Drifting snow climate of the Greenland ice sheet: a study with a regional climate model

The Cryosphere, 6, 891–899, 2012 www.the-cryosphere.net/6/891/2012/ doi:10.5194/tc-6-891-2012 © Author(s) 2012. CC Attribution 3.0 License. The Cryosphere Drifting snow climate of the Greenland ice sheet: a study with a regional climate model J. T. M. Lenaerts1 , M. R. van den Broeke1 , J. H. van Angelen1 , E. van Meijgaard2 , and S. J. Déry3 1 Institute for Marine and Atmospheric research Utrecht, Utrecht University, Utrecht, The Netherlands Netherlands Meteorological Institute, De Bilt, The Netherlands 3 University of Northern British Columbia, Prince George, Canada 2 Royal Correspondence to: J. T. M. Lenaerts () Received: 20 March 2012 – Published in The Cryosphere Discuss.: 3 May 2012 Revised: 18 July 2012 – Accepted: 3 August 2012 – Published: 15 August 2012 Abstract. This paper presents the drifting snow climate of the Greenland ice sheet, using output from a high-resolution (∼ 11 km) regional climate model. Because reliable direct observations of drifting snow do not exist, we evaluate the modeled near-surface climate instead, using automatic weather station (AWS) observations from the K-transect and find that RACMO2 realistically simulates near-surface wind speed and relative humidity, two variables that are important for drifting snow. Integrated over the ice sheet, drifting snow sublimation (SUds ) equals 24±3 Gt yr−1 , and is significantly larger than surface sublimation (SUs , 16 ± 2 Gt yr−1 ). SUds strongly varies between seasons, and is only important in winter, when surface sublimation and runoff are small. A rapid transition exists between the winter season, when snowfall and SUds are important, and the summer season, when snowmelt is significant, which increases surface snow density and thereby limits drifting snow processes. Drifting snow erosion (ERds ) is only important on a regional scale. In recent decades, following decreasing wind speed and rising near-surface temperatures, SUds exhibits a negative trend (0.1 ± 0.1 Gt yr−1 ), which is compensated by an increase in SUs of similar magnitude. 1 Introduction The Greenland ice sheet (GrIS) is the largest body of ice in the Northern Hemisphere, containing approximately 7 m sea level equivalent (IPCC AR4). In the last two decades, numerous Greenland outlet glaciers have accelerated and surface mass balance (SMB) has declined (Rignot et al., 2011), both contributing about equally to recent Greenland mass loss (Van den Broeke et al., 2009). The volume loss of outlet glaciers may be primarily related to oceanic warming (Holland et al., 2008), but the interaction between ocean and outlet glaciers is complex (Nick et al., 2009; Straneo et al., 2011). At the same time, Greenland has experienced significant atmospheric warming in the recent two decades (Box and Cohen, 2006), increasing surface meltwater production and subsequent runoff (Ettema et al., 2009), extending the length of the melt season (Fettweis et al., 2011) and triggering the melt-albedo feedback (Tedesco et al., 2010). To assess the surface mass balance (SMB) of the GrIS, regional climate models are useful tools. In the SMB of the GrIS, precipitation (P ) is the main source of mass, whereas mass is lost by surface (SUs ) and drifting snow sublimation (SUds ), drifting snow erosion (ERds ) and meltwater runoff (RU). ERds is defined as the horizontal divergence of the snow transport (TRds ). Until now, drifting snow processes have usually been neglected in GrIS SMB studies (Fettweis, 2007; Hanna et al., 2005; Ettema et al., 2009). On the Antarctic ice sheet, drifting snow sublimation (SUds ) is an important ablation term in dry and windy areas and ERds redistributes snow on a local to regional scale (Lenaerts and van den Broeke, 2012). Several studies estimated SUds for Greenland (Déry and Yau, 2002; Box et al., 2006) and ERds (Déry and Yau, 2002), using parameterizations based on wind speed (Déry and Yau, 1999), neglecting feedbacks to the overlying atmosphere and the snow surface, which are known to be important (Bintanja, 2001; Lenaerts et al., 2010, 2012a; Gallée et al., 2001). Published by Copernicus Publications on behalf of the European Geosciences Union. 8 892 J. T. M. Lenaerts et al.: Greenland drifting snow climate J. T. M. Lenaerts et al.: Greenland drifting snow climate Here we present the drifting snow climate (1960–2011) of the Greenland ice sheet using a regional atmospheric climate model (RACMO2) at relatively high horizontal resolution (11 km). RACMO2 includes an interactive drifting snow routine (Lenaerts et al., 2012a). We discuss the spatial and temporal variability of SUds and ERds and their impact on the SMB of the GrIS. 2 2.1 Methods Numerical setup The Regional Atmospheric Climate MOdel version 2 (RACMO2 hereafter; Van Meijgaard et al., 2008) combines the dynamical parameterizations from the HIgh Resolution Limited Area Model (HIRLAM) (Undén et al., 2002) with the physical schemes from the European Centre for Medium-Range Weather Forecasts model (ECMWF cycle 23r4, White, 2001). In recent years, RACMO2 has been used to estimate the SMB of Antarctica (Van de Berg et al., 2005; Lenaerts et al., 2012b) and Greenland (Ettema et al., 2009). Modeled precipitation and surface mass balance have been extensively evaluated using available in-situ observations (Ettema et al., 2009). Moreover, Ettema et al. (2010b) showed that RACMO2 provides a realistic simulation of the near-surface climate of the GrIS, although several deficiencies were detected, especially in the snow albedo scheme. To resolve this, we included a snow albedo parameterization based on the growth of snow during dry and wet metamorphosis, so that snow albedo can be physically coupled to snow grain size (Flanner and Zender, 2006). This significantly improved net shortwave radiation in RACMO2 over the Antarctic ice sheet (Kuipers Munneke et al., 2011) and the length of the melt season in Greenland (Van Angelen et al., 2012). In addition, a remote sensing-derived background albedo (from the Moderate Resolution Imaging Spectroradiometer, MODIS) is prescribed for ice (Van Angelen et al., 2012) to capture the spatial variability of albedo in the ablation area when the winter snow has melted (Van den Broeke et al., 2008). Finally, the drifting snow scheme derived from Déry and Yau (1999) has been included in RACMO2. It calculates drifting snow transport and sublimation, and accounts for interactions between the drifting snow layer with both the overlying atmosphere and the underlying snow surface (Lenaerts et al., 2010). Furthermore, we use an empirically derived parameterization for surface snow density. This was derived by Lenaerts et al. (2012a) for Antarctica, such that modeled drifting snow frequency and horizontal transport agree well with available in-situ and remote sensing observations (Lenaerts et al., 2012a). For the GrIS, RACMO2 has 40 levels in the vertical and the model grid has a horizontal spacing of ∼ 11 km. It is forced at its lateral bo (...truncated)