Seasonally dry tropical forests (SDTFs) are well adapted to seasonal and interannual rainfall variability and have mechanisms to control growth dynamics under water limitation. The forest starts flushing the leaves following the first rainfall events of the wet season, after which it grows fast and remains green until litterfall starts at the end of the wet season. Although ecosystem adaptation is known to buffer seasonal rainfall variability, yearly fluctuations in biomass productivity are still observed. In order to understand the origin of these productivity fluctuations, here we analyze the effects of rainfall variability (seasonal, interannual, and spatial) on the growth dynamics of an SDTF located in the semi-arid region of Brazil. We used a coupled soil water and vegetation model to simulate soil moisture and NDVI dynamics in three sites across a rainfall gradient (from 400 to 800 mm year-1) to compute specific assimilation (kA) and respiration (kR) rates for 19 hydrological years. Based on the time-series of the rainfall and kA, the analysis shows that in all three sites kA tends to increase following dry years and to decrease following wet ones, whereas kR is about ten times smaller and does not vary particularly over time. The observed patterns of kA and kR suggest an interannual mechanism to buffer long-term rainfall variability and mitigate water stress by tuning assimilation rates.

Observations of rocks from exhumed shear zones clearly reveal that secondary phases strongly influence the mechanical and microstructural evolution of materials undergoing large-strain shear deformation. Through Zener pinning, secondary phases promote grain size sensitive creep, allowing deformation to localize in fine-grained regions. For the longevity of such shear zones over geological times, fine grain sizes must be maintained between episodes of deformation such that localization will continue in these regions in subsequent deformation events. Experimental studies of static grain growth on single phase materials demonstrate relatively fast rates of grain growth that would serve to undo grain size refinement under natural conditions. Although static grain growth experiments on samples composed of two or more phases indicate slower growth rates of each phase, such studies have typically been carried out on undeformed material. To investigate the effectiveness of secondary phases at inhibiting grain growth after large-strain deformation, analog samples were synthesized from olivine (Ol) and ferropericlase (Fp) powders with Ol:Fp ratios of 1:5 to 5:1. Samples were deformed in torsion in a triaxial gas-medium apparatus to shear strains of γ = 3 - 7 at P = 300 MPa and T = 1523 K to induce mixing between the two phases; specifically, the distribution of phase boundaries followed a random binomial distribution. Subsequently, sections of each sample were statically annealed at P = 0.1 MPa and T = 1523 K for 10 h or 100 h. Grain size measurements obtained via electron backscatter diffraction indicate that after 10 h of post-deformation annealing Ol grains are smaller by a factor of 1.5 and Fp grains are smaller by a factor of 3 than predicted from single-phase grain growth laws. Comparing the ratio of grain sizes of the two phases to the secondary phase fraction yields a power law fit with an exponent of ~ 0.4 in Ol-rich samples and ~ 1 in Fp-rich samples. These results, along with microstructural observations, indicate that secondary phase particles are primarily distributed along grain boundaries in the Ol-rich samples but are randomly dispersed in Fp-rich samples. Our results demonstrate that secondary phases are highly effective at pinning grain size during static annealing following significant deformation.

The roles of heavy ions have long been an important subject in the magnetospheric physics since the first discovery of O+ ions in the magnetosphere as it hinted to the connection between the ionospheric and magnetospheric plasma. Albeit limited, several observations show the importance of ionospheric N+ and molecular ions, including NO+, N2+ and O2+, in the high-altitude ionosphere and magnetosphere. However, the mechanisms responsible for accelerating the ionospheric heavy ions from eV to keV energies are still largely unknown. Developed from the Polar Wind Outflow Model (PWOM), the Seven Ion Polar Wind Outflow Model (7iPWOM) solves the gyrotropic transport equations for all relevant species (e-, H+, He+, N+, O+, N2+, NO+ and O2+) along open magnetic field lines and therefore, has the capability to assess the role of heavy ions in the supersonic ionospheric outflow. However, the hydrodynamic approach is limited to the region where collisions are important. For the altitudes above the collision-dominated region, the hydrodynamic solution becomes increasingly inadequate. Thus, the 7iPWOM applies a kinetic particle-in-cell (PIC) approach that enables the inclusion of wave-particle interactions (WPI) and Coulomb collisions. The simulation results showed that the N+ ions play a key role in the polar wind solution under all conditions. The mechanisms responsible for the energization of outflowing N+ ions are different than those of O+, not only in the collision-dominated region but also at high-altitudes. This means that the local heating sources to O+ and N+ in the polar wind, even in small amounts, can lead to plasma instability and could possibly affect the large-scale transport properties. In addition, the relative abundance of molecular ions, and how they change the polar wind solution, reveals the link between lower thermosphere and the ionosphere. Therefore, tracking the molecular ions helps understand how the “fast ion outflow” acquires sufficient energy in such a short time scale, compared with the dissociative recombination lifetime of the molecular ions, and assess the role of molecular ions in the overall dynamics of the polar wind outflow.

Since mid-2008, automated radio monitoring networks such as the Reverse Beacon Network (RBN) and the Weak Signal Propagation Reporting Network (WSPRNet) have been observing and logging global amateur radio high frequency (HF, 3-30 MHz) communications. These networks are built and operated by amateur radio operators for amateur radio purposes, but the data are also useful for the study of ionospheric space weather and ionospheric science. Ham Radio Science Citizen Investigation (HamSCI) members will present global observations of over 1.9 billion radio reports on the 1.8, 3.5, 7, 14, 21, and 28 MHz amateur radio bands. The observations span an 11-year period from 2009-2020. We find a correlation between HF propagation path characteristics and the solar EUV output through the F10.7 index, along with solar cycle effects, seasonal effects, and sporadic E signatures. We will also discuss impacts of operator behavior and sampling effects caused by the distribution of amateur radio transmitting and receiving stations.

Ice motion in land terminating regions of the Greenland Ice Sheet is controlled in part by meltwater input into moulins. Moulins, large near-vertical shafts that deliver supraglacial water to the bed, modulate local and regional basal water pressure and ice flow by influencing subglacial drainage efficiency on daily to seasonal timescales. Our previous modeling work found that the geometry of a moulin near the water line has substantial effect on subglacial water pressure variations. Here, we develop a new physically based moulin model which can help constrain moulin shape across the ice sheet and its influence on hydraulic head oscillation, and inform the englacial void parameter used in glacier hydrology modeling. The Moulin Shape (MouSh) model (in Matlab and Python) provides new insight into the evolution of subsurface moulin size and shape at hourly to multi-year timescales. The modeled moulin is initialized as a vertical cylinder. The moulin walls melt back above and below the water line due to the dissipation of turbulent energy, open or close due to viscous and elastic deformation, and freeze inward in winter when cold air temperatures and an absence of meltwater allow refreezing. We combine MouSh modeling results with geometric data from two moulins in Pâkitsoq, western Greenland, which we mapped to the water line. The moulins have heterogeneous shapes and volumes in the top 100 m. This suggests that the size and shape of the upper portion is controlled by local and regional pre-existing fractures, which provide preferential paths for water flow and melting, creating stochastic karst-like conduit shapes. Modeling results show that moulin geometry below the water line is influenced by the hydraulic head, which controls the depth-dependent elastic and viscous closure rates, and by the roughness of the walls, which enhances melt-out rates that oppose moulin closure. We show that subglacial water pressure across the ice sheet is likely influenced by moulin geometry, underscoring the need for including moulins in subglacial models.

The Eastern Himalayan region, along with its neighboring area is known to be the part of the ‘Alpine-Himalayan global seismic belt’, a seismically active area of the world. In the past (1897, 1905, 1934, and 1950) four great earthquakes have triggered in this region with a magnitude higher than Mw=8.0. The 2011 (Mw= 6.9) Sikkim and 2015 (Mw= 7.8) Gorkha Nepal earthquakes call attention to the need for a more accurate understanding of seismic characteristics in the Eastern Himalayan region. In the present study, analysis of spatial variation of seismic activity in the Eastern Himalayas and its surroundings is done by analyzing the variation of seismic parameters and fractal dimension (Dc). Considering the seismicity data and tectonic features, the eastern part of the Himalayas is divided into eight seismic source zones. For the comparison of the seismicity between each seismic source zone, seismic parameters like average seismic activity rate at threshold magnitude (λo), maximum possible earthquake magnitude (Mmax), and ‘b-value’ are calculated. Seismic parameters of seismic zones are calculated by considering the incompleteness of earthquake catalogue, for that sub-catalogue is prepared for each seismic zone, which is complete to the different threshold of magnitude. The seismotectonic stress variations in Eastern Himalayas are indicated by the estimated values of b and Dc. The calculated seismic parameters can be used directly for seismic hazard analysis of the study area. Keywords:Eastern Himalayas; Seismicity; Fractal dimension; Seismic source zone

The hydrological behavior and freshwater availability in any typical river basin are highly dependent upon precipitation making it the most crucial input variable for hydrological modelling. Precipitation as an input variable to hydrological models is available in gridded form with various spatiotemporal resolution. The variations in the model inputs could be subjected to uncertainties in the hydrological model simulation, which further affect the estimation of blue water flow (BWF) and green water flow (GWF) of a river basin. In this study, we investigated the effects of three gridded precipitation datasets [Watch forcing data ERA-Interim (WFDEI); Princeton datasets; Indian Meteorological Department (IMD)] on streamflow pattern, BWF, and GWF using a semi-distributed hydrological model [Soil and Water Assessment Tool (SWAT)] and a lumped rainfall-runoff model [Hydrological Simulation model (HYSIM)] in the Damodar river basin situated in eastern India. Both the models are simulated at daily time steps with the calibration of ten years (1994 – 2004) and validation of five years (2005 – 2010) at catchment outlet (Durgapur barrage) using three precipitation datasets. The performance of all the three precipitation products is evaluated on the basis of streamflow simulation for both HYSIM and SWAT model at the basin outlet using the performance indicators viz., Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2) and percent bias (PBIAS). The seasonal and annual variation in precipitation values of the WFDEI, Princeton, and IMD dataset could attribute to the significant variations in the performance indicators. Subsequently, the best performance in streamflow simulation is achieved by HYSIM model compared to SWAT with IMD precipitation input. Both models showed remarkable differences in BWF and GWF estimation due to changes in precipitation inputs. The results also indicate that BWF is more sensitive to precipitation than GWF as BWF is directly generated from precipitation. All the above observations suggest that the choice of appropriate precipitation datasets is essential to examine the catchment hydrological behavior, and it further helps policymakers to make critical water management decisions.

Accurate field data are required to predict elevated runoff and sediment transport to aid post-fire planning. This is especially significant at the small catchment scale, where these runoff processes occur disproportionately, occurring at a higher magnitude and higher frequency. Sources of elevated runoff include soil hydrophobicity, in-channel sediment loading through dry ravel, extensive rill networks, and exposed bare soil. Recovery of these processes are related to vegetation conditions before, during, and after fire. This can be quantified using satellite-based vegetation indices to understand the recovery of the burned area and resulting hydrologic response. The main objective was to demonstrate potential for ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) evaporation measurements in post-fire hydrology by linking to hydrologic signatures (flashiness index and runoff-ratio) to highlight changes and improve vegetation assessments after fire. A case study of the 2018 Holy Fire is presented using a control catchment and a burned catchment. Results show that areas containing higher proportions of montane hardwood and montane hardwood conifers burned at high soil burn severity, a result of the normal to moderately dry period (2012-2016, 2018) that decreased annual evapotranspiration and increased water stress leading up to fire. For 1-year post-fire, ECOSTRESS measured the highest variation between burned and control during the peak dry months (3 to 4 mm/day), which explained the comparatively higher dry-season flows observed for the burned catchment. Summer field visit also showed vegetation regrowth that consisted of mostly annual grasses following a moderately wet year. Preliminary results were adequate; however, not enough time has passed for the system to fully recover, and thus, we present a Google Earth Engine application to help track recovery.

One obstacle to space weather research is the practical challenge of accessing relevant data. Space weather data are housed in disparate repositories, each with its own unique focus, be it solar, magnetospheric, atmospheric, or earth-based. Much of the effort spent acquiring data could instead be spent on space weather research and education. To address this problem, the Space Weather Technology, Research, and Education Center (SWx TREC), at the University of Colorado, Boulder, in collaboration with the Laboratory for Atmospheric and Space Physics (LASP), has developed the Space Weather Data Portal (https://lasp.colorado.edu/space-weather-portal), a tool built by and for the space weather community. Through the Data Portal, previously dispersed space weather data are in one unified place, accessible to scientists, students, and curious individuals. The focus is on the users and their ability to discover, display, compare, overplot, and download relevant data. A user can filter for past events then easily display and download data related to that event, from the moment it occurs on the Sun, as it travels through space and the atmosphere, to the impacts it has on the Earth. Analysis of space weather events via the Data Portal has proved useful for forecaster training and online learning. The community-created Event Library is a short-cut to curated data collections that provide narratives for context and serve as launch pads for further space weather exploration. This presentation will highlight contributions to the Data Portal from the community: datasets, event markers, timelines, and narrated data collections. Your contributions are encouraged as new resources and improvements are deployed every few weeks. Through this iterative, collaborative process, the Data Portal aims to increase awareness of space weather and its impacts and decrease the time between research and real-world applications.

Observational records of rapidly varying magnetic fields strongly constrain our understanding of core flow dynamics and Earth’s dynamo. Archeomagnetic analyses of densely sampled artefacts from the Near-East have suggested that the intensity variation during the first millennium BC was punctuated with two geomagnetic spikes with rates of change of intensity exceeding 1 μT/y, whose extreme behaviour is challenging to explain from a geodynamo perspective. By applying a new transdimensional Bayesian method designed to capture variations on both long and short timescales, we show that the data considered only at the fragment (thermal-unit) level require a complex intensity variation with six spikes, each with a duration between ~30-100 years. However, the nature of the inferred intensity evolution and the number of spikes detected are fragile and highly dependent on the specific treatment of the archeomagnetic data. No spikes are observed when the data are considered only at the level of a group of fragments from the same archeological context, with a minimum of three different artefacts per context. Furthermore, the number of spikes decreases to zero when increasing the error budget for the intensity within reasonable levels of 3-6 μT and the data age uncertainty up to 50 years. Thus, depending on the choices made, the Near-Eastern data are compatible with a broad range of time-dependence, from six spikes at one extreme to zero spikes on the other, the latter associated with much more modest rates of change of ~0.2-0.3 μT/y, comparable to secular variation at other periods and in other regions.

It is accepted that the Perseids Meteor Shower each August is related to the comet Swift Tuttle. It is apparent that the outgassing as the comet travels spreads a cloud of dust along its 133 year long orbital path, and that as the Earth passes through the dust each year, the larger particles burn in the atmosphere creating the meteor shower. Fireballs are also common at the same time, associated with Comet Swift Tuttle. Fireballs are much larger objects than expected from comets outgassing, but the timing and association with the Perseids and Comet Swift Tuttle is significant enough to comfortably assign a large number of mid August fireballs to a relationship with Swift Tuttle. William Cooke of NASA’s Meteoroid Environment Office has plotted the orbits of the fireballs associated with Swift Tuttle as shown in the attached image. They show a large range of orbits, with only one common factor: They all intersect Earth’s orbit in mid-August. Some have very short periods and others long periods. The image shown in this abstract is a diagram of the orbital paths of the fireballs presented by NASA. Studies with iSALE impact hydrocode show that comets hitting Earth at low angles such as 5 degrees or less can skip and continue on a new path at similar speeds to the original orbital speeds. Such a skip will cause part of the comet to drag across the surface of Earth, and both portions of the comet and portions of the Earth’s surface will be carried into space at above Earth escape velocities. Such an occurrence would send some fragments at high speeds similar to or higher than the speed of the comet, and other fragments would be tossed at slower speeds, all depending on the location of the fragments during the impact event. It would be expected that such fragments would travel away from Earth on a wide range of orbits, with the common feature of all of the orbits being the initiation point of the event: the skip impact of Swift Tuttle with Earth. It is proposed that Comet Swift Tuttle has previously impacted Earth at a low angle, causing a skip event which sent fragments of the comet and of the Earth into the wide range of orbits seen in the fireball orbits associated with the Perseids Meteor Showers. The .gif image shows a graphic rendition of such a skip impact. The third image shows the calculated orbits of the resultant debris. The debris from a skip impact produces the same range of orbits as determined by NASA.

Woody plants vary greatly from tall trees to branching shrubs with increasing dryness. Variations in plant allometry are driven by both biotic and abiotic factors, reflecting different plant adaption strategies in different environments. While much is known about the response of plant functional traits to declining rainfall, less is known about how aboveground allometry (e.g. canopy size, height, stems, branching) of woody plants might respond to increasing dryness, limiting our ability to predict changes in woody plants and associated ecosystem functions under future climate change scenarios. Here, we explore how aboveground allometry of different woody genera responds to increasing dryness at 150 sites long an extensive aridity gradient from humid to arid areas. We used regression analyses and Structural Equation Modelling to explore the variation in woody allometry with increasing aridity, and the abiotic (resource availability) and biotic (competition) mechanisms driving such changes. Our results showed that plant height declined, but branching, and canopy width and depth increased with increasing aridity. Woody responses to dryness varied among genera, with increasing aridity associated with wider canopies in Eucalyptus and Callitris, thicker stems in Acacia, but no clear differences in Allocasuarina. Biotic and abiotic factors exerted different effects on the allometry of different genera, with Eucalyptus and Callitris spp. constrained by resource availability, while Acacia and Allocasuarina spp. were regulated mainly by competition. Our results highlight the genus-specific responses in allometric changes and driving mechanisms (resource availability cf. competition) with increasing dryness. Rather than merely shrinking, plants would allocate resources to either canopies or stems to cope with increasing dryness. Under predicted hotter and drier climates, increasing stem or canopy size, and altering branching might be a useful strategy for woody plants to compensate for biomass reduction and maintain ecosystem functions while growing shorter as dryness increases.

The frequent activity taking place at Mount Etna may pose risks to tourists and specialists visiting the summit craters as well as to the nearby population and thus requires constant monitoring. Infrasound recordings play an important role in volcanic observation because shallow explosive activity, shallow tremor processes or other volcanic phenomena coupled with the atmosphere are easier to identify with sound waves travelling through the air than with seismic waves, which are more effective for the characterization of buried sources but are strongly affected by scattering within the volcano edifice. Similar to seismic waves, infrasound signals often interfere with noise sources, in case of Mount Etna mostly wind induced noise. The manual distinction of noisy data from real volcanic signals is often not easy, and becomes unrealistic when a large amount of data has to be processed. Currently five summit craters at Mt Etna are active, showing intermittent and fluctuating levels of activity. This leads to a wide variety of infrasound signal patterns coming along with changing noise levels. In order to distinguish between noise induced signals and different kinds of volcano induced signals – such as infrasound events and tremor – in the waveform data we apply unsupervised pattern recognition techniques. We show that by extracting features from the amplitude spectrum, a simple clustering analysis with K-Means yields a set of different activity regimes. A more indepth analysis of the patterns is possible by means of Self-Organizing maps (SOMs) which also allow for the identification of transitional activity regimes and provide the option to color-code the results for an intuitive interpretation. We create a reference data set from multiple months of infrasound waveforms to include as many activity regimes as possible. We apply the results to a test set of data showing how new data can be treated fast and efficiently.

Distribution: A. Approved for public release – distribution is unlimited QuakeCast is a novel system for short-term earthquake prediction using global ionosphere Total Electron Content (TEC) data. In the last 20 years, earthquakes have caused over 800,000 deaths and $650 billion in economic damage. While current seismic early warning systems provide up to a minute’s notice by detecting an earthquake’s non-damaging P-waves prior to the damaging S-waves, earlier warnings could allow more significant emergency preparations. Electromagnetic ionospheric phenomena have recently been observed many days before major earthquakes, notably in 2015 in Nepal. QuakeCast explores whether such signals could be used to predict earthquakes in a global dataset of ionosphere and earthquake data. The ionosphere data consists of global TEC data from NASA’s Crustal Dynamics Data Information System (CDDIS), for the years 2005-2015 at 15 minute intervals. The earthquake data consists of over 10,000 events of at least M5 from the International Seismological Centre (ISC-GEM) Global Instrumental Earthquake Catalogue over the same time period. We used the dataset to explore the following classification problem: given a 24-hour sequence of ionosphere TEC data in a 30° latitude by 30° longitude window, is the sequence preseismic (an earthquake occurs in the next time step) or nominal (no earthquake)? We built two models to address this. The first is a classical logistic regression model trained on radially binned data. Analysis of the classifier weights indicate a localized effect in the ionosphere: TEC data spatially and temporally closer to the event contributes more significantly to the prediction. The second model is a deep learning ConvLSTM autoencoder trained to reproduce nominal TEC data sequences. Since autoencoders reproduce data which resemble their training data better than data which does not, we used the reconstruction error to classify sequences as anomalous (preseismic) or nominal. Both methods were found to perform significantly better than a random null classifier, indicating that the ionosphere contains information useful for predicting earthquakes at least 15 minutes in advance. While further research is needed to incorporate additional data features and address noise, we believe that these results are a promising development toward forecasting earthquakes using geoelectric signals.

Conventionally, radar-based nowcasting methods have been conducted by shifting or extrapolating the most recent estimated precipitation field along the estimated motion field to lead times up to 3 hr. However, such methods assume that these fields do not evolve during the lead-time period. A more recent nowcasting approach is the short-term ensemble prediction system known as STEPS. STEPS aims to filter small spatial scales of rain as they have an expected short life-time compared to large spatial scales. Besides, it perturbs the precipitation field during extrapolation so that the prediction uncertainties related to the evolution of precipitation is considered, resulting in an ensemble nowcast. In this work, the configuration, implementation, and performance of STEPS are studied for its radar-based ensemble nowcasting application in Germany. Attention is given to the spatial localization (i.e., the adjustment of parameters that control the spatio-temporal evolution of precipitation in large domains). For such purpose, the capability of STEPS is analyzed using multiple rain events collected by the German radar network. Preliminary analysis regarding the spatial scale filtering of STEPS (without perturbation) shows an improvement against conventional extrapolation of 20 to 30 % on the critical success index starting at lead times of 30 min at a rainfall threshold of 0.1 mm hr-1. In terms of the spatial structure, STEPS (without perturbation) provides a consistent nowcast for lead times up to 3 hr. Although small spatial structures are filtered, the spatial structure at scales of 32 km or larger is maintained. The skill of a probabilistic nowcast (STEPS with perturbation) was also analyzed. It is shown that for the probability of exceeding the 5.0 mm hr-1 threshold, the predictability of the nowcast is compromised for lead times on the order of 1 hr. However, the capability of identifying rain and non-rain areas is still valuable: the hit rates are still larger than the false alarm rates. Our preliminary results highlight aspects needed in the configuration of STEPS such as the evolution model and the perturbation model. In this manner, this study can serve as a basis for an improved nowcasting system in Germany and as a reference for forecasters to understand the characteristics of the examined nowcast system.

The prediction of natural disasters in general and earthquakes, in particular, is becoming increasingly critical in providing early warnings and mitigating catastrophes’ effects. This study investigates relations between earthquakes and ionospheric disturbances. Based on analyzing TEC disturbances, we search for the impact of the earthquake-related seismic waves on the ionosphere. The study is designed as cross-sectional investigations, in which the global earthquakes are randomly collected by the cluster sampling method. We use data of 54 permanent GNSS (Global Navigation Satellite Systems) stations and global earthquakes with magnitudes (Mw) from 4.0 to 9.0 in the period from 2006 to March 2020. The selected data ensure strict conditions such as accuracy, the distance from the GNSS stations to the epicenter, and the depth of hypocenter. Probability and statistics are applied to filter, classify and analyze data. The results indicate that TEC fluctuations at the regions occurring earthquakes with magnitudes greater than 6.0 Mw are significant. These TEC anomalies appear from 30 minutes to around two hours before the mainshocks, and the oscillations remain from five to eight minutes. These TEC variations occur from five to eleven days before the great earthquakes. The results reveal the relations between ionospheric anomalies and earthquake-related seismic waves. The findings are the base to filter TEC anomalies generated by earthquakes in building ionospheric models. The study also opens up a prospect for GNSS applications in studying TEC anomalies linked to earthquake precursors as well.

Dengue virus (DENV) is an endemic disease in the hot and humid low-lands of Colombia. We characterize diverse temporal and spatial patterns of monthly series of dengue incidence in diverse regions of Colombia during the period 2007-2017 at different spatial scales, and their association with indices of El Niño/Southern Oscillation (ENSO) and local climatic variables. For estimation purposes, we use linear analysis tools including lagged cross-correlations (Pearson test), cross wavelet analysis (wavelet cross spectrum, and wavelet coherence), as well as a novel nonlinear causality method, PCMCI, that allows identifying common causal drivers and links among high dimensional simultaneous and time-lagged variables. Our results evidence the strong association of DENV cases in Colombia with ENSO indices and with local temperature and rainfall data. El Niño (La Niña) phenomenon is related to the intensification (weakening) of dengue cases at the national level and in most regions and departments, with maximum correlations occurring at shorter time lags in the Pacific region, closer to the Pacific Ocean. This association is mainly explained by the ENSO-driven increase in temperature and decrease in rainfall, especially in the Andes and Pacific regions. The influence of ENSO is not stationary (there is a reduction of DENV cases since 2005) and local climate variables vary in space and time, and thus it is not easy to extrapolate results from one site to another. The association between DENV and ENSO varies at national and regional scales when data are disaggregated by seasons, being stronger in DJF and weaker in SON. Specific regions (Pacific and Andes) control the overall relationship between dengue dynamics and ENSO at national scale, and the departments of Antioquia and Valle del Cauca determine those of the Andes and Pacific regions, respectively. Cross wavelet analysis indicates that the ENSO-DENV relation in Colombia exhibits a strong coherence in the 12 to16-months frequency band, which denotes the frequency locking between the annual cycle and the interannual (ENSO) timescales. Results of nonlinear causality metrics reveal the complex concomitant effects of ENSO and local climate variables (Fig. 1), offering new insights to develop early warning systems for DENV in Colombia.

To understand the influence of the Sun on Earth's system, long and accurate measurements of solar irradiance are a prerequisite. The available direct measurements of solar irradiance since 1978 are clearly not sufficient for this purpose. This stimulated development of models used to reconstruct past solar irradiance variations from alternative observations. The main driver of the irradiance variations on time scales of days to millennia is the evolution of the solar surface magnetic field in form of dark sunspots and bright faculae and network. Therefore, models require input data describing the contributions of these various magnetic regions on the Sun at earlier times. Unfortunately, records that can be used to describe the facular and network contributions are barely longer than the direct irradiance measurements. Thus, irradiance reconstructions to earlier periods have to rely on sunspot data alone. Data that have hardly been used for solar irradiance reconstructions until now are full-disc solar observations in the Ca II K line. Such data exist since 1892 from various observatories and include all the needed information describing faculae and the network. However, they are plagued by a bunch of various problems and artefacts, and recovering the non-linear response of the photographic material to the radiation is non-trivial since the required information is usually missing, too. We have developed a method to process Ca II K observations from various sources and demonstrated the higher accuracy achieved by our method compared to other techniques presented in the literature. Here we use the carefully reduced Ca II K observations from multiple archives to reconstruct solar irradiance variations.