Geology topics

Nikita G. Platonov

Publications and source records attributed to Nikita G. Platonov.

6 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↗

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

Estimating the time of melt onset and freeze onset over Arctic sea-ice area using active and passive microwave data

Accurate calculation of the time of melt onset, freeze onset, and melt duration over Arctic sea-ice area is crucial for climate and global change studies because it affects accuracy of surface energy balance estimates. This comparative study evaluates several methods used to estimate sea-ice melt and freeze onset dates: (1) the melt onset database derived from SSM/I passive microwave brightness temperatures ( T b s) using Drobot and Anderson's [J. Geophys. Res. 106 (2001) 24033] Advanced Horizontal Range Algorithm (AHRA) and distributed by the National Snow and Ice Data Center (NSIDC); (2) the International Arctic Buoy Program/Polar Exchange at the Sea (IABP/POLES) surface air temperatures (SATs); (3) an elaborated version of the AHRA that uses IABP/POLES to avoid anomalous results (Passive Microwave and Surface Temperature Analysis [PMSTA]); (4) another elaborated version of the AHRA that uses T b variance to avoid anomalous results (Mean Differences and Standard Deviation Analysis [MDSDA]); (5) Smith's [J. Geophys. Res. 103 (1998) 27753] vertically polarized T b algorithm for estimating melt onset in multiyear (MY) ice (SSM/I 19V–37V); and (6) analyses of concurrent backscattering cross section ( σ °) and brightness temperature ( T b ) from OKEAN-01 satellite series. Melt onset and freeze onset maps were created and compared to understand how the estimates vary between different satellite instruments and methods over different Arctic sea-ice regions. Comparisons were made to evaluate relative sensitivities among the methods to slight adjustments of the T b calibration coefficients and algorithm threshold values. Compared to the PMSTA method, the AHRA method tended to estimate significantly earlier melt dates, likely caused by the AHRA's susceptibility to prematurely identify melt onset conditions. In contrast, the IABP/POLES surface air temperature data tended to estimate later melt and earlier freeze in all but perennial ice. The MDSDA method was least sensitive to small adjustments of the SMMR–SSM/I inter-satellite calibration coefficients. Differences among methods varied by latitude. Freeze onset dates among methods were most disparate in southern latitudes, and tended to converge northward. Surface air temperatures (IABP/POLES) indicated freeze onset well before the MDSDA method, especially in southern peripheral seas, while PMSTA freeze estimates were generally intermediate. Surface air temperature data estimated latest melt onset dates in southern latitudes, but earliest melt onset in northern latitudes. The PMSTA estimated earliest melt onset dates in southern regions, and converged with the MDSDA northward. Because sea-ice melt and freeze are dynamical transitional processes, differences among these methods are associated with differing sensitivities to changing stages of environmental and physical development. These studies contribute to the growing body of documentation about the levels of disparity obtained when Arctic seasonal transition parameters are estimated using various types of microwave data and algorithms.

Remote Sensing of Environment↗

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↗