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In this analysis, we examine relative contributions from climate change and river discharge regulation to changes in marine conditions in the Hudson Bay Complex using a subset of five atmospheric forcing scenarios from the Coupled Model Intercomparison Project Phase 5 (CMIP5), river discharge data from the Hydrological Predictions for the Environment (HYPE) model, both naturalized (without anthropogenic intervention) and regulated (anthropogenically controlled through diversions, dams, reservoirs), and output from the Nucleus for European Modeling of the Ocean Ice-Ocean model for the 1981-2070 time frame. Investigated in particular are spatiotemporal changes in sea surface temperature, sea ice concentration and thickness, and zonal and meridional sea ice drift in response to (i) climate change through comparison of historical (1981-2010) and future (2021-2050 and 2041-2070) simulations, (ii) regulation through comparison of historical (1981-2010) naturalized and regulated simulations, and (iii) climate change and regulation combined through comparison of future (2021-2050 and 2041-2070) naturalized and regulated simulations. Also investigated is use of the diagnostic known as e-folding time spatial distribution to monitor changes in persistence in these variables in response to changing climate and regulation impacts in the Hudson Bay Complex. Results from this analysis highlight bay-wide and regional reductions in sea ice concentration and thickness in southwest and northeast Hudson Bay in response to a changing climate, and east-west asymmetry in sea ice drift response in support of past studies. Regulation is also shown to amplify or suppress the climate change signal. Specifically, regulation amplifies sea surface temperatures from April to August, suppresses sea ice loss by approximately 30% in March, contributes to enhanced sea ice drift speed by approximately 30%, and reduces meridional circulation by approximately 20% in January due to enhanced zonal drift. Results further suggest that the offshore impacts of regulation are amplified in a changing climate.

In this paper, we examine sea surface temperatures (SSTs) and sea ice conditions in the Hudson Bay Complex as a baseline evaluation for the BaySys 2016-2018 field program time frame. Investigated in particular are spatiotemporal patterns in SST and sea ice state and dynamics, with rankings of the latter to highlight extreme conditions relative to the examined 1981-2010 climatology. Results from this study show that SSTs in northwestern Hudson Bay from May to July, 2016-2018, are high relative to the climatology for SST (1982-2010). SSTs are also warmer in 2016 and 2017 than in 2018 relative to their climatology. Similarly, unusually low sea ice cover existed from August to December of 2016 and July to September of 2017, while unusually high sea ice cover existed in January, February, and October of 2018. The ice-free season was approximately 20 days longer in 2016 than in 2018. Unusually high ice-drift speeds occurred in April of 2016 and 2017 and in May of 2018, coinciding with strong winds in 2016 and 2018 and following strong winds in March 2017. Strong meridional circulation was observed in spring of 2016 and winter of 2017, while weak meridional circulation existed in 2018. In a case study of an extreme event, a blizzard from 7 to 9 March 2017, evaluated using Lagrangian dispersion statistics, is shown to have suppressed sea ice deformation off the coast of Churchill. These results are relevant to describing and planning for possible future pathways and scenarios under continued climate change and river regulation.

Delineating spatial patterns of precipitation isotopes ("isoscapes") is becoming increasingly important to understand the processes governing the modern water isotope cycle and their application to migration forensics, climate proxy interpretation, and ecohydrology of terrestrial systems. However, the extent to which these patterns can be empirically predicted across Canada and the northern United States has not been fully articulated, in part due to a lack of time series precipitation isotope data for major regions of North America. In this study, we use multiple linear regressions of CNIP, GNIP, and USNIP observations alongside climatological variables, teleconnection indices, and geographic indicators to create empirical models that predict the δ18O of monthly precipitation (δ18Oppt) across Canada and the northern United States. Five regionalization approaches are used to separate the study domain into isotope zones to explore the effect of spatial grouping on model performance. Stepwise regression-derived parameterizations quantified by permutation testing indicate the significance of precipitable water content and latitude as predictor variables. Within the Canadian Arctic and eastern portion of the study domain, models from all regionalizations capture the interannual and intraannual variability of δ18Oppt. The Pacific coast and northwestern portions of the study domain show less agreement between models and poorer model performance, resulting in higher uncertainty in simulations throughout these regions. Long-term annual average δ18Oppt isoscapes are generated, highlighting the uncertainty in the regionalization approach as it compounds over time. Additionally, monthly time series simulations are presented at various locations, and model structure uncertainty and 90% bootstrapped prediction bounds are detailed for these predictions. Key Points: Empirical models are developed to simulate 18O of monthly precipitation Precipitable water content describes the most variance in precipitation 18O Uncertainty in modeling monthly and long-term precipitation 18O is assessed

We modified a passive capillary sampler (PCS) to collect snowmelt water for isotopic analysis. Past applications of PCSs have been to sample soil water, but the novel aspect of this study was the placement of the PCSs at the ground-snowpack interface to collect snowmelt. We deployed arrays of PCSs at 11 sites in ten partner countries on five continents representing a range of climate and snow cover worldwide. The PCS reliably collected snowmelt at all sites and caused negligible evaporative fractionation effects in the samples. PCS is low-cost, easy to install, and collects a representative integrated snowmelt sample throughout the melt season or at the melt event scale. Unlike snow cores, the PCS collects the water that would actually infiltrate the soil; thus, its isotopic composition is appropriate to use for tracing snowmelt water through the hydrologic cycle. The purpose of this Briefing is to show the potential advantages of PCSs and recommend guidelines for constructing and installing them based on our preliminary results from two snowmelt seasons. Fil: Penna, D.. Università di Padova; Italia. Universidad Bozen-Bolzano. Bozen-Bolzano; Italia Fil: Ahmad M.. International Atomic Energy Agency. Viena; Austria Fil: Birks, S.J.. Alberta Innovates. Alberta; Canadá Fil: Bouchaou, L.. University Ibn Zohr. Agadir; Marruecos Fil: Brenčič M.. University of Ljubljana. Ljubljana; Eslovenia. Geological Survey of Slovenia. Ljubljana; Eslovenia Fil: Butt, S.. Pakistan Institute of Nuclear Science and Technology. Islamabad; Pakistán Fil: Holko, L.. Institute of Hydrology, Liptovský Mikuláš; Eslovenia Fil: Jeelani, G.. University of Kashmir. Srinagar; India Fil: Martinez, Daniel Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina Fil: Melikadze, G.. Tbilisi State University. Tbilisi; Georgia Fil: Shanley, J.. US Geological Survey. Montpelier; Estados Unidos Fil: Sokratov, S.A.. Moscow State University; Rusia Fil: Stadnyk, T.. University of Manitoba. Winnipeg; Canadá Fil: Sugimoto, A.. Hokkaido University, Sapporo; Japón Fil: Vreča, P.. Jožef Stefan Institute. Ljubljana; Eslovenia