Climate variability in the Upper Jordan Valley around 0.78 Ma, inferences from time-series stable isotopes of Viviparidae, supported by mollusc and plant palaeoecology

Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For this purpose, time series and artificial intelligence models, such as Seasonal AutoRegressive Integrated Moving Average (SARIMA), SARIMA with eXogenous variables (SARIMAX), Artificial Neural Network (ANN), Adaptive Neural-based Fuzzy Inference System (ANFIS), and Random Forest (RF) models were employed with two types of input variables, autoregressive variables (AR-) and a combination of autoregressive and exogenous variables (ARX-). Several statistical methods, including ensemble empirical mode decomposition (EEMD), were used to select the lagged climate indices. Finally, monthly reservoir inflow was forecasted by SARIMA, SARIMAX, AR-ANN, ARX-ANN, AR-ANFIS, ARX-ANFIS, AR-RF, and ARX-RF models. As a result, the use of climate indices in artificial intelligence models showed a potential to improve the model performance, and the ARX-ANN and AR-RF models generally showed the best performance among the employed models.

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Identifying Signatures of Natural Climate Variability in Time Series of Global-Mean Surface Temperature: Methodology and Insights

Journal of Climate ◽

10.1175/2009jcli3089.1 ◽

2009 ◽

Vol 22 (22) ◽

pp. 6120-6141 ◽

Cited By ~ 111

Author(s):

David W. J. Thompson ◽

John M. Wallace ◽

Phil D. Jones ◽

John J. Kennedy

Keyword(s):

Time Series ◽

Climate Variability ◽

Surface Temperature ◽

High Latitude ◽

Volcanic Eruptions ◽

Natural Variability ◽

Natural Climate Variability ◽

Global Mean Surface Temperature ◽

Natural Climate ◽

Global Mean

Abstract Global-mean surface temperature is affected by both natural variability and anthropogenic forcing. This study is concerned with identifying and removing from global-mean temperatures the signatures of natural climate variability over the period January 1900–March 2009. A series of simple, physically based methodologies are developed and applied to isolate the climate impacts of three known sources of natural variability: the El Niño–Southern Oscillation (ENSO), variations in the advection of marine air masses over the high-latitude continents during winter, and aerosols injected into the stratosphere by explosive volcanic eruptions. After the effects of ENSO and high-latitude temperature advection are removed from the global-mean temperature record, the signatures of volcanic eruptions and changes in instrumentation become more clearly apparent. After the volcanic eruptions are subsequently filtered from the record, the residual time series reveals a nearly monotonic global warming pattern since ∼1950. The results also reveal coupling between the land and ocean areas on the interannual time scale that transcends the effects of ENSO and volcanic eruptions. Globally averaged land and ocean temperatures are most strongly correlated when ocean leads land by ∼2–3 months. These coupled fluctuations exhibit a complicated spatial signature with largest-amplitude sea surface temperature perturbations over the Atlantic Ocean.

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The Influence of European Climate Variability Mechanism on Air Temperatures in Romania

Present Environment and Sustainable Development ◽

10.2478/pesd-2014-0001 ◽

2014 ◽

Vol 8 (1) ◽

pp. 5-16 ◽

Cited By ~ 2

Author(s):

Nicoleta Ionac ◽

Monica Matei

Keyword(s):

Time Series ◽

Climate Variability ◽

Large Scale ◽

Empirical Orthogonal Functions ◽

Spatial And Temporal Variability ◽

Orthogonal Functions ◽

Mean Values ◽

European Climate ◽

Air Temperatures ◽

And Shifts

Abstract The present paper investigates on the spatial and temporal variability of maximum and minimum air-temperatures in Romania and their connection to the European climate variability. The European climate variability is expressed by large scale parameters, which are roughly represented by the geopotential height at 500 hPa (H500) and air temperature at 850 hPa (T850). The Romanian data are represented by the time series at 22 weather stations, evenly distributed over the entire country’s territory. The period that was taken into account was 1961-2010, for the summer and winter seasons. The method of empirical orthogonal functions (EOF) has been used, in order to analyze the connection between the temperature variability in Romania and the same variability at a larger scale, by taking into consideration the atmosphere circulation. The time series associated to the first two EOF patterns of local temperatures and large-scale anomalies were considered with regard to trends and shifts in their mean values. The non- Mann-Kendall and Pettitt parametric tests were used in this respect. The results showed a strong correlation between T850 parameter and minimum and maximum air temperatures in Romania. Also, the ample variance expressed by the first EOF configurations suggests a connection between local and large scale climate variability.

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POTENTIAL TOOLS FOR TRACKING OCEAN CLIMATE: VARIABILITY IN STABLE ISOTOPES IN LIVING CORALLINE ALGAE

Journal of Phycology ◽

10.1046/j.1529-8817.1999.00001-57.x ◽

2000 ◽

Vol 36 (s3) ◽

pp. 19-19

Author(s):

R.A. Dunn ◽

D. L. Steller ◽

J. Zachos

Keyword(s):

Stable Isotopes ◽

Climate Variability ◽

Coralline Algae ◽

Ocean Climate

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Pteropod time series from the North Western Mediterranean (1967-2003): impacts of pH and climate variability

Marine Ecology Progress Series ◽

10.3354/meps11322 ◽

2015 ◽

Vol 531 ◽

pp. 193-206 ◽

Cited By ~ 17

Author(s):

EL Howes ◽

L Stemmann ◽

C Assailly ◽

JO Irisson ◽

M Dima ◽

...

Keyword(s):

Time Series ◽

Climate Variability ◽

Western Mediterranean ◽

The North ◽

North Western

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Assessing Landsat Fractional Ground-Cover Time Series across Australia's Arid Rangelands: Separating Grazing Impacts from Climate Variability

10.20944/preprints201703.0236.v1 ◽

2017 ◽

Author(s):

Jason Barnetson ◽

Stuart Phinn ◽

Peter Scarth ◽

Robert Denham

Keyword(s):

Time Series ◽

Climate Variability ◽

Ground Cover ◽

Field Measurements ◽

Change Point Detection ◽

Grazing Impact ◽

Growth Cycles ◽

Grazing Impacts ◽

Arid Rangelands

Suitable measures of grazing impacts on ground cover, that enable separation of the effects of climatic variations, are needed to inform land managers and policy makers across the arid rangelands of the Northern Territory of Australia. This work developed and tested a time-series, change-point detection method for application to time series of vegetation fractional cover derived from Landsat data to identify irregular and episodic ground-cover growth cycles. These cycles were classified to distinguish grazing impacts from that of climate variability. A measure of grazing impact was developed using a multivariate technique to quantify the rate and degree of ground cover change. The method was successful in detecting both long term (> 3 years) and short term (< 3 years) growth cycles. Growth cycle detection was assessed against rainfall surplus measures indicating a relationship with high rainfall periods. Ground cover change associated with grazing impacts was also assessed against field measurements of ground cover indicating a relationship between both field and remotely sensed ground cover. Cause and effects between grazing practices and ground cover resilience can now be explored in isolation to climatic drivers. This is important to the long term balance between ground cover utilisation and overall landscape function and resilience.

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Time-series of tritium, stable isotopes and chloride reveal short-term variations in groundwater contribution to a stream

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-8035-2015 ◽

2015 ◽

Vol 12 (8) ◽

pp. 8035-8089 ◽

Cited By ~ 1

Author(s):

C. Duvert ◽

M. K. Stewart ◽

D. I. Cendón ◽

M. Raiber

Keyword(s):

Time Series ◽

Stable Isotopes ◽

Residence Time ◽

Temporal Dynamics ◽

Low Flow ◽

Short Term ◽

Water Fraction ◽

Highly Nonlinear ◽

Complex Relationships ◽

Over Time

Abstract. A major limitation to the accurate assessment of streamwater transit time (TT) stems from the use of stable isotopes or chloride as hydrological tracers, because these tracers are blind to older contributions. Also, while catchment processes are highly non-stationary, the importance of temporal dynamics in older water TT has often been overlooked. In this study we used lumped convolution models to examine time-series of tritium, stable isotopes and chloride in rainfall, streamwater and groundwater of a catchment located in subtropical Australia. Our objectives were to assess the different contributions to streamflow and their variations over time, and to understand the relationships between streamwater TT and groundwater residence time. Stable isotopes and chloride provided consistent estimates of TT in the upstream part of the catchment. A young component to streamflow was identified that was partitioned into quickflow (mean TT ≈ 2 weeks) and discharge from the fractured igneous rocks forming the headwaters (mean TT ≈ 0.3 years). The use of tritium was beneficial for determining an older contribution to streamflow in the downstream area. The best fits were obtained for a mean TT of 16–25 years for this older groundwater component. This was significantly lower than the residence time calculated for the alluvial aquifer feeding the stream downstream (≈ 76–102 years), outlining the fact that water exiting the catchment and water stored in it had distinctive age distributions. When simulations were run separately on each tritium streamwater sample, the TT of old water fraction varied substantially over time, with values averaging 17 ± 6 years at low flow and 38 ± 15 years after major recharge events. This was interpreted as the flushing out of deeper, older waters shortly after recharge by the resulting pressure wave propagation. Overall, this study shows the usefulness of collecting tritium data in streamwater to document short-term variations in the older component of the TT distribution. Our results also shed light on the complex relationships between stored water and water in transit, which are highly nonlinear and remain poorly understood.

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