Abstract: The objective of this work is to investigate impacts of solar and galactic cosmic rays on flights by considering high realistic paths issued from Flight Plan
updated with Radar Data. Models and approaches used were previously validated by comparison of calculated and measured ambient dose equivalents or
cosmic ray variations during quiet solar activity and GLEs. Thus, to consider continents or sub-continents representative of European international air traffic,
five flights were selected. They link Paris (France) to Los Angeles, New York (United States), Tokyo (Japan), Johannesburg (South Africa) and Sao Paulo (Brazil)
city. Moreover, several hundred realistic paths are considered for each of these flights. The objective is to analyze statistically the ambient dose equivalents
considering path characteristics and GLE/quiet scenario, and thus demonstrate the complexity to assess the real radiation exposure, particularly during solar
event.

Abstract: Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of −0.2 m GTSR generally underestimates extremes, particularly in the tropics. The Dynamic Interactive Vulnerability Assessment model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea-level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39–59% higher estimate of population exposure.

Abstract: Despite widespread efforts to implement climate services, there is almost no literature that systematically analyzes users' needs. This paper addresses this gap by applying a decision analysis perspective to identify what kind of mean sea level rise (SLR) information is needed for local coastal adaptation decisions. We first characterize these decisions, then identify suitable decision analysis approaches and the sea level information required, and finally discuss if and how these information needs can be met given the state of the art of sea level science. We find that four types of information are needed: (i) probabilistic predictions for short-term decisions when users are uncertainty tolerant; (ii) high-end and low-end SLR scenarios chosen for different levels of uncertainty tolerance; (iii) upper bounds of SLR for users with a low uncertainty tolerance; and (iv) learning scenarios derived from estimating what knowledge will plausibly emerge about SLR over time. Probabilistic predictions can only be attained for the near term (i.e., 2030–2050) before SLR significantly diverges between low and high emission scenarios, for locations for which modes of climate variability are well understood and the vertical land movement contribution to local sea levels is small. Meaningful SLR upper bounds cannot be defined unambiguously from a physical perspective. Low- to high-end scenarios for different levels of uncertainty tolerance and learning scenarios can be produced, but this involves both expert and user judgments. The decision analysis procedure elaborated here can be applied to other types of climate information that are required for mitigation and adaptation purposes.

Abstract: The Arctic is warming nearly four times as rapidly as other regions of the planet, challenging the capacity of organisms to cope with shifting resources and maintain thermal balance. Tracking responses of free-living animals in dynamic environments can be challenging, but is increasingly enabled by advanced biologging approaches. We used data gathered from miniaturized bird-borne devices to demonstrate increases in energy expenditure with declining sea ice conditions and warming sea surface temperatures in a dove-sized seabird, the little auk (also named dovekie; Alle alle). This keystone species feeds on sea ice-associated copepods and inhabits large breeding colonies in the High Arctic.

Abstract: The decline of sea-ice thickness, area, and volume due to the transition from multi-year to first-year sea ice has improved the under-ice light environment for pelagic Arctic ecosystems. One unexpected and direct consequence of this transition, the proliferation of under-ice phytoplankton blooms (UIBs), challenges the paradigm that waters beneath the ice pack harbor little planktonic life. Little is known about the diversity and spatial distribution of UIBs in the Arctic Ocean, or the environmental drivers behind their timing, magnitude, and taxonomic composition. Here, we compiled a unique and comprehensive dataset from seven major research projects in the Arctic Ocean (11 expeditions, covering the spring sea-ice-covered period to summer ice-free conditions) to identify the environmental drivers responsible for initiating and shaping the magnitude and assemblage structure of UIBs. The temporal dynamics behind UIB formation are related to the ways that snow and sea-ice conditions impact the under-ice light field. In particular, the onset of snowmelt significantly increased under-ice light availability (>0.1–0.2 mol photons m–2 d–1), marking the concomitant termination of the sea-ice algal bloom and initiation of UIBs. At the pan-Arctic scale, bloom magnitude (expressed as maximum chlorophyll a concentration) was predicted best by winter water Si(OH)4 and PO43– concentrations, as well as Si(OH)4:NO3– and PO43–:NO3– drawdown ratios, but not NO3– concentration. Two main phytoplankton assemblages dominated UIBs (diatoms or Phaeocystis), driven primarily by the winter nitrate:silicate (NO3–:Si(OH)4) ratio and the under-ice light climate. Phaeocystis co-dominated in low Si(OH)4 (i.e., NO3:Si(OH)4 molar ratios >1) waters, while diatoms contributed the bulk of UIB biomass when Si(OH)4 was high (i.e., NO3:Si(OH)4 molar ratios <1). The implications of such differences in UIB composition could have important ramifications for Arctic biogeochemical cycles, and ultimately impact carbon flow to higher trophic levels and the deep ocean.