Annual Greenland accumulation rates (2009–2012) from airborne snow radar

Abstract

Abstract. Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet through increasing surface melt, emphasizing the need to closely monitor its surface mass balance in order to improve sea-level rise predictions. Snow accumulation is the largest component of the ice sheet's surface mass balance, but in situ observations thereof are inherently sparse and models are difficult to evaluate at large scales. Here, we quantify recent Greenland accumulation rates using ultra-wideband (2-6.5 GHz) airborne snow radar data collected as part of NASA's Operation IceBridge between 2009 and 2012. We use a semiautomated method to trace the observed radiostratigraphy and then derive annual net accumulation rates for 2009-2012. The uncertainty in these radar-derived accumulation rates is on average 14 %. A comparison of the radarderived accumulation rates and contemporaneous ice cores shows that snow radar captures both the annual and longterm mean accumulation rate accurately. A comparison with outputs from a regional climate model (MAR) shows that this model matches radar-derived accumulation rates in the ice sheet interior but produces higher values over southeastern Greenland. Our results demonstrate that snow radar can efficiently and accurately map patterns of snow accumulation across an ice sheet and that it is valuable for evaluating the accuracy of surface mass balance models.

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

“…The Snow Radar Layer Detection (SRLD) algorithm was first developed for layer detection on land ice, in which the detection of the a-s interface and s-i interface is determined prior to the subsequent detection of deeper layers within the firn (Koenig et al, 2016). Over sea ice, only the a-s and s-i interfaces are returned by the algorithm.…”

The NASA Eulerian Snow on Sea Ice Model (NESOSIM): Initial model development and analysis

“…Various algorithms have 5 been developed to produce snow depth estimates from the OIB Snow Radar data (Kwok et al, 2017), with the products showing broad agreement in the regional snow depth distributions, but significant intraregional and interannual differences, due primarily to changes in the radar configuration and algorithm tuning. To account for these differences we use the snow depth data from the (i) Snow Radar Layer Detection (SRLD) (Koenig et al, 2016), (ii) NASA Goddard Space Flight Center (GSFC) (Kurtz et al, 2013) and ( …”

Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 1: Greenland (1958–2016)

“…cloud physics (Van Tricht et al, 2016) and turbulent fluxes (Noël et al, 2015;Fausto et al, 2016). Therefore, considerable efforts have been dedicated to evaluating and improving polar RCM output in Greenland (Ettema et al, 2010b;Van Angelen et al, 2013b;Lucas-Picher et al, 2012;Fettweis et al, 2017;Noël et al, 2015;Langen et al, 2017), using in situ SMB observations (Bales et al, 2001(Bales et al, , 2009van de Wal et al, 2012;Machguth et al, 2016), airborne radar measurements of snow accumulation (Koenig et al, 2016;Overly et al, 2016;Lewis et al, 2017) and meteorological records (Ahlstrøm et al, 2008;Kuipers Munneke et al, 2018;Smeets et al, 2018), including radiative fluxes that are required to close the ice sheet surface energy balance (SEB) and hence quantify surface melt energy.…”