Main Modes Of Variability Research Articles

Features of subseasonal concurrent variations between subtropical and polar front jets and their association with temperature anomalies in China

Subseasonal variability of atmospheric circulation in mid-high latitudes is highly correlated to the upper-level jet streams, acting as a major contributor to the global or regional persistent climate anomalies. In this study, the features of subseasonal concurrent variations (SCVs) between East Asian Polar Front Jet (EAPJ) and Subtropical Jet (EASJ), and corresponding atmospheric circulation and temperature anomalies in China are investigated by using the observational data and NCEP/NCAR reanalysis datasets. Results show that the main variability modes of the winter upper-level wind fields on subseasonal time scales are characterized by the meridional shift in opposite directions and out-of-phase variations in the intensity of the EASJ and EAPJ. These concurrent variations between the two jets have a significant oscillation cycle with a period of 10–25 days. Accompanied by the location (intensity) SCV of the two jets, the mid-latitude circulation systems show significant zonal shifts (local intensity changes), resulting in alternations of the atmospheric circulation between zonal and meridional types. The typical meridional circulation is characterized by a strong East Asian trough and its upstream strong high-pressure ridges. Meanwhile, gradient force between Siberian high and the Aleutian low is anomalously strong. As a result, the SCVs between two jets can cause widespread and persistent low temperature anomalies over China, usually lasting more than three days. In the case of the location SCV of the two jets, the low temperature anomalies concentrate in northern China, particularly in north of 40°N. In the case of the intensity SCV of the two jets, the low temperature anomalies occur in most parts of China, notably in south of 40°N. These results are helpful to improve the prediction of atmospheric circulation and temperature anomalies in East Asia during winter.

Mesoscale and climate environmental variability drive krill community changes in the Humboldt Current System

Euphausiids (or “krill”) play a crucial role in the food webs of eastern boundary upwelling systems. Their inter-specific predatory interactions with ecologically and commercially important species highlights the importance of understanding krill variability at different temporal and spatial scales. In the Humboldt Current System (HCS), few studies have addressed the spatio-temporal variability of krill communities and their link with climate and local environmental drivers. We studied the patterns and variability of euphausiid diversity in the coastal area off northern Chile, using zooplankton and CTD-O data, and satellite environmental data from the falls and springs of 2010–2017. The community showed low diversity and evenness, with the endemic species Euphausia mucronata being the most abundant. The environmental variance showed 2 main modes of variability: (1) upwelling-associated changes in the depth of the oxygen minimum zone (OMZ) and in temperature, and (2) interannual variability in salinity, associated with ENSO-driven water-mass changes. The diversity indices and community structure showed large fluctuations in the cross-shore direction, and with latitude. The general pattern showed higher diversity offshore and southward, with few species in the low temperature, shallow OMZ conditions of the coastal band. During the 2013 and 2016 marine heatwaves and the 2015-2016 El Niño, the Subtropical Water Mass was advected southward, causing an increase in salinity and temperature, and a decrease in total krill abundance. However, ENSO variability did not significantly affect the species composition. The changes in community structure were caused by fluctuations in species abundance rather than species presence, as the most abundant species dominated the community throughout the study period. These results indicate that the krill communities of the HCS are highly resilient to climate perturbations, with upwelling-associated gradients being the primary source of variability for euphausiid populations in this ecosystem.

Comprehensive assessment of global spinal sagittal alignment and related normal spinal loads in a healthy population

Abnormal postoperative global sagittal alignment (GSA) is associated with an increased risk of mechanical complications after spinal surgery. Typical assessment of sagittal alignment relies on a few selected measures, disregarding global complexity and variability of the sagittal curvature. The normative range of spinal loads associated with GSA has not yet been considered in clinical evaluation. The study objectives were to develop a new GSA assessment method that holistically describes the inherent relationships within GSA and to estimate the related spinal loads.Vertebral endplates were annotated on radiographs of 85 non-pathological subjects. A Principal Component Analysis (PCA) was performed to derive a Statistical Shape Model (SSM). Associations between identified GSA variability modes and conventional alignment measures were assessed. Simulations of respective Shape Modes (SMs) were performed using an established musculoskeletal AnyBody model to estimate normal variation in cervico-thoraco-lumbar loads.The first six principal components explained 97.96% of GSA variance. The SSM provides the normative range of GSA and a visual representation of the main variability modes. Normal variation relative to the population mean in identified alignment features was found to influence spinal loads, e.g. the lower bound of the second shape mode (SM2-2σ) corresponds to an increase in L4L5-compression by 378.64 N (67.86%).Six unique alignment features were sufficient to describe GSA almost entirely, demonstrating the value of the proposed method for an objective and comprehensive analysis of GSA. The influence of these features on spinal loads provides a normative biomechanical reference, eventually guiding surgical planning of deformity correction in the future.

Different PCA approaches for vector functional time series with applications to resistive switching processes

This paper is motivated by modeling the cycle-to-cycle variability associated with the resistive switching operation behind memristors. Although the data generated by this stochastic process are by nature current–voltage curves associated with the creation (set process) and destruction (reset process) of a conductive filament, the statistical analysis is usually based on analyzing only the scalar time series related to the reset and set voltages/currents in consecutive cycles. As the data are by nature curves, functional principal component analysis is a suitable candidate to explain the main modes of variability associated with these processes. Taking into account this data-driven motivation, in this paper we propose two new forecasting approaches based on studying the sequential cross-dependence between and within a multivariate functional time series in terms of vector autoregressive modeling of the most explicative functional principal component scores. The main difference between the two methods lies in whether a univariate or multivariate PCA is performed so that we have a different set of principal component scores for each functional time series or the same one for all of them. Finally, the sample performance of the proposed methodologies is illustrated by an application on a bivariate functional time series of reset/set curves.

Seasonal influence of tropical Pacific and Atlantic sea surface temperature on streamflow variability in the patia river basin

This research presents a seasonal analysis of the variability of streamflows in the Patía River Basin (PRB) between 1984 and 2018 and the influence exerted by the large-scale climate variability using non-linear principal component analysis (NLPCA), Pearson's correlation, and composite analysis. The study was conduced during the minimum (July–August–September, JAS) and maximum (October–November–December, OND) streamflow periods. The NLPCA depicted a single significant mode of variability for each season with explained variances greater than 75%. The correlation analysis between the main mode of variability during OND and climate indices showed significant results, mainly with the Pacific Ocean and El Niño-Southern Oscillation (ENSO). In contrast, for JAS, the correlations were significant for the indices linked to the Atlantic Ocean. Finally, the composite analysis indicated that the positive (negative) events during JAS, which show the increase (decrease) of streamflow in PRB, are related to negative (positive) anomalies in the Tropical Northern Atlantic band, including the Caribbean Sea and the Gulf of Mexico. In comparison, the positive (negative) events during OND are related to negative (positive) sea surface temperature (SST) anomalies in the tropical Pacific, corresponding to La Niña (El Niño) events. The results provide evidence of the strong influence of climate indices and tropical Pacific and Atlantic SST on seasonal streamflow in the PRB and establish the foundations for seasonal streamflow modelling, relevant for prevention and risk management as well as for adequate planning and management of water resources in the region.

Subpolar Southern Ocean Seasonal Variability of the Geostrophic Circulation From Multi‐Mission Satellite Altimetry

AbstractA novel multi‐satellite product is used to shed light on the sea surface height seasonal cycle and associated geostrophic circulation in the subpolar Southern Ocean. We find three main modes of variability governing the Sea Level Anomaly seasonal cycle, all of them primarily governed by wind forcing. The main mode of seasonal variability is associated with the seasonality of the main subpolar gyres governed by large‐scale wind stress curl, qualitatively consistent with Sverdrup dynamics. The second seasonal mode is related to the Antarctic Slope Current (ASC), governed by the coastal easterlies, with a rapid circumpolar propagation of anomalous sea‐level along the continental slope that is dynamically consistent with the so‐called Southern Mode. The first two modes induce an acceleration of the gyres and the ASC in winter. The third seasonal mode appears to be driven by sea ice‐modulated surface stress and induces an offshore extension of the ASC from autumn to winter.

Late Holocene Hydro-Climate Variability in the Eastern Mediterranean: A Spatial Multi-Proxy Approach

A total of thirteen (13) paleoclimatic coastal and hinterland archives of the broader eastern Mediterranean region were collected and examined statistically in search of underlying trends for the period 2800 to 200 BP. For each archive, a proxy record representative of hydro-climatic changes was selected, normalized using z-factors to facilitate intercomparison, and analyzed statistically. Multivariate statistical analysis was performed using a clustering analysis (HCA) and dimension reduction (PCA), which led to groupings of similar records temporally, and allowed the identification of spatially underlying modes of variability. Two main modes of variability were identified, further supporting complex trajectories of paleoclimatic evolution in the region. The first mode was identified for sites presenting a trend from a wetter to an overall drier phase, with respective changes at major phase shifts at 1400 BP and 1100 BP. All sites were from the southern and northern Balkan region, as well as southwestern Turkey. A contrasting dry to wet trend was identified for a site in the Peloponnese (Greece) and the Levant, with a major phase shift at around 750 BP. The inclusion of different proxies from very different environmental settings and the 200-year window has complicated the connection of established short-term climatic events to the study’s findings.

Drivers of the decadal variability of the North Ionian Gyre upper layer circulation during 1910\u20132010: a regional modelling study

A long simulation over the period 1901–2010 with an eddy-permitting ocean circulation model is used to study the variability of the upper layer circulation in the North Ionian Gyre (NIG) in the Eastern Mediterranean Sea (EMed). The model is driven by the atmospheric forcing from the twentieth century reanalysis data set ERA-20C, ensuring a consistent performance of the model over the entire simulation period. The main modes of variability known in the EMed, in particular the decadal reversals of the NIG upper layer circulation observed since the late 1980s are well reproduced. We find that the simulated NIG upper layer circulation prior to the observational period is characterized by long-lasting cyclonic phases with weak variability during years 1910–1940 and 1960–1985, while in the in-between period (1940–1960) quasi-decadal NIG circulation reversals occur with similar characteristics to those observed in the recent decades. Our simulation indicates that the NIG upper layer circulation is rather prone to the cyclonic mode with occasional kicks to the anticyclonic mode. The coherent variability of the NIG upper layer circulation mode and of the Adriatic Deep Water (AdDW) outflow implies that atmospheric forcing triggering strong AdDW formation is required to kick the NIG into an anticyclonic circulation 1–2 years later. A sensitivity experiment mimicking a cold winter event over the Adriatic Sea supports this hypothesis. Our simulation shows that it is the multi-decadal variability of the salinity in the Adriatic Sea that leads to periods where low salinity prevents strong AdDW formation events. This explains the absence of quasi-decadal NIG reversals during 1910–1940 and 1960–1985.

Observed snow depth trends in the European Alps: 1971 to 2019

Abstract. The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

Seasonal reconstructions coupling ice core data and an isotope-enabled climate model – methodological implications of seasonality, climate modes and selection of proxy data

Abstract. The research area of climate field reconstructions has developed strongly during the past 20 years, motivated by the need to understand the complex dynamics of the earth system in a changing climate. Climate field reconstructions aim to build a consistent gridded climate reconstruction of different variables, often from a range of climate proxies, using either statistical tools or a climate model to fill the gaps between the locations of the proxy data. Commonly, large-scale climate field reconstructions covering more than 500 years are of annual resolution. In this method study, we investigate the potential of seasonally resolved climate field reconstructions based on oxygen isotope records from Greenland ice cores and an isotope-enabled climate model. Our analogue-type method matches modeled isotope patterns in Greenland precipitation to the patterns of ice core data from up to 14 ice core sites. In a second step, the climate variables of the best-matching model years are extracted, with the mean of the best-matching years comprising the reconstruction. We test a range of climate reconstructions, varying the definition of the seasons and the number of ice cores used. Our findings show that the optimal definition of the seasons depends on the variability in the target season. For winter, the vigorous variability is best captured when defining the season as December–February due to the dominance of large-scale patterns. For summer, which has weaker variability, albeit more persistent in time, the variability is better captured using a longer season of May–October. Motivated by the scarcity of seasonal data, we also test the use of annual data where the year is divided during summer, that is, not following the calendar year. This means that the winter variability is not split and that the annual data then can be used to reconstruct the winter variability. In particularly when reconstructing the sea level pressure and the corresponding main modes of variability, it is important to take seasonality into account, because of changes in the spatial patterns of the modes throughout the year. Targeting the annual mean sea level pressure for the reconstruction lowers the skill simply due to the seasonal geographical shift of the circulation modes. Our reconstructions based on ice core data also show skill for the North Atlantic sea surface temperatures, in particularly during winter for latitudes higher than 50∘ N. In addition, the main modes of the sea surface temperature variability are qualitatively captured by the reconstructions. When testing the skill of the reconstructions using 19 ice cores compared to the ones using eight ice cores, we do not find a clear advantage of using a larger data set. This could be due to a more even spatial distribution of the eight ice cores. However, including European tree-ring data to further constrain the summer temperature reconstruction clearly improves the skill for this season, which otherwise is more difficult to capture than the winter season.

Investigating the Effects of Demographics on Shoulder Morphology and Density Using Statistical Shape and Density Modeling.

A better understanding of how the shape and density of the shoulder vary among members of a population can help design more effective population-based orthopedic implants. The main objective of this study was to develop statistical shape models (SSMs) and statistical density models (SDMs) of the shoulder to describe the main modes of variability in the shape and density distributions of shoulder bones within a population in terms of principal components (PCs). These PC scores were analyzed, and significant correlations were observed between the shape and density distributions of the shoulder and demographics of the population, such as sex and age. Our results demonstrated that when the overall body sizes of male and female donors were matched, males still had, on average, larger scapulae and thicker humeral cortical bones. Moreover, we concluded that age has a weak but significant inverse effect on the density within the entire shoulder. Weak and moderate, but significant, correlations were also found between many modes of shape and density variations in the shoulder. Our results suggested that donors with bigger humeri have bigger scapulae and higher bone density of humeri corresponds with higher bone density in the scapulae. Finally, asymmetry, to some extent, was noted in the shape and density distributions of the contralateral bones of the shoulder. These results can be used to help guide the designs of population-based prosthesis components and pre-operative surgical planning.

Assessment of the Teleconnection Patterns Affecting July Precipitation in China and Their Forcing Mechanisms in the Met Office Unified Model

AbstractIn this study, we evaluate the ability of the MetUM to reproduce the Silk Road (SR) and Europe–China (EC) teleconnection patterns and their relationship with precipitation over China. The SR and EC patterns are the main modes of interannual variability of July upper-tropospheric meridional wind. The three main factors to the formation of these patterns are analyzed: 1) the tropical precipitation anomalies, which act as a forcing mechanism; 2) the emission of Rossby waves in the Mediterranean–Caspian Sea region; and 3) the basic state of the tropospheric jet over Eurasia. It was found that the model has some difficulty reproducing the main modes of variability in atmosphere-only mode (SR and EC pattern correlation of 0.31 and 0.54, respectively) with some improvement in coupled mode (pattern correlations of 0.56 and 0.44, respectively). Relaxation experiments were used to assess the impact that improving circulation in key regions has on the teleconnections. It was found that nudging wind and temperatures in the forcing regions within the tropics improved the Silk Road pattern whereas nudging in the region where the jet transitions between the North Atlantic Ocean and Eurasian continent—correcting the basic state—had the most impact on the EC teleconnection pattern. This suggests that while the Silk Road pattern is more sensitive to changes in the forcing, the Europe–China pattern is more sensitive to the basic state.

Characterizing the gradients of structural covariance in the human hippocampus

The hippocampus is a plastic brain structure that has been associated with a range of behavioral aspects but also shows vulnerability to the most frequent neurocognitive diseases. Different aspects of its organization have been revealed by studies probing its different neurobiological properties. In particular, histological work has shown a pattern of differentiation along the proximal-distal dimension, while studies examining functional properties and large-scale functional integration have primarily highlighted a pattern of differentiation along the anterior-posterior dimension.To better understand how these organizational dimensions underlie the pattern of structural covariance (SC) in the human hippocampus, we here applied a non-linear decomposition approach, disentangling the major modes of variation, to the pattern of gray matter volume correlation of hippocampus voxels with the rest of the brain in a sample of 377 healthy young adults. We additionally investigated the consistency of the derived gradients in an independent sample of life-span adults and also examined the relationships between these major modes of variations and the patterns derived from microstructure and functional connectivity mapping.Our results showed that similar major modes of SC-variability are identified across the two independent datasets. The major dimension of variation found in SC runs along the hippocampal anterior-posterior axis and followed closely the principal dimension of functional differentiation, suggesting an influence of network level interaction in this major mode of morphological variability. The second main mode of variability in the SC showed a gradient along the dorsal-ventral axis, and was moderately related to variability in hippocampal microstructural properties.Thus our results depicting relatively reliable patterns of SC-variability within the hippocampus show an interplay between the already known organizational principles on the pattern of variability in hippocampus’ macrostructural properties. This study hence provides a first insight on the underlying organizational forces generating different co-plastic modes within the human hippocampus that may, in turn, help to better understand different vulnerability patterns of this crucial structure in different neurological and psychiatric diseases.

Coastal Erosion Variability at the Southern Laptev Sea Linked to Winter Sea Ice and the Arctic Oscillation

AbstractArctic coastal erosion experiences pronounced effects from ongoing climate change. The Laptev Sea figures among the Arctic regions with the most severe erosion rates. Here, we use unprecedentedly long records of almost 30 years of annual in‐situ coastal erosion rate measurements from Bykovsky Peninsula and Muostakh Island to separate the main modes of variability, which we attribute to large‐scale drivers. The first (lower‐frequency) and second (higher‐frequency) modes are associated with winter sea‐ice cover in the Laptev Sea and with the Arctic Oscillation, respectively, which together account for of the total observed variance. Arctic coastal erosion has so far been neglected in Earth system models (ESMs). The proposed mechanisms set favorable conditions for coastal erosion at large scales (synoptic to planetary scales), compatible with those represented in modern ESMs.

Predictability of Extratropical Upper-Tropospheric Circulation in the Southern Hemisphere by Its Main Modes of Variability

AbstractThe predictability and forecast skill of the models participating in the Climate Historical Forecast Project (CHFP) database is assessed through evaluating the representation of the upper-tropospheric extratropical circulation in the Southern Hemisphere (SH) in winter and summer and its main modes of variability. In summer, the predictability of 200-hPa geopotential height anomalies mainly comes from the ability of the multimodel ensemble mean (MMEM) to forecast the first three modes of interannual variability with high fidelity. The MMEM can reproduce not only the spatial patterns of these modes but also their temporal evolution. On the other hand, in JJA only the second and fourth modes of variability are predictable by the MMEM. These seasonal differences in the performance of the MMEM seem to be related to the role that the sea surface temperature (SST) anomalies have in influencing the variability of each mode. Accordingly, modes that are strongly linked to tropical SST anomalies are better forecast by the MMEM and show less spread among models. The analysis of both 2-m temperature and precipitation anomalies in the SH associated with the predictable modes reveals that DJF predictable modes are accompanied by significant temperature anomalies. In particular, temperatures at polar (tropical) latitudes are significantly correlated with the first (second) mode. Furthermore, these links obtained with observations are also well forecast by the MMEM and can help to improve seasonal forecast of climate anomalies in those regions with low skill.

Changes in the North Pacific Current divergence and California Current transport based on HadGEM2-ES CMIP5 projections to the end of the century

This study aims to evaluate the potential changes to the strength of the North Pacific Current (NPC) and its position of divergence, which would alter the variability of the Alaska (AC) and California (CC) currents throughout the century. For this, the volume transports of these currents were evaluated using the historical CMIP5 and RCP4.5 experimental results of the HadGEM2-ES model from 1985 to 2100. In general, the NPC will migrate to the north, in accord to the northwestward expansion of North Pacific High, which would alter the positioning and the amount of flow entering the AC and CC systems by 2100. Along with the bifurcation shift, changes in the two main modes of variability, i.e., the bifurcation and breathing modes, are expected. The bifurcation mode will become responsible for only 11% of the total variability while presenting a positive anomaly throughout the century. These results indicate the role of NPC migration on the southward transport of enriched waters and the possible impact on the primary productivity along the North American coast.

Coherent South American Monsoon Variability During the Last Millennium Revealed Through High‐Resolution Proxy Records

AbstractThe number of paleoprecipitation records from the South American Monsoon domain that cover the last millennium has increased substantially in past years. However, hitherto most studies focused only on regional aspects, thereby neglecting the role of large‐scale monsoon variability and the mechanisms that link proxy locations in space and time. Here we decompose the South American Monsoon into its main modes of variability by applying a Monte Carlo principal component analysis to a compilation of 11 well‐dated summer paleoprecipitation records from tropical South America. The first mode represents changes in precipitation over the core monsoon domain, while the second mode is characterized by high loadings along the fringes of the South American Monsoon over Southeastern South America and the northern monsoon limit. Composite analysis reveals an enhanced monsoon with a wider, rather than a southward displaced, South Atlantic Convergence Zone during the early Little Ice Age, in contrast to previous interpretations.

NDVI Spatio-temporal Patterns and Climatic Controls Over Northern Patagonia

In this study, we identify the main modes of variability of the Normalized Difference Vegetation Index (NDVI) and their relationships with precipitation and temperature variations across northern Patagonia (36°–45° S). In this approach, we combined a recently developed high-resolution gridded dataset (20 × 20 km) for temperature and precipitation with a re-scaled NDVI grid to spatially match the climate database. Climate–vegetation relationships were analyzed taking into account a wide range of temporal variations (intra- to inter-annual) of both climate and NDVI. An Empirical Orthogonal Function analysis performed on NDVI delimits four regions that are spatially consistent with previous vegetation classifications for northern Patagonia. In addition, these coherent NDVI regions show similarities with the spatial precipitation patterns and the temporal evolution of precipitation over the common period 2001–2010. Both NDVI and precipitation show evident annual cycles over the Mediterranean climatic region in northwestern Patagonia. These annual cycles decrease in amplitude toward the eastern arid rangelands, and to the south on the evergreen all-year-round rainforests. Significant positive relationships between monthly precipitation and NDVI are recorded in the dry temperate rangelands in northeastern Patagonia. In contrast, direct associations between monthly NDVI and precipitation were absent in the Central Patagonia cold grasslands, where seasonal interactions between precipitation, temperature and NDVI appear to be more relevant. Relationships between NDVI and temperature are generally weaker east of the Andes, but significantly positive in late winter/spring over the temperate forests in western North Patagonia. Our results indicate that climate–NDVI relationships in northern Patagonia are biome specifics with the occurrence of temporal lags and precipitation–temperature interactions in the responses of vegetation to climate at some ecosystems.

Spatiotemporal variability of the wind power resource in Argentina and Uruguay

AbstractThe spatiotemporal variability of the wind power resource over Argentina and Uruguay is assessed based on the Modern‐Era Retrospective Analysis for Research and Applications 2 (MERRA2) dataset. Hourly wind speeds were interpolated to 100‐m height, and then, wind power outputs were computed using power curves of three International Electrotechnical Commission (IEC) wind classes. The time series of wind power outputs were filtered using a fast Fourier transform (FFT) to separate regular (annual and daily) from irregular (interannual and synoptic scale) cycles. An empirical orthogonal function analysis was applied to the resulting datasets to obtain the main modes of variability. The results show that the combination of wind power outputs from southern and northern Patagonia broadly follows the average annual electric load. Patagonia exhibits the highest variability on the interannual, annual, and synoptic timescales. On the interannual and synoptic timescales, the variability modes are associated with known and distinct atmospheric circulation modes. The interannual modes of variability are associated with opposite surface level pressure (SLP) anomalies between middle and high latitudes.

Reconstruction of Holocene coupling between the South American Monsoon System and local moisture variability from speleothem δ18O and 87Sr/86Sr records

Investigating controls on past variability of South American hydroclimate is critical to assessing its response to future warming scenarios. δ18O records from South America offer insight into past variability of the South American Monsoon System (SAMS). The controls, however, on precipitation δ18O values can be decoupled from precipitation amount at a given site and, thereby, limit local moisture condition reconstructions. Here we use a principal components analysis to assess the coherence of speleothem and lake core Holocene δ18O records in tropical and subtropical South America to evaluate the extent to which δ18O variability reflects changes in SAMS intensity at different sites across the region. The main mode of variability across Holocene δ18O records (PC1) closely tracks austral summertime insolation, consistent with existing work. Sites towards the periphery of the continent are heavily weighted on PC1, whereas interior sites as not. Further δ18O variability at interior sites bear little similarity to each other and implicate controls, beyond regional monsoon intensity, on these δ18O records. Further, we develop speleothem 87Sr/86Sr records spanning the Holocene from Tamboril Cave (Brazilian Highlands), Paraíso Cave (eastern Amazon Basin), Jaraguá Cave (Mato Grosso do Sul Plateau), and Botuverá Cave (Atlantic coastal plain) to investigate coupling between reconstructed monsoon variability (reflected by PC1) and local moisture conditions (interpreted from 87Sr/86Sr records). We interpret speleothem 87Sr/86Sr variability as a proxy of local moisture conditions, reflecting the degree of water-rock interaction with the cave host rock as driven by variations in water residence time. Speleothem 87Sr/86Sr records from all the sites, except Botuverá cave, do not co-vary with PC1, suggesting that local moisture conditions do not necessarily follow variations in regional monsoon intensity at these interior sites. These speleothem 87Sr/86Sr records generally suggest dry mid-Holocene conditions relative to the early- and late-Holocene, consistent with interpretations of other paleo-moisture records in the region, but timing of wet-dry transitions varies between sites. These results highlight that controls, in addition to SAMS variability, might influence δ18O variability. Further, our results suggest spatially variable local moisture conditions at interior sites that do not uniformly respond to regional monsoon intensity, and stress the need for δ18O-independent reconstructions of moisture conditions.