSE of modeled wind vectors (m s-1) from control (ctrl), nudging (nudg), 

assimilation (assim), and combined assimilation and nudging (assim+nudg) 

simulations, as verified against surface observations during February (left) and 

August (right) 2009 ...................................................................................................93

Figure 3.5.1. Wind vector RMSE (m s-1) for various WRF physical parameterization 

sensitivity tests in September 2004 when using NARR vs. ERA-I as large-scale 

forcing data................................................................................................................94

Figure 3.6.1. Optimized model configuration for CBHAR...............................................97

Figure 3.6.2. Process used to generate each of the 54-hour modeling cycles used to 

produce CBHAR. WPS is the WRF preprocessing system, which creates WRF input 

files. The first 6 hours of each cycle were used as model spin-up and discarded, with 

the remaining 48 hours contributing to the reanalysis. Due to overlap between 

adjacent cycles, the final 1800 UTC analysis was also discarded.............................98

Figure 4.2.1. ΔRMSE and ΔCORR for 2-m air temperature (T) and 10-m wind speed 

(Spd), direction (Dir), U&V components, and wind vector (Vect), as verified for 6-

hourly output against all collected NCDC surface station observations for the entire 

31-year period. For each statistic, values represent the percentage improvement in 

CBHAR relative to ERA-I.......................................................................................102

Figure 4.2.2. Same as Figure 4.2.1, but for verification against coastal stations............102
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Figure 4.2.3. Same as Figure 4.2.1, but for verification against inland stations.............103

Figure 4.3.1. ΔRMSE and ΔCORR for 10-m wind speed (Spd), direction (Dir), U&V 

components, and wind vector (Vect), as verified for 6-hourly output against 

QuikSCAT data for the entire 31-year period. ........................................................104

Figure 4.4.1. Monthly-averaged RMSE for 2-m air temperature (°C) in CBHAR (red) and 

ERA-Interim (blue), as verified against NCDC stations.........................................106

Figure 4.4.2. Monthly-averaged fractional improvement in 2-m air temperature RMSE 

(ΔRMSE) and correlation (ΔCORR) for CBHAR relative to ERA-Interim, as 

verified against NCDC stations...............................................................................106

Figure 4.4.3. Same as Figure 4.4.1, but for 10-m wind speed (m s-1).............................107

Figure 4.4.4. Same as Figure 4.4.2, but for 10-m wind speed.........................................107

Figure 4.4.5. Same as Figure 4.4.1, but for 10-m wind direction (degrees)....................108

Figure 4.4.6. Same as Figure 4.4.2, but for 10-m wind direction....................................108

Figure 4.4.7. Same as Figure 4.4.1, but for 10-m wind vector (m s-1

) ............................109

Figure 4.4.8. Same as Figure 4.4.2, but for 10-m wind vector........................................109

Figure 4.4.9. Hourly-averaged RMSE for 2-m air temperature (°C) in CBHAR (red) and 

ERA-Interim (blue), as verified against hourly-reporting NCDC stations..............111

Figure 4.4.10. Same as Figure 4.4.9, but for 10-m wind vector (m s-1) ..........................111

Figure 4.5.1. RMSE profile for temperature (°C) in CBHAR (red) and ERA-Interim 

(blue) as verified against all available radiosonde measurements for the entire 31-

year period ...............................................................................................................113

Figure 4.5.2. Same as Figure 4.5.1, but for dew point (°C) ............................................113

Figure 4.5.3. Same as Figure 4.5.1, but for wind vector (m s-1

)......................................114

Figure 4.5.4. ΔRMSE for upper-air temperature (red), dew point (blue), and wind vector 

(green) as verified against all available radiosonde measurements for the entire 31-

year period ...............................................................................................................114

Figure 5.1.1. Climatological monthly mean sea level pressure (SLP, hPa) for 1979–2009 

in CBHAR ...............................................................................................................117

Figure 5.1.2. Climatological monthly mean wind speed (m s-1) for 1979–2009 in CBHAR

.................................................................................................................................118
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Figure 5.1.3. Climatological monthly mean wind speed (m s-1) for 1979–2009 in ERAInterim .....................................................................................................................119

Figure 5.1.4. Monthly 95th-percentile wind speed (m s-1

) for 1979–2009 in CBHAR....120

Figure 5.1.5. Monthly 95th-percentile wind speed (m s-1

) for 1979–2009 in ERA-Interim

.................................................................................................................................121

Figure 5.1.6. Probability Density Functions (PDFs) of the climatological monthly surface 

wind speed in CBHAR............................................................................................122

Figure 5.1.7. PDFs of surface wind speed for ERA-Interim (blue) and CBHAR (red)..122

Figure 5.1.8. Hourly mean wind speed (m s-1

) over the ocean (blue) and land (red) in 

CBHAR, averaged over the study region for 1979–2009 .......................................124

Figure 5.1.9. 6-hourly mean wind speed (m s-1

) over the ocean (blue) and land (red) in 

ERA-Interim, averaged over the study region for 1979–2009 ................................125

Figure 5.1.10. Monthly northeast wind direction (0–90°) frequency in CBHAR, 

expressed as a percentage (%).................................................................................126

Figure 5.1.11. Monthly northeast wind direction (0–90°) frequency in ERA-Interim, 

expressed as a percentage (%).................................................................................127

Figure 5.1.12. Frequency of southwest and northwest winds in CBHAR (a and b) and 

ERA-Interim (c and d), averaged over the cold months of January–May and 

October–December and expressed as a percentage (%)..........................................128

Figure 5.1.13. Monthly mean surface wind speed (m s-1) and its linear trends for 1979–

2009 in CBHAR (red) and ERA-Interim (blue)......................................................130

Figure 5.1.14. Monthly mean 95th-percentile wind speed (m s-1) and its linear trends for 

1979–2009 in CBHAR (red) and ERA-Interim (blue)............................................131

Figure 5.1.15. Monthly mean surface wind speed (m s-1

) and its linear trends for 1979–

2009 over ocean (blue) and land (red) in CBHAR..................................................132

Figure 5.1.16. Monthly mean 95th-percentile wind speed (m s-1

) and its linear trends for 

1979–2009 over ocean (blue) and land (red) in CBHAR........................................133

Figure 5.1.17. Monthly mean surface wind speed (m s-1) and its linear trends for 1979–

2009 over ocean (blue) and land (red) in ERA-Interim...........................................134

Figure 5.1.18. Monthly mean 95th-percentile wind speed (m s-1) and its linear trends for 

1979–2009 over ocean (blue) and land (red) in ERA-Interim.................................135
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Figure 5.1.19. Frequency of extreme winds, defined as the percentage (%) of winds 

above the 95th-percentile wind speed, for 1979–2009 in CBHAR (red) and ERAinterim (blue)...........................................................................................................136

Figure 5.1.20. Frequency of surface wind directions (degrees), grouped by quadrant, for 

1979–2009 in CBHAR ............................................................................................137

Figure 5.1.21. Illustration of the Chukchi and Beaufort Sea regions as defined here for 

climatological analysis of winds. The two regions are separated by the 203°E 

meridian extending north from Point Barrow, Alaska. ...........................................138

Figure 5.1.22. Monthly mean surface wind speed (m s-1) and its linear trends for 1979–

2009 over the Chukchi Sea (blue) and Beaufort Sea (red) in CBHAR. All data are 

for ocean points only. ..............................................................................................139

Figure 5.1.23. Monthly 95th-percentile wind speed (m s-1) and its linear trends for 1979–

2009 over the Chukchi Sea (blue) and Beaufort Sea (red) in CBHAR. All data are 

for ocean points only. ..............................................................................................140

Figure 5.1.24. Linear trends in the monthly mean surface wind speed (m s-1

yr-1) at each 

grid point in CBHAR. Positive values indicate an increase and negative values a 

decrease over the 31-year period.............................................................................141

Figure 5.1.25. Linear trends in the monthly 95th-percentile surface wind speed (m s

-1

yr-1) 

at each grid point in CBHAR. Positive values indicate an increase over the 1979–

2009 period, and negative values a decrease...........................................................142

Figure 5.1.26. Monthly means and linear trends of sea ice extent (SIE, ×105 km2) in red, 

2-m air temperature (ocean only, °C) in blue, and SST (°C) in black for 1979–2009 

in CBHAR ...............................................................................................................143

Figure 5.1.27. Linear trend of sea ice concentration (% yr-1) for all months from 1979–

2009 in CBHAR. Shaded colors are significant at the 95% confidence level. Positive 

values indicate an increase over the 1979–2009 period, and negative values a 

decrease. ..................................................................................................................144

Figure 5.1.28. Monthly sea ice extent (SIE, ×105 km2

) and its linear trends for 1979–2009 

in the Chukchi Sea (blue) and Beaufort Sea (red) in CBHAR................................145

Figure 5.1.29. Monthly 2-m air temperature (°C) and its trends for 1979–2009 over ocean 

(blue) and land (red) in CBHAR .............................................................................146

Figure 5.1.30. Linear trend of 2-m air temperature (°C yr-1) for all months from 1979–

2009 in CBHAR. Shaded colors are significant at the 95% confidence level. Positive 

values indicate an increase over the 1979–2009 period, and negative values a 

decrease. ..................................................................................................................147
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Figure 5.1.31. Monthly mean 2-m air temperature (°C) and its trends from 1979–2009 

over the Chukchi Sea (blue) and Beaufort Sea (red) in CBHAR............................148

Figure 5.1.32. Linear SST trends (°C yr-1) for 1979–2009 in CBHAR. Shaded colors are 

significant at the 95% confidence level. Positive values indicate an increase and 

negative values a decrease over the entire period....................................................149

Figure 5.1.33. Monthly mean SST (°C) and its trends for 1979–2009 in the Chukchi Sea 

(blue) and Beaufort Sea (red) in CBHAR ...............................................................150

Figure 5.1.34. Binned wind speed anomalies (m s-1) as a function of binned sea ice 

concentration anomalies in CBHAR. Triangles indicate +/− one standard deviation.

.................................................................................................................................151

Figure 5.1.35. Binned wind speed anomalies (m s-1

) as a function of binned SST 

anomalies (°C) in CBHAR. Each plot represents a different range of sea ice 

concentrations as noted in the figures, i.e., 0%, 0–20%, etc. Triangles indicate +/−

one standard deviation.............................................................................................152

Figure 5.1.36. Same as Figure 5.1.35, except for binned wind speed anomalies (m s-1

) as 

a function of static stability anomalies (s-2). Each plot represents a different range of 

sea ice concentrations as noted in the figures, i.e., 0%, 0–20%, etc. Triangles 

indicate +/− one standard deviation.........................................................................153

Figure 5.2.1. Schematic diagrams for (a) sea breeze and (b) upslope wind....................156

Figure 5.2.2. Vertical cross-sections used in different areas to show the vertical structure 

of land/sea breezes, up/downslope winds, and cold-air damming. The four locations 

A, B, C, and D, shown in orange, were chosen for use in displaying the land/sea 

breeze component at different locations in different seasons. Gray shading over the 

ocean represents the average sea ice concentration over the entire CBHAR period 

(lighter colors signify higher SIC), with the 0.8 value highlighted by the dashed line.

.................................................................................................................................156

Figure 5.2.3. Climatological diurnal anomalies of surface winds (m s-1) relative to winds 

at 0600 AKST, calculated at different hours (AKST) in July in CBHAR ..............158

Figure 5.2.4. Climatological diurnal variation of the on/offshore surface wind component 

(m s-1

) for different months at Points A, B, C, and D in CBHAR, as shown in Figure 

5.2.2. Positive values represent offshore-directed wind, and negative values onshore 

wind. Times are given in AKST..............................................................................160

Figure 5.2.5. Climatological diurnal variation of surface wind speed (a, m s-1) and 

direction (b, °) in January (dashed) and July (solid) at Barrow from the surface 

station observations (blue) and CBHAR data (red). Times are given in AKST. ....161

Figure 5.2.6. Diurnal variation of climatological wind in July along the vertical crosssection C2. Wind vectors include both the horizontal wind component, oriented 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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perpendicular to the shoreline, and the vertical component. Wind speed (m s

-1

) is 

indicated by colors. Solid contours indicate potential temperature (°C). The 

shoreline is located at 0 km, and the orientation of the cross-section is indicated by 

the “S” (south) and “N” (north) labels. ...................................................................162

Figure 5.2.7. Same as Figure 5.2.6, but for the climatological anomalies relative to the 

wind at 0600 AKST, demonstrating the diurnal evolution of the sea breeze..........163

Figure 5.2.8. Climatological wind in January along the vertical cross- sections shown in 

Figure 5.2.2. Arrows represent the wind vectors, composed of a horizontal 

component oriented perpendicular to the shoreline and the vertical component. Wind 

speeds (m s-1) are indicated by colors. Solid contours indicate potential temperature 

(°C). The orientations of the cross-sections are indicated by the directional labels 

(e.g. S, SW, NE, N). ................................................................................................165

Figure 5.2.9. Climatological wind anomalies in July at 1800 AKST, relative to the wind 

at 0600 AKST along the vertical cross-sections shown in Figure 5.2.2, indicating the 

strength of the mountain and sea breezes. Arrows represent wind vectors, composed 

of a horizontal component oriented perpendicular to the shoreline and the vertical 

component. Wind speeds (m s-1

) are indicated by colors. Solid lines indicate 

potential temperature (°C). Orientations of the cross-sections are indicated by the 

directional labels (e.g. S, SW, NE, N).....................................................................166

Figure 5.2.10. Maximum offshore distance (km) out to which the climatological diurnal 

wind variation of the cross-shore component is greater than 0.8 m s-1 for various 

cross-sections, indicating the spatial range that the sea breeze impacts different areas 

along the Chukchi and Beaufort coasts...................................................................167

Figure 5.2.11. Climatological surface wind in (a) January, (b) April, (c) July, and (d) 

October in CBHAR. Wind speeds (m s-1

) are indicated by the vector colors. ........168

Figure 5.2.12. Climatological wind profile in different months along cross-section M1 in 

Figure 5.2.2. Alongshore wind is denoted by colors (m s-1, negative values represent 

flow toward the reader), while on/offshore wind and vertical motion are indicated by 

vectors. The shoreline is located at 0 km, and the orientation of the cross-section is 

indicated by the “SW” (southwest) and “NE” (northeast) labels. ...........................170

Figure 5.2.13. Wind rose at station Mys Shmidta in January (top) and July (bottom) for 

observations (left) and CBHAR data (right) ...........................................................171

Figure 5.3.1. Climatological monthly mean sea level pressure (SLP, hPa) in CBHAR over 

1979–2009. The blue line indicates the boundary between the northern and southern 

sub-domains used for the seasonality analysis. .......................................................173

Figure 5.3.2. Climatological monthly mean SLP (hPa) over the northern (red) and 

southern (blue) sub-domains over 1979–2009 in CBHAR .....................................174
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Figure 5.3.3. Climatological monthly mean SLP (hPa) over 1979–2009 in the 

NCEP/NCAR Reanalysis (Kalnay et al. 1996) .......................................................174

Figure 5.3.4. Climatological monthly-mean storm count, intensity (hPa), duration (hours), 

and CAI (hPa) for 1979–2009 over the CBHAR domain .......................................177

Figure 5.3.5. Seasonal storm intensity (hPa) in CBHAR during 1979–2009 (Early Winter: 

September–December; Late Winter: January–February; Spring: March–May; 

Summer: June–August) ...........................................................................................177

Figure 5.3.6. Standardized storm intensity (red) and frequency of strong winds (95thpercentile wind speed) (black) for each season (Early Winter: September–

December; Late Winter: January–February; Spring: March–May; Summer: June–

August) in CBHAR. Correlations between storm intensity and frequency of strong 

winds are given in parentheses. ...............................................................................178

Figure 5.3.7. Climatological number and intensity (hPa) of intense storms for each month 

from 1979–2009 in CBHAR ...................................................................................179

Figure 5.3.8. Standardized intensity of intense storms (red) and frequency of strong winds 

(95th-percentile wind speed) (black) in (a) early winter (September–December) and 

(b) late winter (January–February) in CBHAR. Number of intense storms in (c) 

early winter and (d) late winter in CBHAR. Correlations between storm intensity 

and frequency of strong winds are given in parentheses.........................................180

Figure 5.3.9. Sample weather charts showing the interplay between storms and the 

Beaufort High in generating strong surface winds. Black contours: SLP (hPa); 

shaded colors: 10-m wind speed (m s-1); wind barbs: 10-m winds. ........................182
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List of Tables

Table 2.1.1. Long-term surface stations in the climatological database..............................6

Table 2.3.1. Computed limits of station pressure. Max and Min are the maximum and 

minimum station pressure of all stations, excluding significant outliers. Stdev(max)

and Stdev(min) are the standard deviations of the maximum and minimum station 

pressures, respectively...............................................................................................21

Table 2.3.2. Values for QC flags in the observational database as a function of the QC 

tests passed by the observed value. P denotes the test was passed, F that it was 

failed, and U that not enough data were available to make a determination.............22

Table 2.3.3. Percentage of flagged data for the three different quality control tests 

(separated by network and variable)..........................................................................23

Table 2.4.1. Temperature trend (°C yr-1) in surface temperature for the period 1979–2009. 

Boldface red type indicates statistical significance at the 95% confidence level 

(boldface black, 90% level).......................................................................................45

Table 3.2.1. Model physical parameterizations in the CTRL simulation..........................50

Table 3.2.2. Identifiers of model physical parameterizations in the sensitivity tests........51

Table 3.2.3. Identifiers of radiation sensitivity tests .........................................................53

Table 3.2.4. Sensitivity tests run for the SHEBA period (parameterizations used other

than those identified here were NOAH LSM and KF cumulus) ...............................67

Table 3.2.5. RMSE of downward longwave irradiance (W m-2), 2-m temperature (°C), 

and 10-m wind vector (m s-1

) for the microphysics sensitivity tests during December 

1997 as verified against SHEBA measurements.......................................................69

Table 3.2.6. Same as Table 3.2.5 but for the radiation sensitivity tests during July 1998 69

Table 3.2.7. Same as Table 3.2.5 but for the PBL sensitivity tests during July 1998 .......70

Table 3.2.8 Sensitivity tests covering August 2009 (parameterizations used other than 

those identified below were Morr microphysics, RRTMG radiation, MYJ PBL, and 

NOAH LSM).............................................................................................................71

Table 3.2.9. RMSE of cumulus sensitivity tests as verified against surface observations of 

long- and shortwave irradiance (W m-2

), 2-m temperature (°C), and 10-m wind 

vector (m s-1)..............................................................................................................71

Table 3.3.1. Sensitivity simulations comparing WRF, Polar WRF, and WRF-ice ...........76
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Table 3.3.2. RMSE of surface air temperature (°C), surface skin temperature (°C), and 

sensible and latent heat fluxes (W m-2) for sensitivity simulations during November–

December 1997 as verified against SHEBA observations........................................76

Table 3.4.1. Configuration of WRF physical parameterizations in the assimilation 

sensitivity tests...........................................................................................................78

Table 3.4.2. Sensitivity tests for evaluating model background error...............................80

Table 3.4.3. Verification of BE tests against different observations for 2-m temperature 

(T) (°C), sea level pressure (SLP) (hPa), and 10-m wind vector (VEC) (m s-1) 

(RMSEs (improved in bold) are averaged over all stations; when verified against 

surface in situ stations, coastal and inland stations are verified separately.).............81

Table 3.4.4. Sensitivity tests used to evaluate assimilation of different observational 

sources.......................................................................................................................84

Table 3.4.5. Same as Table 3.4.3, but for sensitivity tests assimilating different 

observational sources.................................................................................................85

Table 3.4.6. List of MODIS retrieval flags .......................................................................87

Table 3.4.7. MODIS assimilation sensitivity tests............................................................88

Table 3.4.8. Same as Table 3.4.3, but for sensitivity tests assimilating MODIS profiles.88

Table 3.4.9. Same as Table 3.4.3, but for sensitivity tests assimilating COSMIC profiles

...................................................................................................................................90

Table 3.5.1. RMSE of land-use data sensitivity tests as verified against surface 

observations for long- and shortwave irradiance (W m-2

), 2-m temperature (°C), and 

10-m wind vector (m s-1

)...........................................................................................95
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Partial List of Acronyms Used in This Report

AKST Alaska Standard Time

ARM Atmospheric Radiation Measurement

AVHRR Advanced Very High Resolution Radiometer

BE (Model) Background Error

BOEM Bureau of Ocean Energy Management

CAI Cyclone Activity Index

CAM Community Atmosphere Model

CBHAR Chukchi–Beaufort High-Resolution Atmospheric Reanalysis

CFSR Climate Forecast System Reanalysis

CMC Canadian Meteorological Centre

COSMIC Constellation Observing System for Meteorology, Ionosphere, and 

Climate

CRN Climate Reference Network

ECMWF European Centre for Medium-Range Weather Forecasts

ERA-40 ECMWF 40-Year Re-analysis

ERA-I ECMWF Interim Reanalysis

GCM General Circulation Model

GFS Global Forecast System

ISH Integrated Surface Hourly (database)

JAMSTEC Japan Agency for Marine-Earth Science and Technology

JRA-25 Japanese 25-Year Reanalysis

LSM Land-Surface Model

LTER (Arctic) Long Term Ecological Research

LW Longwave (radiation)

MODIS Moderate Resolution Imaging Spectroradiometer

NARR North American Regional Reanalysis

NASA National Aeronautics and Space Administration

NCAR National Center for Atmospheric Research

NCDC National Climatic Data Center

NCEP National Centers for Environmental Prediction

NSIDC National Snow and Ice Data Center

OCS Outer Continental Shelf

OSU Ohio State University

PBL Planetary Boundary Layer

PDF Probability Density Function

QA Quality Assurance

QC Quality Control

QuikSCAT Quick Scatterometer

RAWS Remote Automated Weather Station

RMSE Root-Mean-Square Error

RRTM Rapid Radiative Transfer Model

RRTMG Rapid Radiative Transfer Model for GCMs

SEDNA Sea Ice Experiment: Dynamic Nature of the Arctic

SHEBA Surface Heat Budget of the Arctic

SIC Sea Ice Concentration
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SIT Sea Ice Thickness

SLP Sea Level Pressure

SST Sea Surface Temperature

SW Shortwave (radiation)

SWE Snow Water Equivalent

UAF University of Alaska Fairbanks

USGS United States Geological Survey

UTC Coordinated Universal Time

WERC Water and Environmental Research Center

WHOI Woods Hole Oceanographic Institution

WRF Weather Research and Forecasting (model)

WRFDA WRF Data Assimilation (system)
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Abstract

The Chukchi–Beaufort Seas Alaska OCS region has experienced drastic changes over 

recent decades, as evidenced by a rapid sea ice reduction and more frequent extreme 

weather events. These changes may increase threats to the environment, given the 

potential for oil spills related to offshore drilling. Should an oil spill occur, the surface 

wind field is a crucial parameter for accurately predicting spill transport. As such, a 

mesoscale meteorological modeling study over the Chukchi–Beaufort Seas region has 

been conducted under support from the U.S. Department of the Interior, Bureau of Ocean 

Energy Management. A physically-optimized, Arctic-processes-enhanced Weather 

Forecasting and Research (WRF) model and WRF-based data assimilation system have 

been established; a meteorological buoy was successfully deployed over the highly seaice-dynamic Beaufort Sea; a quality-controlled observational database was developed 

from available sources; and, finally, the high-resolution Chukchi–Beaufort Seas HighResolution Atmospheric Reanalysis (CBHAR) was constructed for the period 1979–2009 

with the optimized model and quality-controlled observations. CBHAR represents an 

improved estimate of the regional atmospheric state throughout the study area. CBHAR 

surface wind fields reveal that winds, particularly extreme winds, have strengthened over 

the Chukchi–Beaufort Seas in the autumn. Intensified onshore winds occur during 

summer due to combined sea and mountain breezes along the eastern Brooks Range and 

Chukotka Mountains. During winter, the mesoscale cold-air damming effect diverts the 

synoptic northeasterly winds to the southeast along the northern slope of the Chukotka

Mountains. In the Brooks Range, downslope winds are the predominant driver of the 

surface wind field.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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1 Introduction

The Chukchi–Beaufort Seas region is currently undergoing significant environmental 

changes, including the fastest rate of decline and maximum observed interannual variance 

of sea ice anywhere in the Arctic (Comiso 2012), along with increased surface wind 

speeds over recent decades as the sea ice retreats (Stegall and Zhang 2012). In addition to 

natural changes, the potential for further offshore development by the oil industry also 

exists in the area (Figure 1.1.1). With oil extraction comes the threat of oil spills, which 

can have serious environmental consequences, leading to increasing attention from the 

government, scientific community, and general public (e.g., Gundlach and Hayes 1978;

Picou et al. 2009; Webler and Lord 2010). Oil spills may impact not only the immediate 

area, but also remote regions due to transport via ocean currents and drifting sea ice. 

Given that the Chukchi–Beaufort coastal areas comprise a particularly vulnerable and 

fragile region, with an ecosystem and environment that are especially sensitive to human 

impacts (Ford and Pearce 2010; Doney et al. 2012), it is therefore of critical importance 

to be able to accurately predict the dispersal and movement of oil spills in the region, and 

to assess the potential environmental impacts should a spill occur. Doing so requires a 

good understanding of surface wind, a crucial parameter for assessing and predicting oil 

spill transport (Reed et al. 1999).

Figure 1.1.1. Potential locations of offshore oil development in the Chukchi–

Beaufort Seas as indicated by the red shaded areas offshore (Source: BOEM leasing 

data provided by Warren Horowitz)

Surface wind is primarily determined by the interaction of prevailing synoptic weather 

patterns with prominent underlying geographic features; many studies of such factors 

impacting local wind regimes have been conducted for various regions around the world 

(e.g., Kozo 1979, 1980; Schwerdtfeger 1974; Parish and Bromwich 1987, 1991; 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Bromwich 1989; Olsson and Harrington 2000; Parish and Cassano 2003; Liu et al. 2008; 

Moore and Pickart 2012). In the Chukchi–Beaufort Seas region, including across the 

Arctic Slope (known colloquially as the North Slope), the Beaufort High and Aleutian 

Low are the two dominant synoptic-scale weather patterns that most heavily influence 

surface winds (Shulski and Wendler 2007; Overland 2009; Moore 2012); when the 

intensity and location of these systems change, the surface winds over the study area vary 

in response (Lynch et al. 2004; Stegall and Zhang 2012). The prevailing synoptic 

circulations are modified by local geographic features, resulting in the generation of

mesoscale circulations (e.g., Dickey 1961; Lynch et al. 2001; Moore and Pickart 2012). 

The geography in the study area is characterized by seasonally ice-covered ocean, along 

with the Brooks Range in northern Alaska and the Chukotka Mountains in eastern Siberia

(Figure 1.1.2). In addition to the influence of topography, the presence of little-to-no 

sunlight during the winter months can cause the surface to become extremely cold, 

resulting in frequent temperature inversions throughout the area. Each of these local 

geographic features interacts with the prevailing synoptic weather systems to generate 

characteristic mesoscale atmospheric circulations that correspondingly influence the 

associated surface winds in the region.

Figure 1.1.2. Study area, encompassing the Chukchi and Beaufort Seas, Chukotka 

Mountains, Arctic (North) Slope, Brooks Range, and parts of Interior Alaska and 

the Canadian Yukon. The mean climatological extent of 80% sea ice concentration 

is highlighted by the dashed line; topographic height (m) over land is shaded.

Due to the complexity of its predominant weather and climate systems, accurate 

numerical modeling of the study area poses a great challenge. To improve the 

performance of modeling the Arctic atmosphere, significant modeling and data analysis 

efforts have been made with the use of the state-of-the-art Weather Research and 

Forecasting (WRF) model (Skamarock et al. 2008). These have included the evaluation 

of WRF performance over a large pan-Arctic model domain (Cassano et al. 2011; Porter 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

3

et al. 2011), as well as the development of Polar WRF (Hines and Bromwich 2008; 

Bromwich et al. 2009; Hines et al. 2011), in which the model’s Arctic land-surface 

processes and representation of sea ice have been improved. Most of these studies have 

focused on the Arctic as a whole, in contrast to the more regional interest of the current 

study. In order to better understand how the changing climate interacts with local finerscale processes, and how these in turn impact the surface wind field over the Chukchi–

Beaufort Seas and Arctic Slope, a comprehensive study of the region’s mesoscale 

meteorology has been conducted and detailed herein. To achieve the best possible surface 

wind field climatology for the study area, two concurrent approaches have been adopted:

1) Retrospective collection and climatological analysis of historical surface 

observations over the period 1979–2009 at a total of 262 sites throughout the study 

area.

2) Construction of a high-resolution regional reanalysis for the entire 31-year period

through the synthesis of numerical simulations, in situ measurements, and satellite 

retrievals in support of climatological studies of the area’s surface wind field.

To implement the second approach, the WRF model and its data assimilation system 

WRFDA (WRF Data Assimilation) (Huang et al. 2009; Barker et al. 2012) have been

applied to the study area (Figure 1.1.2) to generate the 31-year Chukchi–Beaufort HighResolution Atmospheric Reanalysis (CBHAR) at a grid spacing of 10 km, with the goal 

to more accurately capture the finer-scale processes of the region’s surface wind field

than has been possible in prior larger-scale reanalyses. Based on the results of CBHAR, a 

comprehensive climatological analysis of the surface wind field over the Chukchi–

Beaufort Seas and Arctic Slope has been carried out. In the following sections of this 

report, the detailed efforts conducted in this study and the resultant scientific findings are 

documented. The report is arranged as follows: observational data collection and analysis 

are summarized in Section 2; sensitivity tests conducted to physically optimize the model 

configuration for producing CBHAR are given in Section 3; detailed verification of 

CBHAR against the collected observations is described in Section 4; and a climatological 

analysis of CBHAR is detailed in Section 5.

2 Development and Analysis of the Chukchi–Beaufort Region Meteorological 

Database

2.1 Introduction

The study domain encompassed by this project spans from the Russian Chukchi

Peninsula, including Wrangell Island, eastward to the northern Yukon and Northwest 

Territories of Canada, including Banks Island. Within this domain lie the Arctic Slope, 

Brooks Range, and northern Alaskan interior, as well as the Chukchi and Beaufort Seas

(Figure 1.1.2). Geographically, the Arctic Slope is bounded to the south by the Brooks 

Range and to the north by the Arctic Ocean. The mountains provide a natural barrier 

separating this region, climatically, from the rest of Alaska. The ridgeline of the Brooks 

Range is somewhat curved and not precisely oriented along an east-west axis; in the east,
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

4

the mountains are situated much closer to the shoreline than in the west. This topographic 

feature has been shown to significantly influence the region’s synoptic meteorological 

conditions (Dickey 1961; Kozo 1980; Kozo and Robe 1986).

Mean annual temperatures in the Arctic are well below freezing, being around −12°C 

along the northern Alaskan coast (Shulski and Wendler 2007). The winters are long and 

cold and the summers are cool and short; only 3–4 months have mean temperatures above 

freezing. No month reaches a mean temperature of 10°C on the coast, a condition 

necessary for tree growth. Precipitation is quite light at about 1000 mm annually, 75% of 

which falls as snow, which covers the ground for about nine months of the year. The 

Kuparuk weather station, located at the base camp of the Kuparuk oil field and close to 

the Beaufort Sea coast, has the lowest observed mean precipitation in Alaska, receiving 

only 92 mm annually (Shulski and Wendler 2007). However, precipitation in the region 

is normally underreported, due to the collection inefficiency of measuring gauges (termed 

“undercatch”). This is a well-known phenomenon and was first reported for the Arctic

Slope by Black (1954). Further inland, the climate becomes more continental, with 

warmer summers, colder winters, and higher precipitation amounts. 

The exact dividing line between the Alaskan coastal and continental climate regimes is 

not easily discernable. The most recent review of climate divisions for Alaska denotes 

that the climate from the Brooks Range northward encompasses the “North Slope” region 

(Bieniek et al. 2012). This paper utilized a cluster analysis technique using data from 

Barrow, Umiat, Barter Island, and Prudhoe Bay. For the current Bureau of Ocean Energy 

Management (BOEM) project described herein, it’s noted that distinctions exist between 

the climate of locations along the coast and of those stations situated farther inland. 

Umiat, Alaska and Inuvik, Yukon Territory are positioned 115 km and 80 km from the 

coast, respectively. These stations provide a perspective of the inland climatology, which

sometimes differs from the coastal perspective, represented by stations such as Barrow 

and Barter Island. This division will be discussed below as part of the climatological

analysis (Section 2.4.1).

Along the coast, winds are persistent, with calm conditions prevailing only about 1% of 

the time. During the cold season, easterly winds generally predominate as a result of the 

Beaufort High, a semi-permanent feature in the pressure field centered offshore to the 

north of the coast. This high-pressure system produces the Beaufort Gyre, a characteristic

anti-cyclonic flow in the oceanic surface waters. During the short summer season, a more 

local-scale phenomenon in the wind has also been observed. After the snow cover on 

land melts, a noticeable gradient in surface temperature arises from north to south as the 

land warms. This thermal gradient is enhanced by the constant daylight present at this 

time of year, inducing a sea breeze circulation that has been documented for the Alaskan

Beaufort Sea coast (Kozo 1982a, 1982b).

Three additional major pressure patterns exist across the Arctic and sub-Arctic regions:

the Aleutian Low, Icelandic Low, and Siberian High. The Aleutian Low, as the name 

implies, is located in the North Pacific in the area of the Aleutian Island chain. This semipermanent pressure pattern is only evident during the cold season, from roughly October 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

5

through March. This region represents an area through which storm systems tend to track 

(from west to east) and intensify. These systems primarily affect the western and southern 

coasts of Alaska. During summer, the pressure increases, making wind and storm systems 

much less prevalent. In the North Atlantic, meanwhile, prevails the counterpart to the 

Aleutian Low: the Icelandic Low. It possesses quite similar characteristics and 

predominates over the winter pressure regime in the opposite hemisphere. The third 

primary pressure pattern, the Siberian High, is also a cold-season phenomenon that is 

established over the Eurasian continent, primarily over Russia. This pattern is regionally 

induced due to the large snow-covered landmass and results in relatively light winds 

across the continental interior and very low temperatures. A similar, but smaller-scale 

phenomenon occurs in sheltered and low-lying portions of Interior Alaska and Canada 

with the simultaneous occurrence of high pressure, low temperatures, and light winds. 

The seasonal distribution of sea ice in the Beaufort and Chukchi Sea regions has garnered 

a great deal of attention in recent years. There exists a documented thinning and 

reduction in areal extent of the perennial ice pack that is most prevalent in this portion of 

the Arctic. The rate of change in sea ice cover has been found to be occurring faster here 

than in model projections (Stroeve et al. 2012), and, particularly during September (the 

time of year when minimum ice extent occurs), the Chukchi and Beaufort Seas have 

experienced a significant retreat in sea ice extent. 

In the north-south direction, the study domain includes observations from 65°N to about 

75°N latitude. The majority of surface stations in the domain are located in Alaska, while 

offshore in situ data come primarily from oil exploration well sites and buoys, primarily 

located in the shallow near-shore areas of the Chukchi and Beaufort Seas. It is from these

meteorological stations (as described in the metadata accompanying the database) that the

climatology described herein has been developed. Data from these observations were 

originally reported in various text-based formats, and were thus reformatted as part of this 

study in order to obtain uniformity in the data records and for inclusion in the final 

observational database. The processed and quality-controlled (as described below in 

Section 2.3) observational records were ultimately imported into an Oracle database 

platform for distribution to the general public. In addition to the in situ surface 

observations, QuikSCAT satellite-retrieved ocean-surface winds (described in more 

detail in Section 3.4) were also collected and incorporated into the observational 

database. See Appendix A for the database entity relationship diagram and a description 

of the tables included.

As described in the metadata report accompanying the database, the stations used for this

study have widely varying periods of record; some, such as the exploration well sites,

span only a few months, while others, such as the Barrow airport, have a complete 31-

year record. Traditionally, international guidelines for producing a climatology require 30

years of data. For this study region, a 30-year data record is quite limiting and decreases 

the total number of available station locations to eight (Table 2.1.1 and Figure 2.2.1). A 

detailed climatological analysis will be discussed for these eight stations. However, in 

some cases the spatial detail provided by shorter-term stations warrants their inclusion. A 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

6

subset of these stations will accordingly be used to discuss spatial patterns and analysis, 

particularly for offshore areas. 

Table 2.1.1. Long-term surface stations in the climatological database

Station Latitude (°N) Longitude (°E) Elevation (m)

Barrow 71.300 −156.783 1.2

Bettles 66.916 −151.509 196.0

Deadhorse 70.192 −148.477 18.3

Inuvik 68.300 −133.483 68.0

Kotzebue 66.885 −162.579 4.9

Mys Shmidta 68.900 −179.367 4.0

Mys Uelen 66.150 −169.833 3.0

Ostrov Vrangelja 70.983 −178.483 5.0

Summary statistics have been calculated for the entire database on both a monthly and an 

annual basis, and are included with its distribution. Variables included in the summaries 

are surface air temperature, relative humidity, sea level pressure, and wind speed. The 

summarized fields for these variables include the monthly and annual average values

(based on the individual hourly data points), monthly absolute maximum and minimum 

values, and the standard deviation of the monthly values. The statistics were computed 

based on the less-strict quality control measures (f = 3, detailed in Section 2.3). These 

summaries were calculated for all stations in the database as a means of comparison 

among stations. Note, however, that many locations have a short period of record. 

Therefore, a direct comparison of statistics from such stations with those from longerterm sites, such as those listed in Table 2.1.1, is not advised. In this report, however, 

statistics from shorter-term stations are given in order to provide some additional 

characterization of the data-sparse areas of the domain. In addition, summary data for the 

transient sites, such as ships (Ship_Obs-1, Ship_Obs-2, JAMSTEC, and Conoco_Ship),

were calculated over the entire record and range of locations and should be treated as 

such. As these are not stationary sites, their summary statistics should not be compared 

with the other fixed stations. 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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2.2 Meteorological Observing Stations

2.2.1 Land-Based Measurements

Surface meteorological data from 262 stations, maintained by a variety of observing 

networks and various stand-alone projects, were obtained for this study, incorporating all 

available in situ meteorological data located in the domain for the period 1 January 1979 

to 31 December 2009. Only a subset of these stations report for this entire time period 

and many stations have a relatively short period of record (i.e., less than 5 years). The 

following meteorological variables, where available, are included in the database: surface 

air temperature (°C), dew point temperature (°C), relative humidity (%), wind speed 

(m s

-1

), wind direction (degrees), station pressure (hPa), sea level pressure (hPa), 

altimeter (in Hg), shortwave radiation (W m-2), longwave radiation (W m-2), 1-hr 

accumulated precipitation (mm), 6-hr precipitation (mm), 24-hr precipitation (mm), and 

snow depth (cm). Because the file format for each of the various datasets was not initially 

homogenous, the collected data files were standardized into a common format prior to 

quality control. All timestamps were transformed into Coordinated Universal Time 

(UTC) and missing observations identified as −999.0. Metadata for each observation

include the year, month, day, hour, minute, latitude, longitude, and elevation at which the 

observation was made. A description of the various stations in the database, grouped by 

network, is given below. Since surface wind information is of particular importance to 

this study, relevant wind observation metadata are also included in the report (as well as 

in the metadata file accompanying the database). 

A large portion of the stations in the database are represented in the Integrated Surface 

Hourly (ISH) database obtained from the National Climatic Data Center (NCDC),

available at http://lwf.ncdc.noaa.gov/oa/climate/climatedata.html. Within the study 

domain, there are a total of 54 ISH stations in Alaska, United States, 14 in Chukotka,

Russia, and 35 in Yukon and Northwest Territories, Canada (Figure 2.2.1) that reported 

observations during the study period. Elevation for the stations ranges from sea level up 

to 991 m, with an average of 153 m. In the U.S. and Canada, the anemometer height is 

standardized at 10 meters, though this does not necessarily hold true for all Automated 

Surface Observing System (ASOS) locations in the U.S. ISH stations are primarily 

located at airports across the region and are distributed fairly evenly throughout the 

domain. In Alaska, the stations within this network are most sparse in the region between

the Brooks Range and Arctic coast. Also available from the NCDC are station 

observations of snowfall and snow depth, measured once-daily at a total of 49 stations in 

the study domain (Figure 2.2.2). These stations are often co-located with the hourly 

observing stations described above and shown in Figure 2.2.1. 
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Figure 2.2.1. Locations of the 103 stations obtained from the National Climatic Data 

Center (NCDC). Stations denoted with a white circle indicate the long-term NCDC 

stations used for the climatology.

Figure 2.2.2. Locations of the 49 stations with daily snowfall and snow depth 

information obtained from the NCDC
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Hourly data from a total of 32 Remote Automated Weather Stations (RAWS) (Figure 

2.2.3) were obtained through the Western Regional Climate Center 

(http://www.wrcc.dri.edu). This network is designed to assist fire weather monitoring and 

most stations are thus located in remote areas of Alaska to the south of the Brooks Range 

(where fire danger is high). The most northerly station in this network is located at 

Umiat. Elevation for these sites ranges from 45 m above sea level at Kiana to 853 m at 

Helmut Mountain. For some of these stations, data are more often available in the warm 

season, or fire season, with infrequent or entirely missing winter observations. The period 

of record for RAWS sites goes back to 1988 for the longer-term sites, and the early 2000s 

for the shorter-term sites. The instrument configuration for these sites is standardized, 

with an anemometer height of 6.5 m and hourly wind speed observations that represent a 

10-minute average.

Figure 2.2.3. Locations of the 32 stations in the Remote Automated Weather Station 

(RAWS) network

Data for six sites in the vicinity of Prudhoe Bay (Figure 2.2.4) were obtained through the 

Bureau of Ocean Energy Management (BOEM, http://www.boem.gov); five of these had 

been established in a previous pair of BOEM meteorological studies (OCS Study MMS2005-069 and OCS Study MMS-2007-011), with the sixth station at Cross Island 

established as part of a BOEM whaling study (OCS Study MMS-2009-038). Under these 

three studies, data collection began in January 2001 for stations at Endicott, Northstar, 

Milne Point, and Badami, with the Cottle Island and Cross Island stations being added to 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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the network in 2002 (Figure 2.2.4). Cross Island was a temporary station, only reporting

data during the whaling seasons (late August to mid-September) of 2002–07. After 

September 2006, control of the stations at Badami, Cottle Island, and Milne Point was

transferred to the University of Alaska Fairbanks (UAF), Water and Environmental 

Research Center (WERC). The six stations are located at or near the Beaufort Sea coast,

with elevations ranging from 5 to 21 m above sea level (the elevation of Cross Island is 

unspecified, but very near sea level). Hourly-reported winds for the five permanent 

stations are computed using a 60-minute average and anemometer heights are 10 m 

(Badami, Cottle Island, Endicott), 14 m (Milne Point), and 23 m (Northstar). Cross Island 

reported observations every 5 minutes, with an unspecified anemometer height.

Figure 2.2.4. Locations of the 6 stations in the Bureau of Ocean Energy 

Management (BOEM) network

The Water and Environmental Research Center (WERC) at UAF maintains an extensive 

network of surface meteorological stations, primarily in the vicinity of Prudhoe Bay and 

locations southward along the Dalton Highway and the northern foothills of the Brooks 

Range (Figure 2.2.5). The few stations not in these areas are located on the Seward 

Peninsula in western Alaska. These stations have varying lengths of record, with datasets 

covering a period of 2 to 10 years. Metadata for this network of stations are quite good,

with instrument height, make, and model all known. Several of these stations are located 

at higher altitudes, with elevations of more than 1000 m near the northern foothills of the 

Brooks Range. There are a total of 30 stations within the WERC network and hourly data 

were obtained from their website (http://ine.uaf.edu/werc).
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Figure 2.2.5. Locations of the 30 stations in the Water and Environmental Research 

Center (WERC) network

A station situated in the northern foothills of the Brooks Range is located at Toolik Lake 

and is part of the Arctic Long Term Ecological Research (LTER) site

(http://ecosystems.mbl.edu/arc). Meteorological data are available since 1988. For wind 

information, the hourly data represent a one-minute average and anemometer 

instrumentation is mounted at 1- and 5-m heights. 

The Arctic coast is also home to two stations (Barrow and Atqasuk) within the

Atmospheric Radiation Measurement (ARM) network (Figure 2.2.6). This network, as 

the name implies, is primarily focused on measuring incoming and outgoing radiation for 

various spectral wavebands. However, additional meteorological data are also measured,

with anemometers mounted at a height of 10 m. These data are observed at the highest 

frequency of the entire database, with observations made every minute. Data were 

obtained from the ARM website (http://www.arm.gov). 
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Figure 2.2.6. Locations of the 4 stations in the Atmospheric Radiation Measurement 

(ARM) network and the Coastal-Marine Automated Network (C-MAN)

Data from two coastal stations (Prudhoe Bay and Red Dog Dock) were obtained from the 

National Data Buoy Center Coastal-Marine Automated Network (C-MAN) program 

(Figure 2.2.6). These stations are situated at sea level and the anemometer instrument 

heights are 38 m for Prudhoe Bay and 17 m for Red Dog Dock. These data are available 

every six minutes for the period 2005 to 2008 and were downloaded from their website 

(http://www.ndbc.noaa.gov/cman.php). 

Industry provided coastal station data from stations at Point Thomson, Reindeer Island, 

and Wainwright. These data are short-term, reporting for the latter one-half or one-third 

of 2009 and include hourly observations. 

The United States Geological Survey (USGS) provided data for 10 stations in Alaska, 

most located along the Arctic coast, with a few additional inland locations (Figure 2.2.7). 

Hourly data are available for these stations and the starting year varies from 1998 to 

2005. Data were obtained courtesy of the USGS (http://www.gtnp.org/index_e.html) and 

Frank Urban.
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Figure 2.2.7. Locations of the 10 stations in the United States Geological Survey 

(USGS) network

2.2.2 Offshore Measurements

Shorter-term data are available from a variety of exploratory well sites that operated in 

the Beaufort and Chukchi Seas from 1986 to 2010 (Figure 2.2.8). These stations were in 

operation in the Chukchi Sea for one to three months during the open water season, and 

in the Beaufort Sea both during the open water season (1–3 months) and during the 

winter months when they occupied landfast ice. The reporting frequency for these data is 

every hour. The seasons of operation are variable, with the earliest records beginning in 

1986 (Phoenix) and later records ending in 2010 (Point McIntyre Pad 2). These data were 

obtained through BOEM and there exist various data and metadata reports that provide 

additional information (Fairweather Forecasting, 1991, unpublished reports). 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Figure 2.2.8. Locations of the well sites and buoys operating in the Beaufort and 

Chukchi Seas, along with the ice camp SEDNA

Offshore meteorological data also exist from buoys and ships that were operated in the 

study domain during the open water season by the ConocoPhillips and Shell oil 

companies. These represent short-term sites, with data collected for just a few months,

and include data from three buoys and one ship (one buoy operated by ConocoPhillips in 

the Chukchi Sea (2009), and two operated by Shell in the Chukchi Sea (2008–2009) and

Beaufort Sea (2009); the ship was operated by ConocoPhillips in the Chukchi (2008))

(Figure 2.2.8). Buoy data are reported every one or two hours and the ConocoPhillips

ship data are reported every minute. In addition, the BOEM-funded project summarized 

in this report, the Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study,

launched a buoy that operated in the western Beaufort Sea from early August to midSeptember 2009 (Figure 2.2.8). Meteorological data are available for this time period at a 

frequency of five minutes.

Observational data from the scientific research vessel Mirai were obtained from the Japan 

Agency for Marine-Earth Science and Technology (JAMSTEC) from their website 

(http://www.godac.jamstec.go.jp/darwin/e). These data were largely observed within the 

study domain during the months of September and October and cover the years 1999, 

2000, 2002, 2004, 2006, 2008, and 2009. The reporting frequency for these observations 

is 10 minutes. 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Ship data were also obtained through the National Data Buoy Center. Observations from 

various ships traversing the domain comprise this dataset, which spans the years 2003 to 

2009. The observation reporting frequency for these data is variable. When using these 

data, users must be aware of the shifts and changes in latitude and longitude in the time 

series since this dataset incorporates and combines data from multiple, distinct vessels. 

As such, the data cannot be interpreted as a single time series for a single ship track. 

Instead, observations are organized by timestamp, and consecutive values may not belong 

to the same ship track. This is the only dataset included here to have this type of time and 

location discontinuity. 

High-quality offshore data were also available from the Sea Ice Experiment: Dynamic 

Nature of the Arctic (SEDNA), a field campaign that operated an ice camp in the 

Beaufort Sea (http://research.iarc.uaf.edu/SEDNA). A meteorological tower was installed 

in the pack ice and observations taken in April 2007, with data reported at 10-minute 

intervals (Figure 2.2.8). Wind observations were made at heights of 1.9 and 3.1 m, and 

the data represent 10-minute averages. Data were provided courtesy of Jennifer 

Hutchings of the International Arctic Research Center, UAF.

2.3 Quality Control of the Meteorological Database

Two major types of error may occur when performing quality control (QC) on

meteorological data: a Type I error is the erroneous flagging of good data, and a Type II 

error is the failure to flag bad data. The frequency of these two types of error is a good 

indicator for evaluating the performance of QC methods. The purpose of research on QC 

methods is to produce optimal techniques to identify bad data while minimizing the 

frequency of Type I and Type II errors.

One of the objectives of the development of automatic quality assurance (QA) procedures 

for climate data is to reduce the manual workload of human validators. QA procedures 

have been applied by the NCDC (Guttman and Quayle 1990) in a mix of manual and 

automatic checks to assess the validity of weather data from cooperative climatological 

stations. The statistical literature is replete with general guidance about identifying 

outliers in data (e.g., Barnett and Lewis 1994), but literature concerning the application of 

techniques specific to quality assessment of climatological data is scant. General testing 

approaches, such as using threshold and step change criteria, have been designed for the 

review of single-station data to detect potential outliers (Wade 1987; Reek et al. 1992; 

Meek and Hatfield 1994; Eischeid et al. 1995; Shafer et al. 2000). Techniques have been 

expanding recently from procedures based on in-station checks to include procedures for 

inter-station checks (Wade 1987; Gandin 1988; Eischeid et al. 1995; Hubbard et al. 

2005). The latter conduct tests from reference estimates using spatial techniques such as 

inverse weighting or statistical regressions between stations. 

The high temporal-resolution data covering the Beaufort and Chukchi Seas region as 

collected for this project’s observational database will greatly enhance modeling efforts 

from the regional to the global scale, by way of driving or validating meteorological 

models. However, techniques to quality control these datasets are not available within an 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

16

automatic system. This project thus introduced an enhanced system for application to this 

database that is based on the traditional QC methods presented in Hubbard et al. (2005). 

2.3.1 Introduction to Observational Quality Control 

The upper and lower threshold test determines whether a given variable (e.g., daily 

maximum temperature) falls within a specific range for the month in question. This test 

has been in use for some time and is based on the position of a value with respect to the 

standard distribution of observations. When relatively young stations are involved, the 

threshold test is often employed by considering the climatic extremes for the area (Shafer 

et al. 2000). When the limits are determined based on the statistical distribution, it has 

been called the sigma test, which utilizes the standard deviation (Guttman et al. 1988). 

The limits for the threshold test for a given meteorological variable x are determined by:

x − fs < x < x + fs (2.1)

where x is the daily mean (e.g., mean of daily maximum values for January) and s is the 

standard deviation of the daily values (e.g., daily maximum values) for the month in 

question. The variable x may represent any meteorological observation. The variable f is 

a variable related to the confidence level of a test obtained for the two-sided normal 

distribution. Being flagged with f = 3 represents 99.73% confidence that the data entry is 

an outlier (three standard deviations away from the mean), while f = 4 represents 99.99% 

confidence that the data entry is an outlier (four standard deviations away from the 

mean). Multiplying the standard deviation by these values of f gives two different 

confidence intervals that one can choose for identifying outliers. 

An analysis using the methodology of Hubbard et al. (2005) was performed on the entire 

database (1979–2009) to determine the relationship between the percentage of data 

passing the test and various values of f. This procedure allows an informed choice 

regarding how many data points will be flagged in the natural data stream. If the data

stream contained no errors, the values not passing the test would therefore all be Type I 

errors. In operational use, the data flagged as potential Type I errors are considered 

suspect and subjected to further manual checks; a realistic determination of f is therefore 

critical to project staff requirements. Graphs were developed to display the potential Type 

I errors versus f for the threshold test. 

An example of a wind speed observation that failed the threshold test is the following 

observation from Prudhoe Bay (NCDC network). In March 1981, a wind speed value of 

50.068 m s-1 was recorded. This value was caught by the threshold test as being an outlier 

and one that is well outside of the normal limits for this station. This represents a good 

example of how an excessive value was successfully caught by the automated threshold 

test. Incidentally, this value also failed the step change test, which is discussed below. 

Year month day hour minute latitude longitude wind speed

1981 3 6 17 0 70.25 -148.333 50.068
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Another example of the threshold test catching invalid data was for dew point 

temperatures observed at Birch Creek (RAWS) during the summer of 2001. Values 

around −40°C were reported from 26–30 June and were successfully caught by the 

automated threshold test. 

The step change (SC) test determines whether the difference between consecutive values 

of a variable fall within the climatologically expected limits of the daily rate of change 

for the month in question. In this case, the step is defined as the difference between 

values on days i and i +1, for example xi – xi+1. Utilizing this definition of x and 

calculating the associated mean and variance allow Equation 2.1 to again be used. An 

analysis of the data determines the relationship between f and the potential Type I errors 

for the SC test. 

A relatively small number of observations and stations failed the step change test. 

However, data from the Shell buoy in the Chukchi Sea in 2008 failed the SC test when an 

unrealistic temperature change was reported. The following five consecutive observations

from this station illustrate the large jump in temperature at 0200 UTC 15 October 2008

that was caught by this test.

Year month day hour minute latitude longitude air temperature

2008 10 15 0 0 71.508 −164.072 −4.1

2008 10 15 1 0 71.508 −164.072 −3.7

2008 10 15 2 0 71.508 −164.072 −11.4

2008 10 15 3 0 71.508 −164.072 −2.8

2008 10 15 4 0 71.508 −164.072 −1.9

Another example of a step change error that was caught by the automated procedure is 

present in the following hourly data from the Council 1 station (WERC). In this example, 

duplicate reports were made for 1500 UTC 18 June 2008. Each variable for this 

observation except relative humidity was reported as −999.0. This issue happened 

multiple times at this location and the test successfully caught these errors in the Council 

dataset. 

Year month day hour minute latitude longitude relative humidity

2005 6 18 13 0 64.843 −163.705 96.2

2005 6 18 14 0 64.843 −163.705 96.1

2005 6 18 15 0 64.843 −163.705 0.0

2005 6 18 15 0 64.843 −163.705 95.9
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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The persistence test checks the variability of the measurements. When a sensor fails it 

will often report a constant value; thus, the standard deviation (s) will become smaller, 

and if the sensor fails for an entire reporting period, s will be zero. In other cases, the 

instrument may work intermittently and produce reasonable values interspersed with 

zeroed values, thereby greatly increasing the variability for the period. Thus, when the 

variability is either too high or too low the data should be flagged for further checks. The 

first step in this procedure is to calculate the standard deviation sjk from the daily values 

for each month (j) and year (k) of the 31-year record. Likewise, the standard deviation of 

these monthly values (sjk) is calculated over all years to obtain sj and sj ' , as shown in 

Equation 2.2. The persistence test compares the standard deviation for the time period 

being examined with the expected limits as follows:

sj − fsj ' < s < sj + fsj ' (2.2)

The period under consideration passes the persistence test if the above relation holds for 

the specified value of f.

Persistence tests were shown to catch instances of zero wind speed reported for long 

periods of time. For the Prudhoe Bay-Auto site (NCDC), the persistence test caught a 

period of nine days in the winter of 2006 (3–11 January) during which the wind speed 

was reported as a constant 0.0 m s-1. Below is a sample of the output file, and as the two 

QC flags denote, this erroneous persistence was caught at both the f = 3 and f = 4

confidence levels (the meaning of the QC flag values will be discussed in Section 2.3.3).

Year month day hour minute latitude longitude wind speed flags

2006 1 3 2 0 70.4 −148.517 0.0 4 4

2006 1 3 3 0 70.4 −148.517 0.0 4 4

2006 1 3 4 0 70.4 −148.517 0.0 4 4

The persistence test also caught a similar instance for the Barrow CRN station in which 

the anemometer apparently froze. Wind speed values of 0.0 m s-1 were reported for a 

period of 1.5 months during the winter of 2005–06 from late December to mid-February. 

Similar periods of apparent anemometer freeze-up were again found for various 

additional portions of the winter at the Barrow CRN site. 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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-50

-40

-30

-20

-10

0

10

20

4/1/00 4/6/00 4/11/00 4/16/00 4/21/00 4/26/00

Temperature of Betty Pingo

Tmax Tmin

Hourly temperature Upper limit Tmax

Lower limit Tmin

Figure 2.3.1. Example of the maximum and minimum daily temperatures created 

from high-resolution time series, along with the threshold limits used in the 

threshold test for Betty Pingo in April 2000

2.3.2 Enhancement to Basic Quality Control Procedures

The traditional basic QC procedures were originally developed for and tested with daily 

data, and thus need improvement for application to high temporal-resolution data. As part 

of the modification, the time series of the maximum and minimum daily values were 

calculated from the high-resolution data; an example of this is shown in Figure 2.3.1 for 

the Betty Pingo site. The mean (u) and standard deviation (s) of the daily maxima and 

minima can then be calculated from the time series as ux, sx and un, sn, respectively. Thus,

limits can be formed by modifying Equation 2.1 as:

un − fsn < x < ux + fsx (2.3)

This equation forms limits defined by the upper limits of the maximum and lower limits 

of the minimum. The two dashed lines in Figure 2.3.1 indicate the upper and lower limits 

of the values for this example. Any value falling outside would be flagged as an outlier 

for further manual checking. 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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-25

-20

-15

-10

-5

0

5

10

15

20

25

4/1/00 4/6/00 4/11/00 4/16/00 4/21/00 4/26/00

Temperature change of Betty Pingo

Hourly temperature Upper limit Tmax

Lower limit Tmin

Figure 2.3.2. Example of hourly temperature change (°C) during a one-month 

period, along with the limits used in the step change method for Betty Pingo in April 

2000

The diurnal change of a variable (e.g., temperature) was also calculated from the highresolution (hourly or sub-hourly) data (e.g., Figure 2.3.2). The mean and standard 

deviation calculated from the diurnal changes were then used to form the limits. Since 

positive and negative changes are assumed to have the same probability, Equation 2.3 can 

be written as:

schr < usc + fssc (2.4)

where schr is the value change in a single time step, usc is the mean daily range, and ssc the 

standard deviation of the daily range. Any change in value between time steps that falls

beyond the limits fails the test and is flagged as an outlier.

The mean and standard deviation of the daily time series of variables were calculated to 

form the limits expressed in Equations 2.3 and 2.4. Several limits were calculated from 

the daily time series. In the current application, only air temperature, dew point, and 

pressure values have normal distributions, and can thus be QC’d for both sides. The other 

variables do not and thus can only be tested on the high end. 

Certain variables required special treatment when performing the QC checks. Hard limits 

of 0 and 360 degrees were used for wind direction. A 10-inch (254 mm) precipitation 

value was used as an upper limit, in addition to the parameters calculated from the data 

itself, which overrides any possible very high precipitation amounts that may exist in the 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

21

dataset. The station pressure was also given special treatment, whereby the minimum and 

maximum pressures were first obtained from all stations. After manual examination, only 

those with reasonable values were retained in order to calculate the standard deviation of 

the minimum and maximum pressures as shown in Table 2.3.1.

Table 2.3.1. Computed limits of station pressure. Max and Min are the maximum 

and minimum station pressure of all stations, excluding significant outliers. 

Stdev(max) and Stdev(min) are the standard deviations of the maximum and 

minimum station pressures, respectively.

Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.

Max 1088.8 1072.3 1074.4 1072.8 1056.3 1079.2 1054.1 1064.3 1050.4 1082.8 1081.3 1058.0

Stdev(max) 12.2 15.4 11.5 11.4 7.7 11.1 6.8 9.6 6.4 9.1 9.1 12.3

f=3 1125.4 1118.6 1108.9 1107.0 1079.3 1112.4 1074.6 1093.1 1069.7 1110.0 1108.7 1094.9

f=4 1137.6 1134.1 1120.5 1118.5 1087.0 1123.5 1081.4 1102.7 1076.1 1119.1 1117.9 1107.2

Min 911.4 950.0 960.2 940.5 965.2 967.4 967.5 975.4 976.2 960.1 942.9 945.3

Stdev(max) 15.5 14.3 12.8 15.6 11.3 11.8 10.1 8.3 8.3 12.1 12.3 12.2

f=3 864.9 907.2 921.9 893.7 931.2 932.0 937.3 950.5 951.3 923.9 905.9 908.8

f=4 849.5 892.9 909.2 878.1 919.9 920.2 927.3 942.2 943.0 911.9 893.6 896.6

2.3.3 Quality Control Flags

Based on the results of the aforementioned QC tests, each meteorological observation 

value for all stations was assigned two QC flags. These two flags correspond to tests 

performed with differing levels of strictness, the first with a value of f equal to 4 (less 

strict) and the second with f equal to 3 (more strict). The value of the final QC flag is 

calculated as a weighted sum of three individual QC flags, as given by:

flag = a × 4 + b× 2 + c (2.5)

where flag is the final QC flag, a is the flag for the persistence test, b is the flag for the 

step change test, and c is the flag for the threshold test. In all cases, a flag value of 0 

denotes that an observed value passed the particular test, while a value of 1 signifies that 

the test was failed. In the case of the persistence test, a value of 2 further signifies that not 

enough consecutive data points were available in order to determine success or failure. 

For hourly data, a minimum number of 15 valid observations within a 72-hour window 

are required to run the persistence test; otherwise, a value of 2 is assigned to the 

persistence test flag. Missing observations are assigned a QC flag value of −1.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

22

Thus, given Equation 2.5, the final QC flags for all reported observations have values 

ranging from 0 to 11, depending on which of the individual QC tests were passed. The 

range of these is given in Table 2.3.2 as a function of the QC tests passed by the observed 

value.

Table 2.3.2. Values for QC flags in the observational database as a function of the 

QC tests passed by the observed value. P denotes the test was passed, F that it was 

failed, and U that not enough data were available to make a determination.

QC Flag Persistence Step Change Threshold

0 P P P

1 P P F

2 P F P

3 P F F

4 F P P

5 F P F

6 F F P

7 F F F

8 U P P

9 U P F

10 U F P

11 U F F

2.3.4 Results and Summary

The QC methods used in this study detect three types of errors in the observational data. 

These errors are able to be identified using automated procedures because the identified 

outliers are statistically significant relative to the surrounding temporal data and the 

climatologically defined limits. The quality control results for the high-resolution in situ 

data in the Beaufort and Chukchi Seas region show some interesting patterns. For 

example, there were cases in which the QC checks identified suspect data entries such as:

1) Missing values denoted by values other than those specified in the metadata of the 

originating dataset.

2) Incoming shortwave radiation observations that had negative values at some 

stations, suggesting that additional calibration of the shortwave sensors is needed 

in order to limit such values to zero.

3) Typos in the entries of one or more variables for stations that do not measure the 

variable; e.g., the 1-hour precipitation at the stations Buhta Providenja and Cape 

Parry-Auto in the NCDC network. These data entries were also flagged.

From a manual analysis of the identified errors, most of the flagged data were likely 

caused by instrumentation failures or by the miscalibration of sensors, such as those that 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

23

generated negative shortwave radiation values. A significant number of radiation data 

entries from the ARM network have values around 0, which may be caused by sensor 

miscalibration. As a result, a large fraction of data for this variable and network were

flagged.

The threshold test does not work for accumulated variables such as 6- and 24-hour 

precipitation and daily snow depth due to the flagging of too many data at the higher end.

The fraction of flagged data as shown in Table 2.3.3 reflects the overall quality of the 

different monitoring networks in measuring all observed variables. Across the entire 

database, the total percentages of flagged data are as follows: 0.99% for the threshold test, 

0.02% for the step change test, and 1.34% for the persistence test. As shown in the table, 

the persistence test (used to detect a sensor reporting a constant value with no change in 

standard deviation) had the highest failure rate. In addition, a few observation types had 

much larger-than-average flagged percentages, including daily precipitation (NCDC), 

daily snow depth (NCDC), shortwave radiation (Toolik Lake), and 1- and 6-hr 

precipitation (NCDC).

In general, the small fraction of flagged values demonstrates the efficiency of the QC 

methods adopted in this study, allowing those values that are flagged to be manually 

checked in order to verify that they are truly invalid. For high-resolution measurements, 

manually checking the entire database would not be feasible due to the time requirements 

needed for such a task, and the automated procedures demonstrated here thus greatly 

reduce the labor involved.

Table 2.3.3. Percentage of flagged data for the three different quality control tests 

(separated by network and variable)

Network Variable Threshold Step Change Persistence

ARM 1-Hr Precipitation 0 0 0

Dew Point 0.01 5.35E−05 0.03

Longwave 0 0 0.01

Relative Humidity 0.01 3.31E−05 0.10

Shortwave 26.37 7.50E−05 2.07

SLP 0 0 0.20

Station Pressure 0 0 0.20

Temperature 0.02 3.53E−04 0.13

Wind Direction 0 0 4.54

Wind Speed 0.78 1.26E−03 1.43

BOEM Dew Point 0.06 3.60E−03 0.64

Relative Humidity 0 1.06E−03 0.54

Shortwave 0.03 3.19E−03 3.52

SLP 2.41 0 0

Station Pressure 0 6.18E−04 0.03
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Network Variable Threshold Step Change Persistence

Temperature 0.04 0.05 0.83

Wind Direction 0 2.68E−03 0.36

Wind Speed 0.95 0 1.21

Buoys Dew Point 0 0 0.26

Relative Humidity 0 0.07 0

Shortwave 0.03 0 0.09

SLP 0 0 0

Station Pressure 0 0 0

Temperature 0.01 0.03 0.38

Wind Direction 0.10 0 0.65

Wind Speed 0.08 0 0.13

C-MAN SLP 0 0 0.07

Station Pressure 0 0 0.07

Temperature 0.05 2.26E−04 0.19

Wind Direction 0 0 1.46

Wind Speed 1.31 0 1.70

LTER 1-Hr Precipitation 0.02 0.01 37.81

1-m Dew Point 0.07 7.11E−04 0.78

1-m Relative Humidity 7.10E−04 1.42E−03 0.05

1-m Temperature 0.03 0.03 1.30

1-m Wind Speed 1.51 0 1.32

5-m Dew Point 0.07 6.60E−04 0.76

5-m Relative Humidity 0 6.60E−04 0.08

5-m Temperature 0.04 0.03 1.28

5-m Wind Speed 1.30 0 1.23

Shortwave 27.73 2.54E−03 1.27

Station Pressure 2.09 0.02 0.16

Wind Direction 0 0 0

NCDC 1-Hr Precipitation 2.81 0.07 19.63

6-Hr Precipitation 0.21 3.09E−03 18.79

24-Hr Precipitation 3.60 0.04 3.54

Altimeter 0 0.07 0.20

Dew Point 1.20 0.10 0.44

Relative Humidity 2.06 0.11 0.32

SLP 0.00 0.07 0.22

Snow Depth 6.61 0.01 0.83

Station Pressure 1.89 0.04 1.98

Temperature 0.21 0.09 1.30
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Network Variable Threshold Step Change Persistence

Wind Direction 0 0.02 0.50

Wind Speed 0.92 0.05 1.76

RAWS 1-Hr Precipitation 0.19 0.07 6.53

Dew Point 1.69 0.06 1.25

Relative Humidity 2.60E−04 0.02 2.02

Shortwave 0.04 0.01 6.64

Temperature 0.16 0.01 1.64

Wind Direction 3.22E−03 8.98E−04 1.07

Wind Speed 0.99 4.02E−03 6.37

Ships 1-Hr Precipitation 0 0 82.75

Dew Point 0.04 0.01 0.71

Longwave 0 0 1.17

Relative Humidity 0.09 0.06 0.18

Shortwave 0 0 4.99

SLP 0 0.01 0

Station Pressure 0 0.02 0

Temperature 0.10 0.03 1.29

Wind Direction 0.27 0.43 0.24

Wind Speed 0.89 0.05 1.16

Well sites 1-Hr Precipitation 0 0 72.57

Dew Point 0 0 0.77

Relative Humidity 0 3.85E−03 0.15

Shortwave 0.02 0 13.20

Station Pressure 0 0.07 0.29

Temperature 0.02 1.70E−03 0.91

Wind Direction 0 0.01 0

Wind Speed 0.57 3.17E−04 0.53

Altimeter 0 0.07 0

SLP 0 0 0

WERC 1-Hr Precipitation 0.48 3.57E−03 40.82

1-m Dew Point 0.06 3.50E−03 0.99

1-m Relative Humidity 0.13 3.50E−03 0.60

1-m Temperature 0.03 9.61E−04 0.99

3-m Dew Point 0.05 1.71E−03 1.23

3-m Relative Humidity 4.44E−03 1.28E−03 0.51

3-m Temperature 0.04 3.48E−03 1.00

Snow Depth 0.08 3.13E−02 6.25

Station Pressure 2.94E−03 8.82E−03 18.16
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Network Variable Threshold Step Change Persistence

Wind Direction 0.01 2.98E−03 0.33

Wind Speed 1.24 7.89E−03 1.57

Total 0.99 0.02 1.34

As demonstrated here, the applied in-station QC tests can successfully identify outliers in 

the observational dataset. However, spatial tests that consider data from neighboring 

stations can, in some cases, be more robust in identifying errors or outliers when strong 

correlations between different stations exist. Figure 2.3.3 shows a promising example of 

the correlation between temperature observations at West Dock and Betty Pingo in May 

2000. A strong relationship between measurements at station pairs suggests the potential 

opportunity for applying the spatial regression test (SRT, Hubbard et al. 2005) to stations 

that measure the same variables, such as air temperature and wind speed. Further testing 

of this method is required in order to successfully perform this type of test on the entire 

observational dataset.

y = 0.974x - 0.1036

R2 = 0.9622

-30

25

-20

-15

10

5

0

5

-30 -25 -20 -15 -10 -5 0 5

Betty Pingo

West Dock

May 1-31, 2000, hourly air temperature 

Figure 2.3.3. Example of a strong correlation in the hourly air temperatures (°C)

between Betty Pingo and West Dock for May 2000

The three QC methods described in this work perform better when operating on a longterm dataset, and so the short-term periods of record at some stations in the study area 

may not be efficiently quality controlled if only using the methods detailed here. The lack

of a long station history with which to set limits, determine standard deviations, and 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

27

establish other parameters used by the three different QC checks limits their utility when 

applied to short-term datasets.

The automated checks work equally well regardless of network or region. A station-bystation look at the quality control results shows that there are problem stations, rather 

than problem networks or regions, as there appear to be no major systematic differences 

in the QC performance with respect to network or region. Some particularly poorperforming stations are the sites with very short and highly discontinuous records, due to 

the lack of a sufficiently long station history with which to determine the in-station limits, 

and because the step change and persistence tests require periods of continuous data,

without gaps, in order to perform properly. For most of the very short-term sites, this is 

not the case. As an example, measurements at the Tuktoyaktuk 2 site (NCDC) begin in 

January 1979 and end in April 1981; however, only 14% of the hours in that timeframe 

contain observations, with the remaining 86% lacking data. For sites such as these, aside 

from the use of the threshold test, it is nearly impossible to automate quality control 

routines. The only alternative would be to check observations hour by hour with those of 

a neighboring station, if available.

In aggregate, less than 5% of the observational data were flagged (Figure 2.3.4). This 

relatively low percentage of flagged data is indicative of the overall good quality of the 

station measurements. For the most part, these data had gone through some form of 

quality control by their maintaining agencies prior to acquisition by this project. In terms 

of overall performance, the BOEM, buoys, C-MAN, and exploration well site networks 

performed better than the ARM, LTER, NCDC, RAWS, ship, and WERC stations. The 

persistence test was failed more frequently than either the threshold or step change tests

for nearly all stations.

A certain portion of the data was not able to undergo the threshold and step change tests

(Figure 2.3.4) due to the lack of continuity necessary for these tests to be conducted. As a 

result, these particular tests could not be applied for some periods of time at various 

stations. In contrast, the threshold test could be performed on all data, regardless of the 

presence of missing observations.

When separated by variable, the wind direction and wind speed were flagged the most

often, failing QC tests nearly 2% of the time (Figure 2.3.5). In spot-checking the data 

records, it appears that these tests most often identified cases of zero wind speed and/or 

wind direction that lasted for periods of several days. It is assumed that sensor failure due 

to icing was the cause of these events. The spot-checking also found that cases of 

persistent high wind speeds were flagged by the persistence test. Given the climatology 

of the coastal region, it is known that such cases do, in fact, periodically occur. It is 

therefore recommended that users of the database manually check such instances when 

investigating periods of consistently high wind speed in which the persistence test was 

failed.
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Figure 2.3.4. Total percentage of flagged data for the three automated QC checks as 

delineated by the major observational networks. Percentage of data that were not 

able to undergo the step change and persistence tests due to missing observations is 

also given.

Figure 2.3.5. Total percentage of flagged data as categorized by variable and QC

test
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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2.4 Analysis of the Meteorological Database

2.4.1 Climatology of the Long-Term Observations

Temperature Patterns

For most locations in the study domain, the mean temperature of the warmest month is 

below 10°C (Figure 2.4.1). The months of June, July, and August have mean 

temperatures above freezing, while the transitional months of May and September may

be above freezing for some years. February is typically the coldest month, with mean 

temperatures in the −25°C range, with January the coldest for Kotzebue, Umiat, and 

Inuvik. The region inland from the Beaufort and Chukchi Sea coasts, throughout the 

Arctic Slope, exhibits a climate that is more continental in nature, with higher 

temperature variability and warmer summers. See, for example, the temperature 

variability at the inland stations of Umiat and Inuvik as compared to the coastal sites of 

Barrow and Deadhorse (Figure 2.4.2). The Brooks Range acts as a geographical dividing 

line between what is traditionally considered the Arctic Slope and the strong continental 

climate of the interior. This mountain range acts to block southward-moving systems, 

sheltering the interior from the influence of the Arctic marine air mass. 

Figure 2.4.1. Mean monthly temperatures (°C) for the long-term stations in the 

region
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Along the Arctic Slope, there is a general longitudinal gradient in temperature from the 

coast inland that varies with season and local weather conditions, and that exhibits some 

significant variability. As an example, a comparison of monthly temperature observations 

between Barrow and Atqasuk shows that Atqasuk is, on average, colder by approximately 

3°C from November to March. From May through September, however, Barrow is colder, 

with the months of June through August displaying the greatest mean temperature 

difference of 3°C to more than 5°C. The same type of gradient is seen for the Beaufort 

Sea coastal region. This summertime thermal gradient is what allows the sea breeze 

phenomenon to take place. 

A frequency distribution of the observed temperatures in the region illustrates a 

predominantly bimodal distribution (Figure 2.4.2). This occurs throughout the domain 

regardless of proximity to the coast; however, the shape of the distribution varies 

depending on location. Inland locations experience a wider range of temperatures 

throughout the year, whereas coastal locations exhibit a more narrow distribution, with 

fewer extremes. The modes of the distribution are centered in the −20 to −25°C range, 

indicative of wintertime conditions, and 0°C during the short summer season. The tails of 

the distribution are widest for the Umiat, Inuvik, and Bettles locations that are 80 km or 

more from the coast. 

Figure 2.4.2. Frequency distribution of observed temperatures (°C) for the longterm stations

Differences related to location are also evident when looking at temperature extremes. As 

shown in Figure 2.4.3, the range of observed temperatures for Umiat, Inuvik, and Bettles
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

31

is larger than for coastal areas such as those at Ostrov Vrangelja and Barrow, regardless 

of latitudinal differences. 

Figure 2.4.3. Monthly maximum and minimum temperatures (°C) for the long-term 

stations

Winds

Surface wind conditions in the study domain show distinct regional differences that 

depend on local topography, distance to the coastline, and proximity to semi-permanent 

pressure patterns. The dominant feature for coastal locations in the domain is the 

influence of the Beaufort High on the wind direction regime. Along the entire Beaufort 

and portions of the Chukchi coasts, winds exhibit a strong easterly component, associated 

with this synoptic-scale pressure feature that is in place during the cold months. Winds 

transition somewhat during the summer, resulting in a slightly more westerly component. 

The resulting annual wind rose has a strong east-west bimodal distribution. Detailed wind 

rose plots for Barrow are shown for the months of February and August (Figures 2.4.4

and 2.4.5, respectively).

For coastal locations in the far western region bordering the Bering Sea (Mys Uelen), or 

on Wrangell Island (Ostrov Vrangelja) in the far north, the wind regime is more 

influenced by local topography. As such, the wind direction distribution is less east-andwesterly than at locations such as Barrow or Barter Island. For example, Wrangell Island 

also displays a strong bimodal distribution, though instead oriented in the north-south 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

32

direction. The observing location is situated on the southern edge of Wrangell Island and 

it is assumed that local topographic features play a part in determining wind directions 

there.

Figure 2.4.4. February wind rose climatology for Barrow (1979–2009)

Figure 2.4.5. August wind rose climatology for Barrow (1979–2009)
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Wind speed conditions for locations near the coast are consistently high throughout the 

year with few, if any, periods of calm (Figure 2.4.6). Mean monthly values lie in the 5 to 

6 m s-1 range for locations such as Barrow, Deadhorse, and Kotzebue. Some coastal 

locations exhibit seasonal variability in which the lowest wind speeds are found in late 

spring and summer. The Russian stations Mys Shmidta, Mys Uelen, and Ostrov 

Vrangelja all have a seasonal maximum in the late autumn and early winter. For all 

coastal locations, the highest annual wind speeds on the order of 20 to 25 m s-1 are found 

during the winter season, with a minimum during summer (Figure 2.4.7). The winter 

months are the time of year when storms frequently influence the region, as discussed 

later in Section 5.3.

The standard deviation of the hourly wind observations shows a similar seasonal 

variation, with the highest values during winter (3–5 m s-1) and lower values in summer 

(2–3 m s-1) (Figure 2.4.8). Again, this is indicative of the stormy period that occurs

during the cold months, which is characterized by higher variability in wind speeds. Calm 

conditions occur less than 5% of the time in these coastal environments.

Figure 2.4.6. Mean monthly wind speed (m s-1) by month for the long-term stations
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

34

Figure 2.4.7. Maximum observed hourly wind speed (m s-1) by month for the longterm stations

Figure 2.4.8. Standard deviation of hourly wind speed observations (m s-1) by month 

for the long-term stations

For interior locations such as Bettles, located to the south of the Brooks Range, the wind 

direction distribution is quite different than for coastal locations. Generally, the mode is 

not as strong, and in the annual wind rose the directional components are fairly evenly 

distributed. Wind speed shows a seasonal distribution that is opposite to that seen for 

coastal locations. The magnitude is much less (2–3 m s-1 on average) throughout the year,
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

35

and there is a slight increase in speed during the summer months, rather than in the 

winter. There is little influence from large, organized winter storms in the sheltered 

region of the central interior south of the Brooks Range, and therefore wind speeds are 

not high during the cold months. Rather, this area often lies under a dome of high 

pressure in the cold season, with subsiding air and calm conditions. The summer, when 

thermal lows develop and produce thunderstorms, is normally the time when localized 

higher winds occur. Maximum wind speeds observed at interior locations lie in the 10–15 

m s-1 range; in contrast to the average wind speeds, this shows no discernable seasonal 

variability. The standard deviation determined from the hourly wind speed observations 

for each month also shows no seasonal differences, remaining consistently around 2 m s-1

throughout the year. The percentage of time with calm conditions is low, though slightly 

greater than for coastal regions, and ranges from 5% to less than 10%.

Since hourly data are available for each observational network, the diurnal variability in 

wind speed was also examined. During the core winter months, there was no evident 

diurnal variation throughout much of the model domain. For example, from October 

through March there is no discernable diurnal variability in the mean hourly wind speed

at Barrow. However, beginning in April and continuing through September, a variation in 

wind speed arises that corresponds to specific hours of the day. From roughly 2000

Alaska Standard Time (AKST) to approximately 0400 AKST a minimum is observed in 

the hourly wind speed data. In contrast, the highest speeds occur during the late afternoon 

and evening hours, with an increase of around 1 m s-1 relative to the overnight hours. 

Pressure

The pressure field in the study domain shows evidence of several different influences 

throughout the year. The predominant pressure pattern in the northern part of the domain 

is the semi-permanent Beaufort High that occurs during winter (Figure 2.4.9). The 

location and strength of this pattern varies depending on various factors; however, 

significantly higher pressures are evident at this time of year. The peak in sea level

pressure occurs during late winter, with March normally exhibiting the highest values of 

around 1020 hPa. Afterwards, pressures tend to slowly decrease throughout the spring 

and summer, reaching a minimum from July to November of approximately 1012 hPa.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

36

Figure 2.4.9. Mean monthly sea level pressure (hPa) by month for the long-term 

observing stations

Throughout the domain, there is a clear seasonal pattern to the pressure variability, in 

which both the highest and lowest pressures are observed during the cold season. Hourly 

observations are highest from November through March, with values in the 1050 to 1060 

hPa range (Figure 2.4.10). During the warm season, in contrast, maxima lie only in the 

1030 to 1040 hPa range. The opposite holds true for the minima, however, with the 

lowest absolute pressure observations occurring during winter. These values tend to be 

below 980 hPa from October through March, even dropping below 960 hPa in some 

cases (Figure 2.4.11). During summer, however, the minima are only around 990 hPa. 

Given this pattern of variability in pressure, it is no surprise that the standard deviation of 

the hourly observations exhibits a similar seasonal pattern. Mean monthly pressures 

during the cold months have standard deviations of 15 to 16.5 hPa, while the values are 3 

to 4 hPa lower from May through September.

The patterns described here are indicative of the timing of storm systems that traverse the 

region. These are common during winter, and particularly late autumn, and represent 

large organized storm events that affect a large area. Summertime storms, though less 

frequent, are not uncommon and have been shown to produce damaging winds and wave 

action leading to coastal erosion. For the most part, however, these are more localized,

and in the interior portion of the domain summer storms are mostly convective in nature.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Figure 2.4.10. Observed maximum sea level pressure (hPa) by month for the longterm stations

Figure 2.4.11. Minimum observed sea level pressure (hPa) by month for the longterm stations
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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2.4.2 Short-Term Observations

Offshore Locations

There are no long-term offshore observations in the database because the source of these

data are purely temporary or transient platforms such as buoys, exploration well sites, 

ships, and ice camps. Due to their short periods of record, these observations are not 

necessarily indicative of the climatology of their location; rather, they more likely 

represent the local meteorology and synoptic conditions during particular months or over 

a single year. However, analysis of these data (see Figure 2.2.8 for their locations) allows

for an investigation into conditions in offshore areas, a critical region that is of particular 

importance for this study. For most of these stations, observations are only available 

during the late summer and early autumn months.

Wind speed observations from these short-term sites show similar values as compared 

with the long-term coastal locations, with averages of around 6 m s-1. However, the 

monthly averages range from 3 m s-1 (Kuvlum 2, July and August) up to 10 m s-1

(Popcorn, September and October). The standard deviation for the hourly winds is in the 

3–4 m s-1 range. Wind rose information for these offshore short-term sites reveals that the 

northeast and southwest quadrants normally exhibit the highest average wind speeds.

With the exception of the Phoenix well site, no offshore sites reported temperature data 

throughout the year. Phoenix (operating from 1990 to 1991) has mean monthly 

temperatures at or above freezing from June through September. The warmest month is 

August and the coldest is February, with absolute observed maxima and minima of

13.9°C (July) and −42.6°C (February). Monthly summer temperatures for other offshore 

sites in the region exhibit mean values above freezing primarily from June through 

September. In nearly all cases, August is the warmest month, with absolute maximum 

temperatures that approach 14°C. In the case of Northstar, Cottle Island, and Endicott, 

summer temperatures peak from 15 to 20°C. A temperature frequency distribution 

histogram shows that observations most frequently occur in the −10 to 0°C range. At the 

Phoenix well site, the distribution peaks in the −5 to 0°C category.

Observations from the BOEM buoy, in operation during late summer 2009, represent a 

high-quality, though short-term, offshore dataset. Data are also available for Shell- and 

ConocoPhillips-maintained buoys in the Chukchi and Beaufort Seas for 2008 and 2009, 

in addition to the SEDNA field campaign in the far north of the domain, which operated

for two weeks in April 2007. It is difficult to directly compare statistics from these sites 

with those of the long-term stations, as many represent only one or two seasons of data, 

rather than 31 years. However, these short-term sites represent the only offshore in situ 

observations available (other than ship data), though by their nature they are indicative of 

meteorological, rather than climatological, conditions in the region. Winds at the buoy 

sites occur most frequently in the 4 to 6 m s-1 range, and calm conditions prevail less than 

1% of the time (except at the SEDNA ice camp, for which it is 5%). The standard 

deviation of the hourly observations is approximately 3 m s-1. Wind directions are 

somewhat variable, but are primarily northerly for the offshore buoys.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

39

Well sites in the Beaufort and Chukchi Seas represent an additional offshore dataset and,

again, are indicative of meteorological, rather than climatological, conditions of the 

region. Calm wind conditions occur less than 5% of the time and the most frequent wind 

speed is 4 to 6 m s-1. The Popcorn well site, in operation during September and October 

1989, exhibits the highest average wind speed and a high frequency of winds over 12 

m s

-1

, most of which come from an east-northeasterly direction. The standard deviation of

wind speed observations for all the collected well sites is a little more than 3 m s-1. Wind 

rose plots show a general predominance of northeasterly winds, with a second mode in 

the southwest quadrant. Kuvlum 2 and 3 and Wild Weasel, on the other hand, exhibit

more of an east-west bimodal wind direction distribution, influenced by the orientation of 

the Brooks Range.

Near-Shore Observations

Quite a number of shorter-term observing sites are located on land in the near-shore 

region of the Beaufort and Chukchi Seas. There are several BOEM and WERC network 

sites that have five or fewer years of data, particularly in the vicinity of Prudhoe Bay. The 

BOEM and surrounding supplemental locations have been summarized in a previous 

study (Hoefler Consulting Group 2007), which describes the temperature and wind 

patterns for these locations on or near the Beaufort coast. Since the metadata are known 

and the quality of these data is high, the conditions observed here should be taken as 

accurate. Many of the same statistics generated in the Hoefler report are also presented 

here. Wind rose plots illustrate the frequency of onshore winds during the summer 

months; wind speeds are in the 5 m s-1 range along the coast, and decrease farther inland. 

Temperature statistics are comparable to the longer-term stations for both warm and cold 

months, and the frequency distribution of hourly temperature observations also shows the 

same bimodal tendency, with peaks in the −25°C and 0°C ranges.

Data from the WERC network varies in record length, but the spatial representation for 

the Prudhoe Bay region of the Arctic Slope is quite good, in contrast to the rest of the 

domain. For analysis, these stations have been divided into four categories, grouped with 

increasing distance from the coast: coastal, near coast, inland, and Brooks Range, as 

indicated in Figures 2.4.12–2.4.15, respectively. Mean monthly temperatures for these 

stations illustrate differences corresponding to their proximity to the coast. Warmer 

summers are evident for locations farther inland, with monthly temperatures exceeding 

10°C.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

40

Figure 2.4.12. Mean monthly temperature (°C) for coastal stations in the WERC 

network

Figure 2.4.13. Mean monthly temperature (°C) for near-coastal stations in the 

WERC network.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Figure 2.4.14. Mean monthly temperature (°C) for inland stations in the WERC 

network

Figure 2.4.15. Mean monthly temperature (°C) for Brooks Range stations in the 

WERC network
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

42

2.4.3 Inter-Network Comparison at Barrow

There are several surface observing stations in the vicinity of Barrow, representing three 

different networks (NCDC, ARM, and the Climate Reference Network (CRN)). The 

metadata for these networks, such as instrument height, are all known. Therefore, it is 

assumed that differences in the observations accurately represent the microclimatology of 

the Barrow area. Detailed comparisons were performed among these three datasets at 

hourly and monthly time scales. Since comparisons were only made for corresponding 

time frames, the period of record presented here is relatively short, as the CRN and ARM 

sites have only been in place for the last several years, as compared to 31 years for the 

Barrow airport (NCDC) site.

Among the Barrow airport (NCDC), ARM, and CRN sites, the airport has consistently 

higher temperatures. The difference is greatest during the winter months, with the airport 

averaging from 0.5 to 1.4°C warmer. During August, September, and October, the air 

temperatures at the three locations around Barrow are quite similar, with a difference of 

just 0.2°C or less. The location of the airport station is on the southern edge of town, 

while the other two sites are situated a slight distance away from town. The warmer 

conditions in town represent a documented urban heat island effect (Hinkel et al. 2003). 

Up to a 2.2°C temperature difference has been found between temperatures in town and

those of rural areas outside Barrow.

Wind speed was also compared between the Barrow ARM and airport sites (wind speed 

at 10 m is not available from the CRN location). The airport location reports a 

consistently higher average wind speed, with differences on the order of 1 m s-1. This 

observed difference could be the result of differences in instrumentation, since the two 

sites do not use the same make and model of anemometer. Instrument exposure and 

localized influences could also play a role in the observed difference.

2.4.4 Upper-Air Observations

Radiosonde data are available for a subset of stations in the database. These data were 

obtained from the Integrated Global Radiosonde Archive through the NCDC

(http://www.ncdc.noaa.gov/oa/climate/igra). Twice-daily data at 0000 UTC and 1200

UTC are available for the period 1979 to 2009, though the Russian stations only have 

records through 1999 and Barter Island only through 1988. The stations with upper-air 

data include Barrow, Barter Island, Inuvik, Kotzebue, Mys Shmidta, Mys Uelen, and 

Ostrov Vrangelja, all of which are also long-term surface observing stations. The 850-hPa 

level was chosen for detailed analysis as this height lies above the boundary layer and 

thus outside of the direct influence of the surface.

As illustrated in Figure 2.4.16, the modal wind direction at the 850-hPa level varies 

seasonally for most locations. During the cold months, the prevailing direction is 

northeast-to-easterly, presumably in association with the Beaufort High. One exception is 

at Inuvik, which, unlike the others, is not located on the coast, but rather a bit inland on 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

43

the Canadian Mackenzie River Delta. For the majority of the stations, the modal direction 

during the warmer months of June to October is predominantly from the west. 

Figure 2.4.16. Monthly mode of the wind direction (degrees) for the 850-hPa level at 

locations where radiosonde data are available

Figure 2.4.17. May to September wind direction (degrees) mode at the surface and 

850-hPa level for Barrow and Barter Island

Of particular interest in upper-air investigations is the sea breeze, revealed through a

comparison of 850-hPa and surface observations. For Barrow and Barter Island in 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

44

particular, the surface and upper-air levels display distinct differences in modal wind 

direction for the warm season of July to September (Figure 2.4.17), approaching a 

complete reversal of 180 degrees in some cases. This indicates a turning of the wind from 

the surface to 850 hPa, which is only evident during the warm season and is indicative of 

the influence of the sea breeze in the boundary layer.

2.4.5 Climate Trends

In recent years, the Arctic has seen dramatic changes in nearly all aspects of its 

environment. One of the most striking changes has been observed in one of its most 

defining characteristics: sea ice (Stroeve et al. 2012). It has been suggested that due to 

declining concentrations in sea ice, specifically multiyear ice, that strong warming trends 

will be observed in months typically defined by ice cover for Arctic locations with a 

direct coastal influence (Comiso 2012; Maslowski et al. 2012). 

Linear trends in temperature and wind speed were calculated for the long-term observing 

stations in the study area for the years 1979–2009. Trends in temperature were calculated 

from the monthly mean values, both for each month and for the warm season as a whole

(May–September). In calculating the monthly mean values, months were considered 

missing if less than 70% of the hourly observations were available. Missing individual 

monthly values were replaced by the mean value for that month over the observational 

period. 

Mann–Kendall trend tests (Mann 1945; Kendall 1975) were performed on the time series,

both for each month and for the warm season as a whole to determine if the calculated 

trends were significant at the 95% confidence level (α=0.05) for each variable. The 

Mann–Kendall trend test has been widely used in climate studies to determine the 

significance of trends observed in time series of climate data. Non-parametric tests, such 

as Mann–Kendall, are thought to be more suitable for analysis of climate data since the 

tests are not dependent on the normality of the data distribution. In addition, Mann–

Kendall is based on a function of the ranks of the data rather than their actual values (Yue 

et al. 2002; Hamed 2008). As a result, the trend analysis is less sensitive to outliers and 

provides a clearer picture of the overall trend (Hamed 2008). 

Temperature trends for the long-term stations in the study region display wide variability 

in both significance and magnitude. Four stations (Bettles, Deadhorse, Inuvik, and 

Kotzebue) show little or no significant evidence of warming over the study period (Table 

2.4.1). Of this group, only Inuvik and Deadhorse display months with significant positive 

trends (95% confidence level) in temperature: November in Deadhorse (+0.17°C yr-1)

and December in Inuvik (+0.17°C yr-1). For all other months, these four stations show 

both positive and negative trends of varying magnitude, though with none achieving a 

95% confidence level. Among these stations, no clear seasonal pattern is evident. Stations 

under the influence of the Chukchi Sea (Barrow, Mys Shmidta, Mys Uelen, and Ostrov 

Vrangelja), however, display significant warming trends in several months throughout the 

year, most notably for September through December. In the previous group, the only 

significant trends were also seen in these months, and were also positive. On an 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

45

interesting side note, the only months in the Chukchi region displaying negative (though 

not significant) trends in temperature were January and February. This winter cooling is 

consistent with findings in a satellite-based analysis of temperature trends by Comiso 

(2003) for the Chukchi region. Comiso (2003) attributed this trend to a 1–8% increase in

sea ice during these months from 1979–2003, further suggesting a strong link to sea ice 

cover and temperature trends.

Table 2.4.1. Temperature trend (°C yr-1) in surface temperature for the period 

1979–2009. Boldface red type indicates statistical significance at the 95% confidence 

level (boldface black, 90% level).

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann

Barrow −0.03 0.04 0.01 0.13 0.05 0.02 0.03 0.05 0.12 0.22 0.19 0.15 0.08

Bettles −0.09 0.15 −0.14 0.07 −0.01 0.02 0.00 0.00 0.06 0.02 −0.02 0.10 0.01

Deadhorse −0.03 0.07 −0.03 0.10 0.03 0.03 −0.02 −0.02 0.05 0.11 0.17 0.10 0.04

Inuvik 0.07 0.14 −0.02 0.14 0.03 0.01 −0.01 −0.03 0.04 0.04 0.15 0.17 0.06

Kotzebue −0.18 0.05 −0.09 0.08 −0.01 0.02 0.02 0.01 0.06 0.04 −0.02 0.06 0.01

Mys

Shmidta −0.09 −0.06 0.08 0.09 0.13 0.05 0.06 0.11 0.13 0.18 0.16 0.15 0.08

Mys

Uelen −0.09 0.00 0.07 0.09 0.07 0.05 0.03 0.05 0.08 0.10 0.14 0.10 0.06

Ostrov

Vrangelja −0.03 0.00 0.08 0.09 0.09 0.03 0.06 0.06 0.15 0.20 0.16 0.15 0.09

2.5 Summary

This section outlines the climatological conditions at the eight long-term stations across 

the model domain. These locations represent both coastal and inland Arctic 

environments. Clear differences are evident between these two environments, with 

maritime regions displaying lower temperature variability, higher overall temperatures,

and higher wind speeds compared to inland stations. Of the long-term sites, the lowest 

temperature variability is found at Ostrov Vrangelja (in a strong maritime climate 

regime), whereas the highest variability is found at Umiat (in a strong continental climate 

regime). During the warm season, Umiat and Inuvik display quite similar mean 

temperatures—both are inland, though Umiat is much farther from the coast than is 

Inuvik. During the winter months, Umiat exhibits the lowest temperatures overall. A 

frequency distribution for all the observations shows a clear bimodal tendency, with 

peaks in the −25°C and 0°C ranges, indicative of the cold and warm seasons, 

respectively.
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Winds average approximately 4–7 m s-1 for locations in the domain along the coast and 

are in the 2–3 m s-1 range for inland locations. Maximum wind speeds are highest in the 

winter and lowest in the summer for most locations, and peak in the 20–30 m s-1 range. 

For inland sites, maximum wind speeds are 15 m s-1 or less. Wind directions vary by 

location, depending on such factors as proximity to the Beaufort High semi-permanent 

pressure pattern, characterized by strong anticyclonic winds, as well as proximity to the 

coast and local topography. 

Many shorter-term observing stations (both offshore and onshore) are present in the 

database, with some only operating for a one-month time frame. Although data from 

these locations are indicative of meteorological, rather than climatological, conditions, it 

is still of interest to compare some of them to the long-term sites. This comparison is 

especially beneficial in understanding the nature of offshore climate conditions when 

looking at buoy and well site data. Wind and temperature data for offshore sites show 

similarities to the long-term coastal locations. In particular, wind speeds are comparable, 

the frequency of calm conditions is similarly low, and wind directions are likewise 

northerly. Near-shore observations in the BOEM and WERC networks provide another 

means of comparison with the long-term sites. These have the added benefit of providing

high spatial detail in the Prudhoe Bay area.

Through the incorporation of radiosonde observations (measured at Barrow, Barter 

Island, Inuvik, Kotzebue, Mys Shmidta, Mys Uelen, and Ostrov Vrangelja), surface and 

upper-air conditions can be compared. In doing so, wind direction differences are evident 

between the surface and 850-hPa levels in the summer months, indicating a turning of the 

wind and the predominance of a sea breeze circulation.

Finally, monthly temperature trends show the nature of the warming for this region since 

1979. The late autumn months show trends that are statistically significant, with warming 

on the order of 0.15°C yr-1. October is the month with the strongest warming trend 

overall, with Barrow exhibiting a warming of 0.22°C yr-1. For nearly all stations, January 

trends show a slight cooling, which is likely linked to regional trends in sea ice.

3 Development of a Physically Optimized Modeling System for the Chukchi–

Beaufort High-Resolution Atmospheric Reanalysis (CBHAR)

3.1 Introduction

While the analysis of observational data is critical in determining the climatology of the 

Chukchi–Beaufort Seas region, both the sparse distribution and relatively short data 

periods of many of the available stations in the study domain make such an analysis 

incomplete. In order to better understand the finely detailed, regional-scale surface wind 

field over the Chukchi–Beaufort Seas and Arctic Slope, the WRF model and its data 

assimilation system WRFDA have thus been utilized to generate a long-term, highresolution reanalysis over the study area that can be used to supplement the existing 

observational record. The result of this reanalysis effort is the 31-year Chukchi–Beaufort 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

47

High-Resolution Atmospheric Reanalysis (CBHAR), in which numerical model output

was combined with the observational database detailed in Section 2, along with 

additional satellite retrievals, over the 31-year period 1979–2009 through data 

assimilation techniques. Examples of similar reanalysis datasets include the European 

Centre for Medium-Range Weather Forecasts (ECMWF) 40-Year Re-analysis (ERA-40) 

(Uppala et al. 2005) and Interim Reanalysis (ERA-I) (Dee et al. 2011), the Japanese 25-

Year Reanalysis (JRA-25) (Onogi et al. 2007), and the National Centers for 

Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) 

Reanalysis (Kalnay et al. 1996) and North American Regional Reanalysis (NARR) 

(Mesinger et al. 2006). The ERA-40, ERA-I, JRA-25, and NCEP reanalyses are 

comprised of global data at relatively coarse resolutions, making them difficult to apply 

in mesoscale studies. NARR, on the other hand, is a regional reanalysis with 32-km grid 

spacing. However, the domain for the current study lies in the far northwest corner of the 

NARR domain, which causes concern regarding potential boundary effects in the data. 

To counter these problems and develop a high-quality product suitable for highresolution analysis of the climate in the study domain, this effort was devised to generate 

a regional reanalysis with 10-km grid spacing for more accurately capturing the finerscale processes of the region’s surface wind field than was possible in previously existing 

products.

To establish the WRF/WRFDA modeling system for use over the Chukchi–Beaufort Seas 

and Arctic Slope, the foremost challenge was the determination of the most appropriate 

model configuration. The WRF model is a widely used community mesoscale model 

developed by NCAR and its partners. As described by Skamarock et al. (2008), the WRF 

dynamical core solves fully compressible nonhydrostatic governing equations on the 

Arakawa C-grid staggered horizontal grid and terrain-following hydrostatic-pressure 

vertical coordinate using 3rd-order Runge–Kutta time-split integration. Processes that 

occur at the sub-grid scale are described by various physical parameterization schemes

that are selectable by the user depending on their particular needs. Two distinct 

dynamical cores are available: the Advanced Research WRF (ARW), which was 

developed and is maintained by NCAR, and the Nonhydrostatic Mesoscale Model 

(NMM), which was developed by NCEP with support from the Developmental Testbed 

Center. In this study, the ARW dynamical core was used due to the extensive testing and 

improvements that were previously made for its use in simulating the Arctic atmosphere 

(Hines and Bromwich 2008; Bromwich et al. 2009; Hines et al. 2011). WRF also 

contains a bevy of physical parameterization options that simulate microphysical, 

convective, radiative, planetary boundary layer (PBL), surface layer, and land-surface

processes. Choosing the most suitable schemes is critical for generating high-quality 

model output. However, different regions experience variable conditions and present 

unique problems, and the optimal setup for one area may not necessarily be applicable to 

another. This is especially true in the Arctic, which presents many unique modeling 

challenges that parameterization schemes developed for and primarily tested in the 

tropics and mid-latitudes may be unable to adequately address. To confront this 

challenge, extensive model sensitivity simulations were conducted as a part of this study 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

48

to optimize the configuration of physical parameterizations for application over the 

Chukchi–Beaufort Seas and Arctic Slope.

The data assimilation package used to generate the CBHAR reanalysis was WRFDA, 

which has been designed to be a flexible, state-of-the-art atmospheric data assimilation 

system including options for both three-dimensional (3DVAR) and four-dimensional 

(4DVAR) variational data assimilation. In this study, the 3DVAR assimilation technique 

was adopted to generate the final high-resolution reanalysis. 4DVAR was not considered 

due to its relatively high computational cost (Barker et al. 2004, 2012), making it 

unfeasible for the ultimate goal of producing a long-term (31-year) reanalysis. The 

variational data assimilation system WRFDA determines its optimal analysis through the 

use of information that includes model background error (BE) and observational

measurement error. Thus, determining the model BE and selecting the observational data 

to be assimilated were the critical steps necessary for optimizing the WRFDA 

assimilation configuration for the production of CBHAR. As with the determination of 

the best-performing parameterization configuration, this optimization was determined 

through a set of sensitivity experimental simulations.

Another essential effort conducted in the modeling aspect of this project was the 

implementation into the WRF model of more advanced model physical parameterizations 

that have been specifically designed for the unique Arctic environment. One of these 

efforts was the coupling of a thermodynamic sea ice model (Zhang and Zhang 2001); the 

other was the adoption of various improvements to WRF, known as Polar WRF, made by 

the Polar Meteorology Group at the Ohio State University (OSU), such as the use of 

variable sea ice thickness (Hines and Bromwich 2008; Bromwich et al. 2009; Hines et al. 

2011). In addition to these efforts, tests were conducted to optimize the selection of 

model forcing and boundary data. The details of each of these efforts are documented in 

Sections 3.2–3.5.

3.2 Optimization of Model Physical Parameterization Configuration

As introduced in Section 3.1, the WRF model contains a bevy of options that control 

which physical parameterization schemes are used to simulate microphysical, convective, 

radiative, PBL, surface layer, and land-surface processes. The selection of the most 

suitable and best-performing parameterization schemes can be critical for achieving the 

best possible performance from a model. To address this challenge, a series of sensitivity 

test simulations were conducted in order to attempt to optimize the WRF physical 

parameterization configuration for application over the Chukchi–Beaufort Seas and 

Arctic Slope, especially as it relates to the simulation of the area’s surface wind field. 

Each of the classes of model physical parameterizations, including shortwave and 

longwave radiation, microphysics, PBL, and land-surface processes, were independently 

tested. The simulation results were verified against the quality-controlled observational 

database described in Section 2 of this report using the statistical metrics described in 

Appendix B. A total of three sets of sensitivity tests, covering different seasons, were 

performed:
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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• First was a series of one-month tests for September 2004, a time of year which 

represents the transitional season from summer to winter conditions. During this 

period, the study domain includes sea ice, open water, snow cover, and bare 

ground conditions, and can thus serve as a representative period for the varied 

conditions found throughout the year.

• Second, additional simulations were conducted on a domain centered on the 

location of the Surface Heat Budget of the Arctic (SHEBA) field experiment. 

SHEBA produced a unique one-year offshore observational dataset in the Arctic, 

allowing the performance of the various parameterizations to be further tested for 

offshore Arctic conditions.

• Third was a series of one-month tests for August 2009, which represents the 

warmest month in the study area and is thus suitable for comparing the 

performance of different convective parameterizations.

Clearly, the optimized configuration as determined from these sensitivity tests should not 

necessarily be considered as absolutely optimal, since it is effectively impossible to test 

all possible combinations of the available parameterizations for all conditions that could 

potentially exist in the study region. Instead, judicious choices were made in order to 

select a small, representative subset of the possible sensitivity tests to determine what 

might be considered a local maximum in model configuration space. In addition to the

tests described here, previous published work was also researched (e.g., Hines and 

Bromwich 2008; Bromwich et al. 2009; Cassano et al. 2011; Porter et al. 2011; Hines et 

al. 2011; Wilson et al. 2011) and their results were taken into consideration as well. 

3.2.1 Sensitivity Tests: September 2004

Sensitivity Test Design

The modeling domain for the initial sensitivity tests was established as shown in Figure 

1.1.2, covering the Chukchi–Beaufort Seas and adjacent land areas of Russia, Alaska, and 

Canada. The domain has dimensions of 235 by 136 points, with a grid spacing of 10 km 

and 49 vertical levels. The oceanic portion of the domain experiences high variability 

throughout the year in its sea ice coverage, and the land portion similarly varies in its 

snow coverage. Both sea ice and snow cover are important to the thermal contrast that 

exists between land and ocean and that plays a significant role in driving near-surface 

circulation patterns. As such, to properly test the model’s performance, it is essential to 

select a representative simulation time period that contains both ice-free and ice-covered 

ocean, as well as bare and snow-covered land, and also includes the transitional period 

between the two states. The month of September 2004 was therefore chosen as the test 

period, as the study region experiences each of these varied conditions during this time.

When conducting the simulations, the month was subdivided into eight 4.5-day periods, 

which were run independently and initialized every fourth day at 1200 UTC, with the 

first 12 hours of each simulation treated as model spin-up and not used in the validation. 

Thus, in stitching together the final four days of each simulation, hourly time series could 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

50

be created, extending from 0000 UTC 2 September to 0000 UTC 4 October, series which 

were then used for model verification.

In order to perform as complete a test as possible of the available physical 

parameterizations, a control experiment (CTRL) that utilized WRF v3.2 and the 

following model physical parameterizations (Table 3.2.1) was first run:

Table 3.2.1. Model physical parameterizations in the CTRL simulation

Parameterization Configuration

Cumulus Kain–Fritsch scheme (KF) (Kain 2004)

Microphysics WRF Single-Moment 6-class (WSM6) (Hong and Lim 2006)

Longwave radiation Community Atmosphere Model (CAM) longwave (CLW) 

(Collins et al. 2004)

Shortwave radiation CAM shortwave (CSW) (Collins et al. 2004)

Planetary boundary layer Yonsei University (YSU) PBL (Hong et al. 2006)

Land-surface model NOAH land-surface model (LSM) (Chen and Dudhia 2001)

Based around the configuration of the CTRL experiment, one or two of the physical 

parameterizations were changed at a time and various combinations were tested, 

ultimately evaluating a total of eight microphysics, three longwave (LW), four shortwave 

(SW), six PBL, and one land-surface options, as summarized in Table 3.2.2. The 

available cumulus parameterization schemes were also compared, though in a separate set 

of sensitivity simulations for August 2009 (Section 3.2.3), as August represents the 

warmest month in the region and thus a period when more convective events are expected 

to occur. As the Noah LSM is the only land-surface scheme in WRF that includes 

sophisticated land-surface processes and considers some additional Arctic surface 

processes, such as sea ice and frozen soil, it was determined to be the only LSM suitable 

for this study and was thus used for all sensitivity tests. The external forcing data used in 

these tests, utilized both for initialization and boundary conditions, was the North 

American Regional Reanalysis (NARR).

The hourly in situ observations used for verification of the model output in September 

2004 (Figure 3.2.1), collected and quality controlled as described in Section 2 of this 

report, included 96 stations that observed temperature, wind, and pressure; 23 stations 

that measured shortwave irradiance from the ARM (2) and RAWS (21) networks; and 

two ARM stations that measured longwave irradiance. In addition to the in situ data, 

sensitivity test results were also verified against observations from the SeaWinds 

instrument on board the National Aeronautics and Space Administration (NASA) Quick 

Scatterometer (QuikSCAT) satellite. This dataset consists of measurements of surface 

(10-m) wind speed and direction at a spacing of 12.5 km over open, ice-free ocean areas. 

In all cases, model output at the closest land point (for stations) or water point (for 

QuikSCAT data) to the location of the observations was compared with the 

measurements, and the mean bias and correlation coefficients (Appendix B) then
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51

calculated across the entire domain over the entire simulation period in order to evaluate 

the overall performance of the model physical schemes.

Table 3.2.2. Identifiers of model physical parameterizations in the sensitivity tests

CTRL KF cumulus, WSM6 microphysics, CLW Longwave, CSW Shortwave,

YSU PBL, NOAH LSM

Microphysics

Lin Purdue Lin (Lin et al. 1983; Tao et al. 1989)

Ferr Ferrier (Eta) (Ryan 1996)

WSM6 WSM6 as used in CTRL

Godd Goddard (Tao and Simpson 1993)

Thom Thompson (Thompson et al. 2004)

Morr Morrison 2-moment (Morrison et al. 2009)

nThom New Thompson (Thompson et al. 2008) 

MY2m Milbrandt–Yau 2-moment (Milbrandt and Yau 2005)

Longwave Radiation

RLW Rapid Radiative Transfer Model (RRTM) longwave (Mlawer et al. 1997)

CLW CLW as used in CTRL

RgLW RRTMG (RRTM for General Circulation Models (GCMs)) longwave 

(Iacono et al. 2008)

Shortwave Radiation

SSW Dudhia simple shortwave (Dudhia 1989)

GSW Goddard shortwave (Chou and Suarez 1994)

CSW CSW as used in CTRL

RgSW RRTMG (RRTM for GCMs) shortwave (Iacono et al. 2008)

Land-Surface Model

NOAH NOAH as used in CTRL

Planetary Boundary Layer

YSU YSU as used in CTRL

MYJ Mellor–Yamada–Janjic (Mellor and Yamada 1982; Janjic 2002)

ACM2 Asymmetrical Convective Model 2 (Pleim 2007)

QNSE Quasi-Normal Scale Elimination (Sukoriansky et al. 2006)

MYN2 MYNN 2.5-level TKE scheme (Zhang et al. 2011)

Bola Bougeault and Lacarrere (Bougeault and Lacarrere 1989)
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Figure 3.2.1. Distribution of the 96 observational stations over the CBHAR 

modeling domain used for model verification of the physical parameterization 

sensitivity simulations. Two ARM stations are indicated by red dots, 21 RAWS 

stations by magenta dots, and other networks by green dots.

Sensitivity Test Results

Radiation

The parameterization of radiative processes is an important element in the modeling of 

the surface heat budget, and consequently also has an impact on the simulation of surface 

winds. Shortwave and longwave irradiances are modeled separately, and so WRF 

includes a distinct set of options for each. In WRF v3.2, four longwave and six shortwave 

parameterizations are available, of which three of the former and four of the latter are 

currently supported and applicable to the modeling configuration for this study. Thus, a 

total of nine radiation sensitivity tests (Table 3.2.3), with experiment identifiers as given 

in Table 3.2.2, were conducted. These tests included RRTM longwave (RLW) and its 

combination with simple shortwave (SSW), Goddard shortwave (GSW), and CAM 

shortwave (CSW); CAM longwave and its combination with SSW, GSW, CAM, and 

RRTMG shortwave (RgSW); and RRTMG longwave (RgLW) and its combination with 

CSW and RgSW. Some combinations resulted in model instability, causing the 

simulations to crash, so not every possible combination produced verifiable results. The 

ARM sites at Barrow and Atqasuk measure both longwave and shortwave irradiance, and 

their observations were used to verify the sensitivity results.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Table 3.2.3. Identifiers of radiation sensitivity tests

Sensitivity test Physical parameterizations

RLWSSW RLW (RRTM longwave) + SSW (Dudhia simple shortwave)

RLWGSW RLW (RRTM longwave) + GSW (Goddard shortwave)

RLWCSW RLW (RRTM longwave) + CSW (CAM shortwave)

CLWSSW CLW (CAM longwave) + SSW (Dudhia simple shortwave)

CLWGSW CLW (CAM longwave) + GSW (Goddard shortwave)

CLWCSW CLW (CAM longwave) + CSW (CAM shortwave)

CLWRgSW CLW (CAM longwave) + RgSW (RRTMG shortwave)

RgLWCSW RgLW (RRTMG longwave) + CSW (CAM shortwave)

RgLWRgSW RgLW (RRTMG longwave) + RgSW (RRTMG shortwave)

The comparison of downward longwave irradiance bias and correlation among the nine 

radiation sensitivity tests, as verified against the ARM observations (Figure 3.2.2), shows 

that all of the radiation schemes produce a negative bias and that the results are aligned 

into three basic groups, each corresponding to a different longwave radiation scheme 

(RLW, CLW, and RgLW). The CLW group tends to have a relatively large bias and 

small correlation. In contrast, the bias produced by RLW is relatively small, with a 

correlation larger than 0.5. Differences within each group result from the choice of 

shortwave scheme; obviously, the shortwave schemes have little interaction with the 

longwave schemes in the simulation of longwave irradiance as shown here. Time series 

comparisons of observed and modeled longwave irradiance (Figure 3.2.3), averaged over 

the ARM sites, show that the three different longwave radiation schemes (RLW, CLW, 

and RgLW) all tend to exhibit larger fluctuations than the observations, while generally 

underestimating the values.

For shortwave irradiance, observations are available from both the ARM and RAWS 

networks, resulting in a total of 23 stations for verification. The comparison of downward 

shortwave irradiance biases (Figure 3.2.4) shows that the SSW scheme tends to produce a 

negative bias with a relatively small correlation when not paired with the RLW scheme, 

while the rest generate positive biases with similar correlations of around 0.85. The 

comparison of daily averaged downward shortwave irradiance (Figure 3.2.5), averaged 

over the entire simulation period and over the ARM and RAWS stations, confirms that 

the SSW scheme underestimates shortwave irradiance, and that the other schemes all 

overestimate it by about 80 W m-2 during midday. Similar results at both the ARM and 

RAWS sites further confirm that the quality-controlled RAWS measurements are a useful 

addition to this model verification effort. Thus, so far it has been shown that:

• All the longwave schemes underestimate downward longwave irradiance when 

verified against ARM measurements, and biases in the RLW and RgLW schemes are

relatively small.

• The SSW shortwave scheme underestimates downward shortwave irradiance.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

54

• The CSW, GSW, and RgSW shortwave schemes perform similarly in overestimating 

downward shortwave irradiance, though a relatively small bias is seen in RgSW.

The impacts of the radiation schemes on the simulation of 2-m air temperature are 

depicted in Figure 3.2.6, in which averaged diurnal cycles of modeled and observed 

surface temperatures are compared. The CLWSSW combination generates the coldest 

temperatures throughout the entire day. Compared to the cold surface temperatures from

CLWSSW, temperatures produced by the RLWSSW, CLWGSW, CLWCSW, and 

CLWRgSW tests are warmer by about 1°C, and warmer by about 2°C in the RLWGSW, 

RLWCSW, RgLWCSW, and RgLWRgSW tests during both the day and night. Modeled 

temperatures simulated by the different radiation schemes are consistent with the 

irradiance bias seen for each scheme. For instance, the coldest temperatures, produced by

CLWSSW, likely result from the relatively large negative bias in longwave irradiance 

generated by the CLW scheme, combined with the negative bias in shortwave irradiance 

from SSW. When the CLW scheme is replaced by the RLW scheme, which has a 

relatively small negative bias in longwave irradiance, the surface temperature generated 

by RLWSSW is warmer by about 1°C. Further, when the SSW scheme is replaced by 

either the GSW or CSW schemes, which have positive biases in shortwave irradiance 

instead of the negative bias from SSW, the surface temperature in RLWGSW and 

RLWCSW is further increased by an additional 1°C.

During the day, shortwave and longwave radiation schemes interact to impact the 

simulation of surface temperature. Comparison between the modeled and observed 

temperatures (Figure 3.2.6) suggests that the negatively biased longwave irradiance in the 

RLW and RgLW schemes (Figure 3.2.2) serves to offset the positively biased shortwave 

radiance from the GSW, CSW, and RgSW schemes (Figure 3.2.4), resulting in a better 

agreement between simulations and observations during the hours of maximum 

irradiance as shown in the RLWGSW, RLWCSW, RgLWCSW, and RgLWRgSW tests.

In terms of radiation dependence, the performance in simulating nighttime temperatures 

is primarily related to the choice of longwave radiation scheme. Thus, one should expect 

better temperature modeling by the RLW or RgLW schemes, which produce relatively 

small biases in longwave irradiance. However, a better agreement with the observed 

evening temperatures is seen with the CLW scheme, which tends to produce large 

negative biases in longwave irradiance. The reason for this seeming discrepancy between 

bias in the longwave irradiance and accuracy of the temperature simulation is most likely 

due to other processes, such as the presence of near-surface temperature inversions,

involved in the simulation of surface temperature. Over areas with complex topography, 

inversions frequently occur at night and are a persistent challenge for regional- and 

larger-scale models to accurately capture due to the lack of sufficiently fine resolution in 

the lower boundary layer. The effect of inadequately modeling the strength of an 

inversion, making it too weak, is a warm bias in the surface temperature. Thus, the 

offsetting effects of a warm bias due to insufficiently modeling inversion strength and the 

cold bias due to negatively biased longwave irradiance combine to produce better 

nighttime temperature in most tests with the CLW scheme (Figure 3.2.6).
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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The final determination for which radiation schemes were selected was based on an 

overall error analysis. The negative bias in longwave irradiance by the RLW and RgLW 

schemes is relatively small, while at the same time the positive bias in shortwave 

irradiance by the RgSW scheme tends to be the smallest, leading the RgLW and RgSW 

schemes to temporarily be chosen for use in the production of CBHAR, pending the 

results of additional sensitivity tests.

Figure 3.2.2. Downward longwave irradiance bias (W m-2, blue) and correlation 

(red) in the radiation sensitivity tests, as verified against observations from 2 ARM 

stations during September 2004

200

220

240

260

280

300

320

340

360

0 100 200 300 400 500 600 700

Downward LW (w/m2

)

Hours since 00 UTC 2 Sep 2004

Radiation LW Comparisons

OBS

RLWCSW

CLWCSW

RgLWCSW

Figure 3.2.3. Modeled (lines) and observed (dots) downward longwave irradiance 

(W m-2) in the radiation sensitivity tests, averaged over 2 ARM stations during 

September 2004
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Figure 3.2.4. Downward shortwave irradiance bias (W m-2, blue) and correlation

(red) in the radiation sensitivity tests, as verified against observations from 23 ARM 

and RAWS stations during September 2004

Figure 3.2.5. Daily mean downward shortwave irradiance (W m-2) in the radiation 

sensitivity tests, averaged over (a) 2 ARM and (b) 21 RAWS stations during 

September 2004
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5 10 15 20 −3

−2

−1

0

1

2

3

4

5

Hour (AKST)

Surface Temp.(C)

Radiation T Comparisons

OBS

RLWSSW

RLWGSW

RLWCSW

CLWSSW

CLWGSW

CLWCSW

CLWRgSW

RgLWCSW

RgLWRgSW

Figure 3.2.6. Daily mean 2-m temperature (°C) in the radiation sensitivity tests, 

averaged over 96 stations during September 2004

Microphysics

One of the major challenges in atmospheric modeling, and of prime importance for 

controlling the surface heat budget, is the accurate simulation of clouds. This is especially 

true in the Arctic, where the slightest forcing can generate clouds in the bitterly cold 

atmosphere, which at the same time can have dramatic effects on near-surface 

temperatures. WRF v3.2 contains 12 microphysical options, including one that is only 

suitable for use in idealized studies and three that are simplified versions of other options, 

leaving eight choices that could conceivably be selected as the preferred scheme for this 

study. Thus, a total of eight microphysics sensitivity tests, with experiment identifiers as 

given in Table 3.2.2, were conducted. They include Lin (Purdue Lin), Ferr (Ferrier (Eta)), 

WSM6 (WRF Single-Moment 6-Class), Godd (Goddard), Thom (Thompson), Morr 

(Morrison 2-moment), nThom (New Thompson), and MY2m (Milbrandt–Yau 2-

moment).

As was the case for the radiation sensitivity tests, a comparison of downward longwave 

irradiance bias and correlation for the eight microphysics sensitivity tests (Figure 3.2.7) 

shows that negative biases exist for all schemes. Among them, the bias for the Thom 

scheme is relatively small. One phenomenon present in these results is that better 

correlation is always accompanied by larger (negative) biases, though all the correlation 

coefficients are relatively small.
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Figure 3.2.7. Downward longwave irradiance bias (W m-2, blue) and correlation 

(red) in the microphysics sensitivity tests, as verified against observations from 2 

ARM stations during September 2004

The comparison of daily mean downward shortwave irradiance (Figure 3.2.8), averaged 

over the entire simulation period and all 23 stations, shows that the degree of 

overestimation produced by the CSW scheme can be modulated by the choice of 

microphysics scheme. The Ferr and WSM6 schemes produce relatively large biases 

compared to the other schemes.

5 10 15 20

0

50

100

150

200

250

300

350

400

Hour (AKST)

Downward SW (w/m 2

)

Microphysics SW Comparisons

OBS

Lin

Ferr

WSM6

Godd

Thom

nThom

MY2m

Morr

Figure 3.2.8. Daily mean shortwave irradiance (W m-2) in the microphysics 

sensitivity tests, averaged over 23 ARM and RAWS stations during September 2004
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5 10 15 20 −3

−2

−1

0

1

2

3

4

5

Hour (AKST)

Surface Temp.(C)

Microphysics T Comparisons

OBS

Lin

Ferr

WSM6

Godd

Thom

nThom

MY2m

Morr

Figure 3.2.9. Daily mean 2-m temperature (°C) in the microphysics sensitivity tests, 

averaged over 96 stations during September 2004

As shown in Figure 3.2.6, the combination of CLW and CSW (CLWCSW) generates a 

cold bias in the simulation of daytime 2-m surface temperatures. This cold bias exists in 

all the microphysics tests as well (Figure 3.2.9), and the impacts from different 

microphysics schemes are relatively small.

Interactions between Radiation and Microphysics

As seen from the results of the radiation sensitivity tests (Figure 3.2.5), the CSW, GSW, 

and RgSW schemes all perform similarly in overestimating downward shortwave 

irradiance when used with WSM6 microphysics (sensitivity test names ending with 

CSW, GSW, and RgSW). However, when combined with other microphysics schemes, 

the overestimation of shortwave irradiance can be reduced to some degree, as seen when 

comparing the CSW scheme to the GSW scheme (Figures 3.2.10 and 3.2.11). The 

underestimated downward longwave irradiance by the CLW scheme is always stronger 

than that of the RLW scheme no matter which microphysics scheme is used (Figure 

3.2.12).
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Figure 3.2.10. Downward shortwave irradiance bias (W m-2, blue) and correlation 

(red) in the radiation-microphysics sensitivity tests, as verified against observations 

from 23 ARM and RAWS stations during September 2004

5 10 15 20

0

50

100

150

200

250

300

350

400

Hour (AKST)

Downward SW (w/m 2

)

Rad−Thom SW Comparisons

OBS

CLWGSW−Thom

CLWCSW−Thom

RLWGSW−Thom

RLWCSW−Thom

Figure 3.2.11. Daily mean downward shortwave irradiance (W m-2) in the radiationmicrophysics sensitivity tests, averaged over 23 ARM and RAWS stations during 

September 2004
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Figure 3.2.12. Downward longwave irradiance bias (W m-2, blue) and correlation 

(red) in the radiation-microphysics sensitivity tests, as verified against observations 

from 2 ARM stations during September 2004

The performance of the various radiation and microphysics schemes, and their associated 

interactions, can be summarized as:

• The RLW scheme tends to produce relatively small biases in downward longwave 

irradiance when combined with different shortwave and microphysics schemes.

• The CSW scheme tends to produce relatively small biases in downward shortwave 

irradiance when combined with different longwave schemes and most microphysics 

schemes (except WSM6).

Thus, it becomes a challenge to select the optimal radiation schemes for use with a 

microphysics scheme other than WSM6. Most microphysics schemes serve to reduce 

biases in the CSW downward shortwave and RLW downward longwave irradiances. A 

similar reduction in bias is also found in the RgLW downward longwave irradiance (not 

shown). Considering that RRTMG is an updated, enhanced, and streamlined version of 

the older RRTM parameterization, the choice becomes one between the CAM and 

RRTMG schemes. The Thom microphysics scheme seems to reduce the irradiance biases 

more effectively than other schemes, leading it to be temporarily favored for the final 

production of CBHAR. However, according to Hines et al. (2011) and Bromwich et al. 

(2013), the RRTMG radiation and Morr microphysics schemes work reasonably well for 

accurately simulating the polar atmosphere. A further comparison between the CAM and 

RRTMG radiation schemes, and between the Thom and Morr microphysics schemes, will 

be discussed in Section 3.2.2 through the use of offshore SHEBA observations.

Planetary Boundary Layer (PBL)

The most important model physical parameterization option for influencing the 

simulation of near-surface winds is the planetary boundary layer (PBL) scheme. In 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

62

regional-scale model configurations such as the one used here, the number of vertical 

levels in the PBL is insufficient to fully resolve the complex and spatially 

inhomogeneous boundary layer processes. At the same time, accounting for these 

processes is of the utmost importance for calculating surface fluxes and simulating nearsurface weather conditions. The model thus includes schemes to parameterize PBL 

processes and calculate the necessary fluxes. One of the functions of the PBL scheme is 

to calculate the 2-m temperature and moisture and 10-m winds that are ultimately used to 

validate the model output against station observations. These data also contribute to the 

final surface reanalysis that is the ultimate aim of this project. As such, the proper 

selection of the PBL scheme is critical, not only to maximize the performance of the 

model as a whole, but to ensure the accurate calculation of near-surface variables. WRF 

v3.2 includes a total of eight PBL options, six of which are currently supported and 

potentially suitable for the simulations conducted in this project. Thus, a total of six PBL 

schemes (YSU, MYJ, ACM2, QNSE, MYN2, and Bola) were tested in combination with 

the Noah LSM.

The downward longwave irradiance produced by the sensitivity tests YSU, MYJ, and 

ACM2 has a relatively smaller bias and better correlation compared to that from the other 

three schemes (Figure 3.2.13). On the other hand, most of the PBL schemes performed 

similarly in simulating downward shortwave irradiance (Figure 3.2.14). A close look at 

the performance of ACM2 shows it to have the smallest biases in the modeling of both 

shortwave and longwave irradiance. However, a strong noisy signal was discovered in the 

surface temperature field of subsequent ACM2 simulations, a highly unrealistic artifact 

that was not present with the use of other PBL schemes such as YSU and MYJ. This 

signal was found to regularly occur over sea-ice-covered areas, and given that a large 

portion of the study domain is normally covered by sea ice, ACM2 was ruled out from 

consideration in the determination of the final model configuration and from further use 

in this study.

Figure 3.2.13. Downward longwave irradiance bias (W m-2, blue) and correlation 

(red) in the PBL sensitivity tests, as verified against observations from 2 ARM 

stations during September 2004
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Figure 3.2.14. Downward shortwave irradiance bias (W m-2, blue) and correlation

(red) in the PBL sensitivity tests, as verified against observations from 23 ARM and 

RAWS stations during September 2004

Comparison of the PBL impact on the simulation of 2-m temperature (Figures 3.2.15 and

3.2.16) shows that most schemes perform similarly during the day but generate different 

temperatures at night. The YSU scheme produces the warmest surface temperatures,

while MYN2 creates the coldest; the remainder of the schemes perform similarly to one 

another. An overall error analysis (Figure 3.2.16), examining the root-mean-square error 

(RMSE, Appendix B) of the modeled surface temperatures, indicates that the YSU and 

MYJ PBL schemes produce comparable results and generate smaller errors than the 

others.

5 10 15 20 −3

−2

−1

0

1

2

3

4

5

Hour (AKST)

Surface Temp.(C)

PBL T Comparisons

OBS

YSU

MYJ

QNSE

MYN2

ACM2

Bola

Figure 3.2.15. Daily mean 2-m temperature (°C) in the PBL sensitivity tests, 

averaged over 96 stations during September 2004
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Figure 3.2.16. RMSE of 2-m temperature (°C) in the PBL sensitivity tests, as 

verified against observations from 96 stations during September 2004

Among all the types of model physical parameterizations, the PBL scheme has a 

comparatively larger impact on the modeling of the surface wind field. Comparison of the 

10-m wind speed time series, averaged over the 96 stations during the entire simulation 

period of September 2004 (Figure 3.2.17), indicates that the PBL schemes MYJ and 

QNSE tend to generate stronger winds relative to the other schemes. This can be further 

confirmed by the comparison of wind speed biases shown in Figure 3.2.18, in which 

QNSE and MYJ generate slightly larger positive biases than the others. On the other 

hand, the biases in wind direction (Figure 3.2.19) show the opposite result: the MYJ and 

QNSE schemes have overall smaller biases. This presents a challenge in determining 

which PBL scheme should be included in the final model configuration for the 

production of CBHAR. To help address this, the RMSE and correlation of the wind 

vectors were calculated against observations from the 96 stations. The vector correlation 

was calculated following the method introduced by Crosby et al. (1993), in which it is

calculated using the orthogonal components of the vectors. As shown in Figure 3.2.20, 

ACM2 generates the smallest error. However, as mentioned above, ACM2 was not a 

candidate for use in the final model configuration due to its numerical instability. In 

addition, QNSE and MYN2 proved to be too unstable for use in a production simulation, 

often causing the model to crash and exhibiting other unpredictable behavior. Thus, 

synthesizing the results of the simulation of both the surface temperature and wind fields, 

the PBL schemes YSU and MYJ were selected as finalists to be considered for use in the 

final production simulation. 
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100 200 300 400 500 600 700

1

2

3

4

5

6

7

8

Hours since 00 UTC 2 Sep 2004

Wind Speed (m/s)

PBL Wind Comparisons

OBS

YSU

MYJ

QNSE

MYN2

ACM2

Bola

Figure 3.2.17. Time series comparison of 10-m wind speeds (m s-1) in the PBL 

sensitivity tests, averaged over 96 stations during September 2004

Figure 3.2.18. 10-m wind speed bias (m s-1, blue) and correlation (red) in the PBL 

sensitivity tests, as verified against observations from 96 stations during September

2004
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Figure 3.2.19. 10-m wind direction bias (degrees) in the PBL sensitivity tests, as 

verified against observations from 96 stations during September 2004

Figure 3.2.20. 10-m wind vector RMSE (m s-1, blue) and correlation (red) in the PBL 

sensitivity tests, as verified against observations from 96 stations during September

2004

3.2.2 Sensitivity Tests Covering the SHEBA Period

Based on the results presented in Section 3.2.1, a total of five additional sensitivity 

simulations (Table 3.2.4), using a domain centered on the site of the SHEBA field 

experiment (Figure 3.2.21), were conducted to further evaluate WRF physical 

parameterizations in offshore Arctic conditions. SHEBA produced valuable, long-term

(one year) observations in an Arctic region where no fixed stations exist, allowing the 

results of the model physical parameterization tests given in the previous section to be 

amended. As discussed in Section 3.2.1, when a microphysics option other than WSM6 is 

used, the scheme helps to reduce biases both in the downward shortwave irradiance 

produced by CSW and the downward longwave irradiance generated by RgLW; Thom 

microphysics tends to reduce them most effectively. On the other hand, according to 

Hines et al. (2011) and Bromwich et al. (2013), the RRTMG radiation and Morr 
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67

microphysics schemes work reasonably well in simulating the polar atmosphere. To 

clarify matters, additional sensitivity tests were run, comparing results from the CAM and 

RRTMG radiation schemes, as well as the Thom and Morr microphysics schemes, with 

SHEBA measurements. In addition, as discussed in Section 3.2.1, the two PBL schemes 

YSU and MYJ had been previously selected as finalists for use in the production of 

CBHAR. To more effectively distinguish between them, the YSU and MYJ schemes 

were thus also further evaluated through the use of SHEBA measurements.

Table 3.2.4. Sensitivity tests run for the SHEBA period (parameterizations used 

other than those identified here were NOAH LSM and KF cumulus)

Sensitivity test Physical parameterizations Simulation

period

Microphysics 〈

Radiation 〈

PBL 〈

YSU-ThomCLW-CSW

MYJ PBL/Thompson microphysics

/CAM longwave/CAM shortwave Dec 1997

YSU-MorrCLW-CSW

YSU PBL/Morrison microphysics

/CAM longwave/CAM shortwave Dec 1997

YSU-MorrRgLW-RgSW

YSU PBL/Morrison microphysics

/RRTMG longwave/RRTMG shortwave July 1998

YSU-MorrCLW-CSW

YSU PBL/Morrison microphysics

/CAM longwave/CAM shortwave July 1998

MYJ-MorrCLW-CSW

MYJ PBL/Morrison microphysics

/CAM longwave/CAM shortwave July 1998

The modeling domain used for these tests has dimensions of 235 by 235 points with a 

grid spacing of 10 km and 49 vertical levels. It is essentially a modified form of the study 

domain shown in Figure 1.1.2, expanded in the north-south direction and shifted slightly 

northward; the configuration of the vertical levels is identical. As the expanded domain 

extends beyond the limits of NARR, the global ERA-Interim reanalysis was instead used 

to provide initial and boundary conditions to the model. In order to evaluate the 

performance of the model for both summer and winter conditions in the Arctic offshore

region, two different simulation periods were selected. The first period, representing the 

Arctic winter, began at 0000 UTC 1 December 1997 and ended at 0000 UTC 1 January 

1998. The second, representing the Arctic summer, began at 0000 UTC 1 July 1998 and 

ended at 0000 UTC 1 August 1998, which is a particularly ideal period for the evaluation 

of shortwave radiation schemes. In the same manner as the sensitivity tests described in 

Section 3.2.1, the simulation periods were subdivided into a series of 4.5-day periods, 

which were run independently and initialized every fourth day at 1200 UTC, with the 

first 12 hours of each simulation treated as spin-up and not used in the validation.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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Figure 3.2.21. Sensitivity simulation domain encompassing the SHEBA locations for 

tests during December 1997 (white track; the star indicates the SHEBA location at 

the beginning of the month) and July 1998 (orange track)

Both the Thom and Morr schemes include two-moment microphysics, which can improve 

the simulated aggregation process of mixed-phase clouds by allowing for better 

representation of particle number concentration and is essential for reasonably modeling 

clouds in the Arctic (Girard and Curry 2001; Morrison et al. 2003; Shupe and Intrieri 

2004). The accurate simulation of clouds is particularly important in the winter months

due to their emitted longwave radiation, which plays a crucial role in determining the 

surface energy balance in the absence of direct solar forcing. As such, the December 

1997 SHEBA case was selected in order to further evaluate the microphysics schemes. 

Overall, the Morr and Thom schemes performed very similarly at the SHEBA site for the 

simulation of downward longwave irradiance, 2-m temperature, and 10-m wind vectors

(Table 3.2.5). Considering that the Morr scheme has been widely used in modeling 

studies of the polar atmosphere (Hines et al. 2011; Barton and Veron 2012; Claremar et 

al. 2012; Tastula et al. 2012), combined with its strong performance in both these and the 

prior sensitivity tests, a decision was made to use the Morr scheme in the final model 

configuration for generating the 31-year CBHAR reanalysis.
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Table 3.2.5. RMSE of downward longwave irradiance (W m-2), 2-m temperature 

(°C), and 10-m wind vector (m s-1) for the microphysics sensitivity tests during 

December 1997 as verified against SHEBA measurements

Sensitivity test Longwave

(W m-2)

Temperature

(°C)

Wind vector

(m s-1)

YSU-MorrCLW-CSW 21.43 8.27 2.05

YSU-ThomCLW-CSW 21.25 8.22 2.00

Since shortwave radiation is relatively non-existent during the winter months in the study 

region, the July 1998 case was instead selected for the radiation sensitivity tests, so that 

both downward short- and longwave irradiances could be calibrated concurrently. The 

CAM and RRTMG radiation schemes were tested while paired with the Morr 

microphysics scheme and verified against SHEBA measurements (Table 3.2.6). The 

verification results indicate that the CAM and RRTMG schemes perform similarly in the 

simulation of downward longwave irradiance, but RRTMG outperforms CAM in 

modeling downward shortwave irradiance. As a result, an improved surface temperature 

is achieved by the RRTMG scheme. A slight improvement in surface wind vector is also 

found in the simulation using RRTMG. Therefore, a clear decision was made to use 

RRTMG as both the short- and longwave radiation parameterizations in the final model 

configuration for the production of the 31-year CBHAR reanalysis.

Table 3.2.6. Same as Table 3.2.5 but for the radiation sensitivity tests during July 

1998

Sensitivity test Longwave

(W m-2)

Shortwave

(W m-2)

Temperature

(°C)

Wind vector

(m s-1)

YSU-MorrCLW-CSW 23.41 90.96 2.12 2.76

YSU-MorrRgLW-RgSW 23.66 86.04 1.17 2.58

The summer SHEBA case was also used to compare the performance of the YSU and 

MYJ PBL schemes (Table 3.2.7). Overall, these two schemes perform similarly in terms 

of modeling the downward longwave irradiance, surface temperature, and wind vector,

though MYJ outperforms YSU for downward shortwave irradiance. Therefore, MYJ was

chosen to be the PBL parameterization in the final model configuration.
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Table 3.2.7. Same as Table 3.2.5 but for the PBL sensitivity tests during July 1998

Sensitivity test Longwave

(W m-2)

Shortwave

(W m-2)

Temperature

(°C)

Wind vector

(m s-1)

YSU-MorrCLW-CSW 23.41 90.96 2.12 2.76

MYJ-MorrCLW-CSW 23.43 87.51 2.23 2.90

Aside from the above model physical parameterization options, WRF also includes the 

ability to select parameterizations that govern the atmospheric surface layer, land-surface 

treatment, and sub-grid-scale convection processes. As the surface layer represents the 

bottom part of the boundary layer, the surface layer schemes are inextricably linked to the 

choice of PBL, and each PBL option is only designed to work with a select number (often 

only one) of the surface layer schemes. The MYJ PBL scheme, selected for the final 

model configuration, is only compatible with the Eta similarity surface layer, and so this 

was necessarily selected as part of the final model configuration as well. Similarly, of the 

available land-surface models, the only one that is designed to work with the current 

version of the Polar WRF modifications employed in the final configuration (see Section

3.3.2), as well as the most physically advanced model, is the Noah LSM.

3.2.3 Sensitivity Tests Covering August 2009

As with the sensitivity tests conducted for the period of September 2004, the same 

domain (Figure 1.1.2) and integration design as described in Section 3.2.1 were again 

used to conduct another series of sensitivity tests for the period of August 2009 in order 

to evaluate the performance of the available cumulus parameterization schemes. A total 

of four cumulus schemes are available in WRF v3.2 and were tested as shown in Table 

3.2.8. The RMSE of the cumulus sensitivity tests as verified against surface observations

during August 2009 was compared for the variables of longwave and shortwave 

irradiance, 2-m temperature, and 10-m wind vector (Table 3.2.9). The comparison shows 

that the cumulus scheme directly impacts the modeling of irradiance, and both the Grell–

Devenyi and Grell-3D ensemble schemes produce smaller errors for both long- and 

shortwave irradiance, contributing to a relatively small error for surface temperature as 

well. Thus, the more advanced of the two, the Grell-3D ensemble cumulus

parameterization, was selected for use in producing the final CBHAR reanalysis.
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Table 3.2.8 Sensitivity tests covering August 2009 (parameterizations used other 

than those identified below were Morr microphysics, RRTMG radiation, MYJ PBL, 

and NOAH LSM)

Sensitivity test Physical parameterization

KF Kain–Fritsch cumulus

BMJ Betts–Miller–Janjic cumulus (Janjic 1994, 2000)

GD Grell–Devenyi ensemble cumulus (Grell and Devenyi 2002)

Grell-3D Grell-3D ensemble cumulus (Grell and Devenyi 2002)

Table 3.2.9. RMSE of cumulus sensitivity tests as verified against surface 

observations of long- and shortwave irradiance (W m-2), 2-m temperature (°C), and 

10-m wind vector (m s-1)

Sensitivity test Longwave

(W m-2)

Shortwave

(W m-2)

Temperature

(°C)

Wind vector

(m s-1)

KF 34.68 151.09 3.23 3.47

BMJ 33.75 147.59 3.22 3.47

GD 31.02 139.13 3.15 3.41

Grell-3D 31.92 138.88 3.15 3.41

3.3 Improvements to Model Physical Parameterizations

Efforts conducted to implement more advanced model physical parameterizations in the 

WRF model included the coupling of a thermodynamic sea ice model (Zhang and Zhang 

2001) and adoption of some improvements to WRF, termed Polar WRF, made by the 

Polar Meteorology Group at OSU. These two efforts are summarized in Sections 3.3.1

and 3.3.2, respectively.

3.3.1 Coupling of Thermodynamic Sea Ice

CBHAR uses a model domain that is half-covered by ocean, which in turn is seasonally 

covered by sea ice for much of the year. It is therefore naturally important for the WRF 

model to be able to accurately characterize sea ice thermodynamic processes in order to 

properly simulate surface temperatures, and by extension other linked variables such as 

near-surface wind. By default, sea ice processes are treated by the Noah land-surface 

model in a way not altogether different from other types of land cover, which can result

in significant errors in the simulation of surface temperature. For example, the albedo of 

sea ice, which plays a significant role in the calculation of the surface energy balance, 

particularly in the spring through autumn months, is hard coded as a constant value in the 

Noah LSM, and sea ice is assumed to have snow cover all year long. In reality, sea ice 

albedo varies dramatically throughout the year, sometimes even in the same day, as snow 

accumulates and melts on the sea ice surface. The snow-free bare ice often present in the 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

72

summer months has a completely different radiative character than mid-winter sea ice 

topped with a snowpack, leading to very different thermodynamic calculations. If these 

types of differences are not accounted for, surface energy fluxes over much of the domain 

will have significant errors, leading to major biases in the reanalyzed fields. In order to 

address this native deficiency in the Noah LSM, a thermodynamic sea ice model (Zhang 

and Zhang 2001) was coupled to WRF for the production of the final CBHAR reanalysis, 

leading to improved simulation of surface temperatures and fluxes, as described below.

In the coupled model, the treatment of sea ice thermodynamics follows Hibler (1979) and 

Parkinson and Washington (1979). The net energy flux over the sea ice surface HTi is 

given by:

HTi = R − F + H + E (3.1)

where

R = Rs(1−α) + Rl

F = εσTi

4

 

H = ρCpChV(Ta − Ti)

E = LρCpV qa − q ( i)

Here, R is the incoming total radiative flux at the ocean surface, F is the outgoing 

longwave radiative flux from the ocean surface, and H and E are the sensible and latent 

heat fluxes between the atmosphere and ocean, respectively. In addition, Rs and Rl are the 

downward shortwave and longwave radiative fluxes (calculated by the atmospheric 

model), respectively, and α is the surface albedo, which is calculated following Gardner 

and Sharp (2010). ε is the ice-surface emissivity (0.95), σ is the Stefan–Boltzmann 

constant ( 8 5.67 10− × ), and the air density ρ, air heat capacity Cp, surface exchange 

coefficient for heat and moisture Ch, and surface wind speed V are all defined from the 

atmospheric model. L is the latent heat and T and q represent surface temperature and 

specific humidity, respectively, with subscript a representing atmosphere and subscript i

representing sea ice. Ta and qa are defined from the atmospheric model.

A Newton/Raphson iteration scheme is used to resolve the sea ice temperature from the 

above surface energy balances. When snow cover exists on the sea ice, a similar method 

is used to resolve the snow surface temperature. The interface between snow and sea ice 

contains the following balance between the conductive fluxes of snow and sea ice:

Cs

hs

(Ti −Ts ) = Ci

hi

(Tb −Ti) (3.2)

Here, C is the thermal conductivity, h is the thickness, and T is the skin surface 

temperature of the snow or ice layer, with the subscripts s and i denoting snow and ice, 

respectively. Tb represents the bottom temperature of the sea ice layer, i.e. the freezing 

point of the seawater below the ice.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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As mentioned above, a physically based broadband albedo parameterization (Gardner and 

Sharp 2010) was incorporated into the sea ice model. This parameterization calculates 

albedo as a function of ice-grain specific surface area, concentration of light-absorbing 

carbon, solar zenith angle, cloud optical thickness, and depth of the overlying snow layer. 

Combining these terms, given an input snow cover analysis, yields a more flexible, 

dynamic characterization of the sea ice albedo and generates realistic values for all 

conditions throughout the year, without the need for artificial techniques for directly 

specifying the albedo. The new albedo parameterization has the added advantage of 

accounting for the observed spectral shift in broadband albedo that occurs under varying 

cloud conditions and solar angles. To determine the appropriate value for the ice-grain 

surface area in the study region, values of snow cover, solar angle, and cloud thickness 

data taken from model simulations conducted over the entire SHEBA experimental 

period (1997–98) were used, in combination with high-frequency observed albedos. This 

calculated specific surface area was then used in the CBHAR production simulation. For 

the carbon concentration, a typical value for Arctic carbon loading of 7 ppm was taken 

from the observations of Doherty et al. (2010), who measured carbon values throughout 

the Arctic region, including at sites in the Beaufort Sea. 

Though the presence of sea ice plays a major role in the meteorology of the Chukchi–

Beaufort Seas region through modification of the surface condition, due to the remote 

location and difficulty in maintaining observational platforms on the constantly changing 

ice there exist very little observational data gathered from the surface of the ice itself. 

Owing to the lack of such verification data in the CBHAR domain, the performance of 

the model was instead tested on the SHEBA-centered domain used for some of the model 

physical parameterization sensitivity tests (Figure 3.2.21). As before, two cases were 

chosen: one in winter, for the month of December 1997, and the other in summer, for the 

month of July 1998. Both a control simulation without the sea ice model and a sea ice 

coupled simulation were performed for each case and the results were verified against 

SHEBA field measurements.

For the summer simulation (July 1998), WRF was initialized with the ERA-I reanalysis 

and the Canadian Meteorological Centre (CMC) 25-km daily snow depth dataset and run 

for one month. During summer, sea ice surface albedo experiences high variability 

compared to the rest of the year, and so this simulation provided a good opportunity to 

examine the model’s performance in simulating sea ice albedo. The SHEBA campaign 

observed surface albedo via two methods: (1) a high-frequency measurement made at a 

fixed observation tower, and (2) a line measurement, a daily-reported average albedo 

measured at various points along a 500-m survey line, incorporating the effects of 

horizontal variability, including the important impacts on albedo of melt ponds. As seen 

in Figure 3.3.1, the control simulation used the constant surface albedo of 0.8 as set by 

default in the Noah LSM, which is obviously not representative of real-world conditions. 

In contrast, the coupled sea ice model captured the observed variation in albedo relatively 

well, particularly when compared with the point measurements taken at the tower. Since 

the sea ice albedo parameterization does not account for any effects from melt ponds, 

which tend to reduce the ice albedo, the model did not capture the small line-averaged 

albedo values observed at the end of July. The short-term maxima in the tower-observed
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

74

albedo represent fresh snowfall events, which dramatically increase the reflectance of the 

normally bare ice prior to rapidly melting. Such localized events could also not be 

captured by WRF, which took its snow cover conditions from the daily 25-km CMC 

dataset.

Figure 3.3.1. Comparison of surface albedo between SHEBA observations (black 

and grey) and control (blue) and sea ice coupled (red) simulations during July 1998

Ultimately, the primary goal of coupling the sea ice model with WRF is to more 

accurately simulate sea ice surface temperature, and by extension surface fluxes to the 

atmosphere, which can in turn impact the modeling of near-surface meteorology. As icesurface temperatures are relatively constant during the summer months, for the most part 

remaining close to the melting point, a winter case was instead used to evaluate the 

performance of the coupled model in simulating sea ice temperatures. A one-month 

simulation was performed for December 1997 using the same model configuration as the 

summer case. Figure 3.3.2 shows that the coupled model succeeded in improving the 

simulation of sea ice temperatures relative to the control simulation. In particular, the 

relatively strong cold bias produced by the control simulation (up to 10°C in the latter 

half of December 1997) is significantly reduced or eliminated altogether, although the 

coupled model reduced extreme cold minima throughout the entire period.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

75

Figure 3.3.2. Sea ice skin temperature (°C) time series for SHEBA observations 

(black) and control (blue) and coupled sea ice (red) simulations during December

1997

The results presented here, demonstrating an accurate, dynamic calculation of the sea ice 

albedo, combined with an improved simulation of sea ice surface temperature, confirm 

that the sea ice model that has been coupled with WRF serves to significantly improve 

the thermodynamic treatment of sea ice in the model. This coupling was therefore applied

in the final model configuration used for the production of the CBHAR reanalysis.

3.3.2 Adoption of Polar WRF Improvements

The Polar Meteorology Group at OSU has made a package of code changes to the WRF 

model, called Polar WRF, available to the public since late 2009. Polar WRF contains 

several modifications to the standard WRF model, intended to improve the overall 

performance of the model for the polar regions. Chief among these are modifications to 

the Noah LSM that allow WRF to incorporate variable sea ice thickness data, in place of 

the constant 3-m thickness value set by default in the model. The thickness of the ice 

layer impacts heat transfer between the atmosphere and the liquid ocean beneath the ice, 

thus affecting the surface temperature calculation performed by the coupled sea ice model

introduced in Section 3.3.1. In addition, Polar WRF includes a change that sets the 

bottom temperature of sea ice to −1.8°C, instead of the default −2.0°C. These sea ice 

thickness and ice parameter modifications were tested and included in the final WRF 

configuration. It should be noted that the Polar WRF distribution also includes additional 

options, including variable sea ice albedo, an alternate calculation of the snow/ice surface 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

76

energy balance, and a change to the thermal conductivity of the top soil layer for tundra 

land-use categories, representing a peat (organic) layer that is found atop the soil of the 

Arctic Slope. These latter options were not utilized in the final configuration, either due 

to a more advanced option already existing in the coupled sea ice model (in the case of 

variable ice albedo), or as a result of sensitivity testing, which demonstrated degraded 

performance when the options were used in the study area.

In order to check the efficacy of using the selected Polar WRF modifications in 

conjunction with the coupled sea ice model, sensitivity tests were conducted for a twomonth period (Nov–Dec 1997) over the SHEBA domain (Figure 3.2.21). For these tests, 

in addition to the simulation with the standard WRF model (CTRL), simulations were 

conducted with the standard Polar WRF codes (PWRF), as well as with WRF coupled 

with the thermodynamic sea ice model and selected modifications from Polar WRF 

(WRF-ice). A summary of these tests is given in Table 3.3.1. Verification was performed 

against the SHEBA observations, with the RMSE calculated for various thermodynamic 

variables, as given in Table 3.3.2.

Table 3.3.1. Sensitivity simulations comparing WRF, Polar WRF, and WRF-ice

Sensitivity 

test Configuration

CTRL Standard WRF model configured with Morr microphysics, KF cumulus, 

CLW longwave, CSW shortwave, YSU PBL, and NOAH LSM

PWRF Polar WRF with the same configuration as CTRL

WRF-ice

Same configuration as CTRL, except using the coupled thermodynamic 

sea ice model and selected Polar WRF options, namely variable sea ice 

thickness and a bottom sea ice temperature of −1.8°C

Table 3.3.2. RMSE of surface air temperature (°C), surface skin temperature (°C), 

and sensible and latent heat fluxes (W m-2) for sensitivity simulations during 

November–December 1997 as verified against SHEBA observations

Sensitivity 

test

Surface air 

temperature (°C)

Ice skin 

temperature (°C)

Sensible heat 

flux (W m-2)

Latent heat 

flux (W m-2)

CTRL 2.50 4.11 18.35 1.96

PWRF 2.74 3.74 10.88 1.84

WRF-ice 2.50 3.38 11.40 1.85

The results demonstrate that WRF-ice, in which WRF is coupled with the thermodynamic 

sea ice model and selected Polar WRF improvements are adopted, generates better results 

in the simulation of both surface air temperature and sea ice skin temperature relative to 

both the standard WRF and Polar WRF models. Though the sensible heat flux is slightly 

worse, it may, in fact, may be artificially low in the case of Polar WRF, as both the air 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

77

and skin temperatures, which are its primary governing factors, saw decreased accuracy 

relative to the use of the sea ice model. Given these results, demonstrating the success of 

both selectively incorporating Polar WRF options, particularly variable sea ice thickness, 

and coupling the thermodynamic sea ice model with WRF, the use of selected Polar WRF 

improvements and the coupled sea ice model were thus included in the final model 

configuration for the production of CBHAR.

3.4 Optimization of Data Assimilation Configuration

Forecast simulations of as little as a few days can generate errors due to inaccuracy in the 

forcing data and limitations of the model itself. The longer the model runs, the more the 

errors accumulate. This is particularly true for simulations with constrained boundaries as 

conducted for this project. The drift induced by the influence of lateral boundary 

conditions grows with time and can be attributed to the chaotic nature of the flow. Thus, 

it is necessary to constrain the model solution in some form, to ensure that it is not 

allowed to deviate too far from reality. There are generally two ways that this can be 

accomplished:

1) Nudging the model solution to a known, “correct” reanalysis, such as that 

provided to the model at initialization.

2) Assimilating additional observations into the model, thereby generating a new

reanalysis as was done for the initialization of the model simulation, and repeating 

this action whenever additional observations are available until the end of the 

desired simulation period is reached.

Which of the two methods (nudging or assimilation) best improves the model results, and 

could a combination of the two (whereby new analyses are generated via data 

assimilation, with the model simultaneously nudged to the forcing data) perform even 

better? This question was explored in this effort through the use of WRF v3.2 and its data 

assimilation system WRFDA, a flexible, state-of-the-art atmospheric data assimilation 

package. The research efforts investigating the relative performance of both data 

assimilation and reanalysis nudging are summarized in Sections 3.4.1–3.4.4.

3.4.1 Data Assimilation Sensitivity Test Design

Data assimilation has proven to be a useful technique for constraining the model solution

and reducing model errors, as seen in the production of many reanalyses such as ERA-I

and NARR. However, assimilating extra data into an analysis does not necessarily 

improve the quality of the subsequent simulation for all parameters. It is therefore 

necessary to test the effect of assimilating each observation type in order to determine 

whether or not to include it in the final production simulation. In addition, the WRFDA 

assimilation system determines its optimal analysis through the use of information that 

includes model background error (BE) and observational error. The dynamic model 

results are combined with observations using weights inversely related to their respective 

errors. Each observation data type is characterized by its own error distribution, 

dependent on the accuracy of the individual observation system, but model errors are not 

fixed and can be affected by the choice of model domain and configuration. Thus, model 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

78

background errors must be determined prior to the performing of any data assimilation. In 

order to best optimize the WRFDA assimilation system for the production of CBHAR,

two sets of sensitivity experiments were conducted as described in the following two 

sections:

• Section 3.4.2 describes tests for the evaluation of model BE sensitivity.

• Section 3.4.3 details tests for evaluating the potential observation data types to be 

assimilated. 

The model domain used for these tests was the same as in Figure 1.1.2. The physical 

parameterization schemes used in the assimilation tests are listed below (Table 3.4.1):

Table 3.4.1. Configuration of WRF physical parameterizations in the assimilation 

sensitivity tests

Physical parameterization Configuration

Cumulus KF cumulus

Microphysics Morr microphysics

Longwave radiation RgLW longwave

Shortwave radiation RgSW shortwave

Planetary boundary layer YSU PBL 

Land-surface model NOAH LSM

The selected simulation periods included two separate months in 2009: January was

chosen to represent winter conditions and July was used for summer conditions. The 

oceanic portion of the domain experiences high variability throughout the year in its sea

ice coverage and surface temperature, and the land similarly varies in its snow cover and 

thermal profile. This high level of variability thus provides a good opportunity for 

investigating the performance of WRFDA by examining the two extreme months of 

January and July. 

The in situ observational data tested for assimilation by WRFDA included surface 

observations from 122 stations distributed throughout the model domain, along with three 

radiosonde stations, as indicated in Figure 3.4.1. These in situ observations were

collected from different data sources and were quality controlled as described in Section 

2. The satellite retrievals tested with WRFDA included Quick Scatterometer 

(QuikSCAT) ocean-surface winds; temperature and moisture profiles from both the 

Moderate Resolution Imaging Spectroradiometer (MODIS) and Constellation Observing 

System for Meteorology, Ionosphere, and Climate (COSMIC) platforms; and upper-level 

polar winds from both the MODIS and Advanced High Resolution Radiometer 

(AVHRR) platforms. The QuikSCAT surface winds are available over open water at a 

resolution of 12.5 km and cover the period from July 1999 to November 2009. The 

MODIS-retrieved moisture and temperature profiles include 20 vertical levels (from 1000 

to 5 hPa) at a horizontal spacing of 5 km and are available since March 2000. The 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

79

COSMIC retrievals provide temperature and moisture profiles, thinned here to 26 vertical 

levels (from 1000 to 10 hPa), and are available beginning in July 2006. The AVHRRderived polar wind product has a grid spacing of 5 km, and is available from the 

beginning of 1982, while MODIS-derived polar winds have a grid spacing of 2 km and 

are available beginning in July 2002. Both polar wind products consist of a single wind 

observation at each available grid point, reported at varying levels depending on the 

altitude of the clouds used to determine the wind vectors.

Figure 3.4.1. Distribution of the 122 observational stations used in WRFDA 

assimilation sensitivity tests and verification. In situ surface stations are indicated 

by green dots, and radiosonde stations by red diamonds. 

In all of the sensitivity simulations, the model was re-initialized from ERA-I forcing data 

every 2 days and run for 2 days and 6 hours, during which time the available 

observational data were assimilated every 6 hours (including at the initial hour). Only 

data within ±1.5 hours of the assimilation hour were used. The results from the first 6 

hours of each run were used as model spin-up and removed from consideration, with the 

remaining 2-day periods pieced together to form a continuous time series. In addition to 

the assimilation runs, a control simulation (CTRL), in which no data were assimilated but 

which was otherwise identical to the assimilation runs, including the stop-restart cycle 

every 6 hours, was also conducted for the purposes of comparing and evaluating the 

sensitivity results.

3.4.2 Sensitivity of WRFDA to Model Background Error

WRFDA offers the option of using either built-in global model BE (BE-GFS) or usercustomized model BE in its data assimilation process. The built-in BE is generated from 

Global Forecast System (GFS) forecasts, produced by NCAR with a horizontal grid 

spacing of about 80 km, and can be used for any domain due to its global coverage 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

80

(Barker et al. 2004). Customized BE can be calculated following the National 

Meteorological Center (NMC) method (Parrish and Derber 1992), in which the 

differences between 12- and 24-hour model forecasts that are valid at the same time are 

first calculated, with the model BE estimated after averaging all forecast differences over 

a given period of time and the error covariances calculated. In this study, the 12- and 24-

hour forecasts for the entire year of 2009 were first generated with the model 

configuration listed in Table 3.4.1. The differences between the 12- and 24-hour forecasts 

were then averaged, both over the entire year and for each month individually, in order to 

produce both yearly-averaged BE (BE-1yr) and monthly-averaged BE (BE-Jan, BE-Jul), 

respectively.

A total of five simulations, including one control and four sensitivity runs (Table 3.4.2),

were conducted in order to evaluate the sensitivity of WRFDA to model BE in the 

assimilation of in situ surface observations. In addition to the built-in model BE-GFS, 

customized model BEs, including the yearly-averaged BE-1yr and monthly-averaged BE 

for January (BE-Jan) and July (BE-Jul), were independently tested in the sensitivity 

experiments BE-GFS, BE-1yr, BE-Jan, and BE-Jul. The simulation periods included

January and July 2009 for most of the sensitivity experiments, with the exceptions that 

BE-Jan was only used for January 2009 and BE-Jul only for July 2009.

Table 3.4.2. Sensitivity tests for evaluating model background error

Sensitivity

test Data assimilated Background Error (BE) Simulation period

BE-GFS In situ surface 

observations Built-in global BE January & July 2009

BE-1yr In situ surface 

observations

Customized BE from 1-year

(2009) simulation January & July 2009

BE-Jul In situ surface 

observations

Customized BE from 1-month 

(July 2009) simulation July 2009

BE-Jan In situ surface 

observations

Customized BE from 1-month 

(January 2009) simulation January 2009

CTRL No data assimilated January & July 2009

The impacts of the model BEs on assimilation performance were determined through 

verification against the collected radiosondes and surface observations at hours when 

assimilation was not performed, as well as against QuikSCAT winds. Verified quantities 

included sea level pressure (SLP), 2-m air temperature (T), 10-m wind vector (VEC), and 

upper-level temperature and wind vector (Table 3.4.3). When verifying against surface 

observations, the stations were divided into two groups, coastal and inland stations, in 

order to distinguish the performance of the assimilation between complex terrain and flat,

coastal areas. Surface stations located within 30 km of the shoreline were designated as 

coastal stations, with those farther from shore designated as inland stations. Using this 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

81

criterion, there were a total of 56 coastal stations and 66 inland stations available within 

the model domain for this period.

Comparisons of RMSEs among the control and sensitivity simulations as verified against 

all three types of observations for both the January and July cases (Table 3.4.3) 

demonstrate that the built-in model BE (BE-GFS) significantly degrades the model 

performance in the study area, generating much greater errors than the control simulation 

in which no data were assimilated. This comparison suggests that in order to achieve 

positive impacts from data assimilation via WRFDA, usage of the built-in model BE 

should be avoided. On the other hand, the customized model BEs, both yearly- and 

monthly-averaged, generally serve to enhance the model performance, reducing the errors 

for most of the examined variables. A close look at the comparison further demonstrates 

that the improvements in SLP and the surface wind field by the customized BEs are seen 

continually throughout both the cold and warm months. For temperature, however, a 

slight negative effect is present in the January case, even with the use of customized BEs. 

This indicates that accurately modeling the strong surface temperature inversion 

characteristic of Arctic winter remains a challenge for the WRF model. Accordingly, the 

estimated model BEs might not fully represent the actual model performance in such 

conditions, resulting in a degradation of the data assimilation performance. Across the 

experiments, the performance of the monthly- and yearly-averaged BEs is similar, 

indicating that including seasonal variability in the customized BEs is relatively 

insignificant in this study. 

Table 3.4.3. Verification of BE tests against different observations for 2-m 

temperature (T) (°C), sea level pressure (SLP) (hPa), and 10-m wind vector (VEC) 

(m s-1) (RMSEs (improved in bold) are averaged over all stations; when verified 

against surface in situ stations, coastal and inland stations are verified separately.)

Sensitivity

tests

Surface observation Radiosonde QuikSCAT

T (°C) SLP (hPa) VEC (m s-1) T

(°C)

VEC

(m s-1)

VEC

(m s-1) Coast Inland Coast Inland Coast Inland

Jul

CTRL 2.69 3.77 1.15 1.69 3.18 3.28 1.08 2.92 3.42

BE-GFS 2.75 4.34 1.27 1.96 3.34 3.56 1.97 4.26 3.74

BE-1yr 2.44 3.79 1.05 1.68 3.01 3.13 1.08 2.89 3.32

BE-Jul 2.44 3.77 1.02 1.65 3.00 3.10 1.09 2.94 3.32

Jan

CTRL 5.64 6.15 1.54 4.54 3.80 3.93 2.03 4.32

No 

QuikSCAT

during icecovered

season

BE-GFS 6.92 7.54 1.69 5.00 4.22 4.10 4.18 6.34

BE-1yr 5.94 6.03 1.33 4.54 3.66 3.79 2.09 4.30

BE-Jan 6.10 6.04 1.35 4.60 3.67 3.78 2.13 4.33

Assimilating in situ surface observations generates different impacts on coastal and 

inland areas. In the BE-GFS experiment, surface temperature was degraded by 2% (23%) 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

82

in the coastal areas, and by 15% (23%) inland when compared to the CTRL simulation in 

July (January), while the RMSEs for surface wind vector were 5% (11%) and 9% (4%) 

larger in July (January) for the coastal and inland stations, respectively (Table 3.4.3). 

This indicates that the built-in model BE generated by the coarse-resolution simulation 

(~80 km grid spacing) is more problematic during winter and over complex terrain. 

Greater overall improvements can be achieved in the coastal region relative to inland 

areas through the use of customized model BEs. During summer, surface temperature and 

SLP in the coastal areas were improved by 9% and 9–11%, respectively, while the 

impacts were very small inland. For the surface wind field, improvements were similar 

for the coastal and inland areas, with 5% (4%) improvement at the coastal stations and 

5% (4%) inland during July (January). Similar to the difficulty that the model has in 

simulating inversions, the fact that the inland area is characterized by complex 

topography presents a challenge for WRF in accurately capturing many terrain-induced 

finer-scale details. Because model BE does not represent the actual model error relative 

to observations, but rather the average difference between one forecast time and another, 

BE can be underestimated in situations where the model performs inherently poorly. 

Thus, the performance of the assimilation can be hampered due to a less accurate 

estimation of model BE.

Since the coastal area receives a greater benefit from assimilation, the averaged 48-hour 

simulation cycle of the temperature and wind vector RMSEs, as averaged over the 56 

coastal stations, is compared for the month of July 2009 across all the sensitivity tests 

(Figure 3.4.2). Over the averaged simulation cycle, both monthly- and yearly-averaged 

customized model BEs performed very similarly in improving the model results 

throughout the entire diurnal cycle. On the other hand, the built-in model BE (BE-GFS) 

degraded the model results, particularly for near-surface temperature during the day. 

 

Figure 3.4.2. RMSE of (a) 2-m temperature (°C) and (b) 10-m wind vector (m s-1) as 

verified against 56 coastal stations over the 48-hour cold-start model cycle for BE 

experiments in July 2009
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From the comparison of RMSEs calculated against radiosondes (Table 3.4.3), even 

though only in situ surface observations were assimilated by the WRFDA system, it’s 

seen that the upper atmosphere can also be impacted nontrivially when the built-in model 

BE is used; the impacts from customized model BEs were very small, however. The 

built-in BE increased the RMSEs by more than 40–100% relative to the control 

simulation. Comparisons of the RMSE profiles among all the simulations for July 2009 

show that the errors introduced by use of the built-in model BE gradually decrease with 

height, and are equivalent to values of the other simulations at or above 300 hPa (Figure 

3.4.3), indicating that the negative effect is three-dimensional even though only twodimensional data were assimilated.

 

Figure 3.4.3. Monthly-averaged profiles of RMSE, averaged over all radiosonde 

stations for (a) temperature (°C) and (b) wind vector (m s-1) for BE experiments in 

July 2009

As again shown in Table 3.4.3, improvements in wind speed and direction, as verified 

against QuikSCAT winds, can also be achieved by the use of customized model BEs 

even though no QuikSCAT data were assimilated. The built-in model BE, however, 

degraded the performance of WRFDA, increasing errors in both wind speed and 

direction. The improvements seen with the customized BEs are most likely due to 

improved surface winds along the coastal areas where coastal station observations were 

assimilated. 

3.4.3 Performance Analysis of WRFDA Assimilation of Multiple Datasets

A total of six data assimilation tests were conducted, as summarized in Table 3.4.4, to 

evaluate whether assimilation of various observational data types via WRFDA would 

enhance the model performance. The collected surface observations include hourly 2-m

temperature and humidity, 10-m wind, and pressure. Radiosonde profiles include 12-

hourly pressure, geopotential height, temperature, humidity, and wind on vertical levels 

extending from the surface to the stratosphere. QuikSCAT sea-surface winds are 

measured over ice-free ocean areas at a horizontal grid spacing of 12.5 km, and are 
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

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available several times daily in the study domain. MODIS retrievals provide twice-daily 

temperature and moisture profiles on 20 vertical levels (1000, 950, 920, 850, 780, 700, 

620, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 5 hPa) with 5-km grid spacing. 

COSMIC profiles used here include temperature and moisture on 26 vertical levels

(1000, 975, 950, 925, 900, 850, 800, 750, 700, 650, 600, 550, 500, 450, 400, 350, 300, 

250, 200, 150, 100, 70, 50, 30, 20, 10 hPa) and report 2–5 profiles in the study domain 

each day. Polar winds are reported at either a grid spacing of 2 km (MODIS) or 5 km 

(AVHRR), with each available grid point containing a single wind vector observation at 

varying levels, and are available within the study domain nearly every hour of the day. 

All sensitivity experiments herein used the customized model BE from sensitivity 

experiment BE-1yr (Table 3.4.2). When verifying the sensitivity test results against 

observations, only unassimilated data were used in order to maintain independence 

between the simulations and the verification dataset. Since radiosonde profiles are 

typically only available at 0000 UTC and 1200 UTC, which coincide with data 

assimilation times in these tests, model output from the previous hour (i.e., 2300 UTC 

and 1100 UTC) was instead used when verifying against radiosonde observations. The 

relative success achieved through assimilating each type of data is analyzed by 

comparing the RMSEs among the control and sensitivity simulations (Tables 3.4.5, 3.4.8, 

and 3.4.9) and are detailed below.

Table 3.4.4. Sensitivity tests used to evaluate assimilation of different observational 

sources

Sensitivity test Data assimilated

CTRL No data assimilated

SFC In situ surface observations

SONDE Radiosonde profiles

QSCAT QuikSCAT ocean-surface winds

COSMIC COSMIC profiles

PWIND MODIS and AVHRR polar winds

Assimilation of In Situ Surface Observations

Sensitivity experiment SFC (Table 3.4.4), in which the model BE from BE-1yr was used 

for the assimilation of surface station observations, is equivalent to the sensitivity 

experiment BE-1yr in Table 3.4.2 for the July case. As discussed in Section 3.4.2, 

assimilation of in situ surface observations generates improvements in most verified 

variables, particularly surface wind. Greater improvements are seen in the coastal region, 

where surface temperature was improved by 9% and wind by 5%, while the impact 

inland is relatively small. When the model results are verified against QuikSCAT winds, 

improvements are also seen. Therefore, the assimilation of in situ surface observations 

does help to improve the model performance in simulating most surface variables, 

particularly surface winds.
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Assimilation of Radiosonde Profiles

In the study area, a total of three sounding stations (Barrow, Inuvik, and Kotzebue) made 

observations during the experiment period of July 2009; two soundings per day from 

each station were assimilated in the assimilation experiment SONDE (Table 3.4.4). The 

comparison of RMSEs between the CTRL and SONDE simulations (Table 3.4.5) 

indicates that the assimilation of radiosonde measurements not only improves the 

model’s simulation of upper-air variables (where model output one hour prior to the 

assimilation times was used for verification in order to maintain independence from the 

observations), but also affects the simulation of surface variables as verified against 

surface in situ observations and QuikSCAT winds. Due to the very limited number of 

soundings available in the study region, the RMSEs of upper-air temperatures and winds 

were only slightly reduced in this experiment, by about 1–2%. At the surface, 

temperature and SLP were improved by about 2% and 3%, respectively, in the coastal 

region. The surface wind over the ocean was also slightly improved, as verified against 

QuikSCAT data. Overall, this experiment indicates that a consistently positive impact can 

be achieved through the assimilation of radiosonde measurements.

Table 3.4.5. Same as Table 3.4.3, but for sensitivity tests assimilating different 

observational sources

Sensitivity

test

Surface observation Radiosonde QuikSCAT

T (°C) SLP (hPa) VEC (m s-1) T

(°C)

VEC

(m s-1)

VEC

(m s-1 Coast ) Inland Coast Inland Coast Inland

CTRL 2.69 3.77 1.15 1.69 3.18 3.28 1.08 2.93 3.42

SFC 2.44 3.79 1.05 1.68 3.01 3.13 1.08 2.93 3.32

SONDE 2.64 3.77 1.11 1.68 3.16 3.27 1.06 2.91 3.39

QSCAT 2.68 3.78 1.15 1.66 3.18 3.25 1.07 2.93 3.06

Assimilation of QuikSCAT Surface Winds

Assimilation of QuikSCAT ocean-surface winds was evaluated with the assimilation 

experiment QSCAT (Table 3.4.4). QuikSCAT winds provide measurements of surface 

wind vectors over open water in the absence of sea ice (Figure 3.4.4a). During the 

experiment period of July 2009, a meteorological buoy was deployed by this project off 

the Beaufort Sea coast near Barrow, where it collected a month-long offshore 

meteorological dataset of near-surface winds, among other variables 

(http://knik.iarc.uaf.edu/buoy09). In order to better understand the accuracy of 

QuikSCAT winds in the study region, the wind speeds measured by the buoy are 

compared with QuikSCAT retrievals (Figure 3.4.4b). The correlation between the buoy 

measurements and QuikSCAT surface winds for the period of 5 August–15 September 

2009 is 0.83, with an RMSE of just 0.82 m s

-1

, indicating that the quality of QuikSCAT 

winds in the study area is satisfactory.
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Figure 3.4.4. (a) Snapshot of QuikSCAT surface wind speed (m s-1) (color) in the 

model domain at 0551 UTC 18 July 2009; (b) QuikSCAT surface wind speed (m s-1) 

versus BOEM buoy (71.29°N, 152.14°W) observed wind speed (m s-1) in August–

September 2009. The red dot in (a) represents the BOEM buoy location.

During the open water season, e.g. July 2009, when there are QuikSCAT winds available 

in the study area, assimilation of QuikSCAT surface winds demonstrates a positive 

influence on the simulation of the offshore surface wind field. As before, model output 

from experiment QSCAT was verified against both in situ measurements and QuikSCAT 

winds at non-assimilation hours and the RMSE calculated (Table 3.4.5). This comparison 

demonstrates that assimilating QuikSCAT winds can significantly reduce the RMSE of 

surface wind vectors over open water as verified against unassimilated QuikSCAT winds. 

Wind vectors were improved by 11% relative to the control run.

Assimilation of Satellite-Retrieved Profiles

MODIS profiles are available from 2–3 satellite passes per day over the study area, with a 

horizontal grid spacing of about 5 km and 20 vertical levels extending from 1000 to 5 

hPa. After the data is thinned to a spacing of approximately 120 km in order to prevent 

oversaturation, which can cause diminished results (e.g., Liu and Rabier 2002), there 

remain approximately 1900 MODIS profiles per day across the model domain available 

to be assimilated by WRFDA.

MODIS profile data are accompanied by quality control flags that indicate the conditions 

under which they were collected. For example, the cloud mask flag (CM) indicates the 

presence of clouds, and the snow/ice background flag (SI) indicates whether the surface 

was covered by snow or sea ice (Table 3.4.6). To characterize the quality of data marked 

by these flags in the study area, the MODIS profiles for 2009 were divided into groups 

according to their CM and SI flags and verified against sounding observations at the three 

available radiosonde stations. A comparison of temperature RMSE profiles (Figure 3.4.5) 

shows that the profiles retrieved under a snow or ice background condition (SI=0) contain 

errors that are 30–50% larger than those that were not (SI=1). A similar contrast is 

present between profiles retrieved under cloudy conditions (CM=0,1) and clear sky 

(CM=2,3). In addition, the data errors are highly variable with height. The RMSE at 1000 

hPa is up to two times larger than at the upper levels (850 to 300 hPa).
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Table 3.4.6. List of MODIS retrieval flags

Flag Interpretation Key

Cloud Mask (CM)

0 = Confidently Cloudy

1 = Probably Cloudy

2 = Probably Clear

3 = Confidently Clear

Snow/Ice Background (SI) 0 = Yes

1 = No

Figure 3.4.5. Mean RMSE profiles of MODIS-retrieved temperature (°C) with 

different QC flags in 2009, as verified against all radiosonde stations in the study 

area

Considering the variations in MODIS profile quality, a series of sensitivity experiments

was conducted in which MODIS profiles were screened by the CM and SI flags 

examined in Figure 3.4.5 (Table 3.4.7). The purpose of this was to determine the optimal 

configuration for assimilating MODIS profiles via WRFDA in order to generate the bestquality final CBHAR reanalysis. In these experiments, MODIS data were either 

assimilated at all levels (MODIS) or only between 850 and 300 hPa (MODIS-MID). The 

sensitivity simulations were evaluated in the same way as before, with the model results 

(SLP, T, and VEC) verified against unassimilated radiosondes, surface observations, and 

QuikSCAT winds. Comparisons of the results from the sensitivity experiments MODIS, 

MODIS-MID, and CTRL (Table 3.4.8) demonstrate that assimilation of unfiltered 
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MODIS retrievals (experiment ‘MODIS’) made the results worse relative to the CTRL 

simulation for almost all examined variables. The experiment ‘MODIS’ produced surface 

wind (VEC) RMSE more than 2% larger than CTRL over land, and more than 7% larger 

over the ocean. Perhaps surprisingly, the experiment that selectively assimilated vertical 

levels of the retrievals (MODIS-MID) did little to improve the poor performance of the 

‘MODIS’ experiment. The results from MODIS-MID were also worse than CTRL, with 

RMSE about 3% larger for the surface wind and 1% for surface temperature in coastal 

areas. 

Table 3.4.7. MODIS assimilation sensitivity tests

Test Snow/Ice background (SI) Cloud mask (CM) Vertical levels

MODIS All background (0, 1) All sky (0, 1, 2, 3) All levels

MODIS-MID All background (0, 1) All sky (0, 1, 2, 3) 850–300 hPa

MODIS-QA Snow/ice-free background (1) Clear sky (2, 3) All levels

Table 3.4.8. Same as Table 3.4.3, but for sensitivity tests assimilating MODIS 

profiles

Sensitivity

tests

Surface observation Radiosonde QuikSCAT

T (°C) SLP (hPa) VEC (m s-1) T

(°C)

VEC

(m s-1)

VEC

(m s-1 Coast Inland Coast Inland Coast Inland )

CTRL 2.69 3.77 1.15 1.69 3.18 3.28 1.08 2.93 3.42

MODIS 2.75 3.77 1.11 1.66 3.30 3.36 1.16 3.19 3.69

MODIS-MID 2.73 3.79 1.12 1.66 3.27 3.38 1.14 3.09 3.60

In addition to the level-selective experiment MODIS-MID, the MODIS profiles may also 

be screened through the use of the CM and SI flags. Experiment MODIS-QA was 

conducted to examine the model performance when assimilating only MODIS profiles 

that have a snow/ice-free background (SI=1) and were measured under either probably 

clear (CM=2) or confidently clear (CM=3) sky conditions. In order to understand the 

vertical effects of assimilating MODIS retrievals, model-simulated temperature profiles 

from the experiments CTRL, ‘MODIS’, and MODIS-QA were compared at all levels and 

averaged over the entire simulation period of July 2009 at Barrow (Figure 3.4.6). 

Comparisons of the monthly-averaged bias profiles among all the sensitivity tests show 

that the unfiltered experiment ‘MODIS’ overcorrected the warm bias generated by the 

CTRL experiment for the middle and lower atmosphere (below ~500 hPa), and produced

a cold bias of up to 0.5°C. When the MODIS profiles were selectively assimilated on the 

basis of quality assurance flags (experiment MODIS-QA), an improvement is seen in

temperature for almost all levels, reducing both the cold bias in the middle and lower 

atmosphere (below ~300 hPa) and the warm bias in the upper atmosphere (above ~300 

hPa). Selectively assimilating MODIS profiles via the WRFDA system through the use of 

quality assurance flags, such as those indicating snow/ice-free background and/or clear 
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sky conditions, is therefore essential for avoiding degradation of assimilation 

performance.

Figure 3.4.6. Monthly-averaged profiles of temperature bias (°C) at Barrow in July 

2009 for experiments CTRL, MODIS, and MODIS-QA, as verified against 

radiosonde data at various vertical levels

COSMIC-retrieved profiles represent an opportunity for further augmenting the upper-air 

data assimilation in this study, with about 20 profiles per day available in the study 

domain. The profiles have a total of 6 more vertical levels and 7 more levels below 300 

hPa than those from MODIS. When evaluating the effect of assimilating COSMIC 

profiles, the impacts of filtering with quality control flags were not considered due to the 

fact that COSMIC products are not accompanied by QC-flag information, suggesting that 

third-party sources are required in order to conduct a filtering procedure, which could 

result in inconsistencies. The experimental results (COSMIC) were verified separately 

against surface station (coastal and inland stations separately), radiosonde, and 

QuikSCAT observations for temperature, SLP, and wind speed and direction (Table

3.4.9). Generally, the assimilation of COSMIC profiles was able to produce a very slight 

positive effect for both surface and upper-air variables, which initially encouraged their 

use as one of the observation types to be assimilated into CBHAR. However, given the 

very short-term availability of COSMIC data (2006–09), it was ultimately decided to 

remove them from consideration for this application. In light of the positive, though 

slight, impacts of COSMIC assimilation, should the period of the reanalysis be extended 

further, the possibility of their inclusion should be re-examined.
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Table 3.4.9. Same as Table 3.4.3, but for sensitivity tests assimilating COSMIC 

profiles

Sensitivity

test

Surface observation Radiosonde QuikSCAT

T (°C) SLP (hPa) VEC (m s-1) T

(°C)

VEC

(m s-1)

VEC

(m s-1 Coast ) Inland Coast Inland Coast Inland

CTRL 2.69 3.77 1.15 1.69 3.18 3.27 1.08 2.93 3.42

COSMIC 2.71 3.76 1.10 1.67 3.18 3.26 1.07 2.92 3.42

Assimilation of Polar Winds

Polar winds are derived from satellite measurements of infrared and water vapor 

channels, whereby the movements of clouds are tracked from one satellite pass to the 

next and the difference in location used to determine the wind vector at the altitude of the 

tracked clouds. As such, polar winds are only available at cloudy locations, and on a 

single vertical level for each reported grid point. This limited vertical extent, and location 

generally far from the surface, suggests that the assimilation of polar winds may have a 

relatively small impact on the near-surface variables that are the focus of this reanalysis 

effort. Nevertheless, given their long-term and frequent availability (since 1982 for 

AVHRR and 2002 for MODIS, and nearly every hour somewhere in the study domain), a 

sensitivity experiment (PWIND) was conducted in order to determine whether polar 

winds should be included in the final reanalysis configuration. The experimental results 

(PWIND) were verified against surface station observations. Perhaps unsurprisingly, the 

assimilation of these single-level, upper-air winds had only an extremely slight, though 

positive, impact on near-surface variables (not shown). Nevertheless, despite this 

insignificant impact on the surface, it was decided to include them in the final modeling 

configuration both due to the length of their observational record, and the fact that winds 

are a primary focus of this reanalysis effort.

Based on the above sensitivity analysis, it was determined that the assimilation of surface 

observations, radiosondes, QuikSCAT ocean-surface winds, MODIS and AVHRR polar 

winds, and selected MODIS profiles each adds value to the modeling results. The 

observational set assimilated into the final 31-year CBHAR reanalysis thus included in 

situ surface observations, radiosondes, QuikSCAT ocean-surface winds, polar winds, and 

selected MODIS-retrieved profiles.

3.4.4 Optimization of Analysis Nudging Configuration

When performing long-term regional simulations, the use of a nudging technique is 

essential for guiding model solutions toward an existing reanalysis and to prevent lateralboundary-induced drift from growing too large. WRF contains two three-dimensional 

analysis nudging options:

1) Grid point nudging: each grid point is directly nudged toward the larger-scale 

reanalysis dataset; and
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2) Spectral nudging: model fields are decomposed in Fourier series into fields with 

different wavenumbers, and only coarser scales below a given wavenumber are 

then nudged; this technique better allows smaller-scale model information to be 

retained.

Within each nudging option, there exist three different degrees of nudging in the vertical 

dimension: one can nudge all levels, including the boundary layer; one can limit nudging

to those levels above a specified model level; or one can limit nudging to the region 

above the model-defined boundary layer. In addition, one can selectively nudge certain 

variables (among temperature, water vapor, geopotential height (spectral nudging only), 

and winds), leaving the others to be freely determined by the model. Thus, tests are 

needed to determine both which level of nudging performs best and whether to include 

winds, the focus of this reanalysis effort, in the nudge. For spectral nudging, another 

consideration is to determine the optimal wavenumber (which controls the spatial scale of 

waves to be nudged) for the size of the domain being used. To answer these questions, a 

series of nudging sensitivity tests was conducted for two simulation periods, one in the 

winter of 2009 (February) and the other in the summer of the same year (August), with 

the modeling results verified against surface observations. For comparison, a control 

simulation (ctrl) without any analysis nudging was also conducted. The RMSE of 

modeled wind vectors from the various nudging simulation tests (Figure 3.4.7) 

demonstrates that:

1) Analysis nudging generally improves the simulations.

2) Spectral nudging with wavenumber 3 (experiment nudg-spec-all-3) outperforms 

the other nudging algorithms.

3) Nudging at all vertical levels and all variables (experiments with “all” in their 

name) is superior to selective nudging.

Thus, spectral nudging with a wavenumber of 3, on all vertical levels and for all 

variables, was selected for use in the final model configuration for the production of the 

31-year CBHAR simulation.

Both data assimilation and analysis nudging have so far been shown to improve the 

performance of the model simulations. Does the combination of these two techniques 

lead to further improvements? To answer this, simulations using a combination of both 

data assimilation of surface observations and three-wavenumber spectral nudging for the 

same time periods as above were conducted and compared with the modeling results in 

which data assimilation or reanalysis nudging were individually utilized. The 

comparisons (Figure 3.4.8) clearly demonstrate that combining data assimilation and 

spectral nudging produces further improvements over those seen with each technique 

used separately.
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Figure 3.4.7. RMSE of modeled wind vectors (m s-1) from control (ctrl) and various 

nudging simulations, as verified against surface observations during February (top) 

and August (bottom) 2009
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Figure 3.4.8. RMSE of modeled wind vectors (m s-1) from control (ctrl), nudging 

(nudg), assimilation (assim), and combined assimilation and nudging (assim+nudg) 

simulations, as verified against surface observations during February (left) and 

August (right) 2009

3.5 Model Data Selections

Regional atmospheric models that cover a limited area, such as WRF, require 

specification of the lateral boundary conditions that surround the modeled domain. In 

real-data applications, these are provided by gridded data from larger-scale models. 

Selection of the larger-scale forcing data for use with WRF to generate the CBHAR 

reanalysis is summarized in Section 3.5.1. In addition, WRF also requires lower boundary 

conditions, such as land-use information. Selection of the lower boundary data to be used 

by WRF in the production of CBHAR is discussed in Section 3.5.2.

3.5.1 Large-Scale Forcing Data

The initial and boundary conditions needed by WRF simulations are generally derived 

from larger-scale model simulations or reanalyses. While many such global or largerscale regional models exist, for a long-term historical retrospective such as this project 

entails, a large-scale gridded historical dataset, such as a reanalysis, that encompasses the 

same time period is required, of which only a few exist. Some, such as the NCEP/NCAR 

Reanalysis can be eliminated from consideration due to their coarse horizontal resolution 

that limits their accuracy at local and regional scales, making them unsuitable for scaling 

down to the 10-km grid spacing employed for CBHAR without using additional outer 

grids, which would be computationally unfeasible. Others, such as ERA-40 and JRA-25,

do not cover CBHAR’s entire 31-year time period, which would introduce potentially 

problematic discontinuities should they be utilized. Still others, such as the NASA 

Modern-Era Retrospective Analysis for Research and Applications (MERRA) (Rienecker 

et al. 2011) and the NCEP Climate Forecast System Reanalysis (CFSR) (Saha et al. 2010) 

became available to researchers only recently, and too late to be considered for use in this 

project. After removal of the unworkable options, only two remained for consideration: 

the NARR and ERA-I reanalyses.
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NARR, as the name implies, is a regional reanalysis product that covers the entire North 

American continent. It is produced with NCEP’s Eta model, downscaled from the 

NCEP/Department of Energy (DOE) Reanalysis 2, and is available from 1979 through 

the present at a horizontal grid spacing of 32 km and a 3-hourly frequency. ERA-I, on the 

other hand, is a global reanalysis generated with the ECMWF Integrated Forecast System 

(IFS) and is also available from 1979 through the present, albeit at a lower horizontal 

(0.7°, ~77 km by 20 km in the study area) and temporal (6-hourly) resolution. Both 

modeling systems assimilate observational data into model simulations in order to 

produce their respective reanalyses. Given the higher resolution, both spatial and 

temporal, of NARR, one might expect higher accuracy in its representation of mesoscale 

meteorological features. However, this cannot be assumed and so a series of sensitivity 

tests were conducted in order to determine the most accurate source of large-scale forcing 

data that would ultimately be used for the production of CBHAR.

As noted in Section 3.2.1, the initial suite of physical parameterization sensitivity tests 

was carried out using NARR as the forcing data, both due to its relatively high resolution 

and the fact that ERA-I became available just recently, after this study had already begun. 

With the introduction of ERA-I, several sensitivity tests were conducted using the same 

configuration as the sensitivity tests with NARR for September 2004 in order to 

investigate the effectiveness of using the new reanalysis (see Tables 3.2.1 and 3.2.2 in 

Section 3.2.1 for additional details about the configuration of the model physical 

parameterization tests). The wind vector RMSE for both the NARR and ERA-I 

sensitivity tests is compared in Figure 3.5.1, as verified against surface station 

observations. 

Figure 3.5.1. Wind vector RMSE (m s-1) for various WRF physical parameterization 

sensitivity tests in September 2004 when using NARR vs. ERA-I as large-scale 

forcing data
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It is clear that no matter the physical parameterization options used, ERA-I produces 

consistently better results, not only for surface winds but also for every other model 

variable investigated (not shown). From these results, it is obvious that ERA-I is the 

superior choice for the selection of forcing data in order to generate the most accurate 

possible results in the CBHAR reanalysis.

3.5.2 WRF Land-Use Dataset

The WRF package includes two different datasets for providing land-use information to 

the model: the 24-category USGS dataset and the 20-category MODIS land-cover 

classification of the International Geosphere-Biosphere Programme, as modified for use 

with the Noah land-surface model. Not only can the two differ in how they categorize a 

particular plot of land, but also the land-use parameters themselves (e.g., albedo, 

roughness, moisture availability) can vary between the two for the same category. 

Particularly for a region like Alaska, which is complex geographically and 

topographically, and for a study that is focused primarily on surface parameters, it is 

important to make a proper selection and ensure that the most accurate simulation is 

produced. To this end, sensitivity tests were conducted to investigate which land-use 

dataset available in WRF would be used for the final CBHAR configuration.

For these tests, the simulation period of August 2009 was chosen in order to avoid the 

presence of snow cover, which would have potentially masked the effects of the 

differences in the underlying land-use classifications. One-month-long tests for August 

2009 were conducted in concert with tests of WRF’s cumulus parameterizations, as 

detailed in Section 3.2.3. The model configuration with the Grell-3D cumulus scheme 

(Table 3.2.6) was used in the tests USGS and MODIS, in which the USGS and MODIS 

land-use data were used, respectively. RMSE values for the model results as verified 

against surface station observations are shown below in Table 3.5.1.

Table 3.5.1. RMSE of land-use data sensitivity tests as verified against surface 

observations for long- and shortwave irradiance (W m-2), 2-m temperature (°C), and 

10-m wind vector (m s-1)

Sensitivity 

test

Longwave

(W m-2)

Shortwave

(W m-2)

Temperature

(°C)

Wind vector

(m s-1)

USGS 31.9 138.9 3.15 3.41

MODIS 30.8 139.3 3.12 3.37

While the differences in RMSE between simulations using USGS and MODIS datasets 

were, for the most part, relatively small, MODIS land use did outperform USGS for most 

of the variables examined, in particular surface temperature and winds, which are of 

prime importance for this study. Due to this slight, but consistent, advantage recorded 

with the use of the MODIS land-use dataset, it was selected to be part of the final 

CBHAR configuration.
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3.6 Final Model Configuration and Production of CBHAR 

Based on the results of all the sensitivity tests and further model improvements (Sections 

3.2–3.5), including the coupling of a thermodynamic sea ice model with WRF (Section 

3.3.1), a finalized model configuration was developed for use in the production of the 31-

year CBHAR reanalysis that represents one of the focal points of this study. Each 

component of the configuration, from the datasets used to the model physical options 

selected to the procedure used to generate the final reanalysis, is summarized in this 

section. While extensive efforts have been made to optimize the model configuration and 

data usage for generating CBHAR, the use of a necessarily imperfect model, changes in 

the data assimilated over time, and the replacement of instruments which produce the 

observations assimilated may all inject errors into the reanalysis (Trenberth et al. 2001). 

Like every other reanalysis product, the CBHAR reanalysis cannot be free from these 

errors.

3.6.1 Initial and Boundary Conditions

Three-dimensional atmospheric initial and boundary conditions, along with most of the 

surface conditions and the gridded analysis to which WRF was spectrally nudged in 

generating CBHAR, were provided by the ERA-I reanalysis, the selection of which was 

discussed in Section 3.5.1. The fact that the study area is characterized by seasonal sea 

ice over the ocean and snow on land further necessitates that a careful preparation must 

be made for the specification of sea surface temperature (SST), sea ice concentration 

(SIC) and thickness (SIT), snow depth, and snow water equivalent (SWE). To maintain 

consistency with the atmospheric fields, SST was taken from ERA-I for use with the 

CBHAR reanalysis. To provide sea ice conditions, the NASA Bootstrap sea ice 

concentration derived from the Scanning Multichannel Microwave Radiometer (SMMR) 

on board the Nimbus-7 satellite and the Special Sensor Microwave/Imager (SSM/I) 

instrument on Defense Meteorological Satellite Program (DMSP) satellites was acquired 

from the National Snow and Ice Data Center (NSIDC). These data are available at a grid 

spacing of 25 km, and at a daily frequency since July 1987 and semi-daily prior to that. 

SIT was taken from the 6-hourly CFSR, a recently developed reanalysis product 

produced by NCEP at a 0.5° grid spacing. Due to the unavoidable discrepancies between 

the selected SIT and SIC datasets, the SIC distribution was treated as the true areal 

coverage, and for points where SIC was available and SIT was not, the SIT value was set 

to the closest available value in the CFSR dataset.

Snow depth and snow water equivalent are critical variables in the study area but are not 

included over sea ice in ERA-I, and at a relatively low resolution over land. Thus, the 

snow depth on land was acquired from the 32-km NARR reanalysis, which in turn was 

taken from the 47-km daily U.S. Air Force Weather Agency (AFWA) snow depth 

analysis. The AFWA analysis is not produced over sea ice, and so snow data (SWE) on 

ice were taken from the CFSR reanalysis. Since the AFWA analysis only provides snow 

depth, and CFSR only provides SWE, another data source of snow density was required 

in order to calculate SWE over land and snow depth over sea ice. The daily global CMC 

snow depth analysis provided by NSIDC, which includes SWE as calculated from snow 
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density climatological lookup tables, was used for this purpose. CMC data only exist 

since 1998, and so to fill in data for prior years the daily-average snow density values 

calculated for each day of the year over the 1998–2009 period were instead applied.

3.6.2 Final Model Configuration

As determined through the sensitivity tests detailed in Section 3.2, WRF was configured 

to use 10-km grid spacing and 49 vertical levels with the following physical 

parameterization options for the production of the final CBHAR reanalysis: Morrison

microphysics, RRTMG longwave and shortwave radiation, Grell-3D cumulus, MYJ PBL 

and surface layer, and the Noah LSM. As summarized in Section 3.3, a new 

thermodynamic sea ice model was also coupled with WRF and selected Polar WRF 

options were adopted as well, namely the use of variable sea ice thickness data and a 

more realistic bottom sea ice temperature. 

Section 3.4 summarized the results of testing the assimilation of various observational 

data and satellite retrievals with the WRFDA assimilation system in order to produce 

reanalysis output. As a result, WRFDA was configured to assimilate in situ surface 

station and radiosonde observations of temperature, dew point, wind vectors, and 

pressure (geopotential height for radiosondes), satellite-derived QuikSCAT ocean-surface 

winds, polar winds derived from MODIS and AVHRR measurements, and selected 

temperature and moisture profiles retrieved from MODIS data. A customized model 

background error file created specifically for the CBHAR domain was included in the 

CBHAR reanalysis configuration. Spectral nudging to the input ERA-I reanalysis was 

also utilized with a horizontal wavenumber of 3. 

The selection of forcing data was discussed in Sections 3.5 and 3.6.1, with the result that 

ERA-I was used to provide initial and boundary conditions for the three-dimensional 

atmospheric fields, as well as most of the surface fields, in the final WRF simulations. 

For the sea ice and snow data needed by WRF, NASA Bootstrap SIC, CFSR SIT, NARR 

snow depth over land, and CFSR SWE over sea ice were used. A summary of the final 

optimized model configuration is given in Figure 3.6.1. 

Figure 3.6.1. Optimized model configuration for CBHAR
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3.6.3 Production of CBHAR

Once the forcing data was processed for input into the WRF simulation and the model 

configuration finalized, the final step was to produce the CBHAR reanalysis. This was 

accomplished at the Arctic Region Supercomputing Center, UAF through a parallelized, 

iterative process whereby a series of 54-hour simulations, initialized every two days 

throughout the 31-year study period, were produced, as summarized in Figure 3.6.2. 

Within each of these simulations, WRF was first initialized at 1200 UTC and 

observations within 30 minutes of the initialization time were assimilated. The model was 

integrated for an hour, stopped, and more observations assimilated at 1300 UTC. This 

process was repeated for the entire 54-hour cycle, with the first 6 output times (hours 

1200–1700 UTC of the initial day) discarded, having only been used to spin up the 

model. The remaining 48 output hours (the output of each assimilation cycle) were kept 

in order to become part of the final product. In this way, a total of 5663 simulations were 

performed and the final two days of each simulation kept, resulting in the final 31-year 

CBHAR reanalysis.

Figure 3.6.2. Process used to generate each of the 54-hour modeling cycles used to 

produce CBHAR. WPS is the WRF preprocessing system, which creates WRF input 

files. The first 6 hours of each cycle were used as model spin-up and discarded, with 

the remaining 48 hours contributing to the reanalysis. Due to overlap between 

adjacent cycles, the final 1800 UTC analysis was also discarded.

4 Performance Evaluation of the Chukchi-Beaufort High-Resolution Atmospheric 

Reanalysis (CBHAR)

4.1 Introduction

As discussed in the previous sections, great care has been taken to produce the highestquality long-term regional reanalysis possible for the Chukchi–Beaufort Seas region. 

Significant efforts have been carried out in order to assemble a rigorously qualitycontrolled observational database and to assimilate these data into an optimized modeling 

configuration that has been thoroughly tested for varying conditions in the study area. 
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The resultant modeling system has been used to produce a high-resolution reanalysis that 

is designed to represent a highly accurate description of the atmospheric fields in the 

region over the 31-year period 1979–2009. Although this reanalysis is the product of an 

effort ultimately devoted to its accuracy, it is necessary to thoroughly evaluate the final 

output and determine whether the effort has been successful in its objectives. Throughout 

the course of this study, the simulation of the near-surface wind field has been of primary 

importance, and indeed is the most significant parameter to be evaluated. As such, it is 

naturally the primary parameter selected for validation of the CBHAR reanalysis. 

However, since the model is an interactive system, one that simulates numerous 

physically dependent fields, errors in surface wind may also result from model 

discrepancies related to other parameters in the model. It is imperative to evaluate and 

understand the overall performance of the model simulations and to monitor their 

dynamic and thermodynamic consistency. Accordingly, in addition to the surface wind 

field, near-surface temperature has also been utilized for the validation of the reanalysis 

in order to quantify its thermodynamic performance, which has implications for 

applications that rely on the determination of near-surface atmospheric stability, such as 

studies of air pollution transport. To better gauge the relative quality of the CBHAR 

reanalysis fields, the ERA-Interim reanalysis has been used as a baseline for comparison 

and has been verified in a similar manner.

An unfortunate consequence of the necessity to produce as accurate a reanalysis as 

possible, requiring the assimilation of all available observational data, is that no 

independent observational dataset exists for the purposes of model verification. Thus, the 

same observations that were assimilated into CBHAR were necessarily used to verify the 

reanalysis as presented in this section. To mitigate the inherent advantage that this may 

confer on CBHAR relative to ERA-Interim, which was validated in the same manner but 

which undoubtedly assimilated fewer data from local networks and field programs, such 

as RAWS, WERC, and offshore well sites, only observations from the widely available 

NCDC ISH database (see Figure 2.2.1) were utilized in the following surface station 

verification analysis given in Section 4.2. Though ECMWF does not publish a listing of 

station data assimilated into ERA-I, the NCDC database primarily consists of the 

standard METAR-reporting airports and permanent weather stations that would most 

likely be assimilated into any global reanalysis product. Usage of this database alone thus 

reduces as much as possible any advantage inherent to CBHAR as a result of the greater 

number of observations assimilated, and aims to ensure that both CBHAR and ERA-I are 

verified with a common station dataset containing observations that were assimilated into 

each.

In order to evaluate the reanalysis output in offshore areas, which are a prime 

consideration of this study, reanalysis data were also evaluated using QuikSCAT oceansurface winds. Like the NCDC data, QuikSCAT observations were assimilated into both 

CBHAR and ERA-Interim, leading to a fair comparison between the two. In addition, 

though not a focus of this project, upper-air variables were also verified in order to 

evaluate the performance of the reanalysis output above the surface. As with the other 

verification datasets, radiosonde data that had been assimilated into both products were 

used for the evaluation.
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In the following statistical analysis, the metrics focused on will be RMSE and correlation, 

as defined in Appendix B. Combining the use of RMSE, which gives an overall measure 

of the error in a modeled field, with correlation, which evaluates how successfully the 

field matches the observed pattern, can give a reliable picture of the accuracy of modeled 

output. Certainly, if the RMSE is lower and correlation higher for one product relative to 

another, it can safely be said that it contains a more accurate representation of the field in 

question. On the other hand, if both the RMSE and correlation are higher, it suggests the 

presence of a persistent bias in the product with the higher correlation; the modeled 

pattern is more accurate, hence the higher correlation, but the bias increases the overall 

error, leading simultaneously to a larger overall RMSE.

4.2 Verification against Surface Station Observations

The CBHAR reanalysis was verified against the database of quality-controlled NCDC 

station observations assembled as part of this project as described in Section 2, 

comprising over 100 stations across all land areas (along with a few offshore locations) 

throughout the entire model domain. As mentioned in Section 4.1, only NCDC stations 

were used in the verification to avoid conferring an unfair advantage to CBHAR relative 

to the global reanalysis ERA-I, which is less likely to have assimilated data from local 

networks and field programs. The available observed variables of 2-m temperature and

10-m wind speed, wind direction, wind vector, and the zonal (U) and meridional (V) 

wind components were used to validate the model fields. Other observed variables, such 

as dew point, SLP, and precipitation were not calibrated as a part of this study and 

therefore were not used to verify the performance of the reanalysis at the surface. Since 

near-surface temperatures and winds were the primary focus of this reanalysis effort, they 

were selected as the basis on which to determine the quality of the resultant model data. 

Model output fields were interpolated to the station locations using a standard bilinear 

interpolation, and the statistical measures of root-mean-square error (RMSE) and 

correlation (CORR) calculated for a variety of different time groupings. To provide a 

baseline against which CBHAR data could be compared, the ERA-I reanalysis data that 

were used as initial and boundary conditions for the current reanalysis—and to this point 

one of, if not the most accurate reanalysis available for the study region—were verified in 

the same manner. The following comparison of the error present in both reanalyses thus 

quantifies the improvements generated by the current model configuration and reanalysis 

techniques detailed in Section 3.

In many of the following figures, rather than show a side-by-side comparison of the 

actual values of the verification statistics for ERA-I and CBHAR, the relative differences 

between the statistics of the two reanalyses are instead shown. For RMSE and CORR, the 

difference between the two is normalized according to the ERA-I statistic, in order to 

demonstrate the percentage improvement (or degradation) in CBHAR relative to ERA-I. 

The formulas for these normalized statistical differences are given below:

ΔRMSE = RMSEERA − RMSECBHAR

RMSEERA

(4.1)
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ΔCORR = CORRCBHAR −CORRERA

CORRERA

(4.2)

Here, the ERA and CBHAR subscripts refer to the values calculated for the ERA-I and 

CBHAR reanalyses, respectively. In both cases, the numerators are defined such that an 

improvement in CBHAR (i.e., either a reduction in RMSE or an increase in CORR) 

results in a positive value for the statistic.

To begin with, Figure 4.2.1 shows ΔRMSE and ΔCORR for near-surface temperature and 

wind variables as verified over all stations and all 6-hourly output times (0000, 0600, 

1200, and 1800 UTC) for the entire 31-year study period. Only the 6-hr times were used 

in this comparison, rather than the hourly data present in CBHAR, in order to provide a 

proper comparison to ERA-I, which only includes 6-hr data. This comparison shows that 

the error has been greatly reduced for all of the surface wind variables, with 

improvements in RMSE ranging from 13–16%. CORR for the wind variables has 

likewise been significantly improved, showing gains of 8–12%. In addition, the error in 

the surface air temperature has also been improved, though to a lesser degree and without 

a corresponding increase in correlation. These overall results are very encouraging, as 

producing a high-quality surface wind reanalysis is the primary objective of this project. 

The improvement in temperature is also beneficial and is indicative of an improved 

characterization of the near-surface atmospheric stability.

Though it is encouraging that the simulation of surface variables has been significantly 

improved domain-wide, a closer examination is in order. After all, it is to be expected 

that a higher-resolution model, with its corresponding higher-resolution topography, will 

naturally produce more accurate winds, solely by virtue of having more accurately 

specified orographic features. However, as the aim of this project centers on winds over 

ocean areas, such improvements to inland areas are not of the highest concern. To 

investigate how much of this overall improvement in the winds was due to such effects, 

the station set was divided into those stations within 30 km of the coast and those farther 

inland, with separate verifications conducted for each. This division resulted in a total of 

65 stations in the coastal zone and 38 in the inland region. The verification results for the 

coastal and inland subsets are shown in Figures 4.2.2 and 4.2.3, respectively. As with the 

overall results in Figure 4.2.1, both zones exhibit improvements to the RMSE and 

correlation for all wind variables, demonstrating the success of CBHAR in simulating 

winds with increased accuracy for both coastal and inland locations. Interestingly, this 

comparison also shows that the improvement to the RMSE for all of the wind-related 

variables is actually significantly larger in the coastal areas, with reductions in error of 

15–18%, with a much smaller improvement inland. The correlations are improved to a 

greater degree away from the coast, however, suggesting an expected benefit from 

higher-resolution topography. This greater reduction in error near the coast is a very 

positive result, and indicates that major improvements to the winds have been made in 

the areas where they are most needed for the purposes of this project. Compared with the 

winds, 2-m temperature is improved to a lesser extent in both regions, with improvements 

of 5.1% and 1.4% in the inland and coastal areas, respectively.
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Figure 4.2.1. ΔRMSE and ΔCORR for 2-m air temperature (T) and 10-m wind 

speed (Spd), direction (Dir), U&V components, and wind vector (Vect), as verified 

for 6-hourly output against all collected NCDC surface station observations for the 

entire 31-year period. For each statistic, values represent the percentage 

improvement in CBHAR relative to ERA-I. 

Figure 4.2.2. Same as Figure 4.2.1, but for verification against coastal stations
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Figure 4.2.3. Same as Figure 4.2.1, but for verification against inland stations

4.3 Verification against QuikSCAT Ocean-Surface Winds

Naturally, the vast majority of the collected station data for this project come from points 

situated on land. Although it is reassuring that stations near the coast see an enhancement 

in the characterization of surface winds, a question naturally arises about the kinds of 

improvements seen in actual offshore areas. Though a large dataset of offshore in situ 

observations is unavailable, due to the annual encroachment of sea ice in the Chukchi–

Beaufort Seas making the deployment of permanent buoys impossible, the QuikSCAT 

satellite-derived ocean surface wind product, measured by the SeaWinds scatterometer 

and assimilated as part of the production of both CBHAR and ERA-I, offers an 

opportunity to validate surface winds over ocean areas. As shown previously in Figure 

3.4.4b, despite being a satellite product, QuikSCAT data provide a highly accurate 

measure of offshore wind conditions as compared to buoy observations, and are thus 

suitable for use in model validation. Unfortunately, due to the nature of the measurement 

method, QuikSCAT winds are only available for open water areas far from the coastline, 

and are thus limited in their seasonal availability. Despite this limitation, they still 

provide the best option for evaluating model results over wide swaths of the ocean basin

in the study area. As was the case with station observations, the fact that QuikSCAT data 

were assimilated in the production of CBHAR means that they do not represent an 

independent verification dataset. However, as they were also assimilated into ERA-I, a 

direct comparison of the relative performance of the two reanalyses remains valid. It 

should be noted that since QuikSCAT observations are only available since 1999, the 
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verification presented in this section is thus applicable to the last decade of the study 

period.

CBHAR and ERA-I data were validated against the available QuikSCAT winds, 

bilinearly interpolating the reanalysis data to observational locations in the same manner 

as was done for surface station data. Results from this comparison, compiled for the 

entire 31-year period, are given in Figure 4.3.1. Impressive gains are seen for all surface 

wind variables in this comparison, with RMSE reduced by about 16–19% and correlation 

increased 9–17%, values that are remarkably similar to those observed for the coastal 

land stations. This result is achieved despite the fact that there is a presumed reduced 

benefit for higher-resolution models over the open ocean, where resolving neither 

orographic features nor the coastline or sea ice edge is a relevant issue. In addition, the 

relatively small-scale circulations, such as the sea breeze, which develop along and across 

coastal boundaries, are presumably factors of lesser importance far away from the coast 

than they are on land and in immediate offshore areas. In sum, the overall quality and 

consistency of these results, combined with those achieved for the coastal land stations, 

provides great confidence in the accuracy of the offshore winds in CBHAR, and suggests 

that this new reanalysis will greatly benefit applications that require the accurate 

specification of historical surface wind data.

Figure 4.3.1. ΔRMSE and ΔCORR for 10-m wind speed (Spd), direction (Dir), U&V 

components, and wind vector (Vect), as verified for 6-hourly output against 

QuikSCAT data for the entire 31-year period.
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4.4 Seasonal and Diurnal Error Analyses

It has been established in Sections 4.2 and 4.3 that the new CBHAR reanalysis provides a 

significant improvement to surface winds across the entire model domain, and 

particularly in areas near the coast and offshore, as compared to ERA-I. The statistics 

thus far have been calculated over the entire 31-year reanalysis period (or in the case of 

QuikSCAT, the entire 10-year observational period). How do they vary with season and 

time of day? To answer these questions, verifications of CBHAR and ERA-I were 

produced for each of the months of the year and hours of the day, in order to depict the 

seasonal and diurnal evolution of the performance of each of the validated model 

variables. As before, only 6-hourly data were included in the verification in order to make 

a consistent comparison between the two reanalyses.

Figure 4.4.1 shows the average monthly 2-m temperature RMSE for CBHAR and ERA-I, 

while Figure 4.4.2 shows monthly ΔRMSE and ΔCORR. As one would expect, errors are 

generally lower in the reanalyses in the warmer months, when variability in atmospheric 

conditions is at a minimum, and greatly increase during the winter. As suggested by the 

results of the overall error analysis in Section 4.2, the impacts of CBHAR on 

temperatures are relatively small through most of the year. However, the greatest 

improvements, both proportionally and in absolute terms, are seen over the late winter 

months, with errors reduced by nearly 6% in January and April, and 10–11% in February 

and March. These gains are somewhat offset in the summer months of July and August, 

however, when the RMSE is increased by 4–5%. The relative size of the improvements 

tends to mirror the relative magnitudes of the actual RMSE values; when total errors are 

larger, the improvements tend to be larger as well. The marked difference between error 

reductions in winter versus summer perhaps indicates a greater capability of CBHAR to 

represent the surface-based temperature inversions that are so important to near-surface 

weather and atmospheric stability throughout the region in the winter months.

The statistics for the surface wind variables (wind speed, direction, and vector), as shown 

in Figures 4.4.3–4.4.8, on the other hand, display no such mixed results. As with 

temperature, the RMSE for wind speed and wind vector is largest in the winter months, 

when the variability of weather conditions is high, and declines during the summer, when 

the variability is at its minimum. Interestingly, the error in wind direction does not follow 

this same pattern, remaining relatively constant throughout the annual cycle, though with 

a slight decline in the autumn. A key difference between the wind statistics and those of 

temperature, however, is that the percentage gains seen in RMSE and CORR are 

remarkably consistent and consistently positive throughout the entire year, with 

significant error reductions in wind vector of 14–16% and increased correlations of 6–

9%. Similar consistent results are seen individually in the wind speed and direction. 

These results further serve to demonstrate the consistently high quality of the reanalyzed 

surface wind fields of CBHAR and give additional confidence in their overall accuracy.
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Figure 4.4.1. Monthly-averaged RMSE for 2-m air temperature (°C) in CBHAR 

(red) and ERA-Interim (blue), as verified against NCDC stations

Figure 4.4.2. Monthly-averaged fractional improvement in 2-m air temperature 

RMSE (ΔRMSE) and correlation (ΔCORR) for CBHAR relative to ERA-Interim, as 

verified against NCDC stations
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Figure 4.4.3. Same as Figure 4.4.1, but for 10-m wind speed (m s-1)

Figure 4.4.4. Same as Figure 4.4.2, but for 10-m wind speed
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Figure 4.4.5. Same as Figure 4.4.1, but for 10-m wind direction (degrees)

Figure 4.4.6. Same as Figure 4.4.2, but for 10-m wind direction
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Figure 4.4.7. Same as Figure 4.4.1, but for 10-m wind vector (m s-1)

Figure 4.4.8. Same as Figure 4.4.2, but for 10-m wind vector
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Moving on to the diurnal cycle, it was noticed during the course of the verification 

produced for this analysis that, as is to be expected, the reanalysis errors in both ERA-I 

and CBHAR were not consistent among the NCDC stations used for verification. Due to 

different geographical and environmental factors associated with their locations, some 

stations are, naturally, not as accurately represented in the reanalyses as are others. 

However, it was discovered that there was a noticeable bias in the observational reporting 

frequency of certain of these stations, such that the 3- and 6-hourly errors, averaged over 

all available stations, were markedly different from those of the intermediate hours, 

leading to noticeable discontinuities in the average hourly verification statistics. This was 

not a problem for the statistical analyses presented to this point, as only 6-hourly data 

were used for verification, due to ERA-I having a 6-hr frequency. It becomes an issue for 

the hourly verification of CBHAR, however, and so to correct for this observational bias, 

only those stations that regularly report at an hourly interval throughout the entire day, 

and over their entire station history, were selected for use in the following analysis. 

Doing so ensures that the set of observations used for verification at each hour is 

consistent over the entire diurnal cycle, mitigating any possible biases resulting from the 

use of different sets of stations for different hours. This filtering resulted in a set of 42 

hourly-reporting NCDC stations that were used for this analysis.

Figures 4.4.9 and 4.4.10 show the hourly-averaged RMSE of near-surface temperature 

and wind vector, respectively, for CBHAR compared to ERA-I. Temperature error 

exhibits a distinct diurnal variation, peaking for the two reanalyses at 0800–0900 AKST, 

the time of day when the boundary layer begins to activate (aside from the Arctic winter, 

which lacks a diurnal cycle), and reaching its minimum at 2100 AKST, when the PBL 

has greater stability. This error pattern mirrors that of the average monthly errors, which 

peaked during months with greater meteorological variability and fell when variability 

declined. The wind vector RMSE follows a similar pattern, though a peak in the error at 

1500 AKST and with far less diurnal variation. There is relatively consistent behavior 

throughout the course of the period, with temperature and winds always performing 

better in CBHAR than in ERA-I, and with a similar degree of improvement throughout 

the day. It is encouraging to see that the CBHAR RMSE for temperatures, and 

particularly winds, is significantly less than the errors present in ERA-I for all hours. As 

with the other verifications performed, the degree and consistency of the improvements 

seen in CBHAR surface wind demonstrate the fundamental quality of the reanalysis.
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Figure 4.4.9. Hourly-averaged RMSE for 2-m air temperature (°C) in CBHAR (red) 

and ERA-Interim (blue), as verified against hourly-reporting NCDC stations

Figure 4.4.10. Same as Figure 4.4.9, but for 10-m wind vector (m s-1)
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4.5 Verification against Radiosondes

Although not a primary focus of this reanalysis effort, it is still instructive to examine the 

performance of the model in representing the upper atmosphere. Unfortunately, only 

eight radiosonde sites exist in the model domain to serve as a basis for model error 

calculations, and only three of these are available throughout the entire period: Barrow, 

Kotzebue, and Inuvik. While this is not nearly enough to give a proper representation of 

the three-dimensional atmosphere across the entire region, it is still worthwhile to look at 

how the new CBHAR reanalysis compares to ERA-I. It should be noted that, as was the 

case with the QuikSCAT and station data, the radiosonde data used here for verification 

were also assimilated into both CBHAR and ERA-I and hence do not represent an 

independent dataset. However, since this is a relative comparison of the performance of 

the two reanalyses, and both assimilated the same radiosonde data used for verification, 

the comparison remains valid. Given the pivotal role that moisture content plays in 

determining atmospheric stability, the verification presented below was also performed 

on dew point temperatures, in addition to the standard temperature and wind variables. At 

each of the radiosonde locations, both the modeled and observed temperature, dew point, 

and wind fields were vertically interpolated to constant pressure levels every 25 hPa and 

the reanalysis output verified using the model grid point closest to the station location.

Figures 4.5.1–4.5.3 show the vertical RMSE profile for temperature, dew point, and wind 

vector for CBHAR versus ERA-I, respectively, as averaged across the available 

radiosonde locations for the entire 31-year study period. To better illustrate the 

differences in RMSE, Figure 4.5.4 shows ΔRMSE for the three variables. For 

temperature and winds, the expected pattern is seen, with larger errors near the surface 

and around the tropopause, levels where the atmospheric variability is highest. Dew point 

error increases vertically to a maximum in the stratosphere, no doubt largely due to the 

difficulty in making accurate measurements in the low pressure, very low moisture 

conditions at those levels. CBHAR generally tracks ERA-I closely for the 

thermodynamic variables, with a notable improvement in temperature near the surface of 

more than 20%, and with reduced error throughout the boundary layer, through the 850-

hPa pressure level. In addition, mid- and upper-level dew points above 750 hPa show 

significantly increased accuracy in CBHAR. Unfortunately, winds show no such 

improvements at upper levels, though the increase in RMSE is relatively small, around 

5% through most of the profile. In contrast, levels near the surface exhibit better 

performance, with a ΔRMSE of around 10%. This latter result is consistent with the great 

improvements in surface winds demonstrated by verification with surface station and 

QuikSCAT measurements. The improvement in boundary-layer temperatures constitutes 

an enhancement to the representation of near-surface atmospheric stability, which should 

be of benefit to air pollution applications.
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Figure 4.5.1. RMSE profile for temperature (°C) in CBHAR (red) and ERA-Interim 

(blue) as verified against all available radiosonde measurements for the entire 31-

year period

Figure 4.5.2. Same as Figure 4.5.1, but for dew point (°C)
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Figure 4.5.3. Same as Figure 4.5.1, but for wind vector (m s-1)

Figure 4.5.4. ΔRMSE for upper-air temperature (red), dew point (blue), and wind 

vector (green) as verified against all available radiosonde measurements for the 

entire 31-year period
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5 Surface Climate in the Chukchi–Beaufort High-Resolution Atmospheric 

Reanalysis (CBHAR)

In this section, the climatology of the study area, including the interannual variability, 

long-term changes, and extremes of the region’s weather patterns, focusing in particular 

on near-surface winds, will be analyzed using the new CBHAR reanalysis. There is an 

imperative to conduct this analysis for use in various applications, due to rapid ongoing 

environmental changes and a lack of credible surface climate information in the study 

region. CBHAR is particularly well suited to this sort of analysis due to its high 

resolution and well-described regional physics, which allow it to resolve finer-scale 

processes caused by complex topography (i.e., the Brooks Range), variable sea ice and 

ocean properties, and their interactions. This regional analysis will add significant 

information to the understanding of the Arctic climate as a whole. Variables analyzed 

include 2-m temperature, the 10-m wind field, SST, and sea ice concentration/extent. As 

in the previous section, the results are compared to those of ERA-Interim, in order to 

further evaluate the overall performance of CBHAR.

5.1 Large-Scale Surface Wind Field Analysis

5.1.1 Analysis Approach for the Basin-Scale Surface Wind Field

This section will present a detailed climatology of the surface winds in CBHAR,

including analysis of the long-term climatology as well as the linear trends in the surface 

wind speeds. A wind direction climatology will also be discussed. In the following 

analysis, the monthly-average wind speeds were calculated by averaging the data over all 

output times for each month of the 31-year reanalysis period, while the monthly 

maximum wind speeds were determined by identifying the maximum wind speed for 

each month over the same period. To calculate the percentile wind speeds, the speeds 

were first sorted from smallest to largest at each grid point. The rank Rk was then 

calculated as follows:

Rk = N P

100

+ 0.5 (5.1)

where N is the total number of values and P is the percentile (90 or 95 here). Let P95

represent the 95th percentile value. Thus,

P95 = Rdec (A(Rwhole +1)− A(Rwhole ))+ A(Rwhole ) (5.2)

where Rwhole and Rdec are the whole and decimal parts of the rank as calculated with

Equation 5.1, respectively, and A is the sorted array at each grid point.

The trends of monthly-average and 95th-percentile wind speeds were also analyzed for 

the entire 31-year period through the use of a linear regression approach, in which a 

positive value indicates an increase in wind speed and a negative value represents a 

decrease over the 31-year period. To investigate the variation of extreme wind events,
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defined as occurrences of wind speed greater than the monthly 95th-percentile wind as 

calculated over the entire 31-year period, their frequency, expressed as a percentage, was 

calculated over a specified time period. For the wind direction climatology, the directions 

were first grouped into quadrants, and the monthly frequency of wind directions in each 

quadrant then calculated for each month over the 31-year period.

5.1.2 SLP and Surface Wind Speed Climatology

To begin with, the monthly mean SLP from 1979–2009 is analyzed as shown in Figure 

5.1.1. The Beaufort High reaches its peak in March with a pressure of ~1024 hPa and is 

centered to the north of Barrow. By June, it weakens to ~1015 hPa and moves to the east, 

becoming centered in the middle of the Beaufort Sea. From July to September, the 

Beaufort High weakens further, reaching a minimum central SLP of ~1010 to 1012 hPa. 

The High strengthens again from October to December and begins to move back to the

north of Barrow with pressures between 1017 and ~1021 hPa. The largest pressure 

gradient occurs in the Chukchi Sea when both the Beaufort High and Aleutian Low 

strengthen, particularly between October and March, with the gradient reaching its 

maximum in November. Overall, CBHAR successfully captures the climatological 

seasonal evolution of the synoptic-scale Beaufort High and its associated SLP. This 

climatological evolution matches that previously documented using NARR reanalysis 

data, in which the same evolutionary trajectory was found (Stegall and Zhang 2012).

The long-term climatology of the surface wind from 1979–2009 is detailed below. This 

analysis includes climatologies of the average wind speed, 95th-percentile wind speed, 

and wind direction. Figure 5.1.2 shows the 31-year average wind speed for each month of 

the year. Generally, the high climatological wind speeds are distributed from the Bering 

Strait to the shelf area of the Chukchi and Beaufort Seas, with the maximum monthly 

average wind speed occurring over the Bering Strait. The wind speed exhibits an obvious 

seasonality, with higher speeds in winter and lower values in summer. The highest wind 

speeds occur in October and November, reaching up to ~6 m s-1 over the Beaufort Sea 

and ~9 m s-1 over the Chukchi, while the lowest wind speeds appear in June. Both 

CBHAR and a parallel analysis using NARR (Stegall and Zhang 2012) show the 

consistent seasonality of surface wind speeds, though NARR data show the months with 

maximum speeds to be September and October. When compared with other large-scale 

reanalysis products and the in situ observations collected within the study domain, 

however, CBHAR gives a better estimate of the time of the maximum wind speed 

occurrence.
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Figure 5.1.1. Climatological monthly mean sea level pressure (SLP, hPa) for 1979–

2009 in CBHAR
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Figure 5.1.2. Climatological monthly mean wind speed (m s-1) for 1979–2009 in 

CBHAR

The average wind speeds in CBHAR, when compared to NARR (Figure 3 in Stegall and 

Zhang 2012), demonstrate that CBHAR wind speeds are consistently higher. From 

December through April, CBHAR winds over the Beaufort Sea range from ~4 to 6 m s

-1

,

whereas NARR wind speeds are below 4 m s

-1

. During these same months, the Chukchi 

Sea wind speeds in CBHAR range from ~6 m s

-1 to above 8 m s-1

, while NARR wind 

speeds lie between 4 and ~6 m s

-1

. Similarly, from May through August, CBHAR wind 

speeds have values of about 4 to 6 m s

-1

, with NARR largely exhibiting speeds below 4 

m s

-1

, except for July and August when the NARR speeds increase up to 6 m s

-1 over the 

Chukchi Sea. From September through November, NARR wind speeds increase up to a 

maximum of ~9 m s

-1 over the Chukchi Sea in October, while CBHAR wind speeds 

increase up to a high of ~9 m s

-1 in November over the same area. In NARR, winds over 

the Beaufort Sea during these months are primarily at or below ~5 m s

-1

, while in 

CBHAR they are at or below ~6 to 6.5 m s

-1

. The presence of higher wind speeds in 

CBHAR relative to NARR suggests that CBHAR better captures reality, as compared to

other large-scale reanalysis datasets and in situ observations. A major reason for this 

increased accuracy is the use of ERA-Interim as the forcing reanalysis dataset for the 

production of the new regional reanalysis. ERA-I is generally considered the best product 

among the new generation of global reanalysis products, and its quality translates into 
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greater accuracy in CBHAR. Figure 5.1.3 shows the ERA-I average wind speeds over the 

same 31-year period, with the speeds also consistently higher than in NARR. A detailed 

comparison between CBHAR and ERA-I is given below. The other major reason for the 

discrepancy between CBHAR and NARR is the use of optimized model physical 

parameterizations in the CBHAR reanalysis system, which were selected specifically for 

their performance in the study region as detailed in Section 3.

Figure 5.1.3. Climatological monthly mean wind speed (m s

-1

) for 1979–2009 in 

ERA-Interim

In comparison to ERA-I, CBHAR exhibits similar basin-scale seasonality in the surface 

wind field, with the added benefit that CBHAR is able to capture many high-resolution 

features and processes not present in the former’s wind field (Figure 5.1.2 vs. Figure 

5.1.3). Figure 5.1.3 displays the average surface wind speeds for each month in ERA-I 

and confirms the similar seasonality as in CBHAR. This is not unexpected, as ERA-I was 

used to drive the production of CBHAR. Wind speeds over the ocean compare favorably 

between ERA-I and CBHAR, though it is quite clear that CBHAR better captures 

topographically influenced wind features than does ERA-Interim. For example, during 

the autumn and winter seasons, higher wind speeds (~6 m s-1) exist over the Brooks 

Range and the interior of Alaska in CBHAR. This could be a signature of drainage and/or 

mountain/valley breezes, features that are not present in the ERA-I data shown in Figure 
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5.1.3. Another difference between the two reanalyses is that ERA-I has consistently 

higher wind speeds over the coasts, with differences of about 2 m s-1. This difference is 

most likely primarily due to the much coarser grid spacing present in ERA-Interim than 

in CBHAR.

An analysis of the 95th-percentile wind speeds in CBHAR, given in Figure 5.1.4, shows 

that the strongest wind speeds occur in the autumn and winter seasons (October–March). 

A similar geographic pattern and seasonal cycle are seen in the 95th-percentile winds as in 

the climatological means (Figure 5.1.2). The strongest winds occur primarily over the 

Chukchi Sea with speeds of ~14–15 m s-1. Wind speeds of ~12–13 m s-1 can be seen in 

October and November over the Beaufort Sea, and primarily along the Alaska coast. A 

close examination suggests that the area of large 95th-percentile wind speed extends 

farther eastward along the Alaskan Beaufort coast than in the mean wind field, although 

the primary area of highest wind speed is similarly situated over the Bering Strait. 

Figure 5.1.4. Monthly 95th-percentile wind speed (m s-1) for 1979–2009 in CBHAR

For comparison, Figure 5.1.5 shows the 95th-percentile wind speeds in the ERA-I data. A 

very similar seasonality is present as that seen in CBHAR. A very evident difference 

between the two, however, is that the 95th-percentile winds in ERA-I are noticeably 

higher than those of CBHAR, particularly over the Beaufort Sea, with a difference of 

around 2 m s-1. The biggest difference is in November, where most of the offshore areas 
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have wind speeds of ~13 m s-1 to above 15 m s-1 in ERA-I. In CBHAR, however, similar 

magnitudes of the wind speed are primarily limited to the Chukchi Sea. These differences 

are possibly due to the treatment of sea ice in the global model used to produce ERA-I. 

Further investigation would be needed to better understand the large differences seen in 

the month of November, as well as the consistently higher 95th-percentile wind speeds in 

ERA-I. As with the average wind speeds, CBHAR captures possible drainage and/or 

mountain/valley breezes during the autumn and winter months that are lacking in the 

larger-scale reanalysis.

Figure 5.1.5. Monthly 95th-percentile wind speed (m s

-1

) for 1979–2009 in ERAInterim

To further understand the frequency of different wind speeds over the study domain, a 

probability density function (PDF) analysis was conducted for each month of the year 

(Figure 5.1.6). It was found that the peak frequency of any individual speed occurs in 

June and July for a wind speed of around 4 m s-1. For most of the months, the highest 

frequency occurs for wind speeds ranging between 4–6 m s-1. The frequency of wind 

speeds greater than 9 m s-1 increases in October and November. Figure 5.1.7 shows PDFs 

of the surface wind speeds over the entire year and throughout the full 31-year time 

period for both CBHAR and ERA-I. CBHAR exhibits a peak frequency at ~3.5 m s

-1

, 

while the most frequent speed in ERA-I is ~2 m s

-1

. However, the higher wind speeds of 
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~7–13 m s

-1 exhibit greater frequency in ERA-I, which is consistent with the findings

discussed above.

Figure 5.1.6. Probability Density Functions (PDFs) of the climatological monthly 

surface wind speed in CBHAR

Figure 5.1.7. PDFs of surface wind speed for ERA-Interim (blue) and CBHAR (red)
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It is also relevant to investigate possible diurnal signatures in the wind speed for each 

season. In this section, the wind speeds are divided into those over land and ocean points. 

This allows an analysis to be done to identify at which times of year diurnal signatures 

occur over the ocean as well as land. Figure 5.1.8 shows the hourly CBHAR wind speeds 

averaged over the study region for the 31-year period, separated into those over ocean 

(blue) and land (red). From late March through May, a clear diurnal signature exists over 

the ocean. This feature is significantly less pronounced, though still present, through midSeptember, before disappearing for the remainder of the year. Over land, the diurnal 

signature begins in April and is clear until about mid-October. This diurnal signature 

agrees well with that seen by Stegall and Zhang (2012). However, the ocean wind speeds 

are much higher than the NARR wind speeds they observed. This agrees with the 

discussion of the differential wind speeds between CBHAR and NARR given above. 

Another reason for this discrepancy in the domain-averaged speeds is that the study 

region used by Stegall and Zhang (2012) extended from the Bering Strait north to about 

76°N. The CBHAR domain, on the other hand, extends farther north, but more 

importantly it also includes most of the Bering Sea and a small piece of the North Pacific 

Ocean, which include some of the highest average wind speeds in the study area (Figures 

5.1.2 and 5.1.4). In addition, the CBHAR ocean wind speeds are much higher than those 

over land, whereas Stegall and Zhang (2012) found that in NARR, the ocean and land 

wind speeds were much more similar. This difference between CBHAR and NARR 

might be due to the treatment of sea ice in the NARR model vs. that in CBHAR. Also, as 

the wind speeds in the Bering Sea/Pacific Ocean are some of the highest in the region, 

when they are included in the averages shown in Figure 5.1.8, the ocean wind speeds are 

increased relative to those over land. Figure 5.1.9 similarly shows the 31-year 6-hourly 

average wind speeds in ERA-I. As with CBHAR, the ocean wind speeds are significantly 

higher than those over land. However, during the months of March through May the 

diurnal variability is weaker than in CBHAR, most likely due in part to the effect of only 

having data four times per day vs. the hourly data available in CBHAR, causing ERA-I to 

miss the diurnal maxima and minima.
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Figure 5.1.8. Hourly mean wind speed (m s-1) over the ocean (blue) and land (red) in 

CBHAR, averaged over the study region for 1979–2009
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Figure 5.1.9. 6-hourly mean wind speed (m s-1) over the ocean (blue) and land (red) 

in ERA-Interim, averaged over the study region for 1979–2009
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5.1.3 Surface Wind Direction Climatology

To investigate the wind direction climatology, directions were first divided into 

quadrants, with 0–90° representing winds from the northeast (NE), 90–180° from the 

southeast (SE), 180–270° from the southwest (SW), and 270–360° from the northwest

(NW). The monthly frequency of wind directions in each quadrant was then calculated 

for each month over the entire 31-year period. Figure 5.1.10 highlights the climatology of 

the northeast wind direction (0–90°) in CBHAR. Northeast winds clearly prevail for most 

of the year within the study area, a result of the dominant influence of the Beaufort High. 

In the months of June and July, the frequency of the northeast wind direction decreases, 

primarily due to a weakening of the Beaufort High during summer and the formation of a 

summer low in the Arctic. The frequency of NE wind in ERA-I (Figure 5.1.11) is similar 

to that in CBHAR, including its seasonality. However, similar to what was seen in the 

wind speed climatology, ERA-I does not capture the topographic features nearly as well 

as CBHAR. The differences in the NE wind frequency over the Brooks Range and 

eastern Siberia are most likely the result of mesoscale circulations, such as 

mountain/valley breezes and the mountain barrier effect.

Figure 5.1.10. Monthly northeast wind direction (0–90°) frequency in CBHAR, 

expressed as a percentage (%)
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Figure 5.1.11. Monthly northeast wind direction (0–90°) frequency in ERA-Interim, 

expressed as a percentage (%)

Although the northeast wind dominates the study area, wind from other directions can 

also be influential. Thus, southwest and northwest winds in CBHAR, averaged over the 

cold season including the months of January–May and October–December, were also 

analyzed (Figure 5.1.12a,b). This analysis reveals the presence of a high percentage of 

southwest and northwest winds, particularly along the Brooks Range in Alaska and along 

the Chukotka Mountains in eastern Siberia. Some of the anomalous SW winds along the 

Brooks Range, particularly those around its eastern end near Canada, could be the result 

of cold-air damming against the mountain barrier. However, the SW winds near the 

ridgeline of the Brooks Range and along its western section could represent mountain 

downslope breezes that flow from the top of the range toward the Arctic Slope. In a 

similar way, the SW winds present on the northern slope of the Chukotka Mountains are 

also most likely due to the mountain breeze effect. The anomalous NW winds along the 

Chukotka Mountains are the result of the mountain barrier effect.
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Figure 5.1.12. Frequency of southwest and northwest winds in CBHAR (a and b) 

and ERA-Interim (c and d), averaged over the cold months of January–May and 

October–December and expressed as a percentage (%)

A high frequency of northwest winds also occurs in the region around the Mackenzie 

River, including the Mackenzie Delta, in the northern Yukon. Small et al. (2011) also 

observed anomalous NW winds in this region. They conclude that these could be the 

result of NW surface winds overlain with NW 925 hPa geostrophic winds, and observed 

a strong correlation of surface winds with those from 1000 to 850 hPa, as well as a 

tendency towards lower stability, suggesting the mixing of momentum throughout the 

lower atmospheric column (1000 to 850 hPa). They also link this feature to synoptic SLP 

patterns, such as an anomalously strong high-pressure ridge over eastern Siberia and the 

Bering Sea, contributing to a pressure rise over the Brooks Range and cold-air damming 

that results in an alteration of the pressure gradient along the coast. The ERA-I data 

shown in Figure 5.1.12c,d also captures the anomalous features seen in CBHAR. 

However, the frequency (percentage) of these features is lower by about 10%. In 

addition, the detailed mesoscale features seen over the mountains in CBHAR are not 

present in ERA-I. Over the Mackenzie River valley and delta, the anomalous winds are 

present, but lower in frequency (25–30% vs. 35–45%) and much less prominent.
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5.1.4 Variability and Changes in Surface Wind Speed and Direction

Based on the results of the above climatological analysis, the interannual variability and 

long-term changes in the CBHAR monthly surface wind speeds were further examined, 

with a simultaneous comparison made to ERA-Interim. Direct comparisons of monthly 

mean and 95th-percentile wind speeds between CBHAR and ERA-I are shown in Figures 

5.1.13 and 5.1.14, respectively. Both figures show that the trends in CBHAR and ERA-I 

are nearly the same. Again, ERA-I wind speeds, particularly the 95th-percentile winds,

are slightly higher than those in CBHAR, which is consistent with the findings in Section

5.1.2. The trends in CBHAR and ERA-I are nearly the same, and only the trend in 

October is significant at the 95% level using the t-test. Notable increasing trends of 0.27

m s-1 decade-1 and 0.26 m s-1 decade-1 exist for the October 95th-percentile winds in 

CBHAR and ERA-I, respectively.

Given their completely distinct geographic features, this interannual variability analysis 

was also performed over ocean and land areas separately, as shown for CBHAR in Figure 

5.1.15. Generally, ocean surface wind speeds are larger than those on land due to the 

lower surface friction typically present over water surfaces. According to the time series 

shown in Figure 5.1.15, both ocean and land surface wind speeds exhibit interannual 

variability throughout the year, with the amplitude varying from month to month. 

Superimposed on this interannual variability lie varying rates of upward and downward 

trends. However, only the trends in August and October over the ocean, and September

and November over land are significant at the 95% level using the t-test. A pronounced

increasing trend of 0.26 m s-1 decade-1 exists over the ocean in October. 

The interannual variability of extreme surface wind speeds was also examined separately 

over ocean and land areas for CBHAR as shown in Figure 5.1.16, exhibiting very similar 

features to those of the monthly mean winds, except for an obviously larger wind speed.

A stronger increasing trend of 0.48 m s-1 decade-1 occurs in the extreme surface wind 

speed over the ocean in October. For comparison, Figures 5.1.17 and 5.1.18 show the

wind speed trends over land and ocean for the ERA-I data. The trends are very similar to 

those in CBHAR, although the 95th-percentile wind speeds are higher, especially in 

November. Figure 5.1.19 displays the frequency of extreme winds, defined as the 

percentage of winds occurring above the 95th-percentile wind speeds for both CBHAR 

and ERA-I. Both datasets show very similar results. Clearly, the trends in Figure 5.1.19

are similar to the trends seen in the previous figures, with October showing the largest

overall increasing trend. This agrees with the findings of Stegall and Zhang (2012), 

except that they also found relatively strong positive trends in other months, such as 

September, November, and December, in the NARR reanalysis.
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Figure 5.1.13. Monthly mean surface wind speed (m s-1) and its linear trends for 

1979–2009 in CBHAR (red) and ERA-Interim (blue)
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Figure 5.1.14. Monthly mean 95th-percentile wind speed (m s-1) and its linear trends 

for 1979–2009 in CBHAR (red) and ERA-Interim (blue)
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Figure 5.1.15. Monthly mean surface wind speed (m s-1) and its linear trends for 

1979–2009 over ocean (blue) and land (red) in CBHAR
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Figure 5.1.16. Monthly mean 95th-percentile wind speed (m s-1) and its linear trends

for 1979–2009 over ocean (blue) and land (red) in CBHAR
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Figure 5.1.17. Monthly mean surface wind speed (m s-1) and its linear trends for 

1979–2009 over ocean (blue) and land (red) in ERA-Interim
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Figure 5.1.18. Monthly mean 95th-percentile wind speed (m s-1) and its linear trends

for 1979–2009 over ocean (blue) and land (red) in ERA-Interim
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Figure 5.1.19. Frequency of extreme winds, defined as the percentage (%) of winds 

above the 95th-percentile wind speed, for 1979–2009 in CBHAR (red) and ERAinterim (blue)
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Figure 5.1.20. Frequency of surface wind directions (degrees), grouped by quadrant,

for 1979–2009 in CBHAR

This analysis was extended to include wind direction by computing the frequency of 

winds originating from each quadrant and evaluating their interannual variability and 

long-term changes (Figure 5.1.20). The frequency of the wind from the northeast, 

northwest, and southwest directions exhibits year-to-year fluctuations, with just a slight 

decreasing trend. However, the frequency of the wind from the southeast has experienced 

a marked increase over time. Determining the reason for this increase requires further 

investigation into the impact of weather systems on wind direction in the region.

To further explore the geographic variability of the region, the ocean surface wind data 

was split into two distinct regions, the Chukchi Sea and the Beaufort Sea, separated by 

the 203°E meridian that passes through Point Barrow, Alaska (Figure 5.1.21). In this way 

the trends specific to each region can be explored separately. Figures 5.1.22 and 5.1.23

show the trends of wind speed and 95th-percentile wind speed, respectively, for each of 

the two zones. It is immediately apparent that the Chukchi Sea wind speeds are, on 

average, higher than the Beaufort Sea wind speeds for each month. Reasons for this 

difference could be the presence of greater storm activity over the Chukchi and/or 

stronger storms that first pass over the Chukchi Sea before weakening as they move over 
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the Beaufort. Consistent with the trend analysis discussed above, October shows the 

largest increase in wind speeds, with the two regions displaying similar increasing trends 

for this month. As with the mean wind speeds, the Chukchi Sea 95th-percentile wind 

speeds are consistently higher than those over the Beaufort, and October again shows the 

largest increasing trend. However, in this case, the Beaufort Sea winds are increasing at a 

faster rate than the Chukchi winds for October. Another difference between the two 

regions is that the Chukchi winds are increasing more in February than those over the 

Beaufort for both mean and 95th-percentile wind speeds. This increase in the Chukchi Sea 

wind speeds is also present in March and could be a result of either increased storm 

frequency or intensity, and may be a worthwhile topic for future exploration.

Figure 5.1.21. Illustration of the Chukchi and Beaufort Sea regions as defined here 

for climatological analysis of winds. The two regions are separated by the 203°E 

meridian extending north from Point Barrow, Alaska.
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Figure 5.1.22. Monthly mean surface wind speed (m s

-1

) and its linear trends for 

1979–2009 over the Chukchi Sea (blue) and Beaufort Sea (red) in CBHAR. All data 

are for ocean points only.
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Figure 5.1.23. Monthly 95th-percentile wind speed (m s

-1

) and its linear trends for 

1979–2009 over the Chukchi Sea (blue) and Beaufort Sea (red) in CBHAR. All data 

are for ocean points only.
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5.1.5 Spatial Structure of Changes in Surface Wind

While the domain-averaged surface wind speed demonstrates a particular long-term 

trend, such changes may vary dramatically at different locations in the study area. To 

investigate the geographic distribution of surface wind changes, their linear trends were 

computed at each grid point in CBHAR (Figure 5.1.24). The results indicate that the 

largest decrease in monthly mean wind speed has occurred over the northern Chukchi–

Beaufort Sea in January, with the minimum centered near the boundary of the two seas,

well north of the Bering Strait. There is at the same time, however, a noticeable increase 

in wind speeds near the boundary of the Chukchi and Bering Seas in January. A notable 

decrease also appears over ocean areas in May and August, located primarily over the 

Beaufort Sea and the Canada Basin. By contrast, the largest increase in monthly surface 

wind speed has occurred over the entirety of the Chukchi and Beaufort Seas in October. 

In Stegall and Zhang (2012), a similar finding was reported, and they showed a high 

negative correlation between sea ice retreat and higher wind speeds, i.e., less sea ice is 

correlated with stronger winds.

Figure 5.1.24. Linear trends in the monthly mean surface wind speed (m s-1 yr-1) at 

each grid point in CBHAR. Positive values indicate an increase and negative values 

a decrease over the 31-year period.
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A similar analysis for the 95th-percentile wind speeds (Figure 5.1.25) shows some 

differences from the monthly-mean analysis, even though the domain-averaged features 

are shown to be quite similar. Unlike the monthly means, increasing trends dominate 

throughout the year and the decreasing trend in January is confined to a much smaller 

area. Increasing trends occur over nearly the entire ocean surface in February, March, 

May, July, and October. As with the monthly-mean analysis, the largest trend emerges in 

October, with a maximum increase over the Beaufort Sea.

Figure 5.1.25. Linear trends in the monthly 95th-percentile surface wind speed 

(m s

-1 yr-1

) at each grid point in CBHAR. Positive values indicate an increase over 

the 1979–2009 period, and negative values a decrease.

5.1.6 Trends in Sea Ice, 2-m Air Temperature, and SST

The wind field climatology (Section 5.1.2) has shown that the highest wind speeds in the 

study region occur during the autumn, particularly in October. This generally coincides 

with the time of year of the sea ice minimum and, consequently, the maximum amount of 

open ocean. Stegall and Zhang (2012) found that there is a high correlation between sea 

ice extent and wind speed. A detailed description of the sea ice concentration/extent 
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present in CBHAR is therefore important. It is also important to investigate the sea ice 

trends given that the Chukchi/Beaufort Seas region is an area that has seen dramatic sea 

ice loss, particularly over the last decade or so (Jahn et al. 2012).

In this section, the trends in sea ice, 2-m air temperature, and SST are analyzed for the 

months of July through October, when sea ice extent exhibits large fluctuations (Figure 

5.1.26). The decreasing trends in ice extent shown for the study region from July through 

October are significant at the 95% confidence level using the t-test. The largest 

decreasing trend (~0.3×105 km2yr

-1

) occurs in September, the month associated with the 

annual sea ice minimum. A record low extent over the entire Arctic occurred in 

September 2007; however, for the study region, the minimum extent occurred in 

September 2008. August and October also exhibit dramatic decreases in sea ice extent 

(~0.2×105 km2yr

-1

). Accompanying the strong decreasing trends in sea ice during 

September and October, both 2-m air temperature and SST show significant increasing 

trends.

Figure 5.1.26. Monthly means and linear trends of sea ice extent (SIE, ×105 km2) in 

red, 2-m air temperature (ocean only, °C) in blue, and SST (°C) in black for 1979–

2009 in CBHAR
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Figure 5.1.27. Linear trend of sea ice concentration (% yr-1) for all months from 

1979–2009 in CBHAR. Shaded colors are significant at the 95% confidence level.

Positive values indicate an increase over the 1979–2009 period, and negative values 

a decrease.

Figure 5.1.27 shows the linear trends in sea ice concentration (% yr-1) for all months;

shaded colors are significant at the 95% level. The figure clearly shows that a substantial 

portion of the domain has experienced significant sea ice loss for July through October. 

For September and October most of the Chukchi/Beaufort Seas region displays a 

decrease in sea ice concentration. Overall, for the autumn as a whole, the study domain 

exhibits a negative regression in sea ice concentration, which is consistent with largerscale trends as well as observations.

As with the wind speed analysis, the trends in sea ice extent are divided into Chukchi Sea 

and Beaufort Sea regions to investigate if differences exist in the trends between the two 

seas. Figure 5.1.28 shows these trends for July through October. Clearly, the Chukchi Sea 

has a larger decreasing trend than does the Beaufort for each of the four months, 

particularly in September and October. In September of 2007 and 2008 CBHAR shows 

nearly 0 km2 in ice extent in the Chukchi, indicative of the record sea ice loss for those 

years. Lindsay and Zhang (2005) reported that this region is experiencing some of the 
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greatest sea ice thinning of anywhere in the world, along with rising 2-m air temperatures

in the autumn. This supports the dramatic sea ice loss shown in the figures here.

Figure 5.1.28. Monthly sea ice extent (SIE, ×105 km2) and its linear trends for 1979–

2009 in the Chukchi Sea (blue) and Beaufort Sea (red) in CBHAR

The area-averaged 2-m air temperatures in CBHAR over ocean and land demonstrate 

various interannual variability and long-term trends during 1979–2009 (Figure 5.1.29). 

Clearly, September and October show large increases in temperature, with the greatest 

increase over the ocean in October. This agrees with the observed temperature increase as 

well as other studies showing similar results. Figure 5.1.30 displays the 2-m air 

temperature linear trend (°C yr-1) at each grid point in CBHAR for all months of the year. 

Colors shown are significant at the 95% confidence level. The most obvious feature is the 

large area of increasing temperatures in the Chukchi–Beaufort Seas from September 

through December. The summer months (May–August) show little trend over most of the 

region. Figure 5.1.31 shows the area-averaged 2-m air temperature trends over the 

Chukchi and Beaufort Sea regions separately, for September through December. Overall, 

the Chukchi Sea shows a larger increase in temperature than does the Beaufort. Each of 

the Figures 5.1.29–5.1.31 indicates the presence of much warmer air in the latter part of 

the 31-year study period. This warmer air is one potential cause for the substantial sea ice 
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loss in the region. The larger increase in temperature in the Chukchi Sea should and does 

indicate a larger decrease in sea ice extent in that region relative to the Beaufort Sea.

Figure 5.1.29. Monthly 2-m air temperature (°C) and its trends for 1979–2009 over

ocean (blue) and land (red) in CBHAR
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Figure 5.1.30. Linear trend of 2-m air temperature (°C yr-1) for all months from 

1979–2009 in CBHAR. Shaded colors are significant at the 95% confidence level.

Positive values indicate an increase over the 1979–2009 period, and negative values 

a decrease.
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Figure 5.1.31. Monthly mean 2-m air temperature (°C) and its trends from 1979–

2009 over the Chukchi Sea (blue) and Beaufort Sea (red) in CBHAR

Another important variable to analyze is the SST in areas of open, ice-free ocean. 

Warmer SST may contribute to sea ice loss through redistribution and mixing by ocean 

dynamic processes. Figure 5.1.26 above shows the long-term SST trend for the months of 

July through October. The largest increasing trend occurs in September, while the other 

three months show weaker, but still positive, trends as well. Figure 5.1.32 shows the SST 

linear trends at each of the ocean grid points in CBHAR. Clearly, SSTs have increased 

over most the study region, with the broadest area of increase occurring in September. 

Although there are large areas of increasing SSTs in July, August, and October, the areas

of warming are not as large as the maximum area present in September. One very 

noticeable feature of the trends is the decreasing SST in the southeastern Beaufort Sea 

and off the coast of Canada. This could be related to the outflow of fresh water from the 

Mackenzie River/Delta and may be worth further research. Figure 5.1.33 displays the 

separate SST trends for the Chukchi and Beaufort Sea partitions, as was done above for 

the wind speed and air temperature analyses. Clearly, the Chukchi Sea experiences a 

much larger increasing trend for all four months, with the largest in September. The 

Beaufort Sea, on the other hand, only shows an increasing trend in September, with no 

significant trend in the other months, except for a slight decrease in July.
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Figure 5.1.32. Linear SST trends (°C yr-1) for 1979–2009 in CBHAR. Shaded colors

are significant at the 95% confidence level. Positive values indicate an increase and 

negative values a decrease over the entire period.
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Figure 5.1.33. Monthly mean SST (°C) and its trends for 1979–2009 in the Chukchi 

Sea (blue) and Beaufort Sea (red) in CBHAR

5.1.7 Possible Relationships between Wind Speed, Sea Ice, SST, and Boundary Layer 

Stability

In order to explore the relationships between surface wind speed, sea ice, SST, and 

atmospheric boundary layer stability, scatterplots of binned dependent variables as a 

function of binned independent variables, for example, binned wind speed anomalies 

dependent on binned sea ice anomalies, were produced. In this way, an exceptionally 

large amount of data is represented with only a handful of meaningful data points. The 

anomalies are calculated by removing the long-term mean, thereby detrending the data. 

The long-term mean is removed at all times and at all grid points. In the following 

analysis, only the months of September and October are used since not only is this the 

time of year with the highest wind speeds, but it is also when the sea ice minimum 

occurs, thus resulting in the presence of the greatest amount of open ocean.
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Figure 5.1.34. Binned wind speed anomalies (m s-1) as a function of binned sea ice 

concentration anomalies in CBHAR. Triangles indicate +/− one standard deviation.

Figure 5.1.34 shows a clear relationship between wind speed and sea ice concentration. 

The inverse, quasi-linear relationship signifies that smaller sea ice extent is highly 

correlated with higher wind speed. The correlation coefficient is −0.98 and is significant 

at the 99% confidence level. Similarly, Stegall and Zhang (2012) also found a strong 

negative correlation between sea ice extent and wind speed. In other words, more open 

water is linked to higher wind speeds. This more than likely is the result of lower surface 

drag from the smoother ocean surface versus rougher sea ice. However, for the open 

ocean it is worth investigating the possible relationship between SST and wind speed.

The wind speed is displayed as a function of SST in Figure 5.1.35. Between open ocean 

(0% sea ice concentration) and about 80% concentration, an inverse linear relationship

exists between wind speed and SST. The correlations for each sea ice concentration range 

(0%, 0–20%, 20–40%, 40–60%, and 60–80%) are −0.80, −0.86, −0.87, −0.96, and −0.86, 

respectively, and are significant at the 99% confidence level. The relationship for the 80–

100% range is not significant.
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Figure 5.1.35. Binned wind speed anomalies (m s-1) as a function of binned SST 

anomalies (°C) in CBHAR. Each plot represents a different range of sea ice

concentrations as noted in the figures, i.e., 0%, 0–20%, etc. Triangles indicate +/−

one standard deviation.
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Figure 5.1.36. Same as Figure 5.1.35, except for binned wind speed anomalies (m s-1) 

as a function of static stability anomalies (s-2). Each plot represents a different range 

of sea ice concentrations as noted in the figures, i.e., 0%, 0–20%, etc. Triangles 

indicate +/− one standard deviation.
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Following this, the relationship between surface wind speed and atmospheric boundary 

layer stability was analyzed. The near-surface stability (buoyancy) is calculated by:

N2 ≡ g

θ

dθ

dz

= g

{θ}

θ2m −θsst

dz

#

$

% &

'

( (5.3)

where N is the Brunt–Väisälä frequency, θsst is the ocean-surface potential temperature,

θ2m is the atmospheric potential temperature at 2 m, g is gravitational acceleration, {θ} is 

the mean layer potential temperature, and dz is 2 m. 

The wind speed is depicted as a function of static stability in Figure 5.1.36, shown with 

the same sea ice concentration ranges as in Figure 5.1.35. A strong inverse linear 

relationship exists here as well, particularly with increasing sea ice concentration. The 

correlations between wind speed and stability for each range as given in Figure 5.1.36 are 

−0.96, −0.99, −0.98, −0.91, −0.76, and −0.96, and all are significant at the 99% 

confidence level. The relationship between stability and wind speed displayed in Figure 

5.1.36 is not surprising. It is expected that higher wind speed can lead to lower stability, 

and vice versa. The large anomalies in Figure 5.1.36 could result from the opening of 

leads in the sea ice and the subsequent release of latent heat to the atmosphere from the 

ocean water below. Changes in stability and subsequent changes in wind speeds could 

also result from synoptic-scale weather systems, such as low- and high-pressure systems, 

which would generate warm and cold air advection, thus changing the atmospheric 

stability and consequently the wind speeds as well.

5.1.8 Summary

The surface climate analysis using CBHAR shows that the wind speeds (monthly mean 

and monthly 95th percentile) exhibit a clear seasonality, with the lowest wind speeds in 

the spring months and the highest wind speeds in the autumn. The wind direction 

climatology shows that winds from the NE quadrant are the most frequent in the region, 

except during the summer months when the Beaufort High tends to weaken. The 

direction climatology also captures mesoscale features, including anomalous SW winds 

over the Brooks Range, anomalous NW winds over the Chukotka Mountains in eastern 

Siberia, and anomalous NW winds over the Mackenzie River valley in Canada, 

particularly during the cold season months. These anomalous winds could be the result of 

cold-air damming, resulting from a mesoscale force imbalance between the pressure 

gradient and Coriolis forces, as well as the downslope mountain breeze. The linear trend 

analysis of the wind speeds shows that the largest increasing trend occurs in October. 

These results are consistent between CBHAR and ERA-Interim, even though CBHAR 

employed different assimilation technology and assimilated different data. This suggests

that CBHAR accurately captures changes in the large-scale surface climate. Sudden 

changes in trends due to a change in technology (e.g., the introduction of QuikSCAT or 

MODIS observations), such as discontinuities in the trend data or sudden increases or 

decreases in the trends are not present. Both the climatology and trends of the winds in 
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CBHAR agree well with ERA-I data, indicating that the assimilation of different types of 

data as technology changed over time was handled correctly in CBHAR. 

The analysis of the relationships between sea ice and wind speed, though preliminary,

yields useful information. The observed negative linear relationship supports the findings

of Stegall and Zhang (2012), who similarly found a highly negative correlation between 

sea ice extent and wind speed. The relationship between wind speed and SST also shows 

a negative linear relationship. This phenomenon could be related to cold air advection,

given that the primary wind direction is from the NE. There is also a strong linear 

relationship between near-surface stability and wind speed. This is not surprising, since 

higher wind speeds can cause the stability to decrease, and with decreasing stability the 

wind speeds can more easily increase. However, regarding the relationship between wind 

speed and SST, other variables such as the presence of low cloud cover need to be 

researched as well. The results of Section 5.1.7 are very preliminary; additional research 

will be conducted on these topics.

5.2 Mesoscale Wind Fields in CBHAR

5.2.1 Introduction

Both prevailing synoptic weather patterns and prominent underlying geographic features 

play roles in determining the surface wind climate (e.g., Schwerdtfeger 1974; Kozo 1980; 

Moore and Pickart 2012). In the Chukchi–Beaufort Seas region, the surface winds vary in 

response to the intensity and location of the Beaufort High and Aleutian Low, which are 

the dominant synoptic-scale weather patterns in the area, as discussed in Section 2.1

(Shulski and Wendler 2007; Overland 2009; Moore 2012). The surface winds are further 

modulated by local geographic features through both thermodynamic and dynamic 

processes (Kozo 1979, 1980; Liu et al. 2008). The geography in the area is characterized 

by seasonally ice-covered ocean, along with the Brooks Range in northern Alaska and the 

Chukotka Mountains in eastern Siberia. The land and ocean respond differently to 

incoming solar radiation, generating horizontal temperature gradients, which in turn 

cause air to flow from cool to warm areas along the surface, with a reversed 

compensating flow at upper levels (Figure 5.2.1a) (Kozo 1979). Land/sea breezes thus 

become an integral component of the surface wind regime in coastal areas during warm 

seasons. The same mechanism may also exist in mountainous areas, where mountain 

slopes are more readily heated than is the horizontally adjacent air, causing upslope 

winds during the day (Figure 5.2.1b). In cold seasons, the air near the terrain surface is 

cooled faster than the adjacent air through the loss of outgoing longwave radiation, 

resulting in denser surface air over the terrain and a resultant downslope wind (also 

referred to as gravity drainage). Topography can also impact the surface wind through 

other mechanisms. For example, when an air mass characterized by a low Froude number 

(U/NH, where U is the speed of the basic current, N is the Brunt–Väisälä frequency, and 

H is the mountain height) flows toward a mountain barrier, its approach will be blocked 

due to having insufficient kinetic energy to flow over the mountain. The accumulated air

mass alters the distribution of pressure so that the flow is deflected to the left and finally 
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turns, becoming parallel to the mountain range. This effect is known as cold-air damming

(Schwerdtfeger 1974; Kozo 1980).

 

Figure 5.2.1. Schematic diagrams for (a) sea breeze and (b) upslope wind

Figure 5.2.2. Vertical cross-sections used in different areas to show the vertical 

structure of land/sea breezes, up/downslope winds, and cold-air damming. The four 

locations A, B, C, and D, shown in orange, were chosen for use in displaying the 

land/sea breeze component at different locations in different seasons. Gray shading 

over the ocean represents the average sea ice concentration over the entire CBHAR 

period (lighter colors signify higher SIC), with the 0.8 value highlighted by the 

dashed line.

The CBHAR reanalysis has high spatial resolution, with a grid spacing of 10 km, and a 

temporal resolution of just one hour, making it able to resolve finer-scale mesoscale 

processes than is possible in coarser reanalyses, such as ERA-Interim. These processes 
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are usually generated by small-scale thermodynamic and dynamic effects, and include sea 

breezes, up/downslope winds, and cold-air damming, among others. To facilitate the 

following analysis and discussion, the model domain is again shown in Figure 5.2.2, with 

superimposed sea ice and geographic features, along with the vertical cross-sections used 

in the subsequent analysis. In order to reduce biases potentially caused by the use of a 

single vertical section, a series of parallel vertical cross-sections were chosen in the areas 

of interest and the wind fields averaged across these multiple vertical sections. In 

addition, the locations A, B, C, and D as shown in Figure 5.2.2, were selected in order to 

analyze the seasonality of both sea breezes and up/downslope winds as impacted by the 

nearby locations of the shoreline and mountainous terrain.

5.2.2 Sea Breeze and Up/Downslope Wind

Sea breezes and up/downslope winds are both generated by horizontal thermal contrasts 

at the surface, caused by the quite different thermodynamic characteristics of different 

geographic surfaces. For example, water has a much larger heat capacity than soil. Land 

is thus heated faster during the day than adjacent ocean or lake areas in the summer. For 

the same reason, radiative cooling also cools the land faster at night. As a result, 

horizontal temperature contrasts may exist both during the day and at night, driving the 

motion of air currents in coastal areas (Figure 5.2.1a) (Kozo 1979). A similar process 

occurs in the vicinity of mountain valleys, where the air over the mountain and valley 

surfaces experiences differential heating. Air over the warmer surface experiences 

increased heating and tends to ascend to upper levels. The pressure near the surface is 

thus decreased and correspondingly increased at higher levels, resulting in a horizontal 

pressure gradient both near the surface and at upper levels. This then causes cool air to 

flow towards the warmer surface at lower levels and warmer air to flow in the opposite 

direction at higher levels (Figure 5.2.1b), resulting in an up/downslope circulation

pattern.

The diurnal cycle of solar radiative heating produces time-varying horizontal temperature 

contrasts throughout the day. As a consequence, the associated thermodynamically driven 

flows tend to also have a noticeable diurnal variation. Based on this fact, climatological 

sea breezes and up/downslope winds can be identified through the following approach. 

The surface wind field is first averaged over the 31-year record at each hour of the day,

separated by month, in order to obtain the monthly climatological diurnal surface wind. 

The sea breezes and up/downslope winds in a certain month are then identified by 

subtracting out the climatological wind at a given reference time when the local 

thermodynamic effects are minimal. In this study, 0600 AKST was chosen as the 

reference time, based on an analysis of the variation in the climatological diurnal surface 

wind. The results indicate that in July, the diurnal anomalies of surface winds show 

significant diurnal variation in both mountainous and coastal areas (Figure 5.2.3). Along 

the Brooks Range, upslope winds develop when the underlying surface is heated. The 

maximum upslope wind occurs in the late afternoon around 1700 AKST, with maximum 

wind speeds of up to 2–3 m s-1 in the eastern Brooks Range. Along the shoreline, the 

surface wind field is characterized by the sea breeze, with weak onshore flow in the early 

morning and stronger onshore flow in the afternoon. The maximum sea breeze near the 
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shoreline also occurs in the late afternoon at around 1700 AKST, with a maximum wind 

speed of 2 to 3 m s

-1

, though this varies in different areas. Along the Chukchi Sea 

coastline, the sea breeze can reach 3 m s-1 in July, whereas the eastern Beaufort coast 

experiences a relatively weak sea breeze of about 2 m s-1.

Figure 5.2.3. Climatological diurnal anomalies of surface winds (m s

-1

) relative to 

winds at 0600 AKST, calculated at different hours (AKST) in July in CBHAR
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To further examine the geographic dependence of the sea breeze and up/downslope wind, 

four particular locations were chosen for additional analysis as shown in Figure 5.2.2. 

Point A represents a location that is relatively far away (about 60 km) from the shoreline, 

where weak diurnal wind variation is expected and where the sole impact is from the sea 

breeze. Point B is located directly on the shoreline, experiencing the most pronounced

diurnal variation in horizontal temperature contrast and, accordingly, a vigorous sea 

breeze. The surface wind at the inland Point C tends to show weak impacts from both the 

sea breeze and up/downslope winds, due to its location far away (more than 100 km) 

from both the shoreline and the mountain slope. The inland Point D, meanwhile, is 

located on the north slope of the eastern Brooks Range, where the terrain is characterized 

by a steep slope. The horizontal distance between Point D and the shoreline is about 60 

km. By comparing the perpendicular component of the surface winds relative to the 

shoreline at locations A, B, C, and D, the relative impacts of the mountain and sea 

breezes can thus be isolated.

The mountains’ topographic effects exhibit quite different impacts on the surface winds

from month to month (Figure 5.2.4). At Point D, strong downslope winds occur from 

October through March (Figure 5.2.4d) due to the strong radiative cooling effect of the 

mountain slopes in the cold season, resulting in dense cold-air formation near the terrain 

surface. While the dense cold-air flow over the Antarctic ice sheet has been extensively 

studied (e.g., Parish and Cassano 2003), there have been few such studies on the Arctic 

Slope. The strongest downslope wind occurs during the coldest part of the year, from

November to February, with a downslope wind component of 2–3 m s-1. During this 

season, the diurnal variation of surface wind is too weak to identify due to a relative lack 

of solar irradiance. In October and March, the downslope flow is also present throughout 

the day, though with a weak diurnal variation due to surface radiative heating. In March, 

the downslope wind decreases from a maximum of 1.8 m s-1 in the early morning to 

about 1 m s-1 at around 1400 AKST. From April through September, the mountain 

surface receives sufficient heat during the day from solar radiation to induce an upslope 

wind. The diurnal variation of the wind speed is characterized by a decrease in 

downslope wind speed in the morning, followed by an increase in upslope wind in the 

afternoon. From June to August, both the daily downslope and upslope winds at Point D 

last roughly the same amount of time, with a maximum upslope component of about 1 m 

s

-1 and a maximum downslope component of around 2 m s-1

. 

As seen at Points A and B, the sea breeze induced by the land-sea thermal contrast does 

not become identifiable until June (Figure 5.2.4a,b). At the shoreline Point B, the 

maximum sea breeze occurs in June, with a maximum onshore wind of approximately 1

m s-1 in the late afternoon. The reversal from sea breeze to land breeze occurs at about 

2100 AKST. The land breeze reaches its maximum after several hours and lasts for 

several hours more, before the wind switches back to onshore flow in the morning at 

around 0900 AKST. The climatological surface wind at the offshore Point A is directed 

offshore throughout the entire year, though like at Point B it displays a diurnal variation 

from June through September. At the inland Point C, the diurnal variation of the 

on/offshore wind component is also noticeable, which is likely primarily caused by the 
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thermodynamic effects of the mountains, as the point is located more than 100 km away 

from the shoreline.

Figure 5.2.4. Climatological diurnal variation of the on/offshore surface wind

component (m s

-1

) for different months at Points A, B, C, and D in CBHAR, as 

shown in Figure 5.2.2. Positive values represent offshore-directed wind, and 

negative values onshore wind. Times are given in AKST.

A comparison of the climatological diurnal variation of the surface wind was also 

conducted between CBHAR and the in situ observations at Barrow, where hourly

observations are available over the entire CBHAR time period (Figure 5.2.5). The 

impacts of the sea breeze on the surface wind can easily be identified, reflected by the 

obvious diurnal cycle of wind speed and direction. Due to the existence of the Beaufort 

High, the surface wind direction is predominantly from the east-northeast (Figure 5.2.5b). 

The wind direction turns toward the north during the day, when the surface is heated by 

solar radiation, representing a reduced offshore wind component. The comparison also 

indicates a consistency between CBHAR and the station observations.
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Figure 5.2.5. Climatological diurnal variation of surface wind speed (a, m s

-1

) and 

direction (b, °) in January (dashed) and July (solid) at Barrow from the surface 

station observations (blue) and CBHAR data (red). Times are given in AKST.

The vertical structure of the sea breeze and up/downslope winds can be revealed by 

examining the vertical motion and on/offshore wind component along selected crosssections. In order to reduce biases potentially caused by the use of a single vertical 

section, a series of parallel vertical cross-sections were instead identified in the interested 

areas (Figure 5.2.2) and averages of the wind fields across these vertical sections were 

calculated. In total, seven areas were selected for the examination of vertical wind 

structures, including inland areas of the Chukotka Mountains (M1) and the Brooks Range 

(M2 and M3), as well as four coastal areas (C1–C4). Along the vertical sections, the 

horizontal winds were transformed in order to define components perpendicular and 

parallel to the vertical section, i.e., wind components were oriented relative to the 

shoreline or the orientation of the mountain ranges. The climatology of the wind along 

the vertical sections was constructed at each hour of the day for different months.

Given that thermodynamically forced sea and land breezes become most active in June, 

July, and August, the climatological diurnal variation of wind in July was examined, as 

displayed along the vertical section C2 in Figure 5.2.6. In order to better illustrate the 

diurnal variation, the daily evolution of the sea breeze is also depicted in Figure 5.2.7. 

The sea breeze was isolated by subtracting the wind field at 0600 AKST from the wind 

field at each hour of the day. An important thermodynamic feature in the study domain is 

that the surface air temperature over land is consistently warmer than that over the ocean 

in July due to the long daylight hours. In July, the surface air temperature begins to 

increase at about 0500 AKST, when a weak offshore wind with speed less than 0.5 m s-1

and weak downward motion near the shoreline dominates near the surface. As the surface 

warms, upward motion is gradually developed inland about 20 km from the shoreline at 

0900 AKST. At noon, the sea breeze circulation is initially formed, with very weak 

onshore wind across an expanse of just 10 km, crossing the shoreline from the shelf seas 

and directed inland. At the same time, the upper-air motion is oriented toward the 

offshore direction near the shoreline at about 200–300 meters above ground, and

downward motion develops offshore within 50 km of the shoreline. The sea breeze
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Figure 5.2.6. Diurnal variation of climatological wind in July along the vertical 

cross-section C2. Wind vectors include both the horizontal wind component,

oriented perpendicular to the shoreline, and the vertical component. Wind speed 

(m s

-1

) is indicated by colors. Solid contours indicate potential temperature (°C).

The shoreline is located at 0 km, and the orientation of the cross-section is indicated 

by the “S” (south) and “N” (north) labels.
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Figure 5.2.7. Same as Figure 5.2.6, but for the climatological anomalies relative to 

the wind at 0600 AKST, demonstrating the diurnal evolution of the sea breeze

circulation is strengthened in subsequent hours due to the increase in the surface 

temperature contrast, and the horizontal extent of the onshore winds gradually increases 

to 20 km. The strongest sea breeze occurs in the late afternoon at about 1700 AKST, with 

a maximum onshore wind of roughly 1.2 m s-1, located around the shoreline near the 

surface, and a maximum return flow of about 2 m s-1, located 700 meters above ground at 

the shoreline. The sea breeze circulation gradually weakens over the next 12 hours, with 

upward motion disappearing at around 2200 AKST, and offshore winds near the surface 

reestablished at about 0400 AKST. 

Considering the seasonal and geographical dependence of the thermodynamically forced 

sea breeze and up/downslope winds, additional comparisons of the climatological wind 

profiles in January (Figure 5.2.8) and wind profile anomalies in July (Figure 5.2.9) were 
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conducted along different vertical sections (locations shown in Figure 5.2.2). The January 

wind profiles were compared due to presence of little diurnal variation at that time (see 

Figures 5.2.4 and 5.2.5), while the wind profile anomalies calculated between 1800 and 

0600 AKST in July represent the strongest diurnal variations along the various vertical 

cross-sections.

In January, the surface winds are dominated by downslope winds along the north slope of

the eastern Brooks Range (Figure 5.2.8c,g), where terrain height changes dramatically 

and the surface air is significantly cooled. Downslope winds are also a major contributor 

to the surface winds on the south slope of the western Brooks Range (Figure 5.2.8b). In 

addition, strong downward winds flowing from the top of the mountains occur in the 

western coastal area of Alaska (Figure 5.2.8d). This flow pattern is a combination of the 

mountain-slope-induced downward density flow and the background flow associated 

with the Beaufort High. This analysis also indicates that the surface wind along the north 

slope of the western Brooks Range (M2) is dominated by the mountain-parallel 

component (not shown). Weak downslope winds appear along the vertical sections C2 

and C3 (Figure 5.2.8e,f), most likely due to more gradual mountain slopes and a weak 

background flow. When examining the vertical wind field in the area of the Chukotka

Mountains (Figure 5.2.8a), one finds that the strong background winds flow toward the 

mountains, against the direction of any existing downslope winds. The mountains exert a 

strong barrier effect upon the approaching winds, which may lead to the formation of

cold-air damming, as discussed in the following section.

In July (Figure 5.2.9), both sea breeze and upslope winds are expected to contribute to

surface winds as mentioned above. The climatological sea and mountain breezes are 

usually well developed by 1800 AKST and relatively weak at 0600 AKST (see Figures 

5.2.3–5.2.7), thus the climatological wind profile differences between 1800 and 0600 

AKST are able to represent the maximum strength of the diurnal sea and mountain 

breezes. The sea breeze along the vertical section C1, in the presence of a relatively flat 

mountain slope and where mountain thermodynamic effects are very weak, extend out to

about 80 km offshore and 50 km onshore, as defined by wind speed anomalies larger than 

0.8 m s-1 (Figure 5.2.9d). On the other hand, along the vertical section C2, in the absence 

of any mountain slope, the sea breeze has a lesser extent offshore (60 km), but a greater 

extent offshore (>100 km), using the same criterion for the anomaly (Figure 5.2.9e). 

When examining other vertical sections where the mountain slopes are relatively steep 

and the mountain impacts are correspondingly greater (sections M1, C3, and C4), it is 

apparent that upslope winds become stronger due to reinforcement of the sea breeze and 

mountain thermodynamic impacts, producing a significant impact on the surface wind 

field. The sea breeze and upslope winds exhibit different spatial scales, extending from 

about 40–60 km offshore all the way to the top of the mountain onshore, again in terms of 

anomalies greater than 0.8 m s-1 (Figure 5.2.9a,f,g). Along the mountain cross-sections 

M2 and M3, where the distance to the shoreline is too far to receive any sea breeze 

impacts, the upslope winds are most likely the product of mountain thermodynamic 

effects, with relatively stronger upslope winds occurring along the steep eastern Brooks 

Range (section M3) and weaker upslope winds along the more gently sloping western 
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Figure 5.2.8. Climatological wind in 

January along the vertical crosssections shown in Figure 5.2.2. Arrows 

represent the wind vectors, composed of 

a horizontal component oriented 

perpendicular to the shoreline and the 

vertical component. Wind speeds (m s-1) 

are indicated by colors. Solid contours

indicate potential temperature (°C). The 

orientations of the cross-sections are 

indicated by the directional labels (e.g. 

S, SW, NE, N).
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Figure 5.2.9. Climatological wind 

anomalies in July at 1800 AKST, relative to the wind at 0600 AKST along the 

vertical cross-sections shown in Figure 

5.2.2, indicating the strength of the 

mountain and sea breezes. Arrows 

represent wind vectors, composed of a 

horizontal component oriented perpendicular to the shoreline and the vertical

component. Wind speeds (m s-1) are 

indicated by colors. Solid lines indicate

potential temperature (°C). Orientations 

of the cross-sections are indicated by the 

directional labels (e.g. S, SW, NE, N).
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Brooks Range (section M2). Vertically, in the absence of mountain impacts (sections C1 

and C2), a weaker and shallower return flow caused by the sea breeze appears as high as 

1 km above the surface. However, when mountain impacts from relatively steep slopes 

are present (sections M1, M3, C3, and C4), a stronger and deeper upper-air reversal flow 

appears around 1–1.5 km above the surface.

The spatial extent of sea breeze circulations can vary depending on season and latitude

due to differences in solar radiation, geographic location, and the background wind field.

To examine the distance out to which sea breezes may affect offshore locations in the 

study region, a simple method was employed. The distance was measured through the use 

of a criterion specifying that the climatological diurnal wind variation of the cross-shore 

wind component be greater than 0.8 m s

-1

; exceeding this threshold was defined to 

indicate the presence of a sea breeze. The results show that the largest distance that the 

sea breeze extends offshore appears over the Chukchi Sea west of Alaska and north of the 

Chukotka Mountains (Figure 5.2.10). Application of this method also reveals the seasonal 

dependence of the sea breeze, which emerges in June with a spatial extent out to 30–80

km offshore. The affected distance decreases to about 35–50 km in August, and further to 

about 20 km in September.

Figure 5.2.10. Maximum offshore distance (km) out to which the climatological

diurnal wind variation of the cross-shore component is greater than 0.8 m s-1 for 

various cross-sections, indicating the spatial range that the sea breeze impacts

different areas along the Chukchi and Beaufort coasts

5.2.3 Cold-Air Damming

Cold-air damming (Schwerdtfeger 1974; Kozo 1980) is another mesoscale process 

through which topography can affect the surface wind field. When air flow approaches a 

mountain, the air is forced to either ascend over the mountain or be blocked, depending 

on the properties of both the air layer and the topography as measured by a series of 

parameters, including the horizontal scale and slope of the mountain, the direction and 

speed of the large-scale flow, and the stability of the atmosphere, among others. When 
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the Froude number is low (U/NH<1; where U is the speed of the general flow, N is the 

Brunt–Väisälä frequency, and H is the mountain height), the kinetic energy of the lowlevel flow is insufficient for the air to pass completely over the mountain. The flow is 

thus blocked and decelerates as it approaches the barrier. Decelerated flow results in a 

decreased Coriolis force, breaking the geostrophic balance of the large-scale wind. The 

flow is thus deflected to the left and a mass of air accumulates near the base of the 

mountain, which in so doing generates a mesoscale pressure ridge. Eventually, a new 

balance between the large-scale pressure gradient, mesoscale pressure gradient, Coriolis 

force, and friction is reached. Accordingly, a low-level wind maximum is formed parallel 

to the mountain barrier. In the study domain, the occurrence of low Froude number flow 

is common, due to the orientation of the mountain ranges and the stable low-level 

atmosphere. Cold-air damming is therefore expected to be one of the important 

mechanisms affecting the surface wind field. 

The climatological surface winds over the 31-year period are first examined using 

CBHAR in order to get an overall understanding of the occurrence of cold-air damming 

across the entire study domain. In this region, the surface winds are stronger in cold 

months (Figure 5.2.11a,d) and weaker in warm months (Figure 5.2.11c). In January, a 

noticeable feature is that strong surface winds occur over the Chukchi Sea, caused by the 

intense Beaufort High centered over its northern area. Along the Chukotka Mountains, 

the strong northeasterly winds are turned, flowing parallel to the mountain range with 

enhanced speeds, implying the possibility of the occurrence of cold-air damming. This 

turning of the surface winds is not identified in other areas. The vertical wind structure is 

thus analyzed along the Chukotka Mountains (cross-section M1 in Figure 5.2.2) in order 

to investigate the cold-air damming effect.

Figure 5.2.11. Climatological surface wind in (a) January, (b) April, (c) July, and (d) 

October in CBHAR. Wind speeds (m s-1) are indicated by the vector colors.
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One significant feature of climatological surface winds along the Chukotka Mountains is 

the existence of low-level large-scale flow directed toward the mountains from January

through April and from September through December, favoring the occurrence of coldair damming. Climatologically, a low-level wind maximum can be found in these months 

(Figure 5.2.12a,b,c,d,i,j,k,l). In the other months, primarily in the warm season (June–

August), the wind maximum is not identifiable (Figure 5.2.12f,g,h). The maximum is 

larger in December and January, when the Beaufort High is strong and located over the 

northern Chukchi Sea, causing strong northeasterly wind to almost directly impact the 

Chukotka Mountains. This northeasterly wind is deflected to the left and the wind speed 

is also increased over the mountain slope (Figure 5.2.1b). The low-level maximum is 

about 6 m s-1 in the area 40 km inland from the shoreline, at the point where the terrain 

height begins to rise dramatically. The maximum also extends offshore to a fairly large

distance. At the offshore location 50 km away from the shoreline, the wind component 

parallel to the mountain range is more than 4 m s

-1 in January and December (Figure 

5.2.12a,l). From January to April, the maximum decreases from around 6 m s-1 to 3.5 

m s

-1

, and reduces its extent to within 50 km of both sides of the shoreline. From 

September to December, the low-level wind maximum recovers its strength, following 

almost a reversal of the process that occurs from January to April.

A comparison between observations and CBHAR suggests that CBHAR indeed captures 

the cold-air damming process that impacts the surface wind field on the Chukotka 

Mountain slopes and in adjacent areas. For example, the observed climatological

distribution of wind speed and direction for the station Mys Shmidta (Figure 5.2.13a,c), 

located on the shoreline near the mountains, shows that the surface is solely dominated 

by strong northwesterly winds in January, with a maximum wind speed of more than 7 

m s

-1

. CBHAR agrees very well with the observations (Figure 5.2.13b). In July, the 

dominant surface winds flow from the east-southeast, with far fewer occurrences of high 

wind-speed events. 

Along the Brooks Range, the low-level large-scale flow has only a weak component

oriented perpendicular to the mountain range; thus, the climatological vertical wind 

distribution does not show a similar low-level wind maximum, indicating the absence of 

the cold-air damming process and suggesting few impacts of cold-air damming on the 

surface wind field in the Brooks Range area, as compared with the Chukotka Mountains,

in terms of long-term climatology. Despite this, the cold-damming process may still 

occasionally occur at particular times under favorable synoptic conditions and have an

impact on the surface winds.
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Figure 5.2.12. Climatological wind profile in different months along cross-section 

M1 in Figure 5.2.2. Alongshore wind is denoted by colors (m s-1, negative values 

represent flow toward the reader), while on/offshore wind and vertical motion are 

indicated by vectors. The shoreline is located at 0 km, and the orientation of the 

cross-section is indicated by the “SW” (southwest) and “NE” (northeast) labels.
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Figure 5.2.13. Wind rose at station Mys Shmidta in January (top) and July (bottom) 

for observations (left) and CBHAR data (right)

5.2.4 Summary

The analysis given in this section indicates that CBHAR accurately represents mesoscale 

sea breezes and up/downslope winds, which can play important roles in the near-surface 

wind field of the study region. Upslope winds are found to occur from March through

September. The sea breeze begins in May, and reaches its maximum in June, July, and 

August. In July, the sea breeze circulation is well formed by noon and develops 

continuously until 1700 AKST. A corresponding compensating return flow appears about 

1000 m above ground. In addition, the cold-air damming process is also represented in 
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CBHAR and found to be most active near the Chukotka Mountain range, as indicated by 

the presence of a significant low-level wind maximum along the base of the mountains.

5.3 Climatology of Storms and Their Impacts on Surface Winds in CBHAR

5.3.1 Introduction

Storms frequently invade the Chukchi–Beaufort Seas region, impacting both the regional 

climate and environment (e.g., Zhang et al. 2004). They play a fundamental contributing 

role in transporting atmospheric heat energy and moisture into both the study area and the 

Arctic region as a whole. Surface wind associated with these storms exerts dynamic 

forcing, modifying sea ice movements and ocean currents, opening sea ice leads, and 

redistributing sea ice cover. Furthermore, intense storms can induce extreme winds, 

which may give rise to high wave surges, causing flooding, coastal erosion, and property

damage, particularly after the retreat of sea ice (e.g., Lynch et al. 2004). In this section, 

the climatology of storms in the CBHAR domain will be described, and, in particular, 

their impacts on the surface wind field will be analyzed.

Synoptic-scale storms are interactively steered by the atmospheric general circulation 

pattern. In order to better understand the storm climatology, the circulation patterns 

covering the Beaufort and Chukchi Seas are first documented. Regional climatological 

circulation patterns are generally shaped by the influence of the two semi-permanent 

systems in the area: the Beaufort High in the north and the Aleutian Low in the south. 

Therefore, climatologically, higher pressure appears in the north of the study domain and 

lower pressure in the south throughout the course of the year (Figure 5.3.1). Due to the 

presence of the Beaufort High, surface pressure increases over the Chukchi and Beaufort 

Seas from January to May. Lower pressure becomes more dominant in the second half of 

the year as the Aleutian Low gradually strengthens. 

To further examine the seasonal cycle of the surface pressure as governed by the Beaufort 

High and Aleutian Low, the study domain was divided into northern and southern halves

by the blue line shown in Figure 5.3.1. The SLP exhibits a similar increase from January 

to March over both the northern and southern subdomains (Figure 5.3.2). This may 

suggest an increased influence of the intensifying Beaufort High and a decreased 

influence of the weakening Aleutian Low during this time period. The intensity of the 

Beaufort High reaches its maximum in March and decreases afterward. In conjunction 

with this, SLP decreases over the study domain from March to August. In particular, the 

rate of SLP decrease accelerates in the northern part of the domain from May to August,

ending up about 4 hPa lower in August than in May. Note that, during June–August, the 

difference in the areal mean of SLP between the two sub-regions declines. During this 

period, both the Beaufort High and the Aleutian Low are in their weaker phase. 

Beginning in September, both the Beaufort High and Aleutian Low start to intensify. The 

SLP correspondingly increases in the north, but, by contrast, remains relatively constant

in the south until December. The slight southward shift of the Aleutian Low during 

December to January favors the increase in SLP over the study domain at this time 

(Figure 5.3.3).
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Figure 5.3.1. Climatological monthly mean sea level pressure (SLP, hPa) in CBHAR 

over 1979–2009. The blue line indicates the boundary between the northern and 

southern sub-domains used for the seasonality analysis.
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Figure 5.3.2. Climatological monthly mean SLP (hPa) over the northern (red) and 

southern (blue) sub-domains over 1979–2009 in CBHAR

Figure 5.3.3. Climatological monthly mean SLP (hPa) over 1979–2009 in the 

NCEP/NCAR Reanalysis (Kalnay et al. 1996)
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5.3.2 Storm Identification and Tracking Algorithm

A storm identification and tracking algorithm (Zhang et al. 2004) was employed in this 

study in order to investigate the climatology of storms in the study region. To accurately

identify synoptic-scale storms in the high spatial and temporal resolution CBHAR data, 

the algorithm, originally developed for use with GCMs, was modified for its application 

to this analysis. The modified criteria used in this algorithm are:

1) If the SLP at one grid point is lower than at the surrounding grid points within 50 

km, a cyclone candidate is identified with its center at this grid point. To 

minimize the impacts of thermodynamically and dynamically induced highresolution features on the identification of synoptic-scale storms, the SLP field 

was initially smoothed at each grid point using the 25 surrounding grid points. 

2) The minimum pressure gradient calculated between the candidate storm center 

and grid points 50 km away must be larger than 0.05 hPa (100 km)-1

.

3) The SLP gradient between the central point and each of the four surrounding 

points both at a radius of 50 km and 100 km are compared, and at least three of 

them must be negative inward (i.e., the central SLP is lower than the surrounding 

points). The criterion requiring three of the four to be negative, not all, is created 

primarily to allow the inclusion of some storms that have open SLP contours 

directed away from the candidate center.

4) If two candidate storms simultaneously appear within 600 km of one another, they 

are considered to be part of the same storm system. The original algorithm set this 

limit to be 1200 km, but due to the region’s high latitude and complex surface 

forcing, CBHAR has shown that two distinct storms can indeed exist within this 

distance. The limit was therefore reduced in order to more accurately identify 

dynamically separate, yet closely positioned storm centers.

5) The storm has a minimum lifetime of 12 hours. In addition, if the location of a 

storm is within 200 km of a storm center identified one hour earlier, the center is 

considered to have either moved or reformed from its previous location. 

Otherwise, a new storm is considered to have been generated.

In addition to the criteria of Zhang et al. (2004) that were modified for this application, 

the following two additional criteria were newly added specifically for the highresolution data and complex study area used here:

6) The candidate storm’s central SLP must be less than 1005 hPa.

7) The distance traversed during the storm’s entire lifetime must be larger than 250 

km. 

These two criteria are designed to remove any dynamic and thermodynamic lows forced 

by the complex terrain.
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5.3.3 Climatology and Variability of Storm Activity

Following Zhang et al. (2004), four parameters have been employed to describe the storm 

climatology, including the number, duration, and intensity of storms, as well as an 

integrative index, called the cyclone activity index (CAI), which measures the overall 

storm activity in the study region. The analysis is conducted on a monthly basis. The 

number of storms is defined as the number of storm trajectories located within the study 

domain for each month; the duration is the mean time period that all counted storms 

spend within the study domain for each month; and the intensity is the mean difference 

between the central SLP of all counted storms and the climatological monthly-mean SLP 

at the corresponding grid points throughout the duration of each storm for each month. 

The CAI is the sum of the differences between the storm’s central SLP and the 

climatological monthly-mean SLP at the corresponding grid points for all time steps 

within the study domain for each month. This index thus integrates all the calculated 

information about the number, duration, and intensity of the storms.

A storm climatology was developed for the Chukchi–Beaufort Seas by first applying the 

modified storm identification and tracking algorithm described above to the CBHAR data 

from 1979–2009 (Figure 5.3.4). The storm activity demonstrates an obvious seasonal 

cycle. More numerous, but weaker storms occur in summer, and fewer, but stronger 

storms appear in the winter season over the study area. This climatological seasonality is 

precisely consistent with the previous finding for the Arctic Ocean as a whole by Zhang 

et al. (2004). Considering this contrast between the Arctic seasonality and that of the midlatitudes, Serreze and Barrett (2008) conducted a follow-up and additionally found a 

minimum in the number of storms in March. The storm duration displays a much smaller 

seasonal fluctuation, characterized by a mean duration of about 30 hours throughout the 

year. The seasonal cycle of the CAI indicates an overall intensification in storm activity 

from summer to the late winter over the study domain, followed by a weakening in 

spring. Comparison of the four panels in Figure 5.3.4 suggests that interplay between the 

number and intensity of storms makes a major contribution to the seasonal variation in 

the CAI.

The seasonal cycle of storm activity also corresponds well to that of the regional 

atmospheric circulation patterns. The weakening storm activity during spring is in good 

agreement with the variation in the intensities of the Beaufort High and Aleutian Low. As 

discussed above, the Beaufort High is strongest from March through May, with its SLP 

reaching a maximum of 1023 hPa. At the same time, the Aleutian Low weakens 

noticeably from March onwards (Figure 5.3.3).
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Figure 5.3.4. Climatological monthly-mean storm count, intensity (hPa), duration

(hours), and CAI (hPa) for 1979–2009 over the CBHAR domain

Based on the climatological seasonal cycle of storm activity, and considering the seasonal 

evolution of the regional atmospheric circulation pattern as shown in Figure 5.3.1, for the 

next step in the analysis the year was split into four seasons to investigate the year-byyear variability and long-term changes in storm activity. The four seasons are defined as

early winter (September–December, when the Aleutian Low grows strongest); late winter 

(January–February, when the Beaufort High intensifies and the Aleutian Low weakens); 

spring (March–May, when storm activity is at its weakest); and summer (June–August, 

when the Beaufort High is in its weakest state).

Figure 5.3.5. Seasonal storm intensity (hPa) in CBHAR during 1979–2009 (Early 

Winter: September–December; Late Winter: January–February; Spring: March–

May; Summer: June–August)

The intensity of storms over the study domain exhibits obvious interannual variability in 

all four seasons from 1979–2009 (Figure 5.3.5). A slight intensification over the last 31 
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years appears in the late winter season, but there is no significant long-term trend. 

Consistent with the above climatological analysis, the interannual variability of storm 

intensity exhibits seasonal differences, with storms being generally stronger in the late 

winter than in the other seasons. The largest variability occurs in the late winter and the 

smallest variance in summer.

5.3.4 Impacts of Storms on Surface Wind

Strong surface winds are always associated with intense storms. To examine how the 

fluctuation in storm intensity impacts the surface wind field, strong winds, defined as 

those having speeds exceeding the 95th percentile, are identified. The computation of the 

95th-percentile wind speed was introduced in Section 5.1.1. Both the intensity of storms 

and frequency of strong winds demonstrate large interannual variability across the four 

seasons (Figure 5.3.6). The two parameters are well correlated in the late winter and 

summer seasons, with correlation coefficients of 0.46 and 0.40, respectively, which are 

significant at the 95% confidence level. This suggests that the invasion or intensification

of storms over the study area may contribute to an increase in surface pressure gradients, 

in particular when the Beaufort High is also present within the domain, and, in turn, 

increases the frequency of strong winds during these seasons. In contrast, insignificant,

small correlations are present for the early winter and spring seasons, in particular before 

the mid-1990s. This could be attributable to the dominance of the stable Beaufort High. 

In general, storms may not be able to play a dominant role in the steering of surface 

winds.

Figure 5.3.6. Standardized storm intensity (red) and frequency of strong winds

(95th-percentile wind speed) (black) for each season (Early Winter: September–

December; Late Winter: January–February; Spring: March–May; Summer: June–

August) in CBHAR. Correlations between storm intensity and frequency of strong 

winds are given in parentheses.

To further examine the impacts of storms, the analysis will now focus on intense storms.

In particular, a previous study has indicated that a large proportion of strong wind events 
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are related to strong storms (Small et al. 2011). Intense storms are defined here as those 

with a central SLP lower than 990 hPa. The climatological number and intensity of

intense storms in each month are shown in Figure 5.3.7. The seasonal cycle of the 

number of intense storms exhibits differences from that of all storms. The smallest

number of intense storms generally occurs from March to June, with the number 

increasing after June and reaching its maximum in January. The seasonal cycle of the 

intensity has a relatively small amplitude; it is at its strongest in January and becomes

weakest in August. Based on the nature of these seasonal cycles, the focus in the 

following analysis will be on the early and late winter seasons.

Figure 5.3.7. Climatological number and intensity (hPa) of intense storms for each 

month from 1979–2009 in CBHAR

The correlation between the intensity of intense storms and the frequency of strong winds

is noticeably larger than the correlation calculated using all storms, in particular for the 

early winter, which has an increased correlation of 0.44, up from 0.06 (Figure 5.3.8a). 

These changes, as compared with Figure 5.3.6, suggest that intense storms are a 

predominant contributing factor driving the occurrence of strong surface winds. This 

finding has important implications for assessing the impacts of climate change. Zhang et 

al. (2004) found that there has been an intensifying trend of storm activity over the panArctic region. If this has and will continue to occur for the current study area, the recently 

detected and future-projected upward trend in strong surface winds may be attributed to 

the changes in storm frequency that are the result of a warming climate. To examine this,

the yearly frequency of intense storms over the last 31 years is shown in Figure 5.3.8. 

The results indeed show an increased number of intense storms in the early winter. 
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Figure 5.3.8. Standardized intensity of intense storms (red) and frequency of strong 

winds (95th-percentile wind speed) (black) in (a) early winter (September–

December) and (b) late winter (January–February) in CBHAR. Number of intense 

storms in (c) early winter and (d) late winter in CBHAR. Correlations between 

storm intensity and frequency of strong winds are given in parentheses.

5.3.5 More Insight into the Impacts of Storms on Surface Winds: Case Studies

From the above analysis, it can be found that the variability of intense storms explains 

about 20–30% of the total variance of the variability in strong surface winds. Although 

this value suggests that intense storms have a predominant role in the long-term climate 

variability of surface winds, other factors can also interact with storms to impact the 

winds. Considering the regional atmospheric circulation patterns, the interplay between 

storms and the Beaufort High would be key in shaping the strong wind field, including 

three possible scenarios:

1) Storms themselves bring strong winds when moving into or being generated 

within the study domain;

2) Intensification of the Beaufort High causes strong winds; or

3) Both storms and the Beaufort High interact to generate strong winds.

To better understand how the above three scenarios shape the surface wind field, a case 

study has been conducted for 6–7 October 1992. During this period, an intense storm 

moved southeastward from Russia over the Bering Strait, reaching its minimum SLP on 6 

October 1992 (Figure 5.3.9a). Strong winds occurred over the northern Chukchi and 

Beaufort Seas, with the maximum wind speed exceeding 18 m s

-1 along the Alaskan coast 

of the southern Chukchi. Interestingly, a narrow band of strong wind extends from the 
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west coast of Alaska eastward along the Brooks Range, demonstrating topographic 

effects. The storm continued to maintain its intensity on 7 October 1992 (Figure 5.3.9b), 

during which time the storm center moved northward over the Chukchi Sea, bringing the 

associated area of strong winds along with it. At the same time, the strong winds in the 

extreme southern Chukchi Sea moved northward along the coast. In this case, the intense 

storm itself played the predominant role in causing the strong surface winds, as a wellshaped Beaufort High had not been formed.

During the late winter, it is very common that storms vigorously interact with a wellestablished Beaufort High. This can be exemplified by a case occurring on 21 January 

1991 (Figure 5.3.9c). The storm was initially situated over the Chukchi Sea with a central

SLP of 997 hPa. At the same time, the Beaufort High was located to the north with a 

central SLP higher than 1020 hPa. The arrangement of the storm and the Beaufort High 

resulted in large SLP gradients that generated 12–16 m s

-1 winds. In this case, the 

intensity of the storm is moderate. In instances when the storms are stronger, more 

extreme winds can be expected. For example, the case occurring on 19 January 1981 

shows a storm over the eastern Siberia coastal region with a central SLP of 978 hPa 

(Figure 5.3.9d). The obviously larger pressure gradients between the storm and the 

Beaufort High created strong winds with a maximum wind speed exceeding 18 m s

-1

.

According to the regional circulation analysis given above, the Beaufort High reaches its 

peak strength in spring, along with the Aleutian Low, which is not a fixed feature but 

rather represents the time-averaged effect of storms traversing the region. Under this 

circumstance, the Beaufort High predominantly governs the formation of surface winds

in the area. A case involving strong winds on 3 March 1981 illustrates this effect (Figure 

5.3.9e). Here, the Beaufort High central SLP was higher than 1032 hPa, and the 

associated strong winds were around 16 m s

-1 over the Chukchi Sea.

During summer, the Beaufort High is climatologically weak, and as such this season 

generally has the weakest winds, with a 95th-percentile wind speed of less than 9 m s

-1

. 

However, storms that either move over or are generated over the Arctic Ocean can 

produce relatively large winds. For example, a storm occurred over the northern Beaufort 

Sea on 22 July 1996 (Figure 5.3.9f). Its central SLP was 994 hPa, with associated large 

wind speeds of around 9–14 m s

-1

.
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Figure 5.3.9. Sample weather charts showing the interplay between storms and the 

Beaufort High in generating strong surface winds. Black contours: SLP (hPa); 

shaded colors: 10-m wind speed (m s-1); wind barbs: 10-m winds.

6 Summary

With support from BOEM/Department of the Interior (DoI) and through collaborations 

with national and international institutions and colleagues, the “Beaufort and Chukchi 

Seas Mesoscale Meteorology Modeling Study” project has hereby been conducted from 

September 2006 through July 2013. The research team includes Dr. Xiangdong Zhang 

(PI) and Mr. Jeremy Krieger from the University of Alaska Fairbanks, Dr. Jing Zhang 

(Co-PI) and her group from North Carolina A&T State University, and Dr. Martha 

Shulski (Co-PI) and her group from the University of Nebraska–Lincoln. This research 

has built upon the extensive experience and expertise of the team members in mesoscale 

meteorological modeling, data assimilation, observational data processing and quality 
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control, and meteorological and climatological data analysis. Mr. Warren Horowitz at the 

BOEM-Alaska OCS Region Office has overseen and provided management and technical 

guidance, and Dr. Ron Lai at BOEM Headquarters and the Scientific Review Board 

members Dr. Keith Hines, Dr. Peter Olsson, Dr. Igor Polyakov, and Dr. Tom 

Weingartner have provided scientific counsel, all of which has helped this project to 

proceed successfully.

The primary objective of this project was to develop a regional, high-resolution 

atmospheric reanalysis product, focused on surface wind over the Chukchi–Beaufort Seas 

and Arctic Slope. To achieve this objective, the project comprised eight components: (1) 

acquiring and quality controlling surface-based and satellite-retrieved observations from 

all available sources; (2) conducting a field program to collect additional surface 

observations in a selected key and data-sparse area; (3) optimizing model configurations 

and physical parameterizations with improved treatment of the unique Arctic 

environment; (4) customizing a data assimilation system for the best estimation of model 

errors and efficient observational assimilation; (5) constructing a high-resolution regional 

reanalysis for the study area through the use of the optimized modeling system and 

quality-controlled observational data; (6) evaluating the resulting reanalysis product 

against observational data and the latest generation of global reanalysis datasets; (7) 

analyzing the reanalysis data in order to describe the climatology, variability, changes in, 

and extremes of the surface wind field; and (8) disseminating the reanalysis data and 

scientific results to the broader community through conferences, workshops, and 

publications.

This project represented the first time that such a comprehensive and systematic highresolution atmospheric modeling study has been conducted over the Chukchi–Beaufort 

Seas and Arctic Slope. The major resultant project outcome—the Chukchi–Beaufort 

High-Resolution Atmospheric Reanalysis (CBHAR)—is the first three-dimensional, 

thermodynamically and dynamically constrained gridded dataset to be available at such a 

high resolution (10 km in space and 1 hour in time) over the period 1979–2009 in the 

study area. These features make it possible to be used to investigate not only regional 

circulation and weather patterns, but also local thermodynamically and dynamically 

forced finer-scale processes for this remote, climatically harsh area. Major 

accomplishments from this project are highlighted here:

1) Developing the CBHAR reanalysis at resolutions of 10 km in space and 1 hour in 

time, representing a noticeably improved estimate of the regional atmospheric state as 

compared with observations and the new generation of global reanalysis datasets;

2) Establishing a physically optimized Weather Forecasting and Research (WRF) model 

and WRF-based data assimilation system, enhanced for the simulation of Arctic 

processes, over the Chukchi–Beaufort Seas and Arctic Slope;

3) Constructing an integrative, quality-controlled observational database comprised of 

all available data sources, including surface-based meteorological stations, ships, and 

buoys, as well as satellite remotely sensed data for the assimilation system;
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4) Deploying a meteorological buoy over the highly sea-ice-dynamic Beaufort Sea and 

successfully acquiring continuing offshore observations of surface wind and other 

fundamental meteorological and ocean-surface parameters;

5) Using CBHAR to analyze surface climate over the study area, from the large scale to 

synoptic and mesoscales, including climatology, variability, changes, and extreme 

events, and describing new scientific findings;

6) Strengthening national and international collaborations between BOEM, UAF, 

Woods Hole Oceanographic Institution (WHOI), the industry sector including the 

Shell and ConocoPhillips oil companies, and JAMSTEC; and

7) Training three Ph.D. students and one master’s student in mesoscale meteorological 

modeling, data assimilation studies, observational data processing and analysis, and 

Arctic climatological analysis, allowing them to participate among the next 

generation of Arctic scientists.

By using CBHAR data, along with the newly created observational database and the 

latest generation of global reanalysis datasets, this project has delivered the following 

specific scientific findings about the surface wind field in the study area:

1) Monthly mean and extreme (as measured by the 95th percentile) surface wind speeds 

over the ocean have increased markedly since 1979, with the largest increase in 

October, while wind speeds over most land areas have decreased.

2) Climatology of and long-term changes in the mean and extreme surface winds exhibit 

an obvious seasonal cycle and a variable spatial distribution, with stronger winds 

occurring in autumn and along the Alaskan coast.

3) Changes in wind speed are negatively correlated with changes in sea ice cover and 

sea surface temperature, suggesting that retreating sea ice and a warmed ocean favor 

the occurrence of stronger winds.

4) More strong storms occur in winter than in summer in the climatological seasonal 

cycle. The frequency of intense winter storms entering the Chukchi–Beaufort Seas 

region has increased since 1979, playing a significant role in contributing to the 

occurrence of strong surface winds.

5) Northeasterly wind dominates the Chukchi–Beaufort Seas region and exhibits a clear

seasonal cycle, strengthening from September through May and weakening from June 

to August.

6) Diurnally varying sea and mountain breezes are pronounced in the summer, reaching 

their peak intensity in the late afternoon. Combined sea and mountain breezes along 

the eastern Brooks Range and Chukotka Mountains serve to intensify the onshore 

surface winds, the impact of which can reach out to 50–100 km offshore. 
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7) A strong cold-air damming process occurs on the north slope of the Chukotka 

Mountains in winter, producing mesoscale northwesterly winds. Radiative-coolinginduced downslope wind is the primary driver of the wintertime surface wind field in 

the eastern Brooks Range.

For a better understanding of the accomplishments and scientific findings described 

above, each of the research tasks performed during this project is briefly summarized 

below.

• Development of a quality-controlled observational database

Meteorological data from 262 stations in the Beaufort and Chukchi Seas region of the 

Arctic were gathered over the course of the project. These data represent stations from 

several different observational networks operating in the region, fulfilling purposes 

related to aviation, climate, coastal weather, fire weather, hydrology, and surface 

radiation. The locations of these stations accommodate land-based observations as well as 

offshore data from buoys, ships, exploration well sites, and an ice camp. Observed 

variables include: surface air temperature (°C), dew point temperature (°C), relative 

humidity (%), wind speed (m s-1), wind direction (degrees), station pressure (hPa), sea 

level pressure (hPa), altimeter (in Hg), shortwave radiation (W m-2), longwave radiation 

(W m-2), 1-hr accumulated precipitation (mm), 6-hr precipitation (mm), 24-hr 

precipitation (mm), and snow depth (cm). In addition to the data values, any available 

metadata were also gathered. All available data were collected for the 1979–2009 period, 

with most stations reporting on an hourly or sub-hourly basis. Few stations possess data 

records spanning the entire 31 years; most stations have a period of record of less than 10 

years.

A unified data quality control (QC) process has been applied to these data, including

three primary QC evaluation procedures: identifying instances of observations falling 

outside of a normal range (threshold test), instances of consecutive values that are 

excessively different (step change test), and instances of too-high or too-low variability in 

the observations (persistence test). All original data are conserved in the database, and a 

flag is inserted after the reported data column to inform the user whether the value has 

passed or failed each QC check. The fraction of flagged data for the entire database is 

rather low (less than 5%), with the persistence test failing the highest percentage (1.34%), 

followed by the threshold (0.99%) and step change (0.02%) tests. When delineated by 

variable, wind direction and wind speed were flagged the most often, failing QC tests 

nearly 2% of the time. Automated checks identified the existence of problem stations 

(those with a high percentage of flagged data), rather than problem networks or regions. 

In terms of overall performance, the BOEM, buoys, CMAN, and exploration well site 

networks performed better than the ARM, Arctic LTER, NCDC, RAWS, Ship, and 

WERC stations.
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• Optimization and Arctic-process enhancement of the WRF model and WRF-based 

data assimilation system

The WRF model includes a large number of physical parameterizations developed for 

various applications, primarily over the tropical and mid-latitudes, that may not perform 

well in an Arctic environment, including for complex air-sea-ice interactions and the 

seasonally varying land-sea thermal contrast and topographic effects involved. In 

particular, neither the WRF model nor its associated data assimilation system has before 

been well utilized over the Chukchi–Beaufort Seas and Arctic Slope area. An imperative 

exists to evaluate the capability and performance of the WRF model and its data 

assimilation system, and to define the model configuration capable of best representing 

the atmospheric state in this region. This optimization was accomplished through a 

thorough evaluation of the model physical parameterization schemes for the study area, 

including the careful selection of various in situ observations and satellite retrievals for 

assimilation, along with further improvement to the model physical parameterizations 

that require specific considerations for the unique Arctic environment.

Model physical parameterizations evaluated include those governing long- and shortwave 

radiation, cloud microphysics, cumulus convection, and the planetary boundary layer 

(PBL). Through three sets of sensitivity tests covering the periods of September 2004

(representing the seasonal transition from summer to winter), December 1997 and July 

1998 (covering the unique SHEBA field experiment), and August 2009 (the warmest 

month of the year in the study area), the performance of different physical 

parameterization schemes was thoroughly compared. RRTMG (Rapid Radiative Transfer 

Model for GCMs) radiation (Iacono et al. 2008) and CAM (Community Atmosphere 

Model) radiation (Collins et al. 2004) tend to outperform other schemes in simulating

both long- and shortwave irradiances, though the RRTMG shortwave performs slightly 

better than CAM, leading RRTMG to be selected for use in generating the CBHAR 

reanalysis.

Both Thom (Thompson et al. 2004) and Morr (Morisson et al. 2009) microphysics are 

double-moment schemes, and both performed very similarly in simulating downward 

irradiance, surface temperature, and wind vectors. Considering that the Morr scheme has 

been widely used in polar atmospheric modeling studies (Hines et al. 2011; Barton and 

Veron 2012; Claremar et al. 2012; Tastula et al. 2012), a decision was made to use this as 

the microphysics scheme in the final model configuration. When examining cumulus 

schemes, it was found that both the Grell–Devenyi and Grell-3D ensemble schemes 

(Grell and Devenyi 2002) produce smaller errors for long- and shortwave irradiances, and 

also contribute to a relatively small error for surface temperature. Thus, the Grell-3D 

ensemble cumulus parameterization was used in the production of the final CBHAR 

reanalysis.

The YSU (Yonsei University, Hong et al. 2006) and MYJ (Mellor–Yamada–Janjic, 

Mellor and Yamada 1982; Janjic 2002) PBL schemes were found to perform better than 

the other PBL schemes. In terms of modeling downward longwave irradiance, surface 

temperature, and wind vectors, YSU and MYJ perform similarly, though MYJ 
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outperforms YSU for the simulation of downward shortwave irradiance. Therefore, MYJ 

was selected for use as the PBL parameterization in the final model configuration.

In addition to the evaluation of the physical parameterizations built into WRF, the 

particular effects of sea ice upon the overlying atmosphere were also considered. To this 

end, efforts were made to implement more advanced model physical parameterizations 

into WRF for its application to the Arctic environment. The coupling of a thermodynamic 

sea ice model with WRF produces an accurate modeling of sea-ice surface albedo,

resulting in an improved surface temperature, and encouraging this coupling to be applied 

to the final model configuration used for the production of the CBHAR reanalysis.

For an optimal WRFDA configuration in the Chukchi–Beaufort Seas region, this study 

concludes that the usage of customized model background error (BE) is necessary to 

achieve positive impacts from WRFDA assimilation in the study area. Sensitivity 

analyses examining the assimilation of different datasets via WRFDA demonstrate that 1) 

positive impacts are always seen through the assimilation of in situ surface and 

radiosonde measurements; 2) assimilating QuikSCAT winds improves the simulation of 

the surface wind field over ocean and coastal areas; 3) selectively assimilating MODISretrieved profiles under clear-sky and snow-free conditions is essential to avoid 

degradation of assimilation performance, while assimilation of COSMIC retrievals has a 

slight impact upon upper-air temperature alone, most likely due to limited data 

availability; and 4) assimilation of polar winds provides a slight benefit, though their 

impact on the surface fields in the kind of configuration used for this reanalysis is 

extremely limited. The use of three-wavenumber spectral nudging, on all vertical levels 

and for all variables, can effectively reduce model errors. Combining data assimilation 

and spectral nudging produces further improvements, encouraging the inclusion of both 

spectral nudging and data assimilation for generating the CBHAR reanalysis.

Forcing data required by the regional WRF model were also evaluated. WRF simulations 

forced by the newly developed ERA-Interim reanalysis consistently demonstrate better 

results for every model variable investigated. As a result, ERA-Interim was selected as 

the forcing data for producing the CBHAR reanalysis.

• Evaluation of CBHAR data against observations, compared with the latest global 

reanalysis

In order to evaluate the overall performance of CBHAR, the reanalysis data were 

statistically verified against the observational data collected and quality-controlled to 

produce the observational database also created as part of this study. Observations used 

for verification included surface station observations of temperature and winds, 

QuikSCAT ocean-surface winds, and upper-air measurements from radiosondes. To 

better provide an indication of the relative performance of the new reanalysis, the highquality global reanalysis ERA-Interim, which was used to drive the production of 

CBHAR, was also evaluated in a similar manner. It was found that CBHAR greatly and 

consistently outperforms ERA-Interim in the simulation of the examined near-surface 

atmospheric variables in both coastal and offshore regions, as well as inland. Perhaps 
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counterintuitively, the largest relative gains were seen in near- and offshore areas, where 

the higher-resolution topography present in the WRF model was presumably less 

influential.

In addition to the overall verification, the reanalysis results were also verified on diurnal 

and seasonal time scales. Generally, errors for both surface temperatures and winds were 

found to be largest during the cold-season months, falling to a minimum during the 

climatologically less-variable warm season. While the fractional improvement in 

temperature error seen in CBHAR versus ERA-Interim followed a similar pattern (with 

larger relative decreases seen in the winter), surface winds were, notably, consistently 

improved throughout the course of the annual cycle, with declines in wind vector RMSE 

of ~15% and increases in correlation of ~8% for all months. The diurnal cycle errors in 

CBHAR exhibited a similar pattern to those of the annual cycle, with errors in both 

temperature and winds highest during the day, when boundary-layer activity is at its 

peak, and declining during the quieter overnight period. Relative reductions in both 

temperature and wind errors were found to be consistent throughout the diurnal cycle.

• Multi-scale surface climate analysis using CBHAR

By taking advantage of the high resolution of CBHAR, not only were regional-scale 

circulations, weather patterns, and the associated surface wind field able to be analyzed in 

detail, but so were the mesoscale meteorological processes forced by local 

thermodynamic and dynamic effects. To characterize regional climate features and 

describe their changes, the monthly mean and monthly 95th-percentile surface wind 

speeds, wind direction, 2-m air temperature, sea ice concentration, and SST were 

examined in particular. Wind directions were divided into quadrants—NE, SE, SW, and 

NW—in the analysis.

Results show that the highest wind speeds in the study region occur in November, and 

that northeast is the dominant wind direction. Trend analysis of the winds shows that the 

largest increase has occurred in October. Wind speeds and wind speed trends appear 

higher over the ocean than over land, including the largest increasing trend. Furthermore, 

CBHAR shows increasing trends in 2-m air temperature and SST, along with decreasing 

trends in sea ice extent, which are consistent with other findings and observations. Wind 

speed and sea ice extent appear to have an inverse linear relationship with one other, as 

do wind speed and SST; these findings will be investigated further. Comparing CBHAR 

with ERA-Interim data, both datasets exhibited very similar climatological features and 

trends, although ERA-Interim shows higher wind speeds, particularly along the coast. 

This is most likely due to the coarser grid spacing in ERA-Interim, which limits its 

capability in realistically capturing local physical processes, though this requires further 

investigation. CBHAR clearly captures mesoscale features in wind direction, such as the 

anomalous winds over the Brooks Range and the Mackenzie River valley, better than 

ERA-Interim.

In the mesoscale climate analysis, the primary focus was on sea and mountain breezes, 

up/downslope winds along mountain slopes, and cold-air damming, all of which are 
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prominent mesoscale meteorological processes affecting the surface wind field in the 

study area. There have been few studies done on these local thermodynamically and 

dynamically forced mesoscale wind field processes, due to a lack of both continuous and 

plentiful high-resolution data. However, the CBHAR data make it possible for such a

mesoscale study to be conducted. The major geographic features steering the mesoscale 

wind processes include the seasonal variation of sea ice cover and sea surface 

temperature, as well as the Brooks Range in northern Alaska and the Chukotka

Mountains in eastern Siberia.

Mesoscale wind processes have distinct diurnal and seasonal cycles. Thus, the 

climatological diurnal variation of surface winds was analyzed during different seasons. 

In order to understand the vertical structures of this phenomenon, a series of vertical 

cross-sections were constructed at different locations, in order to further investigate

vertical wind profiles. These results show that sea breezes and up/downslope winds play 

important roles in determining the surface winds in the domain throughout different 

seasons of the year. The cold-air damming effect is found to dominate surface winds in 

the area adjacent to the Chukotka Mountains. Along the eastern Brooks Range, 

downslope winds, which are caused by radiative cooling in the mountains, dominate the 

surface winds for about half the year. Sea breezes are active in the warm months from 

June to August, producing significant diurnal wind variation in the coastal areas. The 

effects of sea breezes can extend from around 50 km offshore to about 30 km inland. In

the area adjacent to the Chukotka Mountains, cold-air damming is the dominant effect in

the cold months of October to April, generating enhanced surface winds (with a low-level 

wind maximum) in the area, oriented parallel to the mountain range. In the warm months, 

these surface winds are affected by both sea breezes and upslope winds.

In recent years, since the discovery of the poleward shift of storm tracks and the 

intensification of storm activity over the Arctic (e.g., Zhang et al. 2004), there has been 

increased attention paid to storms in the region. A storm identification and tracking 

algorithm was adapted for application to CBHAR data in the study area, and an 

investigation was made into how storms modulate or impact surface winds. Little 

research has previously been done on this topic. Specifically, all storms entering or 

generated within the Chukchi–Beaufort Seas region over the period 1979–2009 were 

identified using CBHAR data. Based on these data, the seasonal cycle and interannual 

changes in the number of storms and their intensity were analyzed. The relationship 

between storm intensity and strong-wind frequency during each season was then 

examined. To better understand this impact, case studies were also conducted during

different seasons. The results indicate that more numerous, yet weaker storms occur in 

the summer, while fewer, but stronger storms exist in the winter. This is consistent with 

the seasonal cycle for the pan-Arctic region (Zhang et al. 2004). Intense storms produce a 

significant impact upon the occurrence of strong surface winds. In addition, the impact of 

storms on surface winds exhibits seasonality, due to its interaction with the semipermanent Beaufort High.
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7 Recommendations

Based on our experience from this project, including the evaluation of CBHAR, a 

preliminary climate analysis using the CBHAR data, and our interactions with the 

broader community and other potential users of the CBHAR data, we have summarized a 

list of recommendations to BOEM, given below, for planning future projects:

1) Establish a data server and develop a user-friendly interface to disseminate the 3-D 

CBHAR data to the broader community. During the project study, we have received 

requests for CBHAR data for various applications, including air quality modeling and 

assessment, the study of ocean waves and boundary currents, regional climate 

analysis, and hydrology and water resources research. The current project has focused 

on the surface wind field and does not include a platform for releasing 3-D CBHAR 

data. The CBHAR data has a large size (about 50 TB) and is currently stored at the 

Arctic Region Supercomputing Center (ARSC), which is inaccessible to the outside 

community. To benefit the community and allow them to fully exploit this CBHAR 

dataset, we recommend establishing a data server and developing a user-friendly 

interface to disseminate the 3-D CBHAR data to the wider community. During this 

process, different subsets of the data, including various layouts/formats, should be 

developed to meet the needs of different users in its application.

2) Evaluate the suitability of CBHAR for various applications, such as the recently 

planned air quality modeling and assessment project. Although CBHAR data has 

demonstrated better performance than the whole of the latest generation of global 

reanalysis datasets for the region including the Chukchi–Beaufort Seas and the Arctic 

Slope, uncertainties can still be expected to exist in CBHAR, due to model 

discrepancies and the limited availability of and inherent bias in the observations. We 

recommend a comprehensive evaluation of the suitability of CBHAR for various 

scientific and practical applications, including an assessment of the sensitivity of the 

targeted research results and an identification of where the data, including its postprocessing, can be improved. 

3) Extend the CBHAR data time period to the present. CBHAR currently covers a 31-

year time period from 1979–2009. Climate and weather have changed dramatically

during recent years, and the availability of observations and measurements has

continually increased through the efforts of additional field campaigns since the

passing of the International Polar Year. There is an imperative for the scientific 

community to have dynamically and thermodynamically consistent, high-resolution 

meteorological data covering the most recent years. We have implemented a 

physically optimized, WRF-model-based data assimilation system for the 

development of CBHAR, and we recommend using this system, along with the 

collection and quality control of newly available observational data, to extend 

CBHAR to the present time.

4) Conduct continuing climate analysis using CBHAR, particularly for studying 

mesoscale climatology and climate variability and change. We have preliminarily 
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analyzed regional-scale and mesoscale climatology, as well as the variability of and 

changes in the surface wind field and surface air temperature using CBHAR data. To 

better understand the recently observed dramatic climate changes and assess their 

environmental and societal impacts, we recommend continuing this climate analysis, 

which would also serve to assist the policy decision-making process. In particular, we 

recommend taking advantage of CBHAR’s high resolution to further analyze 

mesoscale meteorological processes and features. We have analyzed climatological 

sea/mountain/valley breezes, mountain barrier effects, and cold-damming processes. 

We recommend a continuing analysis of the temporal variability of and changes in 

these mesoscale processes.

5) Develop a new generation of CBHAR with newly available data and newly developed

assimilation technology. During the course of the current project, new observational 

data have become available, and new data assimilation approaches have been 

developed. Some applications have requested even higher spatial and temporal 

resolutions, as well as larger spatial coverage. Based on the physically optimized and 

WRF-model-based data assimilation system established over the course of the current 

project, we wish to recommend the development of a new generation of the CBHAR 

reanalysis at a higher resolution, and with additionally improved treatment of the 

rapidly changing Arctic sea ice and ocean, as well as cold regional land-surface 

processes. The development of a new generation of CBHAR is consistent with that of 

planned global reanalysis products.

6) Conduct a lead-resolving modeling study for investigating coupled atmosphere-sea 

ice-ocean processes over the Chukchi–Beaufort Seas. With amplified warming and 

thinned sea ice, leads, polynyas, and even larger open water areas are often observed 

even during the freezing season in the Chukchi and Beaufort Seas. In this study, we 

have found that the surface wind has been significantly enhanced over the last 30 

years. The enhanced surface wind may further increase sea ice vulnerability, 

increasing the frequency of lead and polynya opening and directly agitating the 

opened water. The removal of ice and the associated increase in ocean mixing will 

enhance sensible and latent heat release, which will in turn reduce atmospheric 

boundary layer stratification and trigger baroclinic instability. This may result in 

feedbacks to the overlying weather patterns and the surface wind field. We 

recommend improving the representation of lead opening and polynya development 

and investigating feedback processes between atmosphere-leads/polynyas-ocean, 

including the creation of very-high-resolution atmospheric surface data sets to support 

such efforts.
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8 References

Barker, D. M., W. Huang, Y.-R. Guo, A. J. Bourgeois, and Q. N. Xiao, 2004: A threedimensional variational data assimilation system for MM5: Implementation and 

initial results. Mon. Wea. Rev., 132, 897–914.

Barker, D. M. et al., 2012: The Weather Research and Forecasting Model's Community 

Variational/Ensemble Data Assimilation System: WRFDA. Bull. Amer. Meteor. 

Soc., 93, 831–843.

Barnett, V. and T. Lewis, 1994: Outliers in statistical data. 3d ed. J. Wiley and Sons, 604 

pp.

Barton, N. P. and D. E. Veron, 2012: Response of clouds and surface energy fluxes to 

changes in sea-ice cover over the Laptev Sea (Arctic Ocean). Clim. Res., 54, 69–

84.

Bieniek, P. A., U. S. Bhatt, R. L. Thoman, H. Angeloff, J. Partain, J. Papineau, F. Fritsch, 

E. Holloway, J. E. Walsh, C. Daly, M. Shulski, G. Hufford, D. F. Hill, S. Calos, R. 

Gens, 2012: Climate divisions for Alaska based on objective methods. J. Climate, 

51, 1276–1289.

Black, R. F., 1954: Precipitation at Barrow, Alaska greater than recorded. Trans. Amer. 

Geophys. Union, 35, 203–206.

Bougeault, P. and P. Lacarrere, 1989: Parameterization of orography-induced turbulence 

in a mesobeta-scale model. Mon. Wea. Rev., 1117, 1872–1890.

Bromwich, D. H., 1989: An extraordinary katabatic wind regime at Terra Nova Bay, 

Antarctica. Mon. Wea. Rev., 117, 688–695. 

Bromwich, D. H., K. M. Hines, and L.-S. Bai, 2009: Development and testing of Polar 

Weather Research and Forecasting model: 2. Arctic Ocean. J. Geophys. Res., 114, 

D08122, doi: 10.1029/2008JD010300.

Bromwich, D. H., F. O. Otieno, K. M. Hines, K. W. Manning, and E. Shilo, 2013: 

Comprehensive evaluation of polar weather research and forecasting model 

performance in the Antarctic. J. Geophys. Res., 118, 274–292.

Cassano, J. J., M. E. Higgins, and M. W. Seefeldt, 2011: Performance of the Weather 

Research and Forecasting (WRF) model for month-long pan-Arctic simulations. 

Mon. Wea. Rev., 139, 3469–3488.

Chen, F. and J. Dudhia, 2001: Coupling an advanced land-surface hydrology model with 

the PSU/NCAR MM5 modeling system. Part I: Model description and 

implementation. Mon. Wea. Rev., 129, 569–585.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

193

Chou, M.-D. and M. J. Suarez, 1994: An efficient thermal infrared radiation 

parameterization for use in general circulation models. NASA Tech. Memo. 

104606, 3, 85 pp.

Claremar, B., F. Obleitner, C. Reijmer, V. Pohjola, A. Waxegard, F. Karner, and A. 

Rutgersson, 2012: Applying a mesoscale atmospheric model to Svalbard glaciers. 

Adv. Meteorol., 321649, doi: 10.1155/2012/321649.

Collins, W. D. et al., 2004: Description of the NCAR Community Atmosphere Model 

(CAM3.0). NCAR Technical Note, NCAR/TN-464+STR, 226 pp.

Comiso, J. C., 2003: Warming trends in the Arctic from clear sky satellite observations. J. 

Climate, 16, 3498–3510.

Comiso, J. C., 2012: Large decadal decline of the Arctic multiyear ice cover. J. Climate, 

25, 1176–1193.

Crosby, D. S., L. C. Breaker, and W. H. Gemmill, 1993: A proposed definition for vector 

correlation in geophysics: Theory and application. J. Atmos. Oceanic Technol., 

10, 355–367.

Dee, D. P. et al., 2011: The ERA-Interim reanalysis: Configuration and performance of 

the data assimilation system. Q. J. R. Meteor. Soc., 137, 553–597.

Dickey, W., 1961: A study of a topographic effect on wind in the Arctic. J. Meteor., 18,

790–803.

Doherty, S. J., S. G. Warren, T. C. Grenfell, A. D. Clarke, and R. E. Brandt, 2010: Lightabsorbing impurities in Arctic snow. Atmos. Chem. Phys., 10, 11647–11680.

Doney, S. C. et al., 2012: Climate change impacts on marine ecosystems. Annu. Rev. 

Mar. Sci., 4, 11–37.

Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon 

experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077–

3107.

Eischeid, J. K., C. B. Baker, T. Karl, and H. F. Diaz, 1995: The quality control of longterm climatological data using objective data analysis. J. Appl. Meteor., 34, 2787–

2795.

Ford, J. D. and T. Pearce, 2010: What we know, do not know, and need to know about 

climate change vulnerability in the western Canadian Arctic: A systematic 

literature review. Environ. Res. Lett., 5, 014008, doi: 10.1088/1748-

9326/5/1/014008.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

194

Gandin, L. S., 1988: Complex quality control of meteorological observations. Mon. Wea. 

Rev., 116, 1137–1156.

Gardner, A. S. and M. J. Sharp, 2010: A review of snow and ice albedo and the 

development of a new physically based broadband albedo parameterization. J. 

Geophys. Res., 115, F01009, doi: 10.1029/2009JF001444.

Girard, E. and J. A. Curry, 2001: Simulation of Arctic low-level clouds observed during 

the FIRE Arctic Clouds Experiment using a new bulk microphysics scheme. J. 

Geophys. Res., 106, 15139–15154.

Grell, G. A. and D. Devenyi, 2002: A generalized approach to parameterizing convection 

combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 

1693, doi: 10.1029/2002GL015311.

Gundlach, E. R. and M. O. Hayes, 1978: Vulnerability of coastal environments to oil spill 

impacts. Mar. Technol. Soc. J., 12(4), 18–27.

Guttman, N., C. Karl, T. Reek, and V. Shuler, 1988: Measuring the performance of data 

validators. Bull. Amer. Meteor. Soc. 69, 1448–1452.

Guttman, N. and R. G. Quayle, 1990: A review of cooperative temperature data 

validation. J. Atmos. Oceanic Technol., 7, 334–339.

Hamed, K. H., 2008: Trend detection in hydrologic data: The Mann–Kendall trend test 

under the scaling hypothesis. J. Hydrol., 349, 350–363.

Hibler III, W. D., 1979: A dynamic thermodynamic sea ice model. J. Phys. Oceanogr., 9, 

815–846.

Hines, K. M. and D. H. Bromwich, 2008: Development and testing of Polar Weather 

Research and Forecasting (WRF) Model. Part I: Greenland ice sheet meteorology. 

Mon. Wea. Rev., 136, 1971–1989.

Hines, K. M., D. H. Bromwich, L.-S. Bai, M. Barlage, and A. G. Slater, 2011: 

Development and testing of Polar WRF. Part III: Arctic land. J. Climate, 24, 26–

48.

Hinkel, K. M., F. E. Nelson, A. E. Klene, and J. H. Bell, 2003: The urban heat island in 

winter at Barrow, Alaska. Int. J. Climatol., 23, 1889–1905.

Hoefler Consulting Group, 2007: Study final report for the Nearshore Beaufort Sea 

Meteorological Monitoring and Data Synthesis Project. Minerals and 

Management Service, 93 pp.

Hong, S.-Y. and J.-O. J. Lim, 2006: The WRF Single-Moment 6-Class Microphysics 

Scheme (WSM6). J. Korean Meteor. Soc., 42, 129–151.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

195

Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an

explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341.

Huang, X. et al., 2009: Four-dimensional variational data assimilation for WRF: 

Formulation and preliminary results. Mon. Wea. Rev., 137, 299–314.

Hubbard, K. G., S. Goddard, W. D. Sorensen, N. Wells, and T. T. Osugi, 2005: 

Performance of quality assurance procedures for an applied climate information 

system. J. Atmos. Oceanic Technol., 22, 105–112.

Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. 

Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations 

with the AER radiative transfer models. J. Geophys. Res, 113, D13103, doi: 

10.1029/2008JD009944.

Jahn, A. et al., 2012: Late-twentieth-century simulation of Arctic sea ice and ocean 

properties in the CCSM4. J. Climate, 25, 1431–1452.

Janjic, Z. I., 1994: The step-mountain Eta coordinate model: Further developments of the 

convection, viscous sublayer and turbulence closure schemes. Mon. Wea. Rev., 

122, 927–945.

Janjic, Z. I., 2000: Comments on “Development and evaluation of a convection scheme 

for use in climate models”. J. Atmos. Sci., 57, 3686.

Janjic, Z. I., 2002: Nonsingular implementation of the Mellor–Yamada level 2.5 scheme 

in the NCEP Meso model. NCEP Office Note, No. 437, 61 pp.

Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. 

Meteor., 43, 170–181. 

Kalnay, E. et al., 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. 

Meteor. Soc., 77, 437–471.

Kendall, M. G., 1975: Rank correlation methods. Griffin, London. 

Kozo, T., 1979: Evidence for sea breezes on the Alaskan Beaufort Sea coast. Geophys. 

Res. Lett., 6, 849–852.

Kozo, T. L., 1980: Mountain barrier baroclinity effects on surface winds along the 

Alaskan Arctic coast. Geophys. Res. Lett., 7, 377–380.

Kozo, T. L., 1982a: An observational study of sea breezes along the Alaskan Beaufort 

Sea coast: Part I. J. Appl. Meteor., 21, 891–905.

Kozo, T. L., 1982b: A mathematical model of sea breezes along the Alaskan Beaufort 

Sea coast: Part II. J. Appl. Meteor., 21, 906–924.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

196

Kozo, T. L. and R. Q. Robe, 1986: Modeling winds and open‐water buoy drift along the 

eastern Beaufort Sea coast, including the effects of the Brooks Range. J. Geophys. 

Res., 91, 13011–13032.

Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field 

in a cloud model. J. Climate Appl. Meteor., 22, 1065–1092.

Lindsay, R. W. and J. Zhang, 2005: The thinning of Arctic sea ice, 1988–2003: Have we 

passed a tipping point? J. Climate, 18, 4879–4894.

Liu, H., P. Q. Olsson, and K. Volz, 2008: SAR observation and modeling of gap winds in 

the Prince William Sound of Alaska. Sensors, 8, 4894–4914.

Liu, Z.-Q. and F. Rabier, 2002: The interaction between model resolution, observation 

resolution and observation density in data assimilation: A one-dimensional study. 

Q. J. R. Meteor. Soc., 128, 1367–1386. 

Lynch, A. H., A. G. Slater, and M. Serreze, 2001: The Alaskan Arctic frontal zone: 

Forcing by orography, coastal contrast, and the boreal forest. J. Climate, 14, 

4351–4362.

Lynch, A. H., J. A. Curry, R. D. Brunner, and J. A. Maslanik, 2004: Toward an integrated 

assessment of the impacts of extreme wind events on Barrow, Alaska. Bull. Amer. 

Meteor. Soc., 85, 209–221.

Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13, 245–259.

Maslowski, W., J. C. Kinney, M. Higgins, and A. Roberts, 2012: The future of Arctic sea 

ice. Annu. Rev. Earth Planet. Sci., 40, 625–654. 

Meek, D. W. and J. L. Hatfield, 1994: Data quality checking for single station 

meteorological databases. Agric. For. Meteorol., 69, 85–109.

Mellor, G. L. and T. Yamada, 1982: Development of a turbulence closure model for 

geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851–875.

Mesinger, F. et al., 2006: North American Regional Reanalysis. Bull. Amer. Meteor. 

Soc., 87, 343–360.

Milbrandt, J. A. and M. K. Yau, 2005: A multimoment bulk microphysics 

parameterization. Part I: Analysis of the role of the spectral shape parameter. J. 

Atmos. Sci., 62, 3051–3064.

Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: 

Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlatedk model for the longwave. J. Geophys. Res., 102, 16663–16682.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

197

Moore, G. W. K., 2012: Decadal variability and a recent amplification of the summer 

Beaufort Sea High. Geophys. Res. Lett., 39, L10807, doi: 

10.1029/2012GL051570.

Moore, G. W. K. and R. S. Pickart, 2012: Northern Bering Sea tip jets. Geophys. Res. 

Lett., 39, L08807, doi: 10.1029/2012GL051537.

Morrison, H. C., M. D. Shupe, and J. A. Curry, 2003: Modeling clouds observed at 

SHEBA using a bulk parameterization implemented into a single-column model. 

J. Geophys. Res., 108, 4255, doi:10.1029/2002JD002229.

Morrison, H. C., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on 

the development of trailing stratiform precipitation in a simulated squall line: 

Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 991–1007.

Olsson, P. Q. and Harrington, J. Y., 2000: Dynamics and energetics of the cloudy 

boundary layer in simulations of off-ice flow in the marginal ice zone. J. 

Geophys. Res., 105, 11889–11899.

Onogi, K. et al., 2007: The JRA-25 Reanalysis. J. Meteor. Soc. Japan, 85, 369–432.

Overland, J. E., 2009: Meteorology of the Beaufort Sea. J. Geophys. Res., 114, C00A07, 

doi: 10.1029/2008JC004861.

Parish, T. R. and D. H. Bromwich, 1987: The surface windfield over the Antarctic ice 

sheets. Nature, 328, 51–54.

Parish, T. R. and D. H. Bromwich, 1991: Continental-scale simulation of the Antarctic 

katabatic wind regime. J. Climate, 4, 135–146.

Parish, T. R. and J. J. Cassano, 2003: The role of katabatic winds on the Antarctic surface 

wind regime. Mon. Wea. Rev., 131, 317–333.

Parkinson, C. L. and W. M. Washington, 1979: A large scale numerical model of sea ice. 

J. Geophys. Res., 84, 311–337.

Parrish, D. and J. Derber, 1992: The National Meteorological Center’s spectral statisticalinterpolation analysis system. Mon. Wea. Rev., 120, 1747–1763.

Picou, J. S. et al., 2009: Community impacts of the Exxon Valdez oil spill: A synthesis 

and elaboration of social science research. Synthesis: Three decades of research 

on socioeconomic effects related to offshore petroleum development in coastal 

Alaska. S. R. Braund and J. Kruse, eds. Anchorage, Alaska: Steven R. Braund & 

Associates, 279–310.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

198

Pleim, J. E., 2007: A combined local and nonlocal closure model for the atmospheric 

boundary layer. Part I: Model description and testing. J. Appl. Meteor. Climatol., 

46, 1383–1395.

Porter, D. F., J. J. Cassano, and M. C. Serreze, 2011: Analysis of the Arctic atmospheric 

energy budget in WRF: A comparison with reanalyses and satellite observations. 

J. Geophys. Res., 116, D22108, doi: 10.1029/2011JD016622.

Reed, M. et al., 1999: Oil spill modeling towards the close of the 20th century: Overview 

of the state of the art. Spill Sci. Technol. Bull., 5, 3–16.

Reek, T., S. R. Doty, and T. W. Owen, 1992: A deterministic approach to the validation 

of historical daily temperature and precipitation data from the Cooperative 

Network. Bull. Amer. Meteor. Soc., 73, 753–765.

Rienecker, M. M. et al., 2011: MERRA: NASA's Modern-Era Retrospective Analysis for 

Research and Applications. J. Climate, 24, 3624–3648.

Ryan, B. F., 1996: On the global variation of precipitating layer clouds. Bull. Amer. 

Meteor. Soc., 77, 53–70.

Saha, S. et al., 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. 

Meteor. Soc., 91, 1015–1057.

Schwerdtfeger, W., 1974: Mountain barrier effect on the flow of stable air north of the 

Brooks Range. Proc. of the 24th Alaskan Science Conf., Fairbanks, 204–208.

Serreze, M. C. and A. P. Barrett, 2008: The summer cyclone maximum over the central 

Arctic Ocean. J. Climate, 21, 1048–1065.

Shafer, M. A., C. A. Fiebrich, D. Arndt, S. E. Fredrickson, and T. W. Hughes, 2000: 

Quality assurance procedures in the Oklahoma Mesonetwork. J. Atmos. Oceanic

Technol., 17, 474–494. 

Shupe, M. D. and J. M. Intrieri, 2004: Cloud radiative forcing of the Arctic surface: The 

influence of cloud properties, surface albedo, and solar zenith angle. J. Climate, 

17, 616–628.

Shulski, M. D. and G. Wendler, 2007: The climate of Alaska. University of Alaska Press, 

216 pp.

Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. 

Huang, W. Wang, and J. G. Powers, 2008: A description of the Advanced 

Research WRF Version 3. NCAR Tech Note, NCAR/TN–475+STR, 113 pp.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

199

Small, D., E. Atallah, and J. Gyakum, 2011: Wind regimes along the Beaufort Sea coast 

favorable for strong wind events at Tuktoyaktuk. J. Appl. Meteor. Climatol., 50, 

1291–1306.

Stegall, S. T. and J. Zhang, 2012: Wind field climatology, changes, and extremes in the 

Chukchi–Beaufort Seas and Alaska North Slope during 1979–2009. J. Climate, 

25, 8075–8089. 

Stroeve, J. C., M. C. Serreze, M. M. Holland, J. E. Kay, J. Malanik, and A. P. Barrett,

2012: The Arctic's rapidly shrinking sea ice cover: A research synthesis. Clim. 

Change, 110, 1005–1027.

Sukoriansky, S., B. Galperin, and V. Perov, 2006: A quasi-normal scale elimination 

model of turbulence and its application to stably stratified flows. Nonlinear 

Process. Geophys., 13, 9–22.

Tao, W.-K., J. Simpson, and M. McCumber, 1989: An ice-water saturation adjustment.

Mon. Wea. Rev., 117, 231–235.

Tao, W.-K. and J. Simpson, 1993: The Goddard cumulus ensemble model. Part I: Model 

description. Terr. Atmos. Ocean. Sci., 4, 35–72.

Tastula, E.-M., T. Vihma, and E. L. Andreas, 2012: Evaluation of Polar WRF from 

modeling the atmospheric boundary layer over Antarctic sea ice in autumn and 

winter. Mon. Wea. Rev., 140, 3919–3935.

Thompson, G., R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter 

precipitation using an improved bulk microphysics scheme. Part I: Description 

and sensitivity analysis. Mon. Wea. Rev., 132, 519–542.

Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of 

winter precipitation using an improved bulk microphysics scheme. Part II: 

Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 5095–

5115.

Trenberth, K. E., D. P. Stepaniak, J. W. Hurrell, and M. Fiorino, 2001: Quality of 

reanalyses in the tropics. J. Climate, 14, 1499–1510.

Uppala, S. M. et al., 2005: The ERA-40 re-analysis. Q. J. R. Meteorol. Soc., 131, 2961–

3012.

Wade, C. G., 1987: A quality control program for surface mesometeorological data. J. 

Atmos. Oceanic Technol., 4, 435–453.

Webler, T. and F. Lord, 2010: Planning for the human dimensions of oil spills and spill 

response. Environ. Manage., 45, 723–738.
Beaufort and Chukchi Seas Mesoscale Meteorology Modeling Study

200

Wilks, D. S., 2006: Statistical methods in the atmospheric sciences. 2nd ed. Academic 

Press, 627 pp.

Wilson, A. B., D. H. Bromwich, and K. M. Hines, 2011: Evaluation of Polar WRF 

forecasts on the Arctic System Reanalysis domain: Surface and upper air analysis. 

J. Geophys. Res., 116, D11112, doi: 10.1029/2010JD015013.

Yue, S., P. Pilon, B. Phinney, and G. Cavadias, 2002: The influence of autocorrelation on 

the ability to detect trend in hydrological series. Hydrol. Processes, 16, 1807–

1829.

Zhang, C., Y. Wang, and K. Hamilton, 2011: Improved representation of boundary layer 

clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke 

cumulus parameterization scheme. Mon. Wea. Rev., 139, 3489–3513.

Zhang, X. and J. Zhang, 2001: Heat and freshwater budgets and pathways in the Arctic 

Mediterranean in a coupled ocean/sea-ice model. J. Oceanogr., 57, 207–237.

Zhang, X., J. E. Walsh, J. Zhang, U. S. Bhatt, and M. Ikeda, 2004: Climatology and 

interannual variability of arctic cyclone activity: 1948–2002. J. Climate, 17, 

2300–2317.
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Appendix A: Structure of the Chukchi–Beaufort Region Meteorological Database
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Entity Relationship Diagram (ERD)

Table Descriptions

STATION Information related to the individual data collection sources. 

Contains the unique STATIONID along with sensor and 

operational information.

STATIONHISTORY Keeps a history of changes made to the STATION table. 

Needed to query the full range of available dates for a 

particular station without querying the actual data. Invalid 

entries should be removed from this table in order to 

maintain a clean history. It is designed to keep track of 

changes such as those to a WBANID or sensor.

STATIONTYPE Lookup table for the available station types such as buoy, 

land, well site, etc.

SOURCEAGENCY Lookup table for the contact information of each data source

COUNTRY Lookup table for the country where data was recorded

PARAMETER Lookup table of available data parameters (e.g., temperature, 

wind speed)

TESTRESULT Lookup table for the quality-control flag definitions

STATIONREADING Relational table containing unique location/temporal stationbased events

STATIONDATA Contains the data collected for each station-reading event

MONTHLYDATA Monthly summary statistics (min,max,avg,stdev) for 

standard climatology stations

YEARLYDATA Summary statistics (min,max,avg,stdev) for standard 

climatology stations aggregated by all available data

SHIPREADING Relational table containing location/temporal events for 

ships

SHIPDATA Contains the data collected for each ship-reading event

QUIKSCATREADING Contains the unique location/temporal events for the 

QuikSCAT dataset

QUIKSCATDATA Contains the values (wind speed and direction) for each 

QuikSCAT reading

WRFREADING Contains the unique grid points, location, and time instances 

of each WRF-model event

WRFDATA Contains the data values associated with each WRF reading
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Appendix B: Statistical Evaluation Metrics

In the discussion of the statistical performance of modeling results throughout this report, 

a number of evaluation metrics are employed. Though these are standard parameters 

frequently used in verification analyses of atmospheric model data, a summary is 

presented here for the sake of completeness. The statistical quantities primarily discussed 

are root-mean-square error (RMSE), mean bias, and correlation. Definitions of these 

metrics are presented below; further discussion can be found in Wilks (2006). All other 

statistical quantities used in the report are defined in the sections in which they are 

introduced.

Root-mean-square error (RMSE)

RMSE is a statistic commonly used to measure the overall accuracy of a modeled field 

through direct comparison with observed values. The deviations of the modeled values 

from their observed counterparts are squared and averaged, before calculating the square 

root of the mean, like so:

RMSE = 1

N (xk − ok )

2

k=1

N

∑

Both here and below, N is the total number of observations, x is the modeled value, and o

is the observed value. Due to the squaring of the deviations, this statistic is sensitive to 

outliers and thus penalizes large modeled errors. Taking the square root allows the 

statistic to retain the same units as the variable being evaluated.

Mean bias

In contrast to RMSE, which evaluates the accuracy of a modeled field at individual 

observational locations, mean bias is a measure of the field as a whole through the 

calculation of the average deviation of the modeled field:

BIAS = 1

N (xk − ok )

k=1

N

∑

Since the deviations are not squared before being summed, a deviation at one station 

location can be offset by the deviation at another. Thus, this quantity is used to present 

the overall bias of a modeled field, but gives no information about the accuracy at 

individual locations.
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Correlation

Correlation, as used here, is a statistical metric used to indicate the accuracy of the 

“shape” of the modeled field, while disregarding any inherent model bias. The standard 

Pearson product-moment correlation coefficient is used here for scalar variables:

CORR = (xk − x )(ok − o) k=1

N

∑

(xk − x )

2 (ok − o)2

k=1

N

k=1 ∑ N

∑

Correlation is a unitless quantity that varies from 0 to 1, with 1 representing a modeled 

field that is perfectly correlated to the observations (i.e., they have an identical “shape”). 

Because the standard correlation can only be used for scalar variables, the correlation of 

the vector wind field is calculated using the technique described in Crosby et al. (1993), 

in which it is computed using the zonal and meridional components of the wind vector (U 

and V, respectively). The vector correlation represents a generalization of the standard 

scalar correlation, and thus has the same properties.
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