Very high resolution regional climate model simulations over Greenland: Identifying added value

Abstract

[1] This study presents two simulations of the climate over Greenland with the regional climate model (RCM) HIRHAM5 at 0.05°and 0.25°resolution driven at the lateral boundaries by the ERA-Interim reanalysis for the period 1989-2009. These simulations are validated against observations from meteorological stations (Danish Meteorological Institute) at the coast and automatic weather stations on the ice sheet (Greenland Climate Network). Generally, the temperature and precipitation biases are small, indicating a realistic simulation of the climate over Greenland that is suitable to drive ice sheet models. However, the bias between the simulations and the few available observations does not reduce with higher resolution. This is partly explained by the lack of observations in regions where the higher resolution is expected to improve the simulated climate. The RCM simulations show that the temperature has increased the most in the northern part of Greenland and at lower elevations over the period 1989-2009. Higher resolution increases the relief variability in the model topography and causes the simulated precipitation to be larger on the coast and smaller over the main ice sheet compared to the lower-resolution simulation. The higher-resolution simulation likely represents the Greenlandic climate better, but the lack of observations makes it difficult to validate fully. The detailed temperature and precipitation fields that are generated with the higher resolution are recommended for producing adequate forcing fields for ice sheet models, particularly for their improved simulation of the processes occurring at the steep margins of the ice sheet.

Inferred basal friction and surface mass balance of the Northeast Greenland Ice Stream using data assimilation of ICESat (Ice Cloud and land Elevation Satellite) surface altimetry and ISSM (Ice Sheet System Model)

“…In the percolation-wet-snow-superimposed ice zones, the firn-densification model from Reeh et al (2005) and Reeh (2008) was used, which includes the effect of ice lensing and is forced by the RACMO2/GR data (Ettema et al, 2009;van Angelen et al, 2012). Above the ELA, the firndensification model from Lucas-Picher et al (2012) forced by HIRHAM5 climatologies was used, which accounts for densification through pore-space closure. Both models depend on atmospheric constraints such as accumulation rate, surface temperature and surface snow density.…”

“…Estimates range from −2.7 to 4.6 mm yr −1 , with errors that are negligible compared with elevation changes due to other factors. Variations of firn compaction rates above the equilibrium line altitude (ELA) are from a 5 × 5 km gridded model forced by the output from the HIRHAM5 (subset of the HIRLAM (High Resolution Limited Area Model) and ECHAM (EC Hamburg) models) regional climate model (Sørensen et al, 2011;Lucas-Picher et al, 2012) and range from −0.016 to 0.146 m yr −1 (negative values of firn densification correspond here to cases where pore space increases relatively by addition of fresh new snow). Finally the corrected elevation changes were converted into ice-equivalent elevation changes using a constant ice density of 917 kg m −3 in the ablation and superimposed ice zones and a simple firn-densification model from (Reeh et al, 2005) and (Reeh, 2008) in the accumulation zone.…”

Mass loss of Greenland's glaciers and ice caps 2003–2008 revealed from ICESat laser altimetry data

“…The dynamical firn model is forced by interpolated output fields from the HIRHAM5 regional climate model at a resolution of 5 Â 5 km [Lucas-Picher et al, 2012]. The retention of melt water in the firn pack is assumed to be confined to the surface layer formed in the same period of time as the melt [Reeh, 2008].…”

Surface mass balance model intercomparison for the Greenland ice sheet

“…GrIS SMB has been estimated through the statistical downscaling of low resolution re-analysis data Hanna et al ( , 2005Hanna et al ( , 2002, and low resolution global climate model data (Huybrechts et al, 2004;Gregory and Huybrechts, 2006) through regional climate models (Box et al, 2004Ettema et al, 2010a, b;Fettweis, 2007;Fettweis et al, 2008;Lucas-Picher et al, 2012) and also through the interpolation of in situ observations from ice cores, snow pits, stake measurements and automatic weather stations ; however, these measurements are limited in both spatial and temporal coverage and are unable to separate components of SMB. The release of high-quality and consistent historical weather reanalysis data has made possible the modelling of the processes controlling SMB over the whole ice sheet.…”