Inferring long memory processes in the climate network via ordinal pattern analysis

We use ordinal patterns and symbolic analysis to construct global climate networks and uncover long and short term memory processes. The data analyzed is the monthly averaged surface air temperature (SAT field) and the results suggest that the time variability of the SAT field is determined by patterns of oscillatory behavior that repeat from time to time, with a periodicity related to intraseasonal oscillations and to El Ni\~{n}o on seasonal-to-interannual time scales.Comment: 10 pages, 13 figures Enlarged version, new sections and figures. Accepted in Chao

Understanding seasonal climate predictability in the Atlantic sector

This dissertation aims at understanding ocean-atmosphere interactions in the Atlantic basin, and how this coupling may lead to increased climate predictability on seasonal-to-interannual time scales. Two regions are studied: the South Atlantic convergence zone (SACZ), and the tropical Atlantic. We studied the SACZ during austral summer and separated its variability into forced and internal components. This was done by applying a signal-to-noise optimization procedure to an ensemble of integrations of the NCAR Community Climate Model (CCM3)forced with observed Sea Surface Temperature (SST). The analysis yielded two dominant responses: (1) a response to local Atlantic SST consisting of a dipole-like structure in precipitation close to the coast of South America; (2) a response to Pacific SST which manifests mainly in the upper-level circulation consisting of a northeastward shift of the SACZ during El Niño events. The land portion of the SACZ was found to be primarily dominated by internal variability, thereby having limited potential predictability at seasonal time scales. We studied two aspects of tropical Atlantic Variability (TAV). First, we investigated the effect of extratropical variability on the gradient mode. We found that the intensive Southern Hemisphere (SH) winter variability can play a pre-conditioning role in the onset of the interhemispheric anomalies in the deep tropics during boreal spring. This SH influence on TAV is contrasted with its northern counterpart that primarily comes from the North Atlantic Oscillation during boreal winter. Second, we explored the importance of ocean dynamics in the predictability of TAV. We used the CCM3 coupled to a slab ocean as a tier-one prediction system. The ocean processes are included as a statistical correction that parameterizes the heat transport due to anomalous linear ocean dynamics. The role of ocean dynamics was studied by comparing prediction runs with and without the correction. We showed that in the corrected region the corrected model outperforms the non-corrected one particularly at long lead times. Furthermore, when the model was initialized with global initial conditions, tropical Atlantic SST anomalies are skillfully predicted for lead times of up to six months. As result, the corrected model showed high skill in predicting rainfall in the ITCZ during boreal spring

Sensitivity of the tropical climate to an interhemispheric thermal gradient: the role of tropical ocean dynamics

This study aims to determine the role of the tropical ocean dynamics in the response of the climate to extratropical thermal forcing. We analyse and compare the outcomes of coupling an atmospheric general circulation model (AGCM) with two ocean models of different complexity. In the first configuration the AGCM is coupled with a slab ocean model while in the second a reduced gravity ocean (RGO) model is additionally coupled in the tropical region. We find that the imposition of extratropical thermal forcing (warming in the Northern Hemisphere and cooling in the Southern Hemisphere with zero global mean) produces, in terms of annual means, a weaker response when the RGO is coupled, thus indicating that the tropical ocean dynamics oppose the incoming remote signal. On the other hand, while the slab ocean coupling does not produce significant changes to the equatorial Pacific sea surface temperature (SST) seasonal cycle, the RGO configuration generates strong warming in the central-eastern basin from April to August balanced by cooling during the rest of the year, strengthening the seasonal cycle in the eastern portion of the basin. We hypothesize that such changes are possible via the dynamical effect that zonal wind stress has on the thermocline depth. We also find that the imposed extratropical pattern affects El Ninõ-Southern Oscillation, weakening its amplitude and low-frequency behaviour

Land–atmosphere coupling in El Niño influence over South America

This study addresses the role of soil moisture and its interaction with the overlying atmosphere in setting up climate anomalies over South America during El Niño years using observations as well as Atmospheric General Circulation Model (AGCM) simulations. It is found that during summertime land–atmosphere interaction is instrumental in setting the spatial pattern and sign of surface air temperature anomalies, and increases substantially the amplitude of precipitation anomalies particularly in southeastern South America. Thus, in order to improve the seasonal forecasts over South America it is necessary to represent properly not only the teleconnection processes but also the regional land–atmosphere interactions

Intraseasonal Predictions for the South American Rainfall Dipole

Funding Information: All authors acknowledge PEDECIBA, Uruguay. N. D. acknowledges Comisión Académica de Posgrado (CAP), Uruguay. N. R. acknowledges the Comisión Sectorial de Investigación Científica (CSIC), Uruguay, group grant “CSIC2018‐FID13‐grupo ID 722.” Publisher Copyright: ©2020. American Geophysical Union. All Rights Reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Peer reviewedPublisher PD

Machine learning prediction of the Madden-Julian oscillation

The socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden–Julian oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, and can promote or enhance extreme events in both, the tropics and the extratropics. Forecasting extreme events on the sub-seasonal time scale (from 10 days to about 3 months) is very challenging due to a poor understanding of the phenomena that can increase predictability on this time scale. Here we show that two artificial neural networks (ANNs), a feed-forward neural network and a recurrent neural network, allow a very competitive MJO prediction. While our average prediction skill is about 26–27 days (which competes with that obtained with most computationally demanding state-of-the-art climate models), for some initial phases and seasons the ANNs have a prediction skill of 60 days or longer. Furthermore, we show that the ANNs have a good ability to predict the MJO phase, but the amplitude is underestimated

Evolution of atmospheric connectivity in the 20th century

We aim to study the evolution of the upper atmosphere connectivity over the 20th century as well as to distinguish the oceanically forced component from the atmospheric internal variability. For this purpose we build networks from two different reanalysis data sets using both linear and nonlinear statistical similarity measures to determine the existence of links between different regions of the world in the two halves of the last century. We furthermore use symbolic analysis to emphasize intra-seasonal, intra-annual and inter-annual timescales. Both linear and nonlinear networks have similar structures and evolution, showing that the most connected regions are in the tropics over the Pacific Ocean. Also, the Southern Hemisphere extratropics have more connectivity in the first half of the 20th century, particularly on intra-annual and intra-seasonal timescales. Changes over the Pacific main connectivity regions are analyzed in more detail. Both linear and nonlinear networks show that the central and western Pacific regions have decreasing connectivity from early 1900 up to about 1940, when it starts increasing again until the present. The inter-annual network shows a similar behavior. However, this is not true of other timescales. On intra-annual timescales the minimum connectivity is around 1956, with a negative (positive) trend before (after) that date for both the central and western Pacific. While this is also true of the central Pacific on intra-seasonal timescales, the western Pacific shows a positive trend during the entire 20th century. In order to separate the internal and forced connectivity networks and to study their evolution through time, an ensemble of atmospheric general circulation model outputs is used. The results suggest that the main connectivity patterns captured in the reanalysis networks are due to the oceanically forced component, particularly on inter-annual timescales. Moreover, the atmospheric internal variability seems to play an important role in determining the intra-seasonal timescale networks

Multidecadal changes in the relationship between extreme temperature events in Uruguay and the general atmospheric circulation

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Data driven models of the Madden-Julian Oscillation : understanding its evolution and ENSO modulation

Acknowledgements N.D. thanks the Comisión Academica de Posgrado (CAP), Universidad de la República, Uruguay. All authors thanks the Programa de Desarrollo de la Ciencias Básicas (PEDECIBA).Peer reviewedPublisher PD

A study of the air-sea interaction in the South Atlantic Convergence Zone through Granger causality

Air-sea interaction in the region of the South Atlantic Convergence Zone (SACZ) is studied using Granger causality (GC) as a measure of directional coupling. Calculation of the area weighted connectivity indicates that the SACZ region is the one with largest mutual air-sea connectivity in the south Atlantic basin during summertime. Focusing on the leading mode of daily coupled variability, GC allows distinguishing four regimes characterized by different coupling: there are years in which the forcing is mainly directed from the atmosphere to the ocean, years in which the ocean forces the atmosphere, years in which the influence is mutual and years in which the coupling is not significant. A composite analysis shows that ocean-driven events have atmospheric anomalies that develop first and are strongest over the ocean, while in events without coupling anomalies develop from the continent where they are strongest and have smaller oceanic extension.Peer ReviewedPostprint (author's final draft