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Abstract Variability in oceanic conditions directly impacts ice loss from marine outlet glaciers in Greenland, influencing the ice sheet mass balance. Oceanic conditions are available from Atmosphere‐Ocean Global Climate Model (AOGCM) output, but these models require extensive computational resources and lack the fine resolution needed to simulate ocean dynamics on the Greenland continental shelf and close to glacier marine termini. Here, we develop a statistical approach to generate ocean forcing for ice sheet model simulations, which incorporates natural spatiotemporal variability and anthropogenic changes. Starting from raw AOGCM ocean heat content, we apply: (a) a bias‐correction using ocean reanalysis, (b) an extrapolation accounting for on‐shelf ocean dynamics, and (c) stochastic time series models to generate realizations of natural variability. The bias‐correction reduces model errors by ∼25% when compared to independent in‐situ measurements. The bias‐corrected time series are subsequently extrapolated to fjord mouth locations using relations constrained from available high‐resolution regional ocean model results. The stochastic time series models reproduce the spatial correlation, characteristic timescales, and the amplitude of natural variability of bias‐corrected AOGCMs, but at negligible computational expense. We demonstrate the efficiency of this method by generating >6,000 time series of ocean forcing for >200 Greenland marine‐terminating glacier locations until 2100. As our method is computationally efficient and adaptable to any ocean model output and reanalysis product, it provides flexibility in exploring sensitivity to ocean conditions in Greenland ice sheet model simulations. We provide the output and workflow in an open‐source repository, and discuss advantages and future developments for our method. 

Ocean temperatures have warmed in the fjords surrounding the Greenland Ice Sheet, causing increased melt along their ice fronts and rapid glacier retreat and contributing to rising global sea levels. However, there are many physical mechanisms that can mediate the glacier response to ocean warming and variability. Warm ocean waters can directly cause melt at horizontal and vertical ice interfaces or promote iceberg calving by weakening proglacial melange or undercutting the glacier front. Sermeq Kujalleq (also known as Jakobshavn Isbræ) is the largest and fastest glacier in Greenland and has undergone substantial retreat, which started in the late 1990s. In this study, we use an ensemble modeling approach to disentangle the dominant mechanisms that drive the retreat of Sermeq Kujalleq. Within this ensemble, we vary the sensitivity of three different glaciological parameters to ocean temperature: frontal melt, subshelf melt, and a calving-stress threshold. Comparing results to the observed retreat behavior from 1985 to 2018, we select a best-fitting simulation which reproduces the observed retreat well. In this simulation, the arrival of warm water at the front of Sermeq Kujalleq in the late 1990s led to enhanced rates of subshelf melt, triggering the disintegration of the floating ice tongue over a decade. The recession of the calving front into a substantially deeper bed trough around 2010 accelerated the calving-driven retreat, which continued nearly unabated despite local ocean cooling in 2016. An extended ensemble of simulations with varying calving thresholds shows evidence of hysteresis in the calving rate, which can only be inhibited by a substantial increase in the calving-stress threshold beyond the values suggested for the historical period. Our findings indicate that accurate simulation of rapid calving-driven glacier retreats requires more sophisticated models of iceberg mélange and calving evolution coupled to ice flow models. 

Greenland’s coastal margins are influenced by the confluence of Arctic and Atlantic waters, sea ice, icebergs, and meltwater from the ice sheet. Hundreds of spectacular glacial fjords cut through the coastline and support thriving marine ecosystems and, in some places, adjacent Greenlandic communities. Rising air and ocean temperatures, as well as glacier and sea-ice retreat, are impacting the conditions that support these systems. Projecting how these regions and their communities will evolve requires understanding both the large-scale climate variability and the regional-scale web of physical, biological, and social interactions. Here, we describe pan-Greenland physical, biological, and social settings and show how they are shaped by the ocean, the atmosphere, and the ice sheet. Next, we focus on two communities, Qaanaaq in Northwest Greenland, exposed to Arctic variability, and Ammassalik in Southeast Greenland, exposed to Atlantic variability. We show that while their climates today are similar to those of the warm 1930s­–1940s, temperatures are projected to soon exceed those of the last 100 years at both locations. Existing biological records, including fisheries, provide some insight on ecosystem variability, but they are too short to discern robust patterns. To determine how these systems will evolve in the future requires an improved understanding of the linkages and external factors shaping the ecosystem and community response. This interdisciplinary study exemplifies a first step in a systems approach to investigating the evolution of Greenland’s coastal margins.