UNAVCO internships provide unique opportunities for students from underrepresented populations to develop their technical and communication skills while aiming to broaden participation in the Earth sciences. UNAVCO, a non-profit that operates the National Science Foundation-funded GAGE facility, has the opportunity to facilitate multiple internship programs, engaging students from across the United States. Prior to 2020, all internship programs were in-residence in Boulder, Colorado. Internship programs included project work and in-person professional development as well as cohort building, multi-level mentoring, and professional networking in the science-rich Boulder area. In light of the COVID-19 pandemic and ‘safer-at-home’ directives nationwide, we successfully converted our three internship programs to a remote format with fully online programming. We present our strategies for successfully planning, managing, and facilitating three online internship programs with 12 student participants ranging from community college to undergraduates to graduate students who all participated from their home locations. We also share our considerations for converting our internship programming to an online format, expanding our interns’ mentoring network and opportunities, and increasing our programs’ flexibility for interns who were personally impacted by national events. We present our process of identifying and engaging online learning experts and working collaboratively with all stakeholders to ensure each of our internships continued to be of the highest quality and our student experiences were not diminished. By offering the full length and robustness of our programs through a remote platform, we kept our promises of education, career advancement, monetary compensation, and community connection to our full 2020 intern cohort. In addition to offering geoscience projects and research experience to each intern, we implemented a robust suite of synchronous, online professional development courses that occurred weekly, including technical writing workshops, Geoscience Career Circles, and Communications Seminars focused on soft skill development. We also describe the support and training that we offered project mentors as they virtually guided students through successfully completing rigorous science projects and a suite of project deliverables including scientific posters, oral presentations, and technical manuscripts.

WW3 is a major spectral wave modeling system known for its applicability in large-scale modeling of sea states and wave climate from large rectilinear grids offshore to high-resolution unstructured grids in coastal zones, being widely used in operational, research and engineering arenas. Over the past few years, the operational global system at NCEP utilizes the multi-grid capabilities, where various rectilinear grids are nested internally to represent the offshore domain with a less computationally expensive coarse grid while the nearshore is run at a high-resolution grids. Alternatively, a single unstructured grid can be used for such applications. Recent advances in the performance of the model on unstructured grids provide the opportunity to run the model on large unstructured grids efficnetly. Here, an assessment of the operational global setup and unstructured wave models on various resolutions are given. The sensitivity to global unstructured mesh design was investigated using triangular meshes with a global minimum element size that ranged from 1.5 km to 6 km with local mesh refinement in storm landfall location. The validation is performed against satellite altimeter data and offshore NDBC network during normal and stormy conditions. We demonstrate that an optimized unstructured WW3 can be used for global applications, with superior performance relative to the complicated multi-grid system.

Southern California has seen a resurgence of winegrowing regions in the past few decades, however the future of winegrape climatic suitability in the area has not been exhaustively explored. This study evaluated the future climate suitability for the cultivation of winegrape and potential global warming impacts on southern California’s winegrowing regions through a series of high-resolution surface air temperature and precipitation projections obtained with the WRF-SSIB regional climate model. Results reveal that by mid-21st-century the surface air temperature will increase by approximately 1.2 °C, while average precipitation will decrease by as much as 11% in the southern winegrowing areas under the Intergovernmental Panel on Climate Change high greenhouse-gas emissions scenario. Evaluation of bioclimatic suitability indices indicate increases in heat accumulation for all major winegrowing areas; including an increase of about 10% in growing-degree day, while morning low temperatures in September may experience increases of approximately 11% in the future, thus impacting negatively the ripening stage of grapevines and leading to changes in wine composition and quality. Additionally, the extent of areas classified under the cool to warm climate suitability categories could decrease by nearly 42% in the study area by 2050. Conditions in southern California are already warm and dry for viticulture and continuing heat accumulation increase, along with rainfall reduction, could potentially place additional stress to winegrape crop in the area, including advanced phenological timing and moisture deficit stress that could lead to decreases in yield. The projected decline in viticulture suitability highlights the need for adaptive capacity within this sector to mitigate the impacts of global warming. Possible mitigating strategies include planting hotter climate grape varieties, moving vineyards to regions that are more suitable in the future, and adopting dry-farming techniques.

Public health communication strategies, including entertainment-education, can effectively change human behavior, improving health outcomes from climate change. Tools from social psychology, including social modeling and building self and collective efficacy, can help us to create a new model for current, culturally-relevant stories that can help communities adapt to climate change. As an example we will share key learnings from Rhythm and Glue, an applied television prototype, based on research from an NSF Advancing Informal STEM Learning submission. Best practices for climate communication include adaptations of entertainment-education techniques for culturally grounded representations of climate engagement positive outliers. As science communication progresses in adapting social psychology and sociology practices for climate communication, we would like to share how this prototype applies the methods and suggests some new directions that further adapt the practices to account for limited resources and media fragmentation challenges. While this work focuses on climate, it has broad implications for future science communication practices.

Most hydropower utilities rely on flow forecasts to manage the water resources of their reservoir systems and to help marketers and schedulers make efficient use of power generating resources. Flow forecast providers and dam operators typically assess the value of flow forecasts by assessing the skill of the forecasts in a verification exercise. Although there are many flow forecasting approaches available—from physics-based approaches associated with statistical pre and post processors and data assimilation, to emerging machine-learning based approaches—there is little consensus on how to choose the best forecast product. Nor are there established methods for translating forecast skill—a summary statistic amalgamating multiple types of errors —to forecast value (benefits or avoided cost) as perceived by a marketer or scheduler. In this work we develop such an approach by combining a water resources management model with a power grid model. Flow forecasts are developed at 85 locations for a varying range of skills, from perfect, to persistent and in-between. Using reservoir and power grid simulations over the Western U.S., we propagate flow forecasts through the power grid model, mapping flow forecast skill to regional hydropower revenues, production costs and carbon emissions. We develop a deeper understanding of the influence of regional and seasonal differences in markets and hydrologic dynamics on forecast value. We discuss future research directions to integrate hydrologic forecasts into decision-making at the utility and wider system scale.

The transport effectiveness of a stream flow event of a certain magnitude in carrying a sediment load is defined as the product of the effect of that event and the frequency with which it occurs. This approach is famously known as Magnitude-Frequency Analysis (MFA). MFA has been commonly used to calculate “effective discharge” which is considered as the stream flow that is responsible for transportation of the majority of the sediments from a river basin over a long period of time. In MFA, the stream flow at a location is assumed to follow a continuous probability distribution whereas the sediment transport is described by a rating curve between stream flow and sediment load. Despite the apparent good fit to the data, there are problems using a rating curve to predict sediment load. The sediment load carried through a stream network can be affected by the variability in stream flow occurring during different months or seasons (i.e. time scales) and consequent hysteresis phenomenon. Prediction of sediment load can be improved by constructing separate rating curves for various time scales. In this study, daily stream flow was assumed to follow the log normal distribution, and rating curves (between stream flow and sediment load) have been developed for total, monsoon, individual months data and considering hysteresis phenomenon for total and monsoon data in transport of sediments. Finally, MFA based effective discharges were calculated at 16 stream gauges in the Godavari River basin, India. The results of this study can find use in the effective planning and functioning of dams/reservoirs.

Whistler-mode waves have often been proposed as a plausible mechanism for pitch angle scattering and energization of electron populations in the solar wind. Theoretical work suggested that whistler waves with wave vectors parallel to the interplanetary magnetic field must counter-propagate (sunward) to the electrons for resonant interactions to occur. However, recent studies reveal the existence of obliquely propagating, high amplitude, and coherent waves consistent with the whistler-mode. Initial results from a particle tracing simulation demonstrated that these waves were able to scatter and energize electrons. That simulation was limited and did not examine a broad range of electron distributions. We have adapted the original particle tracing code for the solar wind with wave parameters observed by the STEREO satellites and to model core, halo and strahl electrons. Simulations are run to record the response of a wide initial phase space volume with uniform waves and wave packets. Using a Hamiltonian analysis, resonant responses at different harmonics of the cyclotron frequency are included in the simulation. A numerical integration scheme that combines the Hamiltonian analysis and the relativistic 3d particle tracing deployed on a high performance cluster enables accurate mapping and large-scale statistical studies of phase space responses. Observations of electron distributions from WIND at 1 AU are used for normalization. This enables extrapolations for core, halo, and strahl electrons evolution with the numerical Green’s function method. Results provide evidence for pitch angle broadening of the strahl and energization of core and halo electrons. This model can also provide results that are applicable to a number of different wave-particle interactions in the heliosphere for comparison to in-situ measurements.

Transport and retention behavior of Graphene Oxide (GO) is influenced by the physical and chemical properties of porous media under subsurface environmental conditions. Fixed-bed column studies using quartz sand and biochar (BC) in different configurations were conducted as a function of ionic strength and flowrate. Colloid filtration theory (CFT) was employed to develop mathematical models based on the one-dimensional convection-dispersion equation using experimental GO breakthrough curves (BTCs) and retention profiles (RPs) obtained from the experimental data. GO transport and retention behavior was modeled using BC and BC-nZVI (BC surface modified with nanoscale zero-valent iron) as filter media to understand the effect of media properties. It was demonstrated that the model can describe measured BTCs and RPs of GO in the sand, BC, and BC-nZVI. The inverse modeling approach was implemented to determine the attachment coefficient (Ka) and maximum solid-phase retention capacity (Smax) using GO BTCs for different experimental conditions. Higher Ka in BC at 10 mM IS indicated the influence of straining which agrees with the depth-dependent retention kinetics. Furthermore, pronounced GO aggregation at higher IS supports the higher Ka values at 10 mM compared to 0.1 mM. In contrast, higher Ka values were predicted in BC-nZVI at lower ionic strength (0.1 mM) primarily due to the attachment of GO onto nZVI where nZVI in BC pores was also favorable for the straining process. This study revealed that CFT including the attachment, straining, and blocking process can effectively describe the GO transport in BC and surface-modified BC-nZVI under subsurface environmental conditions.

Marine cloud brightening (MCB) is the idea that the amount of solar radiation reflected by low clouds might be deliberately increased by augmenting the existing population of aerosol particles with salt particles created from seawater. MCB has been suggested as one of the potentially feasible climate intervention approaches to counteract anthropogenic global warming. Global and process modeling studies have been conducted to assess various aspects of MCB, but many questions remain. Observations evaluating the brightening of clouds using pollution from commercial shipping serve as a useful tool for evaluating potential for brightening, as do studies using detailed microphysical models and large eddy simulations. In this presentation, these different pieces of knowledge will be synthesized using the framework of a simple heuristic model, which aims to estimate bounds on the global radiative forcing possible from MCB given assumptions regarding: (a) the quantity, size, and lifetime of salt particles injected from each vessel; (b) the number of vessels deployed; (c) the relationship between cloud droplet concentration and the aerosol size distribution; (d) the albedo susceptibility of clouds; (e) the strength of the cloud liquid water adjustments to aerosol. This presentation will use the heuristic model to explore questions such as: How much salt mass must be sprayed to achieve a certain forcing, what is the optimal size for the injected particles, and how many ships are really needed to achieve significant cooling?

Serpentinites play a vital role in subduction zone processes: as one of the dominant carriers of water in downgoing plates, they are critical constituents in the global volatile cycle and add complexity to the bulk rheology and density of downgoing and overlying plates, with strong implications for seismicity and mechanical mixing. These rocks can form in a number of tectonic settings where water reacts with peridotite under certain conditions, including at or near mid-ocean ridges and in mantle overlying dehydrating subducted plates. In the HP/LT terrane preserved on the island of New Caledonia, serpentinites outcrop as meter-scale resistant blocks and as highly deformed “matrix” that hosts a range of metasedimentary, metamafic, and meta-ultramafic lenses. The origin of these HP serpentinites has been debated, with competing hypotheses linking protolith to nearby obducted ophiolite or to the subducted oceanic plate. We analyzed 30 serpentinites and associated hybrid rocks from across the HP terrane to discern the tectonic origin of their protoliths and to better understand their reaction history through subduction and exhumation. Whole rock major and trace element and stable isotope geochemistry reveal the existence of at least two distinct types of serpentinites in the HP terrane. Serpentinites in the far NE exhibit elevated HREE’s, δ18O values of 6-10‰, and scatter in major element concentrations. In the SE, a ~1 km2 ultramafic massif contains serpentinites with relatively more depleted REE, δ18O values of 5-7‰, and a more restricted range of major element concentrations, with distinctly higher MgO and lower Al2O3 than samples from the NE. We compare these data to a global geochemical compilation of serpentinites from various tectonic settings. Ongoing Raman spectroscopy work will determine serpentine polymorph(s) and electron probe microanalysis will target isolated relict pyroxene grains and oxide minerals to retrieve additional protolith information. The recognition of multiple types of serpentinites in this HP/LT terrane adds important information to debate about the origin of the ultramafic material, and may speak to complex interactions between the downgoing plate and overlying mantle or to systematic spatial differences in protolith composition or degree of metamorphism or deformation.

Place-based field education is the foundation of Lamont-Doherty Earth Observatory’s Hudson River Field Station. The most effective method of engaging and connecting people with their local environment is through memorable and hands-on field investigations, with the Hudson River itself as the best educational tool. Our ‘Next Generation of Hudson River Educators’ is a six week summer internship program specifically designed to more effectively engage underrepresented minority (URM) students and communities with the Hudson River using a tiered mentoring structure. The high school interns first take a deep dive into the Hudson River to develop a better understanding of the historic to present human connection to the estuary, the dynamism of its biology, physics, chemistry, geology, and topography, and the challenges the Hudson faces, along with potential community solutions. These field investigations build an appreciation for the estuary that they can pass on to their communities. Unfortunately, the coronavirus introduced a unique challenge on place-based education making it impossible to run an in-person program. Through an innovative approach to place-based learning, we decided ‘if we couldn’t bring the students to the river, we would bring the river to the students’. The interns dedicated the first weeks to learning about the Hudson through “Virtual River” videos, games, interactive web activities, and live river demonstrations hosted down by the water. While the content is virtual, it simulates a place-based education that effectively engages students in the geosciences and increases science literacy. The interns then work in teams to create their own communication tools to share Hudson information that is captivating to a multigenerational and diverse audience. In order to offer information that is interesting and relevant to their communities, interns performed interviews to learn directly from their friends, family, and neighbors about their perceptions and existing knowledge of the Hudson. Involving the community in place-based education is crucial because residents provide key information that would be otherwise unknown to a visiting scientist or educator. They then use this information to build their communication outputs that are intended to better connect URM to their local waterways.

Identifying subsurface contamination is challenging as the sources are not directly perceivable. Aquifer contamination only gets noticed when it is measured in one of the observation wells. As remediation of the contaminated sites is expensive and time-consuming, it is essential to locate sources of contamination for efficient remediation design and water resources management. Simulation-optimization approach is popularly used for identification of contaminant sources. However, the numerous runs required for the simulation model by utilizing the optimization algorithm makes this approach computationally expensive. Alternatively, the simulation model can be substituted by a surrogate model which can significantly reduce the computational cost. In this study, a deep neural network (DNN) based surrogate model is proposed for simulating the transport of a reactive contaminant Tritium in a hypothetical aquifer. The DNN is trained by considering injection rates at possible source locations as inputs and concentration at observation wells simulated by meshfree Radial Point Collocation Method (RPCM) as output. RPCM efficiently handles instabilities associated with advection and reaction dominant problems in comparison to grid/mesh-based methods. The backpropagation approach is used to optimize the weights and biases of the DNN using adaptive moment estimation (ADAM) as an optimizer. The performance evaluation of the surrogate model yields Mean Squared Error (MSE) close to zero and correlation coefficient (R2) of 0.99. An inverse model is developed by linking the DNN surrogate model and Particle Swarm Optimizer (PSO). The application of the inverse model show that the DNN-PSO model can predict the injection rates at possible source locations accurately.

Remote sensing approaches based on VIS-NIR spectroscopy can be used for getting near real-time information about soil fertility. However, the main challenge limiting the application of spectroscopy in soil fertility evaluation is finding suitable data pre-processing and calibration strategies. We have compared various pre-processing techniques using the reflectance spectra obtained from AVIRIS-NG hyperspectral images, for quantification of organic carbon (OC), available phosphorus (P) and available potassium (K) in the surface soils of Surendranagar area (Western parts of India) and Raichur (Southern parts of India). Surface (0 - 0.15 m) soil samples were collected from these two areas synchronously with the dates of the AVIRIS-NG campaign. The soil samples were air dried, sieved to <2 mm, and analyzed for OC, P, and K using standard methods. The AVIRIS spectra (spectral range of 380-2500 nm with an interval of 5 nm) corresponding to soil sampling points were extracted. The pre-processing steps were used in the order: Continuum Removal (Yes/No), Moving Window Abstraction (Yes/No), No transformation or Euclidean Normalization or Standard Normal Variate (SNV), No transformation or Savitsky-Golay (SG) first-order smoothing, and No transformation or first derivative OR second derivative. We have used the partial least squares regression (PLSR) to calibrate the model from pre-processed spectra. The PLSR with Continuum Removal, SNV, SG first-order smoothing, and first derivative was selected as the best algorithm for estimating soil properties from the Western parts of India, and the corresponding R2 were 0.77 for OC, 0.79 for P and 0.83 for K (RMSE <0.3 for all the parameters). The PLSR with Moving Window Abstraction, SG first-order smoothing, and second derivative were selected as the best algorithm for estimating soil properties from the Southern parts of India, and the corresponding R2 were 0.54 for OC, 0.49 for P and 0.56 for K (RMSE <0.3 for all the parameters). These results suggest that the optimization of AVIRIS spectra using various pre-processing techniques and modeling approaches is required for rapid and non-destructive assessment and monitoring of soil health for precision agriculture.

Being an agent of environmental hazard, wild fire is a major concern, especially in Mediterranean countries, where warm and dry summers may lead to high levels of vegetation stress. Vegetation fire, whether caused naturally or by humans, its widespread and occurrence depends on land cover (LC) type and state, climate and meteorological conditions, land use. The main concern of the study is quantification the physical processes related to vulnerability of biomass burning across the climatic gradient for Eastern Mediterranean Europe (Bulgaria) using satellite information, ground observations and model outputs for land surface state. Sixteen years (2004-2019) satellite data from LSASAF FRP-PIXEL product at MSG resolution were processed in order to determine the distribution of vegetation fires for the most risky period July-September. The work examines the relationships of wildfire statistics (number of fires and radiative energy released) to surface temperature (according LSASAF LST product), and soil moisture availability, SMA (according SVAT model) as energy-moisture related climate variables. Regression analyses for the main aggregated vegetation types (forest, shrubs, cultivated) reveal statistically significant dependences: - In short-term climatic aspect: a high correlation between monthly mean positive LST anomalies and fire activity as well as negative correlation between SMA anomalies and fire activity; - In long-term climatic aspect: the contribution of different LC types for fire activity is considered. Identified specific fire regime with location of spots with highest fire activity provides knowledge about fire-climate relations on a regional scale, which is important to define more risky regions as well as for projection the effects of global environmental change.

The Ocean City Inlet was created in the early 1930s by a strong hurricane mobilizing the sediment and thus separating Ocean City from Assateague Island, making the Maryland coastal bay a two-inlet system, with Chincoteague Inlet 60 km to the south. Ecological and hydrodynamic impacts of tropical storms on coastal regions have been well-documented. However, little work has been conducted comparing the impacts of extratropical winter storms with hurricanes, specifically at two-inlet systems. Observed winds, water levels, and waves from NOAA’s National Data Buoy Center (NDBC) and predicted data from the North American Regional Reanalysis (NARR) and Finite Volume Community Ocean Model (FVCOM) models were compiled for 2016 to 2018 to compare the magnitude of forces driving volumetric flow during tropical and extratropical storms. Although the inlets are around 60 km apart, the water level responses were similar during the three blizzard events examined. During each blizzard, the water level increased at the initial arrival of the low-pressure system and then decreased at both inlets over the course of the storm, potentially owing to sustained wind pushing water out of the inlets. The wind and wave forcing on both inlets will be compared using field data to validate modeled data for one blizzard and hurricane per each year. This study, along with the validated hydrodynamic models that were utilized, will assist in predicting environmental stressors and potential influences on shoreline zones under varying storm intensities in two-inlet systems.

Over the past decade, the huge success in many large-scale projects like the USArray component of Earthscope gave rise to a massive increase in the data volume available to the seismology community. We assert that the software infrastructure of the field has not kept up with parallel developments in ‘big data’ sciences. As a step towards enabling research at the extreme scale to more of the seismology community, we are developing a new framework for seismic data processing and management we call Massive Parallel Analysis System for Seismologists (MsPASS). MsPASS leverages several existing technologies: (1) Spark as the scalable parallel processing framework, (2) MongoDB as the flexible database system, and (3) Docker and Singularity as the containerized virtual environment. The core of the system builds on a rewrite of the SEISPP package to implement wrappers around the widely accepted ObsPy toolkit. The wrappers automate many database operations and provide a mechanism to automatically save the processing history and provide a mechanism for reproducibility. The synthesis of these components can provide flexibility to adapt to a wide range of data processing workflows. The use of containers enables the deployment to a wide range of computing platforms without requiring intervention by system administrators. We evaluate the effectiveness of the system with a deconvolution processing workflow applied to USArray data. Through extensive documentation and examples, we aim to make this system a sustainable, open-source framework for the community.

Uturuncu volcano in southern Bolivia is something of a “zombie” volcano – presumed dead, but showing signs of life. The volcano has not erupted in 250 kyr, but is exhibiting unrest in the form of ground deformation, seismicity, and active fumaroles. Elucidating the subsurface structure of the volcano is key for interpreting this recent unrest. Magnetotelluric measurements revealed alternating high and low resistivity anomalies at depths <10 km beneath the volcano, with a low-resistivity anomaly directly beneath Uturuncu. A key question is, what is the nature of this anomaly? To what extent is it partial melt, a hydrothermal brine reservoir, or a mature ore body? Knowing the density of this anomaly could distinguish between these scenarios, but existing density models of the area lack sufficient resolution. To address this issue, we collected additional gravity measurements on the Uturuncu edifice with 1.5 km spacing in November 2018. Gradient analysis and geophysical inversion of these data revealed several features: a 5 km diameter, high density anomaly beneath the summit of Uturuncu (1 – 3 km elev.), a 20 km diameter ring-shaped negative density anomaly around the volcano (-3 – 4 km elev.), a NNE trending, positive density anomaly northwest of the volcano (0 – 4 km elev.), and a NW trending, negative density anomaly to the southeast. These structures often (but not always) align with resistivity anomalies, features in new seismic tomography models, and relocated earthquake hypocenters. Based on a joint analysis of these data, we interpret the positive density anomaly as a crystallizing dacite pluton, and the negative density ring anomaly as a zone of hydrothermal alteration. Earthquakes around the edges of the crystallizing pluton may represent escaping fluids as the magma cools. The high density anomaly to the northwest likely represents a solidified pluton, and the low density anomaly to the southeast may represent a fractured fault zone. We posit that the alternating zones of high and low resistivity anomalies represent zones of low and high fluid/brine content, respectively. Based on this analysis we suggest that the unrest at Uturuncu is unlikely to be pre-eruptive. This study shows the value of joint analysis of multiple types of geophysical data in evaluating volcanic subsurface structure.

Snow is among the most reflective of natural surfaces on Earth and in its reflectance, also known as snow surface albedo (SSA), a small SSA decrease can increase its absorption of solar energy. Also, climate models indicate that the reduction of SSA contributes to global warming and near-worldwide melting of cryosphere. The snowpack through South America (SA) is mainly associated to the Andes mountain range, which geologically extends 9000 km approx. from the tropical region (11°N) to the Antarctandes (76°S) in the Antarctic. There, snow is an important source of fresh water for more than 80 million people who live in the 9 countries where this mountain passes. In this study we have analyzed the SSA trend. For that, in accordance with the division proposed by Dussaillant et al. (2019), we have divided the study area into 8 zones: Inner Tropics (11°N-5°S), Outer Tropics (5°S-18°S), Desert Andes (18° S-31°S), Central Andes (31°S-37°S), North Patagonia (37°S-46°S), South Patagonia (46°S-54°S), Fuegian Andes (54°S- 56°S), and Antarctandes (63°S- 76°S). We used daily data of SSA available from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the NASA Terra and Aqua satellites for the last 20 years (Mar 2000 to Feb 2020). The results show that the greatest negative inter and intra-annual trends (i.e. is a decrease in SSA) during the last 20 years were observed in the Central Andes (April, -5.76%; p <0.001), North Patagonia (Nov, -2.33%; p <0.05) and Fuegian Andes (Oct, -1.73%; p <0.05). While the greatest positive variations were detected in Antarctandes (May, 4.76%; p <0.001), Fuegian Andes (Dec, 4%; p <0.05), and Outer Tropics (Dec, 1.67%; p <0.01). The highest SSA decrease observed in the Central Andes is consistent with previous studies carried out by the authors that have shown an association between light-absorbing particles with SSA decrease in various basins there. Our results could serve to better understand the radiative forcing changes generated in the SA cryosphere and its effects on climate change.