European Climate Assessment Research Articles

Near-optimal selection of representative measuring points for robust temperature field reconstruction with the CRO-SL and analogue methods

In this paper we tackle a problem of representative measuring points selection for temperature field reconstruction. This problem is a version of the more general Representative Selection (RS) problem, well-known in computer and data science. In this particular case, the objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a reconstruction error is minimized when reconstructing the monthly average temperature field. We use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer (CRO-SL), which is an evolutionary-type method able to combine several different search procedures within a single population. The CRO-SL is combined with the Analogue Method (AM) to identify the most representative points. This approach exhibits strong performance from experiments with gridded and un-gridded temperature field datasets (European Climate Assessment & Dataset (ECA) and ERA-Interim reanalysis (ERA)). Different aspects such as the error assessment and the comparison with alternative approaches, are discussed in the experimental analysis of this article. We show that the algorithm performs better than a greedy approach, i.e. the best solution for N points is different from the N best individual predictors. The solutions obtained with the proposed methodology are climatologically consistent and include points from Scandinavia, Central and Southern Europe, the Black Sea and Central and South Western Asia as the more representative in the case of the ECA dataset; similar areas are selected for ERA. We have found out that once the number of stations/points goes over a threshold, the improvement in the model is obtained by increasing the density of data in the given zones, instead of adding data from different zones to the algorithm. The method proposed may have direct application in Palaeoclimalogy, where there are a large amount of distributed proxies with scarce information, so the proposed approach could be useful to select the most important ones to reconstruct a desired field.

Ambient temperature and sexual activity: Evidence from time use surveys

Background: Previous research has found that unusually hot temperatures reduce birth rates eight to ten months later. Objective: We examine one of the potential mechanisms behind this relationship: the connection between ambient temperature and sexual activity. Methods: We use individual-level data provided by three waves of the Hungarian Time Use Survey between 1986 and 2010 and daily weather data from the European Climate Assessment & Dataset project. Results: Hot temperatures do not have a significant effect on sexual activity on a given day. Studying the dynamics of the relationship, we found that temperature does not influence sexual activity on subsequent days either. Conclusions: Since high temperatures seem to have no negative effect on sexual activity, the relationship between temperature and sexual activity might be a mechanism of minor importance in the relationship between temperature and birth rates. Contribution: Our paper is the first study of the relationship between ambient temperature and sexual activity that uses time use data.

Evaluation of CLARA-A2 and ISCCP-H Cloud Cover Climate Data Records over Europe with ECA&D Ground-Based Measurements

Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to be validated and compared with other data records, especially ground measurements. In the present study, the spatiotemporal distribution and variability of Total Cloud Cover (TCC) from the Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo And Surface Radiation dataset from AVHRR data—edition 2 (CLARA-A2) and the International Satellite Cloud Climatology Project H-series (ISCCP-H) is analyzed over Europe. The CLARA-A2 data record has been created using measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the polar orbiting NOAA and the EUMETSAT MetOp satellites, whereas the ISCCP-H data were produced by a combination of measurements from geostationary meteorological satellites and the AVHRR instrument on the polar orbiting satellites. An intercomparison of the two data records is performed over their common period, 1984 to 2012. In addition, a comparison of the two satellite data records is made against TCC observations at 22 meteorological stations in Europe, from the European Climate Assessment & Dataset (ECA&D). The results indicate generally larger ISCCP-H TCC with respect to the corresponding CLARA-A2 data, in particular in the Mediterranean. Compared to ECA&D data, both satellite datasets reveal a reasonable performance, with overall mean TCC biases of 2.1 and 5.2% for CLARA-A2 and ISCCP-H, respectively. This, along with the higher correlation coefficients between CLARA-A2 and ECA&D TCC, indicates the better performance of CLARA-A2 TCC data.

Homogenization of daily temperature series in the European Climate Assessment & Dataset

The daily maximum and minimum temperature series of the European Climate Assessment & Dataset are homogenized using the quantile matching approach. As the dataset is large and the detail of metadata is generally missing, an automated method locates breaks in the series based on a comparison with surrounding series and applies adjustments which are estimated using homogeneous segments of surrounding series as reference. A total of 6,500 series have been processed and after removing duplicates and short series, about 2,100 series have been adjusted. Finally, the effect of the homogenization of daily maximum and minimum temperature on trend estimation is shown to produce a much more spatially homogeneous and then plausible picture.

Weather and gastrointestinal disease in Spain: A retrospective time series regression study

BackgroundA few studies in high-income countries have investigated the relationship between ambient temperature and/or precipitation and the occurrence of gastroenteritis. In most of the cases, hot temperatures and heavy precipitation events have been related to increases in infections. This is of concern as climate change predictions indicate an increase of those extreme events. Our aim was to evaluate the association between meteorological variables and daily gastroenteritis hospitalizations in Spain for the period 1997–2013. MethodsWe obtained data on all hospitalizations which occurred in Spain for the study period from administrative databases and selected those with gastroenteritis as the main diagnosis. Meteorological data was obtained from the European Climate Assessment & Dataset. Daily counts of hospitalizations were linked to meteorological variables in a retrospective ecological time series study using quasi-Poisson regression models with overdispersion and applying the Distributed Lag Non-linear Model (DLNM) framework. ResultsBoth high and cold temperatures increased the risk of gastroenteritis hospitalizations (relative risk (RR) = 1.21, 95% confidence interval (CI): 1.09, 1.34; and RR = 1.07, 95% CI: 1.00, 1.15, respectively), whereas heavy precipitation was found protective for those hospitalizations (RR = 0.74, 95% CI: 0.63, 0.86). Hot temperatures increased hospitalizations for gastroenteritis classified as foodborne or idiopathic but not those in the group of Others, which were composed mainly of infections by rotavirus and were associated with cold temperatures. ConclusionsOur findings suggest an important role of ambient temperatures, especially hot temperatures, in increasing gastroenteritis hospitalizations, while the exposure to heavy precipitation events pose opposite and unexpected effects on these infections.

Low-cycle fatigue life of a thermal break system under climatic actions

External insulation is more widely used in Northern and Continental Europe than internal insulation. This technique leads to thermal bridges where the building facade has projecting elements like balconies. The thermal requirements of actual standards lead to restore the continuity of the insulation at the interfaces by using thermal break systems (TBS). These systems are usually made of a box containing the insulation material, and a minimalist structural system able to transmit the shear force and the bending moment from the balcony to the wall. In most cases, structural elements are made of stainless steel, as it is less heat-conducting than normal steel. The paper focuses on a specific TBS that uses shear keys and steel profiles to ensure the transfer of forces. TBS are also submitted to important horizontal cyclic shear deformations, provoked by the variations of the dimensions of the balconies due to climatic effects. The objective of the study presented in the paper is to show that significant yielding under these actions can be accepted during service life. First experimental cyclic loading tests have been performed in order to characterize the behaviour of the TBS, as well as its fatigue strength. Then the loading has been defined on the basis of the database of the ECA&D, the European Climate Assessment and Dataset. Finally, the fatigue resistance of the system has been verified. It is shown that the developed TBS can resist to fatigue loading for great length of balconies, while exhibiting significant yielding during service life.

Spanish vineyard classification according to bioclimatic indexes

Background and Aims Spain has more than one million hectares of vineyards, being the first country in the world, and the third in production of wine. Local zoning studies have been carried out using bioclimatic indexes to show the influence of climate; however, no detailed study has been undertaken at national level that included the current area of vineyards. The aim of this study was to prepare a thermal characterisation and zoning of winegrowing areas in Spain through four bioclimatic indexes. Methods and Results From data of the European Climate Assessment & Dataset (1980–2010), four bioclimatic indexes adapted to viticulture were calculated and integrated into a geographic information system, which allowed the graphical visualisation of their patterns by means of geostatistical interpolation techniques (kriging). The results show that there is a significant spatial variability of the climate from a winegrowing perspective, and each index defines the differences between the climatic classes suitable for viticulture. Conclusions In order to define and assess the climatic differences in wine production and the suitability of the Spanish mainland territory, it is necessary to combine several indexes. Significance of the Study For the first time, the climatic variability of Spain has been described and the peculiarities that differentiate its vineyards have been highlighted. High‐resolution kriged maps have made this study possible.

Short-term effects of atmospheric pressure, temperature, and rainfall on notification rate of community-acquired Legionnaires' disease in four European countries - CORRIGENDUM.

Legionnaires' disease (LD) is caused by the inhalation of aerosols containing Legionella, a Gram-negative bacteria. Previous national- or regional-level studies have suggested an impact of climate on LD incidence. The objective of this study was to investigate the effect of temperature, rainfall, and atmospheric pressure on short-term variations in LD notification rate. EU/EEA Member States report their LD surveillance data to the European Centre for Disease Prevention and Control. Community-acquired LD cases reported by Denmark, Germany, Italy, and The Netherlands with onset date in 2007-2012 were aggregated by onset week and region of residence. Weather variables were extracted from the European Climate Assessment & Dataset project. We fitted Poisson regression models to estimate the association between meteorological variables and the weekly number of community-acquired LD cases. Temperature, rainfall and atmospheric pressure were all associated with LD risk with higher risk associated with simultaneous increase in temperature and rainfall. Temperatures >20 °C were not associated with a higher risk for LD. LD cases occurring during wintertime may be associated with sources less influenced by meteorological conditions.

On the intra-annual variation of cloudiness over the Mediterranean region

The seasonal variability of cloudiness in the Mediterranean region is studied, through the examination of the mean day-to-day intra-annual variation of gridded NCEP/NCAR total cloud cover for the period 1948–2014. A dimensionality reduction process is followed by using a multivariate statistical methodology and three main modes of seasonal variation are found. The first mode is characterized by a winter maximum and a summer minimum and prevails mainly over the Mediterranean and Black Sea areas; a weak see-saw teleconnection over the Alps represents the opposite intra-annual variation. The second mode presents maxima in early autumn and late spring and minima in late summer and winter and prevails over the SW Europe and NW Africa inland regions. The third mode shows a maximum in June and a minimum in October and prevails over the eastern part of central Europe. The main characteristics of these modes are attributed to the seasonal variation of: i) the temperature and humidity of the lower and the middle troposphere affecting static stability and ii) the intensity and the position of the main circulation systems affecting the region, as for example the Azores subtropical anticyclone and the Mediterranean depressions. The examination of inter-decadal changes in the seasonal variation of cloud cover in the above core regions reveals a long-term decrease of cloudiness during the 66-year period 1948–2014. A comparison with contemporary satellite MODerate resolution Imaging Spectroradiometer (MODIS) and surface based European Climate Assessment & Dataset (ECA&D) station cloud cover data is carried out for the above core regions, evidencing a similarity among the data sets as to their seasonal variability, which supports the validity of the NCEP/NCAR reanalysis cloud cover results.

EURO‐CORDEX regional climate model simulation of precipitation on Scottish islands (1971–2000): model performance and implications for decision‐making in topographically complex regions

ABSTRACTDue to their scale and complex topography, islands such as the Hebrides and Shetland Islands are not completely resolved by global climate models, which may impact the quality of data that can be provided about future climate in such locations. In principle, dynamical downscaling may provide helpful additional detail about future local climate. However, there is also the potential for error and uncertainty to cascade through to the regional simulation. Here, we evaluate the simulative skill of the EURO‐CORDEX regional climate model ensemble on regional and local scales in the Hebrides and Shetland Islands, and consider the potential for such models to aid decision‐making in island settings, and other locations characterized by complex topography. Several precipitation indices (accumulated precipitation amount, mean daily precipitation amount, max 1‐day and 5‐day precipitation amounts, simple daily intensity, number of heavy and very heavy precipitation days) are used to assess model performance and identify bias relative to observations. Models are compared regionally, and at specific locations, namely Stornoway in the Hebrides and Lerwick in Shetland, for the period 1971–2000. Regional evaluation utilizes the UKCP09 gridded observational data set and local evaluation at Stornoway Airport and Lerwick utilizes observed mean precipitation and extreme indices from the European Climate Assessment & Dataset project. While no models perform skilfully across all the metrics studied, some models capture aspects of the precipitation climate at each location particularly well. Differences in model performance between the two case study sites highlight the value of evaluating models on multiple spatial scales. The implications of model uncertainty for decision‐making are also discussed.

ECA&D and E-OBS: High-resolution datasets for monitoring climate change and effects on viticulture in Europe

Climate change and climate variability profoundly affect the production of wine. When facing a changing climate, the characteristics of wine produced in each region will change while the natural year-to-year variations in climate will increase variability of income for wine businesses and therefore affect profitability and economic resilience. The challenge posed to the viticulture community is thus to closely monitor these changes to be able to adapt business practices. The European Climate Assessment and Dataset (ECA&D) and its gridded version E-OBS are tools to monitor the changing climatic conditions over Europe, with an emphasis on changes in extreme climatic conditions. In this paper, the potential of ECA&D and E-OBS for viticulture is demonstrated by a few examples. The examples include the changing areal suitable for Chardonnay cultivation, showing an expansion to areas which have been too cold only a few decades ago and retraction of optimal conditions from areas which have been suitable in the recent past. Other examples show the change in the diurnal temperature range in the latter stages of the ripening process of grapes and the variability in heavy precipitation events. Finally, first results of a new dataset for South America are presented.

TO THE QUESTION OF MODERN CHANGES OF CLIMATE OF NORTHWEST BLACK SEA REGION

Purpose. In recent years, climate indices, the majority of which belongs to the category of so-called «extreme indices», widely used in international practice for assessing global and regional climate changes. The purpose of this article is to identify the characteristics of climate change, the North-Western Black Sea region since the beginning of the twentieth century through the use of climate indices. Data & Methods. For the analysis and evaluation of the changes of climate of the North-west Black Sea region have used the series of monthly, seasonal and annual values of the most informative climate indices for weather station Odessa-GMO for the period 1900-2015 from the site of European Climate Assessment & Data (ECA&D) project. In total have used 22 indexes, 12 of which are temperature, 5 – precipitation, 5 – complex. Analysis and assessment of climate change made using the comparative–geographical, graph-analytical and statistical methods of analysis. Results. During 1900-2015 years according to observations at the meteorological station of Оdessa-GMO had been an increase both air temperatures and precipitation amount. In so doing in the long-term course of most of the indices manifested the periodicity of changes with the characteristic average size equal to 62-64 years. The most intensely during this period occurred raising the minimum air temperature. While the coefficient of linear trend of average annual air temperature (TG index) equals 1,23° C / 100 years, for the absolute annual minimum of air temperature (TNn index) this coefficient equals 2.97° C / 100 years. Since the late 70-ies of the last century has been a significant increase in the intensity of warming. In particular, over the last 35 years the average annual air temperature increased by 2° C, the annual minimum air temperature increased by 4° C, more than in 2 times there was an increase the number of «summer days», in 3.5 times increased number of «tropical nights», and in 1,5 times reduced the number of «frosty nights». It should also be emphasized that in the dynamic of precipitation amount over the last 35 years the monotonicity of changes, typical for the temperature indexes, is absent. In the seasonal aspect most intensive growth both of air temperature and precipitation as a whole for period under consideration is typical for winter, the least an intensive –for the summer, which led to a decrease in annual amplitude of air temperature and equalization of distribution within the year of precipitation amount, respectively. At the same time in the last 35 years against the background of a slight increase in summer precipitation significantly increased their intensity.

Short-term effects of atmospheric pressure, temperature, and rainfall on notification rate of community-acquired Legionnaires' disease in four European countries.

Legionnaires' disease (LD) is caused by the inhalation of aerosols containing Legionella, a Gram-negative bacteria. Previous national- or regional-level studies have suggested an impact of climate on LD incidence. The objective of this study was to investigate the effect of temperature, rainfall, and atmospheric pressure on short-term variations in LD notification rate. EU/EEA Member States report their LD surveillance data to the European Centre for Disease Prevention and Control. Community-acquired LD cases reported by Denmark, Germany, Italy, and The Netherlands with onset date in 2007-2012 were aggregated by onset week and region of residence. Weather variables were extracted from the European Climate Assessment & Dataset project. We fitted Poisson regression models to estimate the association between meteorological variables and the weekly number of community-acquired LD cases. Temperature, rainfall and atmospheric pressure were all associated with LD risk with higher risk associated with simultaneous increase in temperature and rainfall. Temperatures >20 °C were not associated with a higher risk for LD. LD cases occurring during wintertime may be associated with sources less influenced by meteorological conditions.

Influence of winter North-Atlantic Oscillation on Climate-Related-Energy penetration in Europe

When considering 100% renewable scenarios, backup generation is needed for stabilizing the network when Climate Related Energy (CRE) such as wind, solar or run-of-the river hydropower are not sufficient for supplying the load. Several studies show that, over relatively short time period (less than 10 years), backup generation needs are reduced by dissipating power densities either in space through grids or time through storage. This study looks at the impact of low time frequency variations of CRE with a specific focus on the time variability induced by the North Atlantic Oscillation (NAO) teleconnection pattern during winter season. A set of eleven regions in Europe and Tunisia is used for highlighting space variability of the winter NAO’s impact. For each of these regions, we combine data from the Weather Research and Forecasting Model and the European Climate Assessment & Dataset for estimating solar-power, wind-power, run-of-the-river hydro-power and the energy load over the 1980–2012 time period. Results show that NAO’s impact on winter penetration rate depends on both the considered energy source and the location. They also highlight a non-linear relation between the NAO’s impact on CRE penetration rates and the level of equipment used for harvesting the CRE sources.

Köppen–Geiger Climate Classification for Europe Recaptured via the Hölder Regularity of Air Temperature Data

In this paper, we make use of the monoHolder nature of surface air temperature data to recapture the Koppen–Geiger climate classification in Europe. Using data from the European Climate Assessment and Dataset (ECA&D), we first show that the Holder exponents of surface air temperature data are statistically related to pressure anomalies. Then, we establish a climate classification based on these Holder exponents in such a way that it allows to recover the Koppen–Geiger climate classification. We show that the two classifications match for a vast majority of stations, and we corroborate these observations with a confirmation test. We compare these results with those obtained with another dataset (NCEP-NCAR Reanalysis Project) to show that the new classification is still well-adapted, before eventually discussing these findings.

CPLFD-GDPT5: High-resolution gridded daily precipitation and temperature data set for two largest Polish river basins

Abstract. The CHASE-PL (Climate change impact assessment for selected sectors in Poland) Forcing Data–Gridded Daily Precipitation & Temperature Dataset–5 km (CPLFD-GDPT5) consists of 1951–2013 daily minimum and maximum air temperatures and precipitation totals interpolated onto a 5 km grid based on daily meteorological observations from the Institute of Meteorology and Water Management (IMGW-PIB; Polish stations), Deutscher Wetterdienst (DWD, German and Czech stations), and European Climate Assessment and Dataset (ECAD) and National Oceanic and Atmosphere Administration–National Climatic Data Center (NOAA-NCDC) (Slovak, Ukrainian, and Belarusian stations). The main purpose for constructing this product was the need for long-term aerial precipitation and temperature data for earth-system modelling, especially hydrological modelling. The spatial coverage is the union of the Vistula and Oder basins and Polish territory. The number of available meteorological stations for precipitation and temperature varies in time from about 100 for temperature and 300 for precipitation in the 1950s up to about 180 for temperature and 700 for precipitation in the 1990s. The precipitation data set was corrected for snowfall and rainfall under-catch with the Richter method. The interpolation methods were kriging with elevation as external drift for temperatures and indicator kriging combined with universal kriging for precipitation. The kriging cross validation revealed low root-mean-squared errors expressed as a fraction of standard deviation (SD): 0.54 and 0.47 for minimum and maximum temperature, respectively, and 0.79 for precipitation. The correlation scores were 0.84 for minimum temperatures, 0.88 for maximum temperatures, and 0.65 for precipitation. The CPLFD-GDPT5 product is consistent with 1971–2000 climatic data published by IMGW-PIB. We also confirm good skill of the product for hydrological modelling by performing an application using the Soil and Water Assessment Tool (SWAT) in the Vistula and Oder basins. Link to the data set: doi:10.4121/uuid:e939aec0-bdd1-440f-bd1e-c49ff10d0a07.

Increased incidence of subarachnoid hemorrhage during cold temperatures and influenza epidemics.

OBJECTIVE This study investigated whether the increased incidence of aneurysmal subarachnoid hemorrhage (SAH) in winter is related to temperature or increased incidence of influenza. Such relationships may elucidate the pathogenesis of intracranial aneurysm rupture. METHODS A nationwide sample of 18,714 patients with SAH was linked with weekly temperature and influenza-like illness consultation data. Poisson regression analyses were used to calculate incidence density ratios (IDRs) with corresponding 95% CIs for the association of SAH incidence with temperature and influenza epidemics; IDRs were adjusted for study year (aIDR). In addition, SAH incidence data from 30 European population-based studies were linked with daily temperature data from the European Climate Assessment. RESULTS The aIDR for SAH during influenza epidemics was 1.061 (95% CI 1.022-1.101) in the univariable and 1.030 (95% CI 0.989-1.074) in the multivariable analysis. This association declined gradually during the weeks after epidemics. Per 1°C temperature drop, the aIDR was 1.005 (95% CI 1.003-1.008) in the univariable and 1.004 (95% CI 1.002-1.007) in the multivariable analysis. In the European population-based studies, the IDR was 1.143 (95% CI 1.129-1.157) per 1°C temperature drop. CONCLUSIONS The incidence of SAH is increased during cold temperatures and epidemic influenza. Future studies with individual patient data are needed to investigate causality between temperature or influenza and SAH.

Relationship between sunshine duration and temperature trends across Europe since the second half of the twentieth century

Global radiation is a fundamental source of energy in the climate system. A significant impact of global radiation on temperature change is expected due to the widespread dimming/brightening phenomenon observed since the second half of the twentieth century. This work describes the analysis of 312 stations with sunshine duration (SD) series, a proxy for global radiation, and temperature series in the European Climate Assessment & Dataset (ECA&D) with data over the period 1961–2010. The relationship between SD and temperature series is analyzed for four temperature variables: maximum (Tmax), minimum (Tmin), mean temperature (Tmean), and diurnal temperature range (DTR). The analyses are performed on annual and seasonal basis. The results show strong positive correlations between SD and temperatures over Europe, with highest correlation for DTR and Tmax during the summer period. These results confirm the strong relationship between SD and temperature trends over Europe since the second half of the twentieth century. This study supports previous suggestions that dimming (brightening) has partially decreased (increased) temperatures thereby modulating the greenhouse gas induced warming rates over Europe.

Characterization of the wind speed variability and future change in the Iberian Peninsula and the Balearic Islands

AbstractWind energy is susceptible to global climate change because it could alter the wind patterns. Then, improvement of our knowledge of wind field variability is crucial to optimize the use of wind resources in a given region.Here, we quantify the effects of climate change on the surface wind speed field over the Iberian Peninsula and Balearic Islands using an ensemble of four regional climate models driven by a global climate model.Regions of the Iberian Peninsula with coherent temporal variability in wind speed in each of the models are identified and analysed using cluster analysis. These regions are continuous in each model and exhibit a high degree of overlap across the models. The models forced by the European Reanalysis Interim (ERA‐Interim) reanalysis are validated against the European Climate Assessment and Dataset wind. We find that regional models are able to simulate with reasonable skill the spatial distribution of wind speed at 10 m in the Iberian Peninsula, identifying areas with common wind variability.Under the Special Report on Emissions Scenarios (SRES) A1B climate change scenario, the wind speed in the identified regions for 2031–2050 is up to 5% less than during the 1980–1999 control period for all models. The models also agree on the time evolution of spatially averaged wind speed in each region, showing a negative trend for all of them. These tendencies depend on the region and are significant at p = 5% or slightly more for annual trends, while seasonal trends are not significant in most of the regions and seasons. Copyright © 2015 John Wiley & Sons, Ltd.

Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation

This article highlights the results of an assessment of representation and usability of global temperature station data for global spatio-temporal analysis. Datasets from the Global Surface Summary of Day (GSOD) and the European Climate Assessment & Dataset (ECA&D) were merged and consisted of 10,695 global stations for the year 2011. Three aspects of data quality were considered: (a) representation in the geographical domain, (b) representation in the feature space (based on the MaxEnt method), and (c) usability i.e. fitness of use for spatio-temporal interpolation based on cross-validation of spatio-temporal regression-kriging models. The results indicate significant clustering of meteorological stations in the combined data set in both geographical and feature space. The majority of the distribution of stations (84%) can be explained by population density and accessibility maps. Consequently, higher elevations areas and inaccessible areas that are sparsely populated are significantly under-represented. Under-representation also reflects on the results of spatio-temporal analysis. Spatio-temporal regression-kriging model of mean daily temperature using 8-day MODIS LST images, as covariate, produces average global accuracy of 2–3 °C. Prediction of temperature for polar areas and mountains is 2 times lower than for areas densely covered with meteorological stations. Balanced spatio-temporal regression models that account for station clustering are suggested.