ICESat Data Showing Decline in Arctic Sea Ice ThicknessICESat Data Showing Decline in Arctic Sea Ice Thickness

Ice Age from tracking model

Ice thickness from ICESat

A younger, thinner Arctic ice cover: Increased potential for rapid,extensive sea-ice loss, GRL Dec 2007,J. A. Maslanik, C. Fowler, J. Stroeve, S. Drobot, J. Zwally, D. Yi, and W. Emery, U. Colorado and NASA Goddard

Sea ice thickness derived from ICESat measurements of sea ice freeboard were correlated with estimates of ice age from a tracking-aging model of ice surviving each summer melt season.

Derived thickness-age relation was used to estimate thickness changes over 24 years (1986 to 2006).

Oldest and thickest multiyear ice has declined significantly.

Remaining ice is thinner and more vulnerable to increasing summer melting.

Jay Zwally, Code 614.1, ICESat Project Science, NASA GSFC

ICESat data is also showing that the VOLUME of ice at the end of summer decreased by 50% in only 4 years (2003 to 2007) while the AREA decreased by 32%.

Hydrospheric and Biospheric Sciences Laboratory

Name: Jay ZwallyEmail: Jay.Zwally@nasa.gov Phone: 301-614-5643

Related References:

Yu, Y., G. A. Maykut, and D. A. Rothrock (2004), Changes in the thickness distribution of Arctic sea ice between 1958 – 1970 and 1993 – 1997, J. Geophys. Res., 109, C08004, doi:10.1029/2003JC001982.

Zwally, H. J., D. Yi, R. Kwok, and Y. Zhao (2007), ICESat measurements of sea-ice freeboard and estimates of sea-ice thickness in the Weddell Sea, J. Geophys. Res., doi:10.1029/2007JC004284, in press.

Kwok, R., H. J. Zwally, and D. Yi (2004), ICESat observations of Arctic sea ice: A first look, Geophys. Res. Lett., 31, L16401, doi:10.1029/2004GL020309.

Maslanik, J., S. Drobot, C. Fowler, W. Emery, and R. Barry (2007), On the Arctic climate paradox and the continuing role of atmospheric circulation in affecting sea ice conditions, Geophys. Res. Lett., 34, L03711, doi:10.1029/2006GL028269.

Stroeve, J., M. M. Holland, W. Meier, T. Scambos, and M. Serreze (2007), Arctic sea ice decline: Faster than forecast, Geophys. Res. Lett., 34, L09501, doi:10.1029/2007GL029703.

Data Sources: ICESat and Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the series of Advanced Very High Resolution Radiometer (AVHRR) sensors. Motion vectors are then blended via optimal interpolation with International Arctic Buoy Program drifting- buoy vectors [Fowler, 2003].

Technical Description of Figures: See figures, which should be self-explanatory.

Scientific Significance: ICESat data enabled a first-time ever establishment of a quantitative relationship between the age of sea ice on the Arctic Ocean and its thickness. Derived thickness-age relation was used to estimate thickness changes over 24 years (1986 to 2006). Oldest and thickest multiyear ice has declined significantly. Remaining ice is thinner and more vulnerable to increasing summer melting. The much-reduced extent of the oldest and thickest ice, in combination with other factors such as ice transport helps explain the recent large and abrupt ice loss in summer.

Hydrospheric and Biospheric Sciences Laboratory

Tipping Point for the Arctic Perennial Ice CoverTipping Point for the Arctic Perennial Ice Cover

Perennial ice cover (or ice at the end of the summer) is currently declining at the rate of 11.4% per decade as observed from satellite data from 1978 to 2007. Acceleration in the decline makes it difficult for ice to recover because of ice-albedo feedback effects which has caused increases in solar heating of the mixed layer on account of the observed increase in open water area of 23% per decade in the Arctic basin since 1978.

Such feedback effect is reflected in the unexpectedly large decline (39% lower than average) in the perennial ice cover in 2007. Concurrently, the sea surface temperature was anomalously warm (4oC higher than average) and has been increasing at the rate of 0.7 oC/decade in the Beaufort/Chukchi Seas region since 1981.

We postulate that the perennial ice has reached its tipping point and will continue to decline.

Figure 2 SST monthly Anomalies in 2007

Figure 1 Ice Extent Monthly Anomalies

-2.2 % per decade 1978-1996

-10.1 % per decade 1996-2007

Hydrospheric and Biospheric Sciences Laboratory

Josefino C. Comiso, Code 614.1, NASA/GSFC

Name: Josefino C. Comiso, NASA/GSFC E-mail: josefino.c.comiso@nasa.gov Phone: 301-614-5708

References:

The slide is primarily a summary of the Fall 2007 AGU talk on “Arctic Perennial Ice Tipping Point,” by J. C. Comiso and cited in

Kerr, Richard A., Climate Tipping Points come in from the cold, AGU meeting briefs, Science, 319, p. 153, 11 January 2008.

Inman, Mason, Global Warming Tipping Points, National Geographic News, 14 December 2007.

Other References:

Comiso, J.C., C.L. Parkinson, R. Gersten, and L. Stock (2008), Accelerated decline in the Arctic sea ice cover, Geophy. Res. Lett. 35, L01703,

doi:10.1029/2007GL031972

Cited as one of the Research Highlights in: Arctic Meltdown/Cryosphere, Harvey Leifert, Nature Reports, Climate Change, Vol. 2, February

2008/www.nature.com/reports/climate

Comiso, J. C. and F. Nishio (2008), Trends in the sea ice cover using enhanced and compatible AMSR-E, SSM/I, and SMMR data, J. Geophys.

Res. 113, C02S07, doi:10.1029/2007JC004257.

Comiso, J. C. (2006), Abrupt Decline in the Arctic Winter Sea Ice Cover, Geophys. Res. Lett.,33, L18504, doi:10.1029/2006GL027341, 2006.

Comiso, J.C. (2006) Arctic warming signals from satellite observations, Weather, 61(3), 70-76, 2006

Data Sources: Sea ice extents were derived from sea ice concentration maps from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and the DMSP/ Special Scanning Microwave Imager (SSM/I) passive microwave brightness temperature data from November 1978 to 2007. Data from the EOS/Aqua Advanced Multichannel Microwave Radiometer (AMSR) were also utilized. Sea Surface Temperature (SST) were derived from the NOAA/Advanced Very High Resolution Radiometer (AVHRR) data from August 1981 to 2007.

Technical Description of Image:Figure 1: Monthly anomalies of the extent of sea ice in the entire Northern Hemisphere from November 1978 to December 2007. The monthly anomalies were derived by subtracting the climatological monthly ice extent (1978 to 2007) from the monthly average ice extents from passive microwave data.

Figure 2: Color coded sea surface temperature (SST) anomaly maps in the Northern Hemisphere in August and September 2007 derived from satellite thermal infrared data. The data from thermal infrared (1981 to 2007) have been shown to be consistent during overlap with those from AMSR-E data from 2002 to 2007 which are less vulnerable to cloud cover. In 2007, in situ measurements indicated a thinning of the ice cover in the underside of as much as 1 m in the Beaufort/Chukchi Sea region, where the anomalies are shown to be relatively high, during the summer.

Scientific significance: The sea ice cover is one of the key components of the Climate system. A continued decline of the perennial ice cover would lead to an ice free Arctic Ocean in the summer in the foreseable future. The impact on the physical characteristics of the Arctic ocean is expected to be profound, including changes in its hydrography, its circulation, and the halocline.. Serious impacts on the productivity, ecology and climate of the region is also expected.

Relevance for future science and relationship to Decadal Survey: The distribution of the sea ice cover is affected by surface temperature and wind circulation which are primarily controlled by the Arctic Oscillation which is responsible for periodic changes in surface temperature and wind circulation. The distribution is also affected by warming associated with increasing greenhouse gases in the atmosphere. The compilation and analysis of several decades of data are needed to better understand the influence of the latter on the declining sea ice cover and the warming Arctic.

Hydrospheric and Biospheric Sciences Laboratory

Blended Snow Grid Values MODIS snow cover and AMSR swe

MODIS snow cover and 0 swe

MODIS cloud and AMSR swe

MODIS no data and AMSR swe

Permanent Snow/Ice

MODIS polar darkness and AMSR swe

Land

MODIS cloud in AMSR swath gap

MODIS 0 snow and AMSR swe

Water

Visible data from MODIS and passive microwave data from AMSR-E have now been blended to produce global, daily snow maps at a resolution of 25 km. The blended-snow ANSA (Air Force/NASA Snow Algorithm) product begins with combined MODIS/AMSR-E data from the Aqua satellite (June of 2002) and continues through the present.

A NEW BLENDED GLOBAL SNOW PRODUCT USING VISIBLE, MICROWAVE AND SCATTEROMETER SATELLITE DATA

JAMES FOSTER, DOROTHY HALL, GEORGE RIGGS, ED KIM, MARCO TEDESCO, SON NGHIEM, RICHARD KELLY, BHASKAR CHOUDHURY

ANSA snow map 15 January 2007This new product is the first to include not only global, daily snow cover extent and snow water equivalent (swe) data but also snowmelt data (not shown).

These products improve forecasts for seasonal water supplies, weather and climate, and snowmelt flooding.

Figure 1: ANSA Snow Map for January 15, 2007. Colorsrepresented on this map show a suite of MODIS/AMSR-E data,including snow and cloud cover and snow water equivalent.

Hydrospheric and Biospheric Sciences Laboratory

References: Foster, J. L., et al.: “A blended global snow product using data from MODIS, AMSR-E and QuickSCAT,” Proceedings

of the 88th Amer. Met. Soc., New Orleans, LA, 2008.Hall, D. K., et al. “Validation of a Blended Snow Product Using Observations and Satellite Images,” Proceedings of the

88th Amer. Met. Soc., New Orleans, LA, Jan., 2008. Foster, J. L., et al.: “Blended Visible (MODIS), Passive Microwave (AMSR-E) and Scatterometer (QuickSCAT) Global

Snow Products,” Proceeding of the 64th Eastern Snow Conference (ESC), St. John, Newfoundland, May 2007.Hall, D. K., et al. “Preliminary Validation of the AFWA-NASA Blended Snow-Cover Product,” Proceeding of the 64th

Eastern Snow Conference (ESC), St. John, Newfoundland, May 2007.Tedesco, M. et al. “Melting Snow from Passive Microwave Observations for a Microwave/Visible Blended Product:

First Results,” Proceeding of the 64th Eastern Snow Conference (ESC), St. John, Newfoundland, May 2007

Data Source: Goddard Snow Team (GOST), which consists of the scientists listed above. Figure 1 derived from MODIS and AMSR-E data.

Technical Description of Image: Figure 1 shows a Northern Hemisphere ANSA (Air Force NASA Snow Algorithm) map of a blended visible and

passive microwave images for January 15, 2007. Eight separate snow categories are shown -- from either or both the MODIS and AMSR-E products. The deeper blue or lapis color represents snow cover from both the visible and microwave products. The purple color shows cloud cover as observed on MODIS – AMSR-E detects snow beneath these clouds. Red portrays areas where MODIS detects snow but AMSR-E misses -- especially evident near the border of the continental snowline where snow depths are typically shallow. On this rendition, the yellow color represents snow that AMSR-E has observed but MODIS does not. This is often a false-positive signal that results when and where the AMSR-E algorithm detects snow in areas where the ground surface is cold but not always snow covered. The lime color shows AMSR-E swath gaps. Sky blue colors, in the high Arctic, are areas dark on MODIS (polar night) – AMSR-E is able to observe the surface at nighttime as well as during the day.

Scientific Significance: Visible data from MODIS and passive microwave data from AMSR-E have now been fused, for the first time, to

produce global, daily snow maps at a resolution of 25 km. The blended-snow ANSA product begins with combined MODIS/AMSR-E data from the Aqua satellite (June of 2002) and continues through the present. In addition, QuikSCAT data (not shown), are utilized to show areas of active snowmelt. QuilSCAT data is available from June 1999 to the present. This is the first time that this data has been included as part of a daily, global snow product. Our blended product has been thus far been validated in the Great Lakes region of the U.S. and in the Cold Lands (CLPX) study area of Colorado.

Hydrospheric and Biospheric Sciences Laboratory

Names: James FosterEmails: James.L.Foster@nasa.govPhones: 301-614-5769

Passive Millimeter-Wave Satellite Sensors Passive Millimeter-Wave Satellite Sensors Can Detect Falling Snow over LandCan Detect Falling Snow over Land

It is important to determine if passive satellite observations can detect frozen precipitation over variable land features. Retrievals of falling snow from satellite data will be critical for fully under-standing the links between the water and energy cycles. Pas-sive radiometers are needed to supple-ment GPM instruments in measuring falling snow & light rain over land.

By combining surface and other information obtained during the (Fig. 1) Canadian CloudSat/Calipso Field Campaign (C3VP), we determined that scattering from precipitation sized ice particles can be detected over land (Fig. 3). We used GLCF forest cover (Fig. 2) to help obtain surface emissivity for forward clear air calculations that we compare to the AMSU-B data.

Fig. 1: C3VP Site Fig. 2: Forest Fraction

46% forest cover in this 50km x 50 km box.

Hydrospheric and Biospheric Sciences Laboratory

Gail Skofronick Jackson, Code 614.6, NASA/GSFC

This plot shows that AMSU-B Brightness Temperatures (TB) during precipitation are

colder than computed TB as caused by scattering of cloud ice particles. For cases with no precipitation, the differences are negative.

Fig. 3: Detection of Snow Scattering Signal

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Name: Gail Skofronick Jackson, NASA/GSFC E-mail: Gail.S.Jackson@nasa.govPhone: 301-614-5720

References:G. Skofronick-Jackson, Ali Tokay, Ben Johnson, and Anne Kramer, “Detection of Falling Snow and Cloud Ice Scattering over Land Surfaces

from Spaceborne Radiometric Observations,” to be submitted to JGR-Atmospheres, spring 2008.

G. M. Skofronick-Jackson, M.-J. Kim, J. A. Weinman, and D.-E. Chang, “A Physical Model to Determine Snowfall over Land by Microwave Radiometry,” IEEE Trans. Geosci. Remote Sens, vol. 42, pp. 1047-1058, 2004.

Data Sources: The C3VP field campaign was held October 2006 through March 2007 70km north of Toronto, Canada. We used Parsivel ground observations at the C3VP site to indicate falling snow precipitation events and ground weather station data to determine surface temperature and snow cover depth. Radiosonde data released at the C3VP site was also employed in this work. The Global Land Cover Facility (www.landcover.org) was used to obtain forest fraction, while papers by Hewison and English provided surface emissivity for the various surface conditions. The NOAA AMSU-B observations from 89 to 183 GHz are the primary data set used for retrievals.

Technical Description of Images:Figure 1: A Google image of a 50km by 50 km box centered at the C3VP site. AMSU-B FOVs diameters are 15km to 25km.

Figure 2: The forest cover fraction in a 50km by 50 km box centered at the C3VP site. (Data from www.landcover.org)

Figure 3: A difference plot between clear-air computed brightness temperatures (TBs) and AMSU-B observations for 11 cases of dropsonde data (for computed TB) within an hour of an AMSU-B overpass where the Parsivel said it was snowing. All of the differences are positive (computed – AMSU-B) indicating that there was cloud ice scattering in the AMSU-B observations. TB computations used surface emissivity as determined using the forest fraction and varying snow cover depth (from ground station data). Though not shown, the clear-air plot (according to the Parsivel) has differences where at least one frequency is positive.

Scientific significance: Surface features are quite variable and yet the signature from such features impacts brightness temperatures at frequencies typically used for precipitation estimates. This work shows that (1) we can detect cloud ice scattering over land, and (2) that rough estimates of surface emissivity may be all that is required for future retrievals.

Relevance for future science and relationship to Decadal Survey: This work is critical in GPM pre-launch physically-based algorithm development for falling snow and light rain over land estimates. By estimating both rain and frozen precipitation, a more complete picture of the water cycle is obtained.

Hydrospheric and Biospheric Sciences Laboratory

A Better Way to Measure Water Vapor in the Upper Troposphere and Lower Stratosphere (UTLS)

Water vapor is an important greenhouse gas whose measurement is essential for understanding climate change and water balance models, especially in the UTLS.

Radiosonde relative humidity sensor capability is limited above ~200 hPa* or at temperatures of -30°C to -40°C (Figure 1). The Chilled Mirror Hygrometer (CMH) measures to ~70 hPa while the Frost Point Hygrometer (FPH) can measure to between 500 hPa and 20 hPa. CMH/FPH comparability (Figure 2).

Meaningful water vapor structure can be obtained to near 70 hPa or occasionally to lower pressure using the CMH. Figure 3 illustrates features not usually observed with the typical radiosonde relative humidity sensor. The CMH identifies cirrus and sub-visual layers.

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Fig. 1: Relative humidity (RH) profile from typical radiosonde ascent

Fig. 2: Comparison between Chilled Mirror Hygrometer (CMH) sensor and Frost Point Hygrometer (FTH)

Fig. 3: Data obtained from a CMH in the sub-tropics

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Frank Schmidlin, Code 614, NASA GSFC

Hydrospheric and Biospheric Sciences Laboratory

Name: Francis J. SchmidlinE-mail: francis.j.schmidlin@nasa.govPhone: 757 824 1618

References:

Mattioli, V., E. R. Westwater, D. Cimini, J. C. Liljegren, B. M. Lesht, S. I. Gutman, and F. J. Schmidlin, 2007: Analysis of Radiosonde and Ground-Based Remotely Sensed PWV Data from the 2004 North Slope of Alaska Arctic Winter Radiometric Experiment. J. Atmos. Oceanic. Technol., Vol. 24. 415-431.

Vömel, H., D. E. David, and K. Smith, 2007: Accuracy of tropospheric and stratospheric water vapor measurements by the cryogenic frost point hygrometer: Instrumental details and observations. J. Geophys. Res. Vol. 112, D08305, doi:10.1029/2006JD007224.

Wang, J., D. J. Carlson, D. B. Parsons, T. F. Hock, D. Lauritsen, H. L. Cole, K. Beierle, and E. Chamberlain, 2003: Performance of operational radiosonde humidity sensors in direct comparison with a chilled mirror dew-point hygrometer and its climate implication. Geophy. Res. Lett., 30, 10.1029/2003GL016985.

Data Sources: Knowledge of water vapor is an important parameter required by NASA, many other US agencies, international groups, and for research studies by Universities. The use of the CMH has increased but because of its 70 hPa limitation preference has been given to the use of the FPH instrument, of which operational ability is contained within NOAA. NASA has used the CMH since 1998 for special purposes but its increased use

is important for future research and validation.

Technical Description of Images:

Figure 1: Relative humidity (RH) profile from typical radiosonde ascent illustrating lack of response of the sensor at cold atmospheric temperatures.

Figure 2: Comparison between Chilled Mirror Hygrometer (CMH) sensor and Frost Point Hygrometer (FTH) is shown. Agreement is within a 2-4 percent relative humidity (RH). This is significant because it illustrates that the CMH will provide comparable RH data to about 70 hPa, roughly 18 km. Using the CMH involves less risk since a liquid cryogen is not required. Research and operational sites should be able to utilize the CMH for water vapor research and NASA satellite validation.

Figure 3: Detailed description of data obtained from a CMH in the sub-tropics. CMHs detect the dew point temperature by cooling a reflective surface until mist or ice forms. A photo detector cell optically detects the mist or ice and the temperature of the surface is recorded. Super-saturation may occur

in clouds, the positive result is that clouds are detected when the typical radiosonde will not. Cirrus and sub-visual cirrus are detected by CMHs.

Scientific Significance: Water vapor information is required for cloud research, satellite validation, climate change studies, transmission of radio waves through the atmosphere; the CMH provides increased measurement reliability over the standard radiosonde instrument.

Relevance for Future Science and Relationship to Decadal Survey: Water vapor is an important gas for which the hydrological cycle greatly depends, and it aids in controlling atmospheric processes, I.e., energy, cloud formation. Water vapor measurements are complex and difficult, nonetheless, are a necessary component for validation of future EOS satellites.

Hydrospheric and Biospheric Sciences Laboratory