scholarly journals Assessing the predictability of MLR models for long-term streamflow using lagged climate indices as predictors: a case study of NSW (Australia)

2018 ◽  
Vol 50 (1) ◽  
pp. 262-281 ◽  
Author(s):  
Rijwana I. Esha ◽  
Monzur A. Imteaz
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Climate Indices ◽  
South Wales ◽  
Forecast Models ◽  
Input Variables ◽  
Combined Impact

Abstract The current study aims to assess the potential of statistical multiple linear regression (MLR) techniques to develop long-term streamflow forecast models for New South Wales (NSW). While most of the past studies were concentrated on revealing the relationship between streamflow and single concurrent or lagged climate indices, this study intends to explore the combined impact of large-scale climate drivers. Considering their influences on the streamflow of NSW, several major climate drivers – IPO (Inter Decadal Pacific Oscillation)/PDO (Pacific Decadal Oscillation), IOD (Indian Ocean Dipole) and ENSO (El Niño-Southern Oscillation) are selected. Single correlation analysis is exploited as the basis for selecting different combinations of input variables for developing MLR models to examine the extent of the combined impacts of the selected climate drivers on forecasting spring streamflow several months ahead. The developed models with all the possible combinations show significantly good results for all selected 12 stations in terms of Pearson correlation (r), root mean square error (RMSE), mean absolute error (MAE) and Willmott index of agreement (d). For each region, the best model with lower errors provides statistically significant maximum correlation which ranges from 0.51 to 0.65.

scholarly journals Historical drought patterns over Canada and their teleconnections with large-scale climate signals

2018 ◽  
Vol 22 (6) ◽  
pp. 3105-3124 ◽  
Author(s):  
Zilefac Elvis Asong ◽  
Howard Simon Wheater ◽  
Barrie Bonsal ◽  
Saman Razavi ◽  
Sopan Kurkute
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Kendall Test ◽  
Wavelet Coherence ◽  
Climate Indices ◽  
Data Sets ◽  
Canadian Prairies ◽  
Agriculture Industry ◽  
Spatio Temporal

Abstract. Drought is a recurring extreme climate event and among the most costly natural disasters in the world. This is particularly true over Canada, where drought is both a frequent and damaging phenomenon with impacts on regional water resources, agriculture, industry, aquatic ecosystems, and health. However, nationwide drought assessments are currently lacking and impacted by limited ground-based observations. This study provides a comprehensive analysis of historical droughts over the whole of Canada, including the role of large-scale teleconnections. Drought events are characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) over various temporal scales (1, 3, 6, and 12 consecutive months, 6 months from April to September, and 12 months from October to September) applied to different gridded monthly data sets for the period 1950–2013. The Mann–Kendall test, rotated empirical orthogonal function, continuous wavelet transform, and wavelet coherence analyses are used, respectively, to investigate the trend, spatio-temporal patterns, periodicity, and teleconnectivity of drought events. Results indicate that southern (northern) parts of the country experienced significant trends towards drier (wetter) conditions although substantial variability exists. Two spatially well-defined regions with different temporal evolution of droughts were identified – the Canadian Prairies and northern central Canada. The analyses also revealed the presence of a dominant periodicity of between 8 and 32 months in the Prairie region and between 8 and 40 months in the northern central region. These cycles of low-frequency variability are found to be associated principally with the Pacific–North American (PNA) and Multivariate El Niño/Southern Oscillation Index (MEI) relative to other considered large-scale climate indices. This study is the first of its kind to identify dominant periodicities in drought variability over the whole of Canada in terms of when the drought events occur, their duration, and how often they occur.

scholarly journals Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Niloufar Nouri ◽  
Naresh Devineni ◽  
Valerie Were ◽  
Reza Khanbilvardi
Keyword(s):  
Population Density ◽  
Large Scale ◽  
Southern Oscillation ◽  
The United States ◽  
Anthropogenic Factors ◽  
Climate Variables ◽  
Term Trend ◽  
Cyclical Variability ◽  
Long Term Trend

AbstractThe annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies.

scholarly journals Structure of ocean circulation between the Galápagos Islands and Ecuador

2013 ◽  
Vol 33 ◽  
pp. 3-12 ◽  
Author(s):  
C. Collins ◽  
A. Mascarenhas ◽  
R. Martinez
Keyword(s):  
Sea Level ◽  
Ocean Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Galapagos Islands ◽  
Surface Flow ◽  
Climate Indices ◽  
Late Winter ◽  
Coastal Current ◽  
Galápagos Islands

Abstract. From 27 March to 5 April 2009, upper ocean velocities between the Galápagos Islands and Ecuador were measured using a vessel mounted ADCP. A region of possible strong cross-hemisphere exchange was observed immediately to the east of the Galápagos, where a shallow (200 m) 300 km wide northeastward surface flow transported 7–11 Sv. Underlying this strong northeastward surface current, a southward flowing undercurrent was observed which was at least 600 m thick, 100 km wide, and had an observed transport of 7–8 Sv. Next to the Ecuador coast, the shallow (< 200 m) Ecuador Coastal Current was observed to extend offshore 100 km with strongest flow, 0.33 m s−1, near the surface. Immediately to the west of the Ecuador Coastal Current, flow was directed eastward and southward into the beginnings of the Peru-Chile Countercurrent. The integral of the surface currents between the Galápagos and Ecuador agreed well with observed sea level differences. Although the correlation of the sea level differences with large scale climate indices (Niño3 and the Southern Oscillation Index) was significant, more than half of the sea level variability was not explained. Seasonal variability of the sea level difference indicated that sea level was 2 cm higher at the Galápagos during late winter and early spring, which could be associated with the pattern of northward surface flows observed by R/V Knorr.

Oceanography of the New Zealand subantarctic region

2021 ◽  
Author(s):  
◽  
Aitana Forcén-Vázquez
Keyword(s):  
New Zealand ◽  
Interannual Variability ◽  
Water Column ◽  
Large Scale ◽  
Southern Oscillation ◽  
Complex Structure ◽  
Positive Trend ◽  
Intermediate Water ◽  
Subantarctic Region

<p>Subantarctic New Zealand is an oceanographycally dynamic region with the Subtropical Front (STF) to the north and the Subantarctic Front (SAF) to the south. This thesis investigates the ocean structure of the Campbell Plateau and the surrounding New Zealand subantarctic, including the spatial, seasonal, interannual and longer term variability over the ocean properties, and their connection to atmospheric variability using a combination of in-situ oceanographic measurements and remote sensing data.  The spatial and seasonal oceanographic structure in the New Zealand subantarctic region was investigated by analysing ten high resolution Conductivity Temperature and Depth (CTD) datasets, sampled during oceanographic cruises from May 1998 to February 2013. Position of fronts, water mass structure and changes over the seasons show a complex structure around the Campbell Plateau combining the influence of subtropical and subantarctic waters.  The spatial and interannual variability on the Campbell Plateau was described by analysing approximately 70 low resolution CTD profiles collected each year in December between 2002 and 2009. Conservative temperature and absolute salinity profiles reveal high variability in the upper 200m of the water column and a homogeneous water column from 200 to 600m depth. Temperature variability of about 0.7 °C, on occasions between consecutive years, is observed down to 900m depth. The presence of Subantarctic Mode Water (SAMW) on the Campbell Plateau is confirmed and Antarctic Intermediate Water (AAIW) reported for the first time in the deeper regions around the edges of the plateau.  Long-term trends and variability over the Campbell Plateau were investigated by analysing satellite derived Sea Level Anomalies (SLA) and Sea Surface Temperature (SST) time series. Links to large scale atmospheric processes are also explored through correlation with the Southern Oscillation Index (SOI) and Southern Annular Mode (SAM). SST shows a strong seasonality and interannual variability which is linked to local winds, but no significant trend is found. The SLA over the Campbell Plateau has increased at a rate of 5.2 cm decade⁻¹ in the last two decades. The strong positive trend in SLA appears to be a combination of the response of the ocean to wind stress curl (Ekman pumping), thermal expansion and ocean mass redistribution via advection amongst others.  These results suggest that the variability on the Campbell Plateau is influenced by the interaction of the STF and the SAF. The STF influence reaches the limit of the SAF over the western Campbell Plateau and the SAF influence extends all around the plateau. Results also suggest different connections between the plateau with the surrounding oceans, e.g., along the northern edge with the Bounty Trough and via the southwest edge with the SAF. A significant correlation with SOI and little correlation with SAM suggest a stronger response to tropically driven processes in the long-term variability on the Campbell Plateau.  The results of this thesis provide a new definitive assessment of the circulation, water masses and variability of the Campbell Plateau on mean, annual, and interannual time scales which will support research in other disciplines such as palaeoceanography, fisheries management and climate.</p>

Variability of Arctic Sea Ice Based on Quantile Regression and the Teleconnection with Large-Scale Climate Patterns

2020 ◽  
Vol 33 (10) ◽  
pp. 4009-4025
Author(s):  
Shuyu Zhang ◽  
Thian Yew Gan ◽  
Andrew B. G. Bush
Keyword(s):  
Sea Ice ◽  
Quantile Regression ◽  
Large Scale ◽  
Southern Oscillation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Climate Indices ◽  
The Pacific ◽  
Arctic Sea ◽  
Climate Patterns

AbstractUnder global warming, Arctic sea ice has declined significantly in recent decades, with years of extremely low sea ice occurring more frequently. Recent studies suggest that teleconnections with large-scale climate patterns could induce the observed extreme sea ice loss. In this study, a probabilistic analysis of Arctic sea ice was conducted using quantile regression analysis with covariates, including time and climate indices. From temporal trends at quantile levels from 0.01 to 0.99, Arctic sea ice shows statistically significant decreases over all quantile levels, although of different magnitudes at different quantiles. At the representative extreme quantile levels of the 5th and 95th percentiles, the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), and the Pacific–North American pattern (PNA) have more significant influence on Arctic sea ice than El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the Atlantic multidecadal oscillation (AMO). Positive AO as well as positive NAO contribute to low winter sea ice, and a positive PNA contributes to low summer Arctic sea ice. If, in addition to these conditions, there is concurrently positive AMO and PDO, the sea ice decrease is amplified. Teleconnections between Arctic sea ice and the climate patterns were demonstrated through a composite analysis of the climate variables. The anomalously strong anticyclonic circulation during the years of positive AO, NAO, and PNA promotes more sea ice export through Fram Strait, resulting in excessive sea ice loss. The probabilistic analyses of the teleconnections between the Arctic sea ice and climate patterns confirm the crucial role that the climate patterns and their combinations play in overall sea ice reduction, but particularly for the low and high quantiles of sea ice concentration.

scholarly journals Predicting and Analyzing Language Specificity in Social Media Posts

2019 ◽  
Vol 33 ◽  
pp. 6415-6422 ◽  
Author(s):  
Yifan Gao ◽  
Yang Zhong ◽  
Daniel Preoţiuc-Pietro ◽  
Junyi Jessy Li
Keyword(s):  
Social Media ◽  
Computational Linguistics ◽  
Large Scale ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Fine Grained ◽  
Argumentation Mining ◽  
Sentence Level ◽  
Prediction Systems ◽  
Mental Health Factors

In computational linguistics, specificity quantifies how much detail is engaged in text. It is an important characteristic of speaker intention and language style, and is useful in NLP applications such as summarization and argumentation mining. Yet to date, expert-annotated data for sentence-level specificity are scarce and confined to the news genre. In addition, systems that predict sentence specificity are classifiers trained to produce binary labels (general or specific).We collect a dataset of over 7,000 tweets annotated with specificity on a fine-grained scale. Using this dataset, we train a supervised regression model that accurately estimates specificity in social media posts, reaching a mean absolute error of 0.3578 (for ratings on a scale of 1-5) and 0.73 Pearson correlation, significantly improving over baselines and previous sentence specificity prediction systems. We also present the first large-scale study revealing the social, temporal and mental health factors underlying language specificity on social media.

scholarly journals Multiscale Spatio-Temporal Changes of Precipitation Extremes in Beijing-Tianjin-Hebei Region, China during 1958–2017

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 462 ◽  
Author(s):  
Xiaomeng Song ◽  
Xianju Zou ◽  
Chunhua Zhang ◽  
Jianyun Zhang ◽  
Fanzhe Kong
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Periodic Oscillation ◽  
Precipitation Extremes ◽  
Climate Indices ◽  
Climate Anomalies ◽  
Wet Precipitation ◽  
Spatio Temporal ◽  
One Year ◽  
Periodic Components

In this study, based on daily precipitation records during 1958–2017 from 28 meteorological stations in the Beijing-Tianjin-Hebei (BTH) region, the spatio-temporal variations in precipitation extremes defined by twelve indices are analyzed by the methods of linear regression, Mann-Kendall test and continuous wavelet transform. The results showed that the spatial patterns of all the indices except for consecutive dry days (CDD) and consecutive wet days (CWD) were similar to that of annual total precipitation with the high values in the east and the low value in the west. Regionally averaged precipitation extremes were characterized by decreasing trends, of which five indices (i.e., very heavy precipitation days (R50), very wet precipitation (R95p), extreme wet precipitation (R99p), max one-day precipitation (R × 1day), and max five-day precipitation (R × 5day)) exhibited significantly decreasing trends at 5% level. From monthly and seasonal scale, almost all of the highest values in R × 1day and R × 5day occurred in summer, especially in July and August due to the impacts of East Asian monsoon climate on inter-annual uneven distribution of precipitation. The significant decreasing trends in annual R×1day and R×5day were mainly caused by the significant descend in summer. Besides, the possible associations between precipitation extremes and large-scale climate anomalies (e.g., ENSO (El Niño Southern Oscillation), NAO (North Atlantic Oscillation), IOD (Indian Ocean Dipole), and PDO (Pacific Decadal Oscillation)) were also investigated using the correlation analysis. The results showed that the precipitation extremes were significantly influenced by ENSO with one-year ahead, and the converse correlations between the precipitation extremes and climate indices with one-year ahead and 0-year ahead were observed. Moreover, all the indices show significant two- to four-year periodic oscillation during the entire period of 1958–2017, and most of indices show significant four- to eight-year periodic oscillation during certain periods. The influences of climate anomalies on precipitation extremes were composed by different periodic components, with most of higher correlations occurring in low-frequency components.

scholarly journals Regional Characteristics of Long-Term Variability of Summer Precipitation in the Poyang Lake Basin and Possible Links with Large-Scale Circulations

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1033
Author(s):  
Hua Zhu ◽  
Handan He ◽  
Hongxiang Fan ◽  
Ligang Xu ◽  
Jiahu Jiang ◽  
...  
Keyword(s):  
Large Scale ◽  
Poyang Lake ◽  
Summer Precipitation ◽  
Climate Indices ◽  
Lake Basin ◽  
Regional Characteristics ◽  
Poyang Lake Basin ◽  
Region I ◽  
Region Ii

Understanding the spatiotemporal regime of summer precipitation at local scales plays a key role in regional prevention and mitigation of floods disasters and water resources management. Previous works focused on spatiotemporal characteristics of a region as a whole but left the influence of associated physical factors on sub-regions unexplored. Based on the precipitation data of 77 meteorological stations in the Poyang Lake basin (PYLB) from 1959 to 2013, we have investigated regional characteristics of summer precipitation in the PYLB by integrating the rotated empirical orthogonal function (REOF) analysis with hierarchical clustering algorithm (HCA). Then the long-term variability of summer precipitation in sub-regions of the PYLB and possible links with large-scale circulations was investigated using multiple trend analyses, wavelet analysis and correlation analysis. The results indicate that summer precipitation variations in the PYLB were of very striking regional characteristics. The PYLB was divided into three independent sub-regions based on two leading REOF modes and silhouette coefficient (SC). These sub-regions were located in northern PYLB (sub-region I), central PYLB (sub-region II), and southern PYLB (sub-region III). The summer precipitation in different sub-regions exhibited distinct variation trends and periodicities, which was associated with different factors. All sub-regions show no trends over the whole period 1959–2013, rather they show trends in different periods. Trends per decade in annual summer precipitation in sub-region I and sub-region II were consistent for all periods with different start and end years. The oscillations periods with 2–3 years were found in summer precipitation of all the three sub-regions. Summer precipitation in sub-region I was significantly positively correlated with the previous Indian Ocean Dipole (IOD) event, but negatively correlated with East Asian Summer Monsoon (EASM). While summer precipitation in sub-region II and sub-region III showed weak teleconnections with climate indices. All of the results of this study are conducive to further understand both the regional climate variations in the PYLB and response to circulation patterns variations.

scholarly journals Probabilistic streamflow forecasts based on hydrologic persistence and large-scale climate signals in central Texas

2010 ◽  
Vol 13 (4) ◽  
pp. 760-774 ◽  
Author(s):  
Wenge Wei ◽  
David W. Watkins
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
River System ◽  
Forecast Model ◽  
Climate Indices ◽  
Lead Times ◽  
Forecast Performance ◽  
Streamflow Forecasts ◽  
Central Texas ◽  
Multiple Predictor

Skillful streamflow forecasts at seasonal lead times may be useful to water managers seeking to provide reliable water supplies and maximize system benefits. In this study, streamflow autocorrelation and large-scale climate information are used to generate probabilistic streamflow forecasts for the Lower Colorado River system in central Texas. A number of potential predictors are evaluated for forecasting flows in various seasons, including large-scale climate indices related to the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO) and others. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. An ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distribution-oriented metrics, and implications for decision making are discussed.

scholarly journals Historical drought patterns over Canada and their relation to teleconnections

2018 ◽  
Author(s):  
Zilefac Elvis Asong ◽  
Howard Simon Wheater ◽  
Barrie Bonsal ◽  
Saman Razavi ◽  
Sopan Kurkute
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Kendall Test ◽  
Wavelet Coherence ◽  
Climate Indices ◽  
Data Sets ◽  
Canadian Prairies ◽  
Agriculture Industry ◽  
Climate Event

Abstract. Drought is a recurring extreme climate event and among the most costly natural disasters in the world. This is particularly true over Canada, where drought is both a frequent and damaging phenomenon with impacts on regional water resources, agriculture, industry, aquatic ecosystems and health. However, nation-wide drought assessments are currently lacking and impacted by limited ground-based observations. This study provides a comprehensive analysis of historical droughts over the whole of Canada, including the role of large-scale teleconnections. Drought events are characterized by the Standardized Precipitation-Evapotranspiration Index (SPEI) over various temporal scales (1, 3, 6, and 12 consecutive months, 6 months from April to September, and 12 months from October to September) applied to different gridded monthly data sets for the period 1950–2013. The Mann Kendall test, Rotated Empirical Orthogonal Function, Continuous Wavelet Transform, and Wavelet Coherence analyses are used, respectively, to investigate the trend, spatiotemporal patterns, periodicity, and teleconnectivity of drought events. Results indicate that southern (northern) parts of the country experienced significant trends towards drier (wetter) conditions although substantial variability exists. Two spatially well-defined regions with different temporal evolution of droughts were identified―the Canadian Prairies and Northern-central Canada. The analyses also revealed the presence of a dominant periodicity of between 8–32 months in the Prairie region, and 8–40 months in the Northern central region. These cycles of low-frequency variability are found to be associated principally to the Pacific-North American (PNA) and Multivariate El Niño/Southern Oscillation Index (MEI) relative to other considered large-scale climate indices. This study is the first of its kind to identify dominant periodicities in drought variability over the whole of Canada in terms of when the drought events occur, the duration, and how often they do so.

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