Local Climatology Research Articles

Quantifying the Wind‐Induced Bias of Rainfall Measurements for the Thies CLIMA Optical Disdrometer

AbstractThe wind‐induced bias of rainfall measurements obtained from non‐catching instruments is addressed in this work with reference to the Laser Precipitation Monitor (LPM) optical disdrometer manufactured by Thies CLIMA. A numerical simulation approach is adopted to quantify the expected bias, involving three different models with increasing complexity. Computational Fluid‐Dynamics simulation of the airflow field around the instrument with an embedded Lagrangian particle‐tracking module to obtain raindrop trajectories are performed by solving the Unsteady Reynolds Averaged Navier‐Stokes (URANS) equations and a Large Eddy Simulation (LES) model. URANS‐uncoupled, LES‐uncoupled, and LES‐coupled approaches are tested to assess the impact of modeling the airflow turbulent fluctuations in detail. Due to the non‐radially symmetric external shape of the instrument, various combinations of the wind speed and direction are considered. Catch ratios for monodisperse rain are obtained as a function of the particle Reynolds number and the wind direction and fitted to obtain site‐independent curves to support application of the simulation results. Based on literature expressions to link the drop size distribution of real rainfall events with the rainfall intensity (which instead depend on the local rainfall climatology at the measurement site), sample collection efficiency curves are obtained from the catch ratios of monodisperse rain. The resulting adjustment curves allow rainfall measurements to be corrected using either a real‐time or post‐processing approach. However, at high wind speed and assuming that the wind blows parallel to the instrument sensing area, the instrument may fail to report precipitation altogether.

Assessment of clear-sky irradiance from 6S affected by local climatology of India

Accurate estimation of solar power generation is crucial for the effective planning and operation of solar energy sector. Solar potential can be estimated at a location from long-term historical irradiance data processed from atmospheric reanalysis datasets. The current study employs a clear-sky radiative transfer model (6S) to simulate the net surface irradiance at four distinct locations in India, comparing our model's output with mean monthly ground-based measurements of surface irradiance. Our findings reveal that the 6S model marginally underestimates solar irradiance, with potential deviations varying depending on the accuracy of input data. This research evaluates the influence of particulate matter concentration and relative humidity on the scattering and absorption of solar radiation. We also state the variability in surface irradiance across the country with the rainfall trends that enhance assessment accuracy. The study reports an annual deviation of 10–15 % in surface irradiance across the country, where rural settlements show lower deviations in simulations. The radiative transfer calculations mentioned in the study are simplistic yet beneficial for a priori evaluation of solar potential. Transmittance estimated from clear-sky models is further implemented in all-sky (with clouds), and thus, model accuracy is an important parameter. Additionally, we explore the trends in aerosol concentrations and the impact of local climatological factors on surface irradiance, providing insights critical for optimizing solar energy utilization.

Climate-informed flood risk mapping using a GAN-based approach (ExGAN)

This study develops a class of robust models for flood risk mapping in highly vulnerable regions by focusing on accurately depicting extreme precipitation patterns aligned with regional climates. By implementing sophisticated hydrodynamics modeling and advanced probabilistic approaches, the present work underscores the efficacy of physical-based methodologies in the flood risk assessment. We propose a machine learning based ExGAN to address the challenge of synthesizing extreme precipitation scenarios which faithfully capture the nuances of local climatology. It is expected that through refined temporal disaggregation, the ExGAN approach exhibits exceptional proficiency in replicating a diverse spectrum of extreme precipitation patterns specific to the vulnerable region under scrutiny. Therefore, using these synthesized scenarios as inputs in a meticulously calibrated hydrological model would enable a comprehensive and detailed flood risk mapping exercise. To demonstrate the robustness of the developed mode, we perform a rigorous testing and validation within the highly susceptible Martil river basin, situated in the northern Mediterranean region of Morocco. The obtained results confirm that extending return periods would provide invaluable insights into the expanding geographical expanse of at-risk areas, clarifying the evolving landscape of vulnerability rather than merely amplifying inherent risk levels. Comparisons against the conventional Monte-Carlo sampling are also carried out in this study and the obtained results highlight significant overestimations within the latter, emphasizing the imperative need to account for diverse uncertainties beyond the basic sampling strategies within the realm of hydrodynamic modeling.

The Overall Collection Efficiency of Catching‐Type Precipitation Gauges in Windy Conditions

AbstractThe wind‐induced bias of catching‐type precipitation measurement instruments is quantified using Computational Fluid Dynamics with embedded liquid (raindrops) and solid (snowflakes) particle tracking. The performance of six common commercial gauges having different outer geometry is compared under a range of Precipitation Intensity (PI) and wind speed conditions by means of the numerically calculated catch ratios. Validation of the simulated aerodynamic behavior and its effect on water drop trajectories is provided by previous wind tunnel experiments. The functional dependence of the overall collection efficiency on the PI is derived as a quantitative measure of the instrument performance under a wind climatology with a uniform probability density function. Instruments with aerodynamic design produce a less pronounced diversion of hydrometeor trajectories—at any given size—than those having more traditional geometry. For liquid precipitation measurements, chimney‐shaped instruments rank low, while inverted conical and Nipher shielded instruments are quite performant solutions. The investigated quasi‐cylindrical gauges have intermediate behavior depending on their detailed aerodynamic features. For solid precipitation, all instruments rank low at light to moderate PI, except the Nipher shielded gauge. Results allow selecting the appropriate instrument for the local precipitation climatology at the measurement site and can be used to apply suitable adjustments to the measured precipitation.

Profiling wind LiDAR measurements to quantify blockage for onshore wind turbines

AbstractFlow modifications induced by wind turbine rotors on the incoming atmospheric boundary layer (ABL), such as blockage and speedups, can be important factors affecting the power performance and annual energy production (AEP) of a wind farm. Further, these rotor‐induced effects on the incoming ABL can vary significantly with the characteristics of the incoming wind, such as wind shear, veer, and turbulence intensity, and turbine operative conditions. To better characterize the complex flow physics underpinning the interaction between turbine rotors and the ABL, a field campaign was performed by deploying profiling wind LiDARs both before and after the construction of an onshore wind turbine array. Considering that the magnitude of these rotor‐induced flow modifications represents a small percentage of the incoming wind speed ( ), high accuracy needs to be achieved for the analysis of the experimental data and generation of flow predictions. Further, flow distortions induced by the site topography and effects of the local climatology need to be quantified and differentiated from those induced by wind turbine rotors. To this aim, a suite of statistical and machine learning models, such as k‐means cluster analysis coupled with random forest predictions, are used to quantify and predict flow modifications for different wind and atmospheric conditions. The experimental results show that wind velocity reductions of up to 3% can be observed at an upstream distance of 1.5 rotor diameter from the leading wind turbine rotor, with more significant effects occurring for larger positive wind shear. For more complex wind conditions, such as negative shear and low‐level jet, the rotor induction becomes highly complex entailing either velocity reductions (down to 9%) below hub height and velocity increases (up to 3%) above hub height. The effects of the rotor induction on the incoming wind velocity field seem to be already roughly negligible at an upstream distance of three rotor diameters. The results from this field experiment will inform models to simulate wind‐turbine and wind‐farm operations with improved accuracy for flow predictions in the proximity of the rotor area, which will be instrumental for more accurate quantification of wind farm blockage and relative effects on AEP.

Ground-based noontime D-region electron density climatology over northern Norway

Abstract. The bottom part of the Earth's ionosphere is the so-called D region, which is typically less dense than the upper regions. Despite the comparably lower electron density, the ionization state of the D region has a significant influence on signal absorption for propagating lower to medium radio frequencies. We present local noon climatologies of electron densities in the upper middle atmosphere (50–90 km) at high latitudes as observed by an active radar experiment. The radar measurements cover 9 years (2014–2022) from the solar maximum of cycle 24 to the beginning of cycle 25. Reliable electron densities are derived by employing signal processing, applying interferometry methods, and applying the Faraday-International Reference Ionosphere (FIRI) model. For all years a consistent spring–fall asymmetry of the electron density pattern with a gradual increase during summer as well as a sharp decrease at the beginning of October was found. These findings are consistent with very low frequency (VLF) studies showing equivalent signatures for nearby propagation paths. It is suggested that the meridional circulation associated with downwelling in winter could cause enhanced electron densities through NO transport. However, this mechanism can not explain the reduction in electron density in early October.

Adjustment of 1 min rain gauge time series using co-located drop size distribution and wind speed measurements

Abstract. A procedure to adjust rainfall intensity (RI) measurements to account for the wind-induced measurement bias of traditional catching-type gauges is proposed and demonstrated with an application to a suitable case study. The objective is to demonstrate that adjustment curves derived from numerical simulations and disdrometer measurements allow for a posteriori correction of rainfall time series based on the wind velocity measurements alone. To quantify the impact of wind on long-term records, 1 min RI measurements from a cylindrical tipping-bucket rain gauge installed at the Hong Kong Observatory are adjusted. Catch ratios derived from the instrument's aerodynamic behaviour under varying wind speed and drop size combinations are obtained by fitting computational fluid-dynamic simulation results already available in the literature. Co-located high-resolution wind speed measurements from a cup and vane sensor and drop size distribution measurements from an optical video disdrometer (the 2DVD or two-dimensional video disdrometer) are used to infer the collection efficiency of the gauge as a function of wind speed and RI alone and to adjust raw data from a 4-year dataset (2018–2021) of 1 min RI measurements. Due to the specific local climatology, where strong wind is often associated with intense precipitation, 80 % of the dataset adjustments are limited to 4 % of the observed RI values. This, however, results in a significant amount of available freshwater resources that would be missing from the calculated hydrological and water management budget of the region should the adjustments be neglected. This work raises the need to quantify the impact of the wind-induced bias at other sites where disdrometer data support a characterisation of the relationship between the drop size distribution and the measured RI. Depending on the local rain and wind climatology, the correction may account for a significant portion of the annual rainfall amount.

Assessing North Atlantic Tropical Cyclone Rainfall Hazard Using Engineered-Synthetic Storms and a Physics-Based Tropical Cyclone Rainfall Model

Abstract In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in the continental United States; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment.

Fine-Scale Climate Projections: What Additional Fixed Spatial Detail Is Provided by a Convection-Permitting Model?

Abstract Convection-permitting (CP) models promise much in response to the demand for increased localization of future climate information: greater resolution of influential land surface characteristics, improved representation of convective storms, and unprecedented resolution of user-relevant data. In practice, however, it is contended that the benefits of enhanced resolution cannot be fully realized due to the gap between models’ computational and effective resolution. Nevertheless, where surface forcing is strongly heterogeneous, one can argue that usable information may persist close to the grid scale. Here we analyze a 4.5-km resolution CP projection for Africa, asking whether and where fine-scale projection detail is robust at sub-25-km scales, focusing on geolocated rainfall features (rather than Lagrangian motion). Statistically significant detail for seasonal means and daily extremes is most frequent in regions of high topographic variability, most prominently in East Africa throughout the annual cycle, West Africa in the monsoon season, and to a lesser extent over Southern Africa. Lake coastal features have smaller but significant impacts on projection detail, whereas ocean coastlines and urban conurbations have little or no detectable impact. The amplitude of this sub-25-km projection detail can be similar to that of the local climatology in mountainous regions (or around a third near East Africa’s lake shores), so potentially beneficial for improved localization of future climate information. In flatter regions distant from coasts (the majority of Africa), spatial heterogeneity can be explained by chaotic weather variability. Here, the robustness of local climate projection information can be substantially enhanced by spatial aggregation to approximately 25-km scales, especially for daily extremes and equatorial regions. Significance Statement Recent substantial increases in the horizontal resolution of climate models bring the potential for both more reliable and more local future climate information. However, the best spatial scale on which to analyze such data for impacts assessments remains unclear. We examine a 4.5-km resolution climate projection for Africa, focusing on seasonal and daily rainfall. Spatially fixed fine-scale projection detail is found to be statistically robust at sub-25-km scales in the most mountainous regions and to a lesser extent along lake coastlines. Elsewhere, the model data may be better aggregated to at least 25-km scales to reduce sampling uncertainties. Such evolving guidance on the circumstances and extent of high-resolution data aggregation will help users gain greater benefit from climate model projections.

Global Application of the Atmospheric River Scale

AbstractAtmospheric rivers (ARs) are narrow, elongated, transient corridors of enhanced water vapor transport that play important roles in the global water cycle. Increasing scientific and societal interests in ARs prompted the introduction of an AR scale (ranks 1–5, from weak to strong) initially focused on western North America. Aided by a global AR detection algorithm, the current study explores the insights the AR scale can help provide from a global perspective. It is found that AR event count is inversely related to AR rank and peaks in midlatitude oceans. Out of all precipitation occurrences, ARs account for an increasing fraction as the precipitation intensity considered increases. Over the oceans, this fraction is composed of comparable contributions from the five AR ranks, but the contribution of AR 5 is much smaller over land. Higher‐ranked ARs tend to initiate at lower latitudes, terminate at higher latitudes, but follow a less sloped track due to longer zonal displacement. Sensitivity analysis indicates that if ARs are defined using a sole IVT threshold of 250 kg m−1 s−1 globally but with the tropics excluded, the spatiotemporal patterns of key AR event characteristics are largely similar to those described above, except AR events are more frequent by a factor of ∼1.5–2 depending on the AR rank. The results demonstrate the potential of the AR scale in helping evaluate and communicate ARs' influences globally, where the uniform scaling facilitates intercomparisons across different regions and the perception of impacts can be adjusted according to local climatology.

Impacts of urban decline on local climatology: A comparison of growing and shrinking cities in the post-industrial Rust Belt

Cities such as Detroit, MI in the post-industrial Rust Belt region of the United States, have been experiencing a decline in both population and economy since the 1970's. These “shrinking cities” are characterized by aging infrastructure and increasing vacant areas, potentially resulting in more green space. While in growing cities research has demonstrated an “urban heat island” effect resulting from increased temperatures with increased urbanization, little is known about how this may be different if a city shrinks due to urban decline. We hypothesize that the changes associated with shrinking cities will have a measurable impact on their local climatology that is different than in areas experiencing increased urbanization. Here we present our analysis of historical temperature and precipitation records (1900–2020) from weather stations positioned in multiple shrinking cities from within the Rust Belt region of the United States and in growing cities within and outside of this region. Our results suggest that while temperatures are increasing overall, these increases are lower in shrinking cities than those cities that are continuing to experience urban growth. Our analysis also suggests there are differences in precipitation trends between shrinking and growing cities. We also highlight recent climate data in Detroit, MI in the context of these longer-term changes in climatology to support urban planning and management decisions that may influence or be influenced by these trends.

Indigenous Knowledge of seasons delivers a new way of considering annual cycles in atmospheric dispersion of pollutants

Poor air quality is recognised as the most important environmental health issue of our time. Meteorological variables like temperature and wind speed can strongly influence air quality and these variables often show clear annual cycles. It is therefore common to analyse atmospheric pollutants within a seasonal framework. However, the commonly used seasons in Australia do not align well with all of the most important annual weather patterns that influence air quality in the Sydney Basin. We used Indigenous perspectives on ‘seasons’ as identified by the co-authors and combined these with statistical analysis of the local climatology. This enabled us to create a set of locally informed ‘quasi-seasons’ that we named IKALC-seasons (Indigenous Knowledge Applied to Local Climatology). Engaging with the IKALC-seasons improved our understanding of temporal variability of air pollution in western Sydney, mainly due to a better identification of the time of year when cold, still weather conditions result in higher levels of fine particulate pollution, carbon monoxide and nitrogen oxides. Although the IKALC seasons identified in this study are intrinsically local in nature, the methodology developed has broadscale application. This approach can be used to identify the times of year when micrometeorological conditions are most likely to drive poor air quality thereby helping to inform effective decision-making about emission controls.

Thunderstorm Activity and Extremes in Vietnam for the Period 2015–2019

Within a meteorological capacity building project in Vietnam, lightning location data and manual (human-observed) thunderstorm day observations were analyzed for the period 2015–2019. The lightning location dataset, based on the global lightning detection system Vaisala GLD360, consists of a total of 315,522,761 lightning strokes. The results indicate that, on average, 6.9 million lightning flashes per year occur in the land areas of Vietnam; this equals a lightning flash density of 20 flashes km−2 yr−1. The largest average annual flash density values occur in three regions in North, Central and South Vietnam. The majority of lightning occurs in the monsoon season (April–September), peaking in May, while in October–March, the lightning activity is very modest. During individual intense thunderstorm days, the flash density may exceed 12 flashes km−2 day−1. Thunderstorms in Central Vietnam are generally more intense, i.e., more lightning is expected on average per one thunderstorm day in Central Vietnam than in other regions. This study is a continuation of several years of meteorological capacity building in Vietnam, and the results suggest that large socio-economic benefits can be received by understanding the local thunderstorm climatology in high detail, especially in a country such as Vietnam, where lightning causes substantial socio-economic losses annually.

Involving Turc-Budyko formula in evaluating gridded precipitation datasets in glaciated catchments

Recently, gridded precipitation datasets (GPDs) have become an essential input in hydrological modeling. Evaluation of their performance is a priori step before their application. When they are applied to a glaciated catchment, the glacier mass balance adds another dimension to the evaluation. This study has attempted to introduce the Turc-Budyko formula for validating the GPDs in glaciated catchments. This approach can identify the over- or under-estimation of catchment precipitation input by the GPDs. As a case study, the approach was applied in Upper Indus Basin (UIB), located in High Mountain Asia. The Turc-Budyko formula and other commonly used evaluation techniques found that APHRODITE underestimated the precipitation by 40 %, although it captured most of inter- and intra-annual variations of local climatology (CC > 0.6). CFSR and HAR were overestimated (106 % and 77 %) and characterize UIB as “Leaky” catchment, whereas underestimated GPDs characterize UIB as “Gaining” catchment. In leaky conditions, glacier storage changes were positive (0.21–0.37 m w.e. yr−1); while gaining conditions made negative glacier storage changes (-0.44 to −0.34 m w.e. yr−1) in UIB. None of the GPDs represented true conditions of glacio-hydrology in UIB. Introducing the Turc-Budyko formula confirmed that direct application of GPDs in hydrological modeling is implausible because GPDs generally cannot produce rational output of streamflow and mass balance simultaneously. It is recommended to locally correct the GPDs before any hydrological application in glacierized catchments.

Diurnal and Seasonal Variability of ERA5 Convective Parameters in Relation to Lightning Flash Rates in Poland

Abstract The relationship between convective parameters derived from ERA5 and cloud-to-ground (CG) lightning flashes from the PERUN network in Poland was evaluated. All flashes detected between 2002 and 2019 were divided into intensity categories based on a peak 1-min CG lightning flash rate and were collocated with proximal profiles from ERA5 to assess their climatological variability. Thunderstorms in Poland are the most frequent in July, between 1400 and 1600 UTC and over the southeastern parts of the country. The highest median of most unstable convective available potential energy (MUCAPE) for CG lightning flash events is from June to August, between 1400 and 1600 UTC (around 900 J kg−1), whereas patterns in 0–6-km wind shear [deep-layer shear (DLS)] are reversed, with the highest median values during winter and night (around 25 m s−1). The best overlap of MUCAPE and DLS (MUWMAXSHEAR parameter) is in July–August, typically between 1400 and 2000 UTC with median values of around 850 m2 s−2. Thunderstorms in Poland are the most frequent in MUCAPE below 1000 J kg−1, and DLS between 8 and 15 m s−1. Along with increasing MUCAPE and DLS, peak CG lightning flash rates increase as well. Compared to DLS, MUCAPE is a more important parameter in forecasting any lightning activity, but when these two are combined together (MUWMAXSHEAR) they are more reliable in distinguishing between thunderstorms producing small and high CG lightning flash rates. Our results also indicate that higher CG lightning flash rates result in thunderstorms more frequently associated with severe weather reports (hail, tornado, wind). Significance Statement Each year severe thunderstorms produce considerable material losses and lead to deaths across central Europe; thus, a better understanding of local storm climatologies and their accompanying environments is important for operational forecasters, emergency managers, and risk estimation. In this research we address this issue by analyzing 18 years of lightning intensity data and collocated atmospheric environments. Thunderstorms in Poland are the most frequent in July between 1400 and 1600 UTC and form typically in environments with low atmospheric instability and moderate vertical shear of the horizontal wind. The probability for storms producing intense lightning increases when both of these environmental parameters reach higher values.

Advancing early warning capabilities with CHIRPS-compatible NCEP GEFS precipitation forecasts

CHIRPS-GEFS is an operational data set that provides daily bias-corrected forecasts for next 1-day to ~15-day precipitation totals and anomalies at a quasi-global 50-deg N to 50-deg S extent and 0.05-degree resolution. These are based on National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System version 12 (GEFS v12) precipitation forecasts. CHIRPS-GEFS forecasts are compatible with Climate Hazards center InfraRed Precipitation with Stations (CHIRPS) data, which is actively used for drought monitoring, early warning, and near real-time impact assessments. A rank-based quantile matching procedure is used to transform GEFS v12 “reforecast” and “real-time” forecast ensemble means to CHIRPS spatial-temporal characteristics. Matching distributions to CHIRPS makes forecasts better reflect local climatology at finer spatial resolution and reduces moderate-to-large forecast errors. As shown in this study, having a CHIRPS-compatible version of the latest generation of NCEP GEFS forecasts enables rapid assessment of current forecasts and local historical context. CHIRPS-GEFS effectively bridges the gap between observations and weather predictions, increasing the value of both by connecting monitoring resources (CHIRPS) with interoperable forecasts.

Development of High Resolution Cloud Cover Climatology Databank Using Merged Manual and Satellite Datasets over Ghana, West Africa

ABSTRACT Accurate and reliable total cloud cover (TCC) observation is essential for astronomy, renewable energy resource assessment, climate impact studies, and agriculture. In order to improve the spatial coverage for a climatological distribution pattern of TCC observation across different climatic zones in Ghana – West Africa, this paper developed a merged database comprising ground-based manual TCC observation dataset (TCCM) at 22 tropical synoptic stations and satellite-based TCC dataset retrieved from the NASA Prediction of Worldwide Energy Resource (POWER) climatological archives (TCCN) spanning 30 years (1983–2013) for each dataset. Firstly, the satellite data was assessed statistically for merging with station data. From the results, it is shown that on the overall, the satellite data (TCCN) is a good representation of local TCC climatology comparative to station observation by a mean percentage deviation of 7.8 ± 1.7, and indices of agreement between 0.7 and 0.99 ± 0.01, indicating strong zonal and seasonal similarities. Moreover, the best station-by-station similarities are over the northern half, being predominantly Savannah climate areas, while the southernmost half show the weakest similarities. This can be attributed to a complex interplay of coastal ocean-land-atmosphere interactions obstructing satellite sensing. Finally, the gridded merged dataset established that December–February is the lowest TCC season countrywide, whereas June–August is the highest TCC season, more pronounced over the southern half, being predominantly Forest climate type and showing significant non-linearity with atmospheric clarity indices. The results have useful applications for solar energy resource assessment, crop yield models, and provides a framework for development of cloud property and cloud radiative effect assessment for climate related studies.

Excess Heat Factor climatology, trends, and exposure across European Functional Urban Areas

In Europe, regional climate change prospects indicate the urgency of adapting to extreme weather events. While increasing temperature trends have already been detected, in the last decades, the adoption of a European heatwave (HW) early-warning index is not yet consensual, partially due to the significant number of alternative algorithms, in some cases adjusted to the measurement of sector-specific impacts (as per the Expert Team on Climate Risk and Sector-specific Indices (ET-SCI)). In particular, the Excess Heat Factor (EHF) has been shown to accurately predict heat-related human health outcomes, in mid-latitude climates, provided that local summer exposure to excess heat is mostly driven by extreme air temperatures, with a lower contribution from relative humidity. Here, annual summaries of EHF-based HW detection were calculated for the European region, using daily maximum and minimum temperatures from the homogenised version of the E-OBS gridded dataset. Annual HW frequencies, duration, mean magnitude, maximum amplitude, and severity were subject to climatology and trend analysis across the European biogeographical regions, considering the 1961–1990 period as the baseline reference for anomaly detection in the more recent (1991–2018) decades. As HW-dependent morbidity/mortality affects mostly the elderly, an EHF-based HW Exposure Index was also calculated, by multiplying the recent probability of severe events per the number of people aged 65, or more, in the European Functional Urban Areas (FUAs). Results show that recent historical EHF-based patterns diverge across European Biogeographical regions, with a clear latitudinal gradient. Both the historical mean and recent trends point towards the greater exposure in the southern European Mediterranean region, driven by the significant increase of HW frequency, duration and maximum severity, especially in the last 3 decades; conversely, annual maximum EHF intensities (i.e., greatest deviations from the local 90th daily mean temperature) are mostly found in the northern and/or high altitude Boreal, Alpine and Continental regions, as a consequence of the latitudinal effect of local climatology on the HWM/HWA indices (this also translates into greater magnitudes of change, in this regions). Nonetheless, by simultaneously considering the probability of Severe HW occurrence in the last three decades, together with the log transformation of people aged 65 or more, results show that greater HW Exposure Indices affect FUAs across the whole Europe, irrespective of its regional climate, suggesting that more meaningful vulnerability assessments, early warning and adaptation measures should be prioritized accordingly.

Multi-decadal retreat of marine-terminating outlet glaciers in northwest and central-west Greenland

Abstract. The retreat and acceleration of marine-terminating outlet glaciers in Greenland over the past 2 decades have been widely attributed to climate change. Here we present a comprehensive annual record of glacier terminus positions in northwest and central-west Greenland and compare it against local and regional climatology to assess the regional sensitivity of glacier termini to different climatic factors. This record is derived from optical and radar satellite imagery and spans 87 marine-terminating outlet glaciers from 1972 through 2021. We find that in this region, most glaciers have retreated over the observation period and widespread regional retreat accelerated from around 1996. The acceleration of glacier retreat coincides with the timing of sharp shifts in ocean surface temperatures, the duration of the sea-ice season, ice-sheet surface mass balance, and meltwater and runoff production. Regression analysis indicates that terminus retreat is most sensitive to increases in runoff and ocean temperatures, while the effect of offshore sea ice is weak. Because runoff and ocean temperatures can influence terminus positions through several mechanisms, our findings suggest that a variety of processes – such as ocean-interface melting, mélange presence and rigidity, and hydrofracture-induced calving – may contribute to, but do not conclusively dominate, the observed regional retreat.

How to select the optimal monitoring locations for an aerobiological network: A case of study in central northwest of Spain

Airborne pollen concentration varies depending on several factors, such as local plant biodiversity, geography and climatology. These particles are involved in triggering pollinosis in a share of worldwide human population, and adequate monitoring is, therefore, important. However, the pollen traps in aerobiological monitoring networks are usually installed in cities, and the features of the whole territory are not taken into account. The aim of this study was to analyze what environmental parameters are more suitable as regards setting up monitoring stations throughout a territory in order to obtain an aerobiological network that can represent environmental diversity. The analysis was carried out in 13 locations in Castilla y León over an 8 year period. This is a favorable territory in which to conduct this type of study owing to its climatic features, orography and biodiversity. The ten most abundant pollen types in the region were analyzed, and a clustering analysis was calculated with different distances so as to obtain homogeneous groups of stations. Moreover, the clusters obtained were analyzed in combination with altitudinal and different bioclimatic parameters, which derived from temperature and precipitation.The result here shows that the Castilla y León aerobiological network RACYL represents most of the environmental variability of the territory. Furthermore, it can be divided into two clusters and five sub-clusters for which the start of the main pollen season is different. This corresponds with the division of the territory as regards bioclimatic conditions. The most important bioclimatic parameters were the seasonality of the precipitation and the maximum temperature of the warmest month, although orography must also be taken into account. All of these help discover the optimal places in which to install traps and could reduce the number of monitoring stations. This study additionally provides data for unmonitored areas with similar bioclimatic conditions to those monitored.