ALBERT

Description: A severe windstorm downstream of Mnt. Öræfajökull in Southeast Iceland is simulated on a grid of 1 km horizontal resolution by using the PSU/NCAR MM5 model and the Advanced Research WRF model. Both models are run with a new, two equation planetary boundary layer (PBL) scheme as well as the ETA/MYJ PBL schemes. The storm is also simulated using six different micro-physics schemes in combination with the MYJ PBL scheme in WRF. Output from a 3 km MM5 domain simulation is used to initialise and drive both the 1 km MM5 and WRF simulations. Both models capture gravity-wave breaking over Mnt. Öræfajökull, while the vertical structure of the lee wave differs between the two models and the PBL schemes. The WRF simulated downslope winds, using the MYJ PBL scheme, are in good agreement with the strength of the observed downslope windstorm, whilst using the new two equation scheme surface winds are considerably less than observed winds. The MM5 simulated surface winds, with the new two equation model, are in better agreement with observations than when using the ETA scheme. Micro-physics processes are shown to play an important role in the formation of downslope windstorms and lifting of the upslope isotherm layer from mountain height to about 1.3 times the mountain height leads to a significant increase in the downslope windstorm.

Description: A severe windstorm downstream of Mt. Öræfajökull in Southeast Iceland is simulated on a grid of 1 km horizontal resolution by using the PSU/NCAR MM5 model and the Advanced Research WRF model. Both models are run with a new, two equation planetary boundary layer (PBL) scheme as well as the ETA/MYJ PBL schemes. The storm is also simulated using six different micro-physics schemes in combination with the MYJ PBL scheme in WRF, as well as one "dry" run. Output from a 3 km MM5 domain simulation is used to initialise and drive both the 1 km MM5 and WRF simulations. Both models capture gravity-wave breaking over Mt. Öræfajökull, while the vertical structure of the lee wave differs between the two models and the PBL schemes. The WRF simulated downslope winds, using both the MYJ and 2EQ PBL schemes, are in good agreement with the strength of the observed downslope windstorm. The MM5 simulated surface winds, with the new two equation model, are in better agreement to observations than when using the ETA scheme. Micro-physics processes are shown to play an important role in the formation of downslope windstorms and a correctly simulated moisture distribution is decisive for a successful windstorm prediction. Of the micro-physics schemes tested, only the Thompson scheme captures the downslope windstorm.

Description: In this paper we describe how recent high resolution Digital Elevation Models (DEMs) can be used as constraints for extracting glacier surface DEMs from old aerial photographs and to evaluate the uncertainty of the mass balance record derived from the DEMs. We present a case study for Drangajökull ice cap, NW-Iceland. This ice cap covered an area of 144 km2 when it was surveyed with airborne LiDAR in 2011. Aerial photographs spanning all or most of the ice cap are available from survey flights in 1946, 1960, 1975, 1985, 1994 and 2005. All ground control points used to constrain the orientation of the aerial photographs were obtained from the high resolution LiDAR DEM (2 m × 2 m cell size and vertical accuracy 〈 0.5 m). The LiDAR DEM was also used to estimate errors of the extracted photogrammetric DEMs in ice and snow free areas, at nunataks and outside the glacier margin. The derived errors of each DEM were used to constrain a spherical variogram model, which along with the derived errors in ice and snow free areas were used as inputs into 1000 Sequential Gaussian Simulations (SGSim). The simulations were used to estimate the possible bias in the entire glaciated part of the DEM. The derived bias correction, varying in magnitude between DEMs from 0.03 to 1.66 m (1946 DEM) was then applied. The simulation results were also used to calculate the 95 % confidence level of this bias, resulting in values between ±0.21 m (in 2005) and ±1.58 m (in 1946). Error estimation methods based on more simple proxies would typically yield 2–4 times larger error estimates. The aerial photographs used were acquired between late June and early October. An additional bias correction was therefore estimated using a degree day model to obtain the volume change between the start of two hydrological years (1 October). This correction corresponds to an average elevation change of ~ −3 m in the worst case for 1960, or about ~ 2/3 of volume change between the 1960 and the 1975 DEMs. The total uncertainty of the derived mass balance record is mostly due to uncertainty of the SGSim bias correction, the uncertainty of the seasonal bias correction and the uncertainty of the interpolated glacier surface where data is lacking. The record shows a glacier-wide mass balance rate of Ḃ = −0.250 ± 0.040 m w.e. a−1 for the entire study period (1946–2011). We observe significant decadal variability including positive periods, peaking in 1985–1994 with Ḃ = 0.26 ± 0.11 m w.e. a−1. There is a striking difference if Ḃ is calculated separately for the western and eastern halves of Drangajökull, with a reduction of eastern part on average ~ 3 times faster than the western part. Our study emphasises the need of applying rigorous geostatistical methods for obtaining uncertainty estimates of geodetic mass balance, the importance of seasonal corrections of DEMs from glaciers with high mass turnover and the risk of extrapolating mass balance record from one glacier to another even over short distances.

Description: In this paper we describe how recent high-resolution digital elevation models (DEMs) can be used to extract glacier surface DEMs from old aerial photographs and to evaluate the uncertainty of the mass balance record derived from the DEMs. We present a case study for Drangajökull ice cap, NW Iceland. This ice cap covered an area of 144 km2 when it was surveyed with airborne lidar in 2011. Aerial photographs spanning all or most of the ice cap are available from survey flights in 1946, 1960, 1975, 1985, 1994 and 2005. All ground control points used to constrain the orientation of the aerial photographs were obtained from the high-resolution lidar DEM. The lidar DEM was also used to estimate errors of the extracted photogrammetric DEMs in ice- and snow-free areas, at nunataks and outside the glacier margin. The derived errors of each DEM were used to constrain a spherical semivariogram model, which along with the derived errors in ice- and snow-free areas were used as inputs into 1000 sequential Gaussian simulations (SGSims). The simulations were used to estimate the possible bias in the entire glaciated part of the DEM and the 95 % confidence level of this bias. This results in bias correction varying in magnitude between 0.03 m (in 1975) and 1.66 m (in 1946) and uncertainty values between ±0.21 m (in 2005) and ±1.58 m (in 1946). Error estimation methods based on more simple proxies would typically yield 2–4 times larger error estimates. The aerial photographs used were acquired between late June and early October. An additional seasonal bias correction was therefore estimated using a degree-day model to obtain the volume change between the start of 2 glaciological years (1 October). This correction was largest for the 1960 DEM, corresponding to an average elevation change of −3.5 m or approx. three-quarters of the volume change between the 1960 and the 1975 DEMs. The total uncertainty of the derived mass balance record is dominated by uncertainty in the volume changes caused by uncertainties of the SGSim bias correction, the seasonal bias correction and the interpolation of glacier surface where data are lacking. The record shows a glacier-wide mass balance rate of Ḃ  = −0.26 ± 0.04 m w.e. a−1 for the entire study period (1946–2011). We observe significant decadal variability including periods of mass gain, peaking in 1985–1994 with Ḃ  = 0.27 ± 0.11 m w.e. a−1. There is a striking difference when Ḃ  is calculated separately for the western and eastern halves of Drangajökull, with a reduction of eastern part on average  ∼  3 times faster than the western part. Our study emphasizes the need for applying rigorous geostatistical methods for obtaining uncertainty estimates of geodetic mass balance, the importance of seasonal corrections of DEMs from glaciers with high mass turnover and the risk of extrapolating mass balance record from one glacier to another even over short distances.