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  1. Abstract. Free-drift estimates of sea ice motion are necessary to produce a seamless observational record combining buoy and satellite-derived sea ice motionvectors. We develop a new parameterization for the free drift of sea ice based on wind forcing, wind turning angle, sea ice state variables(thickness and concentration), and estimates of the ocean currents. Given the fact that the spatial distribution of the wind–ice–ocean transfercoefficient has a similar structure to that of the spatial distribution of sea ice thickness, we take the standard free-drift equation and introducea wind–ice–ocean transfer coefficient that scales linearly with ice thickness. Results show a mean bias error of −0.5 cm s−1(low-speed bias) and a root-mean-square error of 5.1 cm s−1, considering daily buoy drift data as truth. This represents a 35 %reduction of the error on drift speed compared to the free-drift estimates used in the Polar Pathfinder dataset (Tschudi et al., 2019b). Thethickness-dependent transfer coefficient provides an improved seasonality and long-term trend of the sea ice drift speed, with a minimum (maximum)drift speed in May (October), compared to July (January) for the constant transfer coefficient parameterizations which simply follow the peak inmean surface wind stresses. Over the 1979–2019 period, the trend in sea ice drift in this new model is +0.45 cm s−1 permore »decadecompared with +0.39 cm s−1 per decade from the buoy observations, whereas there is essentially no trend in a free-driftparameterization with a constant transfer coefficient (−0.09 cm s−1 per decade) or the Polar Pathfinder free-drift input data(−0.01 cm s−1 per decade). The optimal wind turning angle obtained from a least-squares fitting is 25∘, resulting in a meanerror and a root-mean-square error of +3 and 42∘ on the direction of the drift, respectively. The ocean current estimates obtained from theminimization procedure resolve key large-scale features such as the Beaufort Gyre and Transpolar Drift Stream and are in good agreement with oceanstate estimates from the ECCO, GLORYS, and PIOMAS ice–ocean reanalyses, as well as geostrophic currents from dynamical ocean topography, with aroot-mean-square difference of 2.4, 2.9, 2.6, and 3.8 cm s−1, respectively. Finally, a repeat of the analysis on two sub-sections of thetime series (pre- and post-2000) clearly shows the acceleration of the Beaufort Gyre (particularly along the Alaskan coastline) and an expansion ofthe gyre in the post-2000s, concurrent with a thinning of the sea ice cover and the observed acceleration of the ice drift speed and oceancurrents. This new dataset is publicly available for complementing merged observation-based sea ice drift datasets that include satellite and buoydrift records.« less
    Free, publicly-accessible full text available January 1, 2023
  2. Free, publicly-accessible full text available October 25, 2023
  3. Abstract. Tritium and helium isotope data provide key information on oceancirculation, ventilation, and mixing, as well as the rates of biogeochemicalprocesses and deep-ocean hydrothermal processes. We present here globaloceanic datasets of tritium and helium isotope measurements made by numerousresearchers and laboratories over a period exceeding 60 years. The dataset'sDOI is https://doi.org/10.25921/c1sn-9631, and the data are available athttps://www.nodc.noaa.gov/ocads/data/0176626.xml (last access: 15 March2019) or alternatelyhttp://odv.awi.de/data/ocean/jenkins-tritium-helium-data-compilation/(last access: 13 March 2019) and includes approximately 60 000 valid tritiummeasurements, 63 000 valid helium isotope determinations, 57 000 dissolvedhelium concentrations, and 34 000 dissolved neon concentrations. Somequality control has been applied in that questionable data have been flaggedand clearly compromised data excluded entirely. Appropriate metadata havebeen included, including geographic location, date, and sample depth. Whenavailable, we include water temperature, salinity, and dissolved oxygen. Dataquality flags and data originator information (including methodology) arealso included. This paper provides an introduction to the dataset along withsome discussion of its broader qualities and graphics.
  4. Seasonal predictability of the minimum sea ice extent (SIE) in the Laptev Sea is investigated using winter coastal divergence as a predictor. From February to May, the new ice forming in wind-driven coastal polynyas grows to a thickness approximately equal to the climatological thickness loss due to summer thermodynamic processes. Estimating the area of sea ice that is preconditioned to melt enables seasonal predictability of the minimum SIE. Wintertime ice motion is quantified by seeding passive tracers along the coastlines and advecting them with the Lagrangian Ice Tracking System (LITS) forced with sea ice drifts from the Polar Pathfinder dataset for years 1992–2016. LITS-derived landfast ice estimates are comparable to those of the Russian Arctic and Antarctic Research Institute ice charts. Time series of the minimum SIE and coastal divergence show trends of −24.2% and +31.3% per decade, respectively. Statistically significant correlation ( r = −0.63) between anomalies of coastal divergence and the following September SIE occurs for coastal divergence integrated from February to the beginning of May. Using the coastal divergence anomaly to predict the minimum SIE departure from the trend improves the explained variance by 21% compared to hindcasts based on persistence of the linear trend. Coastal divergencemore »anomalies correlate with the winter mean Arctic Oscillation index ( r = 0.69). LITS-derived areas of coastal divergence tend to underestimate the total area covered by thin ice in the CryoSat-2/SMOS (Soil Moisture and Ocean Salinity) thickness dataset, as suggested by a thermodynamic sea ice growth model.

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  5. 
Research consistently shows that children who have opportunities to actively investigate natural settings and engage in problem-based learning greatly benefit from the experiences. They gain skills, interests, knowledge, aspirations, and motivation to learn more. But how can we provide these rich opportunities in densely populated urban areas where resources and access to natural areas are limited? This project will develop and test a model of curriculum and community enterprise to address that issue within the nation's largest urban school system. Middle school students will study New York harbor and the extensive watershed that empties into it, and they will conduct field research in support of restoring native oyster habitats. The project builds on the existing Billion Oyster Project, and will be implemented by a broad partnership of institutions and community resources, including Pace University, the New York City Department of Education, the Columbia University Lamont-Doherty Earth Observatory, the New York Academy of Sciences, the New York Harbor Foundation, the New York Aquarium, and others.
The project focuses on an important concept in the geological, environmental, and biological sciences that typically receives inadequate attention in schools: watersheds. This project builds on and extends the Billion Oyster Project of the New York Harbormore »School. The project model includes five interrelated components: A teacher education curriculum, a student learning curriculum, a digital platform for project resources, an aquarium exhibit, and an afterschool STEM mentoring program. It targets middle-school students in low-income neighborhoods with high populations of English language learners and students from groups underrepresented in STEM fields and education pathways. The project will directly involve over forty schools, eighty teachers, and 8,640 students over a period of three years. A quasi-experimental, mixed-methods research plan will be used to assess the individual and collective effectiveness of the five project components. Regression analyses will be used to identify effective program aspects and assess the individual effectiveness of participation in various combinations of the five program components. Social network mapping will be used to further asses the overall "curriculum plus community" model.« less