In this work, the potential of the Universal Thermal Climate Index (UTCI) as a heat-related health risk indicator in Europe is demonstrated. The UTCI is a bioclimate index that uses a multi-node ...human heat balance model to represent the heat stress induced by meteorological conditions to the human body. Using 38 years of meteorological reanalysis data, UTCI maps were computed to assess the thermal bioclimate of Europe for the summer season. Patterns of heat stress conditions and non-thermal stress regions are identified across Europe. An increase in heat stress up to 1 °C is observed during recent decades. Correlation with mortality data from 17 European countries revealed that the relationship between the UTCI and death counts depends on the bioclimate of the country, and death counts increase in conditions of moderate and strong stress, i.e., when UTCI is above 26 and 32 °C. The UTCI’s ability to represent mortality patterns is demonstrated for the 2003 European heatwave. These findings confirm the importance of UTCI as a bioclimatic index that is able to both capture the thermal bioclimatic variability of Europe, and relate such variability with the effects it has on human health.
Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can ...help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability.
In human biometeorology, the estimation of mean radiant temperature (MRT) is generally considered challenging. This work presents a general framework to compute the MRT at the global scale for a ...human subject placed in an outdoor environment and irradiated by solar and thermal radiation both directly and diffusely. The proposed framework requires as input radiation fluxes computed by numerical weather prediction (NWP) models and generates as output gridded globe-wide maps of MRT. It also considers changes in the Sun’s position affecting radiation components when these are stored by NWP models as an accumulated-over-time quantity. The applicability of the framework was demonstrated using NWP reanalysis radiation data from the European Centre for Medium-Range Weather Forecasts. Mapped distributions of MRT were correspondingly computed at the global scale. Comparison against measurements from radiation monitoring stations showed a good agreement with NWP-based MRT (coefficient of determination greater than 0.88; average bias equal to 0.42 °C) suggesting its potential as a proxy for observations in application studies.
Tropical cyclones (TCs) are one of the most destructive natural hazards that pose a serious threat to society, particularly to those in the coastal regions. In this work, we study the temporal ...evolution of the regional weather conditions in relation to the occurrence of TCs using climate networks. Climate networks encode the interactions among climate variables at different locations on the Earth’s surface, and in particular, time-evolving climate networks have been successfully applied to study different climate phenomena at comparably long time scales, such as the El Niño Southern Oscillation, different monsoon systems, or the climatic impacts of volcanic eruptions. Here, we develop and apply a complex network approach suitable for the investigation of the relatively short-lived TCs. We show that our proposed methodology has the potential to identify TCs and their tracks from mean sea level pressure (MSLP) data. We use the ERA5 reanalysis MSLP data to construct successive networks of overlapping, short-length time windows for the regions under consideration, where we focus on the north Indian Ocean and the tropical north Atlantic Ocean. We compare the spatial features of various topological properties of the network, and the spatial scales involved, in the absence and presence of a cyclone. We find that network measures such as degree and clustering exhibit significant signatures of TCs and have striking similarities with their tracks. The study of the network topology over time scales relevant to TCs allows us to obtain crucial insights into the effects of TCs on the spatial connectivity structure of sea-level pressure fields.
The Mediterranean region is strongly affected by extreme precipitation events (EPEs), sometimes leading to severe negative impacts on society, economy, and the environment. Understanding such natural ...hazards and their drivers is essential to mitigate related risks. Here, EPEs over the Mediterranean between 1979 and 2019 are analysed, using ERA5, the latest reanalysis dataset from ECMWF. EPEs are determined based on the 99th percentile of their daily distribution (P99). The different EPE characteristics are assessed, based on seasonality and spatiotemporal dependencies. To better understand their connection to large‐scale atmospheric flow patterns, Empirical Orthogonal Function analysis and subsequent non‐hierarchical K‐means clustering are used to quantify the importance of weather regimes to EPE frequency. The analysis is performed for different variables, depicting atmospheric variability in the lower and middle troposphere. Results show a clear spatial division in EPE occurrence, with winter and autumn being the seasons of highest EPE frequency for the eastern and western Mediterranean, respectively. There is a high degree of temporal dependencies with 20% of the EPEs (median value based on all studied grid cells), occurring up to 1 week after a preceding P99 event at the same location. Local orography is a key modulator of the spatiotemporal connections and substantially enhances the probability of co‐occurrence of EPEs even for distant locations. The clustering clearly demonstrates the prevalence of distinct synoptic‐scale atmospheric conditions during the occurrence of EPEs for different locations within the region. Results indicate that clustering, based on a combination of sea level pressure (SLP) and geopotential height at 500 hPa (Z500), can increase the conditional probability of EPEs by more than three (3) times (median value for all grid cells) from the nominal probability of 1% for the P99 EPEs. Such strong spatiotemporal dependencies and connections to large‐scale patterns can support extended‐range forecasts.
This study analyses the spatiotemporal characteristics of extreme precipitation events over the Mediterranean, and their connection to large‐scale atmospheric flow patterns. It is shown that by conditioning the extremes based on the atmospheric variability in the low‐ and mid‐troposphere, their probability increases more than threefold, when using nine clusters to group all the synoptic daily patterns. This finding can support extended‐range forecasts, as for such lead times the NWP models are more skillful in predicting large‐scale patterns than localized extremes.
The mean radiant temperature (MRT) and the Universal Thermal Climate Index (UTCI) are widely used as human biometeorology parameters to assess the linkages between outdoor environment and human ...well‐being. Historically computed from meteorological station measurements, we here present ERA5‐HEAT (Human thErmAl comforT), the first historical dataset of MRT and UTCI as spatially gridded records at the global scale. Derived using climate variables from ERA5, a quality‐controlled reanalysis produced by the European Centre for Medium‐Range Weather Forecasts (ECMWF) within the Copernicus Climate Change Service (C3S), ERA5‐HEAT consists of hourly gridded maps of MRT and UTCI at 0.25° × 0.25° spatial resolution. It currently spans from 1979 to present, and it will be extended in time as updates of ERA5 are made available. ERA5‐HEAT provides two streams, a consolidated and an intermediate one, that are released at 2 or 3 months and 5 days behind real time, respectively. Data are publicly and freely available for download at the Climate Data Store which has been developed as part of C3S. Being the only existing global historical gridded time series of MRT and UTCI to date, ERA5‐HEAT is aimed at a wide range of end users, from scientists to policymakers, with an interest in environment–health applications at any spatial and temporal scale.
ERA5‐HEAT (Human thErmAl comforT) is the first historical dataset of two indices—the mean radiant temperature (MRT) and the Universal Thermal Climate Index (UTCI)—representing human thermal stress and discomfort in outdoor conditions. Derived from ERA5, a climate reanalysis from the European Centre from Medium‐Range Weather Forecasts (ECMWF), ERA5‐HEAT consists of hourly world‐wide gridded maps of MRT and UTCI. It currently spans from 1979 to present and it will be extended in time as updates of ERA5 are made available.
Abstract
The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogeneous ...hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model.
Focusing on ECMWF’s land surface model Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the authors present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. The authors select six poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, and total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration, and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally, the authors investigate the possibility to construct ensembles from the multiple land surface parameters.
In the uncoupled runs the authors find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. The authors demonstrate the robustness of these findings by comparing multiple best-performing parameter sets and multiple randomly chosen parameter sets. The authors find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings.
Finally, the authors construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system.
Riverine plastic pollution is of global concern due to its negative impact on ecosystem health and human livelihood. Recent studies show a strong link between river discharge and plastic transport, ...but the role of floods is still unresolved. We combined high-resolution mismanaged plastic waste data and river flood extents with increasing return periods to estimate flood-driven plastic mobilisation, from local to global scale. We show that 10 year return period floods already tenfold the global plastic mobilisation potential compared to non-flood conditions. In the worst affected regions, plastic mobilisation increases up to five orders of magnitude. Our results suggest a high inter-annual variability in plastic mobilisation, previously ignored by global plastic transport models. Flood defences reduce plastic mobilisation substantially, but regions vulnerable to flooding often coincide with high plastic mobilisation potential during floods. Consequentially, clean-up and mitigation measures and flood risk management are inherently interdependent and need to be managed holistically.
Widespread flooding occurred across northwest Europe during the winter of 2013/14, resulting in large socioeconomic damages. In the historical record, extreme hydrological events have been connected ...with intense water vapour transport. Here we show that water vapour transport has higher medium-range predictability compared with precipitation in the winter 2013/14 forecasts from the European Centre for Medium-Range Weather Forecasts. Applying the concept of potential predictability, the transport is found to extend the forecast horizon by 3 days in some European regions. Our results suggest that the breakdown in precipitation predictability is due to uncertainty in the horizontal mass convergence location, an essential mechanism for precipitation generation. Furthermore, the predictability increases with larger spatial averages. Given the strong association between precipitation and water vapour transport, especially for extreme events, we conclude that the higher transport predictability could be used as a model diagnostic to increase preparedness for extreme hydrological events.
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