Sources of seasonal sea-ice bias for CMIP6 models in the Hudson Bay Complex

The seasonal ice-free period in the Hudson Bay Complex (HBC) has grown longer in recent decades in response to warming, both from progressively earlier sea-ice retreat in summer and later sea-ice advance in fall. Such changes disrupt the HBC ecosystem and ice-based human activities. In this study, we compare 102 simulations from 37 models participating in phase 6 of the Coupled Model Intercomparison Project to the satellite passive microwave record and atmospheric reanalyses. We show that, throughout the HBC, models simulate an ice-free period that averages 30 d longer than in satellite observations. This occurs because seasonal sea-ice advance is unrealistically late and seasonal sea-ice retreat is unrealistically early. We find that much of the ice-season bias can be linked to a warm bias in the atmosphere that is associated with a southerly wind bias, especially in summer. Many models also exhibit an easterly wind bias during winter and spring, which reduces sea-ice convergence on the east side of Hudson Bay and impacts the spatial patterns of summer sea-ice retreat. These results suggest that, for many models, more realistic simulation of atmospheric circulation would improve their simulation of HBC sea ice

Summer extreme cyclone impacts on arctic sea ice

In this study the impact of extreme cyclones on Arctic sea ice in summer is investigated. Examined in particular are relative thermodynamic and dynamic contributions to sea ice volume budgets in the vicinity of Arctic summer cyclones in 2012 and 2016. Results from this investigation illustrate that sea ice loss in the vicinity of the cyclone trajectories during each year was associated with different dominant processes: thermodynamic processes (melting) in the Pacific sector of the Arctic in 2012, and both thermodynamic and dynamic processes in the Pacific sector of the Arctic in 2016. Comparison of both years further suggests that the Arctic minimum sea ice extent is influenced by not only the strength of the cyclone, but also by the timing and location relative to the sea ice edge. Located near the sea ice edge in early August in 2012, and over the central Arctic later in August in 2016, extreme cyclones contributed to comparable sea ice area (SIA) loss, yet enhanced sea ice volume loss in 2012 relative to 2016. Central to a characterization of extreme cyclone impacts on Arctic sea ice from the perspective of thermodynamic and dynamic processes, we present an index describing relative thermodynamic and dynamic contributions to sea ice volume changes. This index helps to quantify and improve our understanding of initial sea ice state and dynamical responses to cyclones in a rapidly warming Arctic, with implications for seasonal ice forecasting, marine navigation, coastal community infrastructure, and designation of protected and ecologically sensitive marine zones

Record winter winds in 2020/21 drove exceptional Arctic sea ice transport

AbstractThe volume of Arctic sea ice is in decline but exhibits high interannual variability, which is driven primarily by atmospheric circulation. Through analysis of satellite-derived ice products and atmospheric reanalysis data, we show that winter 2020/21 was characterised by anomalously high sea-level pressure over the central Arctic Ocean, which resulted in unprecedented anticyclonic winds over the sea ice. This atmospheric circulation pattern drove older sea ice from the central Arctic Ocean into the lower-latitude Beaufort Sea, where it is more vulnerable to melting in the coming warm season. We suggest that this unusual atmospheric circulation may potentially lead to unusually high summer losses of the Arctic’s remaining store of old ice.</jats:p

Diel turbidity cycles in a headwater stream: evidence of nocturnal bioturbation?

Purpose: A small number of recent studies have linked daily cycles in stream turbidity to nocturnal bioturbation by aquatic fauna, principally crayfish, and demonstrated this process can significantly impact upon water quality under baseflow conditions. Adding to this limited body of research, we use high-resolution water quality monitoring data to investigate evidence of diel turbidity cycles in a lowland, headwater stream with a known signal crayfish (Pacifastacus leniusculus) population and explore a range of potential causal mechanisms. Materials and methods: Automatic bankside monitoring stations measured turbidity and other water quality parameters at 30-min resolution at three locations on the River Blackwater, Norfolk, UK during 2013. Specifically, we focused on two 20-day periods of baseflow conditions during January and April 2013 which displayed turbidity trends typical of winter and spring seasons, respectively. The turbidity time-series, which were smoothed with 6.5 hour Savitzky-Golay filters to highlight diel trends, were correlated against temperature, stage, dissolved oxygen and pH to assess the importance of abiotic influences on turbidity. Turbidity was also calibrated against suspended particulate matter (SPM) over a wide range of values via linear regression. Results and discussion: Pronounced diel turbidity cycles were found at two of the three sites under baseflow conditions during April. Spring night-time turbidity values consistently peaked between 21:00 and 04:00 with values increasing by ~10 nephelometric turbidity units (NTU) compared with the lowest recorded daytime values which occurred between 10:00 and 14:00. This translated into statistically significant increases in median midnight SPM concentration of up to 76% compared with midday, with night-time (18:00 – 05:30) SPM loads also up to 30% higher than that recorded during the daytime (06:00 – 17:30). Relating turbidity to other water quality parameters exhibiting diel cycles revealed there to be neither any correlation that might indicate a causal link, nor any obvious mechanistic connections to explain the temporal turbidity trends. Diel turbidity cycles were less prominent at all sites during the winter. Conclusions: Considering the seasonality and timing of elevated turbidity, visual observations of crayfish activity, and an absence of mechanistic connections with other water quality parameters, the results presented here are consistent with the hypothesis that nocturnal bioturbation is responsible for generating diel turbidity cycles under baseflow conditions in headwater streams. However, further research in a variety of fluvial environments is required to better assess the spatial extent, importance and causal mechanisms of this phenomenon

Cannibalism, cell survival, and endocrine resistance in breast cancer

Breast cancer cells often respond to an endocrine therapy by altering expression of specific estrogen-responsive genes and inducing autophagy, a cannibalistic lysosomal pathway. Autophagy eliminates damaged or other organelles, allowing the recovery of the energy stored in their macromolecules to attempt restoration of metabolic homeostasis. Induction of autophagy can result from activation of the unfolded protein response following metabolic stress, the final cell fate often being determined by the extent and duration of autophagy. A study by Gonzalez-Malerva and colleagues builds upon this extensive knowledge, adding HSPB8 to the list of altered genes associated with endocrine resistance in breast cancer and describing the ability of HSPB8 to regulate autophagy and confer tamoxifen resistance

Proteomics: in pursuit of effective traumatic brain injury therapeutics

Effective traumatic brain injury (TBI) therapeutics remain stubbornly elusive. Efforts in the field have been challenged by the heterogeneity of clinical TBI, with greater complexity among underlying molecular phenotypes than initially conceived. Future research must confront the multitude of factors comprising this heterogeneity, representing a big data challenge befitting the coming informatics age. Proteomics is poised to serve a central role in prescriptive therapeutic development, as it offers an efficient endpoint within which to assess post-TBI biochemistry. We examine rationale for multifactor TBI proteomic studies and the particular importance of temporal profiling in defining biochemical sequences and guiding therapeutic development. Lastly, we offer perspective on repurposing biofluid proteomics to develop theragnostic assays with which to prescribe, monitor and assess pharmaceutics for improved translation and outcome for TBI patients

Interstellar Matter and the Boundary Conditions of the Heliosphere

The interstellar cloud surrounding the solar system regulates the galactic environment of the Sun, and determines the boundary conditions of the heliosphere. Both the Sun and interstellar clouds move through space, so these boundary conditions change with time. Data and theoretical models now support densities in the cloud surrounding the solar system of n(HI)=0.22+/-0.06 cm^-3, and n(e-)~0.1 cm-3, with larger values allowed for n(HI) by radiative transfer considerations. Ulysses and Extreme Ultraviolet Explorer satellite HeI data yield a cloud temperature of 6,400 K. Nearby interstellar gas appears to be structured and inhomogeneous. The interstellar gas in the Local Fluff cloud complex exhibits elemental abundance patterns in which refractory elements are enhanced over the depleted abundances found in cold disk gas. Within a few parsecs of the Sun, inconclusive evidence for factors of 2--5 variation in MgII and FeII gas phase abundances is found, providing evidence for variable grain destruction. Observations of the hydrogen pile-up at the nose of the heliosphere are consistent with a barely subsonic motion of the heliosphere with respect to the surrounding interstellar cloud. Uncertainties on the velocity vector of the cloud that surrounds the solar system indicate that it is uncertain as to whether the Sun and alpha Cen are or are not immersed in the same interstellar cloud.Comment: 24 pages 3 figure

The SNAPSHOT study protocol : SNAcking, Physical activity, Self-regulation, and Heart rate Over Time

Peer reviewedPublisher PD

A practitioner's guide to Bayesian estimation of discrete choice dynamic programming models

This paper provides a step-by-step guide to estimating infinite horizon discrete choice dynamic programming (DDP) models using a new Bayesian estimation algorithm (Imai et al., Econometrica 77:1865–1899, 2009a) (IJC). In the conventional nested fixed point algorithm, most of the information obtained in the past iterations remains unused in the current iteration. In contrast, the IJC algorithm extensively uses the computational results obtained from the past iterations to help solve the DDP model at the current iterated parameter values. Consequently, it has the potential to significantly alleviate the computational burden of estimating DDP models. To illustrate this new estimation method, we use a simple dynamic store choice model where stores offer “frequent-buyer” type rewards programs. Our Monte Carlo results demonstrate that the IJC method is able to recover the true parameter values of this model quite precisely. We also show that the IJC method could reduce the estimation time significantly when estimating DDP models with unobserved heterogeneity, especially when the discount factor is close to 1