The files include results from a statistical analysis of the AARI sea ice
chart database in SIGRID format. File names starting with SY include statistics
from analysing data from a single year, while file names starting with MY include
statistics from analysing all available data. The remaining part of the file
names show month (abbreviated to 3 characters) and year (two last digits and only
for SY-files) and a letter (a, b, or c) indicating type of data, where:
a - Total ice concentration and ice thickness
b - Old ice concentration
c - Fast ice
Files of type b and c include only data points where old ice or fast ice was
observed.
Data acquisition
The AARI sea ice charts are based on airborne visual and SLAR data, and
satellite data. Except for in 1972, the charts were split into western and eastern
charts. These 'twin' charts may be a few days apart. The charts should in
principle be issued with a 10-day interval (3 per month), but in some 10-day periods
only the western or the eastern chart exists, and there are also a number of
10-day periods where no charts are available. The sea ice charts cover the period
1967-90.
The sea ice charts have been digitised into SIGRID format at the Arctic and
Antarctic Research Institute (AARI) in St.Petersburg, Russia. The complete data
set was acquired at SINTEF from the World Data Center-A (WDC-A) for Glaciology
[Snow and Ice] maintained at the National Snow and Ice Data Center (NSIDC)
through World Wide Web (http://nsidc.colorado.edu/NSIDC/
wdc-a.html) as three compressed UNIX tar files (aari-72.tar.z,
aari-east.tar.z, aari-west.tar.z).
Data processing and analysis
As this data set is the most comprehensive and detailed sea ice data set
available for Project I.3.1 in 1995, it was necessary to use the data set to derive
a number of sea ice parameters, such as total ice concentration, ice thickness,
concentration of old ice (second-year and multi-year ice) and presence of fast
ice. The purpose of the data processing and analysis was to establish a
statistical sea ice data set for NSR-related evaluations. The statistical data should
be provided as monthly statistics, based on all available ice charts within
each month, for each single year and for all years (multi-year statistics). The
resulting data sets should also be prepared for use in INSROP GIS.
At the data set location (NSIDC), also software utilities to extract sea ice
information from a SIGRID-formatted data set were available. The C-program
(strip_geog.c) was the basis for developing the data processing and analysis
software at SINTEF. The first step was to modify this program to export data in a
format suitable for import into ARC/INFO, as ArcView can use ARC/INFO-formatted
data directly and also because the initial plan was to run the analysis in the
ARC/INFO GRID module. The format chosen was the ARC/INFO ASCIIGRID format. To
display selected ice charts in INSROP GIS (ArcView), software utilities to convert
a SIGRID ice chart to an ARC/INFO point cover were developed. Hence, at this
point there were two options for displaying the original sea ice charts in INSROP
GIS.
The next step was to derive the ice parameters to be statistically analysed
from the original attributes of the data points in each sea ice chart. The SIGRID
data comprise unique codes for each attribute, but as some codes may represent
ranges rather than discrete values, this calls for special treatment to derive
discrete values for use in the statistical analyses. The statistical
parameters to be provided include minimum, mean, median and maximum values, and
probability for a certain criteria (e.g. ice concentration > 70%) to be fulfilled.
The third step was to create single-year monthly statistics. This was
originally planned to be handled by ARC/INFO (aml-code), but because we encountered
problems with handling NODATA values in the statistical analysis and experienced
large requirements for processing time and temporary storage, a change in
strategy was made. Therefore a FORTRAN program (aaristats_sy.f) was developed to
prepare monthly statistical files for each parameter for each year and the
strip-geog.c program was modified further to be a subroutine of the FORTRAN program. By
specifying ice parameter to be derived, threshold value (for ice parameters
where this is required) and input file name, the modified C-program returns the
derived ice parameter in a fixed grid. For grid cells where the source ice chart
has no data value, a NODATA value (101) is used. The FORTRAN program handles
the statistical analysis and stores the results on files in the ARC/INFO
ASCIIGRID format. In this process NODATA values are excluded and the mean monthly
value is derived by taking the sum of the real data values and dividing by the
number of real data values (excl. NODATA values).
The fourth step was to create the multi-year statistics. The aaristats_sy.f
FORTRAN program was used as basis to develop the aari_sy2my.f FORTRAN program.
This program reads the single-year monthly files, runs the statistical analyses,
and stores the multi-year statistics on monthly files in the ARC/INFO ASCIIGRID
format for each parameter. In this process NODATA values are excluded and each
single-year monthly parameter grid is given equal weight, that is, the number
of data sets within each month is not considered. This avoids getting biased
results due to some years having far more original data sets than others, but it
also means that if a month in a year had only one (or two) ice chart value(s)
at a given location, this value will be used as representative for the entire
month.
The last step is to prepare the statistical grid files for use by INSROP GIS.
This was achieved by developing a FORTRAN program that, for each month, reads
the parameter grids for each time period (single-year months and multi-year
months), and stores the data in the INSROP GIS Point ASCII import format. To save
storage space and increase performance, the data are split into several files
for each time period (YY = year [67-90], MM = month [01-12]):
File 1: Total ice concentration and ice thickness (myMMa00.pos, syYYMMa0.pos)
File 2: Old ice concentration (myMMb00.pos, syYYMMb0.pos)
File 3: Fast ice (myMMc00.pos, syYYMMc0.pos)
File 4: Data coverage (myMMd00.pos, syYYMMd0.pos)
Files of Type 1 and 4 include all points with at least one real data value,
while files of Type 2 and 3 comprise only points where presence of old ice (Type
2) or fast ice (Type 3) is observed.
Implementation in INSROP GIS
Due to the size of the statistical data set, only the multi-year data subset
and selected single-year data subsets (1983 and 1990) are implemented in INSROP
GIS. The data were prepared in the INSROP GIS Point ASCII import file format
and implemented using the "Theme - New Theme From ASCII File" menu option in the
View window. The data set is called AARI Sea Ice Statistics.
Deriving ice concentration values
Ice concentration codes are used for total ice concentration and partial ice
concentrations. To solve the problems of some SIGRID ice codes representing
ranges, each ice concentration code is assigned a minimum, mean and maximum ice
concentration value. The mean value is the average of the minimum and maximum
values, and the median value is the median of the same average values. For codes
representing discrete ice concentrations, these values are all equal, but for
codes representing ranges, the range limits are used as the minimum and maximum
values. Table 8.2 shows how the SIGRID codes are recoded (incl. special codes).
Table 8.2 Conversion of SIGRID codes to ice concentration values.
AARI Sea Ice Chart statistics
| Code
| Explanation
| Minimum
| Mean
| Maximum
|
| 00
| Ice free
| 0
| 0
| 0
|
| 01
| Open water (< 1/10)
| 1
| 5
| 9
|
| 02
| Bergy water (1)
| 2
| 2
| 2
|
| 04
| Fast ice
| 100
| 100
| 100
|
| 10
| 1/10
| 10
| 10
| 10
|
| .
| .
|
|
|
|
| 13
| 1/10 - 3/10
| 10
| 20
| 30
|
| .
| .
|
|
|
|
| 71
| 7/10 - 10/10
| 70
| 85
| 100
|
| .
| .
|
|
|
|
| 90
| 9/10
| 90
| 90
| 90
|
| 91
| more than 9/10, less than 10/10
| 91
| 95
| 99
|
| 92
| 10/10
| 100
| 100
| 100
|
| 99
| Unknown (2)
| 101
| 101
| 101
|
| 102
| Land (2)
| 102
| 102
| 102
|
(2) Values greater than 100 are ignored in the statistical analysis
When deriving the probability of ice concentration above a given threshold, the 'range codes' also require special treatment. If, for a given code, the minimum ice concentration value is above the threshold, the probability is 100 percent. Similarly, if the maximum ice concentration value is below the threshold, the probability is 0 percent. However, if the threshold concentration is within the ice concentration range of the given code, a uniform distribution of ice concentrations within the range is assumed, and the fraction of the ice concentration range above the threshold is taken as the probability; e.g. it is assumed that for areas where the ice concentration is coded as 4/10 - 6/10, there is a 50 per cent probability to encounter ice concentrations above 5/10.
Deriving ice thickness values
Ice thickness codes may represent ice thickness or stage of development. To solve the problem of some SIGRID ice codes representing ranges, each code value is assigned a minimum, mean and maximum ice thickness value. The average value is the average of the minimum and maximum values. For codes representing discrete ice thicknesses, these values are all equal, but for codes representing stage of development, associated ice thickness range limits are used as the minimum and maximum values. Table 8.3 shows how the SIGRID codes are recoded (incl. special codes).
Table 8.3 Conversion of SIGRID codes to ice thickness values
| Code
| Explanation
| Minimum
| Mean
| Maximum
|
| 00
| Ice free
| 103
| 103
| 103
|
| 01
| Ice thickness in cm
| 1
| 1
| 1
|
|
|
|
|
|
|
| 50
| Ice thickness in cm
| 50
| 50
| 50
|
| 51
| Ice thickness in 5 cm intervals
| 55
| 55
| 55
|
| .
| .
|
|
|
|
| 60
| Ice thickness in 5 cm intervals
| 100
| 100
| 100
|
| 61
| Ice thickness in 10 cm intervals
| 110
| 110
| 110
|
| .
| .
|
|
|
|
| 70
| Ice thickness in 10 cm intervals
| 200
| 200
| 200
|
| 71
| Ice thickness in 50 cm intervals
| 250
| 250
| 250
|
| .
| .
|
|
|
|
| 74
| Ice thickness in 50 cm intervals
| 400
| 400
| 400
|
| 75
| Ice thickness in 100 cm intervals
| 500
| 500
| 500
|
| .
| .
|
|
|
|
| 79
| Ice thickness in 100 cm intervals
| 900
| 900
| 900
|
| 80
| No stage of development (1)
| 103
| 103
| 103
|
| 81
| New ice
| 1
| 15
| 30
|
| 82
| Nilas, ice rind less than 10 cm
| 1
| 5
| 9
|
| 83
| Young ice
| 10
| 20
| 30
|
| 84
| Gray ice
| 10
| 13
| 15
|
| 85
| Gray-white ice
| 15
| 23
| 30
|
| 86
| First year ice
| 30
| 115
| 200
|
| 87
| Thin first year ice
| 30
| 50
| 70
|
| 88
| Thin first year ice stage 1
| 30
| 40
| 50
|
| 89
| Thin first year ice stage 2
| 50
| 60
| 70
|
| 91
| Medium first year ice
| 70
| 95
| 120
|
| 93
| Thick first year ice
| 120
| 160
| 200
|
| 95
| Old ice
| 120
| 280
| 420
|
| 96
| Second year ice (2)
| 120
| 185
| 250
|
| 97
| Multi year ice (3)
| 240
| 330
| 420
|
| 98
| Glacier ice (4)
| 104
| 104
| 104
|
| 99
| Unknown (4)
| 101
| 101
| 101
|
2) Thicker than thick first year ice and less than 250 cm (Romanov, 1993)
3) According to Romanov (1993)
4) Values 101-104 are ignored in the statistical analysis of ice thickness
At any given ice chart location, there may be information on the thickest, second thickest and third thickest ice. The minimum ice thickness at a location is the minimum thickness registered, while the maximum ice thickness value is the maximum thickness of the thickest ice. When calculating the average ice thickness, the average thickness of all thickness registrations are weighted by the fraction of average associated partial ice concentration to the average total ice concentration. The mean and median ice thicknesses are derived from the average ice thicknesses.
When deriving the probability of ice thicknesses within a given ice thickness range, the same methodology as used to derive probability of ice concentrations above a given threshold is employed. However, as the probabilities are to be valid for a thickness range, not just above a thickness threshold, the fraction of the observed thickness range being within the specified analysis thickness range multiplied with the associated partial ice concentration, is used as the probability percentage.
Deriving old ice concentration values
Information on ice types is included in the Stage of development codes. As the term old ice includes both second year ice and Multi year ice, all partial ice concentrations associated with 'Stage of development' codes 95, 96 and 97, are counted as an 'old ice' concentration value (as specified in Table 8.1). In addition, the partial concentrations of ice with codes representing ice thickness above 200 cm (codes 71-79) are counted as old ice concentration values. All partial 'old ice' concentration values are summarised into one old ice concentration value for each point.
When deriving the probability of old ice above a given threshold, the same methodology as described for total ice concentration is employed, with the additional requirement that partial ice concentration ranges are involved rather than one total ice concentration range.
Deriving fast ice concentration values
Fast ice is shown as code value 08 in the Form of ice codes. For points with Form of ice code equal 08 (Fast ice), the ice concentration is set to 100 (ref. Table 8.1). For other code values (except the unknown code value: 99) the fast ice concentration is set to zero. For each location, the probability of fast ice within a time period is calculated as the percentage of fast ice data values out of the total number of data values with 'Form of ice' code different from unknown.
Technical description - Total ice concentration and ice thicknessShapefile name: my_apr_a.shp (for example)
Path: <NSR_DATA>\icesnow\aari_sta
GeoDataset type: Shapefile with Point features.
Coordinate system: Latitude/longitude in decimal degrees
* My_apr_a.shp
11288 Points, 17 descriptive fields.
Fields: [<Name>] -- <Alias> (type of field)
[Id] -- "Point #" (Numeric, no decimals)
[Nt_val] -- "N" (Numeric, no decimals)
Number of values
[Ctmin] -- "Ctot, min" (Numeric, no decimals)
Minimum total concentration of sea ice, %
[Ctmean] -- "Ctot, mean" (Numeric, no decimals)
Mean total concentration of sea ice, %
[Ctmed] -- "Ctot, median" (Numeric, no decimals)
Median total concentration of sea ice, %
[Ctmax] -- "Ctot, max" (Numeric, no decimals)
Minimum total concentration of sea ice, %
[P_ct10_] -- "P(Ctot > 10%)" (Numeric, no decimals)
Probability of total ice concentration > 10%, %
[P_ct40_] -- "P(Ctot > 40%)" (Numeric, no decimals)
Probability of total ice concentration > 40%, %
[P_ct70_] -- "P(Ctot > 70%)" (Numeric, no decimals)
Probability of total ice concentration > 70%, %
[Thmin] -- "Ice thickness, min" (Numeric, no decimals)
Minimum ice thickness, cm
[Thmean] -- "Ice thickness, mean" (Numeric, no decimals)
Mean ice thickness, cm
[Thmed] -- "Ice thickness, median" (Numeric, no decimals)
Median ice thickness, cm
[Thmax] -- "Ice thickness, max" (Numeric, no decimals)
Maximum ice thickness, cm
[P_th70_120] -- "P( 70 < Th < 120cm)" (Numeric, no decimals)
Probability of ice thickness between 70 and 120 cm, %
[P_th120_20] -- "P(120 < Th < 200cm)" (Numeric, no decimals)
Probability of ice thickness between 120 and 200 cm, %
[P_th200_] -- "P( Th > 200cm)" (Numeric, no decimals)
Probability of ice thickness greater than 200cm, %
[Calc_fld] -- "Calc_fld" (Numeric, no decimals)
Field for storage of calculated values
Technical description - Old ice concentrationShapefile name: my_apr_b.shp (for example)
Path: <NSR_DATA>\icesnow\aari_sta
GeoDataset type: Shapefile with Point features.
Coordinate system: Latitude/longitude in decimal degrees
* My_apr_b.shp
7936 Points, 10 descriptive fields.
Fields: [<Name>] -- <Alias> (type of field)
[Id] -- "Point #" (Numeric, no decimals)
[Noi_val] -- "N" (Numeric, no decimals)
Number of old ice data points
[Coimin] -- "Cold_ice, min" (Numeric, no decimals)
Minimum old ice concentration, %
[Coimean] -- "Cold_ice, mean" (Numeric, no decimals)
Mean old ice concentration, %
[Coimed] -- "Cold_ice, median" (Numeric, no decimals)
Median old ice concentration, %
[Coimax] -- "Cold_ice, max" (Numeric, no decimals)
Maximum old ice concentration, %
[P_coi10_] -- "P(Cold_ice > 10%)" (Numeric, no decimals)
Probability of old ice concentration > 10%, %
[P_coi40_] -- "P(Cold_ice > 40%)" (Numeric, no decimals)
Probability of old ice concentration > 40%, %
[P_coi70_] -- "P(Cold_ice > 70%)" (Numeric, no decimals)
Probability of old ice concentration > 70%, %
[Calc_fld] -- "Calc_fld" (Numeric, no decimals)
Field for storage of calculated values
Technical description - Fast iceShapefile name: my_apr_c.shp (for example)
Path: <NSR_DATA>\icesnow\aari_sta
GeoDataset type: Shapefile with Point features.
Coordinate system: Latitude/longitude in decimal degrees
* My_apr_c.shp
2819 Points, 4 descriptive fields.
Fields: [<Name>] -- <Alias> (type of field)
[Id] -- "Point #" (Numeric, no decimals)
[Nfi_val] -- "N" (Numeric, no decimals)
Number of fast ice data points
[P_cfi10_] -- "P(Cfast_ice > 10%)" (Numeric, no decimals)
Probability of fast ice, %
[Calc_fld] -- "Calc_fld" (Numeric, no decimals)
Field for storage of calculated values