Sheldon D. Drobot

Arctic Sea Ice Extent Plummets in 2007

Arctic sea ice declined rapidly to unprecedented low extents in the summer of 2007, raising concern that the Arctic may be on the verge of a fundamental transition toward a seasonal ice cover. Arctic sea ice extent typically attains a seasonal maximum in March and minimum in September. Over the course of the modern satellite record (1979 to present), sea ice extent has declined significantly in all months, with the decline being most pronounced in September. By mid-July 2007, it was clear that a new record low would be set during the summer of 2007.

A record minimum arctic sea ice extent and area in 2002

[1] Arctic sea ice extent and area in September 2002 reached their lowest levels recorded since 1978. These conditions likely resulted from (1) anomalous warm southerly winds in spring, advecting ice poleward from the Siberian coast (2) persistent low pressure and high temperatures over the Arctic Ocean in summer, promoting ice divergence and rapid melt.

Risk factors for driving into flooded roads

Abstract Motor vehicle-related deaths account for more than half of all flood fatalities in the United States, but to date, very little is known about the risk factors associated with why people drive into flooded roads. Using data from survey questionnaires administered in Denver, CO, and Austin, TX, this paper suggests that people who do not take warnings seriously are more likely to drive through flooded roads, as are people aged 18–35, and those that do not know that motor vehicles are involved in more than half of all flood fatalities. In Denver, people who have not experienced a flood previously and those who do not know they live in flood-prone areas are also more likely to drive into flooded roads.

An improved method for determining snowmelt onset dates over Arctic sea ice using scanning multichannel microwave radiometer and Special Sensor Microwave/Imager data

Ablation of snow over sea ice is an important physical process affecting the Arctic surface energy balance. An improved understanding of the spatial and temporal variations in snowmelt onset could be utilized to improve climate simulations in the Arctic, as well as monitor the Arctic for signs of climate change. Utilizing an updated approach for monitoring snowmelt onset over Arctic sea ice, spatial variability in passive microwave derived snowmelt onset dates is examined from 1979 through 1998. The improved technique, termed the advanced horizontal range algorithm (AHRA), utilizes temporal variations in 18/19 GHz and 37 GHz passive microwave horizontal brightness temperatures obtained from the scanning multichannel microwave radiometer (SMMR) and the Special Sensor Microwave/Imager (SSM/I) to identify snowmelt onset. A qualitative assessment of spatial variability in snowmelt onset discusses the 1979 through 1998 mean snowmelt onset pattern, and it also illustrates that there are significant variations in snowmelt onset on an annual basis. Principal component analysis of the snowmelt onset dates suggests snowmelt onset variability is dominated by a zone of abnormally early (late) snowmelt onset near the Siberian coast and another zone of abnormally late (early) snowmelt onset near Baffin Bay. Statistical analysis between the first principal component and March-June monthly averaged Arctic Oscillation values implies that variations in snowmelt onset are related to alterations in the phase of the spring Arctic Oscillation.

Information sources for flash flood warnings in Denver, CO and Austin, TX

Abstract This research examines sources of information for flash floods in two large metropolitan areas, Denver, CO, and Austin, TX. Previous research has noted that information delivery systems for weather forecasts are geared toward the cultural majority and suggests that inadequate warnings are a primary contributor to deaths and injuries from hazards. This investigation used chi-square analysis to determine the prime warning source preferences and preferred time of day for receiving different media. Results indicate that successful warning messages need to be targeted toward specific sub-populations if the warning is to be received, understood, and responded to properly.

A long-range forecast of Arctic summer sea-ice minimum extent

This paper discusses the development of simple multiple linear regression (MLR) models for predicting the annual pan-Arctic minimum sea-ice extent at monthly intervals from February through August. The predictor data is based on mean monthly weighted indices of sea-ice concentration (WIC), surface skin temperature (WST), surface albedo (WAL), and downwelling longwave flux at the surface (WDL). The final regression equations retain either one or two sea ice and surface energy and radiation balance predictors, and each of the MLR models is superior to climatology, persistence, or random chance models. The mean absolute error (MAE) for the MLR models decreases from 0.36 x 10 6 km 2 in February to 0.15 × 10 6 km 2 in August; the corresponding r2 increases from 0.46 in February to 0.90 in August. In addition to improving long-range predictions, the models provide insight into the physical mechanisms affecting recent large reductions in sea-ice extent.

The onset of Arctic sea-ice snowmelt as detected with passive- and active-microwave remote sensing

Abstract Daily acquisitions from satellite microwave sensors can be used to observe the spatial and temporal characteristics of the Arctic sea-ice snowmelt onset because the initial presence of liquid water in a dry snowpack causes a dramatic change in the active-and passive-microwave response. A daily sequence of backscatter coefficient images from the NASA scatterometer (NSCAT) clearly shows the spatially continuous progression of decreasing backscatter associated with snowmelt onset across the Arctic Ocean during spring 1997. A time series of the active NSCAT backscatter and a scattering index from the passive Special Sensor Microwave/Imager (SSM/I) show similar trends during the time of the melt onset. An NSCATsnowmelt-onset detection algorithm is developed using the derivative of the backscatter with respect to time to select a melt-onset date for each pixel, generating a melt map for the Arctic sea ice. Comparison between this melt map and one previously generated from an SSM/I scattering index shows the NSCAT algorithm predicts the onset occurs 1−10 days earlier than the SSM/I-based algorithm for most portions of multi-year ice.

Advances and challenges in flash flood warnings

Advances in meteorological, hydrological and engineering sciences are fast generating a range of new methodologies for forecasting weather and flood events, including ensemble prediction systems (EPS) and new hydrodynamic models. These advances are in addition to those already made in weather radar and quantitative precipitation forecasting which have enhanced flood warning lead time potential; in integrated real-time monitoring and modeling; and in long-term models. Each of these advances presents new challenges within the complex information and data flows which now exist between actors generating forecasts and actors using them (Fig. 1). At the same time, the natural, physical and engineering sciences, which are generating the technical advances noted above, are being challenged by social science. The social science paradigm focuses on human perceptions of risk information and the under-estimated complexities of communicating risk between actors, and stresses that uncertainty must be managed rather than eradicated. It has already contributed much to a broader and deeper understanding of human behaviour in the face of hazard warnings. It promises to contribute further in addressing the risk communication and response issues now raised by the new wave of technical advances.

Spatial and Temporal Variability in Snowmelt Onset over Arctic Sea Ice

Abstract Climate models suggest surface warming in the Arctic will be rapid and pronounced, implying substantial changes in snowmelt onset are likely. This research therefore examines spatial and temporal variability in passive-microwave derived snow-melt-onset dates over Arctic sea ice. The objectives are to understand better the regional characteristics of snowmelt and to document whether the snowmelt-onset record shows signs of climate change. Snowmelt-onset dates are derived with Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager brightness-temperature data, and they are subsequently stratified into 13 regions to analyze spatial and temporal variability. Results illustrate significant spatial variability in snowmelt onset, with the median annual snowmelt-onset date in one region of the Arctic typically being statistically different from most other regions. The examination of temporal variability also shows large interannual differences in the median snowmelt-onset date in most regions. Additionally, trends towards earlier snowmelt onset are documented in the West Central Arctic, Lincoln Sea, Beaufort Sea and Canadian Arctic Archipelago regions.

Comparison of interannual snowmelt-onset dates with atmospheric conditions

Abstract The snowmelt-onset date represents an important transitional point in the Arctic surface energy balance, when albedo decreases and energy absorption increases rapidly in response to the appearance of liquid water. Interannual variations in snowmelt onset are likely related to large-scale variations in atmospheric circulation, such as described by the Arctic Oscillation (AO). This research therefore examines the relationship between monthly-averaged AO values and mean annual snowmelt-onset dates over Arctic sea ice in 13 regions, from 1979 to 1998. The objective is to statistically relate variations in mean annual regional snowmelt-onset dates to variations in the AO. Additionally, monthly-averaged 500 hPa heights and 2 m air temperatures are used to illustrate a physical link between snow-melt onset and a positive AO phase. Regression analyses demonstrate that variations in the AO explain a significant portion of the variations in snowmelt onset in the West Central Arctic, Laptev Sea, East Siberian Sea, Hudson Bay and Baffin Bay. Synoptic analyses suggest earlier (later) than average snowmelt onset occurs where warm (cold) air advection and increased (decreased) cyclonic activity are present.