NOAA Technical Memorandum GLERL-155

GREAT LAKES ICE CLIMATOLOGY UPDATE: WINTER 2006 – 2011 

DESCRIPTION OF THE DIGITAL ICE COVER DATASET

Jia Wang1

Raymond A. Assel2

Steven Walterscheid2

Anne H. Clites1

Xuezhi Bai2

1

NOAA, Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI 

48108

2

Cooperative Institute for Limnology and Ecosystems Research, 4840 S. State Rd., Ann Arbor, 

MI 48108

September 2012

UNITED STATES

DEPARTMENT OF COMMERCE

Rebecca Blank

Acting Secretary

NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

Jane Lubchenco

Under Secretary for Oceans & Atmosphere

NOAA Administrator

NAT OI NAL OCEANIC AND ATMOSPHERIC ADMINISTRAT OI N

U.S.DEPARTMENT OF COMMERCE
2

NOTICE

Mention of a commercial company or product does not constitute an endorsement 

by the NOAA. Use of information from this publication concerning proprietary 

products or the tests of such products for publicity or advertising purposes is not 

authorized. This is GLERL Contribution No. 1633.

This publication is available as a PDF file and can be downloaded from GLERL’s 

web site: www.glerl.noaa.gov. Hard copies can be requested from GLERL

Information Services, 4840 S. State Rd., Ann Arbor, MI 48108.

pubs.glerl@noaa.gov.

NOAA’s Mission – To understand and predict changes in Earth’s environment 

and conserve and manage coastal and marine resources to meet our nation’s 

economic, social, and environmental needs. 

NOAA’s Mission Goals:

•	 Protect, restore and manage the use of coastal and ocean resources through an

ecosystem approach to management

•	 Understand climate variability and change to enhance society’s ability to plan

and respond

•	 Serve society’s needs for weather and water information

•	 Support the Nation’s commerce with information for safe, efficient, and

environmentally sound transportation

•	 Provide critical support for NOAA’s Mission
3

TABLE OF CONTENTS

1.	 INTRODUCTION .............................................................................................................................5

2.	 DISTRIBUTION OF ICE CHARTS BY WINTER SEASON..........................................................6

3. 	 NIC ASCII GRID FILES...................................................................................................................6

3.1 Spatial Resolution and Grid Size .................................................................................................6

	 3.2 File Structure, Record Format, Ice Codes, and File Names.......................................................10

4.	 QUALITY CONTROL PROCESSING...........................................................................................10

4.1 Ice Codes and Land Mask..........................................................................................................10

4.2 Ice Chart Dates...........................................................................................................................11

5.	 ArcGIS FEATURE CLASSES ........................................................................................................12

	 5.1	Preprocessing .............................................................................................................................12

	 5.2	GIS Processing...........................................................................................................................12

6.	 DATAAVAILABILITY ...................................................................................................................12

7.	 ANALYSIS USING THE UPDATED DATASET 1973-2011 ........................................................15

7.1 Seasonal Cycle ...........................................................................................................................15

7.2 Variations...................................................................................................................................16

7.3 Interannual Variability................................................................................................................17

	 7.4	Long-term Trend ........................................................................................................................17

	 7.4	Periodicity..................................................................................................................................20

8.	 REFERENCES ................................................................................................................................23

9.	 ACKNOWLEDGEMENTS.............................................................................................................24

APPENDICES	

A-1 	 Software to convert 510×516 grid to 1024×1024 CW grid ..........................................................25

A-2	 Comparison of lake-averaged ice cover converted from 510×516 to 1024×1024 grid ................32

A-3	 Land mask applied to NIC ASCII files to create standard land-water grid cells..........................34

A-4	 Software for adding geographic location to ice concentration values for conversion of ice

 grids to ArcGIS .............................................................................................................................36

LIST OF FIGURES

Figure 1. 	 Long-term (1973-2002) mean annual maximum ice cover (in percentage) in the

	 Great Lakes. Note that ice cover (or concentration) ranges from 0% (ice free) to 100%

(complete ice cover). (From Bai et al. 2011.)......................................................................15

Figure 2.	 The NIC twice-weekly climatology of ice cover (in percentage) in the Great Lakes

	 averaged for the period 1973-2011 ......................................................................................15

Figure 3.	 Variation of weekly ice coverage (in %) of the six lakes for the period 1973-2011

	 with one standard deviation (STD) by the vertical bars ......................................................16
4

Figure 4.	 Twice weekly time series of ice cover (in %) of all six lakes (a-f) and total Great Lakes

	 (g) for the period 1973-2011................................................................................................18

Figure 5. 	 Winter mean lake ice cover (in %) of all six lakes (a-f) and total Great Lakes ice cover

	 (g) for the period 1973-2011. The linear lines are the trend in annual lake ice coverage

calculated from the least squares fit method........................................................................19

Figure 6.	 Winter surface air temperature (SAT) trend (

o

C per decade) for the period 1973-2010,

	 calculated using the least squares fit. (from Wang et al. 2012)............................................21

Figure 7.	 Spectral analysis of ice cover anomalies in all six lakes with the linear trends removed.

	 The dotted curves are the 95% significance level. The peaks are marked with the

	 corresponding periods in years. (from Wang et al. 2012)....................................................22

LIST OF TABLES

Table 1. Summary of ice chart date statistics.......................................................................................6

Table 2.	 Distribution of ice chart dates by winter season ....................................................................7

Table 3.	 Winter 2011 similar ice cover patterns ................................................................................11

Table 4. Trends in annual lake ice coverage calculated by linear least squares fit for the period 

	 1973 to 2010 (see text for detail). In row 2, a is the intercept constant (the value in year

1973) (in km2); in row 3, b is the slope of the line (namely, the rate of change in ice area

	 with time, km2 per year). Row 4 defines relative trend in annual lake ice coverage

	 (% per year) = (b/ a) x 100%. Row 5 is the total loss of annual lake ice coverage over

	 the entire 38-year record relative to 1973 (from 1973 to 2010) ..........................................20
5

Great Lakes Ice Climatology Update: Winter 2006 – 2011 

Description of the Digital Ice Cover Dataset

Jia Wang

Raymond A. Assel

Steven Walterscheid

Anne H. Clites

Xuezhi Bai

1. INTRODUCTION

A 6-winter digital ice cover data set consisting of 389 ice charts of total ice concentration (the fraction

of a unit of surface area covered by ice), was downloaded from the National Ice Center (NIC) website,

http://www.natice.noaa.gov/products/great_lakes.html. This report updates GLERL’s 33-winter ice

climatology dataset for total ice concentration (Assel 2003, Assel 2005). Data processing and quality

control procedures, file structure, format, and naming conventions are described. The data are available 

as fixed formatted ASCII grids (2.55 km for winter 2006 and 1.275 km for winters 2007-2011) and

has been converted to ArcMap feature classes for GIS users. Temporal distribution of the ice charts 

is summarized in tables. A planned companion report will contain an analysis of these data including

spatial patterns of dates of first ice, last ice, ice duration, and ice concentration anomalies [relative to a 

30-winter (1973-2002) base period] for each lake by winter season. It will also contain lake averages of

total ice cover concentration that portray the seasonal progression of ice cover each winter.

Since the last updated ice atlas (Assel 2005), there has been significant change in ice cover on the Great

Lakes. This update of the ice atlas should be useful to the research community as well as others who rely

on Great Lakes ice data for planning purposes. The technical memorandum that updated the ice atlas for 

winters 2003, 2004, and 2005 (Assel 2005) did not include ArcGIS feature classes, so those are included

here.

Lake ice cover is a sensitive indicator of regional climate and climate change (Assel et al. 1995; Assel

and Robertson 1995; Magnuson et al. 1997, 2000). Seasonal ice cover repeats each year with large

interannual variability. For example, the maximum ice coverage over all of the Great Lakes was 95% in

1979 and only 11% in 2002 (Bai et al. 2012). Possible contributors include interannual and interdecadal

climate variability, and long-term trends (Assel 1990), some of which may be related to global climate

warming. Even in response to the same climate forcing, Great Lakes ice cover may experience different

spatial and temporal variability due to an individual lake’s orientation, depth (i.e., water heat storage,

Assel et al. 2003), and turbidity (i.e., albedo due to sedimentation). To predict seasonal and interannual

variability of lake ice cover using statistical analysis, the first step is to investigate the predictability, 

which is measured by the ratio of the mean (climatology) to standard deviations (i.e., variability). In

general, the larger the ratio between the two, the higher the predictability. In other words, if the standard

deviation is larger than the mean, the mean is a poor predictor (Assel et al. 2004). In addition, under the

same climate forcing, lake ice may respond differently in the upper and lower Great Lakes.
6

2. DISTRIBUTION OF ICE CHARTS BY WINTER SEASON

The number of ice charts per winter season, Table 1, ranged from 45 to 55, with the exception of the last

winter, 2011, when it was 151. The NIC begin producing daily charts for GLERL in winter 2011 because

of a request from GLERL’s hydrodynamic modelers to provide daily ice chart analysis for their use.

The dates of first ice charts range from the last week in November to the first week in December, and

the dates of the last ice usually occur in early to mid May. Winter 2010 was mild, and the last ice chart

occurred in mid-April. The number of days between the first and last ice chart ranges from 162 to 130,

with a median value of 158. The average number of days between consecutive ice charts ranged from

approximately 3.1 to 1.1 days, with a median value of approximately 3.25 days. The date of individual

ice charts for each winter is given in Table 2.

 Table 1. Summary of ice chart date statistics

3. NIC ASCII GRID FILES

The dates of NIC ASCII grid files are the dates these files were produced at NIC and not necessarily

the date the data in these files was observed. This is discussed further in section 4.2. The following

metadata for NIC ASCII grid files was abstracted from a file on the NIC web site at http://www.natice.

noaa.gov/products/great_lakes.html under Category “Daily Ice Concentration,” and Format “Metadata”

for winter 2009: “Data for the analyses can go back 96 hours from when they are completed.”... “They

are based on an analysis and integration of all available data on ice conditions, including weather and

oceanographic information, visual observations from shore, ship and aircraft, airborne radar, satellite 

imagery and climatological information.” The reader is referred to that web site for more metadata

details.

3.1 Spatial Resolution and Grid Size 

National Ice Center ASCII grid files and NIC graphic ice chart files were downloaded from the NIC

website for winters 2006 through 2011. As in Assel (2003, 2005) and as described in Norton et al.

(2000) the ice data for winter 2006 was mapped into a Mercator projection and re-sampled to a 510×516

pixel grid covering the Great Lakes area with a grid resolution of 2.55 km. The latitude of true scale is

approximately 45° N, actual grid resolution varies with latitude, L ≈ (2.55*cosf/cos45), where f is the

latitude. Starting in winter 2007, and for subsequent winters, the data was re-sampled to a 1024×1024

pixel grid. The grid spacing for the 1024×1024 grid is specified at the equator as 1.8 km. Actual grid

resolution is d cosf, where d is the spatial resolution at the equator, and f is the latitude (Schwab et al.

1992). For example, the grid spacing for the 1024×1024 grid is 1.275 km at 45°N.

Winter 

Season

No. of 

Charts

Date of First 

Ice Chart

Date of Last 

Ice Chart

Days Between 

First and Last 

Ice Chart

Average No. Days

Between Ice Charts

2006 46 28-Nov 04-May 157 3.43 

2007 46 04-Dec 10-May 158 3.46 

2008 55 26-Nov 15-May 171 3.13 

2009 45 08-Dec 07-May 151 3.38 

2010 46 07-Dec 15-Apr 130 2.85 

2011 151 02-Dec 13-May 162 1.08 
7

Table 2. Distribution of ice chart dates by winter season

Winter 2006 Winter 2007 Winter 2008 Winter 2009 Winter 2010

Year Mon Day Jday Year Mon Day Jday Year Mon Day Jday Year Mon Day Jday Year Mon Day Jday

2005 11 28 332 2006 12 4 338 2007 11 26 330 2008 12 8 342 2009 12 7 341

2005 12 1 335 2006 12 7 341 2007 11 29 334 2008 12 11 345 2009 12 9 343

2005 12 5 339 2006 12 11 345 2007 12 3 337 2008 12 15 349 2009 12 14 348

2005 12 8 342 2006 12 14 348 2007 12 6 340 2008 12 18 352 2009 12 17 351

2005 12 12 346 2006 12 18 352 2007 12 10 344 2008 12 22 356 2009 12 21 355

2005 12 15 349 2006 12 21 355 2007 12 13 347 2008 12 25 359 2009 12 24 358

2005 12 20 354 2006 12 25 359 2007 12 17 351 2008 12 29 363 2009 12 28 362

2005 12 22 356 2006 12 28 362 2007 12 20 354 2009 1 1 366 2009 12 31 365

2005 12 26 360 2007 1 1 366 2007 12 24 358 2009 1 5 370 2010 1 5 370

2005 12 29 363 2007 1 4 369 2007 12 27 361 2009 1 8 373 2010 1 6 371

2006 1 2 367 2007 1 8 373 2007 12 31 365 2009 1 12 377 2010 1 7 372

2006 1 5 370 2007 1 11 376 2008 1 3 368 2009 1 15 380 2010 1 9 374

2006 1 9 374 2007 1 15 380 2008 1 7 372 2009 1 19 384 2010 1 10 375

2006 1 12 377 2007 1 18 383 2008 1 10 375 2009 1 22 387 2010 1 12 377

2006 1 16 381 2007 1 22 387 2008 1 14 379 2009 1 26 391 2010 1 13 378

2006 1 19 384 2007 1 25 390 2008 1 17 382 2009 1 29 394 2010 1 14 379

2006 1 23 388 2007 1 29 394 2008 1 21 386 2009 2 2 398 2010 1 19 384

2006 1 26 391 2007 2 1 397 2008 1 24 389 2009 2 5 401 2010 1 21 386

2006 1 30 395 2007 2 5 401 2008 1 28 393 2009 2 9 405 2010 1 25 390

2006 2 2 398 2007 2 8 404 2008 1 31 396 2009 2 12 408 2010 1 28 393

2006 2 6 402 2007 2 12 408 2008 2 4 400 2009 2 16 412 2010 2 1 397

2006 2 9 405 2007 2 15 411 2008 2 7 403 2009 2 19 415 2010 2 4 400

2006 2 13 409 2007 2 19 415 2008 2 11 407 2009 2 23 419 2010 2 8 404

2006 2 16 412 2007 2 22 418 2008 2 14 410 2009 2 26 422 2010 2 15 411

2006 2 20 416 2007 2 26 422 2008 2 18 414 2009 3 2 427 2010 2 18 414

2006 2 23 419 2007 3 1 426 2008 2 21 417 2009 3 5 430 2010 2 22 418

2006 2 27 423 2007 3 5 430 2008 2 25 421 2009 3 10 435 2010 2 25 421

2006 3 2 426 2007 3 8 433 2008 2 28 424 2009 3 12 437 2010 3 1 426

2006 3 6 430 2007 3 12 437 2008 3 3 428 2009 3 17 442 2010 3 4 429

2006 3 9 433 2007 3 15 440 2008 3 6 431 2009 3 19 444 2010 3 9 434

2006 3 13 437 2007 3 19 444 2008 3 10 435 2009 3 24 449 2010 3 10 435

2006 3 16 440 2007 3 22 447 2008 3 13 438 2009 3 26 451 2010 3 11 436

2006 3 20 444 2007 3 26 451 2008 3 17 442 2009 3 31 456 2010 3 13 438

2006 3 23 447 2007 3 29 454 2008 3 19 444 2009 4 1 457 2010 3 14 439

2006 3 27 451 2007 4 2 458 2008 3 20 445 2009 4 2 458 2010 3 16 441

2006 3 30 454 2007 4 5 461 2008 3 25 450 2009 4 7 463 2010 3 17 442

2006 4 3 458 2007 4 9 465 2008 3 26 451 2009 4 9 465 2010 3 18 443

2006 4 6 461 2007 4 12 468 2008 3 27 452 2009 4 14 470 2010 3 23 448

2006 4 10 465 2007 4 16 472 2008 4 1 457 2009 4 16 472 2010 3 24 449

2006 4 13 468 2007 4 19 475 2008 4 2 458 2009 4 20 476 2010 3 25 450

2006 4 17 472 2007 4 23 479 2008 4 3 459 2009 4 23 479 2010 3 30 455

2006 4 20 475 2007 4 26 482 2008 4 8 464 2009 4 27 483 2010 4 1 457

2006 4 24 479 2007 4 30 486 2008 4 9 465 2009 4 30 486 2010 4 5 461

2006 4 27 482 2007 5 3 489 2008 4 10 466 2009 5 4 490 2010 4 8 464
8

Table 2. Distribution of ice chart dates by winter season

Winter 2006 Winter 2007 Winter 2008 Winter 2009 Winter 2010

Year Mon Day Jday Year Mon Day Jday Year Mon Day Jday Year Mon Day Jday Year Mon Day Jday

2006 5 1 486 2007 5 7 493 2008 4 15 471 2009 5 7 493 2010 4 12 468

2006 5 4 489 2007 5 10 496 2008 4 16 472 2010 4 15 471

2008 4 17 473

2008 4 21 477

2008 4 24 480

2008 4 28 484

2008 5 1 487

2008 5 5 491

2008 5 8 494

2008 5 12 498

2008 5 15 501

Winter 2011 Winter 2011 (cont.) Winter 2011 (cont.)

Year Mon Day Jday Year Mon Day Jday Year Mon Day Jday

2010 12 2 336 2011 1 12 377 2011 2 12 408

2010 12 7 341 2011 1 13 378 2011 2 13 409

2010 12 9 343 2011 1 14 379 2011 2 14 410

2010 12 14 348 2011 1 15 380 2011 2 15 411

2010 12 16 350 2011 1 16 381 2011 2 16 412

2010 12 17 351 2011 1 17 382 2011 2 17 413

2010 12 18 352 2011 1 18 383 2011 2 18 414

2010 12 19 353 2011 1 19 384 2011 2 19 415

2010 12 20 354 2011 1 20 385 2011 2 20 416

2010 12 21 355 2011 1 21 386 2011 2 21 417

2010 12 22 356 2011 1 22 387 2011 2 22 418

2010 12 23 357 2011 1 23 388 2011 2 23 419

2010 12 24 358 2011 1 24 389 2011 2 24 420

2010 12 25 359 2011 1 25 390 2011 2 25 421

2010 12 26 360 2011 1 26 391 2011 2 26 422

2010 12 27 361 2011 1 27 392 2011 2 27 423

2010 12 28 362 2011 1 28 393 2011 2 28 424

2010 12 29 363 2011 1 29 394 2011 3 1 425

2010 12 30 364 2011 1 30 395 2011 3 2 426

2010 12 31 365 2011 1 31 396 2011 3 3 427

2011 1 1 366 2011 2 1 397 2011 3 4 428

2011 1 2 367 2011 2 2 398 2011 3 5 429

2011 1 3 368 2011 2 3 399 2011 3 6 430

2011 1 4 369 2011 2 4 400 2011 3 7 431

2011 1 5 370 2011 2 5 401 2011 3 8 432

2011 1 6 371 2011 2 6 402 2011 3 9 433

2011 1 7 372 2011 2 7 403 2011 3 10 434

2011 1 8 373 2011 2 8 404 2011 3 11 435

2011 1 9 374 2011 2 9 405 2011 3 12 436

2011 1 10 375 2011 2 10 406 2011 3 13 437

2011 1 11 376 2011 2 11 407 2011 3 14 438

Jday is the Julian Date. 

Shading indicates dates that were 

adjusted one day earlier than 

downloaded from NIC.
9

Winter 2011 (cont.) Winter 2011 (cont.) Winter 2011 (cont.)

Year Mon Day Jday Year Mon Day Jday Year Mon Day Jday

2011 3 15 439 2011 4 1 456 2011 5 2 487

2011 3 16 440 2011 4 2 457 2011 5 3 488

2011 3 17 441 2011 4 3 458 2011 5 4 489

2011 3 18 442 2011 4 4 459 2011 5 5 490

2011 3 19 443 2011 4 5 460 2011 5 6 491

2011 3 20 444 2011 4 6 461 2011 5 9 494

2011 3 21 445 2011 4 7 462 2011 5 10 495

2011 3 22 446 2011 4 8 463 2011 5 11 496

2011 3 23 447 2011 4 9 464 2011 5 12 497

2011 3 24 448 2011 4 10 465 2011 5 13 498

2011 3 25 449 2011 4 11 466

2011 3 26 450 2011 4 12 467

2011 3 27 451 2011 4 13 468

2011 3 28 452 2011 4 14 469

2011 3 29 453 2011 4 15 470

2011 3 30 454 2011 4 16 471

2011 3 31 455 2011 4 17 472

2011 4 18 473

2011 4 19 474

2011 4 20 475

2011 4 21 476

2011 4 22 477

2011 4 23 478

2011 4 24 479

2011 4 25 480

2011 4 26 481

2011 4 27 482

2011 4 28 483

2011 4 29 484

2011 4 30 485

2011 5 1 486

Table 2. Distribution of ice chart dates by winter season
10

During the 2007 season, 23 ice charts were given in the 510×516 pixel grid, and 23 ice charts were in

the 1024×1024 pixel grid. Software was developed at GLERL to transpose the 510×516 grid ASCII

files to the 1024×1024 grid, see Appendix A-1. A comparison of lake-averaged ice cover for the

1024×1024 grid and 510×516 grid for winter 2007 (23 ice charts) is given in Appendix A-2. There is a

bias for slightly greater lake-averaged ice cover for the 510×516 grids, but as seen in Appendix A-2, the

maximum differences are 2%, and differences are less than 2% in most cases.

3.2 File Structure, Record format, Ice Codes, and File Names

The first six records of each ASCII grid file contain header information in the following order: 1)

the number of columns, 2) number of rows, 3) xllcorner, the X coordinate (longitude) of the lowerleft corner of the grid, expressed in meters from a reference meridian, 4) yllcorner, the Y coordinate

(latitude) of the lower-left corner of the grid expressed in meters from the equator, 5) the cell size in

meters, and 6) the code for no data (-9999). Records 7 through 516 (for the 2.55 km grids), 1030 (for

the 1.275 km grids) contain the ice concentration data in fixed I3 format. Ice concentration values are 0,

5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, and 100 and have units of percent. Land grid cells have a code

of -1. File names encode the year (four digits), month (two digits), and day (two digits) of the ASCII

grid and are proceeded with a “g” for Great Lakes and have a file extension of “.ct”, indicating total ice

concentration. As an example, the file for December 2, 2004 is given as “g20041202.ct”.

4. QUALITY CONTROL PROCESSING

4.1 Ice Codes and Land Mask

The NIC ice codes on the ASCII grid files were converted to GLERL’s ice concentration codes and then

processed through GLERL’s land mask, Appendix A-3, to produce a file with ice concentration codes

and a consistent set of over-water grid cells. Recall that for winter 2006, NIC still produced 510×516

ASCII grids, so the old GLERL land mask (GLmask2.txt) was applied to these data to be consistent

with the NOAA Great Lakes ice atlas data from 1973 to 2005. GLERL developed a new land mask for

the 1024×1024 grid (for winters 2007-2011) based on the NOAA Great Lakes CoastWatch land mask

to be consistent with grid data produced by the GLERL CoastWatch node. Applying the GLERL land

mask, the old 510×516 mask for winter 2006 and the new 1024×1024 mask for winters from 2007

forward, resulted in a number of over-land grid cells on the NIC ASCII file being over-water grid cells

in the GLERL ASCII file. These grid cells were given a code for missing data [-99], and software was

written to estimate the ice concentration for these cells using over-water cells in the proximity of the

missing data cells. These inconsistencies are due to minor differences in the NIC and GLERL [raster]

shoreline in the NIC and GLERL ASCII files. Software similar to that given in Appendix 1 (see “apply

the cw mask to standardize land and water grid cells” in program tran5.for) was developed to fit the

NIC 1024×1024 ASCII grid data to GLERL’s 1024×1024 land mask, and a computer program similar to

fillx07.for in Appendix 1 was used to estimate ice cover concentration for over water grid cells with -99

codes.

A count of the number of grid cells with a code of -99 was calculated for each ASCII grid file after the

land mask was applied. The ASCII file was compared to the original NIC graphic file. If the number of

cells with a code of -99 were small, corrections were applied to the GLERL version of the ASCII file in

a text editor. An example of this occurred on the ASCII file for December 8, 2009 where a small area on

Lake Erie at the head of Long Point was coded 0% ice cover in the ASCII file, and the NIC graphic file

indicated it should have been 100% ice cover. 
11

If there were a large number of cells with a code of -99, that file was not included for further analysis.

This was a relatively rare occurrence. In some cases, the NIC was informed of a questionable raw data

file, and they provided a corrected version of the ASCII file. An example of this occurred for a polygon

in Lake Huron for January 22, 2007 that was coded 30% ice cover, while it should have been 0% ice

cover. The NIC provided a corrected version of that ASCII grid. 

4.2 Ice Chart Dates

Spatial distribution patterns of ice concentration were compared between graphic files produced on

the NIC web site and GLERL graphic files produced from NIC ASCII files downloaded from the NIC

web site. The objective of this comparison was to see if the NIC Graphic and NIC ASCII file had the

same date for the same spatial ice cover patterns. Dates were not always the same for the same spatial

patterns. When dates were different, the graphic file date (which was always a day earlier than the ASCII

grid file date) was used to correct the date on the ASCII file. The rationale for making that adjustment

is that since the graphic file existed a day earlier, it is likely that the ASCII grid file, which is a raster

product produced from ARC/GIS coverage, was created the next day. Lending credence to this rationale

is the fact that the Canadian Ice Service (CIS) and NIC each produced one ice chart each week during

the winter and exchange data during the season in near real-time. The CIS ice chart file is used by NIC

to produce special products such as the ASCII grid file for GLERL’s use and different products for other

users. Thus, it is likely that sometimes the CIS ice chart vector coverage is converted to ASCII raster 

grid coverage a day after they are received at NIC from CIS. The changed dates on ASCII grid files are 

summarized, indicated by shading, in Table 2. Note that no changes were made for winters 2006, 2007,

and 2011. In winter 2011, there were 12 cases when the NIC ASCII file dates were 1 day later than the

NIC graphic files, Table 3. The frequency of 1-day difference in 10 of these 12 cases occurred every 7th

day, indicating that it was CIS data. However, it was not possible to adjust the dates on these 12 NIC

ASCII grids, because there were already other NIC ASCII grid files on these dates. The reason for this is

not known at this time but may be related to the NIC production of ice charts each day instead of twice

per week as was the case in other winters. 

Table 3. Winter 2011

similar ice cover patterns

NIC ASCII NIC Graph

Dec. 28 Dec. 27

Jan. 04 Jan. 03

Jan. 06 Jan. 05

Jan. 11 4an. 10

Jan. 18 Jan. 17

Jan. 25 Jan. 24

Feb. 01 Jan. 31

Feb. 08 Feb. 07

Feb. 15 Feb. 14

Feb. 22 Feb. 21

Mar. 01 Feb. 28

Mar. 08 Mar. 07
12

5. ArcGIS FEATURE CLASSES

5.1 Preprocessing

In order to complete the update of the Great Lakes ice atlas, graphic images (.jpg files) and GIS feature

classes were created for all 389 ice charts for winters 2006 through 2011. In addition, since feature

classes (also known as shapefiles) were never produced for the period 2003-2005 (Assel 2005), those

143 grid files also needed to be converted to complete the climatology for 1973-2011. It should be

noted that the 1973-2002 original Great Lakes ice atlas included products that were based on different

ice attribute files such as ice form and ice age. The 2003-2011 additions to the ice atlas are based on a

single ASCII grid file from the NIC, total ice concentration. For the 2003-2005 ASCII grid files, only

‘observed data’ files for total ice concentration ending with ‘2.ct’ were used from the ftp download site:

http://www.glerl.noaa.gov/ftp/publications/tech_reports/glerl-135/Appendix2/DailyGrids

The first step in the process was to add the geographic location to each of the ASCII grid ice

concentration values. This was accomplished by one of three IDL programs, depending on the year,

since the formats of the ASCII grids are somewhat different.

ASCII grid IDL conversion program

2003-2005 LatLon.pro

2006 Cwmap_auto.pro

2007-2011 Cwmap1024_auto.pro

For the 2003-2005 files, the latitude and longitude are read in from a file (Assel 2005). For all other

input files, a subroutine is called that associates each grid pixel with its corresponding latitude and

longitude coordinates. All of these programs were designed to process a year of ASCII grid files at a

time. Once the ice concentration values were associated with location coordinates, they were ready to be

brought into ArcGIS. The IDL program, Cwmap1024_auto.pro is found in Appendix A-4.

5.2 GIS Processing

Each file was first imported into Excel and saved as a comma-delimited (.csv) file in order to be

imported to ArcView 9.2 (later 10.0). Files were loaded, geographic coordinates set (GCS North

American 1983), land points removed, and finally saved as feature classes (.shp). Symbology was set to

match the previous ice atlas .e00 files as closely as possible. Once the feature class file was complete,

the Great Lakes ice concentration map was exported as a graphic image (.jpg) file. ArcModelBuilder

was used to semi-automate this process. Feature classes (.shp) and graphic image files (.jpg) are zipped

together by winter season.

6. DATA AVAILABILITY

The data is available either for an individual winter season or for all winter seasons together. Instructions

for obtaining data are on the next page. You can also find this data on the GLERL ice cover webpage:

http://www.glerl.noaa.gov/data/pgs/glice/glice.html. This Technical Memorandum is available at http://

www.glerl.noaa.gov/ftp/publications/tech_reports/glerl-155/.
13

Individual Winter Seasons. A total of 582 ice charts are available in ASCII grid files and in ArcGIS 

feature classes. The data are arranged by winter season from 2003 through 2011. Note that winters 2003-

2006 are in the 510×516 grid format, and winters 2007 through 2011 are in the 1024×1024 grid format.

Each winter season contains the following files in Zip compression:

1) Graphic files of total ice concentration

2) ASCII grid files

3) ArcGIS feature classes

An example for winter 2009 is given below.

To view ice chart data for a winter season, place cursor on it and click.

2003 2004 2005

2006 2007 2008

2009 2010 2011

Period of Record. For those users who want a single zipped file for all winters, there is grid2003-

2011.zip for ASCII grids and gis2003-2011.zip for ArcGIS feature classes. 
14

ICE Charts – 2009 Winter

Click on thumbnails for larger images [note not all charts for 2009 are included in this example]

< back to winter season selection page

For 2009 winter data to be used with ArcGIS software, download gis2009.zip (32,260 KB)

For 2009 winter ASCII files, download grid2009.zip (1,156 KB )

For 2009 winter ice chart images, download jpg2009.zip (8,772 KB) 

Dec 22 2008 Dec 29 2008 Jan 5 2009 Jan 12 2009

Jan 19 2009 Jan 26 2009 Feb 2 2009 Feb 9 2009

Feb 16 2009 Feb 23 2009 Mar 2 2009 Mar 10 2009

Mar 17 2009 Mar 24 2009 Mar 31 2009 Apr 7 2009
15

7. THE ANALYSIS USING THE UPDATED DATASET 1973-2011

7.1 Seasonal cycle

The spatial long-term (1973-2002) mean annual maximum ice cover in the Great Lakes was constructed

using this dataset (Figure 1). The seasonal ice cover cycle (Figures 2-3) is computed based on the

1973-2011 data for the six lakes. The typical seasonal ice cycle of the Great Lakes consists of an initial

formation (ice onset) period, followed by a growing period in which the annual maximum areal extent is

reached, then a melting (break-up) period. The typical ice cycle has an ice-on date on all lakes occurring

in December. The growth of ice cover is remarkably similar in all of the Great Lakes. Figure 2 also

shows the progression of maximum ice cover for each lake.

Figure 1. Long-term (1973-2002) 

mean annual maximum ice 

cover (in percent) in the Great 

Lakes. Note that ice cover (or 

concentration) ranges from 0% 

(ice free) to 100% (complete ice 

cover). (from Bai et al. 2011).

Figure 2. The NIC twiceweekly climatology of ice 

cover (in percent) in the 

Great Lakes, averaged from 

the period 1973-2011. 
16

7.2 Variations 

The corresponding weekly variations, defined by standard deviations (SDs) of annual ice cover, are also

shown in Figure 3. The months with the largest SDs are those most sensitive to atmospheric forcing, and

the records with large SDs are the periods for better detecting interannual and decadal variability. One

unique feature of Great Lakes ice cover, in contrast to Arctic sea ice cover (Wang and Ikeda 2001), is

that the SDs are equivalent or even larger in magnitude than the means in the deep-water lakes, while

SDs of sea ice are much smaller than the means in the Arctic Ocean (Wang and Ikeda 2001). This

indicates that: 1) the natural variability of lake ice cover due to internal climate forcing is large (Wang

et al. 2010a); and, 2) the predictability of lake ice cover using both statistical and numerical models is

generally poor, particularly for interannual time scales. For example, Lake Ontario has the largest SDs

compared to its mean, while Lake St. Clair has the smallest SDs. This implies that ice cover in Lake St.

Clair has the best predictability, while ice cover in Lake Ontario has the poorest predictability among all

the lakes, although the overall predictability in all lakes is poor. 

Figure 3. Variation of weekly ice coverage (in percent) of the six lakes for the 

period 1973-2011 with one standard deviation (SD) by the vertical bars.
17

The reason for the poor overall predictability of ice cover is that the internal variability of natural 

climate patterns such as ENSO and AO/NAO are poorly predicted (Wang et al. 2010a, 2012). Bai et al.

(2010, 2011, 2012) revealed that both ENSO and AO/NAO have impacts on lake ice; nevertheless, none

of them dominates the Great Lakes. Using both ENSO and AO/NAO indices, lake ice can be projected

on an intraseasonal time scale, but has poor predictability on the interannual time scales (from year to

year). Thus, short-term numerical prediction based on short-term weather prediction is an alternative to

provide relatively accurate prediction of lake ice on synoptic time scales (Wang et al. 2010b). 

7.3 Interannual variability

Time series of weekly percent ice cover for all six lakes for the period 1973-2011 have been constructed

(Figure 4), defined as the percentage of the total lake area that is covered by ice (Wang et al. 1994). All

lakes have strong interannual variability of ice cover. However, there are two types of regional features:

deep water and shallow water lakes. In the shallow water lakes such as Lakes St. Clair (3 m on average)

and Erie (19 m on average), there is almost complete ice cover in winter except in 2005 for St. Clair,

and 1983, 1991, 1998, 2002, 2005, 2006, and 2010 for Lake Erie. This indicates that using only lake ice

area (in both percentage and square kilometers) is not sufficient to detect the climate signals in Lakes

St. Clair and Erie, since the ice area is constrained by the bathymetry, the so-called boundary constraint

(Wang et al. 1994). Under the boundary constraint, even though the ice area no long increases, ice

thickness can still increase in winter. Thus, lake ice thickness must be used to study lake ice in response

to a changing climate. However, in the deep-water lakes (Superior: 148 m; Huron: 59 m; and Ontario: 85

m), ice cover can be used to detect climate signals, since ice rarely completely covers the lakes. On Lake

Erie, the least ice cover was found in 1983, 1991, and 1998, spaced by 7-8 years, but more frequently

since 1998 with a period of about 3-4 years. This implies that interannual variability of the climate

patterns tends to be greater in the Great Lakes in the past decade. In the deep-water lakes, it is possible

to detect climate signals using the ice cover area, since the boundary constraint is weak. 

To investigate interannual variability, the time series of annual mean lake ice coverage for each lake 

(Figure 4) is calculated by averaging over the ice season from the original data (Figure 4). There is

large interannual variability with temporal correspondence among all the lakes, implying that the major

response of lake ice to the climate forcing is basically uniform over the Great Lakes, since the spatial 

scale of the Great Lakes is small compared to the teleconnection patterns excited by phenomena such as

ENSO and AO/NAO.

7.4 Long-term trend

Ice cover on the Great Lakes has been declining since 1973. Figure 4 and Table 4 show the linear trends

for the six lakes. The linear trend was estimated using the least squares regression (LSR). The linear

equation is in the form: x=a+bt, where x is the ice cover (in %), t is the year starting in 1973, a is the

x-intercept constant (the value of x for t=0 (i.e.,1973), and b is the slope of the line (namely, the rate of

change in x with a time increment of t).

Winter mean ice cover in all lakes shows a significant negative trend (Figure 5), indicating that the ice

extent in the Great Lakes has been decreasing since the 1970s. The negative trends vary from lake to

lake (-0.56%/yr to -1.96 %/yr, Table 4). Lake Superior has the largest negative trend (-1.96 %/yr), Lakes

Ontario and Michigan place second (~-1.6%/yr), and Lakes Erie and St. Clair have the smallest negative

trend: -0.74%/yr and -0.56%/yr, respectively. This translates to the total loss of winter lake ice coverage

over the entire 39-year record (from 1973-2011) relative to 1973 in Table 4 (last row), which varies
18

Figure 4. Twice weekly 

time series of ice cover (in 

percent) of all six lakes (a-f) 

and total Great Lakes (g) for 

Percent

the period 1973-2011. 

Percent Percent Percent Percent Percent Percent
19

Figure 5. Winter mean 

lake ice cover (in percent) 

of all six lakes (a-f) and 

total Great Lakes ice cover 

(g) for the period 1973-

2011. The linear lines are 

the trend in annual lake 

ice coverage calculated 

from the least squares fit 

method. 

Percent Percent Percent Percent Percent Percent Percent
20

from 22% in Lake St Clair to 76% in Lake Superior. The total loss for overall Great Lakes ice coverage

is 63% relative to the 1973 value. Another metric of loss of seasonal average ice cover over the 1973-

2011 winters is the difference (1973 minus 2011) in the regression values for 1973 and 2011. The loss in

seasonal average ice cover from 1973 to 2011 using this metric is 24.4% for Lake Superior, 12.9% for

Lake Michigan, 15.5% for Lake Huron, 15.2% for Lake St. Clair, 11.1% for Lake Erie, 6.5% for Lake

Ontario, and 14.9% for the entire Great Lakes basin.

Note that the trends calculated within a specific period of time such as 1973-2011 can only be applicable

to the same period, and cannot be extrapolated to the future and back to the past. It should not be

interpolated to a period shorter than the time series of the data from which the trends are derived, since

there are decadal and multi-decadal changes in lake ice cover. 

Table 4. Trends in winter lake ice coverage (in %) calculated by linear least squares fit for the period 1973-2011 

(see text for detail). In row 2, a is the intercept constant (the value in year 1973) (in %); in row 3, b is the slope of 

the line (namely, the rate of change in ice area with time, % per year). Row 4 defines relative trend in winter lake 

ice coverage (% per year) = (b/ a) x 100%. Row 5 is the total loss of winter lake ice coverage over the entire 39-

year record (from 1973 to 2011) relative to the 1973 value.

Superior Michigan Huron St. Clair Erie Ontario Total GL

a 32.822 21.362 34.717 77.099 44.405 11.784 28.786

b -0.642 -0.348 -0.431 -0.433 -0.327 -0.198 -0.467

b/a*100% -1.955 -1.629 -1.242 -0.562 -0.737 -1.679 -1.622

Total loss (%) -76 -63 -48 -22 -29 -65 -63

To search factors responsible for the lake ice trend, the winter surface air temperature (SAT) trend over

the Northern Hemisphere was calculated for the period 1973-2010 (Figure 6). The SAT trend over the 

Great Lakes ranges from ~0.4 oC per decade over the lower lakes to ~0.6 oC per decade over the upper 

lakes, with Lake Superior being the highest (0.6 oC per decade). This is consistent with the upward

trend of Lake Superior water temperature (Austin and Colman 2007). They found that summer (July–

September) surface water temperatures have increased approximately 2.5°C over the period 1979–

2006, significantly higher than regional atmospheric warming. This excessive warming of lake water

temperature relative to the local surface air temperature has caused a positive ice/water albedo feedback

(Wang et al. 2005) due to the declining ice cover (Austin and Colman 2007).

7.5 Periodicity

We further examined the spectral characteristics of the 38-year time series of the six lakes (Figure 7).

The main periods are ~8 years and 3-5 years. Lakes Michigan, Huron, St. Clair, and Erie have two

periods: 8 and 3.8 years. The former may be related to AO/NAO, and the latter may be related to ENSO,

since both ENSO and AO have impacts on Great Lakes ice cover (Bai et al. 2010, 2011); and the AO/

NAO has significant decadal to quasi-decadal time scales (7-8 years) (Wang et al. 1994; Mysak et al.

1996; Wang and Ikeda 2001; Wang et al. 2005), while ENSO basically possesses strong interannual time

scales of 3-5 years.
21

Figure 6. Winter surface air temperature (SAT) trend (oC per decade) for the period 1973-2010, calculated 

using the least squares fit. (from Wang et al. 2012)
22

Figure 7. Spectral analysis of lake ice cover anomalies in all six lakes. The dotted curves are the 95% significance level. 

The peaks are marked with the corresponding periods in years.

Lake Superior

Cycles per year

Spectral density

Lake Michigan

Cycles per year

Spectral density

Lake Huron

Cycles per year

Spectral density

Lake St. Clair

Cycles per year

Spectral density

Lake Erie

Cycles per year

Spectral density

Lake Ontario

Cycles per year

Spectral density
23

8. REFERENCES

Assel, R.A. An ice-cover climatology for Lake Erie and Lake Superior for the winter seasons 1897-1898

to 1982-1983. International Journal of Climatology 10:731-748 (1990). http://www.glerl.noaa.gov/pubs/

fulltext/1990/19900009.pdf

Assel, R.A. An Electronic Atlas of Great Lakes Ice Cover. NOAA Great Lakes Ice Atlas, Great Lakes 

Environmental Research Laboratory, Ann Arbor, Michigan 48105 (2003). http://www.glerl.noaa.gov/data/ice/

atlas/

Assel, R.A. Great Lakes ice cover climatology update: Winters 2003, 2004, and 2005. NOAA Technical

Memorandum GLERL-135. NOAA, Great Lakes Environmental Research Laboratory, Ann Arbor, MI, 21 pp.

(2005). http://www.glerl.noaa.gov/ftp/publications/tech_reports/glerl-135/

Assel, R.A., and D.M. Robertson. Changes in winter air temperatures near Lake Michigan, 1851-1993, as

determined from regional lake-ice records. Limnology and Oceanography 40(1):165-176 (1995).

Assel, R.A., D.M. Robertson, M.H. Hoff, and J.H. Selgeby. Climatic change implications from long-term

(1823-1994) ice records for the Laurentian Great Lakes. Annals of Glaciology 21:383-386 (1995). http://www.

glerl.noaa.gov/pubs/fulltext/1995/19950020.pdf

ASSEL, R.A., S. Drobot, and T.E. CROLEY, II. Improving 30-day Great Lakes ice cover outlooks. Journal of 

Hydrometeorology 5(4):713-717 (2004). http://www.glerl.noaa.gov/pubs/fulltext/2004/20040016.pdf

Assel, R. A., K. Cronk, and D. C. Norton. Recent trends in Laurentian Great Lakes ice cover. Climatic Change

57:185-204 (2003). http://www.glerl.noaa.gov/pubs/fulltext/2003/20030001.pdf

Austin, J.A., and S. Colman. Lake Superior summer water temperatures are increasing more rapidly than

regional air temperatures:a positive ice-albedo feedback. Geophysical Research Letters 34, L06604, 

doi:10.1029/2006GL029021 (2007).

Bai, X., J. Wang, C. Sellinger, A. Clites, and R. Assel. The impacts of ENSO and AO on the Interannual

variability of Great Lakes ice cover, NOAA Technical Memorandum GLERL-152, 48 pp. (2010). http://www.

glerl.noaa.gov/ftp/publications/tech_reports/glerl-152

Bai, X., J. Wang, Q. Liu, and Y. Liu. Severe ice conditions in the Bohai Sea, China vs. mild ice conditions in the

Great Lakes during the 2009/2010 winter with strong -AO and El Nino. Journal of Applied Meteorology and 

Climatology 50:1922-1935 (2011).

Bai, X., J. Wang, C. Sellinger, A. Clites, and R. Assel. Interannual variability of Great Lakes ice cover and its

relationship to NAO and ENSO. Journal of Geophysical Research doi:10.1029/2010JC006932 (2012).

Magnuson, J.J., K.E. Webster, R.A. ASSEL, C.J. Bowser, P.J. Dillon, J.G. Eaton, H.E. Evans, E.J. Fee, R.I.

Hall, L.R. Mortsch, D.W. Schindler, and F.H. Quinn. Potential effects of climatic changes on aquatic systems:

Laurentian Great Lakes and Precambrian Shield region. Hydrological Processes 11:825-871 (1997).
24

Magnuson, J.J., D.M. Robertson, B.J. Benson, R.H. Wynne, D.M. Livingstone, T. Arai, R.A. Assel, R.G. Barry,

V. Card, E. Kuusisto, N.G. Granin, T.D. Prowse, K.M. Stewart, and V.S. Vuglinski. Historical trends in lake and

river ice cover in the Northern Hemisphere. Science 289:1743-1746 (2000).

Mysak, L.A., R.G. Ingram, J. Wang and A. van der Baaren. Anomalous sea-ice extent in Hudson Bay, Baffin

Bay and the Labrador Sea during three simultaneous ENSO and NAO episodes. Atmosphere-Ocean 34:313-343

(1996).

Norton, D.C., R.A. Assel, D. Meyers, B.A. Hibner, N. Morse, P.J. Trimble, K. Cronk, and M. Rubens. Great

Lakes ice data rescue project. NOAA Technical Memorandum GLERL-117. NOAA Great Lakes Environmental

Research Laboratory, Ann Arbor, MI, 63 pp. (2000). http://www.glerl.noaa.gov/ftp/publications/tech_reports/

glerl-117

Schwab, D.J., G.A. Leshkevich, and G.C. Muhr. Satellite measurements of surface water temperature in the

Great Lakes: Great Lakes CoastWatch. Journal of Great Lakes Research 18(2):247-258 (1992).

Wang, J. and M. Ikeda. Arctic Sea-Ice Oscillation: Regional and seasonal perspectives. Annals of Glaciology

33:481-492 (2001).

Wang, J, L.A. Mysak and R.G. Ingram.Interannual variability of sea-ice cover in Hudson Bay, Baffin Bay and

the Labrador Sea. Atmosphere-Ocean 32(2): 421-447 (1994).

Wang, J., M. Ikeda, S. Zhang, and R. Gerdes. 2005. Linking the northern hemisphere sea ice reduction trend

and the quasi-decadal Arctic Sea Ice Oscillation. Climate Dynamics 24:115-130, 10.1007/s00382-004-0454-5

(2005).

Wang, J., X. Bai, G. Leshkevich, M. Colton, A. Clites, and B. Lofgren. Severe Great Lakes ice cover during

winter 2008/09. EOS 91(5):41-42 (2010a).

Wang, J., H. Hu, D. Schwab, G. Leshkevich, D. Beletsky, N. Hawley and A. Clites. Development of the

Great Lakes Ice-circulation Model (GLIM): Application to Lake Erie in 2003-2004. Journal of Great Lakes 

Research, 0.1016/j.jglr.2010.04.002 (2010b).

Wang, J., X. Bai, H. Hu, A. Clites, M. Colton, and B. Lofgren. Temporal and spatial variability of Great Lakes

ice cover, 1973-2010. Journal of Climate, 10.1175/2011JCL14066.1 (2012).

9. ACKNOWLEDGEMENTS

This work was partially supported by grants from National Research Council Research Association Fellowship

and NOAA GLERL. Support from U.S. EPA’s Great Lakes Restoration Initiative (GLRI) to climate change and

modeling studies is appreciated. We appreciate Cathy Darnell for her editorial assistance.
25

 Appendix A-1

Software for converting 510×516 grids to 1024×1024 grids in CW format

Documentation of Standard Operating Procedure

1. Use the files created in fixed 516 record format with GLERL ice codes, 

2. Run tran5.for, [function: transpose 510x516 array to 1024x1024 array].

3. Run the results of trand5.for through fillx07.for [function estimates the ice concentrations in cells with missing data].

c*************************************************************************

c name tran5.for, date N0VEMBER 2010, function to transpose 510x516 array to 1024x1024 array.

c FIRST transpose 510x516 to 1020x1032 array, NEXT transpose 1020x1032 array to 1024x1024 array,

c LAST,use CoastWatch [CW] land mask to make standard land water grids for climatic analysis

dimension ib(1020,1032),is(510,516),msk(1024,1024),ice(1024,1024)

dimension ib2(1020,1032)

character count(4)*30, inmap*20,outmap*15

c ib is the fine spatial resolution array

c is is the small low resolution array currently used in the NOAA ice Atlas

c msk is the CoastWatch land water mask array

c ice is the ib array with CW mask applied.

data count/’mask and map both overwater’,

*’mask and map both land’,’mask land and map water’,

*’mask water and map land’/

open(unit=1,file=’tem07ctb’, status=’old’)

open(unit=2,file=’tem07-1’,status=’old’)

open(unit=3,file=’cw1024.txt’,status=’old’)

c read in the cw mask of standardize land and water cells

do i=1,1024

read(3,12)(msk(i,j),j=1,1024)

 enddo

12 format(1024i4)

c read in each of 23 maps

do iouter=1,23

c read the input and output files

29 format(a15)

291 format(a20)

read(1,291)inmap

read(2,29)outmap

open(unit=4,file=inmap,status=’old’)

open(unit=6,file=outmap,status=’unknown’)

c set counters ict jct kct and Lct

ict=0

jct=0

kct=0

Lct=0

c fill in ib with missing data code

do i=1,1020

do j=1,1032

ib(i,j)=-99

 enddo

 enddo

c read in the low resolution ice array

do i=1,510

read(4,10)(is(i,j),j=1,516)

 enddo

10 format(516i3)

c fill ib with the data from array ‘is’

do i=1,510

do j=1,516

it=is(i,j)

ib(i*2-1,J*2-1)=it

ib(i*2-1,J*2)=it

ib(i*2,j*2-1)=it

ib(i*2,j*2)=it

 enddo

 enddo

c now shift ib (in ib2)

c**************************
26

nrows=7

ncols=3

c**************************

do i=1,nrows

do j=1,ncols

ib2(i,j)=-1

 enddo

 enddo

iend=1020-nrows

jend=1032-ncols

do i=1,iend

do j=1,jend

ik=ib(i,j)

ib2(i+nrows,j+ncols)=ik

 enddo

 enddo

c apply the cw mask to standardize land and water grid cells

do i=1,1024

do j=1,1024

ix=ib2(i,j)

im=msk(i,j)

c if the mask is land then make ice array land

if(im.eq.255)ice(i,j)=-1

c if the mask is water then test ib to see if it is land or water

c and set value of ice appropriately

if(im.lt.255.and.ix.ge.0)ice(i,j)=ix

if(im.lt.255.and.ix.eq.-1)ice(i,j)=-99

if(im.lt.255.and.ix.lt.-1)ice(i,j)=-98

if(im.lt.255.and.ix.lt.-1)print*,’error cell < -1 at grid’,i,j

c mask and map both overwater

if(im.lt.255.and.ix.ge.0)ict=ict+1

c mask and map both land

if(im.eq.255.and.ix.eq.-1)jct=jct+1

c mask land and map water

if(im.eq.255.and.ix.gt.-1)kct=kct+1

c mask water and map land

if(im.lt.255.and.ix.eq.-1)Lct=Lct+1

 14 continue

 enddo

 enddo

11 format(1032i3)

do i=1,1024

write(6,15)(ice(i,j),j=1,1024)

 enddo

c print out the counters

if(iouter.eq.1)print51

print52,inmap,outmap,ict,jct,kct,Lct, (ict+jct+kct+Lct),iouter

c this is end of outerdo

 enddo

51 format(3x,

*’infile outfile BOLA BOWA MLMPW MWMPL TOTAL’,

*3x,’Map No.’)

52 format(1x,a20,1x,a15,2x,4(i6,1x),1x,i7,2x,i4)

15 format(1024i3)

 end
27

c===================================================================

c modified from screen print statements for tran5.for

-------------------------------------------------------------------------------------------------------------------

input file out file BOLA BOWA MLMPW MWMPL TOTAL Map

----------------------------------------------------------------

glgrd061204_2550.ctb glgrd061204.dat 147618 880500 19394 1064 1048576 1

glgrd061211_2550.ctb glgrd061211.dat 147618 880500 19394 1064 1048576 2

glgrd061218_2550.ctb glgrd061218.dat 147618 880500 19394 1064 1048576 3

glgrd061225_2550.ctb glgrd061225.dat 147618 880500 19394 1064 1048576 4

glgrd070101_2550.ctb glgrd070101.dat 147618 880500 19394 1064 1048576 5

glgrd070108_2550.ctb glgrd070108.dat 147618 880500 19394 1064 1048576 6

glgrd070115_2550.ctb glgrd070115.dat 147618 880500 19394 1064 1048576 7

glgrd070122_2550.ctb glgrd070122.dat 147618 880500 19394 1064 1048576 8

glgrd070129_2550.ctb glgrd070129.dat 147618 880500 19394 1064 1048576 9

glgrd070205_2550.ctb glgrd070205.dat 147618 880500 19394 1064 1048576 10

glgrd070212_2550.ctb glgrd070212.dat 147618 880500 19394 1064 1048576 11

glgrd070219_2550.ctb glgrd070219.dat 147618 880500 19394 1064 1048576 12

glgrd070226_2550.ctb glgrd070226.dat 147618 880500 19394 1064 1048576 13

glgrd070305_2550.ctb glgrd070305.dat 147618 880500 19394 1064 1048576 14

glgrd070312_2550.ctb glgrd070312.dat 147618 880500 19394 1064 1048576 15

glgrd070319_2550.ctb glgrd070319.dat 147618 880500 19394 1064 1048576 16

glgrd070326_2550.ctb glgrd070326.dat 147618 880500 19394 1064 1048576 17

glgrd070402_2550.ctb glgrd070402.dat 147618 880500 19394 1064 1048576 18

glgrd070409_2550.ctb glgrd070409.dat 147618 880500 19394 1064 1048576 19

glgrd070416_2550.ctb glgrd070416.dat 147618 880500 19394 1064 1048576 20

glgrd070423_2550.ctb glgrd070423.dat 147618 880500 19394 1064 1048576 21

glgrd070430_2550.ctb glgrd070430.dat 147618 880500 19394 1064 1048576 22

glgrd070507_2550.ctb glgrd070507.dat 147618 880500 19394 1064 1048576 23

--------------------------------------------------------------------------------------------------------------------

BOLA = no. of CW base and lake map land cells agree

BOWA = no. of CW base and lake map water cells agree

MLMPA = no. of CW base land cells which are water cells on lake map

MWMPL = no. of CW base water cells which are land cells on lake map

TOTAL = total no. cells
28

c===================================================================================

c fillx07.for NOVEMBER 2010

c function: estimate the ice concentrations for grid cells with missing data

c===================================================================================

c code for cells with missing data is -99. 

c Up to 30 iterations can be made to reduce the number of cells with missing data, missed by the previous iteration.

c A check will be made at the end of each iteration to count the number of cells with missing data.

character infi*15,outf*15,infis(23)*15,outfs(23)*15

dimension ice(1024,1024),nice(1024,1024),isav(30,23),iter(30)

dimension ifxr(21),ifxc(21)

data ifxr/564,565,565,566,566,566,567,567,568,568,569,569,

*570,570,570,571,571,571,572,572,573/

data ifxc/1024,1024,1023,1022,1023,1024,1022,1023,1024,

*1022,1024,1023,1024,1023,1022,1024,1023,1022,1023,1022,1022/

open(unit=1,file=’tem07-1’,status=’old’)

open(unit=2,file=’tem07-2’,status=’old’)

open(unit=8,file=’fillx07.out’,status=’unknown’)

10 format(a15)

15 format(1024i3) 

c the number of maps parameter has to be set each time

num_maps=23

c read in the input and output file names

c****************************

do imap=1,num_maps

c****************************

read(1,10)infi

infis(imap)=infi

read(2,10)outf

outfs(imap)=outf

open(unit=3,file=infi,status=’old’)

open(unit=7,file=outf,status=’unknown’)

c read in the data for one map

do irow=1,1024

read(3,15)(ice(irow,icol),icol=1,1024)

 enddo

c iterate through data 15 times

c nt = the number of times to estimate the nearest neighbor cell

nt=3

do itimes=1,nt

iter(itimes)=itimes

c replace cells with missing data

do irow=1,1024

do ic=1,1024

c set the cell value in the new arrary equal to original cell vealue

nice(irow,ic)=ice(irow,ic)

c if you have a land cell [code -1] do not interpolate

if(nice(irow,ic).eq.-1)goto999

c next 4 ifs - if you are within 3 cells of edge of the array

c do not interpoate - as the grid search will produce

c bogus data because of the array dimension

if(irow.lt.3)goto999

if(irow.gt.1021)goto999

if(ic.lt.3.)goto999

if(ic.gt.1021)goto999

if(ice(irow,ic).eq.-99)then

c here we estimate the value of the cell from the neighbor cells

 

it2=ice(irow,ic-1)

if(it2.ge.0)nice(irow,ic)=it2
29

if(nice(irow,ic).ge.0)goto998

it3=ice(irow,ic+1)

if(it3.ge.0)nice(irow,ic)=it3

if(nice(irow,ic).ge.0)goto998

it4=ice(irow+1,ic)

if(it4.ge.0)nice(irow,ic)=it4

if(nice(irow,ic).ge.0)goto998

it5=ice(irow-1,ic)

if(it5.ge.0)nice(irow,ic)=it5

if(nice(irow,ic).ge.0)goto998

it6=ice(irow-1,ic-1)

if(it6.gt.0)nice(irow,ic)=it6

if(nice(irow,ic).ge.0)goto998

it7=ice(irow-1,ic+1)

if(it7.ge.0)nice(irow,ic)=it7

if(nice(irow,ic).ge.0)goto998

it8=ice(irow+1,ic-1)

if(it8.ge.0)nice(irow,ic)=it8

if(nice(irow,ic).ge.0)goto998

it9=ice(irow+1,ic+1)

if(it9.ge.0)nice(irow,ic)=it9

if(nice(irow,ic).ge.0)goto998

it12=ice(irow,ic-2)

if(it12.ge.0)nice(irow,ic)=it12

if(nice(irow,ic).ge.0)goto998

it13=ice(irow,ic+2)

if(it13.ge.0)nice(irow,ic)=it13

if(nice(irow,ic).ge.0)goto998

it14=ice(irow+2,ic)

if(it14.ge.0)nice(irow,ic)=it14

if(nice(irow,ic).ge.0)goto998

it15=ice(irow-2,ic)

if(it15.ge.0)nice(irow,ic)=it15

if(nice(irow,ic).ge.0)goto998

it16=ice(irow-2,ic-2)

if(it16.gt.0)nice(irow,ic)=it16

if(nice(irow,ic).ge.0)goto998

it17=ice(irow-2,ic+2)

if(it17.ge.0)nice(irow,ic)=it17

if(nice(irow,ic).ge.0)goto998

it18=ice(irow+2,ic-2)

if(it18.ge.0)nice(irow,ic)=it18

if(nice(irow,ic).ge.0)goto998

it19=ice(irow+2,ic+2)

if(it19.ge.0)nice(irow,ic)=it19

if(nice(irow,ic).ge.0)goto998

it20=ice(irow,ic-3)

if(it20.ge.0)nice(irow,ic)=it20

if(nice(irow,ic).ge.0)goto998

it21=ice(irow,ic+3)

if(it21.ge.0)nice(irow,ic)=it21
30

if(nice(irow,ic).ge.0)goto998

it22=ice(irow+3,ic)

if(it22.ge.0)nice(irow,ic)=it22

if(nice(irow,ic).ge.0)goto998

it23=ice(irow-3,ic)

if(it23.ge.0)nice(irow,ic)=it23

if(nice(irow,ic).ge.0)goto998

it24=ice(irow-3,ic-3)

if(it24.gt.0)nice(irow,ic)=it24

if(nice(irow,ic).ge.0)goto998

it25=ice(irow-3,ic+3)

if(it25.ge.0)nice(irow,ic)=it25

if(nice(irow,ic).ge.0)goto998

it26=ice(irow+3,ic-3)

if(it26.ge.0)nice(irow,ic)=it26

if(nice(irow,ic).ge.0)goto998

it27=ice(irow+3,ic+3)

if(it27.ge.0)nice(irow,ic)=it27

if(nice(irow,ic).ge.0)goto998

 else

nice(irow,ic)=ice(irow,ic)

 endif

998 continue

c now for the median ice chart maps make the following estimates for

c noth east L. Ontario Bay of Quinte

itont1=nice(588,958)

itont2=nice(588,959)

itont3=nice(589,956)

itont4=nice(589,957)

itont5=nice(590,955)

itont6=nice(591,928)

itont7=nice(592,926)

itont8=nice(595,934)

if(itont1.eq.-99.and.itont8.ne.-99)nice(588,957)=itont8

if(itont2.eq.-99.and.itont8.ne.-99)nice(588,958)=itont8

if(itont3.eq.-99.and.itont8.ne.-99)nice(589,955)=itont8

if(itont4.eq.-99.and.itont8.ne.-99)nice(589,956)=itont8

if(itont5.eq.-99.and.itont8.ne.-99)nice(590,954)=itont8

if(itont6.eq.-99.and.itont8.ne.-99)nice(591,928)=itont8

if(itont7.eq.-99.and.itont8.ne.-99)nice(592,926)=itont8

c now fill in data for one cell in Georgian Bay

itg1=nice(531,787)

itg2=nice(536,785)

if(itg1.eq.-99.and.itg2.ne.-99)nice(531,787)=itg2

c now fill in data for 21 cells at west St. Lawrence River

do kk=1,21

itg3=ifxr(kk)

itg4=ifxc(kk)

istc=nice(itg3,itg4)

istc2=nice(571,1017)

if(istc.eq.-99.and.istc2.ne.-99)nice(itg3,itg4)=istc2

 enddo

999 continue 

 enddo

 enddo

c save a count of the No. of cells that have -99 (missing data)

ict=0

do km=1,1024

do km2=1,1024

item=nice(km,km2)

if(item.eq.-99)ict=ict+1

 enddo

 enddo

isav(itimes,imap)=ict

c save the results for the next iteration
31

do KSA=1,1024

do KSB=1,1024

ice(ksa,ksb)=nice(ksa,ksb)

 enddo

 enddo

c this next do is for the itimes do loop

 enddo

c write out the results for one map

do MM=1,1024

write(7,15)(nice(mm,iicol),iicol=1,1024)

 enddo

c this next do is for all maps

 enddo

c print the number of cells that have -99 (missing data)

c on each ice chart for each iteration

write(8,62)(iter(k),k=1,nt)

print62,(iter(k),k=1,nt)

c***************************

do imap=1,num_maps

c*************************** 

print60,’completed map’,imap,’inf=’,infis(imap),’outf=’,

*outfs(imap), ‘NO. of cell with -99 =’,

*(isav(itime,imap),itime=1,nt)

write(8,60)’completed map’,imap,’inf=’,infis(imap),’outf=’,

*outfs(imap), ‘NO. of cell with -99 =’,

*(isav(itime,imap),itime=1,nt)

 enddo

c234567

62 format(78x,30(1x,i4))

60 format(1x,a13,1x,i3,1x,a4,a15,2x,a5,a15,2x,a22,20(1x,i4))

 end 

c=====================================================================

c modified screen output from fillx07.for No. Iterations

c input file output file 1 2 3

c completed map 1 inf=glgrd061204.dat outf=glgrd061204.fil NO. of cell with -99 = 11 1 0

c completed map 2 inf=glgrd061211.dat outf=glgrd061211.fil NO. of cell with -99 = 11 1 0

c completed map 3 inf=glgrd061218.dat outf=glgrd061218.fil NO. of cell with -99 = 10 0 0

c completed map 4 inf=glgrd061225.dat outf=glgrd061225.fil NO. of cell with -99 = 9 0 0

c completed map 5 inf=glgrd070101.dat outf=glgrd070101.fil NO. of cell with -99 = 10 1 0

c completed map 6 inf=glgrd070108.dat outf=glgrd070108.fil NO. of cell with -99 = 11 1 0

c completed map 7 inf=glgrd070115.dat outf=glgrd070115.fil NO. of cell with -99 = 7 0 0

c completed map 8 inf=glgrd070122.dat outf=glgrd070122.fil NO. of cell with -99 = 7 0 0

c completed map 9 inf=glgrd070129.dat outf=glgrd070129.fil NO. of cell with -99 = 7 0 0

c completed map 10 inf=glgrd070205.dat outf=glgrd070205.fil NO. of cell with -99 = 7 0 0

c completed map 11 inf=glgrd070212.dat outf=glgrd070212.fil NO. of cell with -99 = 7 0 0

c completed map 12 inf=glgrd070219.dat outf=glgrd070219.fil NO. of cell with -99 = 7 0 0

c completed map 13 inf=glgrd070226.dat outf=glgrd070226.fil NO. of cell with -99 = 7 0 0

c completed map 14 inf=glgrd070305.dat outf=glgrd070305.fil NO. of cell with -99 = 7 0 0

c completed map 15 inf=glgrd070312.dat outf=glgrd070312.fil NO. of cell with -99 = 7 0 0

c completed map 16 inf=glgrd070319.dat outf=glgrd070319.fil NO. of cell with -99 = 7 0 0

c completed map 17 inf=glgrd070326.dat outf=glgrd070326.fil NO. of cell with -99 = 7 0 0

c completed map 18 inf=glgrd070402.dat outf=glgrd070402.fil NO. of cell with -99 = 7 0 0

c completed map 19 inf=glgrd070409.dat outf=glgrd070409.fil NO. of cell with -99 = 8 1 0

c completed map 20 inf=glgrd070416.dat outf=glgrd070416.fil NO. of cell with -99 = 10 1 0

c completed map 21 inf=glgrd070423.dat outf=glgrd070423.fil NO. of cell with -99 = 10 1 0

c completed map 22 inf=glgrd070430.dat outf=glgrd070430.fil NO. of cell with -99 = 10 1 0

c completed map 23 inf=glgrd070507.dat outf=glgrd070507.fil NO. of cell with -99 = 11 1 0

c=======================================================================
32

Appendix A-2

Comparison of lake averaged ice cover converted from 510×516 to 1024×1024 grid.

0 

10 

20 

30 

40 

50 

320 340 360 380 400 420 440 460 480 500 

1024 GD 512 GD

L. Superior

0 

5 

10 

15 

20 

25 

30 

320 340 360 380 400 420 440 460 480 500 

1024 GD 512 GD

L. Michigan

0 

10 

20 

30 

40 

50 

60 

320 340 360 380 400 420 440 460 480 500 

1024 GD 512 GD

L. Huron

0 

20 

40 

60 

80 

100 

320 340 360 380 400 420 440 460 480 500 

1024 GD 512 GD

L. Erie

0 

5 

10 

15 

20 

25 

320 340 360 380 400 420 440 460 480 500 

1024 GD 512 GD

L. Ontario

0 

10 

20 

30 

40 

50 

320 340 360 380 400 420 440 460 480 500 

1024 GD 512 GD

Great Lakes

Lake averaged ice cover for 1024x1024 and 510x516 grid for winter 2007 (23 ice charts). Note the bias is for slightly 

greater lake averaged ice cover for 510x516 grid but as seen on next page differences are 2% or less in all cases. This is 

considered sufficiently accurate for climate studies.
33

-1 

-0.8 

-0.6 

-0.4 

-0.2 

0 

320 340 360 380 400 420 440 460 480 500 

1024-512 GD

L. Superior

-1.2 

-1 

-0.8 

-0.6 

-0.4 

-0.2 

0 

320 340 360 380 400 420 440 460 480 500 

1024-512 GD

L. Michigan

-2 

-1.5 

-1 

-0.5 

0 

320 340 360 380 400 420 440 460 480 500 

1024-512 GD

L. Huron

-1 

-0.8 

-0.6 

-0.4 

-0.2 

0 

0.2 

0.4 

320 340 360 380 400 420 440 460 480 500 

1024-512 GD

L. Erie

-2 

-1.5 

-1 

-0.5 

0 

320 340 360 380 400 420 440 460 480 500 

1024-512 GD

L. Ontario

-1.2 

-1 

-0.8 

-0.6 

-0.4 

-0.2 

0 

0.2 

320 340 360 380 400 420 440 460 480 500 

1024-512 GD

Great Lakes

Differences of 1024 from 510×516 grid for winter 2007 (23 ice charts). Note differences are 2% or less in all cases. This is 

considered sufficiently accurate for climate studies.
34

Appendix A-3

Land mask applied to NIC ASCII files to create standard land-water grid cells

The land mask for the 510×516 grid. An ASCII land mask file is available by clicking on the following:

GLmask2.txt. It is a fixed formatted text file with 510 records. Each record in that file has a format of 516

i1. Land grid cells have a code of 0 or 1, the grid cell codes for lakes and rivers are: Lake Superior = 4, Lake

Michigan = 5, Lake Huron = 6, Lake Erie = 7, Lake St. Clair = 2, Lake Ontario = 8, St. Marys River = 9, Detroit

River = 3.

The land mask for the 1024×1024 grid. An ASCII land mask file is available by clicking on CW1024a.txt. 

File Structure

Record Format (1024i4), 1024 records in this file

 Numeric codes for each grid cell by lake by region

 Lake Superior

 01 Thunder Bay

 02 Black Bay

 03 Nipigon Bay

04 Western Lake Superior

 05 Eastern Lake Superior

06 Whitefish Bay

 St Marys River

 07 St Marys River North

 26 St Marys River South

 Lake Michigan

 11 Green Bay

 12 Grand Traverse Bay

 13 Northern Lake Michigan

 14 Southern Lake Michigan

 Lake Huron

 21 Georgian Bay

 22 North Channel

23 Saginaw Bay

24 Northern Lake Huron

25 Southern Lake Huron

 Lake St. Clair

 30 Lake St. Clair
35

 Detroit River

 31 Detroit River North

 44 Detroit River South

 Lake Erie

41 Western Lake Erie

 42 Central Lake Erie

 43 Eastern Lake Erie

 

 Lake Ontario

51 Western Lake Ontario

 52 Eastern Lake Ontario

-01 Western St. Lawrence River

 255 Land
36

Appendix A-4

Software for adding geographic location to ice concentration values for conversion of ice grids to ArcGIS.

;--------------------------------------------------------------------------

pro cwmap1024_auto,ipx,jpx,lat,lon,conv

;--------------------------------------------------------------------------

; Subroutine cwmap: Return pixel location, given latitude and longitude, or

; latitude and longitude given pixel location within synoptic 1024x1024

; CoastWatch image. (Approximate WGS-84 Mercator Projection - DJS 2007)

; Set conv to -1 for (lat, lon) to (ipx, jpx) conversion,

; anything else for inverse

slat = 38.8744 ; Southern latitude

wlon = -92.4106 ; Western longitude

elon = -75.8690 ; Eastern longitude

f1 = 0.99664 ; N-S scale adjustment for WGS-84

; Constants

alon0 = -wlon

a1 = 1023. / (!dtor * ( elon-wlon ) )

y0 = a1 * alog(tan (!dtor * (45.0 + slat / 2.) ))

; Calculate latitude and longitude if conv = -1, else calculate ipx and jpx

if(conv eq -1) then begin

 ipx=fix(a1*!dtor*(alon0-lon)+0.5)

 jpx=fix(f1*(a1*alog(tan(!dtor*(45.+lat/2.)))-y0)+0.5)

end else begin

 lat = 2.0 * ((( atan ( exp ( (jpx/f1 + y0) / a1) )) / !dtor) - 45.0)

 lon = -1.0*((ipx / (a1 * !dtor)) - alon0)

end

return

end

;------------------------------------------------------------------

;

; calculate lat and lon of ice concentration pixels

; this version is for the 1275m grid (2007 and later)

header=STRARR(1,6)

conc=intarr(1024,1024)

iceconc=intarr(1024,1024)

lat=fltarr(1024,1024)

lon=fltarr(1024,1024)

infile=””

outfile=””

dirstrng=””

datestrng=””

print,”Enter the absolute path of the directory containing the .ct files”

read,dirstrng

cd,dirstrng

file_conversion_index=0L

file_list_array = findfile(“*.ct”)

file_count = N_ELEMENTS(file_list_array)

for file_conversion_index=0,file_count-1 do begin

 numlines=0

 infile = file_list_array[file_conversion_index]

 openr,2,infile

 datestrng=strmid(infile,0,11); start w char 0, 11 chars long filename (2007)

 outfile=dirstrng + datestrng + ‘.ll’

 openw,1,outfile

 readf,2,header

 readf,2,conc

 iceconc = rotate(conc,7);flip the ice grid

 close,2
37

 close,1

 print,max(iceconc)

 print,min(iceconc)

 openw,1,outfile

 print,outfile

 printf,1,’ i j lat lon iceconc’

 for j=0,1023 do begin

 for i=0,1023 do begin

 cwmap1024_auto,i,j,lat,lon,0

 printf,1,i,j,lat,-lon,iceconc(i,j)

 end

 end

 close,1

 endfor

end