Theo Economou

Climate adaptation decision making can be informed by a quantification of current and future climate risk. This is important for understanding which populations and/or infrastructures are most at risk in order to prioritise adaptation action. When assessing the risk of overheating in buildings, many studies use advanced building models to comprehensively represent the vulnerability of the building to overheating, but often use a limited representation of the meteorological (hazard) information which does not vary realistically in space. An alternative approach for quantifying risk is to use a spatial risk assessment framework which combines information about hazard, exposure and vulnerability to estimate risk in a spatially consistent way, allowing for risk to be compared across different locations. Here we present a novel application of an open-source CLIMADA-based spatial risk assessment framework to an ensemble of …

All the data, code and Supplementary Material (including Figures S1–S5) are available online (Economou, Johnson and Dyson (2024)) but can also be accessed at Zenodo (Economou (2023)) with DOI: 10.5281/zenodo.10074436. Note that the station names have been anonymised for confidentiality purposes. This repository comprises a single zipped file, code_data_supplementary_plots_v2.zip, which includes all the supplementary figures referenced in the paper: FigureS1.pdf: trace plot of the MCMC samples for the deviance. FigureS2.pdf: predicted vs observed Tmax values for each station. FigureS3.pdf: QQ plot for each station. FigureS4.pdf: Empirical and predicted autocorrelation plots for the 10 stations with long enough time series. FigureS5.pdf: Empirical autocorrelation plots for all stations (except 11, 16, 19 and 21) up to lag 30 Figure3_All_Stations.pdf: Same as Figure 3 but for all stations …

Precipitation holds significant importance as a climate parameter in various applications, including studies on the impacts of climate change. However, its simulation or projection accuracy is low, primarily due to its high stochasticity. Specifically, climate models often overestimate the frequency of light rainy days while simultaneously underestimating the total amounts of extreme observed precipitation. This phenomenon, known as 'drizzle bias,' specifically refers to the model's tendency to overestimate the occurrence of light precipitation events. Consequently, even though the overall precipitation totals are generally well-represented, there is often a significant bias in the number of rainy days. The present study aims to minimize the "drizzle bias" in model output by developing and applying two statistical approaches. In the first approach, the number of rainy days is adjusted based on the assumption that the relationship between observed and simulated rainy days remains the same in time (thresholding). In the second, a machine learning method (Random Forests or RF) is used for the development of a statistical model that describes the relationship between several climate (modelled) variables and the observed number of wet days. The results demonstrate that employing a multivariate approach yields results that are comparable to the conventional thresholding approach when correcting sub-periods with similar climate characteristics. However, the importance of utilizing RF becomes evident when addressing periods exhibiting extreme events, marked by a significantly distinct frequency of rainy days. These disparities are particularly …

The effects of ‘nature’ on mental health and subjective well-being have yet to be consistently integrated into ecosystem service models and frameworks. To address this gap, we used data on subjective mental well-being from an 18-country survey to test a conceptual model integrating mental health with ecosystem services, initially proposed by Bratman et al. We analysed a range of individual and contextual factors in the context of 14,998 recreational visits to blue spaces, outdoor environments which prominently feature water. Consistent with the conceptual model, subjective mental well-being outcomes were dependent upon on a complex interplay of environmental type and quality, visit characteristics, and individual factors. These results have implications for public health and environmental management, as they may help identify the bluespace locations, environmental features, and key activities, that are most …

Climate models are fundamental tools for assessing historical climate conditions and projecting future ones. However, the results often differ systematically from observational data. The minimization of these differences is known as bias correction. The present study aims to correct the biases between observed daily precipitation values and the respective simulated ones from a EURO-CORDEX climate model. For this purpose, powerful statistical tools—generalized additive models (GAMs)—are used. GAMs are modified to adjust the simulated rainfall with the highest accuracy, and subsequently, they are evaluated by comparison with observational data. The method was applied to two eastern Mediterranean stations (Larissa in Greece and Larnaca in Cyprus) for the period 1981 to 2005. The results from both stations reveal that GAMs offer a valuable and accurate technique for the bias adjustment of daily precipitation.

Climate change adaptation decisions often require the consideration of risk rather than the environmental hazard alone. One approach for quantifying risk is to use a risk assessment framework which combines information about hazard, exposure and vulnerability to estimate risk in a spatially consistent way. In recent years, publicly available, open-source risk assessment frameworks have been made available, including the CLIMADA platform. Such tools are increasingly being used in combination with ensembles of climate model projections to quantify risk on climate time-scales, presenting the ensemble spread as a measure of climate model uncertainty. As climate models are computationally expensive to run, this quantification of uncertainty derived from the ensemble of projections is often limited by the number of members available. We present a novel framework involving the application and extension of the …

The climate is continually changing; therefore, making appropriate adaptation and mitigation decisions is essential. Accurate data is paramount for quantifying climate change impacts and performing risk assessments to support decision-making. Nowadays, climate models are the primary tool for understanding and projecting climate variability. However, their outputs systematically differ from observations, especially for climate variables characterized by strong stochasticity (eg precipitation). An ongoing problem concerning climate models is the “drizzling” bias. Climate models tend to overestimate the frequency and duration of rainfalls resulting in the underestimation of their intensity and severity. Due to that fact, climate models significantly underestimate consecutive dry days and overestimate wet days. The adjustment of these biases is a critical process that should precede the use of data. This work proposes a novel statistical method, the Q-GAM (quantile generalized additive models), to bias-correct daily precipitation values over Europe. This task is challenging due to the stochasticity that characterizes this variable, especially on high temporal resolution. The Q-GAM method combines Quantile Mapping (QM) and Generalized Additive Models (GAMs). It is an approach that preserves the advantages of the well-established QM and overcomes its limitations using the flexibility of GAMs. Hence, Q-GAM can significantly increase the accuracy of model-simulated rainfall, maintaining its variability and correcting the number of dry days, overcoming the “drizzling” bias. This is critical for robust and reliable impact analysis. The specific aim of this study is …

Extreme wave climate provides the basis for safe design of offshore structures and is crucial for planning and executing offshore operations. Covariate modeling of extremes significantly enriches and improves estimates of return levels and exceedance probabilities of extreme sea states. Based on novel observational and hindcast datasets, we formulate a seasonal–directional extreme value model for individual wave heights and present exceedance probabilities for the Ekofisk oil and gas field, a location in the Central North Sea. Subsequently, we elucidate how to downscale estimates of monthly exceedance probabilities and return levels to daily maxima and illustrate how to retrieve consistent results on seamless directional sectors and different seasons, or for the entire covariate space. We conclude with a versatile statistical framework to obtain seasonal–directional extreme waves and reveal a strong seasonal …

Poor air quality remains the largest environmental health risk in Europe, despite the EU policy efforts. Especially in cities, the synergistic interactions between the urban heat island and urban pollution result in premature mortality, associated with cardiovascular and respiratory diseases. Mediterranean urban areas are particularly susceptible under the consideration that the intensity, frequency, and duration of heat waves will increase due to climate change. The LIFE SIRIUS project designates that air quality management needs to go beyond traditional approaches in order to consider synergistic effects. This paper assesses the impact of temperature on daily mortality from 2004 to 2019 in the Republic of Cyprus with the use of a Generalized Additive Model (GAM). The association between mean daily temperature and mortality is nonlinear, implying that a prompt rise in deaths occurs when temperatures are high, while for colder temperatures, the effect is delayed. We report an inverted J-shaped relationship between mean temperature and mortality, with the most prominent effects on human health documented at low temperatures. The population under study appears to be acclimatized to local conditions, as mortality increases after 10 days of exposure to the environmental risk. The results of this study will assist in the definition of city-specific thresholds above which health warnings for the protection of the local population will be issued, in the framework of LIFE SIRIUS.

Recent approaches for large-scale mapping of continuous environmental variables by combining ground observations, remote sensing and machine learning have proposed incorporating computer vision capabilities into the model, so that potentially-complex regression features may be learned automatically from covariate datasets, such as of terrain elevation and other satellite imagery (eg see Kirkwood et al 2022;'Bayesian deep learning for spatial interpolation in the presence of auxiliary information'). Here we present new research using national-scale land-surface geochemical data to explore and compare how the incorporation of computer vision for automatic feature learning affects the predictive performance of geostastistical interpolators both within and beyond the spatial extents of the study areas in which ground observations are collected. We attempt to characterise empirically how well the predictive …

The proliferation of atmospheric datasets is a key outcome from the continued development and advancement of our collective scientific understanding. Yet often datasets describing ostensibly identical processes or atmospheric variables provide widely varying results. As an example, we analyse several datasets representing rainfall over Nepal. We show that estimates of extreme rainfall are highly variable depending on which dataset you choose to look at. This leads to confusion and inaction from policy-focused decision makers. Scientifically, we should use datasets that sample a range of creation methodologies and prioritise the use of data science techniques that have the flexibility to incorporate these multiple sources of data. We demonstrate the use of a statistically interpretable data blending technique to help discern and communicate a consensus result, rather than imposing a priori judgement on the choice of dataset, for the benefit of policy decision making.

Open-source climate risk assessment platforms allow for accessible and efficient estimation of current and future climate risk by combining information about hazard, exposure and vulnerability. Such assessments require making a number of choices, such as which hazard data source to use, and the data and approach taken to represent the exposure and vulnerability. As these choices are, to some extent, subjective, when assessing risk and informing adaptation decisions, alternative options should be considered to understand the uncertainty and sensitivity of risk to uncertain input data and assumptions. We present a novel approach to quantify the uncertainty and sensitivity of risk estimates, using the CLIMADA open-source climate risk assessment platform. This work builds upon a recently developed extension of CLIMADA, which uses statistical modelling techniques to model and stochastically simulate …

BackgroundCholera in Kolkata remains endemic and the Indian city is burdened with a high number of annual cases. Climate change is widely considered to exacerbate cholera, however the precise relationship between climate and cholera is highly heterogeneous in space and considerable variation can be observed even within the Indian subcontinent. To date, relatively few studies have been conducted regarding the influence of climate on cholera in Kolkata.MethodsWe considered 21 years of confirmed cholera cases from the Infectious Disease Hospital in Kolkata during the period of 1999–2019. We used Generalised Additive Modelling (GAM) to extract the non-linear relationship between cholera and different climatic factors; temperature, rainfall and sea surface temperature (SST). Peak associated lag times were identified using cross-correlation lag analysis.ResultsOur findings revealed a bi-annual pattern …

The urban heat island (UHI) is a well-known effect where the temperature is higher in a city compared to a rural area, defined as the temperature difference between an urban and a rural location. This is a challenging phenomenon because it exacerbates the heat stress on human health in addition to the on-going global warming. It is therefore important to understand temporal changes in the UHI effect in a warming climate. To examine the UHI effect intensity and variability, we use 40 years (1980-2019) of observational data (daily maximum, minimum, and mean temperature) from the Global Summary of the Day (GSOD), consisting of about 1000 stations of varying temporal extend, spanning the Middle East and North Africa (MENA) region where a faster warming rate has occurred than the other regions globally.The challenge in using data with such spatial and temporal extend is the need to allow for …

The emission of mono-and sesquiterpenes from terrestrial vegetation plays a significant role in ecological interactions and atmospheric chemistry. Previous research has suggested that global emissions of these hydrocarbons are largely driven by responses to abiotic stress and can be simulated using a fixed exponential relationship (β coefficient) between different forest ecosystems and environmental conditions. However, our meta-analysis of published emission data (89 studies/835 β coefficients) reveals that the relationship between mono-and sesquiterpene emissions and temperature is more complex than previously thought. We have found that co-occurring environmental stresses can amplify the temperature sensitivity of monoterpene emissions, which is primarily related to the specific plant functional type (PFT). In contrast, the temperature sensitivity of sesquiterpene emissions decreases over the years. On …

Harmful algal blooms (HABs) intoxicate and asphyxiate marine life, causing devastating environmental and socio-economic impacts, costing at least $8bn/yr globally. Accumulation of phycotoxins from HAB phytoplankton in filter-feeding shellfish can poison human consumers, prompting harvesting closures at shellfish production sites. To quantify long-term intoxication risk from Dinophysis HAB species, we used historical HAB monitoring data (2009–2020) to develop a new modelling approach to predict Dinophysis toxin concentrations in a range of bivalve shellfish species at shellfish sites in Western Scotland, South-West England and Northern France. A spatiotemporal statistical modelling framework was developed within the Generalized Additive Model (GAM) framework to quantify long-term HAB risks for different bivalve shellfish species across each region, capturing seasonal variations, and spatiotemporal …

Meteorological station measurements are an important source of information for understanding the weather and its association with risk, and are vital in quantifying climate change. However, such data tend to lack spatial coverage and are often plagued with flaws such as erroneous outliers and missing values. Alternative meteorological data exist in the form of climate model output that have better spatial coverage, at the expense of bias. We propose a probabilistic framework to integrate temperature measurements with climate model (reanalysis) data, in a way that allows for biases and erroneous outliers, while enabling prediction at any spatial resolution. The approach is Bayesian which facilitates uncertainty quantification and simulation based inference, as illustrated by application to two countries from the Middle East and North Africa region, an important climate change hotspot. We demonstrate the use of the model in: identifying outliers, imputing missing values, non-linear bias correction, downscaling and aggregation to any given spatial configuration.

High temperatures during the summer months are a common feature in countries with a Mediterranean climate, such as Cyprus and Greece. However, anthropogenic climate change is responsible for the increase in the frequency, intensity and duration of extreme high temperatures in the wider Eastern Mediterranean region, especially since 1990. At the same time, future climate projections show that high temperatures and heatwaves that were observed at the beginning of the 21st century and characterized as extreme will become the norm in the coming years. This study confirms the increasing trend in maximum and minimum temperature for the last four decades in Cyprus. Bioclimatic indices provide a measure of human thermal discomfort caused by the thermal environment. In the present study, the UTCI index from the dataset ERA5-HEAT was used to estimate the heat stress of the average person under conditions of heat events. The spatial distribution of maximum monthly UTCIdaily values was carried out for the period 2004–2019. At the same time, the correlation of patient admissions to hospitals, as well as the relationship of mortality with high UTCIdaily values, was assessed. Mortality data and data from eight public hospitals located in five districts of Cyprus were analyzed as obtained from the Ministry of Health and the Cyprus Statistical Service. The data reveal that UTCIdaily values were positively associated with hospital admissions and mortality in some cases.

The near-surface temperature in Mediterranean climate-type regions has increased overall similarly or more rapidly than the global mean rates. Although these regions have comparable climate characteristics and are located at similar latitudes, recent warming acceleration is most pronounced in the Mediterranean Basin. Here, we investigate the contributions of several climate drivers to regional warming anomalies. We consider greenhouse gases, aerosols, solar irradiance, land–atmosphere interactions, and natural climate variability modes. Our results highlight the dominant role of anthropogenic greenhouse gas radiative forcing in all Mediterranean climate-type regions, particularly those in the northern hemisphere. In the Mediterranean Basin, the recent warming acceleration is largely due to the combined effect of declining aerosols and a negative trend in near-surface soil moisture. While land-atmosphere …

BackgroundThe Middle East and North Africa (MENA) is one of the regions that is most vulnerable to the negative effects of climate change, yet the potential public health impacts have been underexplored compared to other regions. We aimed to examine one aspect of these impacts, heat-related mortality, by quantifying the current and future burden in the MENA region and identifying the most vulnerable countries.MethodsWe did a health impact assessment using an ensemble of bias-adjusted statistically downscaled Coupled Model Intercomparison Project phase 6 (CMIP6) data based on four Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2·6 [consistent with a 2°C global warming scenario], SSP2-4·5 [medium pathway scenario], SSP3-7·0 [pessimistic scenario], and SSP5-8·5 [high emissions scenario]) and Bayesian inference methods. Assessments were based on apparent temperature–mortality …