This study presents a novel bias correction methodology for enhancing the reliability of climate projections, particularly in regions vulnerable to climate change impacts, such as the Southern African Development Community (SADC). This methodology is here applied jointly with a downscaling method to correct systematic distributional biases and refine the spatial resolution of climate model outputs, with the aim of improving the identification of extreme weather events. Our work builds upon a unique bias correction approach that enhances the quantile mapping (QM) technique by fitting a generalised extreme value distribution (GEV) to the tails of the distribution. This approach addresses the limitations of traditional methods when dealing with extreme events, which are often tied to the scarcity of data for such high-impact, yet low-probability occurrences. Moreover, we extend the applicability of our methodology to climate projections by integrating an approach designed to preserve relative trends in climate data. The technique is then applied to the daily mean temperature data of long-term climate projections (until 2100) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The bias correction of historical simulations produces an increase in the ensemble forecast members predicting temperature extremes in agreement with the reference dataset (i.e., the reanalysis ERA5-Land). When analysing future scenarios, there is an expected increase in both the intensity and frequency of extreme events, especially in more pessimistic scenarios, such as SSP585. However, these results vary depending on the specific climate model used. Our results suggest that considering multiple climate models of varying resolutions and comparing their outputs against observations and other climate datasets might be key to a comprehensive understanding of extreme event trends in climate scenarios. This approach accounts for each model’s strengths and limitations, providing a robust analysis of changes in extreme weather phenomena.

This preliminary study introduces the Standardised Windstorm Index (SWI), a novel tool designed to quantify the impact of extreme wind events in different geographical regions. The SWI is developed by first fitting the Weibull distribution to daily maximum wind speed data, followed by an inverse normal transformation to obtain a standardised index. This method enhances the accuracy of extreme wind event detection compared to conventional standardisation techniques. Using seasonal forecasts from the SEAS5 system, the SWI demonstrates its ability to effectively detect tropical cyclones and windstorms in the Southern African Development Community (SADC) region, showing an improvement of more than 20

The aviation sector is experiencing the impacts of climate change on both in-flight and ground operations. One meaningful example is that of heatwaves, as temperature extremes reduce aircraft lift and takeoff performance, requiring lighter loads—including fewer passengers and less cargo— and increasing noise pollution due to shallower climb angles. Heatwaves also deteriorate runway asphalt and compromise comfort inside airport terminals.In this work, we analyze the occurrence of summer heatwaves at 30 European airports by comparing historical data from 1985-2014 with projections for 2035-2065, using data from 10 CMIP6 models across three Shared Socioeconomic Pathways (SSP126, SSP370, SSP585). We apply a dedicated bias correction and downscaling technique to model data to improve spatial resolution and address systematic distributional biases. Heatwaves are identified and measured using a Standardized Heatwave Indicator (SHI), enabling the assessment of any trends in the frequency, length, and intensity of this phenomenon at each airport.Our findings reveal a significant increase in the frequency, duration, and intensity of summer heatwaves across all the airports considered for the period 2035-2065 compared to historical data. Heatwaves are anticipated to become an annual phenomenon, with Southern European airports expected to face the most severe conditions. These airports could in fact experience an average of up to four heatwaves each summer, lasting up to 14 days and reaching intensities exceeding the 99.97th percentile of historical events.This work underscores the urgent need to identify and implement heatwave-aware adaptation measures and operational improvements in aviation, enhancing the climate resilience of this sector to ensure safety and efficiency in operations.

In the context of climate change adaptation, the use of climate predictions is steadily gaining importance. One critical challenge to consider when dealing with climate simulation outputs is the systematic bias affecting the modelled data. While bias correction methods are commonly employed in impact models to assess the effect of climate events on human activities, their effectiveness is often reduced in the case of extreme events, due to the scarcity of data for these low-probability and high-impact phenomena. This study, conducted as part of the European project FOCUS-Africa, is dedicated to advancing innovative climate services in the southern regions of Africa. Our primary objective is to respond to the needs of risk assessment studies, focusing on the impact of extreme events and their implications for climate change adaptation. To this end, we designed a novel bias correction method to consistently correct extreme events of temperature and precipitation, but is adaptable to other climate variables, such as wind speed. Our approach conceptually extends one of the classic Quantile Mapping (QM) methods by improving the description of the tail ends of the distribution through a generalised extreme value distribution (GEV) fitting. Our methodology also incorporates a downscaling component. QM is indeed frequently both as a bias correction method and for downscaling simulations to finer observed scales. Therefore, our method not only corrects the climate data but also enhances the raw resolution of the model outputs (typically around 100 km) to match the 9 km grid of the observational reference. In this study, we applied our technique to daily mean temperature and total precipitation data from three seasonal forecasting systems: SEAS5, System7, and GCFS2.1, developed respectively by ECMWF, Météo-France, and DWD. The bias correction efficiency was tested over the Southern African Development Community (SADC) region, which includes 15 Southern African countries. The performance was verified by comparing each of the three models with a reference dataset, the ECMWF reanalysis ERA5-Land. The results reveal that this novel technique significantly reduces the systematic biases in the forecasting models, yielding further improvements over the classic QM. For both the mean temperature and total precipitation, the bias correction produces a decrease in the Root Mean Squared Error (RMSE) and in the bias between the simulated and the reference data. After bias correcting the data, the ensemble forecasts members that correctly predict the temperature extreme increases. On the other hand, the number of members identifying precipitation extremes decreases after the bias correction, highlighting the challenge of obtaining robust statistics due to the lack of information about extreme events.

Marine microplastics can be colonized by biofouling microbial organisms, leading to a decrease in microplastics' buoyancy. The sinking of biofouled microplastics could therefore represent a novel carbon export pathway within the ocean carbon cycle. Here, we model how microplastics are biofouled by diatoms, their consequent vertical motion due to buoyancy changes, and the interactions between particle-attached diatoms and carbon pools within the water column. We initialize our Lagrangian framework with biogeochemical data from NEMO-MEDUSA-2.0 and estimate the amount of organic carbon exported below 100 m depth starting from different surface concentrations of 1-mm microplastics. We focus on the Mediterranean Sea that is characterized by some of the world's highest microplastics concentrations and is a hotspot for biogeochemical changes induced by rising atmospheric carbon dioxide levels. Our results show that the carbon export caused by sinking biofouled microplastics is proportional to the concentration of microplastics in the sea surface layer, at least at modeled concentrations. We estimate that, while current concentrations of microplastics can modify the natural biological carbon export by < 1%, future concentrations projected under business-as-usual pollution scenarios may lead to carbon exports up to 5% larger than the baseline (1998-2012) by 2050. Areas characterized by high primary productivity, that is, the Western and Central Mediterranean, are those where microplastics-mediated carbon export results to be the highest. While highlighting the potential and quantitatively limited occurrence of this phenomenon in the Mediterranean Sea, our results call for further investigation of a microplastics-related carbon export pathway in the global ocean.

When one is using climate simulation outputs, one critical issue to consider is the systematic bias affecting the modelled data. The bias correction of modelled data is often used when one is using impact models to assess the effect of climate events on human activities. However, the efficacy of most of the currently available methods is reduced in the case of extreme events because of the limited number of data for these low probability and high impact events. In this study, a novel bias correction methodology is proposed, which corrects the bias of extreme events. To do so, we extended one of the most popular bias correction techniques, i.e., quantile mapping (QM), by improving the description of extremes through a generalised extreme value distribution (GEV) fitting. The technique was applied to the daily mean temperature and total precipitation data from three seasonal forecasting systems: SEAS5, System7 and GCFS2.1. The bias correction efficiency was tested over the Southern African Development Community (SADC) region, which includes 15 Southern African countries. The performance was verified by comparing each of the three models with a reference dataset, the ECMWF reanalysis ERA5. The results reveal that this novel technique significantly reduces the systematic biases in the forecasting models, yielding further improvements over the classic QM. For both the mean temperature and total precipitation, the bias correction produces a decrease in the Root Mean Squared Error (RMSE) and in the bias between the simulated and the reference data. After bias correcting the data, the ensemble forecasts members that correctly predict the temperature extreme increases. On the other hand, the number of members identifying precipitation extremes decreases after the bias correction.

AbstractMicroplastic pollution is a ubiquitous environmental threat, in particular to the oceans. In the marine environment, microplastics are not just passively transported by sea currents, but often get contaminated with organic pollutants during the journey. The uptake of chemicals onto microplastics can worsen the adverse effects of microplastics to marine organisms; however, investigation on this urgent phenomenon is hampered by the impossibility of monitoring and tracking such small plastic fragments during their motion at sea. This work aims at addressing the need for an effective modelling of the advection–diffusion processes jointly involving microplastics and the pollutants they carry to further our understanding of their spatiotemporal patterns and ecological impacts, focusing on the Mediterranean Sea. Here we present the conceptual design, methodological settings, and modelling results of a novel, data-informed 2D Lagrangian–Eulerian modelling framework that simultaneously describes (i) the Lagrangian dispersal of microplastic on the sea surface, (ii) the Eulerian advection–diffusion of selected organic contaminants, and (iii) the gradient-driven chemical exchanges between microplastic particles and chemical pollutants in the marine environment in a simple, yet comprehensive way. Crucial to the realism of our model is exploiting the wide variety and abundance of data linked with drivers of Mediterranean marine pollution by microplastics and chemicals, ranging from national censuses to satellite data of surface water runoff and GPS ship tracking, other than the use of oceanographic reanalyses to inform microplastics’ motion at sea. The results of our method applied to a multi-year simulation contribute to a first basin-wide assessment of the role of microplastics as a vehicle of other pollutants of concern in the marine environment. The framework proposed here is intended as a flexible tool to help advance knowledge towards a comprehensive description of the multifaceted threat of marine plastic pollution and an informed support to targeted mitigation policies.

The pervasiveness of microplastics in global oceans is raising concern about their impacts on organisms. While quantifying their toxicity is still an open issue, sampling evidence has shown that rarely are marine microplastics found clean; rather, they are often contaminated by other types of chemical pollutants, some known to be harmful to biota and humans. To provide a first tool for assessing the role of microplastics as vectors of plastic-related organic pollutants (PROPs), we developed a data-informed simulation model that accounts for the intertwined dynamics of Lagrangian microplastic particles transported by surface currents and the Eulerian advection-diffusion of pollutants that partition on them through seawater-particle interaction. Focusing on the Mediterranean Sea and using simple, yet realistic forcings for the input of PROPs, our simulations highlight that microplastics can mediate the export of PROPs across different marine regions. Particle origin, in terms of both source type (either coastal, riverine, or fishing-derived) and geographical location, plays a major role in determining the amount of PROPs conveyed by microplastics during their journey at sea. We argue that quantitative numerical modelling approaches can be focal to shed some light on the vast spatiotemporal scales of microplastics-PROPs interaction, complementary to much-needed field investigation.

Plastic pollution is widespread in the global oceans, but at the same time several other types of hydrophobic pollutants contaminate the marine environment. As more and more evidence highlights, microplastics and polluting chemicals are intertwined via adsorption/desorption processes. A thorough assessment of their total impact on marine ecosystems thus requires that these two kinds of pollution are not considered separately. Here we compare the outcomes of two complementary, data-driven modelling approaches for microplastic dispersal and for Plastic-Related Organic Pollutants (PROPs) in the marine environment. Focusing on the Mediterranean Sea, we simulate two years of Lagrangian particle tracking to map microplastic dispersion from the most impacting sources of pollution (i.e. coastal areas, the watersheds of major rivers, and fishing activities). Our particle sources are data-informed by national census data, hydrological regimes, and vessel tracking data to account for spatial and temporal variability of mismanaged plastic waste generation. These particle-based simulations are complemented with a simulation of the dynamics of primary pollutants in the sea, obtained via an advection-diffusion Eulerian model. While providing further understanding of the spatiotemporal distribution of microplastics and the dynamics of PROPs at a Mediterranean-wide scale, our results call for the development of novel integrated modelling approaches aimed at coupling the dynamics of microplastics with the chemical exchanges occurring through them, thus promoting a holistic description of marine plastic pollution.

The pervasiveness of microplastics in global oceans is raising concern about its impacts on organisms. While quantifying its toxicity is still an open issue, sampling evidence has shown that rarely is marine microplastics found clean; rather, it is often contaminated by other types of chemical pollutants, some known to be harmful to biota and humans. To provide a first tool for assessing the role of microplastics as vectors of plastic-related organic pollutants (PROPs), we developed a data-informed model that accounts for the intertwined dynamics of Lagrangian microplastic particles transported by surface currents and the Eulerian advection-diffusion of chemicals that partition on them through seawater-particle interaction. Focusing on the Mediterranean Sea and using simple, yet realistic forcings for the input of PROPs, our simulations highlight that microplastics can mediate PROP export across different sub-seas. Particle origin, in terms of both source type (either coastal, riverine, or fishing-derived) and geographical location, seems to play a major role in determining the amount of PROPs conveyed by microplastics during their journey at sea. We argue that quantitative numerical modelling approaches can be focal to shed some light on the vast spatial and temporal scales of microplastics-PROPs interaction, complementary to much-needed on-field investigation.

Contamination by microplastics has become common in many environments, and particularly dangerous in marine ecosystems. During their journey at sea, floating microplastic particles affect both the biotic and the abiotic components of the seascape through ingestion by (or other physical interactions with) the marine biota. They also provide support for microbial growth (often resulting in reduced particle buoyancy and sinking), and exchange primary hydrophobic pollutants with seawater. These two latter processes, in particular, regulate the balance on the sea surface layers of both microplastics and the pollutants carried by them. Here we propose a novel modeling framework to account for the interactions between microplastics and hydrophobic chemicals in the marine environment. Our approach couples Lagrangian particle tracking with Eulerian advection-diffusion modeling of pollutants. Focusing on the Mediterranean Sea, we simulate two years of plastic particle motion and the concurrent pollutant exchanges occurring between plastic debris and the surrounding environment. As these interactions are found to significantly affect the concentration patterns of chemical pollution over the simulated period, we argue that further modeling research should account for coupled microplastics-pollutants dynamics towards a comprehensive assessment of the risks caused by plastic pollution on marine ecosystems.

The Mediterranean basin is among the most impacted marine ecoregions globally, being at the same time semi-enclosed by densely populated countries and crossed by trafficked maritime routes. Such anthropogenic pressure threatens both the qual-ity of its waters and the high biodiversity living in them, making the role of Marine Protected Areas (MPAs) crucial for pre-serving species suitable habitats. Under the European Union Marine Strategy Framework Directive, marine litter has been recognized as one of the principal causes of marine pollution, and public awareness on its environmental and biological impacts is raising. Using a quantitative and data-driven modelling approach, here we assess the presence of plastic waste within the feeding grounds of the fin whale Balaenoptera physalus , an endangered cetacean for which there is increasing evidence of impacts due to microplastic ingestion. To this end, we analyze a decade (2000 - 2010) of advection patterns of marine plastic litter, modelled with a Lagrangian approach. Particles are released in the MPA Pelagos, the International Sanctuary for the Protection of Mediterranean Marine Mammals (North-Western Mediterranean, between France, Italy and Monaco), from different sources (i.e., untreated waste along coasts, plastic discharged from rivers and plastic pollution released along maritime shipping routes). Risk of exposure of fin whales to microplastic pollution is evaluated by interlacing plastic litter distribution maps obtained through modelling with maps of suitable habitats obtained from the elaboration of satellite chlorophyll-a data in species-specific visited areas. Our modelling results show that all the three main sources of plastic litter taken into account clearly contribute to impacting cetaceans in the Sanctuary, yet in a different manner. The procedure formalized here can be extended to assess the risk caused by plastic pollution in other MPAs as well as to evaluate possible impacts on other taxa, thus informing targeted actions to tackle the complex issue of marine litter.