Surplus Arctic Ice July 2022

The animation shows Arctic ice extents on day 212 (end of July) starting with 2007, and showing the lack of decline through yesterday.  Years vary as to which regions retain more or less ice.  For example, some years the Russian shelf seas (left side) are mostly open water on day 212.  Kara and Barents seas (top side) fluctuate.  The Northwest passage (bottom), melts early some years in Beaufort Sea, and CAA, but this year not so much. The graph below compares the July monthly ice extents 2007 to 2022 and compared to the SII 16 year average.

Clearly July ice appears as a plateau, and most years MASIE shows greater extents than SII, with differences of only a few 100k km2.  The previous three years were in deficit to average, but July 2022 has returned to surplus years like 2018.  More on MASIE dataset at the end.

The graph shows the melting pattern during July 2022 remained above average all month, and greatly exceeded 2007 and 2020, especially in the last 2 weeks.  July 31, 2022 was 275k km2 in surplus, and exceeded 2020 by 1.2 Wadhams (M km2).

The table below shows ice extents by regions comparing 2022 with 16-year average (2007 to 2022 inclusive) and 2020.

Region 2022212 Day 212 Average 2022-Ave. 2020212 2022-2020
 (0) Northern_Hemisphere 7124997 6849722 275275 5880746 1244251
 (1) Beaufort_Sea 884463 789512 94950 875454 9008
 (2) Chukchi_Sea 575245 525326 49919 533748 41498
 (3) East_Siberian_Sea 787982 740081 47901 329453 458529
 (4) Laptev_Sea 297339 365902 -68563 61979 235360
 (5) Kara_Sea 104430 161090 -56660 95539 8891
 (6) Barents_Sea 0 31433 -31433 23940 -23940
 (7) Greenland_Sea 292278 291488 790 282403 9875
 (8) Baffin_Bay_Gulf_of_St._Lawrence 281955 130715 151240 35368 246587
 (9) Canadian_Archipelago 633235 540268 92967 515499 117735
 (10) Hudson_Bay 169704 135947 33757 92861 76844
 (11) Central_Arctic 3096938 3136284 -39347 3033706 63232

2022 is 275k km2 above average (4%). The main deficits are in Laptev and Kara seas, more than offset by 151k km2 surplus in Baffin Bay, along with additional ice in CAA, and an icy BCE (Beaufort, Chukchi, East Siberian seas).

Footnote on MASIE Data Sources:

MASIE reports are based on data primarily from NIC’s Interactive Multisensor Snow and Ice Mapping System (IMS). From the documentation, the multiple sources feeding IMS are:

Platform(s) AQUA, DMSP, DMSP 5D-3/F17, GOES-10, GOES-11, GOES-13, GOES-9, METEOSAT, MSG, MTSAT-1R, MTSAT-2, NOAA-14, NOAA-15, NOAA-16, NOAA-17, NOAA-18, NOAA-N, RADARSAT-2, SUOMI-NPP, TERRA

Sensor(s): AMSU-A, ATMS, AVHRR, GOES I-M IMAGER, MODIS, MTSAT 1R Imager, MTSAT 2 Imager, MVIRI, SAR, SEVIRI, SSM/I, SSMIS, VIIRS

Summary: IMS Daily Northern Hemisphere Snow and Ice Analysis

The National Oceanic and Atmospheric Administration / National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) has an extensive history of monitoring snow and ice coverage.Accurate monitoring of global snow/ice cover is a key component in the study of climate and global change as well as daily weather forecasting.

The Polar and Geostationary Operational Environmental Satellite programs (POES/GOES) operated by NESDIS provide invaluable visible and infrared spectral data in support of these efforts. Clear-sky imagery from both the POES and the GOES sensors show snow/ice boundaries very well; however, the visible and infrared techniques may suffer from persistent cloud cover near the snowline, making observations difficult (Ramsay, 1995). The microwave products (DMSP and AMSR-E) are unobstructed by clouds and thus can be used as another observational platform in most regions. Synthetic Aperture Radar (SAR) imagery also provides all-weather, near daily capacities to discriminate sea and lake ice. With several other derived snow/ice products of varying accuracy, such as those from NCEP and the NWS NOHRSC, it is highly desirable for analysts to be able to interactively compare and contrast the products so that a more accurate composite map can be produced.

The Satellite Analysis Branch (SAB) of NESDIS first began generating Northern Hemisphere Weekly Snow and Ice Cover analysis charts derived from the visible satellite imagery in November, 1966. The spatial and temporal resolutions of the analysis (190 km and 7 days, respectively) remained unchanged for the product’s 33-year lifespan.

As a result of increasing customer needs and expectations, it was decided that an efficient, interactive workstation application should be constructed which would enable SAB to produce snow/ice analyses at a higher resolution and on a daily basis (~25 km / 1024 x 1024 grid and once per day) using a consolidated array of new as well as existing satellite and surface imagery products. The Daily Northern Hemisphere Snow and Ice Cover chart has been produced since February, 1997 by SAB meteorologists on the IMS.

Another large resolution improvement began in early 2004, when improved technology allowed the SAB to begin creation of a daily ~4 km (6144×6144) grid. At this time, both the ~4 km and ~24 km products are available from NSIDC with a slight delay. Near real-time gridded data is available in ASCII format by request.

In March 2008, the product was migrated from SAB to the National Ice Center (NIC) of NESDIS. The production system and methodology was preserved during the migration. Improved access to DMSP, SAR, and modeled data sources is expected as a short-term from the migration, with longer term plans of twice daily production, GRIB2 output format, a Southern Hemisphere analysis, and an expanded suite of integrated snow and ice variable on horizon. Source:  Interactive Multisensor Snow and Ice Mapping System (IMS)

4 comments

  1. HiFast · 17 Days Ago

    Reblogged this on Climate Collections.

    Like

  2. Pingback: Surplus Arctic Ice July 2022 - Climate- Science.press
  3. Jack Broughton · 14 Days Ago

    Dear Ron,
    I follow your ice reports, despite having little knowledge of this. Are there data available for the longer term ice history? I’d be interested to see what happened over a century ago: a bit like the sea-level charts.

    Like

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