scholarly journals Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach

2019 ◽  
Vol 26 (3) ◽  
pp. 251-266 ◽  
Author(s):  
Jürgen Kurths ◽  
Ankit Agarwal ◽  
Roopam Shukla ◽  
Norbert Marwan ◽  
Maheswaran Rathinasamy ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Indian Subcontinent ◽  
Atlantic Multidecadal Oscillation ◽  
Spatial Diversity ◽  
Wavelet Coherence ◽  
Multiscale Approach ◽  
The North ◽  
Non Linear ◽  
The North Atlantic Oscillation ◽  
Wavelet Coherence Analysis

Abstract. A better understanding of precipitation dynamics in the Indian subcontinent is required since India's society depends heavily on reliable monsoon forecasts. We introduce a non-linear, multiscale approach, based on wavelets and event synchronization, for unravelling teleconnection influences on precipitation. We consider those climate patterns with the highest relevance for Indian precipitation. Our results suggest significant influences which are not well captured by only the wavelet coherence analysis, the state-of-the-art method in understanding linkages at multiple timescales. We find substantial variation across India and across timescales. In particular, El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) mainly influence precipitation in the south-east at interannual and decadal scales, respectively, whereas the North Atlantic Oscillation (NAO) has a strong connection to precipitation, particularly in the northern regions. The effect of the Pacific Decadal Oscillation (PDO) stretches across the whole country, whereas the Atlantic Multidecadal Oscillation (AMO) influences precipitation particularly in the central arid and semi-arid regions. The proposed method provides a powerful approach for capturing the dynamics of precipitation and, hence, helps improve precipitation forecasting.

scholarly journals Unraveling the spatial diversity of Indian precipitation teleconnections via nonlinear multi-scale approach

2019 ◽  
Author(s):  
Jürgen Kurths ◽  
Ankit Agarwal ◽  
Norbert Marwan ◽  
Maheswaran Rathinasamy ◽  
Levke Caesar ◽  
...  
Keyword(s):  
Time Scales ◽  
Southern Oscillation ◽  
Indian Subcontinent ◽  
Spatial Diversity ◽  
Wavelet Coherence ◽  
Multiple Time Scales ◽  
Multiscale Approach ◽  
Multiple Time ◽  
The North ◽  
Wavelet Coherence Analysis

Abstract. A better understanding of precipitation dynamics in the Indian subcontinent is required since India’s society depends heavily on reliable monsoon forecasts. We introduce a nonlinear, multiscale approach, based on wavelets and event synchronization, for unraveling teleconnection influences on precipitation. We consider those climate patterns with highest relevance for Indian precipitation. Our results suggest significant influences which are not well captured by only the wavelet coherence analysis, the state-of-the-art method in understanding linkages at multiple time scales. We find substantial variation across India and across time scales. In particular, El Niño/Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) mainly influence precipitation in the southeast at interannual and decadal scales, respectively, whereas the North Atlantic Oscillation (NAO) has a strong connection to precipitation particularly in the northern regions. The effect of PDO stretches across the whole country, whereas AMO influences precipitation particularly in the central arid and semi-arid regions. The proposed method provides a powerful approach for capturing the dynamics of precipitation and, hence, helps improving precipitation forecasting.

scholarly journals Variability of extreme precipitation over Europe and its relationships with teleconnection patterns

2013 ◽  
Vol 10 (10) ◽  
pp. 12331-12371 ◽  
Author(s):  
A. Casanueva ◽  
C. Rodríguez-Puebla ◽  
M. D. Frías ◽  
N. González-Reviriego
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Southern Oscillation ◽  
Global Climate ◽  
Atlantic Multidecadal Oscillation ◽  
Physical Factors ◽  
Teleconnection Patterns ◽  
The North ◽  
The North Atlantic Oscillation

Abstract. A growing interest in extreme precipitation has spread through the scientific community due to the effects of global climate change on the hydrological cycle and their threat on natural systems more than averaged climatic values. Understanding the variability of hydrological indices and their association to atmospheric processes could help to project the frequency and severity of extremes. This paper evaluates the trend of three precipitation extremes: the number of consecutive dry/wet days (CDD/CWD) and the quotient of the precipitation in days where daily precipitation exceeds the 95th percentile of the reference period and the total amount of precipitation (or contribution of very wet days, R95pTOT). The aim of this study is twofold. First, extreme indicators are compared against accumulated precipitation (RR) over Europe in terms of trends using non-parametric approaches. Second, we analyse the geographic opposite trends found over different parts of Europe by considering their relationships with large-scale processes, using different teleconnection patterns. The study is accomplished for the four seasons using the gridded E-OBS dataset developed within the EU ENSEMBLES project. Different patterns of variability were found for CWD and CDD in winter and summer, with north-south and east–west configurations, respectively. We consider physical factors to understand the extremes variability by linking large-scale processes and hydrological extremes. Opposite association with the North Atlantic Oscillation in winter and summer, and the relationships with the Scandinavian, East Atlantic patterns and El Niño/Southern Oscillation events in spring and autumn gave insight into the trend differences. Significant relationships were found between the Atlantic Multidecadal Oscillation and very extreme precipitation (R95pTOT) during the whole year. The largest extreme anomalies were analysed by composite maps using atmospheric variables and sea surface temperature. The association of extreme precipitation indices and large-scale variables found in this work could pave the way of new possibilities for the projection of extremes in downscaling techniques.

scholarly journals Prediction Models for Annual U.S. Hurricane Counts

2006 ◽  
Vol 19 (12) ◽  
pp. 2935-2952 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger
Keyword(s):  
Prediction Models ◽  
Southern Oscillation ◽  
Atlantic Multidecadal Oscillation ◽  
Skill Levels ◽  
The North ◽  
Out Of Sample ◽  
Squared Prediction Error ◽  
Hurricane Activity ◽  
Modeling Strategy ◽  
The North Atlantic Oscillation

Abstract The authors build on their efforts to understand and predict coastal hurricane activity by developing statistical seasonal forecast models that can be used operationally. The modeling strategy uses May–June averaged values representing the North Atlantic Oscillation (NAO), the Southern Oscillation index (SOI), and the Atlantic multidecadal oscillation to predict the probabilities of observing U.S. hurricanes in the months ahead (July–November). The models are developed using a Bayesian approach and make use of data that extend back to 1851 with the earlier hurricane counts (prior to 1899) treated as less certain relative to the later counts. Out-of-sample hindcast skill is assessed using the mean-squared prediction error within a hold-one-out cross-validation exercise. Skill levels are compared to climatology. Predictions show skill above climatology, especially using the NAO + SOI and the NAO-only models. When the springtime NAO values are below normal, there is a heightened risk of U.S. hurricane activity relative to climatology. The preliminary NAO value for 2005 is −0.565 standard deviations so the NAO-only model predicts a 13% increase over climatology of observing three or more U.S. hurricanes.

scholarly journals Teleconnections between Monthly Rainfall Variability and Large-Scale Climate Indices in Southwestern Colombia

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1863 ◽  
Author(s):  
Teresita Canchala ◽  
Wilfredo Alfonso-Morales ◽  
Wilmar Loaiza Cerón ◽  
Yesid Carvajal-Escobar ◽  
Eduardo Caicedo-Bravo
Keyword(s):  
Water Resources ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Monthly Rainfall ◽  
Wavelet Coherence ◽  
Climate Indices ◽  
Annual Scale ◽  
Wavelet Coherence Analysis

Given that the analysis of past monthly rainfall variability is highly relevant for the adequate management of water resources, the relationship between the climate-oceanographic indices, and the variability of monthly rainfall in Southwestern Colombia at different time scales was chosen as the research topic. It should also be noted that little-to-no research has been carried out on this topic before. For the purpose of conducting this research, we identified homogeneous rainfall regions while using Non-Linear Principal Component Analysis (NLPCA) and Self-Organizing Maps (SOM). The rainfall variability modes were obtained from the NLPCA, while their teleconnection in relation to the climate indices was obtained from Pearson’s Correlations and Wavelet Transform. The regionalization process clarified that Nariño has two regions: the Andean Region (AR) and the Pacific Region (PR). The NLPCA showed two modes for the AR, and one for the PR, with an explained variance of 75% and 48%, respectively. The correlation analyses between the first nonlinear components of AR and PR regarding climate indices showed AR high significant positive correlations with Southern Oscillation Index (SOI) index and negative correlations with El Niño/Southern Oscillation (ENSO) indices. PR showed positive ones with Niño1 + 2, and Niño3, and negative correlations with Niño3.4 and Niño4, although their synchronous relationships were not statistically significant. The Wavelet Coherence analysis showed that the variability of the AR rainfall was influenced principally by the Niño3.4 index on the 3–7-year inter-annual scale, while PR rainfall were influenced by the Niño3 index on the 1.5–3-year inter-annual scale. The El Niño (EN) events lead to a decrease and increase in the monthly rainfall on AR and PR, respectively, while, in the La Niña (LN) events, the opposite occurred. These results that are not documented in previous studies are useful for the forecasting of monthly rainfall and the planning of water resources in the area of study.

Cluster Models

2013 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger
Keyword(s):  
East Coast ◽  
Gulf Coast ◽  
Southern Oscillation ◽  
Time Correlation ◽  
Additional Time ◽  
Feature Clustering ◽  
The North ◽  
Time Space ◽  
Hurricane Activity ◽  
The North Atlantic Oscillation

A cluster is a group of the same or similar events close together. Clusters arise in hurricane origin locations, tracks, and landfalls. In this chapter, we look at how to analyze and model clusters. We divide the chapter into methods for time, space, and feature clustering. Of the three feature clustering is best known to climatologists. We begin by showing you how to detect and model time clusters. Consecutive hurricanes originating in the same area often take similar paths. This grouping, or clustering, increases the potential for multiple landfalls above what you expect from random events. A statistical model for landfall probability captures clustering through covariates like the North Atlantic Oscillation (NAO), which relates a steering mechanism (position and strength of the subtropical high pressure) to coastal hurricane activity. But there could be additional time correlation not related to the covariates. A model that does not account for this extra variation will underestimate the potential for multiple hits in a season. Following Jagger and Elsner (2006), you consider three coastal regions including the Gulf Coast, Florida, and the East Coast (Fig. 6.2). Regions are large enough to capture enough hurricanes, but not too large as to include many non-coastal strikes. Here you use hourly position and intensity data described in Chapter 6. For each hurricane, you note its wind speed maximum within each region. If the maximum wind exceeds 33 m s−1, then you count it as a hurricane for the region. A tropical cyclone that affects more than one region at hurricane intensity is counted in each region. Because of this, the sum of the regional counts is larger than the total count. Begin by loading annual.RData. These data were assembled in Chapter 6. Subset the data for years starting with 1866. . . . > load("annual.RData") > dat = subset(annual, Year >= 1866) . . . The covariate Southern Oscillation Index (SOI) data begins in 1866 . Next, extract all hurricane counts for the Gulf coast, Florida, and East coast regions. . . . > cts = dat[, c("G.1", "F.1", "E.1")] . . .

scholarly journals Impacts of large-scale atmospheric circulation changes due to winter sea-ice retreat on Black Carbon transport and deposition to the Arctic

2016 ◽  
Author(s):  
Luca Pozzoli ◽  
Srdan Dobricic ◽  
Simone Russo ◽  
Elisabetta Vignati
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
North Atlantic ◽  
Large Scale ◽  
Southern Oscillation ◽  
The Arctic ◽  
The North ◽  
Ice Retreat ◽  
The North Atlantic Oscillation ◽  
Sea Ice Retreat

Abstract. Winter warming and sea ice retreat observed in the Arctic in the last decades determine changes of large scale atmospheric circulation pattern that may impact as well the transport of black carbon (BC) to the Arctic and its deposition on the sea ice, with possible feedbacks on the regional and global climate forcing. In this study we developed and applied a new statistical algorithm, based on the Maximum Likelihood Estimate approach, to determine how the changes of three large scale weather patterns (the North Atlantic Oscillation, the Scandinavian Blocking, and the El Nino-Southern Oscillation), associated with winter increasing temperatures and sea ice retreat in the Arctic, impact the transport of BC to the Arctic and its deposition. We found that the three atmospheric patterns together determine a decreasing winter deposition trend of BC between 1980 and 2015 in the Eastern Arctic while they increase BC deposition in the Western Arctic. The increasing trend is mainly due to the more frequent occurrences of stable high pressure systems (atmospheric blocking) near Scandinavia favouring the transport in the lower troposphere of BC from Europe and North Atlantic directly into to the Arctic. The North Atlantic Oscillation has a smaller impact on BC deposition in the Arctic, but determines an increasing BC atmospheric load over the entire Arctic Ocean with increasing BC concentrations in the upper troposphere. The El Nino-Southern Oscillation does not influence significantly the transport and deposition of BC to the Arctic. The results show that changes in atmospheric circulation due to polar atmospheric warming and reduced winter sea ice significantly impacted BC transport and deposition. The anthropogenic emission reductions applied in the last decades were, therefore, crucial to counterbalance the most likely trend of increasing BC pollution in the Arctic.

scholarly journals Intraseasonal and Interannual Variability in North American Storm Tracks and Its Relationship to Equatorial Pacific Variability

2013 ◽  
Vol 141 (10) ◽  
pp. 3610-3625 ◽  
Author(s):  
Kevin M. Grise ◽  
Seok-Woo Son ◽  
John R. Gyakum
Keyword(s):  
North America ◽  
Interannual Variability ◽  
North American ◽  
Southern Oscillation ◽  
Extratropical Cyclones ◽  
The North ◽  
The Pacific ◽  
Cyclone Tracks ◽  
Lagrangian Tracking ◽  
The North Atlantic Oscillation

Abstract Extratropical cyclones play a principal role in wintertime precipitation and severe weather over North America. On average, the greatest number of cyclones track 1) from the lee of the Rocky Mountains eastward across the Great Lakes and 2) over the Gulf Stream along the eastern coastline of North America. However, the cyclone tracks are highly variable within individual winters and between winter seasons. In this study, the authors apply a Lagrangian tracking algorithm to examine variability in extratropical cyclone tracks over North America during winter. A series of methodological criteria is used to isolate cyclone development and decay regions and to account for the elevated topography over western North America. The results confirm the signatures of four climate phenomena in the intraseasonal and interannual variability in North American cyclone tracks: the North Atlantic Oscillation (NAO), the El Niño–Southern Oscillation (ENSO), the Pacific–North American pattern (PNA), and the Madden–Julian oscillation (MJO). Similar signatures are found using Eulerian bandpass-filtered eddy variances. Variability in the number of extratropical cyclones at most locations in North America is linked to fluctuations in Rossby wave trains extending from the central tropical Pacific Ocean. Only over the far northeastern United States and northeastern Canada is cyclone variability strongly linked to the NAO. The results suggest that Pacific sector variability (ENSO, PNA, and MJO) is a key contributor to intraseasonal and interannual variability in the frequency of extratropical cyclones at most locations across North America.

Dynamics of the ENSO Teleconnection and NAO Variability in the North Atlantic–European Late Winter

2020 ◽  
Vol 33 (3) ◽  
pp. 907-923 ◽  
Author(s):  
Bianca Mezzina ◽  
Javier García-Serrano ◽  
Ileana Bladé ◽  
Fred Kucharski
Keyword(s):  
North Atlantic ◽  
Southern Oscillation ◽  
Reanalysis Data ◽  
Late Winter ◽  
Sea Level Pressure ◽  
The North ◽  
Nao Index ◽  
Eddy Heat Flux ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

AbstractThe winter extratropical teleconnection of El Niño–Southern Oscillation (ENSO) in the North Atlantic–European (NAE) sector remains controversial, concerning both the amplitude of its impacts and the underlying dynamics. However, a well-established response is a late-winter (January–March) signal in sea level pressure (SLP) consisting of a dipolar pattern that resembles the North Atlantic Oscillation (NAO). Clarifying the relationship between this “NAO-like” ENSO signal and the actual NAO is the focus of this study. The ENSO–NAE teleconnection and NAO signature are diagnosed by means of linear regression onto the sea surface temperature (SST) Niño-3.4 index and an EOF-based NAO index, respectively, using long-term reanalysis data (NOAA-20CR, ERA-20CR). While the similarity in SLP is evident, the analysis of anomalous upper-tropospheric geopotential height, zonal wind, and transient-eddy momentum flux, as well as precipitation and meridional eddy heat flux, suggests that there is no dynamical link between the phenomena. The observational results are further confirmed by analyzing two 10-member ensembles of atmosphere-only simulations (using an intermediate-complexity and a state-of-the-art model) with prescribed SSTs over the twentieth century. The SST-forced variability in the Northern Hemisphere is dominated by the extratropical ENSO teleconnection, which provides modest but significant SLP skill in the NAE midlatitudes. The regional internally generated variability, estimated from residuals around the ensemble mean, corresponds to the NAO pattern. It is concluded that distinct dynamics are at play in the ENSO–NAE teleconnection and NAO variability, and caution is advised when interpreting the former in terms of the latter.

scholarly journals Dynamic wavelet correlation analysis for multivariate climate time series

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Josué M. Polanco-Martínez ◽  
Javier Fernández-Macho ◽  
Martín Medina-Elizalde
Keyword(s):  
Time Series ◽  
Tropical Cyclone ◽  
North Atlantic ◽  
Large Scale ◽  
Southern Oscillation ◽  
Atlantic Multidecadal Oscillation ◽  
Last Millennium ◽  
Climate Data ◽  
The North ◽  
Climate Time Series

AbstractThe wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale reconstructed climate variables characterizing North Atlantic: i.e. sea surface temperatures (SST) from the tropical cyclone main developmental region (MDR), the El Niño-Southern Oscillation (ENSO), the North Atlantic Multidecadal Oscillation (AMO), and tropical cyclone counts (TC). We examine the former three large-scale variables because they are known to influence North Atlantic tropical cyclone activity and because their underlying drivers are still under investigation. WLMC results obtained for these multivariate climate time series suggest that: (1) MDRSST and AMO show the highest correlation with each other and with respect to the TC record over the last millennium, and: (2) MDRSST is the dominant climate variable that explains TC temporal variability. WLMC results confirm that this method is able to capture the most fundamental information contained in multivariate climate time series and is suitable to investigate correlation among climate time series in a multivariate context.

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