scholarly journals Distinguishing Trends and Shifts from Memory in Climate Data

2018 ◽  
Vol 31 (23) ◽  
pp. 9519-9543 ◽  
Author(s):  
Claudie Beaulieu ◽  
Rebecca Killick
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Information Criterion ◽  
Steady Increase ◽  
Climate Data ◽  
Surface Warming ◽  
Abrupt Changes ◽  
Short Memory Process ◽  
The 1960S ◽  
And Shifts

The detection of climate change and its attribution to the corresponding underlying processes is challenging because signals such as trends and shifts are superposed on variability arising from the memory within the climate system. Statistical methods used to characterize change in time series must be flexible enough to distinguish these components. Here we propose an approach tailored to distinguish these different modes of change by fitting a series of models and selecting the most suitable one according to an information criterion. The models involve combinations of a constant mean or a trend superposed to a background of white noise with or without autocorrelation to characterize the memory, and are able to detect multiple changepoints in each model configuration. Through a simulation study on synthetic time series, the approach is shown to be effective in distinguishing abrupt changes from trends and memory by identifying the true number and timing of abrupt changes when they are present. Furthermore, the proposed method is better performing than two commonly used approaches for the detection of abrupt changes in climate time series. Using this approach, the so-called hiatus in recent global mean surface warming fails to be detected as a shift in the rate of temperature rise but is instead consistent with steady increase since the 1960s/1970s. Our method also supports the hypothesis that the Pacific decadal oscillation behaves as a short-memory process rather than forced mean shifts as previously suggested. These examples demonstrate the usefulness of the proposed approach for change detection and for avoiding the most pervasive types of mistake in the detection of climate change.

Advances in the data rescue and digitization of historical wind speed observations in Sweden: the WINDGUST project

2021 ◽  
Author(s):  
Erik Engström ◽  
Cesar Azorin-Molina ◽  
Lennart Wern ◽  
Sverker Hellström ◽  
Christophe Sturm ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Global Climate ◽  
Current Status ◽  
Weather Data ◽  
Climate Data ◽  
Intergovernmental Panel ◽  
Staff Members ◽  
The 1960S

<p>Here we present the progress of the first work package (WP1) of the project “Assessing centennial wind speed variability from a historical weather data rescue project in Sweden” (WINDGUST), funded by FORMAS – A Swedish Research Council for Sustainable Development (ref. 2019-00509); previously introduced in EGU2019-17792-1 and EGU2020-3491. In a global climate change, one of the major uncertainties on the causes driving the climate variability of winds (i.e., the “stilling” phenomenon and the recent “recovery” since the 2010s) is mainly due to short availability (i.e., since the 1960s) and low quality of observed wind records as stated by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).</p><p>The WINDGUST is a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI) and the University of Gothenburg aimed at filling the key gap of short availability and low quality of wind datasets, and improve the limited knowledge on the causes driving wind speed variability in a changing climate across Sweden.</p><p>During 2020, we worked in WP1 to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. In the process we followed the “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization. Our protocol consisted on: (i) designing a template for digitization; (ii) digitizing papers by an imaging process based on scanning and photographs; and (iii) typing numbers of wind speed data into the template. We will report the advances and current status, challenges and experiences learned during the development of WP1. Until new year 2020/2021 eight out of thirteen selected stations spanning over the years 1925 to 1948 have been scanned and digitized by three staff members of SMHI during 1,660 manhours.</p>

scholarly journals A Nonparametric Approach to the Removal of Documented Inhomogeneities in Climate Time Series

2013 ◽  
Vol 52 (5) ◽  
pp. 1139-1146 ◽  
Author(s):  
Chiara Ambrosino ◽  
Richard E. Chandler
Keyword(s):  
Time Series ◽  
Reanalysis Data ◽  
Climate Data ◽  
Nonparametric Approach ◽  
Satellite Technology ◽  
Abrupt Changes ◽  
Regression Framework ◽  
Climate Time Series ◽  
Climate Analysis

AbstractClimate data often suffer from artificial inhomogeneities, resulting from documented or undocumented events. For a time series to be used with confidence in climate analysis, it should only be characterized by variations intrinsic to the climate system. Many methods (e.g., direct or indirect) have been proposed according to the data characteristics (e.g., location, variable, or data completeness). This paper is focused on the abrupt-changes problem (when the properties of a time series change abruptly), when their timing is known, and suggests that a nonparametric regression framework provides an appealing way to correct for discontinuities in such a way as to recognize and allow for the existence of other structures such as seasonality and long-term smooth trends. The approach is illustrated by using reanalysis data for southern Africa, for which discontinuities are present because of the introduction of satellite technology in 1979.

Trends and shifts in time series of climate data generated by GCM from 2006 to 2090

2019 ◽  
Vol 10 (5) ◽  
pp. 212-229
Author(s):  
M. L. SANE ◽  
S. SAMBOU ◽  
S. DIATTA ◽  
I. LEYE ◽  
D. M. NDIONE ◽  
...  
Keyword(s):  
Time Series ◽  
Climate Data ◽  
And Shifts

scholarly journals Bayesian time-series analyses reveal a complex impact of climate change on Classic Maya conflict

2019 ◽  
Author(s):  
William Carleton ◽  
Dave Campbell ◽  
Mark Collard
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Classic Maya ◽  
Classic Period ◽  
Climate Data ◽  
Late Classic ◽  
Impact Of Climate Change ◽  
Increasing Temperature ◽  
Bayesian Time Series ◽  
The Impact

Researchers disagree about the impact of climate change on conflict among the Maya during the Classic period (ca. 250-900 CE). Some contend that increasing aridity exacerbated conflict, while others have found that increasing temperature ramped up conflict. Here, we report a study in which we sought to resolve this disagreement. We collated annually-resolved conflict and climate data, and then created a Bayesian time-series model for analysing count-based prehistoric and historic data. We carried out three analyses, one covering more or less the whole of the Classic Period (292-900 CE), one focused on the Early Classic (292-600 CE), and one that concentrated on the Late Classic (600-900 CE). Our analyses indicated that climate change likely did impact Classic Maya conflict levels, but our results differed from those of previous studies in two important ways. First, we found that the impact of climate change is only evident during the Late Classic. Second, we found that while increasing summer temperature exacerbated conflict, increasing aridity suppressed it. Thus, our study offers a new, more complex perspective on Classic Maya climate-conflict dynamics. It also has implications for our understanding of other aspects of Classic Maya history and for the debate about the likely impact of the current bout of climate change on conflict levels.

Methodology of Climate Change Impact Assessment on Forests

2019 ◽  
pp. 200-219
Author(s):  
Mostafa Jafari
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Global Warming ◽  
Human Life ◽  
Future Climate Change ◽  
Present Climate ◽  
Climate Data ◽  
Empirical Observation ◽  
Climate Change Impact Assessment ◽  
Change Impact

Climate change is one of the challenging issues in various countries. Climate change and climate variability and global warming and its effects on natural resources, plants, animals, and on human life are among the subjects that received the attention of scientists and politicians in recent years. Climate change challenges need to be considered in various dimensions. To both understand the present climate and to predict future climate change, it is necessary to have both theory and empirical observation. Any study of climate change involves the construction (or reconstruction) of time series of climate data. How these climate data vary across time provides a measure (either quantitative or qualitative) of climate change. Types of climate data include temperature, precipitation (rainfall), wind, humidity, evapotranspiration, pressure, and solar irradiance. This chapter explores a methodology of measuring climate change's impact on forests.

The Historiography of Environmental History

2018 ◽  
Author(s):  
J. R. McNeill
Keyword(s):  
Climate Change ◽  
Environmental History ◽  
Industrial Pollution ◽  
Full Scale ◽  
Environmental Concerns ◽  
The Us ◽  
The World ◽  
History Of ◽  
The 1960S ◽  
The Relationship

This chapter discusses the emergence of environmental history, which developed in the context of the environmental concerns that began in the 1960s with worries about local industrial pollution, but which has since evolved into a full-scale global crisis of climate change. Environmental history is ‘the history of the relationship between human societies and the rest of nature’. It includes three chief areas of inquiry: the study of material environmental history, political and policy-related environmental history, and a form of environmental history which concerns what humans have thought, believed, written, and more rarely, painted, sculpted, sung, or danced that deals with the relationship between society and nature. Since 1980, environmental history has come to flourish in many corners of the world, and scholars everywhere have found models, approaches, and perspectives rather different from those developed for the US context.

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

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