Climate downscaling for estimating glacier mass balances in northwestern North America: Validation with a USGS benchmark glacier

Zhang, Jing; Bhatt, Uma S.; Tangborn, Wendell V.; Lingle, Craig S.

doi:10.1029/2007gl031139

Cited by 17 publications

(9 citation statements)

References 17 publications

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“…The adjustment for SRTM and ICESat used published surface snow densities, which range from about 550 kg m -3 at about 450 m elevation to 350 kg m -3 at 2400 m elevation (Sharp, 1951(Sharp, , 1958Alford, 1967). An estimate of winter surface snow density was based on Zwally and Li (2002). The adjustment for the Intermap DEM relied on an estimated surface snow density (fresh snow) of 200 kg m -3 .…”

Section: Snow Accumulation Adjustmentsmentioning

confidence: 99%

Airborne and spaceborne DEM- and laser altimetry-derived surface elevation and volume changes of the Bering Glacier system, Alaska, USA, and Yukon, Canada, 1972–2006

Muskett¹,

Lingle²,

Sauber

et al. 2009

J. Glaciol.

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Using airborne and spaceborne high-resolution digital elevation models and laser altimetry, we present estimates of interannual and multi-decadal surface elevation changes on the Bering Glacier system, Alaska, USA, and Yukon, Canada, from 1972 to 2006. We find: (1) the rate of lowering during 1972-95 was 0.9 AE 0.1 m a -1 ; (2) this rate accelerated to 3.0 AE 0.7 m a -1 during 1995-2000; and (3) during 2000-03 the lowering rate was 1.5 AE 0.4 m a -1 . From 1972 to 2003, 70% of the area of the system experienced a volume loss of 191 AE 17 km 3 , which was an area-average surface elevation lowering of 1.7 AE 0.2 m a -1 . From November 2004 to November 2006, surface elevations across Bering Glacier, from McIntosh Peak on the south to Waxell Ridge on the north, rose as much as 53 m. Up-glacier on Bagley Ice Valley about 10 km east of Juniper Island nunatak, surface elevations lowered as much as 28 m from October 2003 to October 2006. NASA Terra/MODIS observations from May to September 2006 indicated muddy outburst floods from the Bering terminus into Vitus Lake. This suggests basal-englacial hydrologic storage changes were a contributing factor in the surface elevation changes in the fall of 2006.

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Section: Snow Accumulation Adjustmentsmentioning

confidence: 99%

Airborne and spaceborne DEM- and laser altimetry-derived surface elevation and volume changes of the Bering Glacier system, Alaska, USA, and Yukon, Canada, 1972–2006

Muskett¹,

Lingle²,

Sauber

et al. 2009

J. Glaciol.

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“…[7] The development of climate models with the ability to simulate the timing and duration of summer snowmelt is key to accurately projecting future glacier change [e.g., Machguth et al, 2009;Radic and Hock, 2006;Van de Wal and Wild, 2001;Zhang et al, 2007] and for capturing variations and feedbacks of high-latitude climate systems [e.g., Brown and Mote, 2009;Roesch, 2006]. However, validation of regional model output in Antarctica is difficult owing to the scarcity of surface observations, particularly inland of coastal stations.…”

Section: Introductionmentioning

confidence: 99%

Trends in Antarctic Peninsula surface melting conditions from observations and regional climate modeling

et al. 2013

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[1] Multidecadal meteorological station records and microwave backscatter time-series from the SeaWinds scatterometer onboard QuikSCAT (QSCAT) were used to calculate temporal and spatial trends in surface melting conditions on the Antarctic Peninsula (AP). Four of six long-term station records showed strongly positive and statistically significant trends in duration of melting conditions, including a 95% increase in the average annual positive degree day sum (PDD) at Faraday/Vernadsky, since 1948. A validated, threshold-based melt detection method was employed to derive detailed melt season onset, extent, and duration climatologies on the AP from enhanced resolution QSCAT data during 1999-2009. Austral summer melt on the AP was linked to regional-and synoptic-scale atmospheric variability by respectively correlating melt season onset and extent with November near-surface air temperatures and the October-January averaged index of the Southern Hemisphere Annular Mode (SAM). The spatial pattern, magnitude, and interannual variability of AP melt from observations was closely reproduced by simulations of the regional model RACMO2. Local discrepancies between observations and model simulations were likely a result of the QSCAT response to, and RACMO2 treatment of, ponded surface water, and the relatively crude representation of coastal climate in the 27 km RACMO2 grid.

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“…A small number of studies have investigated the use of reanalysis data for modeling glacier mass balance using linear regression models [ Rasmussen and Conway , 2004; Rasmussen and Kohler , 2007], precipitation‐temperature‐area‐altitude models [ Zhang et al , 2007], degree‐day models [ Hanna et al , 2005; Radić and Hock , 2006; Hock et al , 2007], and energy balance models [ Reichert et al , 2001; Hock et al , 2007]. Reanalysis products have also been used to investigate the relation between large‐scale circulation patterns and glacier mass balance [ Gardner and Sharp , 2007; Shea and Marshall , 2007] and as a tool for statistically downscaling general circulation model (GCM) projections to derive future glacier volume changes [ Reichert et al , 2001; Radić and Hock , 2006].…”

Section: Introductionmentioning

confidence: 99%

Modeling the surface mass balance of a high Arctic glacier using the ERA‐40 reanalysis

et al. 2010

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[1] Investigating glacier mass balance via the use of numerical models provides an important insight into glacier-climate interactions and allows for the assessment of future changes in sea level and local water resources. The application of mass balance models to unmonitored regions has previously been inhibited by the inadequate spatial coverage of local meteorological observations. One way to overcome this problem is through the use of climate reanalysis products. In this paper we evaluate the ability of the European Centre for Medium-Range Forecasts ERA-40 reanalysis to drive a surface mass balance model of Midre Lovénbreen, northwest Spitsbergen, Svalbard. The ERA-40 reanalysis is validated and bias-corrected against long-term observations from two nearby in situ weather stations. The model is calibrated over the period of mass balance observations ) using a downhill simplex parameter optimization technique to minimize the error between observed and simulated mass balances. The calibrated model is then used to extend the mass balance record of Midre Lovénbreen back to the beginning of the reanalysis in 1958. Overall, the ERA-40 reanalysis is found to correspond sufficiently well with surface observations to be used for mass balance modeling. When driven using the ERA-40 reanalysis, the calibrated surface mass balance model performs very well. The area-averaged cumulative errors for winter, summer, and net balances are all very small over the period 1968-2001 (<0.3 m water equivalent (w.e.)). For the centerline stakes the correlation coefficients between observed and modeled net, winter, and summer balances are 0.83, 0.84, and 0.77, respectively. Using the hindcasted mass balances, a mean cumulative mass loss of −17.8 ± 3.7 m w.e. is estimated over the 44 year period 1958-2001.

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Climate downscaling for estimating glacier mass balances in northwestern North America: Validation with a USGS benchmark glacier

Cited by 17 publications

References 17 publications

Airborne and spaceborne DEM- and laser altimetry-derived surface elevation and volume changes of the Bering Glacier system, Alaska, USA, and Yukon, Canada, 1972–2006

Airborne and spaceborne DEM- and laser altimetry-derived surface elevation and volume changes of the Bering Glacier system, Alaska, USA, and Yukon, Canada, 1972–2006

Trends in Antarctic Peninsula surface melting conditions from observations and regional climate modeling

Modeling the surface mass balance of a high Arctic glacier using the ERA‐40 reanalysis

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