Euro-Mediterranean Center for Climate Change

Coastal communities worldwide face increasing risks from sea level rise (SLR) and more frequent coastal flooding, threatening densely populated areas. This study applies a Coastal Flood Risk Modelling Framework to evaluate permanent inundation from SLR and episodic flooding from extreme events across Asia and the Pacific, while also estimating the costs and benefits of adaptation. Using probabilistic projections of mean and extreme sea levels under five Shared Socio-economic Pathway (SSP) scenarios, ranging from ambitious mitigation (SSP1-1.9) to high fossil-fuel development (SSP5-8.5), the framework integrates hazard, exposure, and vulnerability data to estimate economic impacts. Current annual economic losses from coastal flooding in Asia and the Pacific amount to $26.8 billion, severely affecting Southeast Asia, South Asia, and the Pacific Islands. By 2050, expected annual economic damages are projected to rise between $143.7 and $197.8 billion just considering SLR effects (depending on the scenario), with further exacerbation by the century’s end. Atoll nations, including Kiribati, Maldives, RMI and Tuvalu will face the most severe losses relative to GDP. These findings underscore the urgent need for holistic adaptation measures including, grey, green and hybrid solutions, adaptive planning, resilient infrastructure, improved governance systems, alongside mitigation policies to reduce future emissions and consequent flood risks.

Abstract El Niño–Southern Oscillation (ENSO) is a complex phenomenon that significantly impacts global weather patterns and ecosystems. Improving its prediction is therefore of a high societal value. However, general circulation models (GCMs) present severe biases when predicting ENSO, and their skill remains comparable to that of vastly simpler empirical models such as linear inverse models (LIMs). LIMs, however, rely on linear dynamics, and they have inherent limitations in capturing the behavior of nonlinear phenomena. In this context, Koopman operator theory has emerged as a powerful mathematical framework, offering a novel perspective for analyzing complex nonlinear systems. Unlike LIMs, Koopman operators enable linear representations of nonlinear dynamics in infinite-dimensional function spaces, allowing for richer dynamical insights. While previous studies have demonstrated the potential of Koopman methods for ENSO forecasting, a fundamental question remains: How much data are actually needed to obtain robust Koopman operator estimates? In this study, this issue is addressed by performing a sensitivity analysis on the reliability of Koopman-based forecasts as a function of data length. Using 2000 years of tropical SST preindustrial Community Earth System Model, version 2 (CESM2), data, this study assesses the skill of the Niño-3.4 forecasts within the nonlinear Koopman framework and compares it to the benchmark set by LIMs. Our findings reveal nuances in the robustness of Koopman operator estimates, particularly when using shorter training periods. However, a notable breakthrough emerges: the higher skill of the Niño-3.4 Koopman ensemble forecasts (KEFs), which showcase consistent improvements over linear models, along with improved representation of western Pacific variability and more reliable forecasts of extreme El Niño and La Niña events. Significance Statement This study aims to improve our ability to predict El Niño–Southern Oscillation (ENSO) variability, whose El Niño and La Niña events have major effects on global weather and ecosystems. We explored a new data-driven approach using Koopman decomposition, which provides a solid dynamical framework to analyze and possibly understand highly nonlinear systems. By applying this method to long records of sea surface temperature data, we found that it can significantly improve the accuracy of El Niño and La Niña forecasts, especially for predictions made well in advance. This advancement could lead to more reliable forecasts, helping communities better prepare for and respond to ENSO events.

Abstract The term “Mediterranean tropical-like cyclone” or “medicane” has been used in different ways by different authors. Identification of medicanes has been based on features observed from satellite imagery or on diagnostics applied to numerical weather prediction model outputs. In the absence of an official definition, medicanes are generally considered to be cyclones over the Mediterranean sharing physical processes with tropical cyclones. Nevertheless, recent studies on the dynamics of several systems widely recognized as medicanes show different underlying development mechanisms. A commonly agreed definition is critical and necessary to assess their climatology in past and future climates, as well as to consistently identify such systems in weather forecasts. The scientific community working on Mediterranean cyclones hereby proposes a definition, which is based solely on Earth observations: “A medicane is a mesoscale cyclone that develops over the Mediterranean Sea and displays tropical-like cyclone characteristics: a warm core extending into the upper troposphere, an eye-like feature in its center with spiral cloud bands around, an almost windless center surrounded by nearly-symmetric sea surface wind circulation with maximum wind speed within a few tens of km from the center.” Significance Statement Nearly two decades have passed since the term “medicane,” short for “Mediterranean hurricane,” has become commonly accepted to represent Mediterranean vortices with satellite-observed characteristics typical of tropical cyclones. The absence of an official or commonly accepted definition has created some confusion within the scientific and operational community. To overcome this ambiguity, here, we propose a phenomenological definition that can serve as a reference for future scientific research and operational applications, to be applied to any cyclonic system in the Mediterranean that bears structural similarities to tropical cyclones. This simple definition will assure consistency and, at the same time, can be used with the outputs from numerical models, working as a reference for future scientific research or operational applications.

Abstract. The Adriatic Sea (eastern Mediterranean Basin) is traditionally considered a natural laboratory for studying a number of oceanographic processes of global interest, including coastal dynamics, dense water formation, and thermohaline circulation. More recently, the intensification of the effects of climate change and the increasing awareness of its possible consequences on the natural and socio-economic assets of the Adriatic Basin have opened new research questions and reframed most of the existing ones into a multi-decadal timescale. In this perspective, a description of the possible evolution of the physical oceanographic processes is one of the key requirements for addressing the multi-disciplinary challenges set by climate change, but up to now it has not been possible to combine, for this basin, a sufficiently high resolution in the process description with an estimate of the uncertainty associated with the predictions. This work presents an ensemble modelling approach (AdriE – Adriatic Sea Ensemble) for the kilometre-scale description of hydrodynamics in the Adriatic Sea in an end-of-century time frame. Addressing 3D circulation and thermohaline dynamics within the Regional Ocean Modelling System (ROMS), the ensemble consists of six climate runs encompassing the period from 1987 to 2100 in a severe RCP8.5 scenario forced by the SMHI-RCA4 regional climate model, driven by as many different CMIP5 general climate models made available within the EURO-CORDEX initiative. The climate ensemble is flanked by a dedicated evaluation run for the 1987–2010 period, in which SMHI-RCA4 has been driven by reanalysis fields approximating the best available boundary conditions, thus isolating the intrinsic sources of uncertainty in the RCA4–ROMS modelling chain. In order to allow a direct comparison, the assessment of the model skills in the evaluation run borrows, as far as possible, data and approaches used for the evaluation of a recent kilometre-scale, multi-decadal modelling effort for this region. The model performances are mostly aligned with the state-of-the-art reference. In particular, good results in describing the main features of marine heatwaves and cold spells, such as timing, intensity, and interannual variability, indicate that the AdriE ensemble can effectively be used for studies on the occurrence and effects of thermal extremes in the basin. Future projections suggest an increase in temperature and salinity at upper and intermediate depths, resulting in an overall decrease in water density and possibly in deep ventilation rates. Projected variations are stronger in summer and autumn, and in these seasons the ensemble range is larger than the spatial variability of the quantities and occasionally comparable with the intensity of the climate signal, highlighting the importance of an ensemble approach to treat the climate variability at this timescale. The dataset presented in this study, which can be used for the analysis of coastal and continental margin processes of general interest, is fully available upon request to the corresponding author, and monthly averages of the main quantities are available for each run on a dedicated Zenodo repository.

The historical City of Venice, with its lagoon, has been severely exposed to repeated marine flooding since historical times due to the combined effects of sea level rise (SLR) and land subsidence (LS) by natural and anthropogenic causes. Although the sea level change in this area has been studied for several years, no detailed flooding scenarios have yet been realized to predict the effects of the expected SLR in the coming decades on the coasts and islands of the lagoon due to global warming. From the analysis of geodetic data and climatic projections for the Shared Socioeconomic Pathways (SSP1-2.6; SSP3-7.0 and SSP5-8.5) released in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), we estimated the rates of LS, the projected local relative sea level rise (RSLR), and the expected extent of flooded surfaces for 11 selected areas of the Venice Lagoon for the years 2050, 2100, and 2150 AD. Vertical Land Movements (VLM) were obtained from the integrated analysis of Global Navigation Satellite System (GNSS) and Interferometry Synthetic Aperture Radar (InSAR) data in the time spans of 1996–2023 and 2017–2023, respectively. The spatial distribution of VLM at 1–3 mm/yr, with maximum values up to 7 mm/yr, is driving the observed variable trend in the RSLR across the lagoon, as also shown by the analysis of the tide gauge data. This is leading to different expected flooding scenarios in the emerging sectors of the investigated area. Scenarios were projected on accurate high-resolution Digital Surface Models (DSMs) derived from LiDAR data. By 2150, over 112 km2 is at risk of flooding for the SSP1-2.6 low-emission scenario, with critical values of 139 km2 for the SSP5-8.5 high-emission scenario. In the case of extreme events of high water levels caused by the joint effects of astronomical tides, seiches, and atmospheric forcing, the RSLR in 2150 may temporarily increase up to 3.47 m above the reference level of the Punta della Salute tide gauge station. This results in up to 65% of land flooding. This extreme scenario poses the question of the future durability and effectiveness of the MoSE (Modulo Sperimentale Elettromeccanico), an artificial barrier that protects the lagoon from high tides, SLR, flooding, and storm surges up to 3 m, which could be submerged by the sea around 2100 AD as a consequence of global warming. Finally, the expected scenarios highlight the need for the local communities to improve the flood resiliency plans to mitigate the consequences of the expected RSLR by 2150 in the UNESCO site of Venice and the unique environmental area of its lagoon.

"Gathering users and developers to shape together the next-generation ocean reanalyses: Ocean reanalyses workshop of the European Copernicus Marine Service" published on 01 Apr 2024 by American Meteorological Society.

Global food production faces significant challenges, acting as a primary driver of land use change, biodiversity loss, and greenhouse gas (GHG) emissions, while a significant part of the world's population still struggles with food security and nutrition. In response, the EAT-Lancet Commission has proposed a healthy and sustainable planetary diet aimed at reducing resource-intensive foods like meat, starchy vegetables, and eggs, while suggesting a 67% increase in global per capita unsaturated oil consumption (e.g., soybean, sunflower, rapeseed) and the maintenance of the current palm oil intake. Using a spatially explicit land allocation algorithm, we assess how future global food oil demand for the expected 9.2 billion people by 2050 might impact forests and other carbon-rich ecosystems, along with associated land use change GHG emissions. We also evaluate the potential consequences of substituting palm oil with other vegetable oils, noting their different health and environmental implications. Results show that the projected 74% rise in global vegetable oil production for food by 2050 would require 317 million hectares of land-a 68% increase compared to maintaining current consumption. This could escalate pressure on forests and threaten global food security, potentially causing 115-120 million hectares of deforestation and 1163-1210 Mt. CO2 per year of GHG emissions from land use change, an 87% rise compared to maintaining current consumption rates. However, the EAT-Lancet diet foresees a reduction in other high-impact foods, potentially freeing other lands and reducing overall projected global food GHG emissions. Another relevant finding reveals that replacing palm oil with other oils would result in increasing land needs, up to 385 million hectares with a potential 148 million hectares of deforestation, and GHG emissions, up to 1525 Mt. CO2 per year, thus not representing a conclusive and viable solution towards sustainability. Instead, along with the growing importance of certification schemes for sustainable and deforestation-free food supply chains, ensuring sustainable production of all vegetable oils emerges as a critical strategy to prevent the conversion of biodiverse and carbon-rich lands.

Due to complex interactions, changes in any one area of food systems are likely to impact—and possibly depend on—changes in other areas. Here we present the first annual monitoring update of the indicator framework proposed by the Food Systems Countdown Initiative, with new qualitative analysis elucidating interactions across indicators. Since 2000, we find that 20 of 42 indicators with time series have been trending in a desirable direction, indicating modest positive change. Qualitative expert elicitation assessed governance and resilience indicators to be most connected to other indicators across themes, highlighting entry points for action—particularly governance action. Literature review and country case studies add context to the assessed interactions across diets, environment, livelihoods, governance and resilience indicators, helping different actors understand and navigate food systems towards desirable change.

Abstract

Abstract. A well-validated storm surge numerical model is crucial, offering precise coastal hazard information and serving as a basis for extensive databases and advanced data-driven algorithms. However, selecting the best model setup based solely on common error indicators like the root-mean-square error (RMSE) or Pearson correlation does not always yield optimal results. To illustrate this, we conducted 34-year high-resolution simulations for storm surge under barotropic (BT) and baroclinic (BC) configurations using atmospheric data from ERA5 and a high-resolution downscaling of the Climate Forecast System Reanalysis (CFSR) developed by the University of Genoa (UniGe). We combined forcing and configurations to produce three datasets: (1) BT-ERA5, (2) BC-ERA5, and (3) BC-UniGe. The model performance was assessed against nearshore station data using various statistical metrics. While RMSE and Pearson correlation suggest BT-ERA5, i.e., the coarsest and simplest setup, is the best model (followed by BC-ERA5), we demonstrate that these indicators are not always reliable for performance assessment. The most sophisticated model (BC-UniGe) shows worse values of RMSE or Pearson correlation due to the so-called “double penalty” effect. Here we propose new skill indicators that assess the ability of the model to reproduce the distribution of the observations. This, combined with an analysis of values above the 99th percentile, identifies BC-UniGe as the best model, while ERA5 simulations tend to underestimate the extremes. Although the study focuses on the accurate representation of storm surge by the numerical model, the analysis and proposed metrics can be applied to any problem involving the comparison between time series of simulation and observation.