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Comparison of Interannual Snowmelt-Onset Dates with Atmospheric Conditions University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Earth and Atmospheric Sciences, Department Papers in the Earth and Atmospheric Sciences of 2001 Comparison of Interannual Snowmelt-Onset Dates with Atmospheric Conditions Sheldon D. Drobot National Center for Atmospheric Research, [email protected] Mark R. Anderson University of Nebraska-Lincoln, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/geosciencefacpub Part of the Earth Sciences Commons Drobot, Sheldon D. and Anderson, Mark R., "Comparison of Interannual Snowmelt-Onset Dates with Atmospheric Conditions" (2001). Papers in the Earth and Atmospheric Sciences. 173. https://digitalcommons.unl.edu/geosciencefacpub/173 This Article is brought to you for free and open access by the Earth and Atmospheric Sciences, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Papers in the Earth and Atmospheric Sciences by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Annals of Glaciology 33 2001 # International Glaciological Society Comparison of interannual snowmelt-onset dates with atmospheric conditions Sheldon D. Drobot, Mark R. Anderson Department of Geosciences, 214 Bessey Hall, University of Nebraska, Lincoln, NE 68588-0340, U.S.A. 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 overArctic sea ice in13 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, LaptevSea, 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. INTRODUCTION tive of this paper is therefore to examine how atmospheric variations relate to snowmelt onset. The Arctic Oscillation Snow on sea ice plays an integral role in the Arctic's energy #AO) is used to represent atmospheric variability, because balance, owing to its influence on surface albedo, sensible- other studies on atmosphere^sea-ice interactions #e.g. Deser and latent-heat exchanges, and because of its considerable and others, 2000) note sea-ice variations are strongly asso- spatial and temporal variability.The significance of snow is ciated with AO variations. The AO is defined as a see-saw particularly emphasized during the snowmelt period between anomalously high and low pressure over the Arctic, #Groisman and others,1994), when the surface albedo drops and is considered the dominant mode of low-frequency from values of >0.8 for dry snow-covered surfaces, to values atmospheric variability in the Northern Hemisphere of <0.3 for wet snow-covered surfaces #Goodison and #Thompson andWallace,1998). Regression analyses are used others,1999).The change in albedo from a highly reflective to statistically associate monthly-averaged AO values with surface to a highly absorptive surface is expedited by the mean annual regional snowmelt-onset dates. In addition, a temperature^albedo feedback #Curry and others, 1995), where an increase in air temperature enhances surface melt, reducing the spatial coverage of snow, decreasing albedo and in turn leading to greater absorbed energy and warmer air temperatures. Based partly upon this temperature^ albedo feedback, general circulation models typically pre- dict enhanced climate warming in the Arctic #e.g. Maxwell and others, 1998; Gates and others, 1999), highlighting the need to better understand spatial and temporal variations in snowmelt. Observational studies of passive-microwave-derived snowmelt onset, defined as the point in time when brightness temperatures increase in response to liquid-water inclusions around snow grains, demonstrate that there is significant spatial and temporal variability #Abdalati and Steffen,1995; Mote and Anderson, 1995; Smith, 1998). A regional assess- ment #Fig.1) of the spatial and temporal variability in snow- melt-onset dates from 1979 to 1998 #Anderson and Drobot, 2001) illustrates large variations in the timing of snowmelt Fig. 1. Regional study sites: 1. East Central Arctic; 2. Mid onset between regions in a given year, and between years Central Arctic; 3. West Central Arctic; 4. Lincoln Sea; 5. within a given region #Fig. 2). Anderson and Drobot #2001) Beaufort Sea; 6. Canadian Arctic Archipelago; 7. Hudson hypothesize the snowmelt-onset variations are at least partly Bay; 8. Baffin Bay; 9. Sea of Okhotsk;10. Barents Sea;11. related to variations in atmospheric conditions. The objec- Kara Sea;12. Laptev Sea;13. East Siberian Sea. 79 Drobot and Anderson: Comparison ofsnowmelt-onset dates with atmospheric conditions Fig. 2.Temporal variability in median annual melt-onset dates for each region. case-study in 1990 further defines the relationship between Although the AHRA has been verified for use over first-year 500 hPa heights, 2 m air temperatures and snowmelt onset. and multi-year sea-ice surfaces, anomalous snowmelt-onset dates, due to pixel averaging, sensor errors, polynyas, etc., may still be present in the snowmelt-onset dataset. In most DATA cases, however, this should not affect the overall results. The snowmelt dates are subsequently stratified into the Snowmelt-onset dates are derived with 25 km2 daily-averaged same 13 regions as Anderson and Drobot #2001) in order to brightness-temperature data from Scanning Multichannel examine the regional associations between snowmelt onset Microwave Radiometer #SMMR) and Special Sensor Micro- and the AO. Note that the regions north of 80³ N closely wave/Imager #SSM/I) radiometers. An improved version of resemble regions developed by Smith #1998), but they are the Anderson #1997) algorithm is used to compute snowmelt shifted 45³ in this study from the work of Smith #1998) in order onset over first-year and multi-year sea-ice surfaces from 1979 to keep snowmelt-onset dates in the Lincoln Sea in one region. to 1998.The new algorithm, termed the Advanced Horizontal Atmospheric data consist of monthly-averaged AO Range algorithm #AHRA), monitors the difference between values, 500 hPa heights and 2 m air temperatures, all derived 18 GHz #SMMR) or 19GHz #SMM/I) and 37 GHz horizon- from U.S. National Centers for Environmental Prediction/ tal brightness temperatures over a 20 day window. At the National Center for Atmospheric Research #NCEP/NCAR) snowmelt-onset point, liquid-water inclusions form around re-analysis data. The re-analysis data provide a consistent snow grains, and the difference between 18 or 19 GHz minus source for atmospheric data, because re-analysis data are 37 GHz horizontal brightness temperatures changes from based on a static data-assimilation scheme, quality control is being positive to near zero or negative. By monitoring the superior to first-order stations, the data analysis is global in 20 day window for signs of a consistent change in the 18 or extent and more observations are used #Kalnay and others, 19GHz ^ 37 GHz brightness temperature, the occurrence 1996).The monthly-averaged AO values were acquired from and variability in snowmelt onset are monitored. Additional the Joint Institute for the Study of Atmosphere and Ocean details on the AHRA are provided in a companion paper #JISAO) at the University of Washington, while the 500 hPa #Anderson and Drobot, 2001) as well as Drobot #2000). height and 2 m air-temperature data were collected from 80 Drobot and Anderson: Comparison ofsnowmelt-onset dates with atmospheric conditions Table 1. Monthly averaged AO values, 1979^98 Table 2. Per cent variation of mean annual snowmelt-onset dates that are explained by variations in monthly-averaged AO values. Statistically significant relationships = 0.05) Year Feb. Mar. Apr. May June are represented by bold text 1979 ^0.51 ^0.75 ^1.41 ^0.20 0.70 1980 ^0.11 ^1.34 ^0.30 ^1.04 0.52 Region r2 values 1981 0.19 ^1.44 0.54 0.33 ^0.51 Feb. Mar. Apr. May June 1982 1.28 1.06 1.35 ^0.31 ^1.16 1983^0.87^0.13^0.87^0.480.29 y * * * * 1984 0.20 ^2.03 ^0.29 0.49 ^0.07 1. East Central Arctic 0.00 0.01 0.01 0.09 0.00 * * * * * 1985 ^1.50 0.57 0.45 ^0.39 ^0.48 2. Mid Central Arctic 0.14 0.19 0.18 0.04 0.00 * * * * * 1986 ^2.03 2.37 ^0.01 0.35 0.59 3.West Central Arctic 0.22 0.21 0.19 0.05 0.00 y y * y y 1987 ^1.03 ^1.54 0.51 0.35 ^0.73 4. Lincoln Sea 0.04 0.00 0.04 0.01 0.03 * * y * * 1988 ^0.69 ^0.22 ^0.47 ^0.76 0.12 5. Beaufort Sea 0.19 0.01 0.04 0.00 0.01 * * * 1989 3.46 1.54 ^0.30 0.81 0.25 6. Canadian Arctic Archipelago 0.00y 0.08 0.04 0.00y 0.01 * * * * * 1990 3.28 3.31 1.86 1.18 0.20 7. Hudson Bay 0.01 0.02 0.26 0.03 0.00 y * y y * 1991 ^0.13 ^0.50 0.57 0.58 ^0.29 8. Baffin Bay 0.24 0.00 0.00 0.13 0.13 * y * * y 1992 1.68 1.19 ^0.66 1.37 ^0.26 9.
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