Fig 1 - uploaded by Emily Osborne
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a) SSMIS-derived surface melt area as a percentage of the ice sheet area during 2018 (solid red), in addition to the 1981-2010 average (dashed blue) and interdecile and interquartile ranges (shaded) b) melt anomaly (in number of melting days) with respect to the 1981-2010 period during the summer of 2018 estimated from spaceborne passive microwave observations.
Source publication
The Arctic Report Card (hereafter 'ARC') has been issued annually since 2006. It is a timely and peer-reviewed source for clear, reliable and concise environmental information on the current state of different components of the Arctic environmental system relative to historical records. The ARC is intended for a wide audience, including scientists,...
Citations
... These maxima are several degrees above the maximum T a values that several aridzone passerine birds are able to tolerate (125). These challenges are not confined to hot deserts; warming in the Arctic is proceeding at twice the rate observed over the rest of the globe, and warm extremes in these regions, although modest in terms of absolute temperatures, may have catastrophic consequences for species that are adapted to function at temperatures 10 -20°C lower than have been recently observed (100). Unless urgent, concerted global action is taken, climate change may well create a future where many parts of the planet are habitable only for mammals and birds that have been genetically engineered to modify their heat balance (e.g., insulation, metabolic rate) or for enhanced heat tolerance. ...
Understanding the heat tolerances of small mammals and birds has taken on new urgency with the advent of climate change. Here, we review heat tolerance limits, pathways of evaporative heat dissipation that permit the defense of body temperature during heat exposure, and mechanisms operating at tissue, cellular, and molecular levels.
In the Arctic, air temperatures are increasing and sea ice is declining, resulting in larger waves and a longer open water season, all of which intensify the thaw and erosion of ice-rich coasts. Climate change has been shown to increase the rate of Arctic coastal erosion, causing problems for Arctic cultural heritage, existing industrial, military, and civil infrastructure, as well as changes in nearshore biogeochemistry. Numerical models that reproduce historical and project future Arctic erosion rates are necessary to understand how further climate change will affect these problems, and no such model yet exists to simulate the physics of erosion on a pan-Arctic scale. We have coupled a bathystrophic storm surge model to a simplified physical erosion model of a permafrost coastline. This Arctic erosion model, called ArcticBeach v1.0, is a first step toward a physical parameterization of Arctic shoreline erosion for larger-scale models. It is forced by wind speed and direction, wave period and height, sea surface temperature, all of which are masked during times of sea ice cover near the coastline. Model tuning requires observed historical retreat rates (at least one value), as well as rough nearshore bathymetry. These parameters are already available on a pan-Arctic scale. The model is validated at three study sites at 1) Drew Point (DP), Alaska, 2) Mamontovy Khayata (MK), Siberia, and 3) Veslebogen Cliffs, Svalbard. Simulated cumulative retreat rates for DP and MK respectively (169 and 170 m) over the time periods studied at each site (2007–2016, and 1995–2018) are found to the same order of magnitude as observed cumulative retreat (172 and 120 m). The rocky Veslebogen cliffs have small observed cumulative retreat rates (0.05 m over 2014–2016), and our model was also able to reproduce this same order of magnitude of retreat (0.08 m). Given the large differences in geomorphology between the study sites, this study provides a proof-of-concept that ArcticBeach v1.0 can be applied on very different permafrost coastlines. ArcticBeach v1.0 provides a promising starting point to project retreat of Arctic shorelines, or to evaluate historical retreat in places that have had few observations.
Arctic amplification (AA) - referring to the enhancement of near-surface air temperature change over the Arctic relative to lower latitudes - is a prominent feature of climate change with important impacts on human and natural systems. In this review, we synthesize current understanding of the underlying physical mechanisms that can give rise to AA. These mechanisms include both local feedbacks and changes in poleward energy transport. Temperature and sea ice-related feedbacks are especially important for AA, since they are significantly more positive over the Arctic than at lower latitudes. Changes in energy transport by the atmosphere and ocean can also contribute to AA. These energy transport changes are tightly coupled with local feedbacks, and thus their respective contributions to AA should not be considered in isolation. It is here emphasized that the feedbacks and energy transport changes that give rise to AA are sensitively dependent on the state of the climate system itself. This implies that changes in the climate state will lead to changes in the strength of AA, with implications for past and future climate change.
Microbial communities in the coastal Arctic Ocean experience extreme variability in organic matter and inorganic nutrients driven by seasonal shifts in sea ice extent and freshwater inputs. Lagoons border more than half of the Beaufort Sea coast and provide important habitats for migratory fish and seabirds; yet, little is known about the planktonic food webs supporting these higher trophic levels. To investigate seasonal changes in bacterial and protistan planktonic communities, amplicon sequences of 16S and 18S rRNA genes were generated from samples collected during periods of ice-cover (April), ice break-up (June), and open water (August) from shallow lagoons along the eastern Alaska Beaufort Sea coast from 2011 through 2013. Protist communities shifted from heterotrophic to photosynthetic taxa (mainly diatoms) during the winter–spring transition, and then back to a heterotroph-dominated summer community that included dinoflagellates and mixotrophic picophytoplankton such as Micromonas and Bathycoccus. Planktonic parasites belonging to Syndiniales were abundant under ice in winter at a time when allochthonous carbon inputs were low. Bacterial communities shifted from coastal marine taxa (Oceanospirillaceae, Alteromonadales) to estuarine taxa (Polaromonas, Bacteroidetes) during the winter-spring transition, and then to oligotrophic marine taxa (SAR86, SAR92) in summer. Chemolithoautotrophic taxa were abundant under ice, including iron-oxidizing Zetaproteobacteria. These results suggest that wintertime Arctic bacterial communities capitalize on the unique biogeochemical gradients that develop below ice near shore, potentially using chemoautotrophic metabolisms at a time when carbon inputs to the system are low. Co-occurrence networks constructed for each season showed that under-ice networks were dominated by relationships between parasitic protists and other microbial taxa, while spring networks were by far the largest and dominated by bacteria-bacteria co-occurrences. Summer networks were the smallest and least connected, suggesting a more detritus-based food web less reliant on interactions among microbial taxa. Eukaryotic and bacterial community compositions were significantly related to trends in concentrations of stable isotopes of particulate organic carbon and nitrogen, among other physiochemical variables such as dissolved oxygen, salinity, and temperature. This suggests the importance of sea ice cover and terrestrial carbon subsidies in contributing to seasonal trends in microbial communities in the coastal Beaufort Sea.
