scholarly journals The Importance of Climate Variability to Wind-Driven Modulation of Hypoxia in Chesapeake Bay

2010 ◽  
Vol 40 (6) ◽  
pp. 1435-1440 ◽  
Author(s):  
Malcolm E. Scully
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Chesapeake Bay ◽  
Wind Direction ◽  
Large Scale ◽  
Nitrogen Loading ◽  
Nutrient Inputs ◽  
Westerly Direction ◽  
Hypoxic Volume

Abstract Extensive hypoxia remains a problem in Chesapeake Bay, despite some reductions in estimated nutrient inputs. An analysis of a 58-yr time series of summer hypoxia reveals that a significant fraction of the interannual variability observed in Chesapeake Bay is correlated to changes in summertime wind direction that are the result of large-scale climate variability. Beginning around 1980, the surface pressure associated with the summer Bermuda high has weakened, favoring winds from a more westerly direction, the direction most correlated with observed hypoxia. Regression analysis suggests that the long-term increase in hypoxic volume observed in this dataset is only accounted for when both changes in wind direction and nitrogen loading are considered.

scholarly journals Influence of low-frequency variability on high and low groundwater levels: example of aquifers in Paris Basin

2022 ◽  
Author(s):  
Lisa Baulon ◽  
Nicolas Massei ◽  
Delphine Allier ◽  
Matthieu Fournier ◽  
Hélène Bessiere
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Large Scale ◽  
Low Frequency ◽  
Paris Basin ◽  
Dominant Type ◽  
Groundwater Levels ◽  
Low Frequency Variability ◽  
Frequency Components

Abstract. Groundwater levels (GWL) very often fluctuate over a wide range of timescales (infra-annual, annual, multi-annual, decadal). In many instances, aquifers act as low-pass filters, dampening the high-frequency variability and amplifying low-frequency variations (from multi-annual to decadal timescales) which basically originate from large-scale climate variability. In the aim of better understanding and ultimately anticipating groundwater droughts and floods, it appears crucial to evaluate whether (and how much) the very high or very low GWLs are sensitive to such low-frequency variability (LFV), which was the main objective of the study presented here. As an example, we focused on exceedance and non-exceedance of the 80 % and 20 % GWL percentiles respectively, in the Paris Basin aquifers over the 1976–2019 period. GWL time series were extracted from a database consisting of relatively undisturbed GWL time series regarding anthropogenic influence (water abstraction by either continuous or periodic pumping) over Metropolitan France. Based on this dataset, our approach consisted of exploring the effect of GWL low-frequency components on threshold exceedance and non-exceedance by successively filtering out low-frequency components of GWL signals using maximum overlap discrete wavelet transform (MODWT). Multi-annual (~7-yr) and decadal (~17-yr) variabilities were found to be the predominant LFVs in GWL signals, in accordance with previous studies in the northern France area. Filtering out these components (either independently or jointly) to (i) examine the proportion of high level (HL) and low level (LL) occurrences generated by these variabilities, (ii) estimate the contribution of each of these variabilities in explaining the occurrence of major historical events associated to well-recognized societal impacts. A typology of GWL variations in Paris Basin aquifers was first determined by quantifying the variance distribution across timescales. Four GWL variation types could be found according to the predominance of annual, multi-annual or/and decadal variabilities in these signals: decadal dominant (type iD), multi-annual and decadal dominant (type iMD), annual dominant (type cA), annual and multi-annual dominant (type cAM). We observed a clear dependence of high and low GWL to LFV for aquifers exhibiting these four GWL variation types. In addition, the respective contribution of multi-annual and decadal variabilities in the threshold exceedance varied according to the event. In numerous aquifers, it also appeared that the sensitivity to LFV was higher for LL than HL. A similar analysis was conducted on the only available long-term GWL time series which covered a hundred years. This allowed us to highlight a potential influence of multidecadal variability on HL and LL too. This study underlined the key role of LFV in the occurrence of HL and LL. Since LFV originates from large-scale stochastic climate variability as demonstrated in many previous studies in the Paris Basin or nearby regions, our results point out that i) poor representation of LFV in General Circulation Models (GCM) outputs used afterwards for developing hydrological projections can result in strong uncertainty in the assessment of future groundwater extremes (GWE), ii) potential changes in the amplitude of LFV, be they natural or induced by global climate change, may lead to substantial changes in the occurrence and severity of GWE for the next decades. Finally, this study also stresses the fact that due to the stochastic nature of LFV, no deterministic prediction of future GWE for the mid- or long term horizons can be achieved even though LFV may look periodic.

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 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.

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 Close co-variation between soil moisture and runoff emerging from multi-catchment data across Europe

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Navid Ghajarnia ◽  
Zahra Kalantari ◽  
René Orth ◽  
Georgia Destouni
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Large Scale ◽  
Temperature Anomaly ◽  
Important Variable ◽  
Climatic Variables ◽  
New Approach ◽  
European Climate ◽  
Moisture Conditions

AbstractSoil moisture is an important variable for land-climate and hydrological interactions. To investigate emergent large-scale, long-term interactions between soil moisture and other key hydro-climatic variables (precipitation, actual evapotranspiration, runoff, temperature), we analyze monthly values and anomalies of these variables in 1378 hydrological catchments across Europe over the period 1980–2010. The study distinguishes results for the main European climate regions, and tests how sensitive or robust they are to the use of three alternative observational and re-analysis datasets. Robustly across the European climates and datasets, monthly soil moisture anomalies correlate well with runoff anomalies, and extreme soil moisture and runoff values also largely co-occur. For precipitation, evapotranspiration, and temperature, anomaly correlation and extreme value co-occurrence with soil moisture are overall lower than for runoff. The runoff results indicate a possible new approach to assessing variability and change of large-scale soil moisture conditions by use of long-term time series of monitored catchment-integrating stream discharges.

scholarly journals Natural Climate Variability During the Holocene

Radiocarbon ◽  
2007 ◽  
Vol 49 (2) ◽  
pp. 837-854 ◽  
Author(s):  
V A Dergachev ◽  
O M Raspopov ◽  
F Damblon ◽  
H Jungner ◽  
G I Zaitseva
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Ice Cores ◽  
Cosmogenic Isotope ◽  
Radiocarbon Age ◽  
Earth’S Climate ◽  
Climatic Time Series ◽  
Natural Climate ◽  
Isotope Content

High-precision radiocarbon age calibration for different terrestrial samples allows us to establish accurate boundaries for many climatic time series. At the same time, the fluctuations of 14C content reflect solar variability. A bispectrum analysis of long-term series of the 14C content deduced from decadal measurements in tree rings demonstrates the existence of amplitude modulation, with a period of main modulation of ∼2400 yr. In 14C time series for the last 11 kyr, major oscillations are distinguished at 8.5–7.8, 5.4–4.7, 2.6–2.2, and 1.1–0.4 cal kyr BP with ∼2400-yr periodicity. High amplitudes in cosmogenic isotope content with a periodicity of about 2400 yr appear synchronous to cooling events documented in Greenland ice cores, to the timing of worldwide Holocene glacier expansion, and to the periods of lake-level changes. This paper focuses on revealing solar forcing on the Earth's climate and about the nature, significance, and impact of sharp Holocene climate variability on human societies and civilizations.

scholarly journals Recent Evidence of Large-Scale Receding Snow Water Equivalents in the European Alps

2017 ◽  
Vol 18 (4) ◽  
pp. 1021-1031 ◽  
Author(s):  
Christoph Marty ◽  
Anna-Maria Tilg ◽  
Tobias Jonas
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Water Cycle ◽  
Critical Role ◽  
European Alps ◽  
Measurement Series ◽  
Snow Water ◽  
Snow Mass

Abstract Snow plays a critical role in the water cycle of many mountain regions and heavily populated areas downstream. In this study, changes of snow water equivalent (SWE) time series from long-term stations in five Alpine countries are analyzed. The sites are located between 500 and 3000 m above mean sea level, and the analysis is mainly based on measurement series from 1 February (winter) and 1 April (spring). The investigation was performed over different time periods, including the last six decades. The large majority of the SWE time series demonstrate a reduction in snow mass, which is more pronounced for spring than for winter. The observed SWE decrease is independent of latitude or longitude, despite the different climate regions in the Alpine domain. In contrast to measurement series from other mountain ranges, even the highest sites revealed a decline in spring SWE. A comparison with a 100-yr mass balance series from a glacier in the central Alps demonstrates that the peak SWEs have been on a record-low level since around the beginning of the twenty-first century at high Alpine sites. In the long term, clearly increasing temperatures and a coincident weak reduction in precipitation are the main drivers for the pronounced snow mass loss in the past.

scholarly journals Tropical Cyclone Footprints in Long-Term Mean State and Multiscale Climate Variability in the Western North Pacific as Seen in the JRA-55 Reanalysis

2021 ◽  
pp. 1-46
Author(s):  
Sho Arakane ◽  
Huang-Hsiung Hsu
Keyword(s):  
Climate Variability ◽  
North Pacific ◽  
Western North Pacific ◽  
Large Scale ◽  
Lower Troposphere ◽  
Monsoon Trough ◽  
Climate Projections ◽  
Anticyclonic Circulation ◽  
Mean State

AbstractThe monsoon trough and subtropical high have long been acknowledged to exert a substantial modulating effect on the genesis and development of TCs in the western North Pacific (WNP). However, the potential upscaling effect of TCs on large-scale circulation remains poorly understood. This study revealed the considerable contributions of TCs to the climate mean state and variability in the WNP between 1958 and 2019, characterized by a strengthened monsoon trough and weakened subtropical anticyclonic circulation in the lower troposphere, enhanced anticyclonic circulation in the upper troposphere, and warming throughout the troposphere. TCs constituted distinct footprints in the long-term mean states of the WNP summer monsoon, and their contributions increased intraseasonal and interannual variance by 50%–70%. The interdecadal variations and long-term trends in intraseasonal variance were mainly due to the year-to-year fluctuations in TC activity. The size of TC footprints was positively correlated with the magnitude of TC activity.Our findings suggest that the full understanding of climate variability and changes cannot be achieved simply on the basis of low-frequency, large-scale circulations. Rather, TCs must be regarded as a crucial component in the climate system, and their interactions with large-scale circulations require thorough exploration. The long-term dataset created in this study provides an opportunity to study the interaction between TCs and TC-free large-scale circulations to advance our understanding of climate variability in the WNP. Our findings also indicate that realistic climate projections must involve the accurate simulations of TCs.

scholarly journals Pacific Decadal Oscillation Climate Variability and Temporal Pattern of Winter Flows in Northwestern North America

2010 ◽  
Vol 11 (4) ◽  
pp. 917-933 ◽  
Author(s):  
M. N. Khaliq ◽  
P. Gachon
Keyword(s):  
Time Series ◽  
Water Resources ◽  
North America ◽  
Climate Variability ◽  
Pacific Decadal Oscillation ◽  
Large Scale ◽  
Temporal Variations ◽  
Average Percentage ◽  
Daily Streamflow ◽  
Northwestern North America

Abstract There is growing concern about the effects of large-scale oceanic atmospheric climate variability, such as the Pacific decadal oscillation (PDO), on regional hydrology and water resources. In this paper, the effects of PDO on temporal patterns of winter (January–March) flow in northwestern North America (NWNA), which is believed to be a PDO-sensitive region, is studied for the period 1943–2007 using daily streamflow data from a much larger set of 179 stations, compared to previous studies in which only smaller subsets of these stations were analyzed. Time series of winter flows were divided into two nonoverlapping blocks corresponding to change points detected in time series of December–March mean monthly PDO indices. Both parametric and nonparametric measures of correlation and average percentage differences and average standardized differences from the period-of-record mean were explored. Like some of the previous studies, it is found that, on average, winter flows tend to be higher (lower) during the warm (cold) phase of the PDO and that establishes the physical link between large-scale climate variability and basin response. It is shown that the serial structure of time series of PDO indices conforms to that of a stochastic process with long-term persistence (LTP). Based on this finding and the climate–streamflow physical link, it is plausible to investigate temporal variations in winter flows with the LTP hypothesis, in addition to assuming merely independence (IND) or short-term persistence (STP). The results of the analysis demonstrate that the LTP mechanism, in combination with the STP, is able to explain more than half of the significant trends noted, with the IND assumption suggesting that the significance of trends reported in previous studies in NWNA may have been overstated. This result has important implications for future planning of regional water resources.

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