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Icelandic glaciers have been losing mass since the Little Ice Age in the mid-to-late 1800s, with higher mass loss rates in the early 21st century, followed by a slowdown since 2011. As of yet, it remains unclear whether this mass loss slowdown will persist in the future. By reconstructing the contemporary (1958–2019) surface mass balance of Icelandic glaciers, we show that the post-2011 mass loss slowdown coincides with the development of the Blue Blob, an area of regional cooling in the North Atlantic Ocean to the south of Greenland. This regional cooling signal mitigates atmospheric warming in Iceland since 2011, in turn decreasing glacier mass loss through reduced meltwater runoff. In a future high-end warming scenario, North Atlantic cooling is projected to mitigate mass loss of Icelandic glaciers until the mid-2050s. High mass loss rates resume thereafter as the regional cooling signal weakens.

Icelandic glaciers have been losing mass since the Little Ice Age in the mid-to-late 1800s, with higher mass loss rates in the early 21st century, followed by a slowdown since 2011. As of yet, it remains unclear whether this mass loss slowdown will persist in the future. By reconstructing the contemporary (1958–2019) surface mass balance of Icelandic glaciers, we show that the post-2011 mass loss slowdown coincides with the development of the Blue Blob, an area of regional cooling in the North Atlantic Ocean to the south of Greenland. This regional cooling signal mitigates atmospheric warming in Iceland since 2011, in turn decreasing glacier mass loss through reduced meltwater runoff. In a future high-end warming scenario, North Atlantic cooling is projected to mitigate mass loss of Icelandic glaciers until the mid-2050s. High mass loss rates resume thereafter as the regional cooling signal weakens., Physical and Space Geodesy, Mathematical Geodesy and Positioning

The flow regimes of glacier-fed rivers are sensitive to climate change due to strong climate–cryosphere–hydrosphere interactions. Previous modelling studies have projected changes in annual and seasonal flow magnitude but neglect other changes in river flow regime that also have socio-economic and environmental impacts. This study employs a signature-based analysis of climate change impacts on the river flow regime for the deglaciating Virkisá river basin in southern Iceland. Twenty-five metrics (signatures) are derived from 21st century projections of river flow time series to evaluate changes in different characteristics (magnitude, timing and variability) of river flow regime over sub-daily to decadal timescales. The projections are produced by a model chain that links numerical models of climate and glacio-hydrology. Five components of the model chain are perturbed to represent their uncertainty including the emission scenario, numerical climate model, downscaling procedure, snow/ice melt model and runoff-routing model. The results show that the magnitude, timing and variability of glacier-fed river flows over a range of timescales will change in response to climate change. For most signatures there is high confidence in the direction of change, but the magnitude is uncertain. A decomposition of the projection uncertainties using analysis of variance (ANOVA) shows that all five perturbed model chain components contribute to projection uncertainty, but their relative contributions vary across the signatures of river flow. For example, the numerical climate model is the dominant source of uncertainty for projections of high-magnitude, quick-release flows, while the runoff-routing model is most important for signatures related to low-magnitude, slow-release flows. The emission scenario dominates mean monthly flow projection uncertainty, but during the transition from the cold to melt season (April and May) the snow/ice melt model contributes up to 23 % of projectio

Glacio-hydrological models (GHMs) allow us to develop an understanding of how future climate change will affect river flow regimes in glaciated watersheds. A variety of simplified GHM structures and parameterisations exist, yet the performance of these are rarely quantified at the process level or with metrics beyond global summary statistics. A fuller understanding of the deficiencies in competing model structures and parameterisations and the ability of models to simulate physical processes require performance metrics utilising the full range of uncertainty information within input observations. Here, the glacio-hydrological characteristics of the Virkisá River basin in southern Iceland are characterised using 33 signatures derived from observations of ice melt, snow coverage and river discharge. The uncertainty of each set of observations is harnessed to define the limits of acceptability (LOA), a set of criteria used to objectively evaluate the acceptability of different GHM structures and parameterisations. This framework is used to compare and diagnose deficiencies in three melt and three run-off-routing model structures. Increased model complexity is shown to improve acceptability when evaluated against specific signatures but does not always result in better consistency across all signatures, emphasising the difficulty in appropriate model selection and the need for multi-model prediction approaches to account for model selection uncertainty. Melt and run-off-routing structures demonstrate a hierarchy of influence on river discharge signatures with melt model structure having the most influence on discharge hydrograph seasonality and run-off-routing structure on shorter-timescale discharge events. None of the tested GHM structural configurations returned acceptable simulations across the full population of signatures. The framework outlined here provides a comprehensive and rigorous assessment tool for evaluating the acceptability of different GHM process hy