Events

Abstract:

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.

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Abstract:

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.

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Abstract:

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.

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Abstract:

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.

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Abstract:

Rivers play a central role in shaping the Earth's surface and ecosystems through physical, chemical, and biological interactions. The intensity, time, and location of these interactions change as rivers continuously migrate across the landscape. In recent decades, human activity and climate change have altered river hydrology and sediment fluxes, leading to changes in river positions. Climate warming, increasing flood extremes, and human-induced land use changes have slowed river migration rates in some river reaches while accelerating them in others. However, a comprehensive, spatially continuous, large-scale perspective on and understanding of these recent changes in the rate of river position shifts is lacking.

To address this knowledge gap, we created a continuous global dataset of yearly river positions and migration rates over the past four decades. The continuous annual river positions were detected using Landsat-derived surface-water datasets and processed in Google Earth Engine, a cloud-based parallel-computation platform. The resulting river extents and centerlines reflect their yearly permanent positions, corresponding to the river locations during base flow. This approach improves the representation of position changes derived from geomorphological rather than hydrological processes. To analyze river position changes across different patterns and complexities at large scales, we developed and applied a global reach-based quantification method for river mobility rates.

Results show that while some alluvial rivers maintain a stable annual pace of mobility, others exhibit trends in migration rates. For instance, the Amazon Basin, which has experienced significant deforestation and hydrological modifications, has shown increased rates of river position change in recent decades, impacting floodplain forests and communities. In this talk, we will discuss the advantages, limitations, and applications of the detected yearly river positions and mobility rates, offer insights into the forcings driving changes in river positions and their environmental outcomes, and highlight current and future impacts on one of Earth’s most vulnerable hydrologic systems.

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Abstract:

A prominent mode of large earthquakes is self-healing slip pulses, which feature a finite slipping length. The latter implies that the rise time of the displacement waveforms of such earthquakes is significantly shorter than the source duration, in contrast to expanding crack-like earthquakes. The slipping length emerges from an interplay between leading-edge contact breakage and trailing-edge re-strengthening (healing), which is intrinsically related to the generic frictional rate and state dependence of faults. Our understanding of slip pulse earthquakes lags behind that of their crack-like counterparts. We show that steady-state slip pulses are intrinsically unstable, yet that the spatiotemporal dynamics of unsteady slip pulses are surprisingly and fundamentally related to the corresponding steady-state family of solutions, leading to a reduced-dimensionality description. We further show that the development of instability of growing pulses is slow, explaining their emergence in natural and manmade frictional systems. The theory culminates in an equation of motion for unsteady slip pulses, and is discussed in relation to large-scale numerical simulations, laboratory earthquakes and geophysical observations.

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Abstract:

While AI has been disrupting conventional weather

forecasting, we are only beginning to witness the

impact of AI on long-term climate simulations. The

fidelity and reliability of climate models have been

limited by computing capabilities. These limitations

lead to inaccurate representations of key processes

such as convection, cloud, or mixing or restrict the

ensemble size of climate predictions. Therefore, these

issues are a significant hurdle in enhancing climate

simulations and their predictions.

Here, I will discuss a new generation of climate

models with AI representations of unresolved ocean

physics, learned from high-fidelity simulations, and

their impact on reducing biases in climate

simulations. The simulations are performed with

operational ocean model components. I will further

demonstrate the potential of AI to accelerate climate

predictions and increase their reliability through the

generation of fully AI-driven emulators, which can

reproduce decades of climate model output in seconds

with high accuracy

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Abstract:

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.

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Abstract:

Forecasting extreme weather relies on the intrinsic predictability of the atmospheric flow, the model resolution needed to represent key processes, and the quality of the initial conditions used to initiate forecasts. In the seminar, I shall present a unified multiscale perspective showing how recent work from my group links these elements into a coherent framework for understanding predictability in the Mediterranean region.

We shall begin at the large scale, where dynamical-systems diagnostics show that Atlantic–European weather regimes are dynamically grounded states with characteristic stability and persistence. These regimes shape the background flow in which Mediterranean extremes develop, thereby defining the intrinsic limits and opportunities for extended-range predictability.‎1 This large-scale structure naturally informs how specific high-impact systems evolve.

At the synoptic scale, a newly developed Lagrangian framework allows us to analyze Mediterranean cyclones within their full potential-vorticity (PV) architecture. The same dynamical features that govern regime persistence help explain why some cyclones maintain long predictability horizons while others amplify uncertainty rapidly, depending on their depth, PV structure, and regional context.‎2 This insight flows directly into our analysis of compound “wet” and “windy’’ extremes, which preferentially arise during particularly persistent atmospheric configurations, effectively the multivariate expression of the dynamical behaviour captured at both the regime and cyclone scales.‎3

At the mesoscale, realizing this dynamical predictability in practice requires sufficient model resolution. High-resolution simulations are essential for capturing sea-breeze interactions, mountain–valley circulations, and other thermally driven flows that modulate extremes in the Eastern Mediterranean, features that coarse reanalysis systematically underestimate.‎4

Finally, we turn to improving the accuracy of initial conditions. Here, Syncope, a high-frequency acoustic sensing system, resolves boundary-layer gusts and turbulence structures that are overlooked by operational networks, providing more realistic near-surface information for initializing numerical weather prediction model simulations.‎5

Together, these studies form a consistent multiscale narrative showing how advances in dynamical understanding, high-resolution modelling, and improved boundary-layer observations can jointly advance the predictability of extreme weather.

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Abstract:

Earth's landscapes are shaped by competition between tectonic plates that push bedrock upward and river networks that remove mass. Transport of countless rock fragments is a fundamental aspect of this action, resonating with many other near-surface processes across the hydrosphere, biosphere, and geosphere. Identifying how efficiently rock fragments are transported away, considering their properties and ecohydrological feedbacks during weather events, has remained a persistent scientific challenge since the dawn of computational geomorphology. With recent advances in terrain remote sensing and analysis techniques, hydroclimate observations/models, and computational methods for describing dynamic topography, a research frontier is emerging, paving the way for a promising new era in the science of surface processes and topographic forms.

 

The seminar first presents a new application of a theory for heterogeneous sediment transport in mountainous gravel-bed rivers. A set of numerical experiments discovered process-form relations that emerge from sediment grains' lithological heterogeneity. Then, the talk will present a first-of-its-kind Earthcasting approach that integrates high-resolution event-scale rainfall forcing into a Holocene-scale landscape evolution research framework. Within that timescale, the importance of the interaction between soil grains and ecohydrological processes in shaping fast-evolving landforms is highlighted. Lastly, paleo-rainfall regimes capable of triggering erosion-deposition cycles and possible future transitions to a unique climate-erosion state by the 21st century will be demonstrated.

 

The findings have the potential to shift paradigms in the interpretation of sediment records and landscape forms. The newly developed methodologies enable unprecedented quantification of surface processes with respect to material properties and climate forcings, which open opportunities toward a transformational understanding of landscape evolution.

 

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Abstract:

Despite covering over a third of Earth’s land surface, arid regions remain among the least understood hydrological environments. Practically every component of the desert water cycle is more poorly constrained than its counterpart in wetter regions. Yet deserts are home to over 20% of the global population and are disproportionately vulnerable to hydrometeorological hazards such as droughts, floods, and the accelerating impacts of climate change. A better understanding of the desert water cycle is therefore not only a scientific challenge, but a critical need for sustainable water resource and risk management in drylands.

In this talk, I will present three studies that illuminate different aspects of the desert water cycle:

(a)  how satellite observations can be used to infer the (underwater) topography — and thus the water volume — of remote desert lakes;

(b) what atmospheric ingredients link moisture, rain, and floods in the hyperarid Sahara, and how these relate to the desert's paleo- (and future?) climate; and

(c)  how misjudged flood risk management on the desert margin contributed to the deadliest hydrometeorological disaster of the 21st century in Derna, Libya.

Together, these studies illustrate how unconventional combinations of satellite data and modelling can overcome the challenges of limited in situ observations to reconstruct, quantify, and ultimately understand hydrological processes in deserts. They also challenge longstanding assumptions about runoff generation and risk mitigation in arid regions, pushing the boundaries of what we thought we could know in some of the world's most water-scarce landscapes.

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Abstract:

Advances in spectral and structural remote sensing are transforming how

we study and monitor plant ecophysiology across scales, from individual

trees to entire agricultural regions. This lecture will explore how

hyperspectral imaging, LiDAR-based 3D canopy modeling, and artificial

intelligence can be integrated to quantify plant functional traits, monitor

crop dynamics, and support precision agriculture. Through three case

studies, we will demonstrate the power of these approaches in capturing

structural and physiological complexity: (1) Satellite-based detection of

bloom shifts and phenological patterns in California’s almond orchards,

revealing climate-driven variations in flowering dynamics; (2) Fusion of

thermal, multispectral, and LiDAR data to estimate plant water status and

its relationship to fruit cracking, linking spectral signals with physiological

stress responses; and (3) Crop-type mapping and multi-year monitoring

of Israeli agricultural systems using Sentinel-1 and Sentinel-2 data

combined with machine learning for national-scale agricultural

assessment. Together, these studies illustrate how spectral

ecophysiology, combining remote sensing and artificial intelligent, offers

new opportunities to bridge plant function, management, and

sustainability in agricultural landscapes under changing environmental

conditions.

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Abstract:

Twenty-seven percent of the world’s terrestrial area is classified as arid or hyper-arid, regions that are second only to oceans in the sparsity of measurement sites. Contrary to popular perception, these desert areas are dynamic ecosystems that respond sensitively to changes in water availability, temperature, and carbon dioxide (CO2) levels. As such, they can serve as important indicators and potentially moderators of climate change. Efforts to understand the dynamics and feedback mechanisms between the main players affecting desert weather and climate can be divided, by-and-large, into two groups: (1) addressing the most pressing knowledge gaps of desert weather and climate systems; and (2) exploring processes that have not previously been considered but are hypothesized to be more important than presumed, representing a realm of "unknown unknowns". One example to the “unknown unknowns” realm is related to non-rainfall water inputs (i.e., fog, dew, and atmospheric water vapor adsorption). Traveling between the Negev, Namib, and Sahara deserts, we will look into this largely overlooked phenomenon. We will point to the similarities between these deserts and ask how widespread this phenomenon may be. Spoiler - we don't know, but we sure need to.

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Abstract:

Clouds are among the most influential components of Earth’s radiation budget, modulating radiative transfer across the electromagnetic spectrum. As a result, even processes that contribute relatively weak radiative effects, such as those occurring in clouds’ surroundings, can be substantial compared to clear-sky conditions and therefore important to Earth’s energy budget and the climate it sustains. Over the past two decades, studies have highlighted several mechanisms contributing to the radiative signatures around clouds, including three-dimensional radiative transfer, enhanced aerosol humidification, and subvisible cloud features. Recent work by Eytan et al. (2025) has provided the first quantification of the top-of-atmosphere (TOA) radiative impact of these near-cloud regions. Their findings suggest a shortwave effect of ~9 W/m² over the ocean in the local afternoon, implying that clouds indirectly amplify the aerosol direct radiative effect. In the longwave, a mean effect of ~1 W/m² corresponds to the radiative forcing of an additional ~90 ppm of CO₂, highlighting these regions' climate relevance. In this talk, I will introduce a new framework for partitioning the sky into three radiative categories: cloudy, pure clear-sky, and cloud-influenced clear-sky. I will demonstrate how this refined classification reveals near-cloud regions' hidden but crucial contribution to all-sky radiative fluxes. We will explore how these contributions vary with cloud type, spatial cloud patterns, and background aerosol loading. By explicitly accounting for these previously overlooked regions, this new paradigm opens the door to a more comprehensive understanding of the processes involved in the cloud’s role in Earth’s energy budget and in aerosol–cloud interactions, which are two of the largest sources of uncertainty in climate projections according to the latest IPCC report. Ultimately, this work aims to establish a more unified approach to treating the atmosphere, from dry aerosols to clouds, and to deepen our understanding of how clouds and their surrounding environments influence Earth’s climate. In doing so, it offers a promising path toward reducing one of the most persistent uncertainties in climate change projections.

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Abstract:

Natural disasters like floods and droughts pose significant threats to communities worldwide, making accurate and timely forecasting essential for mitigation and response. This presentation will delve into the development and implementation of AI-based models for natural disaster forecasting, with a specific focus on floods and droughts.

We will explore Google's machine learning-driven hydrologic model for riverine flood forecasting, which has been operational for several years and provides predictions up to seven days in advance. Additionally, we will discuss a flash flood model currently in development. The talk will also cover a machine learning model for drought forecasting, which provides predictions at a three-month lead time.

A key focus of this discussion will be the robust evaluation of these models. We'll examine various methods for assessing their skill, including comparisons against historical data, satellite observations, and established performance benchmarks across different regions. This presentation will highlight how advanced AI can enhance our ability to predict and prepare for natural disasters, ultimately supporting global resilience efforts.

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Abstract:

The macromolecular composition of phytoplankton shapes the nutrition available to marine ecosystems and regulates the interwoven global cycles of carbon and nutrients. Despite these fundamental roles, there are currently no mechanistic, predictive models of the global distribution of phytoplankton macromolecular composition and its variation in response to environmental changes. Here, we simulate the cellular allocation of proteins, carbohydrates, and lipids in a global ocean model in the present day and over the 21st century under a climate change scenario. Our simulations indicate systematic spatial variations in phytoplankton macromolecular composition, which are consistent with available observations. Our model simulations further suggest variable geographic responses to climate change. Specifically, phytoplankton in polar regions are projected to have more carbohydrates and lipids at the expense of proteins, due to warming and relief from light limitation. We compiled and analyzed in situ macromolecular measurements of polar phytoplankton spanning several decades, finding trends consistent with our model predictions. Our findings indicate that changes in the macromolecular composition of phytoplankton can serve as indicators of shifting environmental conditions. Such changes will reshape the nutritional landscape at the base of the marine food web and alter global biogeochemical cycles.

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Lunar volatiles, especially water, hold the key to sustaining long-term human presence on the Moon and beyond. I will cover the latest discoveries in volatile stability, distribution, sources, and transport. Due to the Moon's monotonic decrease in spin axis obliquity, perennially shadowed regions near the poles have shrunk with time. Thus, comparing the observations against theoretical models affords the opportunity to constrain the history of ice accumulation in these regions. These constraints offer both fundamental insights and practical value.

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Abstract:

Human exposure to atmospheric aerosols is increasingly recognized as the leading global environmental determinant of health and longevity. However, ground-based monitoring remains sparse in many regions of the world. Deep learning offers immense potential to advance understanding of air quality by leveraging large satellite remote sensing datasets. However, the spatial heterogeneity and autocorrelation of ground-based measurements pose challenges to the training and testing of algorithms for true out of sample prediction. Thus, algorithms benefit from additional process-based constraints from a chemical transport model and targeted ground-based measurements of aerosol chemical composition.  This talk will highlight recent advances in applying deep learning by building on satellite remote sensing, global modeling, and ground-based measurements to improve understanding of air quality and planetary health from global toward urban scales

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Abstract:

Scientific background: Longwave Multispectral (MS) infra-red (IR) imaging from satellites is

important in many environment/agriculture monitoring tasks; however, it is limited to a

coarse spatial resolution in the range of 100 [m] to 1000 [m], which does not allow observing

fields details. Super Resolution methods to support multispectral acquired by satellites, e.g.,

Spatial resolution of earth observing in the longwave 8-12 micron, thermal infra-red is

significantly lagged behind the visible range. Recently, a swarm of nanosatellites (1-10 kg) has

been used to achieve a high spatial resolution. While this technology shows outstanding

spatial resolution of only a few meters, it is currently carried only in visible and Near Infra-Red

cameras. Thus, equipping nanosatellites with longwave imagery and improving their relatively

low spatial resolution is an important challenge.

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Abstract:

Despite covering over a third of Earth’s land surface, arid regions remain among the least understood hydrological environments. Practically every component of the desert water cycle is more poorly constrained than its counterpart in wetter regions. Yet deserts are home to over 20% of the global population and are disproportionately vulnerable to hydrometeorological hazards such as droughts, floods, and the accelerating impacts of climate change. A better understanding of the desert water cycle is therefore not only a scientific challenge, but a critical need for sustainable water resource and risk management in drylands.

In this talk, I will present three studies that illuminate different aspects of the desert water cycle:

(a)  how satellite observations can be used to infer the (underwater) topography — and thus the water volume — of remote desert lakes;

(b) what atmospheric ingredients link moisture, rain, and floods in the hyperarid Sahara, and how these relate to the desert's paleo- (and future?) climate; and

(c)  how misjudged flood risk management on the desert margin contributed to the deadliest hydrometeorological disaster of the 21st century in Derna, Libya.

Together, these studies illustrate how unconventional combinations of satellite data and modelling can overcome the challenges of limited in situ observations to reconstruct, quantify, and ultimately understand hydrological processes in deserts. They also challenge longstanding assumptions about runoff generation and risk mitigation in arid regions, pushing the boundaries of what we thought we could know in some of the world's most water-scarce landscapes.

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Abstract:

While AI has achieved remarkable advancements in areas such as image recognition and natural language processing, its application in Earth and environmental sciences is still emerging. Unprecedented data from satellites, remote sensors, and in-situ measurements offers new opportunities to improve physics-based model forecasts of environmental systems with AI and to gain deeper insights. However, extreme systems as weather and climate events, pose distinct challenges for AI, such as limited sampling of rare events, non-trivial data augmentation, errors-in-variables, and complexities of transfer learning across diverse tasks. In this talk, I will explore these challenges and showcase AI architectures designed to address them. I will use specific examples of forecasting dust storms, precipitation extremes, and drought events in the Middle East.

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Abstract:

In recent years, the landscape of artificial intelligence (AI) has been reshaped by the rapid emergence of Foundation Models (FMs). These versatile models have garnered widespread attention for their remarkable ability to transcend the boundaries of traditional, bespoke AI solutions and to generalize to a large set of downstream tasks.   In this presentation we will describe the development of geospatial FMs with earth observation and weather data and discuss the initial results of such models when fine-tuned to various applications including flood detection, CO2 monitoring, nature-based carbon sequestration. We will also show how such foundation models can be a new and exciting tool for assisting and accelerating scientific discovery.

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Abstract:

Western boundary currents (WBCs)—such as the Gulf Stream and Kuroshio—are prominent features of the wind-driven surface ocean circulation. Their structure and dynamics have traditionally been explained by the seminal models of Stommel (1948) and Munk (1950), which emphasize the roles of wind-stress curl, friction, and the planetary vorticity gradient (β-effect). However, these classical theories largely overlook the influence of basin geometry. In this talk, we revisit the Stommel–Munk framework through a non-dimensional approach that isolates two key parameters: frictional damping and the domain aspect ratio, defined as the meridional-to-zonal extent of the ocean basin. Analytical solutions and numerical simulations show that WBC transport increases strongly with the aspect ratio—cubic in Stommel’s model and linear in Munk’s. This geometric dependence helps explain why the East Australian Current is weaker than other WBCs. Extending these insights to paleoclimate, we demonstrate that tectonic changes during the Cretaceous modified basin shapes, weakening gyre circulation and thereby reducing poleward oceanic heat transport. This reduction likely contributed to the larger meridional sea surface temperature gradients observed during that period. Our findings underscore the fundamental role of basin geometry in shaping both modern and ancient ocean circulation.

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Abstract:

Distributed Fiber Optic Sensing (DFOS) is revolutionizing seismology thanks to dense measurements at an unprecedented scale. In this talk, I will describe the main principles behind the technology, as well as multiple scientific and practical questions that we could answer with fiber-optic sensing: vehicle tracking in urban environments, microearthquake location and fault plane reconstruction, an inversion approach to jointly resolve subsurface and structural parameters, and finally – a recent experiment in which we deployed a joint fiber-accelerometer in an abandoned well near the Kinneret, targeting local undetected earthquakes.

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Abstract:

We will discuss the recent findings examining the physical impacts of climate change on the Eastern Mediterranean Sea coastal environment using long-term in-situ data. Specifically, we explore explores three decades of previously inaccessible data on surface waves and sea surface temperature, obtained from two buoys moored off the Israeli coastline, augmented with data from several coastline temperature sensors, and the sea level measurements. Our findings reveal only a moderate increase in sea surface temperature of 2.65°C per century, contradicting the current local scientific consensus of faster warming trends. Moreover, we will see that the widely used reanalysis models grossly overestimate the multiannual trends while underestimating the actual temperature values. Of particular interest is the identified alteration in the seasonal cooling-warming cycles, with shrinking transitional season periods that are replaced by prolonged summer and winter periods. While the extremes, in the form of Marine Heatwaves were found to become more frequent and severe.

Maritime storm activity was observed to intensify over the observed period, with a sharp increase in storms’ intensity during the early 2000s. Such an increase was also accompanied by the rise in the occurrence of Rogue waves, including a notable 11.5-meter wave near Haifa in February 2015. A notable difference in the weather patterns causing significant waves in the North and the South along the Israel coats is also noted. The sea level rise trend was found to be 2.3 mm per year, in good agreement with the published estimates.

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Abstract:

N2 fixation is the primary pathway by which bioavailable nitrogen is added to the

oceans. However, the drivers of N2 fixation on orbital timescales are uncertain. We

present high-resolution foraminifera-bound (FB) δ15N records from the Western

and Eastern tropical Atlantic Ocean (WTA and ETA respectively) throughout the

late Pliocene (~3.60 to ~1.97 Ma), where WTA ODP Site 999 represents N2

fixation changes and EEA ODP Site 662 represents changes in pycnocline δ15N.

Our results show that, compared to the past 160 ka, N2 fixation in the WTA was

significantly lower throughout the late Pliocene as reflected by an average of ~2 ‰

higher FB-δ15N values. A possible explanation to the higher Pliocene FB-δ15N in

the WTA could be lower rates of global denitrification that were balanced by lower

global N2 fixation levels. We suggest that this reduced N2 fixation was due to

decreased excess P in the pycnocline/subsurface ocean, driven by lower global

water column denitrification. This finding implies a coupling between decreased

water column denitrification and reduced level N2 fixation rates under warmer

climates.

On orbital timescales, our N2 fixation record display obliquity-paced cycles that

progressively intensified after the Northern Hemisphere glaciation intensification ~

2.8 Ma, and the onset of equatorial upwelling pulses documented during glacial

periods in the EEA (ODP Site 662; [1]). The observed changes in N2 fixation of the

last 160 ka were previously explained by precession-paced upwelling in the EEA

that imported excess P into the oligotrophic WTA [2]. However, precessional

cyclicity is not dominant in the Pliocene FB- δ15N, which calls for other candidates

to explain the variations after 2.8 Ma. The best explanation is a response to sealevel

paced sedimentary denitrification. Glacial lower sea levels exposed

continental shelves, reducing regional benthic denitrification and inhibiting the

supply of excess P, thereby limiting N2 fixation in the WTA, whereas interglacial

submerged shelves increased excess P availability.

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Abstract:

The pre-industrial Holocene is unique among past

interglacials due to a modest, but notable increase in

atmospheric CO2 and methane (CH4) during the latter half

of the period despite an expected decrease given orbital

parameters. Although the causes for this increase,

anthropogenic or natural are debated, all climate models

simulate an increase in global mean temperature in

response to the increase in the greenhouse gases. Yet,

many proxy reconstructions, interpreted to reflect the

mean annual temperatures, indicate peak temperatures in

the first half of the Holocene, arguably exceeding modern

mean annual temperatures followed by cooling through the

preindustrial period. This significant model-data

discrepancy, known as the Holocene temperature

conundrum, and the debate on the cause of the CO2

increase has undermined confidence in future climate

model predications. In this talk I’ll offer new perspectives

on both issues.

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Abstract:

In this seminar, two new models will be presented. The first new model is a first-principles description of the propagation of light in a cloud, based on a classical solution to Maxwell's equations rather than radiative transfer theory. The second new model is a fully three-dimensional, time-dependent model of the regions of possible sprite inception in the mesosphere, based on the classical method of images from electrostatics rather than finite differencing in space. The reason why each model is unique, the problems each model can solve, and the kinds of results each model can produce will be discussed

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Abstract:

Sub-mesoscale (SMS, <10 km scale) ocean circulation is characterized by high vorticity and deviation from geostrophic balance. It can result in large effects on biology and chemistry due to the large vertical velocities (x10-100 than mesoscale) and resulting down/up welling circulations, as well as significant effects on material retaining and dispersion. 

Modelling and observing the Submesoscale is challenging due to stringent demands on spatio-termpoal resolution, and due to its strong interactions with both larger (mesoscale) and smaller (turbulence and waves) circulations. I will report on the first Sub-mesoscale-resolving numerical modelling study in the East Mediterranean Sea, and (likely universal) findings on the patterns of cross-scale energy exchange between the Sub-Mesoscale and mesoscale circulation, which controls the seasonal evolution of both circulations. Secondly I will show in the model boundary current variability can spawn Sub-Mesoscales year-round (while open ocean formation is largely limited to winter). This will be backed by our (and the first) systematic observations of a Sub-mesoscale vortex formed in summertime via boundary current meandering.

 

 

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Abstract:

Mesoscale eddies dominate global ocean kinetic energy and are responsible for efficiently transferring ocean properties. The influence of ocean eddies in the western boundary currents on storm tracks has been studied in recent years, and their importance in regulating mid-latitude precipitation is now recognized. Unlike western boundary currents, mesoscale eddies in the Mediterranean Sea (MS) are smaller and less intense. Yet, the MS is rich in mesoscale activity, and its proximity to densely populated regions suggests that even a small change may have a large impact, which remains underexplored.

 

In this talk, I present several recent studies in which we investigated the interactions between mesoscale eddies and cyclones in the Mediterranean region. These studies focused on specific Mediterranean tropical-like cyclones (medicanes), analyzing their evolution under different sea conditions using observations and model simulations. We find that mesoscale eddies in the MS can change the intensity and track of cyclones and, consequently, affect their resulting rainfall distribution over land. In general, warm-core eddies tend to intensify cyclones and increase precipitation above them relative to cold-core eddies. Additionally, we observe a general increase in surface ocean biogeochemical properties, such as phytoplankton and chlorophyll, following cyclone passages. This increase is driven by upwelling and vertical mixing, though the relative importance of these processes differs between warm- and cold-core eddies.

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Abstract:

This talk tests the ability of natural freshwater lakes and margins to attenuate the emissions of

the greenhouse gas methane (CH4) to the atmosphere under warming climate. I will show how

microbial communities manage to survive and mitigate methane emissions under energy

limited, highly reduced conditions of deep methanogenic lake sediments, through redox

couplings of methane to Mn-Fe-N. Complex redox couplings between those species were also

explored in thermokarst lakes and margins, which are extensively formed by permafrost thaw

in the Arctic. The cycles were quantified using geochemical and microbial profiles, together

with stable isotope probing experiments close to natural conditions. The profiles and

incubations show active microbial population that exhibit surprisingly both aerobic and

anaerobic methane oxidation in methanogenic sediments and upland Arctic soils, fueled by

nitrogen and iron redox cycles.

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Abstract: Our Laboratory focuses on research that drives technological, environmental and social change. It includes advanced technologies in the social aspect of environment management, embracing the complexity of the human-environment relationship, and physical model development for complex and non-trivial real-world problems in the era of climate change. Our ultimate goal is to bridge the gap between machine learning and geoscience for sustainability and environmental management at the national and international (mainly in the Mediterranean) scales. We understand that machine learning, in general, and deep learning, in particular, offer promising tools to build new data-driven models for Earth system components and thus build our understanding of ecosystems. Yet, accepting that data-driven machine learning approaches in geoscientific research cannot replace physical modelling but strongly complement and enrich it. Our primary scientific interests are developing hybrid approaches, coupling physical processes (physical laws and physics-domain-specific knowledge) with the versatility of data-driven machine learning, also known as physics-aware machine learning, to better understand the ecosystems, biodiversity, dynamic processes and environmental responses to stressors, and emphasizing sustainability and decision support system development aligned with the UN Sustainable Development Goals (SDGs). Close abstractClose abstract

Abstract: causal relationships between evolution and oxygenation of the ocean are vigorously debated. At the heart of these uncertainties are inconsistencies among reconstructed timelines for the rise of O2 in marine habitats. Attempts to reconstruct the timing of marine oxygenation are often based on redox-sensitive geochemical proxies that are prone to post-depositional alteration. Thus, developing new proxies, more resistant to such alteration, is an important direction forward for constraining major changes in atmospheric and marine oxygen levels. Here, we utilize U–Pb dating in dolomite to reconstruct their (re)crystallization ages and initial 207Pb/206Pb ratios; we find that they are systematically younger and lower than expected, respectively. These observations are explained by the resetting of the U–Pb system long after deposition, followed by further evolution in a closed system. Initial 207Pb/206Pb ratios have decreased from expected terrestrial values in the interval between deposition and (re)crystallization, consistent with U decay, and can therefore be used to reconstruct the initial 238U/206Pb ratios during deposition. Within our dataset initial 238U/206Pb ratios remained low in Proterozoic to mid-Paleozoic samples and increased dramatically in samples from the late-Paleozoic–early- Mesozoic Eras. This rise is attributed to a higher ratio of U to Pb in seawater that in turn influenced the fluid composition of carbonate crystallization sites. Accordingly, we interpret the temporal shift in initial 238U/206Pb ratios to reflect a late-Paleozoic increase in oxygenation of marine environments, corroborating previously documented shifts in some redox-sensitive proxies. This timeline is consistent with evolution-driven mechanisms for the oxygenation of late Paleozoic marine environments and with suggestions that Neoproterozoic and early Paleozoic animals thrived in oceans that overall and on long time scales were oxygen-limited compared to the modern ocean. Close abstractClose abstract

Abstract: Seismic waves excited by human activity frequently obscure signals due to tectonic processes and are discarded as a nuisance. Seismic noise-field analysis is, however, a powerful tool for characterizing anthropogenic activities. In this talk, I will briefly review the seismological fingerprints of anthropogenic noise sources and then present a scheme devised to identify precursory activity leading to the October 7 terrorist attack. The precursory activity in Gaza included massive mobilization, documented by multiple media outlets. Favorable conditions arose due to a temporary lack of anthropogenic activity in Israel, allowing remote seismic stations to record signals due to Gaza vehicle traffic in the early hours of Oct. 7. Seismogram analysis reveals a widespread signal that abruptly emerged above the nighttime noise levels about 20 minutes before the attack began. Statistical analysis suggests the signal is highly anomalous; tests for significance indicate that pre-attack inter-station correlations would emerge by chance only once every 18,000 years. Tripartite array analysis was used to detect surface waves, locate their sources, and demarcate the extent of preattack activity within the Gaza Strip. The signal’s amplitude, frequency, and spatiotemporal distribution appear to be aligned with vehicular traffic emanating from the south-central region of the Gaza Strip and extending towards its peripheries in the half-hour window preceding the invasion. This provides valuable tactical information and suggests embedding seismic noise-field analysis into decision-making protocols could enhance preparedness for terrorist attacks. Close abstractClose abstract

Abstract: Climate change is having an impact on agricultural production and food security. Rising temperatures, changes in rainfall patterns and extreme weather events can reduce crop yields, sometimes dramatically. However, climate change can also offer new opportunities, by generating more favorable climatic conditions for agricultural production in certain regions that were previously less productive. In order to assess the positive and negative impacts of climate change on agriculture and identify effective adaptation strategies, scientists have produced massive amounts of data during the last two decades, conducting local experiments in agricultural plots and using models to simulate the effect of climate on crop yields. In most cases, these data are not pooled together and are analyzed separately by different groups of scientists to assess the effects of climate change at a local level, without any attempt to upscale the results at a larger scale. Yet, if brought together, these data represent a rich source of information that are relevant to analyze the effect of climate across diverse environmental conditions. The wealth of data available has led to the emergence of a new type of scientific activity, involving the retrieval of all available data on a given subject and their synthesis into more robust and generic results. In this talk, I review the statistical methods available to synthesize data generated in studies quantifying the effect of climate change on agriculture. I discuss both the most classic methods - such as meta-analysis - and more recent methods based on machine learning. In particular, I show how this approach can be used to map the impact of climate change on a large scale (national, continental and global) from local data. I illustrate these methods in several case studies and present several research perspectives in this area. Close abstractClose abstract

Abstract: Microorganisms play a crucial role in the weathering processes that transform rock into soil through chemical and physical mechanisms essential for nutrient cycling, nitrogen fixation, carbon storage, and organic matter decomposition. This intricate relationship between microbial life and landscapes forms the backbone of ecosystem dynamics and biogeochemical processes. Microbes influence rock weathering and soil production, adapting to their surroundings and creating distinct communities across various landscapes. These complex interactions and feedback mechanisms are pivotal to understanding the co-evolution of microbial communities and landscapes over time. However, existing research on microbial contributions to weathering and soil production has predominantly focused on relatively short timescales and small spatial scales. Understanding the interplay between the evolution of microbial communities and their role in weathering processes over geomorphic timescales within transient landscapes is important for a more complete understanding of how landscapes evolve as well as the impact of geomorphic changes on microbial community establishment and evolution. The main objective of this study is to elucidate the long-term dynamics of microbial communities and their role in weathering processes over millennial timescales. To achieve this, we focused on recently deglaciated basins in the Eastern Sierra Nevada, CA, examining bacterial community composition in three phases of the weathering process: exposed rock at the surface, saprolite—the weathered rock found beneath soil, and soil. Sampling along an elevational transect, we collected 25 samples of rock, soil, and saprolite, and evaluated their bacterial composition using 16S rRNA and metagenomic sequencing. Results show that both soil and saprolite samples exhibited diverse and similar microbial communities, indicating a developmental relationship between these habitats despite distinct geochemical compositions. In contrast, rock habitats are less diverse, and their composition resembles those of young deglaciated landscapes. Our findings point to a link between microbial community composition and rock-to-soil weathering processes, suggesting that the majority of weathering processes occur within the soil column (saprolite and soil), with exposed rock maintaining a steady state. The stability of these microbial communities over extended timescales suggests a potentially significant role for microbial weathering in landscape evolution. This finding underscores the importance of considering microbial contributions in future geomorphic studies, as they may play a key role in shaping the Earth's surface. Moving forward, we plan on coupling a long-term, landscape-scale geomorphic perspective with 'omics approaches from microbial ecology to comprehensively understand the complex relationships between microbial life and landscapes, ultimately advancing our knowledge of ecosystem dynamics and health. Close abstractClose abstract

Abstract: Aerosol-cloud interactions are extensively studied to understand the climatic effect of anthropogenic aerosols, as the latter can change the radiative properties of clouds. Despite the clear presence of different cloud morphologies (i.e., the spatial variation of cloud thickness), the impact of aerosol-cloud interactions under different cloud morphologies is often overlooked. I will show that accounting for cloud morphology is essential for a better process understanding and for an accurate assessment of the radiative forcing due to aerosol-cloud interactions. Close abstractClose abstract

Abstract: Flood is the outcome of complex processes interacting at a range of scales. Flood generation and its magnitude depend on different precipitation and surface properties. As the climate becomes warmer globally, precipitation patterns are changing and, consequently, altering flood regimes. Resolving the expected changes in flood properties requires examining projections of precipitation features most correlated with floods. While the redistribution of mean annual precipitation amounts is generally known, the trends in many other essential factors controlling floods are yet to be resolved. For example, flash flood magnitude is sensitive to space-time rainstorm properties such as areal coverage or storm speed. Still, knowledge of how these properties are affected by global warming is lacking. Maximal rain rates for duration relevant to the watershed’s response time are also crucial parameters controlling the flood discharge. There is some understanding of how extreme rain rates change, but the magnitude and sign depend on the rain duration considered. Changes in frequency and the intra-seasonal distribution of precipitation events also affect flood regimes. Finally, watersheds of different properties are sensitive to different precipitation features, and thus, different watersheds may respond differently to global warming. In this talk, we will present the complexity of flood response under global warming and then focus on two questions: 1) how does global warming affect heavy precipitation events (HPEs) in the eastern Mediterranean, and 2) how these effects are imprinted in the resulting floods in small-medium Mediterranean watersheds. We simulated 41 eastern Mediterranean HPEs with the high-resolution weather research and forecasting (WRF) model. Each event was simulated twice: under historical conditions and at the end of the 21st-century conditions (RCP8.5 scenario) using the “pseudo global warming” approach. Comparison of precipitation patterns from the paired simulations revealed that heavy precipitation events in our region are expected to become drier and more spatiotemporally concentrated, i.e., we expect higher rain rates on smaller coverage areas and shorter storm durations that, in total, yield lower amounts of rainfall. These effects have some contradicting signs, and their full hydrological impact on streamflow peak discharge and volume was further explored. Ensembles of spatially-shifted rainfall data from the simulated HPEs were input to a high-resolution distributed hydrological model (GB-HYDRA) representing four small-medium-size watersheds (18–69 km2) in the eastern Mediterranean (Ramot Menashe). Flow volume is significantly reduced in future HPEs, while the change in flood peak is more complicated due to the combined effect of precipitation amount (decreasing) and precipitation rate (increasing). For the watersheds examined in this research, which are mostly agricultural, flood peaks at the watershed outlets are mostly reduced. The dynamics of flood generation at sub-watersheds of different sizes and properties are further examined in this research to understand scenarios for lowering or increasing flood peaks. This study emphasizes that detecting and quantifying global warming impact on space-time precipitation patterns is essential for flood regime projection. Close abstractClose abstract

Abstract: Located in a highly sensitive subtropical climate area and a densely populated area, Lake Kinneret is poised to undergo both natural and human-induced transformations in the coming decades. The lake is thermally stratified throughout most of the year and mixes thoroughly each winter when the epilimnion (upper layer) water temperature reaches equilibrium with the hypolimnion (bottom layer) water temperature by surface cooling and turbulence. Both the stratified and the fully mixed periods has a significant role in the Kinneret’s ecological system. Observation shows that air above the Lake is warming in a rate of 0.4oC/decade, while the epilimnion and hypolimnion are warming in a rate of 0.3oC/decade and 0.1oC/decade, respectively, for the last 50 years. Therefore, stratification strength and duration is anticipated to change and impact the lake’s ecosystem. Additionally, the sequence of drought periods and the expected future rise in water demands from Lake Kinneret formed the basis for the government's decision to channel desalinated water, via the natural course of the Tzalmon Stream, to the lake to ensure its operational functionality at high levels. Using a 3D hydrodynamic model forced by short and long-term forecasts the above scenarios are examined and analyzed. A simulation forced by regional atmospheric RCP4.5 climate change scenario spanning from 2010-2070 show continuous warming followed by abrupt cooling of the lake water around the year 2065. This result, presumably due to enhanced latent heat loss, suggest a restrain the dramatic anticipated change in the lake stratification. Close abstractClose abstract

Abstract: Quantifying land-atmosphere fluxes of carbon-dioxide (CO2) and methane (CH4) is essential for evaluating carbonclimate feedbacks. Greenhouse gas satellite missions aim to provide global observational coverage of greenhouse gas concentrations and thus improve inversions of landatmosphere exchange fluxes. However, in key regions such as the humid tropics current missions obtain very few valid measurements. Leveraging recent advances in the global analysis of high-resolution optical imagery on cloudcomputing platforms and deep learning algorithms for cloud segmentation, we quantitatively diagnose the sources for low data yields in the tropics. We find that the main cause for low data yields are frequent shallow cumulus clouds. We find that increasing the spatial resolution of observations to 200 m would increase yields by 2–3 orders of magnitude and allow regular measurements in the wet season. Thus, the key to effective tropical greenhouse gas observations likely lies in regularly acquiring high-spatial resolution data. Close abstractClose abstract

Abstract: The sediment – bottom-water interface is suggested as a key control on the chemical composition of the ocean by studies of trace elements in the ocean water-column, yet data regarding trace element fluxes and interactions taking place in the top ten cm of abyssal sediments are scarce. To bridge this gap, I analysed the trace and major element composition of porewater and sediment of red-clay sediment from the abyssal North Pacific, and hydrothermally influenced sediment from the Mid-Atlantic Ridge. The top sediment at both study regions is aerobic, nevertheless, there is large variability in the porewater concentrations of many elements at the top five cm. The North Pacific red-clay sediment is a source of cobalt, nickel, copper, arsenic, vanadium and barium to the deep-ocean, the magnitude of these fluxes is consistent with fluxes calculated based on the water-column distribution of most elements, and are equivalent to the global supply of these elements by rivers. The hydrothermally influenced sediment is a strong source of copper, zinc and cobalt up to three km from the vent due to oxidation of sulfide minerals. Close to the vents, the sediment is high in iron oxyhydroxides that adsorb the oxyanions vanadate, arsenate and phosphate, acting as a sink for these elements. The results of this study highlight the importance of red-clay sediment in shaping the chemical composition of the ocean, and suggest an important role for hydrothermally influenced sediment in modulating the contributions of hydrothermal vents to ocean biogeochemistry. Close abstractClose abstract

Abstract: The impact of anthropogenic aerosols on clouds is a leading source of uncertainty in estimating the effect of human activity on the climate system. The challenge lies in the scale difference between clouds (~1-10 km) and general circulation and climate (~1000 km). To address this, we utilize three different novel sets of simulations that allow to resolve convection while also including a epresentation of large-scale processes. Our findings demonstrate that aerosol-cloud interaction intensifies tropical overturning circulation. Employing a weak temperature gradient approximation, we attribute variations in circulation to clear-sky humidity changes driven by warm rain suppression by aerosols. In two sets of simulations accounting for sub-tropical-tropical coupling, we show that aerosol-driven sub-tropical rain suppression leads to increased advection of cold and moist air from the sub-tropics to the tropics, thus enhancing tropical cloudiness. The increased tropical cloudiness has a strong cooling effect by reflecting more of the incoming solar radiation. The classical “aerosol-cloud lifetime effect” is shown here to have a strong remote effect (sub-tropical aerosols increase cloudiness in the tropics), thus widening the concept of cloud adjustments to aerosol perturbation with important implications for marine cloud brightening. Close abstractClose abstract

Abstract: The use of SOM in atmospheric science has grown popular over the recent years. The SOM's strength lies in its ability to project the continuum of a given dynamical system to an easily understood spectrum of dominant states. The SOM relies on a neural network, where each grid-point in each node (cluster) is assigned with a specific weight for a given input parameter. The SOM then operates competitively, shifting individual members between the nodes to minimize internal node variability while maximizing the distances between the nodes. Here, two novel SOM applications are demonstrated, recently used to classify Mediterranean cyclones from an upper-level PV perspective. Each approach yields the potential to enhance the understanding of different aspects of Mediterranean cyclone's predictability and is readily applicable to other regions of interest. Close abstractClose abstract