scholarly journals Identifying global patterns of stochasticity and nonlinearity in the Earth System

2016 ◽  
Author(s):  
Fernando Arizmendi ◽  
Marcelo Barreiro ◽  
Cristina Masoller
Keyword(s):  
Time Series ◽  
Extreme Values ◽  
Surface Air Temperature ◽  
Reanalysis Data ◽  
Earth System ◽  
Entropy Analysis ◽  
Solar Forcing ◽  
The Earth ◽  
Geographical Regions ◽  
Global Patterns

Abstract. By comparing time-series of surface air temperature (SAT, monthly reanalysis data from NCEP CDAS1 and ERA Interim) with respect to the top-of-atmosphere incoming solar radiation (the insolation), we perform a detailed analysis of the SAT response to solar forcing. By computing the entropy of SAT time-series, we also quantify the degree of stochasticity. We find spatial coherent structures which are characterized by high stochasticity and nearly linear response to solar forcing (the shape of SAT time-series closely follows that of the isolation), or vice versa. The entropy analysis also allows to identify geographical regions in which there are significant differences between the NCEP CDAS1 and ERA Interim datasets, which are due to the presence of extreme values in one dataset but not in the other. Therefore, entropy maps are a valuable tool for anomaly detection and model inter-comparisons.

scholarly journals Identifying global patterns of stochasticity and nonlinearity in the Earth System

2016 ◽  
Author(s):  
Fernando Arizmendi ◽  
Marcelo Barreiro ◽  
Cristina Masoller
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Distance Measure ◽  
Extreme Values ◽  
Surface Air Temperature ◽  
Entropy Measure ◽  
The Earth ◽  
Geographical Regions ◽  
Atmospheric Phenomena ◽  
Low Entropy

Abstract. We demonstrate that two simple measures of time series analysis are able to capture different dynamical and statistical properties of large-scale atmospheric phenomena. We consider two surface air temperature (SAT) datasets, covering a spatial grid of points over the Earth surface (NCEP CDAS1 and ERA Interim reanalysis). In each location we analyze i) the distance between the lagged SAT time series and the insolation (i.e., the local top-of-atmosphere incoming solar radiation), and ii) the Shannon entropy computed from the probability distribution function (pdf) of SAT values. The distance quantifies the similarity between the lagged SAT waveform and the isolation waveform, while the entropy, as defined in information theory, measures the degree of disorder or uncertainty of each time series, which we refer to as stochasticity: the entropy captures the shape of the SAT pdf and is maximum when the pdf is uniform. With the distance measure we uncover well-defined spatial patterns formed by regions with similar SAT response to solar forcing, while with the entropy measure, we uncover regions that have SAT pdf of similar shape. The entropy analysis also allows identifying the geographical regions in which SAT time series has extreme values (i.e., values which are extreme in the local statistics), because the long-tail shape of the pdf is captured as low entropy values. We uncover significant differences between the NCEP CDAS1 and ERA Interim datasets in specific geographical regions, which are due to the presence of extreme values in one dataset but not in the other. In this way, the entropy maps are a valuable tool for anomaly detection and model inter-comparisons.

Benchmarking GPS stations: an improved way to identify the GPS sensitivity

2021 ◽  
Author(s):  
Anna Klos ◽  
Jürgen Kusche ◽  
Artur Lenczuk ◽  
Grzegorz Leszczuk ◽  
Janusz Bogusz
Keyword(s):  
Time Series ◽  
Global Positioning System ◽  
Spatial Density ◽  
Earth System ◽  
Global Networks ◽  
The Earth ◽  
Mass Redistribution ◽  
Wide Range ◽  
Global Positioning ◽  
Temporal And Spatial

<p>Global Positioning System (GPS) stations are affected by a plethora of real and system-related signals and errors that occur at various temporal and spatial resolutions. Geophysical changes related to mass redistribution within the Earth system, common mode components, instability of GPS monuments or thermal expansion of ground, all contribute to the GPS-derived displacement time series. Different spatial resolutions that real and system-related errors occur within are covered thanks to the global networks of GPS stations, characterized presently by an unprecedented spatial density. Various temporal resolutions are covered by displacement time series which span even 25 years now, as estimated for the very first stations established. However, since the GPS sensitivity remains unrecognized, retrieving one signal from this wide range of processes may be very uncertain. Up to now, a comparison between GPS-observed displacement time series and displacements predicted by a set of models, as e.g. environmental loading models, was used to demonstrate the accuracy of the model to predict the observed phenomena. Such a comparison is, however, dependent on the accuracy of models and also on the sensitivity of individual GPS stations. We present a new way to identify the GPS sensitivity, which is based on benchmarking of individual GPS stations using statistical clustering approaches. We focus on regional sets of GPS stations located in Europe, where technique-related signals cover real geophysical changes for many GPS permanent stations and those located in South America and Asia, where hydrological and atmospheric loadings dominate other effects. We prove that combining GPS stations into smaller sets improves our understanding of real and system-related signals and errors.</p>

Recovering climate-related mass transport signals by current and next-generation gravity missions

2020 ◽  
Author(s):  
Roland Pail ◽  
Henryk Dobslaw ◽  
Annette Eicker ◽  
Laura Jensen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Mass Transport ◽  
Gravity Field ◽  
Transport Processes ◽  
Measuring System ◽  
Climate Projections ◽  
Earth System ◽  
Next Generation ◽  
The Earth

<p>Gravity field missions are a unique geodetic measuring system to directly observe mass transport processes in the Earth system. Past and current gravity missions such as CHAMP, GRACE, GOCE and GRACE-Follow On have improved our understanding of large-scale mass changes, such as the global water cycle, melting of continental ice sheets and mountain glaciers, changes in ocean mass that are closely related to the mass-related component of sea level rise, which are subtle indicators of climate change, on global to regional scale. Therefore, mass transport observations are also very valuable for long-term climate applications. Next Generation Gravity Missions (NGGMs) expected to be launched in the midterm future have set high anticipations for an enhanced monitoring of mass transport in the Earth system with significantly improved spatial and temporal resolution and accuracy. This contribution will present results from numerical satellite mission performance simulations designed to evaluate the usefulness of gravity field missions operating over several decades for climate-related applications. The study is based on modelled of mass transport time series obtained from future climate projections until the year 2100 following the representative emission pathway RCP8.5 Numerical closed-loop simulations will assess the recoverability of mass variability signals by means of different NGGM concepts, e.g. GRACE-type in-line single-pair missions, Bender double-pair mission being composed of a polar and an inclined satellite pair, or high-precision high-low tracking missions following the MOBILE concept, assuming realistic noise levels for the key payload. In the evaluation and interpretation of the results, special emphasis shall be given to the identification of (natural or anthropogenic) climate change signals in dependence of the length of the measurement time series, and the quantification of robustness of derived trends and systematic changes.</p>

On the Ensemble Transfer Entropy Analysis of Non-Stationary Geophysical Time Series: The Case of Magnetospheric Response

2020 ◽  
Author(s):  
Mirko Stumpo ◽  
Giuseppe Consolini ◽  
Tommaso Alberti ◽  
Virgilio Quattrociocchi
Keyword(s):  
Time Series ◽  
Fundamental Question ◽  
Entropy Analysis ◽  
Crucial Point ◽  
Information Theoretic ◽  
Model Free ◽  
Statistical Framework ◽  
The Earth ◽  
Geophysical Time Series ◽  
Dynamical Coupling

<p>The fundamental question what causes what has always been the motivating motto for natural sciences, being the study of causality a crucial point for characterizing dynamical relationships. In the framework of complex dynamical systems, both linear statistical tools and Granger causality models drastically fail to detect causal relationships between time series, while a powerful model-free statistical framework is offered by the information theory. </p><p>Here we discuss how to deal with the problem of measuring causal information in non-stationary complex systems by considering a local estimation of the information-theoretic functionals via an ensemble-based statistics. Then, its application for investigating the dynamical coupling and relationships between the solar wind and the Earth’s magnetosphere is also presented. </p>

scholarly journals Simulation of the time-variable gravity field by means of coupled geophysical models

2011 ◽  
Vol 4 (1) ◽  
pp. 27-70 ◽  
Author(s):  
Th. Gruber ◽  
J. L. Bamber ◽  
M. F. P. Bierkens ◽  
H. Dobslaw ◽  
M. Murböck ◽  
...  
Keyword(s):  
Time Series ◽  
Gravity Field ◽  
Mass Distribution ◽  
Time Variable ◽  
Data Sets ◽  
Earth System ◽  
Time Variable Gravity ◽  
The Earth ◽  
Gravity Fields ◽  
Variable Gravity

Abstract. Time variable gravity fields, reflecting variations of mass distribution in the system Earth is one of the key parameters to understand the changing Earth. Mass variations are caused either by redistribution of mass in, on or above the Earth's surface or by geophysical processes in the Earth's interior. The first set of observations of monthly variations of the Earth gravity field was provided by the US/German GRACE satellite mission beginning in 2002. This mission is still providing valuable information to the science community. However, as GRACE has outlived its expected lifetime, the geoscience community is currently seeking successor missions in order to maintain the long time series of climate change that was begun by GRACE. Several studies on science requirements and technical feasibility have been conducted in the recent years. These studies required a realistic model of the time variable gravity field in order to perform simulation studies on sensitivity of satellites and their instrumentation. This was the primary reason for the European Space Agency (ESA) to initiate a study on "Monitoring and Modelling individual Sources of Mass Distribution and Transport in the Earth System by Means of Satellites". The goal of this interdisciplinary study was to create as realistic as possible simulated time variable gravity fields based on coupled geophysical models, which could be used in the simulation processes in a controlled environment. For this purpose global atmosphere, ocean, continental hydrology and ice models were used. The coupling was performed by using consistent forcing throughout the models and by including water flow between the different domains of the Earth system. In addition gravity field changes due to solid Earth processes like continuous glacial isostatic adjustment (GIA) and a sudden earthquake with co-seismic and post-seismic signals were modelled. All individual model results were combined and converted to gravity field spherical harmonic series, which is the quantity commonly used to describe the Earth's global gravity field. The result of this study is a twelve-year time-series of 6-hourly time variable gravity field spherical harmonics up to degree and order 180 corresponding to a global spatial resolution of 1 degree in latitude and longitude. In this paper, we outline the input data sets and the process of combining these data sets into a coherent model of temporal gravity field changes. The resulting time series was used in some follow-on studies and is available to anybody interested via a Website.

scholarly journals The WGLC global gridded lightning climatology and time series

2021 ◽  
Vol 13 (7) ◽  
pp. 3219-3237
Author(s):  
Jed O. Kaplan ◽  
Katie Hong-Kiu Lau
Keyword(s):  
Time Series ◽  
Detection Efficiency ◽  
Earth System ◽  
Maritime Continent ◽  
Lightning Stroke ◽  
The Earth ◽  
The World ◽  
The Tropics ◽  
Direct Use

Abstract. Lightning is an important atmospheric phenomenon and has wide-ranging influence on the Earth system, but few long-term observational datasets of lightning occurrence and distribution are currently freely available. Here, we analyze global lightning activity over the second decade of the 21st century using a new global, high-resolution gridded time series and climatology of lightning stroke density based on raw data from the World Wide Lightning Location Network (WWLLN). While the total number of strokes detected increases from 2010–2014, an adjustment for detection efficiency reduces this artificial trend. The global distribution of lightning shows the well-known pattern of greatest density over the three tropical terrestrial regions of the Americas, Africa, and the Maritime Continent, but we also noticed substantial temporal variability over the 11 years of record, with more lightning in the tropics from 2012–2015 and increasing lightning in the midlatitudes of the Northern Hemisphere from 2016–2020. Although the total number of strokes detected globally was constant, mean stroke power decreases significantly from a peak in 2013 to the lowest levels on record in 2020. Evaluation with independent observational networks shows that while the WWLLN does not capture peak seasonal lightning densities, it does represent the majority of powerful lightning strokes. The resulting gridded lightning dataset (Kaplan and Lau, 2021a, https://doi.org/10.5281/zenodo.4774528) is freely available and will be useful for a range of studies in climate, Earth system, and natural hazards research, including direct use as input data to models and as evaluation data for independent simulations of lightning occurrence.

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