Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison

Abstract This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance.</jats:p

Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system

Two CryoSat-2 sea ice thickness products derived with independent algorithms are used to initialize a coupled ice-ocean modeling system in which a series of reanalysis studies are performed for the period of March 15, 2014–September 30, 2015. Comparisons against moored upward looking sonar, drifting ice mass balance buoy, and NASA Operation IceBridge ice thickness data show that the modeling system exhibits greatly reduced bias using the satellite-derived ice thickness data versus the operational model run without these data. The model initialized with CryoSat-2 ice thickness exhibits skill in simulating ice thickness from the initial period to up to 6 months. We find that the largest improvements in ice thickness occur over multi-year ice. Based on the data periods examined here, we find that for the 18-month study period, when compared with upward looking sonar measurements, the CryoSat-2 reanalyses show significant improvement in bias (0.47–0.75) and RMSE (0.89–1.04) versus the control run without these data (1.44 and 1.60, respectively). An ice drift comparison reveals little change in ice velocity statistics for the Pan Arctic region; however some improvement is seen during the summer/autumn months in 2014 for the Bering/Beaufort/Chukchi and Greenland/Norwegian Seas. These promising results suggest that such a technique should be used to reinitialize operational sea ice modeling systems

High-throughput glycomic analyses reveal unique oligosaccharide profiles of canine and feline milk samples.

Oligosaccharides are important components of milk, serving as substrates for the intestinal microbiota, acting as antimicrobials that prevent pathogen colonization, and supporting the developing gastrointestinal immune system of neonates. Nutrient composition of canine and feline milk samples has been described previously, but little is known about the oligosaccharide content. Therefore, the objective of this study was to characterize canine and feline milk samples using a high-throughput glycomics approach. 23 dogs (9 Labrador retriever and 14 Labrador retriever x golden retriever crossbreed) and 6 domestic shorthair cats were recruited to the study. Milk samples were collected by manual expression at time points after parturition. Samples were collected across 2 phases per species, differentiated by maternal diet. Following extraction, oligosaccharide content was determined by liquid chromatography-mass spectrometry (LC-MS). In canine milk samples, 3 structures accounted for over 90% of all oligosaccharides detected across two diet groups. These were 3'-sialyllactose, 6'-sialyllactose, and 2'-fucosyllactose. In feline samples, a more diverse range of oligosaccharides was detected, with up to 16 structures present at relative abundance &gt;1% of the total. Difucosyllactose-N-hexaose b, 3'-sialyllactose and lacto-N-neohexaose were all detected at abundances &gt;10% in feline milk samples. Statistically significant differences (p&lt;0.05) in oligosaccharide abundances were observed between collection time points and between diet groups within species. These data explore the oligosaccharide content of canine and feline maternal milk, representing an opportunity to generate a fundamental understanding of the nutritional needs of new-born puppies and kittens

Neural correlates of multisensory integration of ecologically valid audiovisual events

Oligosaccharides are important components of milk, serving as substrates for the intestinal microbiota, acting as antimicrobials that prevent pathogen colonization, and supporting the developing gastrointestinal immune system of neonates. Nutrient composition of canine and feline milk samples has been described previously, but little is known about the oligosaccharide content. Therefore, the objective of this study was to characterize canine and feline milk samples using a high-throughput glycomics approach. 23 dogs (9 Labrador retriever and 14 Labrador retriever x golden retriever crossbreed) and 6 domestic shorthair cats were recruited to the study. Milk samples were collected by manual expression at time points after parturition. Samples were collected across 2 phases per species, differentiated by maternal diet. Following extraction, oligosaccharide content was determined by liquid chromatography-mass spectrometry (LC-MS). In canine milk samples, 3 structures accounted for over 90% of all oligosaccharides detected across two diet groups. These were 3'-sialyllactose, 6'-sialyllactose, and 2'-fucosyllactose. In feline samples, a more diverse range of oligosaccharides was detected, with up to 16 structures present at relative abundance >1% of the total. Difucosyllactose-N-hexaose b, 3'-sialyllactose and lacto-N-neohexaose were all detected at abundances >10% in feline milk samples. Statistically significant differences (p<0.05) in oligosaccharide abundances were observed between collection time points and between diet groups within species. These data explore the oligosaccharide content of canine and feline maternal milk, representing an opportunity to generate a fundamental understanding of the nutritional needs of new-born puppies and kittens