scholarly journals Trophic Relationships between Juvenile Salmon during a 22-year Time Series of Climate Variability in Southeast Alaska

2019 ◽  
pp. 97-100
Author(s):  
Emily Fergusson ◽  
Andy Gray ◽  
Jim Murphy
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Trophic Relationships ◽  
Juvenile Salmon ◽  
Southeast Alaska

scholarly journals Climate variability and salmonellosis in Singapore – A time series analysis

2018 ◽  
Vol 639 ◽  
pp. 1261-1267 ◽  
Author(s):  
Joel Aik ◽  
Anita E. Heywood ◽  
Anthony T. Newall ◽  
Lee-Ching Ng ◽  
Martyn D. Kirk ◽  
...  
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Time Series Analysis ◽  
Series Analysis

scholarly journals The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 374 ◽  
Author(s):  
Taereem Kim ◽  
Ju-Young Shin ◽  
Hanbeen Kim ◽  
Sunghun Kim ◽  
Jun-Haeng Heo
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Climate Variability ◽  
Large Scale ◽  
Climate Indices ◽  
Exogenous Variables ◽  
Inflow Forecasting ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Reservoir Inflow

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.

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

2009 ◽  
Vol 22 (22) ◽  
pp. 6120-6141 ◽  
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.

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

2014 ◽  
Vol 8 (1) ◽  
pp. 5-16 ◽  
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.

Multivariate Time Series Analysis of the Canadian Fisheries System in Lake Superior

1987 ◽  
Vol 44 (S2) ◽  
pp. s171-s181 ◽  
Author(s):  
Y. Cohen ◽  
J. N. Stone
Keyword(s):  
Time Series ◽  
Fish Species ◽  
Lake Trout ◽  
Interspecific Interactions ◽  
Lake Superior ◽  
Multivariate Time Series ◽  
Policy Decision ◽  
Trophic Relationships ◽  
Catch Per Unit Effort ◽  
Intraspecific Interactions

Data for the Canadian fisheries system in Lake Superior were organized into monthly time series of catch and effort from January 1963 through December 1976 for six fish species. Multivariate, autoregressive (ARMA) models were identified for the system based on data for the first 140 mo. Forecasts were compared to data for the last 28 mo. The structure of the models indicate that (1) within the system, AR processes, as opposed to MA processes, were of overriding importance, (2) intraspecific interactions (inferred from data on catch-per-unit-effort, CPUE) were more prevalent than interspecific interactions, (3) interactions within the system occurred with lags of 1, 4, 12, 24, 25, 28, and 36 mo, (4) some of the trophic relationships among the fish species were revealed by the models, and (5) CPUE time series of lake trout (Salvelinus namaycush) affected, but was not affected by, the CPUE time series of other species. The models were used to forecast catch and CPUE for the last 28 mo, and the data were generally within one standard error of the forecasts. The models may help policy decision makers to explore the effects of inputs (e.g. quota regulations) and feedbacks within the fisheries' system on outputs (e.g. production, CPUE).

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

2015 ◽  
Vol 531 ◽  
pp. 193-206 ◽  
Author(s):  
EL Howes ◽  
L Stemmann ◽  
C Assailly ◽  
JO Irisson ◽  
M Dima ◽  
...  
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Western Mediterranean ◽  
The North ◽  
North Western

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

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.

scholarly journals Assessment of Groundwater Resource in Climate Variability Context at Sinematiali (North of Côte d'Ivoire)

2020 ◽  
Vol 1 (5) ◽  
Author(s):  
Omer Zephir De Lasme ◽  
Avy Stephane Koffi ◽  
Dodo Guy Gnali Cedric
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Climate Variability ◽  
Water Resources Management ◽  
Integrated Water Resources Management ◽  
Statistical Test ◽  
Groundwater Resource ◽  
Resources Management ◽  
Pettitt Test ◽  
Software Models

Study of climate variability gets great importance for integrated water resources management. This work examines impact of climate variability on the evolution of water resources in the Bandama sub-watershed at Sinematiali with a view of better management. The time series of rainfall and discharge were used as a database for this purpose. Known calculation hydrologic methods of Nicholson, Maillet as well as the statistical test for breaking detection (Pettitt test) were applied. The effective rain and recharge were estimated by using the ESPERE software models over the period 1980 to 1987. Climate variability is characterized by alternative season of wet, normal, and dry periods, and a pluviometry break occurred in 1984 year. The annual effective rain was assessed from 30 to 570 mm while recharge of aquifers estimated between 2 and 333 mm. This work constitutes a fundamental base for modeling water resources management at Sinematiali.

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