Atmospheric deposition of micro-nutrients like Fe has been shown to be important for ocean biogeochemistry. The largest source of atmospheric Fe and other elements (e.g., Ca, Al, Si, and Ti) is desert dust, although there are significant non-dust sources in some regions (e.g., combustion, sea salts, volcanoes). However, past estimates of these elements have been substantially uncertain due to limited information about the composition of the desert source regions. Here we use elemental distributions estimated from new Earth Surface Mineral Dust Source Investigation (EMIT) observations, which provide mineralogical composition at the surface of the Earth based on imaging spectroscopy measurements from the International Space Station. We focus on total elemental amounts, not on the soluble fraction. We add in other sources of these elements (anthropogenic and natural) and compare to a compilation of available surface concentration data from stations over land and from shipborne observations. The combined observational and model synthesis provides new information about the distribution and deposition of these elements. Our results suggest that the modeled distribution is similar to available observations, but discrepancies still exist in both natural desert dust regions as well as regions dominated by anthropogenic sources. Comparisons between the model estimated Ca/Al ratios and observations in some dust dominated regions suggest an underestimate of Ca/Al ratios. Global budgets for Ca, Al, Fe, Si, and Ti suggest that desert dust remains the dominant source, although volcanic and anthropogenic contributions are important in some regions. Changes in elemental distributions since preindustrial times were also estimated.

Non-technical summary This study looks at future water deficit in glacier-fed river basins in Asia and the Andes under three possible global development pathways. The results show that a world with high population growth and low technological progress faces the greatest water stress. Scenarios with better technology or lower climate impacts reduce water deficits. Glacier meltwater increases temporarily under stronger warming but declines later in the century. Overall, the study highlights the need for climate mitigation and better water management to reduce future water scarcity.Technical summary This study assesses water scarcity in selected glacierized basins across Asia and the Andes under three Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5). Using a novel integration of the Open Global Glacier Model (OGGM), the Xanthos hydrological framework, and the Global Change Assessment Model (GCAM), we estimate water availability and demand while accounting for glacier runoff and its temporal dynamics. Results reveal SSP3-7.0 as the most water-scarce scenario due to high water demand, higher population and low technological development. Instead, SSP5-8.5 results in slightly lower water scarcity risks than SSP3-7.0 due to its higher technological efficiency and lower population. Finally, SSP1-2.6 results in lower cumulative surface water deficits due to lower climate change impacts, better water and energy technology, and lower population. Glacier runoff has a peak in its contribution under severe climate scenarios (SSP3-7.0 and SSP5-8.5) and experiences a decline in the second half of the 21st century. The findings underscore the importance of effective mitigation to avoid peak-water occurrence under high emissions scenarios and adaptation measures, such as improving irrigation efficiency and reducing withdrawals, to address anthropogenic-induced water scarcity.Social media summary Increase water scarcity and glacier runoff decline under severe climate change scenarios in Asian and Andean basins.

Outcrops and cores are primary sources of information about the Earth's past. Quantitative analyses rely on geochronologies that take into account highly variable sedimentation and erosion rates as well as gaps from missing strata. Using 23 geochronologies from the Holocene, Quaternary, Phanerozoic and Precambrian, we apply Haar fluctuation analysis to statistically characterize the number of measurements per unit time - the measurement densities. The analysis determines the densities' (multifractal) scaling regimes and exponents; collectively, the analyses span over nine orders of magnitude in time scale. The measurement density is a new paleoindicator that we show is typically correlated with the primary paleoindicator, biasing and complicating its statistical interpretation. We also analyze the distribution of gaps linking the latter's (probability) scaling with series incompleteness and the length Sadler effect. The density characteristics are needed to unbias spectra and other statistical characterizations.

Geological time is punctuated by events that define biostrata and the Geological Time Scale’s (GTS) hierarchy of eons, eras, periods, epochs, ages. Paleotemperatures and macroevolution rates, have already indicated that the range ≈ 1 Myr to (at least) several hundred Myrs) is a scaling (hence hierarchical) “megaclimate” regime. We apply analysis techniques including Haar fluctuations, structure functions trace moment and extended self-similarity to the temporal density of the boundary events (r(t)) of two global and four zonal series. We show that r(t) itself is a new paleoindicator and we determine the fundamental multifractal exponents characterizing the mean fluctuations, the intermittency and the degree of multifractality. The strong intermittency allows us to show that the (largest) megaclimate scale is at least ≈ 0.5 Gyr. We also analyze a Precambrian series going back 3.4Gyrs directly confirming this limit and allowing us to quantatively compare the Phanerozoic with the Proterozoic eons.We find that the probability distribution of the intervals (“gaps”) between boundaries and find that its tail is also scaling with an exponent qD≈ 3.3 indicating huge variability with occasional very large gaps such that it’s third order statistical moment barely converges. The scaling in time implies that record incompleteness increases with its resolution (the “Resolution Sadler effect”), while scaling in probability space implies that incompleteness increases with sample length (the “Length Sadler effect”). The density description of event boundaries is only a useful characterization over time intervals long enough for there to be typically one or more events. In order to model the full range of scales (and low to high r(t)), we introduce a compound Poisson-multifractal model in which the multifractal process determines the probability of a Poisson event. The model well reproduces all the observed statistics.Scaling changes our understanding of life and the planet and it is needed for unbiasing many statistical paleobiological and geological analyses, including unbiasing spectral analysis of the bulk of geodata that are derived from cores.

Iron (Fe) input into remote oceans strongly influences the effectiveness of the biological pump and atmospheric carbon dioxide (CO2 atm). Several pathways contribute to the delivery of Fe to the ocean surface, with mineral dust aerosols being fundamental beyond continental margins. The greatest variability in dust emissions occurs at glacial-interglacial timescales. While the absolute amount of dust is important, another key variable is the amount of soluble Fe within dust particles. However, the effect of this variable on past ocean biogeochemistry is not well documented by observations. Using the cGENIE Earth system model, we conducted sensitivity simulations of the role of dust-borne soluble Fe ocean inputs on the pre-industrial and Last Glacial Maximum (LGM) global CO2 atm concentration. We found that the progressive enhancement of dust-borne Fe deposition and solubility in glacial oceans led to enhanced biological productivity. This shift in nutrient dynamics contributed to a 28% increase in global particulate organic carbon export. The resulting nonlinear drawdown in LGM CO2 atm (compared to the Holocene) increases up to a saturation value of similar to 30 ppmv, approximately one-third of the documented 80-100 ppmv change in CO2 atm between glacial and interglacial periods during the late Quaternary. The region between 35 degrees S and the transition between the Subantarctic and Polar Fronts is particularly critical for regulating CO2 atm. These results provide new insights into the role of Fe solubility in oceanic carbon fixation, emphasizing the need for a more complete understanding of dust particle mineralogy and its interactions with atmospheric and ocean chemistry in past and future climates.

During the Last Glacial Maximum, changes in the thickness of the Patagonian Ice Sheet modified southern Andean topography. However, the resulting atmospheric feedbacks remain poorly constrained. Using an atmosphere-land coupled model, we isolated the climate response to prescribed ice-sheet thicknesses. Our results indicate that a thicker ice sheet generates a decrease in low level zonal winds, a westward shift in precipitation, a temperature increase along the western margin of Eastern Patagonia, and an increase in storm activity over Patagonia. A decrease in thickness generates the opposite pattern. Our findings suggest that the Patagonian Ice Sheet not only responded to climate change, but also actively modulated it-highlighting the role of topographic forcing in shaping atmospheric circulation over the Southern Hemisphere mid-latitudes.

Mineral dust aerosols are pivotal components of Earth's atmosphere and significantly influence the climate system. Investigating their impacts through ice core records offers unique insights into paleoclimate dynamics. Various techniques, including direct and indirect measurements of concentrations and size distributions, unravel the complex story of dust provenance, transport, and deposition. Dust records correlate with temperature reconstructions, reflecting the interconnectedness of climatic processes. Enhanced characterization methods promise a deeper understanding of the role of dust in the Earth's history. The current state of knowledge on paleoclimatic mineral dust in ice cores is reviewed, including its measurement, hemispheric differences, and climatic interpretation.

Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers.

Geological time is punctuated by events that define biostrata and the Geological Time Scale's (GTS) hierarchy of eons, eras, periods, epochs, ages. Paleotemperatures and macroevolution rates, have already indicated that the range ti 1 Myr to (at least) several hundred Myrs is a scaling (hence hierarchical) "megaclimate" regime. We apply analysis techniques including Haar fluctuations, structure functions, trace moment and extended self similarity to the temporal density of the boundary events (rho(t)) of two global and four zonal series. We show that rho(t) itself is a new paleoindicator and we determine the fundamental multifractal exponents characterizing the mean fluctuations, the intermittency and the degree of multifractality. The strong intermittency allows us to show that the (largest) megaclimate scale is at least ti 0.5 Gyr. We find that the tail of the probability distribution of the intervals ("gaps") between boundaries is also scaling with an exponent qD ti 3.3 indicating huge variability with occasional very large gaps such that it's third order statistical moment barely converges. The scaling in time implies that record incompleteness increases with its resolution (the "Resolution Sadler effect"), while scaling in probability space implies that incompleteness increases with sample length (the "Length Sadler effect"). The density description of event boundaries is only a useful characterization over time intervals long enough for there to be typically one or more events. In order to model the full range of scales and densities, we introduce a compound multifractal-Poisson process in which the subordinating multifractal process determines the probability of a Poisson event and that this new process is close to the observed statistics. Scaling changes our understanding of life and the planet and it is needed for unbiasing many statistical paleobiological and geological analyses, including unbiasing spectral analysis of the bulk of geodata that are derived from paleoclimatic and paleoenvironmental archives.

Antarctica, which has always been of great interest to researchers worldwide, is currently attracting considerable attention owing to climate change and other topics. In this context, bibliometric analysis allows the identification of hot topics, scientific productivity, cooperation, research gaps and strategic areas of potential interest. We conducted a bibliometric study to evaluate the global production of Antarctic research between 1980 and 2023 and analysed Spanish National Antarctic Programme (NAP) production as a case study. Scientific publications were reviewed and classified based on their main themes, key word co-occurrence and international collaborations. We found that scientific production worldwide and in the Spanish NAP has progressively increased since 1980. Globally, the main areas of research are the geosciences, oceanography and atmospheric sciences. However, the Spanish NAP, which reported 2287 publications, has focused more on the geosciences and ecology. Spanish Antarctic researchers have mainly collaborated with researchers from the USA, the UK, Germany and Italy. Our research highlights the importance of strengthening research plans to diversify and facilitate international collaboration, promoting a more interdisciplinary approach to address the current and future challenges identified by the scientific community. In this context, specific opportunities for developing a Spanish NAP strategic plan are discussed.

Southern-sourced Antarctic Intermediate Water (AAIW) and Subantarctic Mode Water (SAMW) are currently major sinks of atmospheric CO2. During the last deglaciation, atmospheric CO2 levels increased significantly during two specific time periods, Heinrich Stadial 1 (H1) ~18-14.6 ka BP (thousand years ago before present) and the Younger Dryas (YD) ~12.8-11.5 ka BP. Model simulation and proxy data studies suggest that AAIW/SAMW was crucial in explaining these changes during H1 and YD, but its variability and properties in the Southeast Pacific Ocean are still largely unknown. Here, we present records of benthic foraminiferal carbon isotopes, Mg/Ca-based water temperatures, paleosalinity reconstructions, and sortable silt mean grain size variations over the last 30 thousand years from Ocean Drilling Program (ODP) Site 1233, in the Southeast Pacific Ocean, which is bathed in AAIW/SAMW. Our proxy data suggest an increased northward circulation of high pCO2/ nutrient-enriched AAIW/SAMW during H1 and YD. Our data provides support for AAIW/SAMW as one of the important conduits for deglacial oceanic outgassing in the eastern equatorial Pacific upwelling.

To date, the Icelandic Ice Sheet (IIS) and Patagonian Ice Sheet (PIS) have been poorly understood with regard to their configuration, dynamics, and evolution during the last glacial cycle. The few glaciological modelling studies of the IIS and PIS to date have placed minimal attention on addressing model uncertainties. As such, their inferential value is poorly interpretable. To address this, we present the results of history matchings of the 3D Glacial Systems Model (GSM) against curated sets of paleo constraints for the last glacial cycle IIS and PIS. History matching identifies a set of model simulations that are not ruled out given available data constraints and robust uncertainty analysis (including both model and data uncertainties). As such, it aims to “bracket reality” as opposed to the much more difficult task of determining a meaningful most likely chronology.The GSM is a thermo-mechanically coupled glaciological model with hybrid shallow ice and shallow shelf/stream physics. The climate forcing consists of a fully coupled energy balance climate model and glacial indexed climate forcing using the results of PMIP3 (Paleo Model Intercomparison Project). Approximate 30 GSM ensemble parameters partially account for uncertainties in climate, basal drag, and marine ice processes. The GSM configuration includes fully coupled visco-elastic glacio-isostatic adjustment enabling physically self-consistent relative sealevel predictions. Our presentation focuses on bracketing chronologies for the last glacial cycle IIS and PIS as well as disentangling the relative contribution of atmospheric and marine forcings on mass loss during the deglaciation.

With few exceptions, paleodata are irregularly sampled; this poses numerous challenges for the statistical characterization of paleoindicators, this includes the indicators needed to understand the climate and macroevolution. The key variable is the measurement density - the number of measurements per unit time (r(t)). Our study used 27 paleoindicators collectively spanning time scales from years to hundreds of millions of years. Using Haar fluctuation analysis and for all the series, we show that r(t) has two scaling regimes. At high frequencies, there is a low intermittency (quasi-Gaussian) scaling regime (intermittency parameter C1 ≈ 0). Over this regime, the fluctuation exponent H is negative implying that the chronologies become more uniform at longer time scales, r(t) is commonly close to a Gaussian white noise (H = -1/2). In contrast, at low frequencies, r(t) is highly intermittent (large C1), but it also has positive H so that fluctuations tend to grow with scale but in a highly intermittent fashion. In this this regime, “gaps” at all scales are important. The two regimes have simple physical interpretations: the high frequency behaviour can be explained by fairly smooth (but scaling) sedimentation rates, whereas the low frequencies can be explained by scaling erosion processes that introduce gaps over a wide range of scales (in conformity with the Sadler effect). To confirm this interpretation, we introduce a simple multiplicative sedimentation - erosion model that is close to the data. Finally, we empirically show that the gaps typically have extreme power law probability tails so that the series are not only scaling in time, but also in probability space. A key issue for paleontologists is the effect of variable r(t) on the paleoindicator estimates themselves (e.g. on paleotemperatures T(t)). Using Haar fluctuations we determined the fluctuation - fluctuation correlation R(Δt) = < Δ r(Δt) ΔT(Δt) >. When R(Δt) is small, the measurements and indicators are statistically independent so that the biases due to r(t) variability on paleoindicator statistics are easy to correct. However, at large Δt, the correlations are frequently large, and this poses additional difficulties in data interpretation. Strong correlations were observed in the Quaternary, but not the Holocene or Phanerozoic. Our study spans more than 8 orders of magnitude in time scale and it shows that it is wrong to theorize paleoseries as being fundamentally regularly sampled but interspersed with occasional data “holes” that can be dealt with using conventional techniques such as interpolation. While Haar fluctuation analysis is insensitive to the chronology variability - and if needed can easily be statistically corrected for any biases that it introduces - this is not true of existing spectral estimators that are extremely sensitive to scaling data gaps.

Las redes neuronales informadas por física (PINNs, por sus siglas en inglés) se han vuelto cada vez mas populares, especialmente para resolver ecuaciones diferenciales parciales (EDPs). Las PINNs pueden incorporar información física sobre el proceso en la arquitectura de la red neuronal, reduciendo el espacio de solución y convirtiéndolas en una alternativa cuando hay datos limitados, dispersos e irregulares disponibles. El objetivo de esta tesis es construir y evaluar el rendimiento de una red neuronal informada por física para medir los flujos de polvo durante los periodos del Último Máximo Glacial y Holoceno. Esta metodología combina el análisis de datos con principios físicos para mejorar la precisión de la predicción. Los resultados muestran que las PINNs son una alternativa prometedora a los métodos estadísticos como Kriging cuando hay información limitada disponible. En este estudio se incorporó la modelización física de la deposición de polvo y las PINNs predijeron con precisión los flujos realistas de polvo a lo largo de las direcciones de viento dominantes. Los resultados de este estudio son prometedores, mostrando que las PINNs pueden ser utilizadas como una alternativa efectiva cuando hay datos limitados e irregulares disponibles.

Iron emissions from human activities, such as oil combustion and smelting, affect the Earth's climate and marine ecosystems. These emissions are difficult to quantify accurately due to a lack of observations, particularly in remote ocean regions. In this study, we used long-term, near-source observations in areas with a dominance of anthropogenic iron emissions in various parts of the world to better estimate the total amount of anthropogenic iron emissions. We also used a statistical source apportionment method to identify the anthropogenic components and their sub-sources from bulk aerosol observations in the United States. We find that the estimates of anthropogenic iron emissions are within a factor of 3 in most regions compared to previous inventory estimates. Under- or overestimation varied by region and depended on the number of sites, interannual variability, and the statistical filter choice. Smelting-related iron emissions are overestimated by a factor of 1.5 in East Asia compared to previous estimates. More long-term iron observations and the consideration of the influence of dust and wildfires could help reduce the uncertainty in anthropogenic iron emissions estimates. Human activities, such as smelting and oil combustion, release smoke and particles into the atmosphere. These particles often contain iron, which not only absorbs sunlight, contributing to atmospheric warming, but also serves as a nutrient for phytoplankton in various ocean regions. However, the precise extent of human-induced iron emissions remains uncertain due to a lack of comprehensive monitoring data. In this study, we leverage a global data set of iron observations to refine our estimates of iron emissions attributed to human activities. Additionally, we examine other co-released substances, such as carbon and nickel, to identify specific emission sources of iron. We employ statistical techniques to distinguish human-caused iron emissions from those originating from natural sources like dust and wildfires. Moreover, we utilize iron oxide observations to constrain emissions originating from East Asia and Norway, which are estimated to originate largely from smelting emissions. Through the analysis of long-term data sets, we provide lower and upper bounds to human-caused iron emissions. Furthermore, we investigate the impact of reduced observation numbers and a sparse network on the range of estimated iron emissions. Our findings highlight the critical role of observation quality in accurately assessing iron emissions from human activities. Anthropogenic total iron emissions are constrained to a factor of 3 in most global regions using long-term aerosol observations The number of sites, interannual variability, and site selection filter can affect the model-observation comparison uncertainty by 15%-50% Smelting-related emissions are constrained to a factor of 1.5 using iron oxide observations from East Asia

Fog water represents an alternative, abundant and currently unexploited fresh water resource in the coastal Atacama Desert ( 20°S). Here, the stratocumulus clouds meet the Coastal Cordillera, producing highly dynamic advective marine fog, a major feature of the local climate that provides water to a hyper-arid environment. One of the main issues that arises in harvesting fog water is our limited understanding of the spatial and inter-annual variability of fog clouds and their associated water content. Here we assess the role of regional-wide El Niño Southern Oscillation (ENSO) forcing on local inter-annual fog-water yields along the coast of Atacama. We contrast 17 years of continuous fog-water data, with local and regional atmospheric and oceanographic variables to determine the link between them and the inter-annual dynamics of fog in northern Chile. Sea surface temperature (SST) in ENSO zone 1 + 2 shows significant correlations with offshore and coastal Atacama SST, as well as with local low cloud cover and fog water yields, which go beyond the annual cycle beat, exposing a potential causal link and influence of ENSO on fog along the Atacama. On the inter-annual time scale, we found that when ENSO 3 + 4 zone SST, specifically during summer, overcome a > 1°C temperature threshold, they incite significantly higher summer fog water yields and explain 79

Abstract. Aerosol particles are an important part of the Earth system, but their concentrations are spatially and temporally heterogeneous, as well as variable in size and composition. Aerosol particles can interact with incoming solar radiation and outgoing long wave radiation, change cloud properties, affect photochemistry, impact surface air quality, and when deposited impact surface albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High aerosol concentrations at the surface represent an important public health hazard. There are substantial datasets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modelling community. Here we present a new compilation of PM2.5 and PM10 aerosol observations including composition, a methodology for comparing the datasets to model output, and show the implications of these results using one model. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations, and especially particle composition to understand the current distribution of aerosol particles. Most climate models exclude 10–30 % of the aerosol particles in both PM2.5 and PM10 size fractions across large swaths of the globe in their current configurations, with ammonium nitrate and agricultural dust aerosol being the most important omitted aerosol types. The dataset is available on Zenodo (https://zenodo.org/records/10459654, Mahowald et al., 2024).

Temperature and mineral dust records serve as valuable palaeoclimatic indicators for studying atmospheric variability across different temporal scales. In this study, we employed Haar fluctuations to analyse global spatiotemporal atmospheric variability over the Last Glacial Cycle, capturing both high- and low-frequency information within the records, regardless of uniform or non-uniform sampling. Furthermore, we utilised Haar fluctuations to compute fluctuation correlations, thereby enhancing our understanding of palaeoclimate dynamics. Our findings reveal a latitudinal dependency in the transition from macroweather to climate regimes (τc), with polar regions experiencing shorter transitions compared to the tropics and mid-latitudes. These transitions occur at approximately 1/100th of glacial cycle length scales, suggesting a dominant forcing mechanism beyond Milankovitch cycles. Additionally, our analysis shows that polar regions have larger fluctuation amplitudes than lower latitudes as a consequence of the polar amplification effect. Furthermore, fluctuation correlations demonstrate faster synchronisation between the poles themselves compared to lower-latitude sites, achieving high correlation values within 10 kyr. Therefore, our findings suggest a consistent climate signal propagating from the poles to the Equator, representing the first empirical evidence supporting the hypothesis that the poles play a pivotal role as climate change drivers, influencing the variability in climatic transitions worldwide.