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Some of the largest climatic changes in the Arctic have been observed in Alaska and surrounding marginal seas. Near-surface air temperature (T2m), precipitation (P), snowfall, and sea ice changes have been previously documented, often in disparate studies. Here we provide an updated, long-term trend analysis (1957-2021; n=65 years) of such parameters in ERA5, NOAA NClimGrid, NOAA NCEI Alaska climate division, and composite sea ice products preceding the upcoming Fifth National Climate Assessment (NCA5) and other near-future climate reports. In the past half century, annual T2m has broadly increased across Alaska, and during winter, spring, and autumn on the North Slope and North Panhandle (T2m>0.50°C decade−1). P has also increased across climate divisions, and appears strongly interrelated with temperature-sea ice feedbacks on the North Slope, specifically with increased (decreased) open water (sea ice extent). Snowfall equivalent (SFE) has decreased in autumn and spring, perhaps aligned with a regime transition of snow to rain, while winter SFE has broadly increased across the state. Sea ice decline and melt season lengthening also have a pronounced signal around Alaska, with the largest trends in these parameters found in the Beaufort Sea. Alaska’s climatic changes are also placed in context against regional and contiguous U.S. air temperature trends, and show ~50% greater warming in Alaska relative to the lower-48 states. Alaska T2m increases also exceed those of any contiguous U.S. sub-region, positioning Alaska at the forefront of U.S. climate warming.

Low-salinity meltwater from Arctic sea ice and its snow cover accumulates and creates under-ice meltwater layers below sea ice. These meltwater layers can result in the formation of new ice layers, or false bottoms, at the interface of this low-salinity meltwater and colder seawater. As part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), we used a combination of sea ice coring, temperature profiles from thermistor strings and underwater multibeam sonar surveys with a remotely operated vehicle (ROV) to study the areal coverage and temporal evolution of under-ice meltwater layers and false bottoms during the summer melt season from mid-June until late July. ROV surveys indicated that the areal coverage of false bottoms for a part of the MOSAiC Central Observatory (350 by 200 m2) was 21%. Presence of false bottoms reduced bottom ice melt by 7–8% due to the local decrease in the ocean heat flux, which can be described by a thermodynamic model. Under-ice meltwater layer thickness was larger below first-year ice and thinner below thicker second-year ice. We also found that thick ice and ridge keels confined the areas in which under-ice meltwater accumulated, preventing its mixing with underlying seawater. While a thermodynamic model could reproduce false bottom growth and melt, it could not describe the observed bottom melt rates of the ice above false bottoms. We also show that the evolution of under-ice meltwater-layer salinity below first-year ice is linked to brine flushing from the above sea ice and accumulating in the meltwater layer above the false bottom. The results of this study aid in estimating the contribution of under-ice meltwater layers and false bottoms to the mass balance and salt budget for Arctic summer sea ice.

We investigate sea ice conditions during the 2020 melt season, when warm air temperature anomalies in Spring led to early melt onset, an extended melt season and the second-lowest September minimum Arctic ice extent observed. We focus on the region of the most persistent ice cover and examine melt pond depth retrieved from ICESat-2 using two distinct algorithms in concert with a time series of melt pond fraction and ice concentration derived from Sentinel-2 imagery to obtain insights about the melting ice surface in three dimensions. We find melt pond fraction derived from Sentinel-2 in the study region increased rapidly in June, with the mean melt pond fraction peaking at 16 % +/- 6 % on 24 June 2020, followed by a slow decrease to 8 % +/- 6 % by 3 July, and remained below 10 % for the remainder of the season through 15 September. Sea ice concentration was consistently high (>95 %) at the beginning of the melt season until 4 July, and as floes disintegrated, decreased to a minimum of 70 % on July 30, then became more variable ranging from 75 % to 90 % for the remainder of the melt season. Pond depth increased steadily from a median depth of 0.40 m +/- 0.17 m in early June, peaked at 0.97 m +/- 0.51 m on 16 July, even as melt pond fraction had already started to decrease. Our results demonstrate that by combining high-resolution passive and active remote sensing we now have the ability to track evolving melt conditions and observe changes in the sea ice cover throughout the summer season.

The Good Life Model is an evidence-informed service extension program that seeks to improve the quality of daily life for people living with dementia (PLWD) and their caregivers through an integrated, replicable health professional learning service approach. To provide this service, caregivers referred from a neurology clinic are matched to Dementia Ambassadors (DAs), nursing and health profession faculty and graduate students who are trained in family-centric and nonpharmacological approaches. DAs serve as coaches to telephonically provide disease education, referrals and linkages, support and skills in managing caregiver-identified care challenges. Good Life offers a service-learning approach for developing a workforce that is prepared to care for families living with dementia, while improving the quality of daily life for both people living with dementia and their caregivers.DAs involve various health professionals and students and receive training tailored to their level of expertise in dementia care. Regardless of dementia expertise, key competencies include 1) knowledge about dementia types and etiologies, 2) core clinical signs (e.g., behavioral and psychological symptoms, cognitive and functional decline), 3) knowledge of caregiver experiences, 4) ability to assess care challenges and serve as a coach, and 5) ability to rapidly develop rapport and engage in active listening. Caregivers receive support through bimonthly one hour telephone sessions for up to 12 sessions over 6 months. Biweekly case debriefings provide ongoing oversight and support for DAs.To date, 36 caregivers have been referred, with 19 enrolled having completed 123 sessions. Of the 8 caregivers who have completed the 6 month program, all have elected to continue optional post-program sessions.In providing access and support to underserved populations with a nonpharmacological phone intervention, Good Life demonstrates not only the need for service extension practices as part of standard dementia care to support PLWD and caregivers, but highlights the importance of preparing a workforce to help caregivers manage challenges. Furthermore, caregivers to date having unanimously elected to continue post-program sessions attests to the rapid and powerful relationship developed between DAs trained in these practices and the caregivers who gain value from participating.

Over 16 million caregivers of people living with dementia require support in a range of issues, including self-care, disease education, and guidance for how to manage behavioral and psychological symptoms of dementia (BPSD). Non-pharmacological interventions are needed to address these areas, and online applications have been shown to be safe and effective. To ensure the efficacy of such interventions, racially, ethnically, geographically, and socioeconomically diverse participants must be recruited to increase the generalizability of study outcomes. This protocol paper describes a recruitment plan using Geographic Information Systems (GIS) to reach a representative sample of caregivers across the United States for a national Phase III clinical study. Using publicly available census data from the American Community Survey (ACS), combined with location data for local aging resources such as Area Agencies on Aging (AAA), recruitment will be derived from data analysis conducted in ESRI ArcGIS v10.7.1. Datasets including age, gender, income, and education will be assessed nationally at the county and census tract spatial scale in a nine-step process to develop recruitment priority areas containing high concentrations of eligible participants living in the community. Overall, the current protocol will demonstrate the value of GIS in tailoring targeted outreach strategies to recruit community-dwelling populations through local resource institutions. This novel approach may have far-reaching implications in future recruitment initiatives and help to secure racially/ethnically diverse samples.

Kartlegging av ferskvannslag og dannelse av falsk bunnis under sjøisen ved sommarsmelting i sentrale Polhavet Sjøisen i Polhavet er i rask endring. Den er blitt mye tynnere i løpet av de siste tiårene, havet og atmosfæren er i endring, og dette påvirker det marine økosystemet i Polhavet. Klimaprosesser i Arktis er følgelig viktige for hele planeten, og polare prosesser er viktige elementer i klimamodeller. Dette var motivasjonen for ekspedisjonen «Multidisciplinary drifting Observatory for the Study of Arctic Climate» (MOSAiC) i 2019-2020, og målsetningen var å samle inn data om atmosfære, havis, hav og det marine økosystem, og koblete prosesser gjennom et helt år i det sentrale Polhavet. Norsk polarinstitutt deltok i ekspedisjonen med flere forskere støttet av prosjektet HAVOC finansiert av Forskningsrådet. Under issmelting på sommeren samles ofte smeltevann fra is og snø i tynne lag under isen som påvirker koblingen mellom hav og is. Dette bidrar til isdannelse midt på sommeren når mesteparten av isen smelter. Falsk bunnis dannes når det kalde ferskvannet møter enda kaldere saltvann under isen og danner en såkalt «false bottoms» på grensesnittet mellom ferskvann og saltvann. Det er lite forstått om utbredelsen av slike ferskvannslag og hvordan de påvirker sjøisen på sommeren. Under MOSAiC ble det gjennomført kartlegging av disse vannlagene i drivisen, og man fant at de befant seg oftere under tynnere første års is. Dette kan ha påvirkning på blant annet smelting av isen og for is assosierte planter og dyr, særskilt om det skjer enda mer smelting. Disse prosesser må dermed bli bedre representert i klimamodeller slik at endringer i havisen kan bedre forutsies i fremtiden. Quantifying false bottoms and under-ice meltwater layers beneath Arctic summer sea ice with fine-scale observations During the Arctic melt season, relatively fresh meltwater layers can accumulate under sea ice as a result of snow and ice melt, far from terrestrial freshwater inputs. Such under-ice meltwater layers, sometimes referred to as under-ice melt ponds, have been suggested to play a role in the summer sea ice mass balance both by isolating the sea ice from saltier water below, and by driving formation of ‘false bottoms’ below the sea ice. Such layers form at the interface of the fresher under-ice layer and the colder, saltier seawater below. During the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition in the Central Arctic, we observed the presence of under-ice meltwater layers and false bottoms throughout July 2020 at primarily first-year ice locations. Here, we examine the distribution, prevalence, and drivers of under-ice ponds and the resulting false bottoms during this period. The average thickness of observed false bottoms and freshwater equivalent of under-ice meltwater layers was 0.08 m, with false bottom ice comprised of 74–87% FYI melt and 13–26% snow melt. Additionally, we explore these results using a 1D model to understand the role of dynamic influences on decoupling the ice from the seawater below. The model comparison suggests that the ice-ocean friction velocity was likely exceptionally low, with implications for air-ice-ocean momentum transfer. Overall, the prevalence of false bottoms was similar to or higher than noted during other observational campaigns, indicating that these features may in fact be common in the Arctic during the melt season. These results have implications for the broader ice-ocean system, as under-ice meltwater layers and false bottoms provide a source of ice growth during the melt season, potentially reduce fluxes between the ice and the ocean, isolate sea ice primary producers from pelagic nutrient sources, and may alter light transmission to the ocean below.

Accurate multidecadal radiative flux records are vital to understand Arctic amplification and constrain climate model uncertainties. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes was conducted using in situ measurements from MOSAiC flux stations. The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by +11.40 Wm–2 and underestimates the upwelling shortwave flux by –15.70 Wm–2 and downwelling longwave fluxes by –12.58 Wm–2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0°C). The biases in downwelling shortwave and longwave fluxes suggest that the atmosphere represented in CERES is too optically thin. The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. Additionally, the results show that the spectral surface albedo used in SYN1deg overestimates albedo in visible and mid-infrared bands. A series of radiative transfer model perturbation experiments are performed to quantify the factors contributing to the differences. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm–2) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm–2). In addition, the differences between perturbation experiments using hourly and monthly MOSAiC surface albedo suggest that approximately 25% of the sea ice surface albedo variability is explained by factors not correlated with daily sea ice concentration variability. Biases in net shortwave and longwave flux can be reduced to less than half by adjusting both albedo and cloud inputs toward observed values. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.

Melt ponds on sea ice play an important role in the Arctic climate system. Their presence alters the partitioning of solar radiation: decreasing reflection, increasing absorption and transmission to the ice and ocean, and enhancing melt. The spatiotemporal properties of melt ponds thus modify ice albedo feedbacks and the mass balance of Arctic sea ice. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition presented a valuable opportunity to investigate the seasonal evolution of melt ponds through a rich array of atmosphere-ice-ocean measurements across spatial and temporal scales. In this study, we characterize the seasonal behavior and variability in the snow, surface scattering layer, and melt ponds from spring melt to autumn freeze-up using in situ surveys and auxiliary observations. We compare the results to satellite retrievals and output from two models: the Community Earth System Model (CESM2) and the Marginal Ice Zone Modeling and Assimilation System (MIZMAS). During the melt season, the maximum pond coverage and depth were 21% and 22 ± 13 cm, respectively, with distribution and depth corresponding to surface roughness and ice thickness. Compared to observations, both models overestimate melt pond coverage in summer, with maximum values of approximately 41% (MIZMAS) and 51% (CESM2). This overestimation has important implications for accurately simulating albedo feedbacks. During the observed freeze-up, weather events, including rain on snow, caused high-frequency variability in snow depth, while pond coverage and depth remained relatively constant until continuous freezing ensued. Both models accurately simulate the abrupt cessation of melt ponds during freeze-up, but the dates of freeze-up differ. MIZMAS accurately simulates the observed date of freeze-up, while CESM2 simulates freeze-up one-to-two weeks earlier. This work demonstrates areas that warrant future observation-model synthesis for improving the representation of sea-ice processes and properties, which can aid accurate simulations of albedo feedbacks in a warming climate.

The magnitude, spectral composition, and variability of the Arctic sea ice surface albedo are key to understanding and numerically simulating Earth’s shortwave energy budget. Spectral and broadband albedos of Arctic sea ice were spatially and temporally sampled by on-ice observers along individual survey lines throughout the sunlit season (April–September, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The seasonal evolution of albedo for the MOSAiC year was constructed from spatially averaged broadband albedo values for each line. Specific locations were identified as representative of individual ice surface types, including accumulated dry snow, melting snow, bare and melting ice, melting and refreezing ponded ice, and sediment-laden ice. The area-averaged seasonal progression of total albedo recorded during MOSAiC showed remarkable similarity to that recorded 22 years prior on multiyear sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) expedition. In accord with these and other previous field efforts, the spectral albedo of relatively thick, snow-free, melting sea ice shows invariance across location, decade, and ice type. In particular, the albedo of snow-free, melting seasonal ice was indistinguishable from that of snow-free, melting second-year ice, suggesting that the highly scattering surface layer that forms on sea ice during the summer is robust and stabilizing. In contrast, the albedo of ponded ice was observed to be highly variable at visible wavelengths. Notable temporal changes in albedo were documented during melt and freeze onset, formation and deepening of melt ponds, and during melt evolution of sediment-laden ice. While model simulations show considerable agreement with the observed seasonal albedo progression, disparities suggest the need to improve how the albedo of both ponded ice and thin, melting ice are simulated.