Atmospheric drivers of melt on Larsen C Ice Shelf: Surface energy budget regimes and the impact of foehn

Recent ice shelf retreat on the east coast of the Antarctic Peninsula has been principally attributed to atmospherically driven melt. However, previous studies on the largest of these ice shelves—Larsen C—have struggled to reconcile atmospheric forcing with observed melt. This study provides the first comprehensive quantification and explanation of the atmospheric drivers of melt across Larsen C, using 31-months' worth of observations from Cabinet Inlet, a 6-month, high-resolution atmospheric model simulation and a novel approach to ascertain the surface energy budget (SEB) regime. The dominant meteorological controls on melt are shown to be the occurrence, strength, and warmth of mountain winds called foehn. At Cabinet Inlet, foehn occurs 15% of the time and causes 45% of melt. The primary effect of foehn on the SEB is elevated turbulent heat fluxes. Under typical, warm foehn conditions, this means elevated surface heating and melting, the intensity of which increases as foehn wind speed increases. Less commonly—due to cooler-than-normal foehn winds and/or radiatively warmed ice—the relationship between wind speed and net surface heat flux reverses. This explains the seemingly contradictory results of previous studies. In the model, spatial variability in cumulative melt across Larsen C is largely explained by foehn, with melt maxima in inlets reflecting maxima in foehn wind strength. However, most accumulated melt (58%) occurs due to solar radiation in the absence of foehn. A broad north-south gradient in melt is explained by the combined influence of foehn and non-foehn conditions

Foehn jets over the Larsen C Ice Shelf, Antarctica

Previously unknown foehn jets have been identified to the east of the Antarctic Peninsula (AP) above the Larsen C Ice Shelf. These jets have major implications for the east coast of the AP, a region of rapid climatic warming and where two large sections of ice shelf have collapsed in recent years. During three foehn events across the AP, leeside warming and drying is seen in new aircraft observations and simulated well by the Met Office Unified Model (MetUM) at ∼1.5 km grid spacing. In case A, weak southwesterly flow and an elevated upwind inversion characterise a highly nonlinear flow regime with upwind flow blocking. In case C strong northwesterly winds characterise a relatively linear case with little upwind flow blocking. Case B resides somewhere between the two in flow regime linearity. The foehn jets – apparent in aircraft observations where available and MetUM simulations of all three cases – are mesoscale features (up to 60 km in width) originating from the mouths of leeside inlets. Through back trajectory analysis they are identified as a type of gap flow. In cases A and B the jets are distinct, being strongly accelerated relative to the background flow, and confined to low levels above the Larsen C Ice Shelf. They resemble the ‘shallow foehn’ of the Alps. Case C resembles a case of ‘deep foehn’, with the jets less distinct. The foehn jets are considerably cooler and moister relative to adjacent regions of calmer foehn air. This is due to a dampened foehn effect in the jet regions: in case A the jets have lower upwind source regions, and in the more linear case C there is less diabatic warming and precipitation along jet trajectories due to the reduced orographic uplift across the mountain passes

Projected Changes to Wintertime Air‐Sea Turbulent Heat Fluxes Over the Subpolar North Atlantic Ocean

In wintertime over the subpolar North Atlantic Ocean (SPNA), the strongest surface sensible and latent heat fluxes typically occur just downstream of the sea-ice edge. The recent retreat in Arctic wintertime sea ice is changing the distribution of these turbulent heat fluxes, with consequences for the formation of the dense waters that feed into the Atlantic Meridional Overturning Circulation. Projections of turbulent heat flux over the SPNA are investigated using output from the HadGEM3-GC3.1 climate model, produced as part of the sixth phase of the Coupled Model Inter-Comparison Project. Comparison of two model resolutions (MM: 60 km atmosphere—1/4° ocean and HH: 25 km–1/12°) shows that the HH configuration more accurately simulates historic sea ice and turbulent heat flux distributions. The MM configuration tends to produce too much sea ice in the SPNA, affecting the turbulent heat flux distribution; however, it displays improved performance during winters with less sea ice, increasing confidence in future projections when less sea ice is predicted. Future projections are presented for low (SSP1-2.6) and high (SSP5-8.5) emissions pathways. The simulations agree in predicting that, with climate change, the SPNA will see reductions in wintertime sea ice and air-sea turbulent fluxes later in the 21st century, particularly in the Labrador and Irminger Seas and the interior of the Nordic Seas, and a notable reduction in their decadal variability. These effects are more severe under the SSP5-8.5 pathway. The implications for SPNA ocean circulation are discussed

Ship-based estimates of momentum transfer coefficient over sea ice and recommendations for its parameterization

A major source of uncertainty in both climate projections and seasonal forecasting of sea ice is inadequate representation of surface–atmosphere exchange processes. The observations needed to improve understanding and reduce uncertainty in surface exchange parameterizations are challenging to make and rare. Here we present a large dataset of ship-based measurements of surface momentum exchange (surface drag) in the vicinity of sea ice from the Arctic Clouds in Summer Experiment (ACSE) in July–October 2014, and the Arctic Ocean 2016 experiment (AO2016) in August–September 2016. The combined dataset provides an extensive record of momentum flux over a wide range of surface conditions spanning the late summer melt and early autumn freeze-up periods, and a wide range of atmospheric stabilities. Surface exchange coefficients are estimated from in situ eddy covariance measurements. The local sea-ice fraction is determined via automated processing of imagery from ship-mounted cameras. The surface drag coefficient, CD10n, peaks at local ice fractions of 0.6–0.8, consistent with both recent aircraft-based observations and theory. Two state-of-the-art parameterizations have been tuned to our observations, with both providing excellent fits to the measurements

An evaluation of surface meteorology and fluxes over the Iceland and Greenland Seas in ERA5 reanalysis: the impact of sea ice distribution

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Renfrew, I. A., Barrell, C., Elvidge, A. D., Brooke, J. K., Duscha, C., King, J. C., Kristiansen, J., Cope, T. L., Moore, G. W. K., Pickart, R. S., Reuder, J., Sandu, I., Sergeev, D., Terpstra, A., Vage, K., & Weiss, A. An evaluation of surface meteorology and fluxes over the Iceland and Greenland Seas in ERA5 reanalysis: the impact of sea ice distribution. Quarterly Journal of the Royal Meteorological Society, (2020): 1-22, doi:10.1002/qj.3941.The Iceland and Greenland Seas are a crucial region for the climate system, being the headwaters of the lower limb of the Atlantic Meridional Overturning Circulation. Investigating the atmosphere–ocean–ice processes in this region often necessitates the use of meteorological reanalyses—a representation of the atmospheric state based on the assimilation of observations into a numerical weather prediction system. Knowing the quality of reanalysis products is vital for their proper use. Here we evaluate the surface‐layer meteorology and surface turbulent fluxes in winter and spring for the latest reanalysis from the European Centre for Medium‐Range Weather Forecasts, i.e., ERA5. In situ observations from a meteorological buoy, a research vessel, and a research aircraft during the Iceland–Greenland Seas Project provide unparalleled coverage of this climatically important region. The observations are independent of ERA5. They allow a comprehensive evaluation of the surface meteorology and fluxes of these subpolar seas and, for the first time, a specific focus on the marginal ice zone. Over the ice‐free ocean, ERA5 generally compares well to the observations of surface‐layer meteorology and turbulent fluxes. However, over the marginal ice zone, the correspondence is noticeably less accurate: for example, the root‐mean‐square errors are significantly higher for surface temperature, wind speed, and surface sensible heat flux. The primary reason for the difference in reanalysis quality is an overly smooth sea‐ice distribution in the surface boundary conditions used in ERA5. Particularly over the marginal ice zone, unrepresented variability and uncertainties in how to parameterize surface exchange compromise the quality of the reanalyses. A parallel evaluation of higher‐resolution forecast fields from the Met Office's Unified Model corroborates these findings.This study was part of the Iceland Greenland Seas Project. Funding was from the NERC AFIS grant (NE/N009754/1), the ALERTNESS (Advanced models and weather prediction in the Arctic: enhanced capacity from observations and polar process representations) project (Research Council of Norway project number 280573), the Trond Mohn Foundation (BFS2016REK01), and the National Science Foundation grant OCE‐1558742. The Leosphere WindCube v2 and the Wavescan buoy are part of the OBLO (Offshore Boundary Layer Observatory) infrastructure funded by the Research Council of Norway (project number 227777)

Reducing Parametrization Errors for Polar Surface Turbulent Fluxes Using Machine Learning

Turbulent exchanges between sea ice and the atmosphere are known to influence the melting rate of sea ice, the development of atmospheric circulation anomalies and, potentially, teleconnections between polar and non-polar regions. Large model errors remain in the parametrization of turbulent heat fluxes over sea ice in climate models, resulting in significant uncertainties in projections of future climate. Fluxes are typically calculated using bulk formulae, based on Monin-Obukhov similarity theory, which have shown particular limitations in polar regions. Parametrizations developed specifically for polar conditions (e.g. representing form drag from ridges or melt ponds on sea ice) rely on sparse observations and thus may not be universally applicable. In this study, new data-driven parametrizations have been developed for surface turbulent fluxes of momentum, sensible heat and latent heat in the Arctic. Machine learning has already been used outside the polar regions to provide accurate and computationally inexpensive estimates of surface turbulent fluxes. To investigate the feasibility of this approach in the Arctic, we have fitted neural-network models to a reference dataset (SHEBA). Predictive performance has been tested using data from other observational campaigns. For momentum and sensible heat, performance of the neural networks is found to be comparable to, and in some cases substantially better than, that of a state-of-the-art bulk formulation. These results offer an efficient alternative to the traditional bulk approach in cases where the latter fails, and can serve to inform further physically based developments

Reducing parametrization errors for polar surface turbulent fluxes using machine learning

Turbulent exchanges between sea ice and the atmosphere are known to influence the melting rate of sea ice, the development of atmospheric circulation anomalies and, potentially, teleconnections between polar and non-polar regions. Large model errors remain in the parametrization of turbulent heat fluxes over sea ice in climate models, resulting in significant uncertainties in projections of future climate. Fluxes are typically calculated using bulk formulae, based on Monin-Obukhov similarity theory, which have shown particular limitations in polar regions. Parametrizations developed specifically for polar conditions (e.g. representing form drag from ridges or melt ponds on sea ice) rely on sparse observations and thus may not be universally applicable. In this study, new data-driven parametrizations have been developed for surface turbulent fluxes of momentum, sensible heat and latent heat in the Arctic. Machine learning has already been used outside the polar regions to provide accurate and computationally inexpensive estimates of surface turbulent fluxes. To investigate the feasibility of this approach in the Arctic, we have fitted neural-network models to a reference dataset (SHEBA). Predictive performance has been tested using data from other observational campaigns. For momentum and sensible heat, performance of the neural networks is found to be comparable to, and in some cases substantially better than, that of a state-of-the-art bulk formulation. These results offer an efficient alternative to the traditional bulk approach in cases where the latter fails, and can serve to inform further physically based developments