scholarly journals Co-seismic eruption and intermittent turbulence of a subglacial discharge plume revealed by continuous subsurface observations in Greenland

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Evgeny A. Podolskiy ◽  
Naoya Kanna ◽  
Shin Sugiyama
Keyword(s):  
Coherent Structures ◽  
Time Series Data ◽  
Time Variable ◽  
The Arctic ◽  
Series Data ◽  
Intermittent Turbulence ◽  
Plume Dynamics ◽  
Key Driver ◽  
Subsurface Monitoring

AbstractIn the Arctic, subglacial discharge plumes have been recently recognised as a key driver of fjord-scale circulation. However, owing to the danger that accompanies prolonged observations at plumes, no time-series data are available. Here, we present results showing the chaotic and irregular dynamics of a plume revealed by continuous subsurface monitoring directly on the calving front of a Greenlandic glacier. We found intense fluctuations in the current and scalars (temperature and salinity), recognised shallow and deep tidal modulation and anomalies due to co-seismic drainage of an ice-dammed lake via the plume, and observed rapid and marked changes in stratification. Our analysis uncovers energy cascade intermittency with coherent structures, corresponding to upwelling pulses of warm water. Prior to our research, in situ evidence of time-variable plume dynamics was absent and limited to snapshots, therefore, our study and approach will enable researchers to transition from an episodic view of a plume to a continuously updated image.

scholarly journals Evaluation of Approaches to Depicting First Nations, Inupiat and Inuvialuit Environmental Information in GIS Format: Options for the Handling of Spatial Information in the Arctic Borderlands Ecological Knowledge Co-Op Database

2021 ◽  
Author(s):  
Jadah Elizabeth Folliott
Keyword(s):  
Climate Change ◽  
First Nations ◽  
Spatial Information ◽  
Time Series Data ◽  
Atmospheric Transport ◽  
Data Gathering ◽  
The Arctic ◽  
Series Data ◽  
Ecological Knowledge ◽  
Sea Levels

As the pace of climate change continues to accelerate in the North, traditional environmental knowledge systems are increasingly recognized by researchers, land use planners, government agencies, policy-makers and indigenous peoples as important contributors to environmental impact and climate change assessment and monitoring. Increasing temperatures, melting glaciers, reductions in the extent and thickness of sea ice, thawing permafrost and rising sea levels all provide strong evidence of increasing temperatures in the Arctic. This warming climate has the potential to change migration patterns, the diversity, range, and distribution of animal and plant species, and increase contaminants in the food chain from atmospheric transport of organic pollutants and mercury, thus raising concerns regarding the safety of traditional foods. Since 1996, the Arctic Borderlands Ecological Knowledge Co-op (ABEKC) has systematically recorded First Nations, Inupiat and Inuvialuit observations of landscape changes in the lower Mackenzie, Northern Yukon and eastern Alaska. Time-series data (regarding berry, caribou, fish, weather, ice and snow, plants, and other animal observations) have been obtained through annual interviews with the most active fishers, harvesters and hunters in the communities of Aklavik, Arctic Village, Fort McPherson, Kaktovik, Old Crow, and more recently, in Inuvik, Tsiigehtchic, and Tuktoyaktuk. An evaluation of the spatial utility of the ABEKC database and the many steps that are involved in the collection, storage, and organization of the Co-op’s data was documented. The ABEKC database provided an excellent opportunity to explore the problem of depicting complex qualitative information on northern landscape change in an intelligible GIS format. Initial attempts to develop the database in spatial format were critically evaluated and recommendations were provided in order to explore whether the data gathering and subsequent mapping process can be improved, whether more useful information can be obtained from the data, and to ensure the proper handling of the data in future years.

A data-driven stochastic parametrisation of intermittent turbulence in the stably stratified atmospheric boundary layer

2021 ◽  
Author(s):  
Vyacheslav Boyko ◽  
Sebastian Krumscheid ◽  
Nikki Vercauteren
Keyword(s):  
Boundary Layer ◽  
Time Series Data ◽  
Stable Stratification ◽  
Data Driven ◽  
Series Data ◽  
Similarity Function ◽  
Intermittent Turbulence ◽  
Stochastic Perturbations ◽  
Model Based Clustering ◽  
Flux Gradient

<p>We present results on the modelling of intermittent turbulence in the nocturnal boundary layer using a data-driven approach. In conditions of high stratification and weak wind, the bulk shear can be too weak to sustain continuous turbulence, and the sporadic submeso motions play an important role for the turbulence production. We show a way to stochastically parametrise the effect of the unresolved submeso scales and include it into a 1.5-order turbulence closure scheme. This is achieved by introducing a stochastic equation, which describes the evolution of the non-dimensional flux-gradient stability correction for momentum ($\phi_m$). The unperturbed equilibrium solution of the equation follows the functional form of the universal similarity function. The stochastic perturbations reflect the instantaneous excursions from its equilibrium state, and the distribution of values covers the scatter found in observations at high stability.</p><p>The non-stationary parameters of this equations are estimated from a time-series data of the FLOSS2 experiment using a model-based clustering approach. The clustering analysis of the parameters shows a scaling relationship with the local gradient Ri number, leading to a suggested closed-form model for the stochastic flux-gradient stability correction. The spatial correlation in height of the perturbations is included in the model as well. The resulting equation captures the transition of the stability correction across and beyond the critical Ri up to a value of 10. The out-of-sample prediction shows a valid transient dynamics into and within the regime of strongly-stable stratification.</p>

The Digital Earth Viewer: A new visualization approach for geospatial time series data

2021 ◽  
Author(s):  
Valentin Buck ◽  
Flemming Stäbler ◽  
Everardo Gonzalez ◽  
Jens Greinert
Keyword(s):  
Time Series Data ◽  
Environmental Data ◽  
Series Data ◽  
Multiple Sources ◽  
Data Types ◽  
New Approach ◽  
Digital Earth ◽  
Spatio Temporal ◽  
Diverse Data

<p>The study of the earth’s systems depends on a large amount of observations from homogeneous sources, which are usually scattered around time and space and are tightly intercorrelated to each other. The understanding of said systems depends on the ability to access diverse data types and contextualize them in a global setting suitable for their exploration. While the collection of environmental data has seen an enormous increase over the last couple of decades, the development of software solutions necessary to integrate observations across disciplines seems to be lagging behind. To deal with this issue, we developed the Digital Earth Viewer: a new program to access, combine, and display geospatial data from multiple sources over time.</p><p>Choosing a new approach, the software displays space in true 3D and treats time and time ranges as true dimensions. This allows users to navigate observations across spatio-temporal scales and combine data sources with each other as well as with meta-properties such as quality flags. In this way, the Digital Earth Viewer supports the generation of insight from data and the identification of observational gaps across compartments.</p><p>Developed as a hybrid application, it may be used both in-situ as a local installation to explore and contextualize new data, as well as in a hosted context to present curated data to a wider audience.</p><p>In this work, we present this software to the community, show its strengths and weaknesses, give insight into the development process and talk about extending and adapting the software to custom usecases.</p>

scholarly journals Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes

2020 ◽  
Vol 35 (3) ◽  
pp. 793-806
Author(s):  
William Gregory ◽  
Michel Tsamados ◽  
Julienne Stroeve ◽  
Peter Sollich
Keyword(s):  
Sea Ice ◽  
Time Series Data ◽  
Gaussian Process Regression ◽  
Forecast Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Series Data ◽  
Lead Times ◽  
Sea Ice Concentration ◽  
Sea Ice Extent

Abstract Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.

scholarly journals An evaluation of the performance of Sea-Bird Scientific's SeaFET™ autonomous pH sensor: considerations for the broader oceanographic community

Ocean Science ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 751-768 ◽  
Author(s):  
Cale A. Miller ◽  
Katie Pocock ◽  
Wiley Evans ◽  
Amanda L. Kelley
Keyword(s):  
Spectral Analysis ◽  
Time Series Data ◽  
Single Point ◽  
Ph Sensor ◽  
Calibration Method ◽  
Series Data ◽  
South Central ◽  
Autonomous Sensor ◽  
Robust Measurements

Abstract. The commercially available Sea-Bird SeaFET™ provides an accessible way for a broad community of researchers to study ocean acidification and obtain robust measurements of seawater pH via the use of an in situ autonomous sensor. There are pitfalls, however, that have been detailed in previous best practices for sensor care, deployment, and data handling. Here, we took advantage of two distinctly different coastal settings to evaluate the Sea-Bird SeaFET™ and examine the multitude of scenarios in which problems may arise confounding the accuracy of measured pH. High-resolution temporal measurements of pH were obtained during 3- to 5-month field deployments in three separate locations (two in south-central Alaska, USA, and one in British Columbia, Canada) spanning a broad range of nearshore temperature and salinity conditions. Both the internal and external electrodes onboard the SeaFET™ were evaluated against robust benchtop measurements for accuracy using the factory calibration, an in situ single-point calibration, or an in situ multi-point calibration. In addition, two sensors deployed in parallel in Kasitsna Bay, Alaska, USA, were compared for inter-sensor variability in order to quantify other factors contributing to the sensor's intrinsic inaccuracies. Based on our results, the multi-point calibration method provided the highest accuracy (< 0.025 difference in pH) of pH when compared against benchtop measurements. Spectral analysis of time series data showed that during spring in Alaskan waters, a range of tidal frequencies dominated pH variability, while seasonal oceanographic conditions were the dominant driver in Canadian waters. Further, it is suggested that spectral analysis performed on initial deployments may be able to act as an a posteriori method to better identify appropriate calibration regimes. Based on this evaluation, we provide a comprehensive assessment of the potential sources of uncertainty associated with accuracy and precision of the SeaFET™ electrodes.

Random Walks through Climate Networks: Sea Ice Prediction with Bayesian Inference

2020 ◽  
Author(s):  
William Gregory ◽  
Michel Tsamados ◽  
Julienne Stroeve ◽  
Peter Sollich
Keyword(s):  
Bayesian Inference ◽  
Sea Ice ◽  
Time Series Data ◽  
Gaussian Process Regression ◽  
Forecast Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Series Data ◽  
Sea Ice Concentration ◽  
Spring Predictability Barrier

&lt;p&gt;&lt;span&gt;Spatial predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. However, with sea ice variability likely to increase under continued anthropogenic warming, increasingly complex tools are required in order to make accurate forecasts. In this study, predictions of both Arctic and Antarctic summer sea ice extents are made using a complex network statistical approach. This method exploits statistical relationships within geo-spatial time series data in order to construct regions of spatio-temporal homogeneity -- nodes, and subsequently derive teleconnection links between them. The nodes and links of the networks here are generated from monthly sea ice concentration fields in June(November), July(December) and August(January) for Arctic(Antarctic) forecasts, hence lead times extend from 1 to 3 months. Network information is then utilised within a linear Gaussian Process Regression forecast model; a Bayesian inference technique. Network teleconnection weights are used to generate priors over functions in the form of a random walk covariance kernel; the hyperparameters of which are determined by the empirical Bayesian approach of type-II maximum likelihood. We also show predictions of all other months in order to ascertain the presence of a spring predictability barrier in observational data, for both hemispheres.&lt;/span&gt;&lt;/p&gt;

scholarly journals Biogeochemical variations at the Porcupine Abyssal Plain Sustained Observatory (PAP-SO) in the northeast Atlantic Ocean, from weekly to inter-annual time scales

2014 ◽  
Vol 11 (8) ◽  
pp. 12415-12439
Author(s):  
S. E. Hartman ◽  
Z.-P. Jiang ◽  
D. Turk ◽  
R. S. Lampitt ◽  
H. Frigstad ◽  
...  
Keyword(s):  
Wind Speed ◽  
Time Series Data ◽  
Mixed Layer Depth ◽  
Co2 Flux ◽  
Abyssal Plain ◽  
Carbon Dioxide Partial Pressure ◽  
Series Data ◽  
Concentration Data ◽  
Northeast Atlantic

Abstract. We present high-resolution autonomous measurements of carbon dioxide partial pressure p(CO2) taken in situ at the Porcupine Abyssal Plain sustained observatory (PAP-SO) in the Northeast Atlantic (49° N, 16.5° W; water depth of 4850 m) for the period 2010 to 2012. Measurements of p(CO2) made at 30 m depth on a sensor frame are compared with other autonomous biogeochemical measurements at that depth (including chlorophyll a-fluorescence and nitrate concentration data) to analyse weekly to seasonal controls on p(CO2) flux in the inter-gyre region of the North Atlantic. Comparisons are also made with in situ regional time-series data from a ship of opportunity and mixed layer depth (MLD) measurements from profiling Argo floats. There is a persistent under saturation of CO2 in surface waters throughout the year which gives rise to a perennial CO2 sink. Comparison with an earlier dataset collected at the site (2003 to 2005) confirms seasonal and inter-annual changes in surface seawater chemistry. There is year-to-year variability in the timing of stratification and deep winter mixing. The 2010 to 2012 period shows an overall increase in p(CO2) values when compared to the 2003–2005 period. This is despite similar surface temperature, wind speed and MLD measurements between the two periods of time. Future work should incorporate daily CO2 flux measurements made using CO2 sensors at 1 m depth and the in situ wind speed data now available from the UK Met Office Buoy.

scholarly journals The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

2020 ◽  
Vol 12 (12) ◽  
pp. 1979
Author(s):  
Dandan Xu ◽  
Deshuai An ◽  
Xulin Guo
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Series Data ◽  
Area Index ◽  
Plant Area Index ◽  
The Impact

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000.

scholarly journals Contextualizing time-series data: quantification of short-term regional variability in the San Pedro Channel using high-resolution in situ glider data

2018 ◽  
Vol 15 (20) ◽  
pp. 6151-6165 ◽  
Author(s):  
Elizabeth N. Teel ◽  
Xiao Liu ◽  
Bridget N. Seegers ◽  
Matthew A. Ragan ◽  
William Z. Haskell ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Water Column ◽  
Time Series Data ◽  
Series Data ◽  
Regional Variability ◽  
Modes Of Variability ◽  
Subsurface Chlorophyll Maximum ◽  
San Pedro

Abstract. Oceanic time series have been instrumental in providing an understanding of biological, physical, and chemical dynamics in the oceans and how these processes change over time. However, the extrapolation of these results to larger oceanographic regions requires an understanding and characterization of local versus regional drivers of variability. Here we use high-frequency spatial and temporal glider data to quantify variability at the coastal San Pedro Ocean Time-series (SPOT) site in the San Pedro Channel (SPC) and provide insight into the underlying oceanographic dynamics for the site. The dataset could be described by a combination of four water column profile types that typified active upwelling, a surface bloom, warm-stratified low-nutrient conditions, and a subsurface chlorophyll maximum. On weekly timescales, the SPOT station was on average representative of 64 % of profiles taken within the SPC. In general, shifts in water column profile characteristics at SPOT were also observed across the entire channel. On average, waters across the SPC were most similar to offshore profiles, suggesting that SPOT time series data would be more impacted by regional changes in circulation than local coastal events. These results indicate that high-resolution in situ glider deployments can be used to quantify major modes of variability and provide context for interpreting time series data, allowing for broader application of these datasets and greater integration into modeling efforts.

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