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Geology topics

G. I. Belchansky

Publications and source records attributed to G. I. Belchansky.

10 recordsLinked to original sources
USGS2008

Fluctuating Arctic Sea ice thickness changes estimated by an in situ learned and empirically forced neural network model

Sea ice thickness (SIT) is a key parameter of scientific interest because understanding the natural spatiotemporal variability of ice thickness is critical for improving global climate models. In this paper, changes in Arctic SIT during 1982-2003 are examined using a neural network (NN) algorithm trained with in situ submarine ice draft and surface drilling data. For each month of the study period, the NN individually estimated SIT of each ice-covered pixel (25-km resolution) based on seven geophysical parameters (four shortwave and longwave radiative fluxes, surface air temperature, ice drift velocity, and ice divergence/convergence) that were cumulatively summed at each monthly position along the pixel's previous 3-yr drift track (or less if the ice was <3 yr old). Average January SIT increased during 1982-88 in most regions of the Arctic (+7.6 ?? 0.9 cm yr-1), decreased through 1996 Arctic-wide (-6.1 ?? 1.2 cm yr-1), then modestly increased through 2003 mostly in the central Arctic (+2.1 ?? 0.6 cm yr-1). Net ice volume change in the Arctic Ocean from 1982 to 2003 was negligible, indicating that cumulative ice growth had largely replaced the estimated 45 000 km3 of ice lost by cumulative export. Above 65??N, total annual ice volume and interannual volume changes were correlated with the Arctic Oscillation (AO) at decadal and annual time scales, respectively. Late-summer ice thickness and total volume varied proportionally until the mid-1990s, but volume did not increase commensurate with the thickening during 1996-2002. The authors speculate that decoupling of the ice thickness-volume relationship resulted from two opposing mechanisms with different latitudinal expressions: a recent quasi-decadal shift in atmospheric circulation patterns associated with the AO's neutral state facilitated ice thickening at high latitudes while anomalously warm thermal forcing thinned and melted the ice cap at its periphery. ?? 2008 American Meteorological Society.

Journal of Climate↗
USGS2005

Spatial and temporal variations in the age structure of Arctic sea ice

Spatial and temporal variations in the age structure of Arctic sea ice are investigated using a new reverse chronology algorithm that tracks ice-covered pixels to their location and date of origin based on ice motion and concentration data. The Beaufort Gyre tends to harbor the oldest (>10 years old) sea ice in the western Arctic while direct ice advection pathways toward the Transpolar Drift Stream maintain relatively young (10 years old (10+ year age class) were observed during 1989-2003. Since the mid-1990s, losses to the 10+ year age class lacked compensation by recruitment due to a prior depletion of all mature (6-10 year) age classes. Survival of the 1994 and 1996-1998 sea ice generations reestablished most mature age classes, and thereby the potential to increase extent of the 10+ year age class during the mid-2000s.

Geophysical Research Letters↗
USGS2005

Variations in the Arctic's multiyear sea ice cover: A neural network analysis of SMMR-SSM/I data, 1979-2004

A 26-year (1979-2004) observational record of January multiyear sea ice distributions, derived from neural network analysis of SMMR-SSM/I passive microwave satellite data, reveals dense and persistent cover in the central Arctic basin surrounded by expansive regions of highly fluctuating interannual cover. Following a decade of quasi equilibrium, precipitous declines in multiyear ice area commenced in 1989 when the Arctic Oscillation shifted to a pronounced positive phase. Although extensive survival of first-year ice during autumn 1996 fully replenished the area of multiyear ice, a subsequent and accelerated decline returned the depletion to record lows. The most dramatic multiyear sea ice declines occurred in the East Siberian, Chukchi, and Beaufort Seas.

Geophysical Research Letters↗
USGS2005

Polar climate: Arctic sea ice

Recent decreases in snow and sea ice cover in the high northern latitudes are among the most notable indicators of climate change. Northern Hemisphere sea ice extent for the year as a whole was the third lowest on record dating back to 1973, behind 1995 (lowest) and 1990 (second lowest; Hadley Center&ndash;NCEP). September sea ice extent, which is at the end of the summer melt season and is typically the month with the lowest sea ice extent of the year, has decreased by about 19% since the late 1970s (Fig. 5.2), with a record minimum observed in 2002 (Serreze et al. 2003). A record low extent also occurred in spring (Chapman 2005, personal communication), and 2004 marked the third consecutive year of anomalously extreme sea ice retreat in the Arctic (Stroeve et al. 2005). Some model simulations indicate that ice-free summers will occur in the Arctic by the year 2070 (ACIA 2004).

Arctic↗
USGS2004

Spatial and temporal multiyear sea ice distributions in the Arctic: A neural network analysis of SSM/I data, 1988-2001

Arctic multiyear sea ice concentration maps for January 1988-2001 were generated from SSM/I brightness temperatures (19H, 19V, and 37V) using modified multiple layer perceptron neural networks. Learning data for the neural networks were extracted from ice maps derived from Okean and ERS satellite imagery to capitalize on the stability of active radar multiyear ice signatures. Evaluations of three learning algorithms and several topologies indicated that networks constructed with error back propagation learning and 3-20-1 topology produced the most consistent and physically plausible results. Operational neural networks were developed specifically with January learning data, and then used to estimate daily multiyear ice concentrations from daily-averaged SSM/I brightness temperatures during January. Monthly mean maps were produced for analysis by averaging the respective daily estimates. The 14-year series of January multiyear ice distributions revealed dense and persistent cover in the central Arctic surrounded by expansive regions of highly fluctuating interannual cover. Estimates of total multiyear ice area by the neural network were intermediate to those of other passive microwave algorithms, but annual fluctuations and trends were similar among all algorithms. When compared to Radarsat estimates of multiyear ice concentration in the Beaufort and Chukchi Seas (1997-1999), average discrepancies were small (0.9-2.5%) and spatial coherency was reasonable, indicating the neural network's Okean and ERS learning data facilitated passive microwave inversion that emulated backscatter signatures. During 1988-2001, total January multiyear ice area declined at a significant linear rate of -54.3 x 10 3 km 2 /yr -1 (-1.4%/yr -1 ). The most persistent and extensive decline in multiyear ice concentration (-3.3%/yr -1 ) occurred in the southern Beaufort and Chukchi Seas. In autumn 1996, a large multiyear ice recruitment of over 106 km 2 (mostly in the Siberian Arctic) fully replenished the previous 8-year decline in total area, but it was followed by an accelerated and compensatory decline during the subsequent 4 years. Seventy-five percent of the interannual variation in January multiyear sea ice area was explained by linear regression on two atmospheric parameters: the previous inter's (JFM) Arctic Oscillation index as a proxy to melt duration and the previous year's average sea level pressure gradient across the Fram Strait as a proxy to annual ice export. Consecutive year changes (1994-2001) in January multiyear ice volume were significantly correlated with duration of the intervening melt season (R 2 = 0.73, -80.0 km 3 /d -1 ), emphasizing a large thermodynamic influence on the Arctic's mass sea ice balance during summers with anomalous melt durations.

Journal of Geophysical Research↗
USGS2004

Duration of the Arctic sea ice melt season: Regional and interannual variability, 1979-2001

Melt onset dates, freeze onset dates, and melt season duration were estimated over Arctic sea ice, 1979–2001, using passive microwave satellite imagery and surface air temperature data. Sea ice melt duration for the entire Northern Hemisphere varied from a 104-day minimum in 1983 and 1996 to a 124-day maximum in 1989. Ranges in melt duration were highest in peripheral seas, numbering 32, 42, 44, and 51 days in the Laptev, Barents-Kara, East Siberian, and Chukchi Seas, respectively. In the Arctic Ocean, average melt duration varied from a 75-day minimum in 1987 to a 103-day maximum in 1989. On average, melt onset in annual ice began 10.6 days earlier than perennial ice, and freeze onset in perennial ice commenced 18.4 days earlier than annual ice. Average annual melt dates, freeze dates, and melt durations in annual ice were significantly correlated with seasonal strength of the Arctic Oscillation (AO). Following high-index AO winters (January–March), spring melt tended to be earlier and autumn freeze later, leading to longer melt season durations. The largest increases in melt duration were observed in the eastern Siberian Arctic, coincident with cyclonic low pressure and ice motion anomalies associated with high-index AO phases. Following a positive AO shift in 1989, mean annual melt duration increased 2–3 weeks in the northern East Siberian and Chukchi Seas. Decreasing correlations between consecutive-year maps of melt onset in annual ice during 1979–2001 indicated increasing spatial variability and unpredictability in melt distributions from one year to the next. Despite recent declines in the winter AO index, recent melt distributions did not show evidence of reestablishing spatial patterns similar to those observed during the 1979–88 low-index AO period. Recent freeze distributions have become increasingly similar to those observed during 1979–88, suggesting a recurrent spatial pattern of freeze chronology under low-index AO conditions.

Journal of Climate↗
USGS2004

Diminishing sea ice in the western Arctic Ocean

Since the advent of satellite passive microwave radiometry (1978), variations in sea ice extent and concentration have been carefully monitored from space. An estimated 7.4% decrease in sea ice extent has occurred in the last 25 yr (Johannessen et al. 2004), with recent record minima (e.g., Maslanik et al. 1999; Serreze et al. 2003) accounting for much of the decline. Comparisons between the time series of Arctic sea ice melt dynamics and snowmelt dates at the NOAA–CMDL Barrow Observatory (BRW) reveal intriguing correlations. Melt-onset dates over sea ice (Drobot and Anderson 2001) were cross correlated with the melt-date time series from BRW, and a prominent region of high correlation between snowmelt onset over sea ice and the BRW record of melt dates was approximately aligned with the climatological center of the Beaufort Sea Anticyclone (BSA). The BSA induces anticyclonic ice motion in the region, effectively forcing the Beaufort gyre. A weak gyre caused by a breakdown of the BSA diminishes transport of multiyear ice into this region (Drobot and Maslanik 2003). Similarly, the annual snow cycle at BRW varies with the position and intensity of the BSA (Stone et al. 2002, their Fig. 6). Thus, variations in the BSA appear to have far-reaching effects on the annual accumulation and subsequent melt of snow over a large region of the western Arctic. A dramatic increase in melt season duration (Belchansky et al. 2004) was also observed within the same region of high correlation between onset of melt over the ice pack and snowmelt at BRW (Fig. 5.7). By inference, this suggests linkages between factors that modulate the annual cycle of snow on land and processes that influence melting of snow and ice in the western Arctic Ocean.

Bulletin of the American Meteorological Society↗
USGS1999

Comparative analysis of multisensor satellite monitoring of Arctic sea-ice

This report represents comparative analysis of nearly coincident Russian OKEAN-01 polar orbiting satellite data, Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) imagery. OKEAN-01 ice concentration algorithms utilize active and passive microwave measurements and a linear mixture model for measured values of the brightness temperature and the radar backscatter. SSM/I and AVHRR ice concentrations were computed with NASA Team algorithm and visible and thermal-infrared wavelength AVHRR data, accordingly

Book↗
USGS1996

Assessing variability and trends in Arctic sea ice distribution using satellite data

Trends in the annual minimum, minimum monthly-mean, and the sea ice extent at the end of August were investigated for the Barents and western Kara Seas and adjacent parts of the Arctic Ocean during 1966 to 1994 using data from Russian ice maps (1974-1994), Kosmos-Okean and ALMAZ SAR satellite series (1984-1994), and published literature. Four definitions of sea ice extent were examined based on thresholds of ice concentration: >90%, >70%, >40% and >10% (E1, E2, E3, and E4, respectively). Root-mean-square differences between sea ice maps and satellite-image sea ice classifications for coincident areas were subjected to Monte-Carlo analyses to construct confidence intervals for the 20-year ice-map trends. With probability p=0.8, the average 20-year change in the minimum monthly-mean sea ice extent (followed in brackets by the average change in the absolute annual minimum ice extent) was between 30-60% [19-71%], 29-61% [15-67%], 31-63%[18-69%] and 18-48% [7-55%] in the Barents sea; (-24)-(-4)% [(-25)-(12)%], (-27)-(-9)% [(-34)-(-4)%], (-32)-(-15)% [(-39)-(-9)%] and (-33)-(-15)%[(-38)-(-8)%] in the western Kara sea; and (-3)-19% [(-8)-29%], (-4)-18% [(-11)-26%,] (-6)-16% [(-11)(-24)%] and (-7)-15% [(-12)-24%] in the combined Barents and Kara Seas, for sea ice concentration E1-E4, respectively. Including published data from 1966-1983, the trend in minimum monthly-mean sea ice extent for the combined 30-year period showed an average increasing of 11.8% in the Barents Sea and of 47.4% reduction in the western Kara Sea; sea ice extent at the end of August showed an average reduction of 4.7% in the Barents Sea.

Conference Paper↗