The influence that meteorological, climatological and environmental factors had on historical disease outbreaks is often speculated upon, but little investigated. Here, we explore potential associations between pandemic disease and climate over the last 2,500 years in Mediterranean history, focusing on ancient disease outbreaks and the Justinianic plague in particular. We underscore variation in the quality, quantity and interpretation of written evidence and proxy information from natural archives, the comlexity of identifying and disentangling past climatological and environmental drivers, and the need to integrate diverse methodologies to discern past climate-disease linkages and leverage historical experiences to prepare for the rapid expansion of novel pathogenic diseases. Although the difficulties entailed in establishing historical climate-pandemic linkages persist to the present, this is a research area as urgent as it is complex and historical perspectives are desperately needed.

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Accurate representation of the Atlantic-Mediterranean exchange in climate models is important for a reliable simulation of the North Atlantic ocean circulation. We evaluate the performance of ten global climate models in representing Mediterranean Overflow Water (MOW) over the recent period 1986 to 2005 by using various performance metrics. The metrics are based on the representation of the climatological mean state and the spatiotemporal variability of temperature, salinity and volume transports. Based on analyses and observations, we perform a model ranking by calculating absolute, relative and total relative errors (Ej) over each performance metric and model. The majority of models simulate at least six metrics well. The equilibrium depth of the MOW, the mean Atlantic-Mediterranean exchange flow and the dominant pattern of the MOW are represented reasonably well by most of the models. Of those models considered, MPI-ESM-MR, MPI-ESM-LR, CSIRO-Mk3-6-0 and MRI-CGCM3 provide the best MOW representation (Ej = 0.14, 0.19, 0.19, 0.25, respectively). They are thus likely to be the most suitable choices for studies of MOW dependent processes. However, the models experience salinity, temperature and transport biases and do not represent temporal variability accurately. The implications of our results for future model analysis of the Mediterranean overflow are discussed.

<p>Atmospheric and marine heatwaves (AHW/MHW) have been observed around the world and are expected to increase in intensity and frequency under future climate change. Despite numerous studies that have examined AHW or MHW independently, only few regional studies investigated potential associations between these two types of extreme events. However, the co-occurrence of AHW and MHW could have broader and greater environmental, human, and economic impacts than an individual event, such as changes in species distributions, land and marine mass mortalities, or increased heat stress in coastal areas due to interactions between warm and moist air over the ocean. Based on research on AHW and MHW, we propose a comprehensive and globally applicable definition that relates the two extreme events and the two realms, and allows comparison with past and present concurrent and single events. Our definition is based on a conditional approach: We define a concurrent heatwave as an extreme event where sea surface temperature (SST) and 2 m air temperature (T<sub>air</sub>) exceed their daily 90th percentiles, based on a 30-year historical baseline period, for at least 5 and 3 consecutive days, respectively (Perkins & Alexander 2013; Hobday et al. 2016). Thereby, we account for a potential lagged relationship between the two extremes by calculating and choosing the lag that provides the maximum probability of observing a MHW and an AHW simultaneously or delayed. In this work, we show the results of the most common heatwave metrics, such as duration, frequency, intensity, and cumulative intensity, for concurrent and single heatwaves in the Mediterranean Sea, Western Australia, and the Northwest Atlantic. We use SSTs from Advanced Very High-Resolution Radiometer (AVHRR) satellite data (NOAA OISST V2) as well as T<sub>air</sub> from the ECMWF Reanalysis v5 (ERA5), both provided daily and globally on a high resolution (0.25°) for the period 1982 – 2022. In the Mediterranean Sea, we find concurrent heatwaves to be shorter and less frequent, but more intense and cumulatively intense than their single variants. For concurrent events, the MHW component (SST) is observed to be most intense in summer and spring, and the AHW component (T<sub>air</sub>) in fall and winter. Moreover, the MHW appears to determine the strength of the concurrent heatwave in that region.</p>

<p>Investigating the global connectivities of extreme events is vital for accurate risk reduction and adaptation planning. While human and natural systems have a certain resilience level against single extremes, they may be unable to cope with multiple extreme events whose impacts tend to be amplified in a non-linear relationship. Concurrent droughts and heatwaves are frequently linked to severe damage in socioeconomic sectors such as agriculture, energy, health, and water resources. They can also have detrimental effects on natural ecosystems. Here, we detect global scale dependencies of large-scale droughts and heatwaves using an AI-enhanced point process-based approach, where large-scale events are defined to occur when a certain amount of grid points (e.g., 20%) of a given region of interest experiences heatwave or drought conditions. The classic inhomogeneous and non-stationary J-function can determine whether the occurrence of the events shows clustering, inhibition or independence. However, the analysis and interpretation of this function are usually affected by a high degree of subjectiveness, and its application for large datasets and/or ensembles is challenging. The proposed AI-based automated interpretation tool replaces a subjective and user-dependent approach. Monte Carlo simulations based on standard point process models, reflecting the aforementioned dependence structures, are utilized, allowing the dependence structure to be labeled and the classification problem to be trained using Deep Learning algorithms. To identify the global connectivities of large-scale droughts and heatwaves, we first detect extreme events at the grid scale based on appropriately selected indices. A cluster analysis pinpoints areas with similar drought and heatwave patterns, thus identifying the regions of interest for the large-scale events. For these events we compute the J-functions, and the dependence structure of the large-scale events is then classified by the AI-tool. Links to teleconnections (such as the El Niño-Southern Oscillation and the North Atlantic Oscillation) can be further identified by analyzing the dependencies conditioning on the teleconnection phase under consideration. The proposed tool can be used in diverse research questions where a point process approach is appropriate, and thus has applications beyond climate science.</p>