scholarly journals Sea-ice thickness in the coastal northeastern Chukchi Sea from moored ice-profiling sonar

2017 ◽  
Vol 63 (241) ◽  
pp. 888-898 ◽  
Author(s):  
YASUSHI FUKAMACHI ◽  
DAISUKE SIMIZU ◽  
KAY I. OHSHIMA ◽  
HAJO EICKEN ◽  
ANDREW R. MAHONEY ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Heat Loss ◽  
Time Series Data ◽  
Chukchi Sea ◽  
Spatial Scales ◽  
Heat Losses ◽  
Series Data ◽  
Ice Thickness ◽  
Coastal Polynya

ABSTRACTTime series ice-draft data were obtained from moored ice-profiling sonar (IPS), in the coastal northeastern Chukchi Sea during 2009/10. Time series data show seasonal growth of sea-ice draft, occasionally interrupted by coastal polynya. The sea-ice draft distribution indicates a slightly lower abundance of thick, deformed ice compared with the eastern Beaufort Sea. In January, a rapid increase in the abundance of thick ice coincided with a period of minimal drift indicating compaction again the coast and dynamical thickening. The overall mean draft and corresponding derived thickness are 1.27 and 1.38 m, respectively. The evolution of modal ice thickness observed can be explained mostly by thermodynamic growth. The derived ice thicknesses are used to estimate heat losses based on ERA-interim data. Heat losses from the raw, 1 s IPS data are ~50 and 100% greater than those calculated using IPS data averaged over spatial scales of ~20 and 100 km, respectively. This finding demonstrates the importance of subgrid-scale ice-thickness distribution for heat-loss calculation. The heat-loss estimate based on thin ice data derived from AMSR-E data corresponds well with that from the 1 s observed ice-thickness data, validating heat-loss estimates from the AMSR-E thin ice-thickness algorithm.

Growth rings across the Tree of Life: demographic insights from biogenic time series data

2021 ◽  
pp. 77-96
Author(s):  
Margaret E. K. Evans ◽  
Bryan A. Black ◽  
Donald A. Falk ◽  
Courtney L. Giebink ◽  
Emily L. Schultz
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Spatial Scales ◽  
Ocean Dynamics ◽  
Individual Growth ◽  
Series Data ◽  
Rate Variation ◽  
Sampling Effort ◽  
Growth Rings ◽  
Demographic Processes

Biogenic time series data can be generated in a single sampling effort, offering an appealing alternative to the slow process of revisiting or recapturing individuals to measure demographic rates. Annual growth rings formed by trees and in the ear bones of fish (i.e. otoliths) are prime examples of such biogenic time series. They offer insight into not only the process of growth but also birth, death, movement, and evolution, sometimes at uniquely deep temporal and large spatial scales, well beyond 5–30 years of data collected in localised study areas. This chapter first reviews the fundamentals of how tree-ring and otolith time series data are developed and analysed (i.e. dendrochronology and sclerochronology), then surveys growth rings in other organisms, along with microstructural or microcompositional variation in growth rings, and other records of demographic processes. It highlights the answers to demographic questions revealed by these time series data, such as the influence of environmental (atmospheric or ocean) conditions, competition, and disturbances on demographic processes, and the genetic versus plastic basis of individual growth and traits that influence growth. Lastly, it considers how spatial networks of biogenic, annually resolved time series data can offer insights into the importance of macrosystem atmospheric and ocean dynamics on multispecies, trophic dynamics. The authors encourage demographers to integrate the complementary information contained in biogenic time series data into population models to better understand the drivers of vital rate variation and predict the impacts of global change.

scholarly journals Sea-ice thickness in the Weddell Sea, Antarctica: a comparison of model and upward-looking sonar data

2007 ◽  
Vol 46 ◽  
pp. 419-427 ◽  
Author(s):  
Angelika H.H. Renner ◽  
Victoria Lytle
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Time Series Data ◽  
Global Climate Models ◽  
Weddell Sea ◽  
Salt Flux ◽  
Series Data ◽  
Ice Thickness ◽  
Freshwater Flux ◽  
Sea Ice Thickness

AbstractSea-ice thickness is a key parameter for estimates of salt fluxes to the ocean and the contribution to global thermohaline circulation. Observations of sea-ice thickness in the Southern Ocean are sparse and difficult to collect. An exception to this data gap is time-series data from upward-looking sonars (ULS) which sample the drifting sea ice continuously. In this study we use ULS data from ten different locations over periods ranging from 9 to 25 months to compare with model data. Although these data are limited in space and time, they provide a qualitative indication of the ability of global climate models (GCMs) to adequately represent Southern Ocean sea ice. We compare the ULS data to output from four different GCMs (BCCR-BCM2.0, ECHAM5/MPI-OM, UKMO-HadCM3 and NCAR CCSM3) which were used for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. They simulate the ice thickness reasonably well, but in most cases average model ice thickness is less than thicknesses derived from ULS data. The seasonal cycle produced by the models correlates well with the ULS except for locations near Maud Rise, where in summer the ULS find a low concentration of thick ice floes. This overly thin ice will have implications for both the salt flux to the central Weddell Sea during the growth season and the freshwater flux during the melt season. Using satellite-derived ice-drift data to calculate transports in the Weddell Sea, we find that the underestimation of ice thickness results in underestimated salt fluxes.

scholarly journals A 30 year monthly 5 km gridded surface elevation time series for the Greenland Ice Sheet from multiple satellite radar altimeters

2021 ◽  
Author(s):  
Baojun Zhang ◽  
Zemin Wang ◽  
Jiachun An ◽  
Tingting Liu ◽  
Hong Geng
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Data ◽  
Spatial Scales ◽  
European Space Agency ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Surface Elevation ◽  
Series Data ◽  
Satellite Radar

Abstract. A long-term time series of ice sheet surface elevation change (SEC) is important for study of ice sheet variation and its response to climate change. In this study, we used an updated plane-fitting least-squares regression strategy to generate a 30 year surface elevation time series for the Greenland Ice Sheet (GrIS) at monthly temporal resolution and 5 × 5 km grid spatial resolution using ERS‐1, ERS‐2, Envisat, and CryoSat‐2 satellite radar altimeter observations obtained between August 1991 and December 2020. The accuracy and reliability of the time series are effectively guaranteed by application of sophisticated corrections for intermission bias and interpolation based on empirical orthogonal function reconstruction. Validation using both airborne laser altimeter observations and the European Space Agency GrIS Climate Change Initiative (CCI) product indicated that our merged surface elevation time series is reliable. The accuracy and dispersion of errors of SECs of our results were 19.3 % and 8.9 % higher, respectively, than those of CCI SECs, and even 30.9 % and 19.0 % higher, respectively, in periods from 2006–2010 to 2010–2014. Further analysis showed that our merged time series could provide detailed insight into GrIS SEC on multiple temporal (up to 30 years) and spatial scales, thereby providing opportunity to explore potential associations between ice sheet change and climatic forcing. The merged surface elevation time series data are available at http://dx.doi.org/10.11888/Glacio.tpdc.271658 (Zhang et al., 2021).

scholarly journals A hierarchical framework for segmenting movement paths

2019 ◽  
Author(s):  
Wayne M. Getz
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Animal Movement ◽  
Spatial Scales ◽  
Process Models ◽  
Series Data ◽  
Temporal Scales ◽  
Environmental Covariates ◽  
Spatio Temporal ◽  
State And Local

AbstractComparative applications of animal movement path analyses are hampered by the lack of a comprehensive framework for linking structures and processes conceived at different spatio-temporal scales. Although many analyses begin by generating step-length (SL) and turning-angle (TA) distributions from relocation time-series data—some of which are linked to ecological, landscape, and environmental covariates—the frequency at which these data are collected may vary from sub-seconds to several hours, or longer. The kinds of questions that may be asked of these data, however, are very much scale-dependent. It thus behooves us to clarify how the scale at which SL and TA data are collected and relate to one another, as well as the kinds of ecological questions that can be asked. Difficulties arise because the information contained in SL and TA time series is not semantically aligned with the physiological, ecological, and sociological factors that influence the structure of movement paths. I address these difficulties by classifying movement types at salient temporal scales using two different kinds of vocabularies. The first is the language derived from behavioral and ecological concepts. The second is the language derived from mathematically formulated stochastic walks. The primary tools for analyzing these walks are fitting time-series and stochastic-process models to SL and TA statistics (means, variances, correlations, individual-state and local environmental covariates), while paying attention to movement patterns that emerge at various spatial scales. The purpose of this paper is to lay out a more coherent, hierarchical, scale-dependent, appropriate-complexity framework for conceptualizing path segments at different spatio-temporal scales and propose a method for extracting a simulation model, referred to as M3, from these data when at a relatively high frequencies (ideally minute-by-minute). Additionally, this framework is designed to bridge biological and mathematical movement ecology concepts; thereby stimulating the development of conceptually-rooted methods that facilitates the formulation of our M3 model for simulating theoretical and analyzing empirical data, which can then be used to test hypothesis regarding mechanisms driving animal movement and make predications of animal movement responses to management and global change.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
Sign in / Sign up

Export Citation Format

Share Document