Sequential tests of causality between environmental time series: With application to the global warming theory

2020 ◽  
Author(s):  
Carlo Grillenzoni ◽  
Elisa Carraro
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Sequential Tests ◽  
Environmental Time Series

scholarly journals Business Days Time Series Weekly Trend and Seasonality

2021 ◽  
Vol 5 (1) ◽  
pp. 26
Author(s):  
Karlis Gutans
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Exchange Rates ◽  
Temperature Data ◽  
Adjustment Method ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Time Series Decomposition ◽  
The World

The world changes at incredible speed. Global warming and enormous money printing are two examples, which do not affect every one of us equally. “Where and when to spend the vacation?”; “In what currency to store the money?” are just a few questions that might get asked more frequently. Knowledge gained from freely available temperature data and currency exchange rates can provide better advice. Classical time series decomposition discovers trend and seasonality patterns in data. I propose to visualize trend and seasonality data in one chart. Furthermore, I developed a calendar adjustment method to obtain weekly trend and seasonality data and display them in the chart.

scholarly journals Feature-preserving interpolation and filtering of environmental time series

2015 ◽  
Vol 72 ◽  
pp. 71-76 ◽  
Author(s):  
Gregoire Mariethoz ◽  
Niklas Linde ◽  
Damien Jougnot ◽  
Hassan Rezaee
Keyword(s):  
Time Series ◽  
Feature Preserving ◽  
Environmental Time Series

scholarly journals Towards Reliable Probabilistic Time-Series Projections of Global Mean Surface Temperature

2021 ◽  
Author(s):  
Philip G. Sansom ◽  
Donald Cummins ◽  
Stefan Siegert ◽  
David B Stephenson
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Surface Temperature ◽  
Climate Model ◽  
Future Projections ◽  
Perfect Model ◽  
Global Mean Surface Temperature ◽  
Time Series Approach ◽  
Energy Balance Models ◽  
Global Mean

Abstract Quantifying the risk of global warming exceeding critical targets such as 2.0 ◦ C requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. This approach gives reliable projections of GMST when tested in a perfect model setting. When applied to observations this leads to projected warming of 2.2 ◦ C (1.7 ◦ C to 2.9 ◦ C) in 2100 compared to pre-industrial conditions, 0.4 ◦ C lower than a comparable IPCC anomaly estimate. The probability of staying below the critical 2.0 ◦ C warming target in 2100 more than doubles to 0.28 compared to only 0.11 from a comparably IPCC estimate.

On case-crossover methods for environmental time series data

2007 ◽  
Vol 18 (2) ◽  
pp. 157-171 ◽  
Author(s):  
Heather J. Whitaker ◽  
Mounia N. Hocine ◽  
C. Paddy Farrington
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Case Crossover ◽  
Environmental Time Series

Unsupervised Fraud Detection in Environmental Time Series Data

2020 ◽  
pp. 257-277
Author(s):  
Xiaochun Wang ◽  
Xiali Wang ◽  
Mitch Wilkes
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fraud Detection ◽  
Series Data ◽  
Environmental Time Series

Environmental Time Series Prediction with Missing Data by Machine Learning and Dynamics Recostruction

2021 ◽  
pp. 26-33
Author(s):  
Francesco Camastra ◽  
Vincenzo Capone ◽  
Angelo Ciaramella ◽  
Tony Christian Landi ◽  
Angelo Riccio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Missing Data ◽  
Time Series Prediction ◽  
Environmental Time Series

Elevation-Dependent Climate Change in the European Alps

2020 ◽  
Author(s):  
Michael Kuhn ◽  
Marc Olefs
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Global Warming ◽  
Atmospheric Pressure ◽  
Atmospheric Aerosol ◽  
European Alps ◽  
Model Simulations ◽  
Vapor Cloud ◽  
Pressure Water ◽  
Time Changes

Elevation-dependent climate change has been observed in the European Alps in the context of global warming and as a consequence of Alpine orography. It is most obvious in elevation-dependent warming, conveniently defined as the linear regression of the time series of temperatures against elevation, and it reaches values of several tenths of a degree per 1,000 m elevation per decade. Observed changes in temperature have forced changes in atmospheric pressure, water vapor, cloud condensation, fluxes of infrared and solar radiation, snow cover, and evaporation, which have affected the Alpine surface energy and water balance in different ways at different elevations. At the same time, changes in atmospheric aerosol optical depth, in atmospheric circulation, and in the frequency of weather types have contributed to the observed elevation-dependent climate change in the European Alps. To a large extent, these observations have been reproduced by model simulations.

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