Modes Of Low-frequency Variability Research Articles

Spatiotemporal pattern of successive hydro-hazards and the influence of low-frequency variability modes over Canada

Successive hydro-hazards, particularly dry and wet weather whiplash, can severely impact societies and infrastructure undermining the resilience measures developed based on the traditional univariate approaches. Understanding the spatial and temporal characteristics of such lagged compound events is important to improve the efficacy of disaster risk measures (DRMs). This study analyzes the successive dry-wet and wet-dry (SDW/SWD) spells based on the run theory across Canada and proposes a compound magnitude index to identify the corresponding hotspots across 268 watersheds for the historical period of 1963–2012. The frequency, transition time, and magnitude of SDW/SWD events are assessed considering the variable monthly 20th percentile (dry spell) and a fixed 90th percentile (wet spell) of natural streamflow values as thresholds. The potential influence of low-frequency variability modes such as Multivariate El Niño–Southern Oscillation (ENSO) index (MEI) and the North Atlantic Oscillation (NAO) index on the regional hydro-hazards, with a transition time of less than a month, is assessed based on the Bayesian quantile regression approach. Results indicate that the coastal areas, including the west and east coast and the Great Lakes region, are the hotspots for such abrupt transitions. Furthermore, the spatial and temporal characteristics of SDW/SWD events are largely variable and different from those of individual events. The magnitude of these events is influenced by low frequency variability modes particularly at higher quantiles, suggesting that strong phases of MEI and NAO can potentially lead to SDW and SWD events. The findings of this study support the future development of robust DRMs and effective water resource management strategies.

A 50-year cycle of sea surface temperature regulates decadal precipitation in the tropical and South Atlantic region

Northeast Brazil and Western Africa are two regions geographically separated by the Atlantic Ocean, both home to vulnerable populations living in semi-arid areas. Atlantic Ocean modes of variability and their interactions with the atmosphere are the main drivers of decadal precipitation in these Atlantic Ocean coastal areas. How these low-frequency modes of variability evolve and interact with each other is key to understanding and predicting decadal precipitation. Here we use the Self-Organizing Maps neural network with different variables to unravel causality between the Atlantic modes of variability and their interactions with the atmosphere. Our study finds an 82% (p<0.05) anti-correlation between decadal rainfall in Northeast Brazil and Western Africa from 1979 to 2005. We also find three multi-decadal cycles: 1870-1920, 1920-1970, and 1970-2019 (satellite era), pointing to a 50-year periodicity governing the sea surface temperature anomalies of Tropical and South Atlantic. Our results demonstrate how Northeast Brazil and Western Africa rainfall anti-correlation was formed in the satellite era and how it might be part of a 50-year cycle from the Tropical and South Atlantic decadal variability.

Identifying Shifts in Modes of Low-Frequency Circulation Variability Using the 20CR Renalysis Ensemble

Abstract Principal component analysis (PCA) is a widely used technique to identify modes of low-frequency variability of atmospheric circulation and their spatial changes. However, it turns out that PCA is highly sensitive to the period analyzed and the length of the time window used. Its results can vary considerably if the period is shifted by even 1 year. We present temporal variability of modes from the late nineteenth century using moving PCA of winter (DJF) monthly mean 500-hPa height anomalies for 20–50-yr moving periods with 1-yr step. We employ the congruence coefficient to compare spatial patterns of the modes and identify their substantial changes. Shorter moving periods are more susceptible to sudden fluctuations in mode patterns from one period to the next, while longer periods yield more stable results. We strongly recommend applying a moving PCA to detect spatial changes in modes of low-frequency variability, as it unveils any hidden sudden changes in the modes. These changes can be influenced by many aspects, such as data quality, sampling variability, and length of the analyzed period. Spatial patterns of the Atlantic–European modes are more stable across ensemble members than those over the Pacific and North America, especially before the 1920s. During this period, North Atlantic and European modes explain more variance in the ensemble mean than in ensemble members, while the reverse holds for Pacific and North American modes. In data-sparse regions, modes in ensemble members exhibit greater variability. The process of averaging then leads to weaker modes in the ensemble mean, explaining less variance compared to ensemble members.

On the Spatial Scale Dependence of Long‐Term Persistence in Global Annual Precipitation Data and the Hurst Phenomenon

AbstractPrecipitation deficits are the main physical drivers of droughts across the globe, and their level of persistence can be characterized by the Hurst coefficient H (0.5 &lt; H &lt; 1), with high H indicating strong long‐term persistence (LTP). Previous analyses of point and gridded annual global precipitation datasets have concluded that LTP in precipitation is weak (H ∼ 0.6) which is inconsistent with higher values of H for large river basins, for example, the Nile. Based on an analysis of gridded annual precipitation data for eight selected regions distributed across the globe, an important new finding is that H increases with the spatial scale of averaging, with mean H values at the grid and regional scale of 0.66 and 0.83, respectively. The discovery of enhanced LTP at the regional scale of averaging of precipitation has important implications for characterizing the severity of regional droughts, as well as LTP in the annual flows of large rivers and recharge to major aquifers. Teleconnections with known modes of low frequency variability in the global climate system are demonstrated using correlation analysis and stepwise regression. Despite having several constituent regions exhibiting LTP, the Northern Hemisphere surprisingly has no LTP; this is shown to result from different modes of low frequency climatic variability canceling each other out. LTP for the Southern Hemisphere is moderate, and weak for Global average precipitation. LTP in Blue Nile basin scale precipitation is shown to explain the Hurst Phenomenon in naturalized annual flows for the River Nile, more than 70 years after its discovery by Hurst.

Influence of low frequency variability modes on spatiotemporal patterns of temperature and precipitation in Northwestern North America

ABSTRACT This study performs a comprehensive analysis of the influence of low-frequency variability modes (LFVMs) on the average and extreme temperature and precipitation in the extended winter (November–February) over Northwestern North America (NWNA). Maximum covariance analysis is applied to quantify the covariability between LFVMs and regional climate variables, followed by composite analysis to assess the influence of each LFVM on extremes at local scales using high-resolution gridded observations. This study extends previous analyses by characterizing the spatial patterns of regional climate variations associated with a suite of LFVMs. This includes the associations between negative phases of North Atlantic Oscillation (NAO) and extreme precipitation over western NWNA and the central Columbia basin and the positive phase of the North Tropical Atlantic (NTA) with increases in the minimum and maximum temperature of NWNA except for the western Fraser basin. Overall, the analyses show that LFVM effects are more pronounced over the eastern and southcentral parts of NWNA during extended winter.

Warming Pattern over the Northern Hemisphere Midlatitudes in Boreal Summer 1979–2020

Abstract Significant surface air temperature warming during summer 1979–2020 is not uniformly distributed in the northern midlatitudes over land but rather is confined to several longitudinal sectors including Europe, central Siberia and Mongolia, and both coasts of North America. These hot spots are accompanied by a chain of high pressure ridges from an anomalous, circumglobal Rossby wave train in the upper troposphere. From reanalysis data and several baseline experiments from phase 6 of the Coupled Model Intercomparison Project (CMIP6), we find that the circulation trend pattern is associated with fluctuations of the Atlantic multidecadal variability (AMV) and the interdecadal Pacific oscillation. The phase shift of AMV in the 1990s is particularly noteworthy for accelerating warming averaged over the northern midlatitude land. The amplitude of the observed trend in both surface air temperature and the upper-level geopotential height generally falls beyond the range of multidecadal trends simulated by the CMIP6 preindustrial control runs, supporting the likelihood that anthropogenic forcing played a critical role in the observed trend. On the other hand, the fidelity of the simulated low-frequency modes of variability and their teleconnections, especially on multidecadal time scales, is difficult to assess because of the relatively short observational records. Our mechanistic modeling results indicate that synoptic eddy–mean flow interaction is a key to the formation of the anomalous wave train but how the multidecadal modes can modulate the synoptic eddies through atmosphere–ocean and atmosphere–land interactions remains poorly understood. This gap in our knowledge makes it challenging to quantify the roles of the low-frequency modes and external forcings in causing the observed multidecadal trends.

Predictability of the low-frequency modes of the Arctic Ocean heat content variability: a perfect model approach

Abstract The problem of potential predictability of the temperature of the upper layer of the Arctic Ocean for the data of pre-industrial climate modelling run by the INM-CM5 Earth system model developed at the INM RAS is considered. The main attention is paid to the analysis of predictability of the phases of the dominant modes of low-frequency variability of the Arctic Ocean circulation. The initial estimate of its predictability is made by using the method of analogues and calculating the resonances of the invariant measure. Then this estimate is verified by direct ensemble calculations with the model. The results obtained indicate that the maximum predictability time interval reaches ten years for 15-year average values of heat content and corresponds to the states with maximum positive anomalies along the leading low-frequency variability modes.

European extreme precipitation: The effects of spatio-temporal resolution of the data

European wintertime precipitation is known to be skilfully estimated in reanalysis data and model simulations since it is highly correlated with large scale, low frequency modes of variability, namely the North Atlantic Oscillation (NAO). Since the NAO is mainly a wintertime mode of variability, the skill of estimating precipitation becomes more limited in other seasons, most importantly in summer, when precipitation is mainly a result of mesoscale convection. In this study, we use the Weather Research and Forecast (WRF) model, to show the added value of using a high resolution, convection-permitting model to estimate precipitation extremes. The results show that WRF succeeds to correct the failure of ERA-Interim reanalysis to capture the positive trends over the last decades of European extreme precipitation in summer and transition seasons, that are indicated by observational data (E-OBS) and previous literature. Partial improvements are evident using ERA5 reanalysis, specifically in Spring and in Autumn. In winter, changes in European extreme precipitation over the last decades are dominated by variations in the NAO index, and are well reproduced both in reanalysis data and in the high resolution WRF downscaling.

Synoptic climatology of nuisance flooding along the Atlantic and Gulf of Mexico coasts, USA

Nuisance flooding (NF; also known as high-tide, or “sunny day” flooding) is an increasingly prevalent phenomenon afflicting coastal developments worldwide. NF often results when high tides, exacerbated by global sea level rise, coincide with onshore-directed surface winds. The elevated tidal condition, compounded by frictionally forced ocean currents, then inundates coastal infrastructure, posing a non-life-threatening inconvenience for local residents. Even though NF events are formed by a coupling of atmospheric and oceanic conditions, the large-scale atmospheric circulations favorable for NF have received little attention. Using a 40 year NF database derived from coastal gauge data, this study identifies the mid-tropospheric synoptic atmospheric patterns within days of NF events along the Atlantic and Gulf of Mexico coasts of the USA. ERA-Interim reanalysis supplies 500 hPa geopotential height patterns over North America that coincide with NF events. Subsequently, a principal components analysis is performed upon all NF-associated 500 hPa patterns, disaggregated by region (New England, Mid-Atlantic, and Gulf Coast), to identify dominant intra-variability NF-related circulations. Consistent with prior work, results show that NF frequency is increasing with time. Further, each region is characterized by a unique set of dominant NF synoptic patterns. Most patterns have clear physical associations to NF mechanisms, such as a nearby 500 hPa trough that would support a surface cyclone and strong onshore surface winds, or a mid-level high-pressure cell that would direct onshore-oriented return flow along its western periphery. However, some patterns correspond less obviously to oft-referenced NF mechanisms and may instead reflect low-frequency modes of variability and/or localized influences.

Remaining error sources in bias-corrected climate model outputs

Bias correction methods have now emerged as the most commonly used approach when applying climate model outputs to impact studies. However, comparatively much fewer studies have looked at the limitations of bias correction caused by the very nature of the climate system. Two main sources of errors can affect the efficiency of bias correction over a future period: climate sensitivity and internal variability of the climate system. The former is related to differences in the forcing response between a climate model and the real climate system, whereas the latter results from the chaotic nature of the climate system. Using a “pseudo-reality” approach, this study investigates the contribution of these two sources of error to remaining biases of climate model after bias correction for future periods. The pseudo-reality approach uses one climate model as a reference dataset to correct other climate models. Results indicate that bias correction is beneficial over the reference period and in near future periods. However, large biases remain in future periods. The difference in climate sensitivities is the main contributor to the remaining biases in corrected data. Internal variability affects the near and far future similarly and may dominate in the near future, especially for precipitation. The impact of differences in climate sensitivity between the reference dataset and climate model data cannot be eliminated, while the impact of internal variability can be lessened by using a reference period for as long as possible to filter out low-frequency modes of variability.

West Pacific teleconnection pattern in dynamical seasonal predictions: how is it connected to the Atlantic atmospheric mean bias?

This study examines the West Pacific (WP) pattern, one of the primary modes of low-frequency variability during boreal winter, isolated in 11 global climate model seasonal hindcasts. WP mode separation are verified using three metrics: selected mode number, explained variance, and pattern correlation coefficient of the loading vector. When it comes to the pattern reproducibility, it turns out that the WP is tightly linked to the Atlantic jet and stationary wave. To diagnose details of mean biases in terms of WP reproducibility, atmospheric mean fields are composited for two groups: one group replicates well the key feature, a north–south dipole pattern, while the other manifests considerable displacements and magnitude disparities. The group with a lower pattern correspondence to the observation shows noticeable biases with the southeastward-shifted Atlantic dipole of the stationary wave and downstream-expanded Atlantic jet, which is attributed to the increase in meridional gradient of near surface temperature and resultant enhanced local baroclinicity. Magnified jet tail in the eastern North Atlantic (NA) can intensify the barotropic energy conversion thereby the wave energy actively passes toward both east and west. The excessive wave energy over the Arabian Peninsula and the central North Pacific can be favorable for the formation of a ridge, therefore it possibly leads to disorganized WP patterns at both ends of NA basin. A series of analysis suggests the importance of a realistic winter climatology simulation over NA for better WP representation through the interaction between different scales as well as different basins.

A New Perspective toward Cataloging Northern Hemisphere Rossby Wave Breaking on the Dynamic Tropopause

Abstract Rossby wave breaking (RWB) events are a common feature on the dynamic tropopause and act to modulate synoptic-scale jet dynamics. These events are characterized on the dynamic tropopause by an irreversible overturning of isentropes and are coupled to troposphere-deep vertical motions and geopotential height anomalies. Prior climatologies have focused on the poleward streamer, the equatorward streamer, or the reversal in potential temperature gradient between the streamers, resulting in differences in the frequencies of RWB. Here, a new approach toward cataloging these events that captures both streamers is applied to the National Centers for Environmental Prediction Reanalysis-2 dataset for 1979–2011. Anticyclonic RWB (AWB) events are found to be nearly twice as frequent as cyclonic RWB (CWB) events. Seasonal decompositions of the annual mean find AWB to be most common in summer (40% occurrence), which is likely due to the Asian monsoon, while CWB is most frequent in winter (22.5%) and is likely due to the equatorward shift in mean baroclinicity. Trends in RWB from 1980 to 2010 illustrate a westward shift in North Pacific AWB during winter and summer (up to 0.4% yr−1), while CWB in the North Pacific increases in winter and spring (up to 0.2% yr−1). These changes are hypothesized to be associated with localized changes in the two-way interaction between the jet and RWB. The interannual variability of AWB and CWB is also explored, and a notable modality to the frequency of RWB is found that may be attributable to known low-frequency modes of variability including the Arctic Oscillation, the North Atlantic Oscillation, and the Pacific–North American pattern.

Low frequency variability and sensitivity of the Atlantic meridional overturning circulation in selected IPCC climate models

Abstract The structure of main modes of the decadal and multidecadal variability of the Atlantic meridional overturning circulation (AMOC) is analyzed for the climate models INM-CM5 (INM RAS), CCSM4 (NCAR), MPI-LR and MPI-MR (MPI). It is shown that oscillations with characteristic periods of 25–35 and 50–70 years are recognized in the models, and the corresponding spatial structures are variations of the intensity and position of the AMOC. Relations connecting changes of thermohaline circulation with external impacts on the system are constructed for the above models. The analysis of stability of calculations relative to the length of data series used for calculations is performed. The optimal impacts leading to the greatest response of the AMOC and influence functions causing its response along the leading modes of low-frequency variability are constructed. The optimal impacts have the form of density anomalies localized in the North Atlantic, and the structures of the corresponding responses are dominated by leading low-frequency modes of variability. The structure of optimal impacts varies greatly from model to model.

Potential Predictability of Multidecadal Oscillations of Sea Surface Temperature in the Arctic and Their Sensitivity to External Forcings

The low-frequency variability of sea surface temperature and salinity in the Arctic is analyzed using the data of the 1200-year preindustrial experiment with the INM-CM5 climate model developed in the Marchuk Institute of Numerical Mathematics of Russian Academy of Sciences. It is shown that the leading variability pattern is a regular coupled oscillation of temperature and salinity with the period of about 50 years. The empirical method based on the fluctuation-dissipation theorem was applied to evaluate influence functions which provide the optimum excitation of this oscillation phases. It is demonstrated that salinity anomalies are the main driver of this variability. The time of potential predictability of sea surface temperature and salinity was determined using the analog method, it equals about six years for 15-year means. The main source of long-term predictability is the spatial pattern associated with the leading mode of low-frequency variability of the analyzed parameters in the Arctic.

Ocean modelling and altimeter data reveal the possible occurrence of intrinsic low-frequency variability of the Kuroshio Extension

ABSTRACT In this paper the combined use of model simulations and altimeter data suggests that, during the interval 1998–2006, the Kuroshio Extension (KE) low-frequency variability was mainly of intrinsic origin. The model is based on the primitive equations for a reduced-gravity ocean, is eddy-permitting and relatively idealized, but it includes essential elements of realism and is driven by a time-independent climatological wind field, so that the modelled variability is necessarily of intrinsic oceanic origin. The altimeter data are provided by AVISO and include all the data available to date. A new composite index Λ[Φ] specifically conceived for the KE is used in the analysis of both model and altimeter data (Φ gives the mean latitudinal position of the KE jet and Λ is a measure of the high-frequency variability of the KE path). A set of sensitivity numerical experiments shows that the frontal dynamics is extremely sensitive to the horizontal eddy viscosity. A self-sustained relaxation oscillation emerging from a reference simulation is represented by a loop in the (Λ, Φ)-plane and is characterized by four phases, each one being controlled by a specific dynamical mechanism. An interval ranging from January 1998 to March 2006 is identified in the altimeter data, during which the altimeter-derived index is very similar to the modelled one. A detailed analysis based also on sea surface height maps shows that the real KE evolution possesses four phases characterized by basically the same dynamical behaviour recognized in the model loop. The conclusion is that, during that interval, the KE dynamics was controlled by an oceanic intrinsic mode of low-frequency variability and that the directly driven dynamics and ocean-atmosphere coupled modes of variability did not overshadow the intrinsic evolution. The self-sustained vs excited character of the relaxation oscillation is finally discussed.

Data-driven prediction strategies for low-frequency patterns of North Pacific climate variability

The North Pacific exhibits patterns of low-frequency variability on the intra-annual to decadal time scales, which manifest themselves in both model data and the observational record, and prediction of such low-frequency modes of variability is of great interest to the community. While parametric models, such as stationary and non-stationary autoregressive models, possibly including external factors, may perform well in a data-fitting setting, they may perform poorly in a prediction setting. Ensemble analog forecasting, which relies on the historical record to provide estimates of the future based on past trajectories of those states similar to the initial state of interest, provides a promising, nonparametric approach to forecasting that makes no assumptions on the underlying dynamics or its statistics. We apply such forecasting to low-frequency modes of variability for the North Pacific sea surface temperature and sea ice concentration fields extracted through Nonlinear Laplacian Spectral Analysis. We find such methods may outperform parametric methods and simple persistence with increased predictive skill.

The spring relationship between the Pacific-North American pattern and the North Atlantic Oscillation

The Pacific-North American pattern (PNA) and North Atlantic Oscillation (NAO) are two dominant modes of low-frequency variability in the Northern Hemisphere winter. Generally these two patterns are separable and uncorrelated in both space and time. However, dating back to the 1970s there have been studies which have linked the Aleutian and Icelandic low intensities, and shown that a significant temporal correlation does, on occasion, occur. Each of these semi-permanent low pressure systems is a part of the PNA and NAO patterns, respectively, and therefore, given a link between the two semi-permanent low pressure systems, we explore whether a link between the PNA and NAO also appears. Recently studies have found such a link during the winters of certain decades. The present study, documents an observed relationship between the PNA and NAO which is consistently present during the spring and early summer. This relationship is shown to be due to the fact that the PNA and NAO are spatially overlapping projections of the same pattern of variability, given by the first EOF of 500 hPa geopotential height. Both the PNA and NAO patterns correlate more strongly to the first EOF’s spatial pattern, resembling the Aleutian low-Icelandic low seesaw pattern, than the rotated EOF (REOF) loading patterns used to compute their respective indices. Furthermore, when the two patterns are correlated, the effect of El-Nino on the Northern Hemisphere is separate from the pattern associated with the PNA. Finally, we examine a 21-year period in the winter for which the PNA and NAO are significantly correlated, and find that the conclusions drawn from the spring hold true. The results presented herein demonstrate that defining the PNA and NAO based upon typical loading patterns may not be the ideal approach; especially given that the patterns are spatially similar and covary in time.

Observed Trends in Canada’s Climate and Influence of Low-Frequency Variability Modes

Abstract Trends in Canada’s climate are analyzed using recently updated data to provide a comprehensive view of climate variability and long-term changes over the period of instrumental record. Trends in surface air temperature, precipitation, snow cover, and streamflow indices are examined along with the potential impact of low-frequency variability related to large-scale atmospheric and oceanic oscillations on these trends. The results show that temperature has increased significantly in most regions of Canada over the period 1948–2012, with the largest warming occurring in winter and spring. Precipitation has also increased, especially in the north. Changes in other climate and hydroclimatic variables, including a decrease in the amount of precipitation falling as snow in the south, fewer days with snow cover, an earlier start of the spring high-flow season, and an increase in April streamflow, are consistent with the observed warming and precipitation trends. For the period 1900–2012, there are sufficient temperature and precipitation data for trend analysis for southern Canada (south of 60°N) only. During this period, temperature has increased significantly across the region, precipitation has increased, and the amount of precipitation falling as snow has decreased in many areas south of 55°N. The results also show that modes of low-frequency variability modulate the spatial distribution and strength of the trends; however, they alone cannot explain the observed long-term trends in these climate variables.

Evolution of Eurasian teleconnection pattern and its relationship to climate anomalies in China

The Eurasian teleconnection pattern (EU) is a major mode of low-frequency variability in the Northern Hemisphere winter, with notable impacts on the temperature and precipitation anomalies in Eurasia region. To investigate the structure, life cycle and dynamical mechanisms of EU pattern, diagnostic analyses are conducted to clarify EU pattern evolution, with an emphasis on EU development and decay. In the developing stage, a geopotential height anomaly over North Atlantic emerges 6 days before EU peak phase and other three geopotential height anomalies appear one by one in the following days. During this period, all geopotential height anomalies experience considerable growth and the four-center structure of EU pattern forms 2 days before peak phase. The obvious wave train structure appears at 300 hPa. The EU pattern growth is driven by both relative vorticity advection and transient eddy fluxes. After the peak phase, the geopotential height anomaly over North Atlantic becomes weak as it decays earlier than other anomaly centers, which leads to the classic three-center structure of EU teleconnection pattern. The complete life cycle of EU pattern experience considerable growth and decay within 10 days. During the decaying stage, the horizontal divergence and the transient eddy fluxes play important roles. Additionally, the relationship of EU pattern to winter climate anomalies in China is also analyzed focusing on the decaying stage. The impact of EU pattern on temperature and precipitation in China are significant in 2–4 days after EU peak phase and the distribution of temperature and precipitation anomaly has obvious regional differences.

Multi-annual variability of streamflow in La Plata Basin. Part I: observations and links to global climate

The observed multi-annual variability of streamflow for three rivers (Paraná River (PR), Uruguay River (UR) and Negro River (NR)) in La Plata Basin (LPB) during the twentieth century is analysed. Several spectral methods (singular spectrum analysis, maximum entropy method and multi-taper method) are applied to annual and seasonal run-off time series in order to capture low frequency variability (LFV) modes and pseudo-periodic patterns. Very robust quasi-periods in the three to six years band are detected for the three rivers, suggesting a strong link with El Niño-Southern Oscillation. Pseudo-cycles of eight to nine years appear in NR and UR. No quasi-periods above 10 years are obtained for any of the rivers. The three rivers exhibit significant LFV components associated with increasing trends that show a clear seasonality. For PR, LFV modes appear between May and December while for UR this occurs during December to April. Both periods cover the whole annual cycle, giving rise to complementary behaviours in the timing of trends of these two rivers. Remarkably, for the three rivers, the most intense increasing trend and the minimum streamflow occur at the same time of the annual cycle. Six global climate indices are selected in order to analyse the relationship between the multi-year streamflow variability modes in LPB and those of global climate: Niño 3.4 (N3.4), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), Southern Annular Mode (SAM) and Global Temperature Trend (GTT). Similar to the rivers, almost all these indices present very robust modes of variability in the 3–4.5 years band. Except for N3.4 and PDO, the indices show significant LFV modes that exhibit a pronounced increase at the common end of their records. The NAO, AMO and SAM seem to behave in a nearly cyclic way with periods longer than 60 years while the GTT increases steadily along the whole register.