scholarly journals Inferring late-Holocene climate in the Ecuadorian Andes using a chironomid-based temperature inference model

2016 ◽  
Vol 12 (5) ◽  
pp. 1263-1280 ◽  
Author(s):  
Frazer Matthews-Bird ◽  
Stephen J. Brooks ◽  
Philip B. Holden ◽  
Encarni Montoya ◽  
William D. Gosling
Keyword(s):  
Bayesian Method ◽  
Mean Squared Error ◽  
Weighted Averaging ◽  
Mean Annual Temperature ◽  
Calibration Data ◽  
Tropical Andes ◽  
Data Set ◽  
Inference Models ◽  
Calibration Data Set ◽  
Study Sites

Abstract. Presented here is the first chironomid calibration data set for tropical South America. Surface sediments were collected from 59 lakes across Bolivia (15 lakes), Peru (32 lakes), and Ecuador (12 lakes) between 2004 and 2013 over an altitudinal gradient from 150 m above sea level (a.s.l) to 4655 m a.s.l, between 0–17° S and 64–78° W. The study sites cover a mean annual temperature (MAT) gradient of 25 °C. In total, 55 chironomid taxa were identified in the 59 calibration data set lakes. When used as a single explanatory variable, MAT explains 12.9 % of the variance (λ1/λ2 =  1.431). Two inference models were developed using weighted averaging (WA) and Bayesian methods. The best-performing model using conventional statistical methods was a WA (inverse) model (R2jack =  0.890; RMSEPjack =  2.404 °C, RMSEP – root mean squared error of prediction; mean biasjack =  −0.017 °C; max biasjack =  4.665 °C). The Bayesian method produced a model with R2jack =  0.909, RMSEPjack =  2.373 °C, mean biasjack =  0.598 °C, and max biasjack =  3.158 °C. Both models were used to infer past temperatures from a ca. 3000-year record from the tropical Andes of Ecuador, Laguna Pindo. Inferred temperatures fluctuated around modern-day conditions but showed significant departures at certain intervals (ca. 1600 cal yr BP; ca. 3000–2500 cal yr BP). Both methods (WA and Bayesian) showed similar patterns of temperature variability; however, the magnitude of fluctuations differed. In general the WA method was more variable and often underestimated Holocene temperatures (by ca. −7 ± 2.5 °C relative to the modern period). The Bayesian method provided temperature anomaly estimates for cool periods that lay within the expected range of the Holocene (ca. −3 ± 3.4 °C). The error associated with both reconstructions is consistent with a constant temperature of 20 °C for the past 3000 years. We would caution, however, against an over-interpretation at this stage. The reconstruction can only currently be deemed qualitative and requires more research before quantitative estimates can be generated with confidence. Increasing the number, and spread, of lakes in the calibration data set would enable the detection of smaller climate signals.

scholarly journals Quantifying late-Holocene climate in the Ecuadorian Andes using a chironomid-based temperature inference model

2016 ◽  
Author(s):  
Frazer Matthews-Bird ◽  
Stephen J. Brooks ◽  
Philip B. Holden ◽  
Encarni Montoya ◽  
William D . Gosling
Keyword(s):  
Late Holocene ◽  
Bayesian Method ◽  
Ice Age ◽  
Weighted Averaging ◽  
Mean Annual Temperature ◽  
Calibration Data ◽  
Tropical Andes ◽  
Data Set ◽  
Cold Temperatures ◽  
Inference Models

Abstract. Presented here is the first chironomid calibration dataset for tropical South America. Surface sediments were collected from 59 lakes across Bolivia (15 lakes), Peru (32 lakes) and Ecuador (12 lakes) between 2004 and 2013 over an altitudinal gradient from 150 m above sea level (a.s.l) to 4655 m a.s.l, between 0-­17°S and 64-­78°W. The study sites cover a mean annual temperature (MAT) gradient of 25°C. In total, 55 chironomid taxa were identified in the 59 calibration data-­set lakes. When used as a single explanatory variable, MAT explains 12.9% of the variance (λ1/λ2= 1.431). Two inference models were developed using weighted averaging and Bayesian methods. The best performing model using conventional statistical methods was a WA (inverse) model (R2jack= 0.890, RMSEPjack= 2.404, Mean biasjack= -­0.017, Max biasjack= 4.665). The Bayesian method produced a model with R2jack= 0.909, RMSEPjack= 2.373, Mean biasjack= 0.598, Max biasjack= 3.158. Both models were used to infer past temperatures from a c. 3000 yr record from the tropical Andes of Ecuador, Laguna Pindo. Inferred temperatures fluctuated around modern day conditions but showed significant departures at certain intervals (c. 1600 cal yr BP; c. 3000-­2500 cal yr BP). Both methods (WA/Bayesian) showed similar patterns of temperature variability; however, the magnitude of fluctuations differed. In general the WA method was more variable often inferring unrealistically cold temperatures (c. -­7±2.5°C relative to the modern). The Bayesian method provided temperature anomaly estimates for cool periods that lay within the expected range of the Holocene (c. -­3±3.4°C). The chironomid-­based MAT recon struction from the Laguna Pindo fossil record suggests that periods of low solar output not only affect the tropics through changes in precipitation, but also directly affect tropical temperatures. Inferred temperatures were 2-­3°C colder relative to the modern during the widely recognised 3500-­2500 cal yr BP cooling event. Long-­term cooling during the late-­Holocene culminating in the Little Ice Age (LIA) is not apparent in the Laguna Pindo record. A cooling by 1-­2°C relative to the modern during the LIA is recorded in a single fossil sa

scholarly journals Cariaco Basin Calibration Update: Revisions to Calendar and 14C Chronologies for Core Pl07-58Pc

Radiocarbon ◽  
2004 ◽  
Vol 46 (3) ◽  
pp. 1161-1187 ◽  
Author(s):  
Konrad A Hughen ◽  
John R Southon ◽  
Chanda J H Bertrand ◽  
Brian Frantz ◽  
Paula Zermeño
Keyword(s):  
Tree Ring ◽  
Cariaco Basin ◽  
Calibration Data ◽  
Data Set ◽  
Varve Chronology ◽  
Pine Tree ◽  
Tree Ring Data ◽  
Calibration Data Set ◽  
Recent Version ◽  
Radiocarbon Calibration

This paper describes the methods used to develop the Cariaco Basin PL07-58PC marine radiocarbon calibration data set. Background measurements are provided for the period when Cariaco samples were run, as well as revisions leading to the most recent version of the floating varve chronology. The floating Cariaco chronology has been anchored to an updated and expanded Preboreal pine tree-ring data set, with better estimates of uncertainty in the wiggle-match. Pending any further changes to the dendrochronology, these results represent the final Cariaco 58PC calibration data set.

Developing a modern pollen-climate calibration data set for Norway

Boreas ◽  
2010 ◽  
Vol 39 (4) ◽  
pp. 674-688 ◽  
Author(s):  
ANNE E. BJUNE ◽  
H. JOHN B. BIRKS ◽  
SYLVIA M. PEGLAR ◽  
ARVID ODLAND
Keyword(s):  
Calibration Data ◽  
Data Set ◽  
Modern Pollen ◽  
Calibration Data Set

scholarly journals A New 14C Calibration Data Set for the Last Deglaciation Based on Marine Varves

Radiocarbon ◽  
1997 ◽  
Vol 40 (1) ◽  
pp. 483-494 ◽  
Author(s):  
Konrad A. Hughen ◽  
Jonathan T. Overpeck ◽  
Scott J. Lehman ◽  
Michaele Kashgarian ◽  
John R. Southon ◽  
...  
Keyword(s):  
Global Climate ◽  
Ice Core ◽  
Younger Dryas ◽  
Core Layer ◽  
Cariaco Basin ◽  
Calibration Data ◽  
Last Deglaciation ◽  
Data Set ◽  
The Last Deglaciation ◽  
Calibration Data Set

Varved sediments of the tropical Cariaco Basin provide a new 14C calibration data set for the period of deglaciation (10,000 to 14,500 years before present: 10–14.5 cal ka bp). Independent evaluations of the Cariaco Basin calendar and 14C chronologies were based on the agreement of varve ages with the GISP2 ice core layer chronology for similar high-resolution paleoclimate records, in addition to 14C age agreement with terrestrial 14C dates, even during large climatic changes. These assessments indicate that the Cariaco Basin 14C reservoir age remained stable throughout the Younger Dryas and late Allerød climatic events and that the varve and 14C chronologies provide an accurate alternative to existing calibrations based on coral U/Th dates. The Cariaco Basin calibration generally agrees with coral-derived calibrations but is more continuous and resolves century-scale details of 14C change not seen in the coral records. 14C plateaus can be identified at 9.6, 11.4, and 11.7 14C ka bp, in addition to a large, sloping “plateau” during the Younger Dryas (∼10 to 11 14C ka bp). Accounting for features such as these is crucial to determining the relative timing and rates of change during abrupt global climate changes of the last deglaciation.

scholarly journals Dating the Volcanic Eruption at Thera

Radiocarbon ◽  
2004 ◽  
Vol 46 (1) ◽  
pp. 325-344 ◽  
Author(s):  
Christopher Bronk Ramsey ◽  
Sturt W Manning ◽  
Mariagrazia Galimberti
Keyword(s):  
Radiocarbon Dating ◽  
Aegean Sea ◽  
Data Sets ◽  
Calibration Data ◽  
East Mediterranean ◽  
Data Set ◽  
Western Turkey ◽  
Profound Influence ◽  
Calibration Data Set ◽  
Prehistoric Aegean

The eruption of the volcano at Thera (Santorini) in the Aegean Sea undoubtedly had a profound influence on the civilizations of the surrounding region. The date of the eruption has been a subject of much controversy because it must be linked into the established and intricate archaeological phasings of both the prehistoric Aegean and the wider east Mediterranean. Radiocarbon dating of material from the volcanic destruction layer itself can provide some evidence for the date of the eruption, but because of the shape of the calibration curve for the relevant period, the value of such dates relies on there being no biases in the data sets. However, by dating the material from phases earlier and later than the eruption, some of the problems of the calibration data set can be circumvented and the chronology for the region can be resolved with more certainty.In this paper, we draw together the evidence we have accumulated so far, including new data on the destruction layer itself and for the preceding cultural horizon at Thera, and from associated layers at Miletos in western Turkey. Using Bayesian models to synthesize the data and to identify outliers, we conclude from the most reliable 14C evidence (and using the INTCAL98 calibration data set) that the eruption of Thera occurred between 1663 and 1599 BC.

Highly accurate modeling by using 2-dimensional calibration data set for model-based OPC verification

2006 ◽  
Author(s):  
Cheol-kyun Kim ◽  
Jae-Seung Choi ◽  
Byung-Ho Nam ◽  
DongGyu Yim
Keyword(s):  
Calibration Data ◽  
Data Set ◽  
Model Based ◽  
Calibration Data Set

scholarly journals The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data

SOIL ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 565-578
Author(s):  
Wartini Ng ◽  
Budiman Minasny ◽  
Wanderson de Sousa Mendes ◽  
José Alexandre Melo Demattê
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Soil Properties ◽  
Sample Size ◽  
Training Sample ◽  
Calibration Data ◽  
Learning Models ◽  
Data Set ◽  
Calibration Data Set ◽  
Machine Learning Models

Abstract. The number of samples used in the calibration data set affects the quality of the generated predictive models using visible, near and shortwave infrared (VIS–NIR–SWIR) spectroscopy for soil attributes. Recently, the convolutional neural network (CNN) has been regarded as a highly accurate model for predicting soil properties on a large database. However, it has not yet been ascertained how large the sample size should be for CNN model to be effective. This paper investigates the effect of the training sample size on the accuracy of deep learning and machine learning models. It aims at providing an estimate of how many calibration samples are needed to improve the model performance of soil properties predictions with CNN as compared to conventional machine learning models. In addition, this paper also looks at a way to interpret the CNN models, which are commonly labelled as a black box. It is hypothesised that the performance of machine learning models will increase with an increasing number of training samples, but it will plateau when it reaches a certain number, while the performance of CNN will keep improving. The performances of two machine learning models (partial least squares regression – PLSR; Cubist) are compared against the CNN model. A VIS–NIR–SWIR spectra library from Brazil, containing 4251 unique sites with averages of two to three samples per depth (a total of 12 044 samples), was divided into calibration (3188 sites) and validation (1063 sites) sets. A subset of the calibration data set was then created to represent a smaller calibration data set ranging from 125, 300, 500, 1000, 1500, 2000, 2500 and 2700 unique sites, which is equivalent to a sample size of approximately 350, 840, 1400, 2800, 4200, 5600, 7000 and 7650. All three models (PLSR, Cubist and CNN) were generated for each sample size of the unique sites for the prediction of five different soil properties, i.e. cation exchange capacity, organic carbon, sand, silt and clay content. These calibration subset sampling processes and modelling were repeated 10 times to provide a better representation of the model performances. Learning curves showed that the accuracy increased with an increasing number of training samples. At a lower number of samples (< 1000), PLSR and Cubist performed better than CNN. The performance of CNN outweighed the PLSR and Cubist model at a sample size of 1500 and 1800, respectively. It can be recommended that deep learning is most efficient for spectra modelling for sample sizes above 2000. The accuracy of the PLSR and Cubist model seems to reach a plateau above sample sizes of 4200 and 5000, respectively, while the accuracy of CNN has not plateaued. A sensitivity analysis of the CNN model demonstrated its ability to determine important wavelengths region that affected the predictions of various soil attributes.

scholarly journals A fast MR-thermometry method for quantitative assessment of temperature increase near an implanted wire

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250636
Author(s):  
Marylène Delcey ◽  
Pierre Bour ◽  
Valéry Ozenne ◽  
Wadie Ben Hassen ◽  
Bruno Quesson
Keyword(s):  
Human Brain ◽  
Temperature Rise ◽  
Temperature Increase ◽  
Fiber Optic ◽  
Calibration Data ◽  
Measured Temperature ◽  
Pacemaker Lead ◽  
Mr Thermometry ◽  
Data Set ◽  
Calibration Data Set

Purpose To propose a MR-thermometry method and associated data processing technique to predict the maximal RF-induced temperature increase near an implanted wire for any other MRI sequence. Methods A dynamic single shot echo planar imaging sequence was implemented that interleaves acquisition of several slices every second and an energy deposition module with adjustable parameters. Temperature images were processed in real time and compared to invasive fiber-optic measurements to assess accuracy of the method. The standard deviation of temperature was measured in gel and in vivo in the human brain of a volunteer. Temperature increases were measured for different RF exposure levels in a phantom containing an inserted wire and then a MR-conditional pacemaker lead. These calibration data set were fitted to a semi-empirical model allowing estimation of temperature increase of other acquisition sequences. Results The precision of the measurement obtained after filtering with a 1.6x1.6 mm2 in plane resolution was 0.2°C in gel, as well as in the human brain. A high correspondence was observed with invasive temperature measurements during RF-induced heating (0.5°C RMSE for a 11.5°C temperature increase). Temperature rises of 32.4°C and 6.5°C were reached at the tip of a wire and of a pacemaker lead, respectively. After successful fitting of temperature curves of the calibration data set, temperature rise predicted by the model was in good agreement (around 5% difference) with measured temperature by a fiber optic probe, for three other MRI sequences. Conclusion This method proposes a rapid and reliable quantification of the temperature rise near an implanted wire. Calibration data set and resulting fitting coefficients can be used to estimate temperature increase for any MRI sequence as function of its power and duration.

A high mountain pollen-altitude calibration set for palaeoclimatic use in the tropical Andes

The Holocene ◽  
2006 ◽  
Vol 16 (1) ◽  
pp. 105-117 ◽  
Author(s):  
Valenti Rull
Keyword(s):  
Altitudinal Gradient ◽  
Transfer Functions ◽  
Weighted Averaging ◽  
High Mountain ◽  
Mean Square ◽  
Tropical Andes ◽  
Training Set ◽  
Inference Models ◽  
Mountain Environments ◽  
M 12

The numerical relationship between modem pollen assemblages and altitude in high mountain environments from the northern Andes is analysed, in order to found inference models that allow estimating palaeoaltitudes and palaeotemperatures from past pollen records. The calibration set (DM) consists of a 50-sample altitudinal transect between-2300 and-4600 m altitude. The overall and individual pollen responses to altitude were tested by correspondence analysis (CA), generalized linear regression (HOF) and weighted averaging (WA). Transfer functions were derived by weighted averaging partial least squares (WA-PLS) regression. Overall, altitude is the main controlling factor for the composition of pollen assemblages, as shown by the high correlation between altitude and the first CA component (r =-0.88). Individually, around 35% of the 82 pollen taxa show a significant response to altitude through monotonic or unimodal functions. The best transfer function obtained has a good statistical performance, as shown by the determination coefficient (r2tck =0.78). The prediction power, as measured by the root mean square error of prediction (RMSEP), is of 256 m (12% of the total altitudinal gradient), which is equivalent to-1.5C. These parameters fall within the performance range of the inference models developed elsewhere using pollen and other biological proxies. It is concluded that the DM training set is useful to reconstruct Pleistocene and major Holocene palaeoclimatic trends. This study demonstrates the suitability of establishing reliable transfer functions for palaeoclimatic estimation in the highest altitudes of the tropical Andes, and encourages their continued improvement.

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