Empirical estimate of the signal content of Holocene temperature proxy records

Abstract. Proxy records from climate archives provide evidence about past climate changes, but the recorded signal is affected by non-climate related effects as well as time uncertainty. As proxy based climate reconstructions are frequently used to test climate models and to quantitatively infer past climate, we need to improve our understanding of the proxy records’ signal content as well as the uncertainties involved. In this study, we empirically estimate signal-to-noise ratios (SNRs) of temperature proxy records used in global compilations of the mid to late Holocene. This is achieved through a comparison of proxy time series from close-by sites of three compilations and model time series data at the proxy sites from two transient Holocene climate model simulations. In all comparisons, we found the mean correlations of the proxy time series on centennial to millennial time scales to be rather low (R

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Empirical estimate of the signal content of Holocene temperature proxy records

Climate of the Past ◽

10.5194/cp-15-521-2019 ◽

2019 ◽

Vol 15 (2) ◽

pp. 521-537 ◽

Cited By ~ 2

Author(s):

Maria Reschke ◽

Kira Rehfeld ◽

Thomas Laepple

Keyword(s):

Time Series ◽

Climate Models ◽

Climate Model ◽

Model Simulation ◽

Correlation Structure ◽

Past Climate ◽

Time Uncertainty ◽

Proxy Records ◽

Spatial Correlation Structure ◽

Temperature Proxy

Abstract. Proxy records from climate archives provide evidence about past climate changes, but the recorded signal is affected by non-climate-related effects as well as time uncertainty. As proxy-based climate reconstructions are frequently used to test climate models and to quantitatively infer past climate, we need to improve our understanding of the proxy record signal content as well as the uncertainties involved. In this study, we empirically estimate signal-to-noise ratios (SNRs) of temperature proxy records used in global compilations of the middle to late Holocene (last 6000 years). This is achieved through a comparison of the correlation of proxy time series from nearby sites of three compilations and model time series extracted at the proxy sites from two transient climate model simulations: a Holocene simulation of the ECHAM5/MPI-OM model and the Holocene part of the TraCE-21ka simulation. In all comparisons, we found the mean correlations of the proxy time series on centennial to millennial timescales to be low (R<0.2), even for nearby sites, which resulted in low SNR estimates. The estimated SNRs depend on the assumed time uncertainty of the proxy records, the timescale analysed, and the model simulation used. Using the spatial correlation structure of the ECHAM5/MPI-OM simulation, the estimated SNRs on centennial timescales ranged from 0.05 – assuming no time uncertainty – to 0.5 for a time uncertainty of 400 years. On millennial timescales, the estimated SNRs were generally higher. Use of the TraCE-21ka correlation structure generally resulted in lower SNR estimates than for ECHAM5/MPI-OM. As the number of available high-resolution proxy records continues to grow, a more detailed analysis of the signal content of specific proxy types should become feasible in the near future. The estimated low signal content of Holocene temperature compilations should caution against over-interpretation of these multi-proxy and multisite syntheses until further studies are able to facilitate a better characterisation of the signal content in paleoclimate records.

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Relative Contribution of Greenhouse Gases and Ozone-Depleting Substances to Temperature Trends in the Stratosphere: A Chemistry–Climate Model Study

Journal of Climate ◽

10.1175/2009jcli2955.1 ◽

2010 ◽

Vol 23 (1) ◽

pp. 28-42 ◽

Cited By ~ 38

Author(s):

Richard S. Stolarski ◽

Anne R. Douglass ◽

Paul A. Newman ◽

Steven Pawson ◽

Mark R. Schoeberl

Keyword(s):

Time Series ◽

Greenhouse Gases ◽

Climate Model ◽

Time Series Data ◽

Temperature Time Series ◽

Series Data ◽

Future Evolution ◽

The Past ◽

Ozone Depleting Substances ◽

Temperature Time

Abstract The temperature of the stratosphere has decreased over the past several decades. Two causes contribute to that decrease: well-mixed greenhouse gases (GHGs) and ozone-depleting substances (ODSs). This paper addresses the attribution of temperature decreases to these two causes and the implications of that attribution for the future evolution of stratospheric temperature. Time series analysis is applied to simulations of the Goddard Earth Observing System Chemistry–Climate Model (GEOS CCM) to separate the contributions of GHGs from those of ODSs based on their different time-dependent signatures. The analysis indicates that about 60%–70% of the temperature decrease of the past two decades in the upper stratosphere near 1 hPa and in the lower midlatitude stratosphere near 50 hPa resulted from changes attributable to ODSs, primarily through their impact on ozone. As ozone recovers over the next several decades, the temperature should continue to decrease in the middle and upper stratosphere because of GHG increases. The time series of observed temperature in the upper stratosphere is approaching the length needed to separate the effects of ozone-depleting substances from those of greenhouse gases using temperature time series data.

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Climate Change and Precipitation: Applying Global Climate Model Projections to Local Precipitation Time Series Data in Philadelphia

World Environmental and Water Resources Congress 2017 ◽

10.1061/9780784480618.048 ◽

2017 ◽

Cited By ~ 1

Author(s):

S. Malter ◽

J. Rockwell ◽

M. Maimone

Keyword(s):

Climate Change ◽

Time Series ◽

Climate Model ◽

Time Series Data ◽

Global Climate ◽

Global Climate Model ◽

Series Data ◽

Precipitation Time Series

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Peramalan Data dengan Teknik Pemulusan Simple Moving Average (Studi Kasus Harga Saham Harian PT Bank BRI Syariah Tbk)

AL-MUQAYYAD: Jurnal Ekonomi Syariah ◽

10.46963/jam.v3i2.195 ◽

2020 ◽

Vol 3 (2) ◽

pp. 136-143

Author(s):

Anne Mudya Yolanda ◽

M. Ridhwan

Keyword(s):

Time Series ◽

Time Series Data ◽

Moving Average ◽

Series Data ◽

Future Period ◽

Data Comparison ◽

Price Data ◽

Closing Price ◽

Forecast Data ◽

Model Time Series

Time series analysis is used to model time series data and forecast data for future periods. This research was conducted to predict data with a simple smoothing technique, namely the Simple Moving Average of PT Bank BRI Syariah Tbk's stock closing price data. The closing price of shares was analyzed using three average criteria, namely 3, 5, 20, and 100 of the most recent data. Comparison of accuracy with SSE, MSE, and MAPE showed that the best in predicting daily stock closing price data was the Simple Moving Average using the latest 3 data with a prediction result for the future period of Rp. 748, -.

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Managing the effects of extreme sub-daily rainfall and flash floods—a practitioner's perspective

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2019.0550 ◽

2021 ◽

Vol 379 (2195) ◽

pp. 20190550 ◽

Cited By ~ 3

Author(s):

Murray Dale

Keyword(s):

Climate Change ◽

Time Series ◽

Flood Risk ◽

Climate Models ◽

Time Series Data ◽

Daily Rainfall ◽

Series Data ◽

Rainfall Events ◽

Short Period ◽

The Uk

Extreme sub-daily rainfall affects flooding in the UK and urban pollution management. Water utilities in the UK need to understand the characteristics of this rainfall, and how it may change in the future in order to plan for and manage these impacts. There is also significant interest from infrastructure owners and urban authorities exposed to flood risk from short-period, intense rainfall events. This paper describes how UK flood risk guidance incorporates allowances for climate change and how recent research using convection-permitting climate models is helping to inform this guidance. The guidance documents are used by engineers and scientists in the modelling of sewer networks, smaller river catchments and urban drainage areas and provide values to ‘uplift' rainfall event data used as model inputs to reflect climate change model projections. With an increasing focus on continuous simulation modelling using time series rainfall, research into adjusting time series data to reflect future rainfall characteristics in convection-permitting climate models is discussed. Other knowledge gaps for practitioners discussed are the potential changing shape (profile) of future rainfall events and future changes in antecedent wetness conditions. The author explains the challenge of developing simple and effective guidance for practitioners from the complex scientific output. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.

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A Stochastic Rainfall Generator Approach for Applying Downscaled Global Climate Model Projections to Local Precipitation Time Series Data in Philadelphia

World Environmental and Water Resources Congress 2017 ◽

10.1061/9780784480601.022 ◽

2017 ◽

Author(s):

M. Maimone ◽

J. Rockwell ◽

S. Malter

Keyword(s):

Time Series ◽

Climate Model ◽

Time Series Data ◽

Global Climate ◽

Global Climate Model ◽

Series Data ◽

Precipitation Time Series

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Satellite-derived sulfur dioxide (SO<sub>2</sub>) emissions from the 2014–2015 Holuhraun eruption (Iceland)

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-4851-2019 ◽

2019 ◽

Vol 19 (7) ◽

pp. 4851-4862 ◽

Cited By ~ 6

Author(s):

Elisa Carboni ◽

Tamsin A. Mather ◽

Anja Schmidt ◽

Roy G. Grainger ◽

Melissa A. Pfeffer ◽

...

Keyword(s):

Time Series ◽

Sulfur Dioxide ◽

Large Scale ◽

Climate Model ◽

Time Series Data ◽

Series Data ◽

Data Set ◽

Model Simulations ◽

So2 Flux ◽

Erupted Mass

Abstract. The 6-month-long 2014–2015 Holuhraun eruption was the largest in Iceland for 200 years, emitting huge quantities of sulfur dioxide (SO2) into the troposphere, at times overwhelming European anthropogenic emissions. Weather, terrain and latitude made continuous ground-based or UV satellite sensor measurements challenging. Infrared Atmospheric Sounding Interferometer (IASI) data are used to derive the first time series of daily SO2 mass present in the atmosphere and its vertical distribution over the entire eruption period. A new optimal estimation scheme is used to calculate daily SO2 fluxes and average e-folding time every 12 h. For the 6 months studied, the SO2 flux was observed to be up to 200 kt day−1 and the minimum total SO2 erupted mass was 4.4±0.8 Tg. The average SO2 e-folding time was 2.4±0.6 days. Where comparisons are possible, these results broadly agree with ground-based near-source measurements, independent remote-sensing data and values obtained from model simulations from a previous paper. The results highlight the importance of using high-resolution time series data to accurately estimate volcanic SO2 emissions. The SO2 mass missed due to thermal contrast is estimated to be of the order of 3 % of the total emission when compared to measurements by the Ozone Monitoring Instrument. A statistical correction for cloud based on the AVHRR cloud-CCI data set suggested that the SO2 mass missed due to cloud cover could be significant, up to a factor of 2 for the plume within the first kilometre from the vent. Applying this correction results in a total erupted mass of 6.7±0.4 Tg and little change in average e-folding time. The data set derived can be used for comparisons to other ground- and satellite-based measurements and to petrological estimates of the SO2 flux. It could also be used to initialise climate model simulations, helping to better quantify the environmental and climatic impacts of future Icelandic fissure eruptions and simulations of past large-scale flood lava eruptions.

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

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

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

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

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.

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