Errors in generating time-series and in dating events at late quaternary millenial (radiocarbon) time-scales: Examples from Baffin Bay, NW Labrador Sea, and East Greenland

Author(s):  
John T. Andrews ◽  
Donald C. Barber ◽  
Anne E. Jennings
2007 ◽  
Vol 37 (6) ◽  
pp. 1445-1454 ◽  
Author(s):  
Sunke Schmidt ◽  
Uwe Send

Abstract The depth of winter convection in the central Labrador Sea is strongly influenced by the prevailing stratification in late summer. For this late summer stratification salinity is as important as temperature, and in the upper water layers salinity even dominates. To analyze the source of the spring and summer freshening in the central region, seasonal freshwater cycles have been constructed for the interior Labrador Sea, the West Greenland Current, and the Labrador Current. It is shown that none of the local freshwater sources is responsible for the spring–summer freshening in the interior, which appears to occur in two separate events in April to May and July to September. Comparing the timing and volume estimates of the seasonal freshwater cycles of the boundary currents with the central Labrador Sea helps in understanding the origin of the interior freshwater signals. The first smaller pulse cannot be attributed clearly to either of the boundary currents. The second one is about three times stronger and supplies 60% of the seasonal summer freshwater. Transport estimates and calculated mixing properties provide evidence that its source is the West Greenland Current. The finding implies a connection also on interannual time scales between Labrador Sea surface salinity and freshwater sources in the West Greenland Current and farther upstream in the East Greenland Current. The freshwater input from the West Greenland Current thus also is the likely pathway for the known modulation of Labrador Sea Water mass formation by freshwater export from the Arctic (via the East Greenland Current), which implies some predictability on longer time scales.


1981 ◽  
Vol 118 (4) ◽  
pp. 337-354 ◽  
Author(s):  
A. C. Higgins ◽  
N. J. Soper

SummaryCretaceous-Palaeogene sediments of the Kangerdlugssuaq area on the continental margin of Central East Greenland were deposited in an embayment of an extensive pre-NE Atlantic shelf sea. Pre-Sparnacian sediments are thin (150 m), incomplete and of siliciclastic type, formed in shallow marine waters. Sparnacian times saw the onset of vigorous basaltic vulcanicity, marking the initial rifting episode between Greenland and Eurasia. Uplift immediately prior to the vulcanicity is evidenced by an unconformity at the base of the Sparnacian, above which basement-derived arkosic sandstones and conglomerates are followed by about 1.5 km of coarse volcaniclastics, basaltic flows of dominantly picritic composition, pro-grading hyaloclastite wedges and thin siltstones with abundant organic detritus. Very rapid subsidence accompanied this early phase of vulcanicity, maintaining the top of the pile close to sea level and allowing the deposition of a further kilometre of waterlain tuffs in the embayment. Sedimentation extended northwards and eastwards on to basement rocks at this period, with the formation of a non-marine sequence which includes coals.The overlying plateau tholeiites overlap the earlier volcanics; their depositional area was extremely extensive along the East Greenland margin and bears no relationship to the Kangerdlugssuaq sedimentary embayment, although their thickest development, 4 km or more, was attained in that region. Eruption rate of the pile exceeded subsidence for a period and it is dominantly subaerial.This sequence of events is compared with the similar history of sedimentation and basaltic vulcanicity on the west coast of Greenland, and it is inferred that just as the East Greenland sequence records the initiation of spreading between Greenland and Rockall-Faeroe at anomaly 24−25 time, so that of West Greenland marks the propagation of the Labrador Sea spreading axis through the Davis Strait into Baffin Bay at anomaly 26−27 time.


2020 ◽  
Vol 33 (12) ◽  
pp. 5155-5172
Author(s):  
Quentin Jamet ◽  
William K. Dewar ◽  
Nicolas Wienders ◽  
Bruno Deremble ◽  
Sally Close ◽  
...  

AbstractMechanisms driving the North Atlantic meridional overturning circulation (AMOC) variability at low frequency are of central interest for accurate climate predictions. Although the subpolar gyre region has been identified as a preferred place for generating climate time-scale signals, their southward propagation remains under consideration, complicating the interpretation of the observed time series provided by the Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array–Western Boundary Time Series (RAPID–MOCHA–WBTS) program. In this study, we aim at disentangling the respective contribution of the local atmospheric forcing from signals of remote origin for the subtropical low-frequency AMOC variability. We analyze for this a set of four ensembles of a regional (20°S–55°N), eddy-resolving (1/12°) North Atlantic oceanic configuration, where surface forcing and open boundary conditions are alternatively permuted from fully varying (realistic) to yearly repeating signals. Their analysis reveals the predominance of local, atmospherically forced signal at interannual time scales (2–10 years), whereas signals imposed by the boundaries are responsible for the decadal (10–30 years) part of the spectrum. Due to this marked time-scale separation, we show that, although the intergyre region exhibits peculiarities, most of the subtropical AMOC variability can be understood as a linear superposition of these two signals. Finally, we find that the decadal-scale, boundary-forced AMOC variability has both northern and southern origins, although the former dominates over the latter, including at the site of the RAPID array (26.5°N).


Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.


2012 ◽  
Vol 29 (4) ◽  
pp. 613-628 ◽  
Author(s):  
Steven L. Morey ◽  
Dmitry S. Dukhovskoy

Abstract Statistical analysis methods are developed to quantify the impacts of multiple forcing variables on the hydrographic variability within an estuary instrumented with an enduring observational system. The methods are applied to characterize the salinity variability within Apalachicola Bay, a shallow multiple-inlet estuary along the northeastern Gulf of Mexico coast. The 13-yr multivariate time series collected by the National Estuary Research Reserve at three locations within the bay are analyzed to determine how the estuary responds to variations in external forcing mechanisms, such as freshwater discharge, precipitation, tides, and local winds at multiple time scales. The analysis methods are used to characterize the estuarine variability under differing flow regimes of the Apalachicola River, a managed waterway, with particular focus on extreme events and scales of variability that are critical to local ecosystems. Multivariate statistical models are applied that describe the salinity response to winds from multiple directions, river flow, and precipitation at daily, weekly, and monthly time scales to understand the response of the estuary under different climate regimes. Results show that the salinity is particularly sensitive to river discharge and wind magnitude and direction, with local precipitation being largely unimportant. Applying statistical analyses with conditional sampling quantifies how the likelihoods of high-salinity and long-duration high-salinity events, conditions of critical importance to estuarine organisms, change given the state of the river flow. Intraday salinity range is shown to be negatively correlated with the salinity, and correlated with river discharge rate.


2021 ◽  
Vol 11 (12) ◽  
pp. 5615
Author(s):  
Łukasz Sobolewski ◽  
Wiesław Miczulski

Ensuring the best possible stability of UTC(k) (local time scale) and its compliance with the UTC scale (Universal Coordinated Time) forces predicting the [UTC-UTC(k)] deviations, the article presents the results of work on two methods of constructing time series (TS) for a neural network (NN), increasing the accuracy of UTC(k) prediction. In the first method, two prepared TSs are based on the deviations determined according to the UTC scale with a 5-day interval. In order to improve the accuracy of predicting the deviations, the PCHIP interpolating function is used in subsequent TSs, obtaining TS elements with a 1-day interval. A limitation in the improvement of prediction accuracy for these TS has been a too large prediction horizon. The introduction in 2012 of the additional UTC Rapid scale by BIPM makes it possible to shorten the prediction horizon, and the building of two TSs has been proposed according to the second method. Each of them consists of two subsets. The first subset is based on deviations determined according to the UTC scale, the second on the UTC Rapid scale. The research of the proposed TS in the field of predicting deviations for the Polish Timescale by means of GMDH-type NN shows that the best accuracy of predicting the deviations has been achieved for TS built according to the second method.


2020 ◽  
Author(s):  
Markus Kienast ◽  
Nadine Lehmann ◽  
Carolyn Buchwald ◽  
Sam Davin ◽  
Julie Granger ◽  
...  

2015 ◽  
Vol 12 (8) ◽  
pp. 7437-7467 ◽  
Author(s):  
J. E. Reynolds ◽  
S. Halldin ◽  
C. Y. Xu ◽  
J. Seibert ◽  
A. Kauffeldt

Abstract. Concentration times in small and medium-sized watersheds (~ 100–1000 km2) are commonly less than 24 h. Flood-forecasting models then require data at sub-daily time scales, but time-series of input and runoff data with sufficient lengths are often only available at the daily time scale, especially in developing countries. This has led to a search for time-scale relationships to infer parameter values at the time scales where they are needed from the time scales where they are available. In this study, time-scale dependencies in the HBV-light conceptual hydrological model were assessed within the generalized likelihood uncertainty estimation (GLUE) approach. It was hypothesised that the existence of such dependencies is a result of the numerical method or time-stepping scheme used in the models rather than a real time-scale-data dependence. Parameter values inferred showed a clear dependence on time scale when the explicit Euler method was used for modelling at the same time steps as the time scale of the input data (1–24 h). However, the dependence almost fully disappeared when the explicit Euler method was used for modelling in 1 h time steps internally irrespectively of the time scale of the input data. In other words, it was found that when an adequate time-stepping scheme was implemented, parameter sets inferred at one time scale (e.g., daily) could be used directly for runoff simulations at other time scales (e.g., 3 or 6 h) without any time scaling and this approach only resulted in a small (if any) model performance decrease, in terms of Nash–Sutcliffe and volume-error efficiencies. The overall results of this study indicated that as soon as sub-daily driving data can be secured, flood forecasting in watersheds with sub-daily concentration times is possible with model-parameter values inferred from long time series of daily data, as long as an appropriate numerical method is used.


Sign in / Sign up

Export Citation Format

Share Document