Events

A suite of field experiments illuminate the intimate involvement of moisture in the progressive physical disintegration (cracking) process of surface rocks, even in extremely dry deserts, and in the formation of asymmetrical hyperarid landscapes on Earth and Mars.

Long-term high-resolution orbital imaging at Mars has led to extraordinary advances in understanding martian ice and its connection to climate.  Icy seasonal phenomena such as flows in gullies, avalanches, and exotic defrosting patterns characterize the present climate. Interannual variability over a martian decade helps us deduce climatic averages and current trends. Observations of polar ice layers have characterized periodicities related to orbital change over longer timescales up to millions of years.

Here, I’ll describe the HiRISE camera and its continued mission to describe a dynamic Mars over 20 years of observations, with a special focus on north polar avalanches.  HiRISE has uniquely high resolution and benefits from high signal-to-noise (even at the poles); a near-polar orbit that allows imaging of almost any location within two weeks; color bands that are sensitive to ice; and sufficient imaging stability to construct high-quality meter-scale DTMs. The scientific impact of HiRISE owes much to rapid data releases and community targeting via our online tool HiWISH, ensuring acquisition and analysis of data relevant to today’s scientific questions. 

Atmospheric mineral dust is a well-established source of nutrients to marine ecosystems, yet its contribution to terrestrial plant nutrition has long been underestimated, largely due to the assumption that nutrient acquisition occurs predominantly through root uptake from soils. Here, we present evidence from controlled greenhouse experiments under ambient and elevated CO₂, laboratory simulations of leaf microenvironments, isotopic and geochemical tracing, and field fertilization experiments conducted in both a Mediterranean ecosystem and a tropical forest in Puerto Rico, demonstrating that plants can directly acquire nutrients through their leaf surfaces following atmospheric dust deposition. Using rare earth elements and Nd isotopes, we distinguish nutrients derived from soils from those delivered by deposited atmospheric particles. Laboratory simulations show that mildly acidic leaf surfaces, together with organic acids secreted by leaves, enhance mineral dissolution and facilitate foliar uptake of dust-borne nutrients. In a pioneering Mediterranean field experiment explicitly designed to isolate foliar uptake, we quantified the bioavailable fraction of key nutrients supplied by dust, including P, Fe, Mn, and Cu, and observed clear enrichment of multiple micronutrients in leaf tissues following dust application. These field-based measurements enabled the construction of a global geospatial framework integrating dust deposition with soil nutrient fluxes, indicating that dust-derived inputs can constitute a meaningful fraction of total nutrient supply across large regions, and that during dust events, short-term foliar inputs can rival or exceed soil-derived fluxes. Complementary field observations in a tropical forest in Puerto Rico further reveal foliar nutrient responses consistent with direct dust uptake. Building on these results, we outline a pathway for incorporating foliar dust uptake into Earth system representations of terrestrial nutrient cycling by explicitly accounting for atmospheric nutrient inputs at the canopy level and their interaction with soil-derived fluxes. Together, these findings identify foliar dust uptake as an overlooked but consequential nutrient acquisition pathway and highlight its relevance in highly weathered, nutrient-limited tropical forests, where atmospheric inputs may play a critical role in regulating nutrient availability and carbon–nutrient interactions.

Earth system data is rapidly increasing in volume as new observation systems generate data at rates of terabytes/day and modelling systems continue to

increase their resolution and the number of ensemble members. Coping with such amounts of data presents

substantial challenges to Earth system researchers who often find it difficult to identify suitable tools and concepts to efficiently process such data to the extent that is necessary to obtain statistically meaningful results. Conversely, computer scientists are generally more familiar with the technical aspects of data handling, but they have difficulties to understand the domain-specific aspects of Earth system data with respect to Earth’s geometry or important data and

metadata properties that cannot be neglected. This seminar builds on a one semester university course (slides and videos are available at https://

b2drop.eudat.eu/s/iwYob4QonXHjEPH ) and will discuss selected aspects in an interactive fashion,

including best practices for handling massive amounts

of (Earth system) data, the role of metadata and quality control and more.

Recent years have seen a revolution in weather

prediction. Since 2023, data-driven weather

models are outperforming even the best

numerical models that have been developed

over several decades around the world. AI

weather predictions are a lot faster and cheaper

and they often detect future weather patterns

earlier than their numerical counterparts. The

models also appear robust and can be used to

extrapolate to unseen climate conditions, at

least within a limited range. However, several

challenges remain, in particular when it comes

to long-term climate projections and multiscale

feedbacks within the Earth system. The

talk will review the recent achievements of AI

in weather and climate modelling and discuss

the current limitations.

17Oexcess is the deviation of d17O from the generally accepted 17O-18O mass dependent reference line. In rainfall, 17Oexcess depends mainly on relative humidity at the moisture source region, with lower relative humidity corresponding to higher 17Oexcess. In some cases, however, rainfall 17Oexcess is influenced by atmospheric processes like partial re-evaporation of the raindrops or moisture recycling. We examine how does 17Oexcess in CaCO3 record 17Oexcess of its parent water and apply it to paleo hydrology in Soreq Cave (Israel) and in Devils Hole (Nevada, USA).

In Soreq Cave, 17Oexcess of 50 per meg was obtained in the weighted mean modern rainfall, consistent with the low relative humidity at the moisture source region of the Eastern Mediterranean Sea. 17Oexcess of paleo water were reconstructed from Soreq Cave speleothems, at an age range of 0 - 160 ka. In most of the record values are similar to that in modern cave water, but a few events suggest higher relative humidity, consistent with a more marine storm trajectory. The values at the Last Glacial Maximum suggest low relative humidity and likely indicate the penetration of very cold air.

In Devils Hole, 17Oexcess in modern and interglacial reconstructed water is higher than expected by relative humidity, suggesting significant moisture recycling in this continental site. In glacial periods, however, 17Oexcess suggest much less evaporation of water from land surfaces.

After an earthquake occurs, slip models of the event may be estimated from geodetic observations. This process generally requires static coseismic Green's functions, which must be calculated via a forward model which includes an approximation of the material properties, topography, and fault geometry in the region of interest. Until recently, the lack of seafloor geodetic instrumentation and the use of unrealistically simple forward models have resulted in poor resolution of near-trench slip in subduction zone settings.  In this talk, I will present an investigation into the effects of 3D structure, particularly topography, on forward models of earthquake deformation and on earthquake static slip estimates. I will show that models which neglect 3D structure yield inaccurate estimates of near-trench slip, particularly when seafloor geodetic data are utilized in the inversion.

Climate change is a global phenomenon, yet its fingerprints vary

substantially across regions. This talk highlights a range of these

regional patterns using observational records and climate model

simulations, analyzed with machine learning and complementary

statistical tools.

The first part of the talk examines the magnitude of climate

change across temporal and spatial scales, showing how longterm

warming reshapes seasonal and diurnal temperature cycles

in different regions.

The second part examines how quickly climate mitigation signals

can be detected against regional climate variability, highlighting

where the effects of emission reductions are likely to emerge

sooner or later across the globe.

The final part of the talk addresses the question of climate

change acceleration. Despite rapidly increasing greenhouse gas

emissions, recent studies suggest that the global mean warming

rate remains linear. We revisit this issue by shifting the focus

from global averages to regional scales, where we detect

significant acceleration in warming across a substantial fraction

of the world.

The Dead Sea region has seen a rapid increase in sinkhole formation, posing serious environmental and infrastructure risks. The Geological Survey of Israel monitors sinkhole-related land subsidence along the western shore using InSAR, but current detection relies on manual interpretation of interferometric phase data, which is time-consuming and error-prone.

In this talk, I present an AI-based Deep Learning framework for automated detection of sinkhole-related subsidence from InSAR data. The model learns interferometric phase deformation patterns, rather than visual features, and is trained using expert-labeled subsidence maps from years of operational monitoring. I demonstrate the model’s ability to generalize across spatial and temporal settings using multiple evaluation schemes and object-level performance metrics. Results show effective detection of subsidence areas, promising generalization to unseen regions, and the ability to reconstruct large-scale subsidence trends from patch-level predictions.

Many fossil materials have embedded signals that enable aspects of the past to be reconstructed. These signals however can be altered or lost due to processes that take place once the fossil material is buried (diagenesis). Thus extracting reliable signals can be a major challenge. Here I present a new approach to better understanding diagenesis that I apply to bone.

Satellite instruments, such as TROPOMI, are routinely

used to quantify tropospheric nitrogen dioxide (NO2)

based on its narrowband light absorption in the UV/

visible spectral range. The key limitation of such

retrievals is that they can only return the „vertical

column density“ (VCD), defined as the integral of the

NO2 concentration profile. The profile itself, which

describes the vertical distribution of NO2, remains

unknown.

This presentation showcases „NitroNet“, the first NO2

profile retrieval for TROPOMI. NitroNet is a neural

network, which was trained on synthetic NO2 profiles

from the regional chemistry and transport model WRFChem,

operated on a European domain for the month of

May 2019. The neural network receives NO2 VCDs from

TROPOMI alongside ancillary variables (meteorology,

emission data, etc.) as input, from which it estimates NO2

concentration profiles.

The talk covers:

• an introduction to satellite remote sensing of NO2.

• the theoretical underpinnings of NitroNet, how the

model was trained, and how it was validated.

• practical new applications that NitroNet enables.

The soils of the Golan Heights plateau, in northern Israel, are underlaid by basaltic rocks ranging in age from ~5.5 to 0.1 Ma. Volcanism in this region is associated with the development of the Red Sea rift, and in accordance with the propagation of the rift, the age of the volcanic units displays a general northward decrease. Topographic position, field evidence and morphology, indicate that nearly all of the soils were formed in situ by weathering of the basaltic bedrock and it has been generally assumed the soils form a chronosequence. While the soils are predominantly of basaltic origin, the contribution of allochthonous aeolian sediments to the soils have long been recognized, mainly through the presence of quartz grains, typical to the regional dust.

Based on geochemical mass balance calculations, we found that not only are the soil ages decoupled from the ages of the underlying basalts, they represent up to a few thousand years of soil production, at most (Zaarur et al., 2024). This time frame is orders of magnitude shorter than the basalt age, challenging the prevalent assumption that the soils form a chronosequence. In addition, new OSL measurements provide independent soil ages, based on dust burial in the soils.

OSL measurements were conducted on soils collected from sites from the southern and central Golan, and include samples of mature Vertisols covering the oldest Pliocene basalts on the southern plateau, and soils collected from deep crevasses in the basalts. The ages of all measured samples range between ~0.4 and ~7 ka. Older particles are restricted to deep and protected microenvironments. These results strengthen the chemical modeling findings, that suggested that the soils represent up to a few thousand years of soils accumulation. Furthermore, these results point to a massive soil-loss event that pre-dates the accumulation and development of these soils.

In addition, our findings strongly suggest that erosion is a significant factor controlling soil formation and accumulation on the plateau, despite the generally flat morphology of the Golan Heights. The erosion is likely associated with tectonic activity along the Dead Sea transform, with the development of the Kinarot and Hula valleys, and with the consequential development of drainage systems of various sizes on the plateau.