scholarly journals Interannual Variability of GPS Heights and Environmental Parameters over Europe and the Mediterranean Area

2021 ◽  
Vol 13 (8) ◽  
pp. 1554
Author(s):  
Letizia Elia ◽  
Susanna Zerbini ◽  
Fabio Raicich
Keyword(s):  
Time Series ◽  
Interannual Variability ◽  
Environmental Parameters ◽  
Mediterranean Area ◽  
Space Time ◽  
The Arctic ◽  
Climate Indices ◽  
Geological Processes ◽  
Second Mode ◽  
The Mediterranean

Vertical deformations of the Earth’s surface result from a host of geophysical and geological processes. Identification and assessment of the induced signals is key to addressing outstanding scientific questions, such as those related to the role played by the changing climate on height variations. This study, focused on the European and Mediterranean area, analyzed the GPS height time series of 114 well-distributed stations with the aim of identifying spatially coherent signals likely related to variations of environmental parameters, such as atmospheric surface pressure (SP) and terrestrial water storage (TWS). Linear trends and seasonality were removed from all the time series before applying the principal component analysis (PCA) to identify the main patterns of the space/time interannual variability. Coherent height variations on timescales of about 5 and 10 years were identified by the first and second mode, respectively. They were explained by invoking loading of the crust. Single-value decomposition (SVD) was used to study the coupled interannual space/time variability between the variable pairs GPS height–SP and GPS height–TWS. A decadal timescale was identified that related height and TWS variations. Features common to the height series and to those of a few climate indices—namely, the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA), and the multivariate El Niño Southern Oscillation (ENSO) index (MEI)—were also investigated. We found significant correlations only with the MEI. The first height PCA mode of variability, showing a nearly 5-year fluctuation, was anticorrelated (−0.23) with MEI. The second mode, characterized by a decadal fluctuation, was well correlated (+0.58) with MEI; the spatial distribution of the correlation revealed, for Europe and the Mediterranean area, height decrease till 2015, followed by increase, while Scandinavian and Baltic countries showed the opposite behavior.

PCA study of the interannual variability of GPS heights and environmental parameters

2020 ◽  
Author(s):  
Letizia Elia ◽  
Susanna Zerbini ◽  
Fabio Raicich
Keyword(s):  
Time Series ◽  
Interannual Variability ◽  
Water Storage ◽  
Environmental Parameters ◽  
Space Time ◽  
Terrestrial Water Storage ◽  
Coupled Modes ◽  
Entire Study ◽  
Terrestrial Water ◽  
The Mediterranean

<p>Time series of GPS coordinates longer than two decades are now available at many stations around the world. The objective of our study is to investigate large networks of GPS stations to identify and analyze spatially coherent signals present in the coordinate time series and, at the same locations, to identify and analyze common patterns in the series of environmental parameters and climate indexes. The study is confined to Europe and the Mediterranean area, where 107 GPS stations were selected from the Nevada Geodetic Laboratory (NGL) archive on the basis of the completeness and length of the data series. The parameters of interest for this study are the stations height (H), the atmospheric surface pressure (AP), the terrestrial water storage (TWS) and the various climate indexes, such as NAO (North Atlantic Oscillation), AO (Artic Oscillation), SCAND (Scandinavian Index) and MEI (Multivariate ENSO Index). The Empirical Orthogonal Function (EOF) is the methodology adopted to extract the main patterns of space/time variability of these parameters. We also focus on the coupled modes of space/time interannual variability between pairs of variables using the singular value decomposition (SVD) methodology. The coupled variability between all the afore mentioned parameters is investigated. It shall be pointed out that EOF and SVD are mathematical tools providing common modes on the one hand, and statistical correlations between pairs of parameters on the other. Therefore, these methodologies do not allow to directly infer the physical mechanisms responsible for the observed behaviors which should be explained through appropriate modelling. Our study has identified, over Europe and the Mediterranean, main modes of variability in the time series of GPS heights, atmospheric pressure and terrestrial water storage. For example, regarding the station heights, the EOF1 explains about 30% of the variance and the spatial pattern is coherent over the entire study area. The SVD analysis of coupled parameters, namely H-AP, TWS-AP and H-TWS, showed that most of the common variability is explained by the first 3 modes. In particular, 70% for the H-AP, 67% for the TWS-AP and 49% for the H-TWS pair. Moreover, we correlated the stations heights with the NAO, AO, SCAND and MEI indexes to investigate the possible influence of climate variability on the height behavior. To do so, the stations heights were represented using the first three EOFs to reduce the potential effect of local anomalies. More than 30 stations, over the total of 107, show significant correlations up to about 0.3 with the AO and SCAND indexes. The correlation coefficients with MEI turn out to be significant and up to 0.5 for about half of the stations.</p>

scholarly journals Sources of predictability over the Mediterranean at seasonal time-scale: building up an empirical forecasting model

2021 ◽  
Author(s):  
Esteban Rodríguez-Guisado ◽  
Ernesto Rodríguez-Camino
Keyword(s):  
Time Series ◽  
Global Climate ◽  
Supplementary Information ◽  
Climate Indices ◽  
Least Squares Regression ◽  
Dynamical Models ◽  
Flexible Tool ◽  
Probabilistic Forecasts ◽  
The Mediterranean ◽  
The Impact

<p>Although most operational seasonal forecasting systems are based on dynamical models, empirical forecasting systems, built on statistical relationships between present and future at seasonal time horizons conditions of the climate system, provide a feasible and realistic alternative and a source of supplementary information. Here, a new empirical model based on partial least squares regression is presented. Originally designed as a flexible tool, the model can be run with many configurations including different predictands, resolutions, leads and aggregation times. To be able of producing forecast for any selected configuration, the model automatically selects predictors from an initial pool, containing global climate indices and specific predictors for the Mediterranean region unveiled in the frame of the MEDSCOPE project. Additionally, the model explores spatial fields, generating time series based on spatial averages of areas well correlated with the predictand. These time series are added to the initial pool of candidate predictors.  We present here results from a configuration producing probabilistic forecasts of seasonal (3 month averages) temperature and precipitation, their verification and comparison against a selection of state-of-the-art seasonal forecast systems based on dynamical models in a hindcast period (1994-2015). The model is able to produce spatially coherent anomaly patterns, and reach levels of skill comparable to those based on dynamical models. As predictors can be easily removed or incorporated, the model can provide information on the impact of a particular predictor on skill, so it can be used to help in the search and understanding of new sources of predictability. Evaluation of soil moisture impact on summer temperature predictability is shown as an example</p>

scholarly journals Development of an empirical model for seasonal forecasting over the Mediterranean

2019 ◽  
Vol 16 ◽  
pp. 191-199
Author(s):  
Esteban Rodríguez-Guisado ◽  
Antonio Ángel Serrano-de la Torre ◽  
Eroteida Sánchez-García ◽  
Marta Domínguez-Alonso ◽  
Ernesto Rodríguez-Camino
Keyword(s):  
Empirical Model ◽  
Global Climate ◽  
Mediterranean Area ◽  
Climate Indices ◽  
Teleconnection Patterns ◽  
Sensitivity Experiments ◽  
Global Indices ◽  
Temperature And Precipitation ◽  
The Mediterranean ◽  
Smooth Transitions

Abstract. In the frame of MEDSCOPE project, which mainly aims at improving predictability on seasonal timescales over the Mediterranean area, a seasonal forecast empirical model making use of new predictors based on a collection of targeted sensitivity experiments is being developed. Here, a first version of the model is presented. This version is based on multiple linear regression, using global climate indices (mainly global teleconnection patterns and indices based on sea surface temperatures, as well as sea-ice and snow cover) as predictors. The model is implemented in a way that allows easy modifications to include new information from other predictors that will come as result of the ongoing sensitivity experiments within the project. Given the big extension of the region under study, its high complexity (both in terms of orography and land-sea distribution) and its location, different sub regions are affected by different drivers at different times. The empirical model makes use of different sets of predictors for every season and every sub region. Starting from a collection of 25 global climate indices, a few predictors are selected for every season and every sub region, checking linear correlation between predictands (temperature and precipitation) and global indices up to one year in advance and using moving averages from two to six months. Special attention has also been payed to the selection of predictors in order to guaranty smooth transitions between neighbor sub regions and consecutive seasons. The model runs a three-month forecast every month with a one-month lead time.

scholarly journals Prediction of Autumn Precipitation over the Middle and Lower Reaches of the Yangtze River Basin Based on Climate Indices

Climate ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 53
Author(s):  
Heng Qian ◽  
Shi-Bin Xu
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Future Climate Change ◽  
Climate Indices ◽  
The Yangtze River ◽  
Second Mode ◽  
The Yangtze River Basin

Autumn precipitation (AP) has important impacts on agricultural production, water conservation, and water transportation in the middle and lower reaches of the Yangtze River Basin (MLYRB; 25°–35° N and 105°–122° E). We obtain the main empirical orthogonal function (EOF) modes of the interannual variation in AP based on daily precipitation data from 97 stations throughout the MLYRB during 1980–2015. The results show that the first leading EOF mode accounts for 30.83% of the total variation. The spatial pattern shows uniform change over the whole region. The variance contribution of the second mode is 16.13%, and its spatial distribution function shows a north-south phase inversion. Based on previous research and the physical considerations discussed herein, we include 13 climate indices to reveal the major predictors. To obtain an acceptable prediction performance, we comprehensively rank the climate indices, which are sorted according to the values of the new standardized algorithm of information flow (NIF, a causality-based approach) and correlation coefficient (a traditional climate diagnostic tool). Finally, Tropical Indian Ocean Dipole (TIOD), Arctic Oscillation (AO), and other four indicators are chosen as the final predictors affecting the first mode of AP over the MLYRB; NINO3.4 SSTA (NINO3.4), Atlantic-European Circulation E Pattern (AECE), and other four indicators are the major predictors for the second mode. In the final prediction experiment, considering the time series prediction of principal components (PCs) to be a small-sample problem, the Bayesian linear regression (BLR) model is used for the prediction. The experimental results reveal that the BLR model can effectively capture the time series trends of the first two modes (the correlation coefficients are greater than 0.5), and the overall performance is significantly better than that of the multiple linear regression (MLR) model. The prediction factors and precipitation prediction results identified in this study can be referenced to rapidly obtain climatological information for AP over the MLYRB and improve the regional prediction of AP elsewhere, which will also help policymakers prepare appropriate adaptation and mitigation measures for future climate change.

Spatial Distribution of Pre-Warm Front Rainfall in the Mediterranean Area

1988 ◽  
Vol 19 (1) ◽  
pp. 53-64 ◽  
Author(s):  
C. Corradini ◽  
F. Melone
Keyword(s):  
Mediterranean Area ◽  
Compact Structure ◽  
Large Variability ◽  
Air Mass ◽  
Orographic Effects ◽  
Warm Front ◽  
Average Rainfall ◽  
Convective Motions ◽  
Rainfall Type ◽  
The Mediterranean

Evidence is given of the distribution of pre-warm front rainfall at the meso-γ scale, together with a discussion of the main mechanisms producing this variability. An inland region in the Mediterranean area is considered. The selected rainfall type is commonly considered the most regular inasmuch as it is usually unaffected by extended convective motions. Despite this, within a storm a large variability in space was observed. For 90% of measurements, the typical deviations from the area-average total depth ranged from - 40 to 60 % and the storm ensemble-average rainfall rate over an hilly zone was 60 % greater than that in a contiguous low-land zone generally placed upwind. This variability is largely explained in terms of forced uplift of air mass over an envelope type orography. For a few storms smaller orographic effects were found in locations influenced by an orography with higher slopes and elevations. This feature is ascribed to the compact structure of these mountains which probably determines a deflection of air mass in the boundary layer. The importance of this type of analysis in the hydrological practice is also emphasized.

Warfare and Environment in the Ancient World

2013 ◽  
pp. 127-140
Author(s):  
J. Donald Hughes
Keyword(s):  
Natural Environment ◽  
Armed Conflict ◽  
Near East ◽  
Mediterranean Area ◽  
Ancient World ◽  
Direct Effects ◽  
Environmental Properties ◽  
The Mediterranean ◽  
Ancient Warfare ◽  
The Military

This chapter deals with ancient warfare and the environment. Hunting was often been considered as a form of warfare, and art frequently portrayed humans in battle with animals. Armed conflict had its direct influences on the environment. Along with damage to settled agriculture, warfare had affected other lands such as pastures, brush lands, and forests. It is noted that birds, pigs, bears, rodents, snakes, bees, wasps, scorpions, beetles, assassin bugs, and jellyfish have been employed as weaponized animals in ancient warfare, which, in the Mediterranean area and Near East, had vital environmental properties. The direct effects of battle have been shown by ancient historians, but just as important were the influences of the military-oriented organization of societies on the natural environment and resources.

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