The low-lying, arid coastal regions of the Southern Mediterranean basin, extending over 4600 km, face daunting sea level rise and hydroclimatic changes due to shifting weather patterns. The impacts of the above on coastal urban buildings and infrastructure still need to be more qualified and understood. Alexandria, a historic and densely populated port city representative of several coastal cities in the Southern Mediterranean, has experienced over 280 building collapses near its shorelines over the past two decades, with the root causes still being investigated. We explore the decadal changes in coastal and hydroclimatic drivers along the city's coastline using a GIS-based multi-criteria analysis in the areas where buildings collapsed from 1974 to 2021. Our results suggest that collapses are correlated to severe coastal erosion due to sediment imbalance caused by the decades-long inefficient landscape and urban expansion along the historic city's waterfront. This severe erosion, combined with sea level rise, upsurges seawater intrusion, which raises the groundwater levels in coastal aquifers, disrupting soil stability and accelerating corrosion in building foundations until they collapse. We identified a coastal area of high vulnerability with over 7,000 buildings at risk, surpassing any other vulnerable zone in the Mediterranean Basin. We conclude that several coastal and densely urbanized areas in the Southern Mediterranean are at greater risk of building collapses due to similar hydroclimatic changes. Therefore, we propose a landscape-based coastal mitigation approach to implement adaptive transformations to curb these risks that apply to Alexandria and other southern Mediterranean cities facing the same challenges.

As the likelihood of temporarily exceeding 1.5 °C of global warming rises, understanding the response of the ocean-climate system to overshooting this warming level is of increasing importance. Here, we apply the Adaptive Emissions Reduction Approach to the Earth System Model GFDL-ESM2M to conduct novel overshoot scenarios which temporarily exceed 1.5 °C of global warming to 2.0, 2.5 and 3.0 °C, alongside a complementary scenario that stabilizes global temperature at 1.5 °C. The simulation framework allows to isolate impacts attributable to the temperature overshoots alone, both during their peaks and after their reversals, in simulation timeframes spanning from 1861 to 2500. Our results reveal that, while global sea surface temperatures eventually retrace to 1.5 °C stabilization levels, substantial residual ocean surface warming persists regionally, particularly in the North Atlantic (regional average of up to +3.1 °C in the 3°C overshoot scenario) and the Southern Ocean (+1.2 °C). The residual warming is primarily attributed to the recoveries of the Atlantic and Southern Ocean meridional overturning circulation, resulting in a reversed pattern of disproportionate surface warming in low-latitude oceans found during the transient peak of the overshoot. Excess subsurface heat storage in low and mid-latitudes furthermore prevents steric sea level rise from reverting to 1.5 °C stabilization levels in any overshoot scenario, with sea level remaining up to 32 % higher in the 3 °C overshoot scenario. Both peak overshoot impacts and persistent changes following overshoot reversal bear significant implications for future assessments of coastlines, regional climates, marine ecosystems, and ice sheets.

Climate change could irreversibly modify Southern Ocean ecosystems. Marine ecosystem model (MEM) ensembles can assist policy making by projecting future changes and allowing the evaluation and assessment of alternative management approaches. However, projected future changes in total consumer biomass from the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP) global MEM ensemble highlight an uncertain future for the Southern Ocean, indicating the need for a region-specific ensemble. A large source of model uncertainty originates from the Earth system models (ESMs) used to force FishMIP models, particularly future changes to lower trophic level biomass and sea ice coverage. To build confidence in regional MEMs as ecosystem-based management tools in a changing climate that can better account for uncertainty, we propose the development of a Southern Ocean Marine Ecosystem Model Ensemble (SOMEME) contributing to the FishMIP 2.0 regional model intercomparison initiative. One of the challenges hampering progress of regional MEM ensembles is achieving the balance of global standardised inputs with regional relevance. As a first step, we design a SOMEME simulation protocol, that builds on and extends the existing FishMIP framework, in stages that include: detailed skill assessment of climate forcing variables for Southern Ocean regions, extension of fishing forcing data to include whaling, and new simulations that assess ecological links to sea-ice processes in an ensemble of candidate regional MEMs. These extensions will help advance assessments of urgently needed climate change impacts on Southern Ocean ecosystems.

The ocean oxygen (O2) inventory has declined in recent decades but the estimates of O2 trend are uncertain due to its sparse and irregular sampling. A refined estimate of deoxygenation rate is developed using machine learning techniques and biogeochemical Argo array. The source data includes historical shipboard (bottle and CTD-O2) profiles from 1965 to 2020 and biogeochemical Argo profiles after 2005. Neural network and random forest algorithms were trained using approximately 80 % of this data and the remaining 20% for validation. The training data is further divided into 5-fold decadal groups to perform cross validation and hyperparameter tuning. Through different combinations of algorithm types and predictor variable sets, an ensemble of gridded monthly O2 datasets was generated with similar skills (root-mean-square error ~ 13-18 micro-mol/kg and R2 ~ 0.9). The largest errors are found in the oxycline and frontal regions with strong lateral and vertical gradients. The mapping was repeated with shipboard data only and with both shipboard and Argo data. The effect of including Argo data on the estimated global deoxygenation trends has a major impact with an 56% increase while reducing the uncertainty by 40% as measured by the ensemble spread. This study demonstrates the importance of new biogeochemical Argo arrays in relatively data-poor regions such as the Southern Ocean.

As the urgency to evaluate the impacts of climate change on marine ecosystems increases, there is a need to develop robust projections and improve the uptake of ecosystem model outputs in policy and planning. Standardising input and output data is a crucial step in evaluating and communicating results, but can be challenging when using models with diverse structures, assumptions, and outputs that address region-specific issues. We developed an implementation framework and workflow to standardise the climate and fishing forcings used by regional models contributing to the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP) and to facilitate comparative analyses across models and a wide range of regions, in line with the FishMIP 3a protocol. We applied our workflow to three case study areas-models: the Baltic Sea Mizer, Hawai’i-based Longline fisheries therMizer, and the southern Benguela ecosystem Atlantis marine ecosystem models. We then selected the most challenging steps of the workflow and illustrated their implementation in different model types and regions. Our workflow is adaptable across a wide range of regional models, from non-spatially explicit to spatially explicit and fully-depth resolved models and models that include one or several fishing fleets. This workflow will facilitate the development of regional marine ecosystem model ensembles and enhance future research on marine ecosystem model development and applications, model evaluation and benchmarking, and global-to-regional model comparisons.

This study examines a scenario combining a prolonged solar minimum, like the historical Maunder Minimum, with the increased CO2 emissions characteristic of the post-industrial era. Two scenarios were developed in NCAR/CESM 2.0 implemented at LAMMOC/UFF to create simulations from 1850 to 2000, it contrasts differing radiative forcings from 1950 onward—one reflecting actual observed changes, including rising CO2 levels, and the other simulating a decrease in solar output like that during the Maunder Minimum but with continued CO2 growth. The results were validated against ERA5 data and 20th-century reanalysis. By calculating meridional averages at 30-degree latitude intervals, distinct regional impacts of the Maunder Minimum were identified. Notably the simulated Maunder Minimum reduced global warming by and even mitigated 70% in HS in the last decade of the 20th century. However, this attenuation was lower in the HN, especially in the 30-60N region where no attenuation was observed.

A document by Tianqi Zhang. Click on the document to view its contents.

The Fisheries and Marine Ecosystems Model Intercomparison Project (FishMIP) has dedicated a decade to unravelling the future impacts of climate change on marine animal biomass. FishMIP is now preparing a new simulation protocol to assess the combined effects of both climate and socio-economic changes on marine fisheries and ecosystems. This protocol will be based on the Ocean System Pathways (OSPs), a new set of socio-economic scenarios derived from the Shared Socioeconomic Pathways (SSPs) widely used by the Intergovernmental Panel on Climate Change (IPCC). The OSPs extend the SSPs to the economic, governance, management and socio-cultural contexts of large pelagic, small pelagic, benthic-demersal and emerging fisheries, as well as mariculture. Comprising qualitative storylines, quantitative model driver pathways and a “plug-in-model” framework, the OSPs will enable a heterogeneous suite of ecosystem models to simulate fisheries dynamics in a standardised way. This paper introduces this OSP framework and the simulation protocol that FishMIP will implement to explore future ocean social-ecological systems holistically, with a focus on critical issues such as climate justice, global food security, equitable fisheries, aquaculture development, fisheries management, and biodiversity conservation. Ultimately, the OSP framework is tailored to contribute to the synthesis work of the IPCC. It also aims to inform ongoing policy processes within the United Nations Food and Agriculture Organisation (FAO). Finally, it seeks to support the synthesis work of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), with a particular focus on studying pathways relevant for the United Nations Convention on Biological Diversity (CBD).

Understanding climate change impacts on global marine ecosystems and fisheries requires complex marine ecosystem models, forced by global climate projections, that can robustly detect and project changes. The Fisheries and Marine Ecosystems Model Intercomparison Project (FishMIP) uses an ensemble modelling approach to fill this crucial gap. Yet FishMIP does not have a standardised skill assessment framework to quantify the ability of member models to reproduce past observations and to guide model improvement. In this study, we apply a comprehensive model skill assessment framework to a subset of global FishMIP models that produce historical fisheries catches. We consider a suite of metrics and assess their utility in illustrating the models’ ability to reproduce observed fisheries catches. Our findings reveal improvement in model performance at both global and regional (Large Marine Ecosystem) scales from the Coupled Model Intercomparison Project Phase 5 and 6 simulation rounds. Our analysis underscores the importance of employing easily interpretable, relative skill metrics to estimate the capability of models to capture temporal variations, alongside absolute error measures to characterise shifts in the magnitude of these variations between models and across simulation rounds. The skill assessment framework developed and tested here provides a first objective assessment and a baseline of the FishMIP ensemble’s skill in reproducing historical catch at the global and regional scale. This assessment can be further improved and systematically applied to test the reliability of FishMIP models across the whole model ensemble from future simulation rounds and include more variables like fish biomass or production.

The ensemble-based tangent linear model (ETLM), which evolves the static background error covariance (BEC) in time in the data assimilation window, is beneficial for improving the analysis and avoids the need for developing the tangent linear forecast model and its adjoint as in four-dimensional variational data assimilation (4DVar). This study proposes to apply the filtered ETLM (FETLM), which is composed of ensemble perturbations at limited time slots and localized with a quasi-Gaussian filter in the way that avoids calculation of the inverse matrix. Tests with the Lorenz (1996) model showed that FETLM evolved the static BEC as in 4DVar and improved the analysis. In experiments with the FETLM implemented in the hourly-updated regional forecast system, the Rapid Refresh Forecast System, the imbalance of the analysis was mitigated due to the flow-dependent BEC. Moreover, the cross-variable covariance in the FETLM made it possible to assimilate radar reflectivity effectively even without hydrometeors as control variables.

Magnetic reconnection is a fundamental process known to play a crucial role in electron acceleration and heating, however, the mechanism of electron energization during reconnection is still not fully understood. This study introduces a novel electron acceleration mechanism in which electrons can be accelerated by secondary reconnection in the separatrix region. The secondary reconnection occurs in a thin current sheet resulted from the shear of the out-of-plane Hall magnetic fields of the primary magnetopause reconnection. It results in intense electron energy fluxes towards the primary X-line. This result will likely be an important piece in the puzzle of particle acceleration by reconnection.

Previous studies on future scenarios identified two key effects of increasing CO2 on the African summer monsoon (ASM): Rising CO2 leads to an enhancement in moisture supply, favoring an increase in ASM precipitation (the thermodynamic effect). However, it also results in a weakening in mean atmospheric flow, thus facilitating a dryness across the ASM region (the dynamic effect). Therefore, the ultimate change in ASM precipitation stems from the balance of both the thermodynamic and dynamic effects. This study further examines the impact of rising CO2 on ASM rainfall, by taking into account various climate states. Our results suggest that an increase in CO2 during warm interglacial periods has a stronger influence from thermodynamic factors than from dynamic factors, resulting in an enhancement in ASM rainfall. In contrast, if CO2 increases under cold glacial climate backgrounds, its dynamic impact dominates a reduction of rainfall in the ASM region.

In this work, we investigated the seasonal cycle of atmospheric potential oxygen (APO), a unique tracer of air-sea gas exchanges of molecular oxygen (O2) and carbon dioxide (CO2), expressed as APO = O2 + 1.1×CO2. APO data were obtained from flask air samples collected since late 1990s at three Japanese ground stations and on commercial cargo ships sailing between Japan and Australia/New Zealand, North America, and Southeast Asia. We also analyzed the APO spatial distribution and seasonal cycles with simulations from an atmospheric transport model, using climatological oceanic O2 fluxes from a previous study as input. Model simulations reproduced the observed APO seasonal cycles generally well, but with larger amplitudes and earlier occurrence of seasonal minima and maxima than in the observations. Moreover, the observed seasonal cycles exhibited larger APO enhancements than the simulations in autumn and early winter, especially in the northern North Pacific at 20°N-60°N. These enhancements remained when refining the comparison by adjusting the simulated APO peak-to-peak amplitudes and seasonal phases to the observations. This suggests additional O2 emissions in the North Pacific, not well expressed in the air-sea O2 fluxes used as input for our model simulations. The average autumn enhancement at 40°N-60°N was approximately twice that measured at 20°N-40°N. Confirming previous studies, our results indicate two distinct mechanisms possibly contributing to the additional oceanic O2 emissions: outgassing from a subsurface shallow oxygen maximum at 20°N-40°N and autumn phytoplankton bloom at 40°N-60°N.

• Recent studies for the ascent and eruption of Enceladus' plume have neglected the role of exsolution and expansion of dissolved volatiles. • Volatile-driven direct ocean-to-jet liquid water ascent is generally consistent with observations if combined with a sublimation co-plume. • This mode of ascent could preserve ocean bulk content in jets, leading to jet sampling strategies being preferred for future missions.

Reliable uncertainty model calculation in subsurface engineering from pore- and grain-scale to field-scale relies on sufficient data, but subsurface dataset acquisition remains a challenge, particularly in domains where data collection is expensive or time-consuming, such as Computed Topography (CT) imaging for digital rock images. While AI-based data augmentation may assist the model training, it still requires many training images as well as the quality assessment of generated data. Yet, most data quantitative metrics flatten spatial data into vectors; therefore, removing the essential spatial relationships within the data. We evaluate topology-based metrics for quality assessment of AI-based image augmentation, coupled with digital rocks augmentation practice using the Single image Generative Adversarial Network (SinGAN) for binarized (segmented) images. Compared to most traditional dimensionality reduction methods that process images into a flattened vector, we propose topological image analysis for dimensionality reduction while preserving the essential geometric and topological features of the high-dimensional data. To demonstrate our proposed approach, we evaluate the generated images starting from four distinct digital rock samples, sorted sandstone, synthetic sphere pack, limestone, and poorly sorted sandstone, using Minkowski functionals, image graph network-based measures, graph Laplacian-based measures, local trend maps, and a homogeneity-heterogeneity classifier. Our workflow suggests that AI-based digital rock augmentation, combined with topological dimensionality reduction offers a powerful tool for enhanced quality assessment and diagnostic of digital rock augmentation and improved interpretation to support decision-making.   

A document by Jasper Vrugt. Click on the document to view its contents.

This study investigates high-frequency mapping of downward shortwave radiation (DSR) at Earth's surface using the Advanced Baseline Imager (ABI) instrument mounted on Geostationary Operational Environmental Satellite-R Series (GOES-R). The existing GOES-R DSR product (DSR ABI) offers hourly temporal resolution and spatial resolution of 0.25 o. To enhance these resolutions, we explore machine learning (ML) for DSR estimation at the native temporal resolution of GOES-R Level-2 Cloud and Moisture Imagery (CMI) product (five minutes) and its native spatial resolution of two-kilometer at nadir. We compared four common ML regression models through the Leave-One-Out Cross-Validation (LOOCV) algorithm for robust model assessment against ground measurements from AmeriFlux and SURFRAD networks. Results show that Gradient Boosting Regression (GBR) achieves the best performance (R² = 0.916, RMSE = 88.05 W m-2) with efficient computation compared to Long Short-Term Memory (LSTM), which exhibited similar performance. DSR estimates from the GBR model (DSR ALIVE) outperform DSR ABI across various temporal resolutions and sky conditions. DSR ALIVE agreement with ground measurements at SURFRAD networks exhibits high accuracy at high temporal resolutions (five-minute intervals) with R² exceeding 0.85 and RMSE=122 W m-2. We conclude that GBR offers a promising approach for high-frequency DSR mapping from GOES-R, enabling improved applications for near-real-time monitoring of terrestrial carbon and water fluxes.

The investigation into the physical mechanisms and behavior of geoelectric fields as potential precursors of earthquakes is a crucial yet challenging task in seismo-electromagnetics. Assuming the electrokinetic effect as a mechanism, we propose a novel numerical modeling algorithm based on the framework of the LAC GRTM to explore the behavior of geoelectric fields in stratified porous media during earthquake preparation. This algorithm incorporates two innovative aspects: (1) employing the Jordan decomposition to address the degeneracy problem encountered in quasi-stationary scenarios, and (2) utilizing digital linear filters for Hankel transforms to conduct the wavenumber integration. The accuracy of the algorithm in computing static displacements in an extremely low porosity half-space model is verified through comparison with analytical solutions. Numerical results from a half-space model demonstrate a strong consistency between the vertical electric field and filtration displacement. Notably, the maximum amplitude of the vertical electric field can reach approximately 2878.8 mV/km, which is detectable by receivers located at considerable distances from the epicenter. In addition, three distinct types of pre-earthquake electric fields are identified: (1) the localized electric field induced by slow P waves, (2) the interface electric responses, and (3) the direct converted electric field from the source. Results from a multi-layer porous model show the significant influence of the water table on the amplitude of the vertical electric field. This can be attributed to the influence of water saturation and permeability on the coefficient that combines the vertical electric field and the vertical filtration displacement. We also analyze the temporal evolution of different fields during earthquake preparation, which demonstrates a robust correlation between the temporal characteristics of pore pressure and that of the vertical electric field. This indicates that the vertical electric field offers an effective means to detect pore pressure evolution during earthquake preparation. Its sensitivity to the strike, rake, and dip angles of potential faults indicates its potential for determining fault geometry related to an impending earthquake. Furthermore, fluctuations in displacements enable a rough estimation of variations in stress and confining pressure. These observations underscore the advantage of integrating geoelectric precursor data with continuous displacement observations to enhance our understanding of earthquake preparation mechanisms.