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We investigated the influence of the El Niño‐Southern Oscillation (ENSO) on inter‐annual precipitation variability in the far‐eastern Pacific (FEP) and northern South America (NSA) using an approach based on phase synchronization (PS). First, we carried out a detailed analysis of observational data to define the inter‐annual variability, eliminate the seasonal residual frequencies in hydro‐climatic anomalies, and assess the statistical significance of PS. Additionally, we characterized the seasonality of regional patterns of sea surface temperature, surface pressure levels, low‐level winds and precipitation anomalies associated with the ENSO states. We found that the positive (negative) precipitation anomalies experienced in the FEP and NSA differ from those previously reported in the literature. In particular, the Guianas (northeastern Amazon) and the Caribbean constitute two regions with negative (positive) rainfall anomalies during El Niño (La Niña), separated by a zone of non‐significant anomalies along the Orinoco Low‐level Jet corridor. Moreover, we showed that the ENSO signal is phase‐locked with inter‐annual rainfall and low‐level wind variability in most of the study regions. Furthermore, we found consistency in the PS between the Central and Eastern Pacific El Niño indices and hydroclimatic anomalies over the Pacific. However, some areas exhibited PS, although they did not show significant precipitation anomalies, suggesting that the influence of ENSO on tropical climatology manifests not only in terms of the magnitude of anomalies but also in terms of the phases only. Our approach advances the understanding of climatic anomalies in tropical regions and provides new insights into the non‐linear interactions between ENSO and hydroclimatic processes in tropical Americas. [ABSTRACT FROM AUTHOR]

Understanding the variability of low-level atmospheric circulation regimes is key for understanding the dynamics of monsoon systems. The South American Monsoon is characterized by strong year-long trade winds that are channeled southward into the South American Low-Level Jet after crossing the Amazon basin, which in turn is elementary for the moisture transport to Southern South America. In this study, we utilize streamflow wind networks, a type of climate networks that tracks the local flow of the wind field, together with the analysis of composites of wind, precipitation, and geopotential height fields, to investigate the variability of the South American low-level circulation. The streamflow wind networks are used here as they are able to directly track the wind flow and encode its spatiotemporal characteristics in their topology. We focus on intraseasonal variations in terms of active and break monsoon phases on the one hand, and on the interannual variability in terms of the impacts of the El Niño-Southern Oscillation on the other hand. Our findings highlight the importance of the South American Low-Level Jet, its spatial position and variability. Our study reveals the relation of the active and break regimes to anomalous high- and low-pressure systems over the southern Atlantic that are connected to Rossby wave trains from the southern Pacific, as well as the impact of these regimes on the cross-equatorial low-level flow. In addition, the streamflow networks that we use demonstrate significant shifts of the dominant wind flow pattern during El Niño and La Niña episodes. [ABSTRACT FROM AUTHOR]

Vegetation fires influence global vegetation distribution, ecosystem functioning, and global carbon cycling. Specifically in South America, changes in fire occurrence together with land-use change accelerate ecosystem fragmentation and increase the vulnerability of tropical forests and savannas to climate change. Dynamic global vegetation models (DGVMs) are valuable tools to estimate the effects of fire on ecosystem functioning and carbon cycling under future climate changes. However, most fire-enabled DGVMs have problems in capturing the magnitude, spatial patterns, and temporal dynamics of burned area as observed by satellites. As fire is controlled by the interplay of weather conditions, vegetation properties, and human activities, fire modules in DGVMs can be improved in various aspects. In this study we focus on improving the controls of climate and hence fuel moisture content on fire danger in the LPJmL4-SPITFIRE DGVM in South America, especially for the Brazilian fire-prone biomes of Caatinga and Cerrado. We therefore test two alternative model formulations (standard Nesterov Index and a newly implemented water vapor pressure deficit) for climate effects on fire danger within a formal model–data integration setup where we estimate model parameters against satellite datasets of burned area (GFED4) and aboveground biomass of trees. Our results show that the optimized model improves the representation of spatial patterns and the seasonal to interannual dynamics of burned area especially in the Cerrado and Caatinga regions. In addition, the model improves the simulation of aboveground biomass and the spatial distribution of plant functional types (PFTs). We obtained the best results by using the water vapor pressure deficit (VPD) for the calculation of fire danger. The VPD includes, in comparison to the Nesterov Index, a representation of the air humidity and the vegetation density. This work shows the successful application of a systematic model–data integration setup, as well as the integration of a new fire danger formulation, in order to optimize a process-based fire-enabled DGVM. It further highlights the potential of this approach to achieve a new level of accuracy in comprehensive global fire modeling and prediction. [ABSTRACT FROM AUTHOR]

The climate of South America exhibits pronounced differences between rainy and dry seasons, associated with specific synoptic features such as the establishment of the South Atlantic convergence zone. Here, we analyze the spatiotemporal correlation structure and in particular teleconnections of daily rainfall associated with these features by means of evolving complex networks. A modification of Pearson’s correlation coefficient is introduced to handle the intricate statistical properties of daily rainfall. On this basis, spatial correlation networks are constructed, and new appropriate network measures are introduced in order to analyze the temporal evolution of the networks’ characteristics. We particularly focus on the identification of coherent areas of similar rainfall patterns and previously unknown teleconnection structures between remote areas. We show that the monsoon onset is characterized by an abrupt transition from erratic to organized regional connectivity that prevails during the monsoon season, while only the onset times themselves exhibit anomalous large-scale organization of teleconnections. Furthermore, we reveal that the two mega-droughts in the Amazon basin were already announced in the previous year by an anomalous behavior of the connectivity structure. [ABSTRACT FROM AUTHOR]

The South American Andes are frequently exposed to intense rainfall events with varying moisture sources and precipitation-forming processes. In this study, we assess the spatiotemporal characteristics and geographical origins of rainfall over the South American continent. Using high-spatiotemporal resolution satellite data (TRMM 3B42 V7), we define four different types of rainfall events based on their (1) high magnitude, (2) long temporal extent, (3) large spatial extent, and (4) high magnitude, long temporal and large spatial extent combined. In a first step, we analyze the spatiotemporal characteristics of these events over the entire South American continent and integrate their impact for the main Andean hydrologic catchments. Our results indicate that events of type 1 make the overall highest contributions to total seasonal rainfall (up to $$50\,\%$$ ). However, each consecutive episode of the infrequent events of type 4 still accounts for up to $$20\,\%$$ of total seasonal rainfall in the subtropical Argentinean plains. In a second step, we employ complex network theory to unravel possibly non-linear and long-ranged climatic linkages for these four event types on the high-elevation Altiplano-Puna Plateau as well as in the main river catchments along the foothills of the Andes. Our results suggest that one to two particularly large squall lines per season, originating from northern Brazil, indirectly trigger large, long-lasting thunderstorms on the Altiplano Plateau. In general, we observe that extreme rainfall in the catchments north of approximately $$20^{\circ }$$ S typically originates from the Amazon Basin, while extreme rainfall at the eastern Andean foothills south of $$20^{\circ }$$ S and the Puna Plateau originates from southeastern South America. [ABSTRACT FROM AUTHOR]

We investigate the temporal evolution of moisture divergence and its spatial clustering properties over South America. Our analysis focuses on dependencies on the phase of the El Niño Southern Oscillation (ENSO). Moisture divergence is computed from daily reanalysis data of vertically integrated moisture flux provided by Modern-Era Retrospective Analysis for Research and Applications for the time period from 1979 to 2010. We use a sliding-window approach to construct a sequence of complex networks, each obtained from synchronization of events of strong positive (negative) moisture divergence, which we interpret as strong evapotranspiration (precipitation) events. We make the following three key observations: (1) Moisture divergence values over the Amazon rainforest are typically higher during positive ENSO periods (El Niño events). (2) The spatial coherence of strong positive (upwelling) events assumes a characteristic pattern of reduced coherence in this area during El Niño conditions. This influence of ENSO on moisture divergence and its spatial coherence is dominated by the El Niño events of 1982, 1987, and 1997. (3) The clustering characteristics of the obtained climate networks qualitatively agree with the spatial distribution of connected regions with simultaneous events (i.e., events that occur at the same time), but provide a more detailed view on the spatial organization of strong atmospheric upwelling events. Interestingly, no comparable results are found for negative extremes of moisture divergence (strong precipitation events). [ABSTRACT FROM AUTHOR]

In this study we introduce two new node-weighted difference measures on complex networks as a tool for climate model evaluation. The approach facilitates the quantification of a model's ability to reproduce the spatial covariability structure of climatological time series. We apply our methodology to compare the performance of a statistical and a dynamical regional climate model simulating the South American climate, as represented by the variables 2 m temperature, precipitation, sea level pressure, and geopotential height field at 500 hPa. For each variable, networks are constructed from the model outputs and evaluated against a reference network, derived from the ERA-Interim reanalysis, which also drives the models. We compare two network characteristics, the (linear) adjacency structure and the (nonlinear) clustering structure, and relate our findings to conventional methods of model evaluation. To set a benchmark, we construct different types of random networks and compare them alongside the climate model networks. Our main findings are: (1) The linear network structure is better reproduced by the statistical model statistical analogue resampling scheme (STARS) in summer and winter for all variables except the geopotential height field, where the dynamical model CCLM prevails. (2) For the nonlinear comparison, the seasonal differences are more pronounced and CCLM performs almost as well as STARS in summer (except for sea level pressure), while STARS performs better in winter for all variables. [ABSTRACT FROM AUTHOR]

Wet season precipitation in the South America monsoon system exhibits a dipole-like variability between the South Atlantic convergence zone (SACZ) and southeastern South America (SESA) [2]. This variability affects areas, e.g. around Buenos Aires, São Paulo and Rio de Janeiro, that are both: important for regional food supplies and densely populated. Here, we want to investigate the mechanism behind this variability pattern.In order to do so we analyze this complex interaction between SESA, SACZ and Rossby wave trains emanating from the southern Atlantic with phase synchronization techniques and a conceptual model. From nonlinear systems theory we know that if two oscillating systems are brought into contact, their phases may synchronize [6]. This concept can be applied to climatic time series as well [7]. For this data-driven phase synchronization approach we use NASA's Modern Era Restrospective analysis for Reasearch and Applications 2 (MERRA2) Reanalysis data [3]. First, we extract intraseasonal oscillations with Singular Spectrum Analysis (SSA) [4,5], then we reconstruct their corresponding phases with a Hilbert transform [6]. The resulting phase difference time series stay close to zero for the entire observation period and thus show the phase synchronicity between the two subsystems SACZ and SESA in South American and the Rossby Waves. Complementary to the data-driven approach, we also introduce a conceptual model that shows that the spatial pattern of a Principal Component Analysis of precipitation data can be explained by a traveling wave such as the Rossby Waves. Our approach shows that the dipolar precipitation pattern is not just caused by a direct interaction between the subsystems in SACZ and SESA, but is driven by the propagation of Rossby waves, which potentially has implications for the synoptic-scale weather forecast.[1] M. Gelbrecht, N. Boers, J. Kurths, Phase coherence between precipitation in South America and Rossby waves. Sci. Adv. 4, eaau3191 (2018)[2] J. Nogues-Paegle, K. Mo: Alternating Wet and Dry Conditions over South America, Mon. Wea. Rev., 125, 279–291 [3] M. Rienecker et. al: MERRA: NASA's Modern-Era Retrospective analysis for Research and Analysis, J. Climate, 24, 3624–3648[4] Broomhead, D. S., and G. P. King, Extracting qualitative dynamics from experimental data, Phys. D, 20, 217–236, 1986a[5] Vautard, R., and M. Ghil, Singular spectrum analysis in non-linear dynamics, with applications to paleoclimatic time series, Phys. D, 35, 395–424, 1989.[6] M. G. Rosenblum, A. S. Pikovsky, J. Kurths, Phase synchronization of chaotic oscillators.Phys. Rev. Lett. 76, 1804–1807 (1996).[7] D. Maraun, J. Kurths: Epochs of phase coherence between El Nino/Southern Oscillation and Indian monsoon, GEOPHYSICAL RESEARCH LETTERS, VOL. 32, L15709, 2005 [ABSTRACT FROM AUTHOR]