Climate Reanalysis Data Research Articles

Accurate and efficient AI-assisted paradigm for adding granularity to ERA5 precipitation reanalysis

Scientific inquiry has long relied on deterministic algorithms for systematic problem-solving and predictability. However, the rise of artificial intelligence (AI) has revolutionized data analysis, allowing us to uncover complex patterns in large datasets. In this study, we combine these two approaches by using AI to improve the reconstruction of past precipitation events, which is crucial for understanding climate change. Our objective is to leverage AI to map large-scale atmospheric proxies from the ERA5 climate reanalysis and multi-satellite historical precipitation data from the NASA-IMERG GPM constellation to observed precipitation, enhancing the accuracy and the resolution of climate reanalysis. Accurate climate reanalyses are essential, as they provide the most realistic representations of past atmospheric conditions, serving as benchmarks against which climate models are validated. Our AI-enhanced method offers a more accurate and computationally efficient solution compared to deterministic high-resolution precipitation downscaling methods. Additionally, it shows the capability to generalize predictions to new, previously unobserved locations, making it applicable across various regions. By integrating AI with traditional reanalysis techniques, we open up new opportunities for climate science and geosciences, with the potential to improve the accuracy and reliability of climate data, contributing to a better understanding of climate dynamics.

Inter-comparison of GNSS- IPWV with ERA-5 IPWV and monitoring of convective events over the Indian region

The present study deals with (i) An Inter-comparison study of ground based Global Navigation Satellite System (GNSS) derived Integrated Precipitable Water Vapour (IPWV) with 5th generation global climate reanalysis data of European Centre for Medium Range Weather Forecast (ERA-5) (ii) IPWV thresholds of GNSS data (iii) case studies of IPWV analysis. It is found that both the datasets (GNSS and ERA-5) are strongly correlated & the correlation coefficient ranging between 0.97 and 0.99. Monthly Thresholds of IPWV (MTI) are generated with 2017 -2020 data sets from Indian GNSS station having continuous data and found very useful input as value addition to know the possibility of building up /decaying of convection in day to day daily forecast issued to the public. Case studies shows an increase of IPWV, 3 to 4 hour prior to the occurrence of convective event around the GNSS station and found very useful for nowcasting. Therefore, utilization of IPWV (model as wells GNSS based) have incremented value in enriching the understanding about the weather event and its forecasting. This supplement information along with other analysis products (model, surface, upper air, satellite or radar etc) can support further in appropriate decision making to the forecasters, decision makers and the end users of the society.

PreciDBPN: A customized deep learning approach for hourly precipitation downscaling in eastern China

Long-term series of high-resolution gridded precipitation datasets are essential for hydrological and meteorological research. Producing high-resolution precipitation data from regional models demands substantial computational resources and labor. Global Reanalyses offer long-term coverage and effectively capture annual and seasonal precipitation patterns. However, they have inadequate resolution and frequently have difficulties depicting extreme conditions. This study proposes an efficient and accurate approach for generating long-term series of high spatial and temporal resolution precipitation. It is achieved by leveraging deep learning techniques to integrate the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) global climate reanalysis (ERA5, 0.25°, hourly) data with high-resolution precipitation fusion datasets. Considering the heavy-tailed distribution of precipitation, we developed the PreciDBPN model structure, which combines a classification network with a super-resolution network and incorporates physically relevant indices into the model's input. We trained and evaluated the PreciDBPN and baseline models in eastern China using the China Meteorological Administration Land Data Assimilation System (CLDAS) precipitation dataset (0.0625°, hourly, 2017–2022). When compared to baseline methods and ERA5, our model excels in multiple metrics and provides a more precise representation of relative rainfall frequency. Independent verification was performed using station observations during the period of 2010–2015 when CLDAS data were unavailable. During this verification, the PreciDBPN demonstrated exceptional performance and greater robustness compared to the baseline models. Because our method can efficiently downscale precipitation and bias-correct reanalysis data using minimal computational resources, it can be used to generate high-resolution precipitation datasets (0.0625°, hourly) from 1979 to 2022 while correcting for heavy precipitation underestimations in reanalysis data.

Satellite observations of Arctic blowing dust events >82°N

AbstractThis study reports satellite evidence for the most northerly blown dust activity yet observed on Earth. A systematic inspection of high‐resolution satellite imagery identified active dust events and their sources >82°N in Peary Land, Greenland. In the absence of any local weather measurements, for all observed dust activity a focus period in April 2020 with multiple dust plumes, reanalysis climate data found the majority of dust events to be associated with wind speeds exceeding a typical threshold value for blowing sand and dust uplift. Wind direction variability points to dust‐raising by cold airflow down‐valley winds, likely from nearby ice masses.

Machine learning-based investigation of forest evapotranspiration, net ecosystem productivity, water use efficiency and their climate controls at meteorological station level

Evapotranspiration (ET), net ecosystem productivity (NEP), and ecosystem water use efficiency (EWUE) of forests are changing due to climate change. Traditional models using coarse-scale climate reanalysis data fail to capture local meteorological and hydrological conditions accurately. This study combines in situ meteorological observations, remote sensing, and advanced datasets (forest age, rooting depth, soil moisture) to estimate ET, NEP, and EWUE at forest meteorological stations via machine learning. About 60.6 % of stations showed a decrease in NEP from 2003 to 2010 to 2011–2019, while 63.9 % showed an increase in ET, and 58.9 % showed a decrease in EWUE. NEP and EWUE significantly declined in forests older than 60 years, with younger forests exhibiting higher NEP. EWUE in different forest types is driven by varying mechanisms, with DBF sites influenced by VPD and ENF sites by RSDN. EWUE of regions with inconsistent VPD data between site and reanalysis, such as northwestern North America, showed divergences from previous reanalysis-based studies but aligned more with atmospheric inversion findings. Slight summer VPD increases boosted NEP, especially in high-latitude areas, while early spring phenology and increased spring VPD reduced summer water availability. Incorporating more site-specific observations, such as plant traits, could enhance understanding of climate-plant-ecosystem relationships. This study underscores the potential of meteorological station-level data to improve forest carbon and water flux dynamics understanding, aiding forest management for carbon neutrality and climate adaptation.

Characterizing climate pathways using feature importance on echo state networks

AbstractThe 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data‐driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black‐box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. We are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

Reconstructing Younger Dryas ground temperature and snow thickness from cave deposits

Abstract. The Younger Dryas stadial was characterised by a rapid shift towards cold-climate conditions in the North Atlantic realm during the last deglaciation. While some climate parameters including atmospheric temperature and glacier extent are widely studied, empirical constraints on permafrost temperature and snow thickness are limited. To address this, we present a regional dataset of cryogenic cave carbonates (CCCs) from three caves in Great Britain that formed at temperatures between −2 and 0 °C. Our CCC record indicates that these permafrost temperatures persisted for most of the Younger Dryas. By combining ground temperatures with surface temperatures from high-resolution ground-truthed model simulations, we demonstrate that ground temperatures were approximately 6.6 ± 2.3 °C warmer than the mean annual air temperature. Our results suggest that the observed temperature offset between permafrost and the atmosphere can be explained by an average snow thickness between 0.2 and 0.9 m, which persisted for 233 ± 54 d per year. By identifying modern analogues from climate reanalysis data, we demonstrate that the inferred temperature and snow cover characteristics for the British Isles during the Younger Dryas are best explained by extreme temperature seasonality, comparable to continental parts of today's Arctic Archipelago. Such a climate for the British Isles necessitates a winter sea ice margin at approximately 45° N in the North Atlantic Ocean.

Climate change scenario analysis in Spree catchment, Germany using statistically downscaled ERA5-Land climate reanalysis data

This study focuses on the statistical downscaling of ERA5-Land reanalysis data using the Statistical DownScaling Model (SDSM) to generate climate change scenarios for the Spree catchment. Linear scaling was used to reduce the biases of the Global Climate Model for precipitation and temperature. The statistical analyses demonstrated that this method is a promising and straightforward way of correcting biases in climate data. SDSM was used to generate climate change scenarios, which considered three emission scenarios: RCP 2.6, RCP 4.5, and RCP 8.5. The results indicated that higher precipitation is expected under higher emission scenarios. Specifically, the summer and autumn seasons were projected to experience up to 50 mm more rainfall in the next 80 years, and the temperature was projected to increase by up to 1∘C by 2100. These projections of climate data for different scenarios are useful for assessing water management studies for agricultural and hydrologic applications considering changing climate conditions. This study highlights the importance of statistical downscaling and scenario generation in understanding the potential impacts of climate change on water resources. The results of this study can provide valuable insights into water resource management, especially on adapting to changing climate conditions.

Capturing the net ecosystem CO2 exchange dynamics of tidal wetlands with high spatiotemporal resolution by integrating process-based and machine learning estimations

Accurate estimation of the net ecosystem CO2 exchange (NEE) at regional scales is of great significance for studying the carbon sink potential of coastal wetland ecosystems and their responses to global climate change. However, current NEE estimation methods are mainly developed for terrestrial ecosystems and are therefore unsuitable for NEE estimation with high spatiotemporal resolution estimation in coastal wetlands subjected to sub-daily tidal flooding. In this study, we proposed a high spatiotemporal resolution NEE estimation method for coastal marsh wetlands that properly considered tidal influence by combining the advantages of process-based modeling and machine learning. This method was verified and applied in the Changjiang estuary and Liaohe estuary marsh wetlands based on eddy covariance and environmental measurements, climate reanalysis data, and satellite images. The proposed method had good performance in the NEE estimation of tidal marsh wetlands, with Phragmites australis, Spartina alterniflora, and Suaeda salsa having coefficients of determination (R2) of 0.850, 0.676, and 0.658, respectively, and root mean square error (RMSE) values of 7.211 μmol m−2 s−1, 8.105 μmol m−2 s−1, and 0.109 μmol m−2 s−1, respectively. By integrating the tide level and salinity, the NEE estimation accuracy for each vegetation type was improved. The total annual NEE values of the Changjiang estuary and Liaohe estuary marsh wetlands in 2022 were estimated to be −0.297 and −0.444 Tg C yr−1, respectively. This study demonstrated that integrating process-based model and machine learning estimation can reliably capture the NEE dynamics of coastal wetlands, providing a useful tool to quantify coastal blue carbon potential with high spatiotemporal resolution at large scales.

Acceleration of daily land temperature extremes and correlations with surface energy fluxes

Assessment of climate reanalysis data for land (ECMWF Re-Analysis v5; ERA5-Land) covering the last seven decades reveals regions where extreme daily mean temperatures are rising faster than the average rate of temperature rise of the 6 months of highest background warmth. However, such extreme temperature acceleration is very heterogeneous, occurring only in some places including regions of Europe, the western part of North America, parts of southeast Asia and much of South America. An ensemble average of Earth System Models (ESMs) over the same period also shows acceleration across land areas, but this enhancement is much more spatially uniform in the models than it is for ERA5-Land. Examination of projections from now to the end of the 21st Century, with ESMs driven by the highest emissions Shared Socio-economic Pathway scenario (SSP585) of future changes to atmospheric greenhouse gases, also reveals larger warming during extreme days for most land areas. The increase in high-temperature extremes is driven by different processes depending on location. In northern mid-latitudes, a key driver is often a decrease in the evaporative fraction of the available energy, consistent with soil drying. By contrast, the acceleration of high-temperature extremes in tropical Africa is primarily due to increased available energy. These two drivers combine via the surface energy balance to equal the sensible heat flux, which we find is often strongly correlated with the areas where the acceleration of high-temperature extremes is largest.

Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data

Recent years have witnessed an increasing interest in air pollutants and their effects on human health. More generally, it has become evident how human, animal and environmental health are deeply interconnected within a One Health framework. Ground level air monitoring stations are sparse and thus have limited coverage due to high costs. Satellite and reanalysis data represent an alternative with high spatio-temporal resolution. The idea of this work is to build an Artificial Intelligence model for the estimation of surface-level daily concentrations of air pollutants over the entire Italian territory using satellite, climate reanalysis, geographical and social data. As ground truth we use data from the monitoring stations of the Regional Environmental Protection Agency (ARPA) covering the period 2019–2022 at municipal level. The analysis compares different models and applies an Explainable Artificial Intelligence approach to evaluate the role of individual features in the model. The best model reaches an average R2 of 0.84 ± 0.01 and MAE of 5.00 ± 0.01 μg/m3 across all pollutants which compare well with the body of literature. The XAI analysis highlights the pivotal role of satellite and climate reanalysis data. Our work can facilitate One Health surveys and help researchers and policy makers.

Retroceso del glaciar del Carihuairazo y sus implicaciones en la comunidad de Cunucyacu

The retreat of glaciers is a reality throughout the Andes Mountain range, especially in low-altitude mountains. One of these cases is the loss of the remaining ice mass in Carihuairazo (Tungurahua, Ecuador), which in recent years has experienced a considerable retreat. This research aims to characterize the retreat of this glacier and its implications for the nearby community (Cunucyacu) through the application of a multi-source methodology, which includes the collection of glacier aerial photographs, data from nearby meteorological stations, the use of global climate reanalysis data, interviews with community members, and mountaineers who work and frequent the area. To characterize the glacier’s mass evolution, a hydroglaciological model was applied, using input data from meteorological series, and its parameters were calibrated with the photographic record of the glacier’s outline. The results show a glacier loss of 99% of its surface in 1956 (0.34 ) by 2021. The model successfully simulates the glacier area variation over 67 years, revealing a continuous decrease since 1978, with short periods of recovery and equilibrium, where temperature is the variable that best explains the glacier’s retreat. However, the model fails to consider the effect of external factors, such as the eruption of the Tungurahua volcano that could enhance the glacier retreat. The Carihuairazo glacier is in a situation of inevitable disappearance, highlighting the vulnerabilities of communities facing this phenomenon as a consequence of climate change.

From climate perceptions to actions: A case study on coffee farms in Ethiopia.

Increasing temperatures and shifting precipitation patterns have major consequences for smallholder farmers, especially in the Global South. Our study examined spatial patterns and climatic drivers of farmers' perceptions of climate change, and how these perceptions translated into adaptation actions. We interviewed 56 farmers in southwestern Ethiopia and analyzed ERA5-Land reanalysis climate data from 1971 to 2020. The majority of farmers perceived the recorded temperature increase as well as a decrease and shift in the timing of rainfall. Perceived climate change varied with local climate factors and not with the rate of climate change itself. Farmers' adaptation practices showed associations with local temperature, but not with farmers' perceptions of climate change. Our findings highlight that even if farmers perceive climate change, perceptions are most common in areas where climate action is already urgent, and perceptions may not translate into adaptation. Thus, targeted and timely information and extension programs are crucial.

Mountain greening and rising temperatures erode habitats of ironwort (Sideritis), an important natural medicinal resource

Societal Impact StatementNative medicinal plants contribute essential health benefits to populations globally, constituting a major natural resource that human societies rely on. Being an integral part of terrestrial biodiversity, medicinal plants are detrimentally affected by ongoing climate and land‐use change, yet comprehensive studies on the risk that extinction will pose to medicinal biodiversity are lacking. Responding to ongoing scientific calls for conserving medicinal biodiversity, this study provides an integrated assessment of the impacts of environmental change on ironwort (Sideritis), a group of closely related endemic plants of great cultural significance as local medicinal resources in the Balkan Mountains.Summary Mountain habitats harbour unique biodiversity and provide vital resources for human well‐being, including natural medicinal resources, yet they are amongst the environments most impacted by global change. While there is ample evidence of recent rapid climate and land‐use change on mountain ecosystems, the impacts of these processes on the habitats of culturally important medicinal plants are still poorly understood. Here, we assess the potential loss of mountain habitats for medicinal plant resources over the past four decades using the culturally important ironwort, a group of endemic medicinal plants of the Balkan Mountains extensively used by local human populations and the pharmaceutical industry for treating cough and cold and gastrointestinal disorders. We used information collected from major European natural history museums to guide extensive field campaigns across 15 separate mountain ranges. We integrate field data with thousands of satellite images, station‐validated climate reanalysis data and habitat suitability modelling. We finally used machine learning to assess the relative roles of climate and vegetation rates of change in driving rates of habitat suitability change. We show that rising temperatures and ‘mountain greening’ erode the habitats of ironwort at alarming rates. About 50% of the total habitat area across all considered mountain ranges shows a significant decline in habitat suitability. These past trends will most likely continue in the future and could lead to widespread local extinction of the species and other medicinal plants that share similar ecological preferences, threatening their future contributions to societal well‐being.

Jsmetrics v0.2.0: a Python package for metrics and algorithms used to identify or characterise atmospheric jet streams

Abstract. The underlying dynamics controlling jet streams are complex, but it is expected that they will have an observable response to changes in the larger climatic system. A growing divergence in regional surface warming trends across the planet, which has been both observed and projected since the start of the 20th century, has likely altered the thermodynamic relationships responsible for jet stream formation and control. Despite this, the exact movements and trends in the changes to the jet streams generally remain unclear and without consensus in the literature. The latest IPCC report highlighted that trends both within and between a variety of observational and modelling studies were inconsistent (Gulev et al., 2021; Lee et al., 2021). Trends in jet streams were associated with low to medium confidence, especially in the Northern Hemisphere. However, what is often overlooked in evaluating these trends is the confused message in the literature around how to first identify, and then characterise, the jet streams themselves. We classify the methods for characterising jet streams in the literature into three broad strategies: statistics that isolate individual values from the wind speed profile (jet statistics), methods for quantifying the sinuosity of the upper air (waviness metrics), and algorithms that identify a mask related to the coordinates of fast-flowing wind throughout the horizontal and/or vertical plane (jet core algorithms). While each approach can capture particular characteristics and changes, they are subject to the spatial and temporal specifications of their definition. There is therefore value in using them in combination to assess parametric and structural uncertainty and to carry out sensitivity analyses. Here, we describe jsmetrics version 0.2.0, a new open-source Python 3 module with standardised versions of 17 metrics that have been used for jet stream characterisation. We demonstrate the application of this library with two case studies derived from ERA5 climate reanalysis data.

Making climate reanalysis and CMIP6 data processing easy: two “point-and-click” cloud based user interfaces for environmental and ecological studies

Climate reanalysis and climate projection datasets offer the potential for researchers, students and instructors to access physically informed, global scale, temporally and spatially continuous climate data from the latter half of the 20th century to present, and explore different potential future climates. While these data are of significant use to research and teaching within biological, environmental and social sciences, potential users often face barriers to processing and accessing the data that cannot be overcome without specialist knowledge, facilities or assistance. Consequently, climate reanalysis and projection data are currently substantially under-utilised within research and education communities. To address this issue, we present two simple “point-and-click” graphical user interfaces: the Google Earth Engine Climate Tool (GEEClimT), providing access to climate reanalysis data products; and Google Earth Engine CMIP6 Explorer (GEECE), allowing processing and extraction of CMIP6 projection data, including the ability to create custom model ensembles. Together GEEClimT and GEECE provide easy access to over 387 terabytes of data that can be output in commonly used spreadsheet (CSV) or raster (GeoTIFF) formats to aid subsequent offline analysis. Data included in the two tools include: 20 atmospheric, terrestrial and oceanic reanalysis data products; a new dataset of annual resolution climate variables (comparable to WorldClim) calculated from ERA5-Land data for 1950-2022; and CMIP6 climate projection output for 34 model simulations for historical, SSP2-4.5 and SSP5-8.5 scenarios. New data products can also be easily added to the tools as they become available within the Google Earth Engine Data Catalog. Five case studies that use data from both tools are also provided. These show that GEEClimT and GEECE are easily expandable tools that remove multiple barriers to entry that will open use of climate reanalysis and projection data to a new and wider range of users.

Estimated evaporation of lakes by climate reanalysis data and artificial neural networks

Evaporation, together with precipitation, is the most important component of the hydrological cycle, and knowledge of the local values of lake evaporation has applications in reservoir design and management. The objective of this study was to estimate lake evaporation at locations without meteorological monitoring using ERA5 reanalysis data and artificial neural networks (ANNs). Data from 32 automatic stations in the state of Mato Grosso were used to estimate evaporation using the method of Penman (1948). The evaporation values were related to ERA5 data and radiation data at the top of the atmosphere using multilayer perceptron ANN models. The Mann-Kendall test was used for trend analysis in the estimated monthly evaporation series. From the analysis of the results, it is concluded that it is possible to quantify the spatial and temporal distribution of evaporation from lakes with data from ERA5 reanalysis and the use of ANNs. The historical evaporation series for the period 1980 to 2019 showed a positive trend in certain parts of the Brazilian Savanna and Amazon biomes. Isolated areas of the Pantanal biome also showed a positive trend for monthly evaporation. The proposed methodology allows for the precise and accurate estimation of evaporation from liquid surfaces at locations without meteorological monitoring.

Rain-on-snow responses to warmer Pyrenees: a sensitivity analysis using a physically based snow hydrological model

Abstract. Climate warming is changing the magnitude, timing, and spatial patterns of mountain snowpacks. A warmer atmosphere may also induce precipitation phase shifts, resulting in a decreased snowfall fraction (Sf). The combination of Sf and snowpack directly influences the frequency and intensity of rain-on-snow (ROS) events, a common cause of flash-flood events in snow-dominated regions. In this work, we investigate ROS patterns and their sensitivity to temperature and precipitation changes in the Pyrenees by modeling ROS through a physically based snow model. This model is forced with reanalysis climate data for elevations of 1500, 1800, and 2400 m perturbed using a range of temperature and precipitation values consistent with 21st century climate projections. ROS patterns are characterized by their frequency, rainfall quantity, and snow ablation. The highest ROS frequency for the historical climate period (1980–2019) is found in the 2400 m zones of the southwest Pyrenees (17 d yr−1). The maximum ROS rainfall amount is detected in 1800 m areas of the southeast (45 mm d−1, autumn), whereas the highest ROS ablation is found in the 2400 m zones of the northwest (−10 cm d−1, summer). When air temperature increases from 1 to 4 ∘C compared to the historical climate period, ROS rainfall amount and frequency increase at a constant rate during winter and early spring for all elevation zones. For the rest of the seasons, non-linear responses of ROS frequency and ablation to warming are found. Overall, ROS frequency decreases in the shoulders of the season across eastern low-elevation zones due to snow cover depletion. However, ROS increases in cold, high-elevation zones where long-lasting snow cover exists until late spring. Similarly, warming induces greater ROS ablation (+10 % ∘C−1) during the coldest months of the season, 2400 m elevations, and northern sectors, where the deepest snow depths are found. In contrast, small differences in ROS ablation are found for warm and marginal snowpacks. These results highlight the different ROS responses to warming across the mountain range, suggest similar ROS sensitivities in near-mid-latitude zones, and will help anticipate future ROS impacts in hydrological, environmental, and socioeconomic mountain systems.

Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020 in Greece

Existing assessments of the thermal-related impact of the environment on humans are often limited by the use of data that are not representative of the population exposure and/or not consider a human centred approach. Here, we combine high resolution regional retrospective analysis (reanalysis), population data and human energy balance modelling, in order to produce a human thermal bioclimate dataset capable of addressing the above limitations. The dataset consists of hourly, population-weighted values of an advanced human-biometeorological index, namely the modified physiologically equivalent temperature (mPET), at fine-scale administrative level and for 10 different population groups. It also includes the main environmental drivers of mPET at the same spatiotemporal resolution, covering the period from 1991 to 2020. The study area is Greece, but the provided code allows for the ease replication of the dataset in countries included in the domains of the climate reanalysis and population data, which focus over Europe. Thus, the presented data and code can be exploited for human-biometeorological and environmental epidemiological studies in the European continent.

Protocol of an individual participant data meta-analysis to quantify the impact of high ambient temperatures on maternal and child health in Africa (HE2AT IPD)

IntroductionGlobally, recognition is growing of the harmful impacts of high ambient temperatures (heat) on health in pregnant women and children. There remain, however, major evidence gaps on the extent to...