Search for: All records

Supraglacial lakes on the Greenland Ice Sheet (GrIS) can impact both the ice sheet surface mass balance and ice dynamics. Thus, understanding the evolution and dynamics of supraglacial lakes is important to provide improved parameterizations for ice sheet models to enable better projections of future GrIS changes. In this study, we utilize the growing inventory of optical and microwave satellite imagery to automatically determine the fate of Greenland-wide supraglacial lakes during 2018 and 2019; cool and warm melt seasons respectively. We develop a novel time series classification method to categorize lakes into four classes: 1) refreezing, 2) rapidly draining, 3) slowly draining, and 4) buried. Our findings reveal significant interannual variability between the two melt seasons, with a notable increase in the proportion of draining lakes in 2019. We also find that as mean lake depth increases, so does the percentage of lakes that drain, indicating that lake depth may influence hydrofracture potential. However, we also observe that non-draining lakes are deeper during the cooler 2018 melt season, suggesting that additional factors may predispose lakes to drain earlier in a warmer year. Our automatic classification approach and the resulting two-year ice-sheet-wide dataset provide unprecedented insights into GrIS supraglacial lake dynamics and evolution, offering a valuable resource for future research. 

Supraglacial lakes form on the surface of the Greenland Ice Sheet during the summer months and can directly impact ice sheet mass balance by removing mass via drainage and runoff or indirectly impact mass balance by influencing ice sheet dynamics. Here, we utilize the growing inventory of optical and microwave satellite imagery to automatically determine the fate of Greenland-wide supraglacial lakes during 2018 and 2019, a cool and warm melt season respectively. We use a machine learning time series classification approach to categorize lakes into four different categories: lakes that 1) refreeze, 2) rapidly drain, 3) slowly drain, and 4) become buried lakes at the end of the melt season. We find that during the warmer 2019, not only was the number of lake drainage events higher than in 2018, but also the proportion of lakes that drained was greater. By investigating mean lake depths for these four categories, we show that drained lakes were, on average, 22% deeper than lakes that refroze or became buried lakes. Interestingly, drained lakes had approximately the same maximum depth in 2018 and 2019; however, lakes that did not drain were 29% deeper in 2018, a cooler year. Our unique two-year dataset describing the fate of every Greenland supraglacial lake provides novel insight into lake drainage and refreeze in a relatively warm and cool year, which may be increasingly relevant in a warming climate. 

Abstract Antarctic firn is critical for ice-shelf stability because it stores meltwater that would otherwise pond on the surface. Ponded meltwater increases the risk of hydrofracture and subsequent potential ice-shelf collapse. Here, we use output from a firn model to build a computationally simpler emulator that uses a random forest to predict ice-shelf effective firn air content, which considers impermeable ice layers that make deeper parts of the firn inaccessible to meltwater, based on climate conditions. We find that summer air temperature and precipitation are the most important climatic features for predicting firn air content. Based on the climatology from an ensemble of Earth System Models, we find that the Larsen C Ice Shelf is most at risk of firn air depletion during the 21st century, while the larger Ross and Ronne-Filchner ice shelves are unlikely to experience substantial firn air content change. This work demonstrates the utility of emulation for computationally efficient estimations of complicated ice sheet processes. 

Abstract In 2023, the first Polar Postdoc Leadership Workshop convened to discuss present and future polar science issues and to develop leadership skills. The workshop discussions fostered a collective commitment to inclusive leadership within the polar science community among all participants. Here, we outline challenges encountered by underrepresented groups in polar sciences, while also noting that progress has been made to improve inclusivity in the field. Further, we highlight the inclusive leadership principles identified by workshop participants to bring to the polar community as we transition into leadership roles. Finally, insights and practical knowledge we gained from the workshop are shared, aiming to inform the community of our commitment to inclusive leadership and encourage the polar community to join us in pursuing action toward our shared vision for a more welcoming polar science future. 

Abstract. Earth system models are essential tools for understandingthe impacts of a warming world, particularly on the contribution of polarice sheets to sea level change. However, current models lack full couplingof the ice sheets to the ocean and are typically run at a coarse resolution(1∘ grid spacing or coarser). Coarse spatial resolution isparticularly a problem over Antarctica, where sub-grid-scale orography iswell-known to influence precipitation fields, and glacier models requirehigh-resolution atmospheric inputs. This resolution limitation has beenpartially addressed by regional climate models (RCMs), which must be forcedat their lateral and ocean surface boundaries by (usually coarser) globalatmospheric datasets, However, RCMs fail to capture the two-way couplingbetween the regional domain and the global climate system. Conversely,running high-spatial-resolution models globally is computationallyexpensive and can produce vast amounts of data. Alternatively, variable-resolution grids can retain the benefits of highresolution over a specified domain without the computational costs ofrunning at a high resolution globally. Here we evaluate a historicalsimulation of the Community Earth System Model version 2 (CESM2)implementing the spectral element (SE) numerical dynamical core (VR-CESM2)with an enhanced-horizontal-resolution (0.25∘) grid over theAntarctic Ice Sheet and the surrounding Southern Ocean; the rest of theglobal domain is on the standard 1∘ grid. We compare it to1∘ model runs of CESM2 using both the SE dynamical core and thestandard finite-volume (FV) dynamical core, both with identical physics andforcing, including prescribed sea surface temperatures (SSTs) and sea ice concentrations fromobservations. Our evaluation reveals both improvements and degradations inVR-CESM2 performance relative to the 1∘ CESM2. Surface massbalance estimates are slightly higher but within 1 standard deviation ofthe ensemble mean, except for over the Antarctic Peninsula, which isimpacted by better-resolved surface topography. Temperature and windestimates are improved over both the near surface and aloft, although theoverall correction of a cold bias (within the 1∘ CESM2 runs) hasresulted in temperatures which are too high over the interior of the icesheet. The major degradations include the enhancement of surface melt aswell as excessive cloud liquid water over the ocean, with resultant impactson the surface radiation budget. Despite these changes, VR-CESM2 is avaluable tool for the analysis of precipitation and surface mass balanceand thus constraining estimates of sea level rise associated with theAntarctic Ice Sheet. 

Abstract Supraglacial lakes on the Greenland Ice Sheet (GrIS) can impact both the ice sheet surface mass balance and ice dynamics. Thus, understanding the evolution and dynamics of supraglacial lakes is important to provide improved parameterizations for ice sheet models to enable better projections of future GrIS changes. In this study, we utilize the growing inventory of optical and microwave satellite imagery to automatically determine the fate of Greenland‐wide supraglacial lakes during 2018 and 2019; low and high melt seasons respectively. We develop a novel time series classification method to categorize lakes into four classes: (a) Refreezing, (b) rapidly draining, (c) slowly draining, and (d) buried. Our findings reveal significant interannual variability between the two melt seasons, with a notable increase in the proportion of draining lakes, and a particular dominance of slowly draining lakes, in 2019. We also find that as mean lake depth increases, so does the percentage of lakes that drain, indicating that lake depth may influence hydrofracture potential. We further observe rapidly draining lakes at higher elevations than the previously hypothesized upper‐elevation hydrofracture limit (1,600 m), and that non‐draining lakes are generally deeper during the lower melt 2018 season. Our automatic classification approach and the resulting 2‐year ice‐sheet‐wide data set provide new insights into GrIS supraglacial lake dynamics and evolution, offering a valuable resource for future research. 

Abstract. Earth system models (ESMs) allow us to explore minimally observed components of the Antarctic Ice Sheet (AIS) climate system, both historically andunder future climate change scenarios. Here, we present and analyze surface climate output from the most recent version of the National Center forAtmospheric Research's ESM: the Community Earth System Model version 2 (CESM2). We compare AIS surface climate and surface mass balance (SMB) trendsas simulated by CESM2 with reanalysis and regional climate models and observations. We find that CESM2 substantially better represents the mean-state AIS near-surface temperature, wind speed, and surface melt compared with its predecessor, CESM1. This improvement likely results from theinclusion of new cloud microphysical parameterizations and changes made to the snow model component. However, we also find that grounded CESM2 SMB(2269 ± 100 Gt yr−1) is significantly higher than all other products used in this study and that both temperature andprecipitation are increasing across the AIS during the historical period, a trend that cannot be reconciled with observations. This study provides acomprehensive analysis of the strengths and weaknesses of the representation of AIS surface climate in CESM2, work that will be especially useful inpreparation for CESM3 which plans to incorporate a coupled ice sheet model that interacts with the ocean and atmosphere. 

Abstract. The Greenland Ice Sheet (GrIS) rapid mass loss is primarily driven by an increase in meltwater runoff, which highlights the importance of understanding the formation, evolution, and impact of meltwater features on the ice sheet. Buried lakes are meltwater features that contain liquid water and exist under layers of snow, firn, and/or ice. These lakes are invisible in optical imagery, challenging the analysis of their evolution and implication for larger GrIS dynamics and mass change. Here, we present a method that uses a convolutional neural network, a deep learning method, to automatically detect buried lakes across the GrIS. For the years 2018 and 2019 (which represent low- and high-melt years, respectively), we compare total areal extent of both buried and surface lakes across six regions, and we use a regional climate model to explain the spatial and temporal differences. We find that the total buried lake extent after the 2019 melt season is 56 % larger than after the 2018 melt season across the entire ice sheet. Northern Greenland has the largest increase in buried lake extent after the 2019 melt season, which we attribute to late-summer surface melt and high autumn temperatures. We also provide evidence that different processes are responsible for buried lake formation in different regions of the ice sheet. For example, in southwest Greenland, buried lakes often appear on the surface during the previous melt season, indicating that these meltwater features form when surface lakes partially freeze and become insulated as snowfall buries them. Conversely, in southeast Greenland, most buried lakes never appear on the surface, indicating that these features may form due to downward percolation of meltwater and/or subsurface penetration of shortwave radiation. We provide support for these processes via the use of a physics-based snow model. This study provides additional perspective on the potential role of meltwater on GrIS dynamics and mass loss. 

Abstract Antarctic ice‐shelf stability is threatened by surface melt, which has been implicated in several ice‐shelf collapse events over recent decades. Here, we first analyze cumulative days of wet snow/ice status (“melt days”) for melt seasons from 1980 to 2021 over Antarctica's ice shelves using passive and active microwave satellite observations. As these observations do not directly reveal meltwater volumes, we calculate these using the physics‐based multi‐layer snow model SNOWPACK, driven by the global climate‐reanalysis model Modern‐Era Retrospective analysis for Research and Applications Version 2. We find a strong non‐linear relationship between melt days and meltwater production volume. SNOWPACK's calculation of melt days shows agreement with observations of both cumulative days, and spatial and interannual variability. Highest melt rates are found on the Peninsula ice shelves, particularly in the 1992/1993 and 1994/1995 austral summers. Over all ice shelves, SNOWPACK calculates a small, but significant, decreasing trend in both annual melt days and meltwater production volume over the 41 years.