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This research presents a case study of the biases and discontinuities that were introduced in observed long-term mean wind-speed and gust data-series due to anemometer changes in a meteorological station in northern Spain, operated by the Spanish State Meteorological Agency: San Sebastian-Igueldo. Field and wind-tunnel experiments with predefined conditions have been presented in the literature, however this research uses a real case study to assess the impact of anemometer changes on wind speed measurements due to three factors being: (i) the 3-cup anemometer model (SEAC vs. THIES companies); (ii) sensor height (∼19.95 m vs. ∼20.45 m) and (iii) sensor age (20-years old vs. new). Our results show (a) substantial biases in the measured wind speed and daily peak wind gusts, with the new THIES anemometer reporting stronger surface winds than the old SEAC anemometer; (b) opposing biases under weak (negative) and moderate-strong (positive) winds; and (c) significant breakpoints in the long-term wind data-series, which highlight the importance of data homogenization. National Weather Services and climate assessment groups will benefit from these findings since errors in wind speed and gust measurements can be minimized by implementing systematic observation protocols. Robust anemometer observations provide a basis for accurate quantification of the magnitude of changes and the variability of surface winds. This research was funded by the following projects: IBER-STILLING (RTI2018–095749-A-I00, MCIU/AEI/FEDER,UE); VENTS (GVA-AICO/2021/023) and the CSIC Interdisciplinary Thematic Platform (PTI) Clima (PTI-CLIMA).

[EN] Most studies on wind variability have deepened into the stilling vs. reversal phenomena at global to regional scales, while the long-term changes in local-scale winds such as sea-breezes (SB) represent a gap of knowledge in climate research. The state-of-the-art of the wind variability studies suggests a hypothetical reinforcement of SB at coastal stations. We frst developed a robust automated method for the identifcation of SB days. Then, by using homogenized wind observations from 16 stations across Eastern Spain, we identifed 9,349 episodes for analyzing the multidecadal variability and trends in SB speeds, gusts and occurrence for 1961¿2019. The major fnding is the opposite trends and decoupled variability of SB speeds and gusts: the SB speeds declined signifcantly in all seasons (except for winter), and the SB gusts strengthened at the annual scale and in autumn¿winter, being most signifcant in autumn. Our results also show that the SB occurrence has increased across most of Eastern Spain, although presenting contrasting seasonal trends: positive in winter and negative in summer. We found that more frequent anticyclonic conditions, NAOI+and MOI+are positively linked to the increased winter occurrence; however, the causes behind the opposite trends in SB speeds and gusts remain unclear. The SB changes are complex to explain, involving both large-scale circulation and physical-local factors that challenge the understanding of the opposite trends. Further investigation is needed to assess whether these trends are a widespread phenomenon, while climate models could simulate the drivers behind these decoupled SB changes in a warmer climate., We thank AEMET for the observed wind speed data. This research was funded by the following projects: IBER-STILLING (RTI2018-095749-A-I00, MCIU/AEI/FEDER,UE); VENTS (GVA-AICO/2021/023) and the CSIC Interdisciplinary Thematic Platform (PTI) Clima (PTI-CLIMA). C.A.M was granted by Ramon y Cajal fellowship (RYC-2017-22830), and supported by a 2021 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation. This study is also supported by Unidad Asociada CSIC-Universidad de Vigo: Grupo de Fisica de la Atmosfera y del Océano. The authors wish to acknowledge the anonymous reviewers for their detailed and helpful comments to the original manuscript.

Surfing is one of the most popular activities in coastal tourism resorts. However, the sport depends strongly on the met-ocean weather conditions, particularly on the surface wind-generated waves that reach the coast. This study provides examples of how users? needs and user perspectives are considered by climate data specialists to develop needed, highly useful information addressing human and social needs. In this vein, the climate analysis of such data can provide input on the expected length of a surfing season, according to the surfer?s level of expertise. In addition, other water sports, such as SUP Wave and windsurfing, among others, might be indicated when surfing conditions are not optimal. Finally, the safety of surfers and other tourists who venture into the sea is also dependent on those conditions. We collaborated with the surfing community to define a series of indices for quantifying surfing days (SD), surfing days stratified by surfers? skills (SDS), alternate offers (AOs), and surfers? and swimmers? safety (SuS and SwS). These are of general applications but require wind and wave data at a very fine scale as the input. To illustrate the potential of our indices, we applied them to the Somo beach (Cantabria, Spain). We downscaled a global wave hindcast dataset covering a 30-year period to a spatial resolution of 100 m to obtain wave-surfing information at Somo?s surf spot. The results confirmed Somo?s status as a year-round surf spot, with SD values of 229.5 days/year and monthly values between 22 days/month and 16 days/month. SDS showed different seasonal peaks according to the surfers? skills. Beginners? conditions occurred more often in the summer (18.1 days/month in July), intermediate surfers? conditions appeared in the transitional seasons (14.1 days/month in April), and advanced and big-wave riders in the winter (15.1 days/month in January and 0.7 days/month, respectively). The AO index identified the SUP wave values of 216 days/year. Wind water sports presented values of 141.6 days/year; conversely, SUP sports were possible on only 7.4 days/year. SuS and SwS identified different seasonal hazard values, decreasing from the winter, autumn, and spring to minimum values in the summer. This research is within the INDECIS project (INDECIS is part of ERA4CS, an ERA-NET initiated by JPI Climate and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), and ANR (FR) with co-funding by the European Union Grant 690462). This article publication was possible with the support of the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya, the European Union (UE), and the European Social Fund (ESF) (Doctoral Research Grant 2021FI_B2 00147—Formació personal investigador novell).

This study analyses quality controlled and homogenized near-surface wind speed series (SWS) at 87 meteorological stations distributed across Spain and Portugal for 1961–2019. Multidecadal variability analysis of both mean and gusts SWS confirms for the first time in the region the cessation of the stilling (decline of SWS) and a possible weak reversal phenomenon (increase in SWS) in the last decades, varying its onset from 1999 to 2018, depending on the season and variable. Different atmospheric circulation drivers are evaluated to explain the stilling and reversal phenomena. Among the chosen teleconnection indices, the Western Mediterranean Oscillation index (WeMOi) is the one that presents the highest (positive) correlation with SWS, although the correlation (negative) with the North Atlantic Oscillation index and the Mediterranean Oscillation index in winter is also statistically significant (p < 0.05). Results show that the interannual variability of the WeMOi could exert a strong influence on both the stilling and reversal phenomena, as it displays statistically significant negative trends for 1961–2010 and positive ones (non-significant) for 2010–2019, in agreement with the observed SWS trends. The use of the Jenkinson and Collison weather type classification reveals the marked influence of certain weather types in modulating SWS changes (i.e., west, northwest, and anticyclonic types). We proposed the increase in atmospheric thermal stability and the northward shift of the jet stream as principal causes of the stilling phenomenon in this region. Our results improve our understanding of wind changes, and highlight the importance of regional assessments to discern their socioeconomic and environmental impacts., E.U.-C. was supported by the FPI fellowship (PRE2019-090148), and C.A.-M. by the Ramon y Cajal fellowship (RYC-2017-22830). This study was conducted in the framework of the IBER-STILLING project RTI2018-095749-A-100 (MCIU/AEI/FEDER,UE), the VENTS project AICO/2021/023 (GVA) and the CSIC Interdisciplinary Thematic Platform (PTI) Clima. (PTI-CLIMA).

Conventional time series analysis of observed near-surface wind speed (SWS) have focused both on mean values and on the sign, magnitude and statistical significance of trends. Specifically, a decrease in the SWS has been detected in continental surfaces of the planet's mid-latitudes from 1979 to 2010 approximately, the so-called stilling phenomenon; and an increase from 2010 until now, the reversal phenomenon. However, although various hypotheses have been proposed in the scientific literature, the mechanisms behind these phenomena and what evolution this parameter will follow in the future are still understudied, mainly because the response of a variable dependent on atmospheric circulation, such as wind speed, to a warming climate is uncertain. This study aims to use spectral analysis (Fourier and wavelet) to determine the most significant frequency modes associated with the SWS time series in the Iberian Peninsula (IP), for both mean wind speed and daily peak wind gusts, as well as its temporal evolution for 1961-2019. Subsequently, this study will also attempt to relate these modes to those corresponding to various modes of ocean-atmosphere variability such as the El Niño-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) or, due to the influence of sudden stratospheric warming (SSW) in the European troposphere, the polar vortex. The ultimate goal will be to explore possible sources of predictability in the medium-long term for SWS across the IP, which would have direct applications in areas such as: wind-power generation, agriculture, air quality, insurance and fishing industries, among many others socioeconomic and environmental issues.

This paper extends the work of previous research by investigating surfing practices and surf-recreation companies from a behavioral perspective. The study’s main aim is to gain insights into the role of meteorological/climatological information in decision-making related to the surf-tourism activities market. This information was gathered employing an online survey that asked respondents about where they surf and how they check forecasts for surfing. Climate services (CS) are promoted to support the decision-making process to better prepare for and adapt to the risks and opportunities of climate variability and change. The current market for CS is still in its early stages. In this paper, we report the findings from our recent investigation into the actual and potential market for CS for the Iberian Peninsula surf-tourism sector. Based on surfers’ and surf companies’ demands, it was found that an improved surfing climate service (herein, SCS) will have clear implications in the management of these tourism areas and provide insights into whether surfing activities may be successful. At the same time, such services can help to manage adaptive actions in regard to the impacts of climate change in surfing areas.

Trabajo presentado en EGU General Assembly, celebrada en modalidad virtual del 4 al 8 de mayo de 2020., In a context of global climate change, the scientific community has evidenced a significant decrease in wind speed, a phenomenon known as «stilling». This climate trend has mainly been observed over mid-latitude continental surfaces since the 1980s. On the contrary, other studies have detected an increase in wind speed over ocean surfaces; and there is little conclusive scientific evidence on trends in wind speed across the troposphere. Furthermore, a reversal in global terrestrial stilling has recently been documented in few regional and global studies since the 2010s. The causes associated with the climate variability of wind speed have not yet been resolved and there are many uncertainties behind the «stilling» and «recovery» phenomenon because neither the quantity nor the quality of wind speed observations is adequate. This contribution shows an overview of the IBER-STILLING project (RTI2018-095749-A-I00) funded by the Spanish Ministry of Science, Innovation and Universities. This project aims to move forward on the assessment of wind speed and wind gusts variability and underlying causes globally, with emphasis on the Spanish territory and surrounding ocean (Atlantic) and sea (Mediterranean) surfaces. The IBER-STILLING project will collect and generate climate information of wind speed from different data sources; climate data will be subject to a comprehensive protocol for quality control and homogenization. The statistical analysis of these climate databases will allow characterizing trends and climatic cycles of wind speed, allowing a pioneering global analysis of wind speed over continental and ocean surfaces, and across the boundary layer and the entire troposphere. The project will also conduct wind-tunnel experiments to quantify biases introduced by anemometers devices.

Monitoring and management of several environmental and socioeconomic sectors require climate data that can be summarized using a set of standard and meaningful climate metrics. This study describes a newly developed gridded dataset for the whole of Europe, which employed a set of 125 climate indices spanning different periods based on data availability, but mainly 1950–2017 and 1979–2017. This dataset comprehensively summarizes climate variability in Europe for a wide range of climate variables and conditions, including air temperature, precipitation, biometeorology, aridity, continentality, drought, amongst others. Climate indices were computed at different temporal scales (i.e. monthly, seasonal and annual) and mapped at a grid interval of 0.25°. We intend to update these indices on an annual basis. This dataset is freely available to research and end-user communities., Measurement(s)climateTechnology Type(s)computational modeling techniqueFactor Type(s)air temperature • precipitation • sea level pressure • wind at 10 m • wind gust at 10 m • TOA radiation • radiation • insolation • snow density • snow depth • snowfall • total cloud cover • low cloud coverSample Characteristic - EnvironmentclimateSample Characteristic - LocationEurope Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12029427

A fundamental key to understanding climate change and its implications is the availability of databases with wide spatial coverage, over a long period of time, with constant updates and high spatial resolution. This study describes a newly gridded data set and its map viewer “European Climatology and Trend Atlas of Climate Indices” (ECTACI), which contains four statistical parameters (climatology, coefficient of variation, slope, and significant trend) from 125 standard climate indices for the whole Europe at 0.25° grid intervals from 1979 to 2017 at various temporal scales (monthly, seasonal, and annual). In addition, this study shows, for the first time, the general trends of a wide variety of updated standard climate indices at seasonal and annual scales for the whole of Europe, which could be a useful tool for climate analysis and its impact on different sectors and socioeconomic activities. The data set and ECTACI map viewer are available for free (http://ECTACI.csic.es/).

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org. Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.

A dense monthly precipitation dataset of Ireland and Northern Ireland was homogenized with several modern homogenization methods. The efficiency of these homogenizations was tested by examining the similarity of homogenization results both in the real data homogenization and in the homogenization of a simulated dataset. The analysis of homogenization results shows that the real dataset is characterized by a large number of, but mostly small, non-climatic biases, and a moderate reduction of such biases can be achieved with homogenization. Finally, a combination of the ACMANT and Climatol homogenization results was applied to improve the data accuracy before the trend calculations. These two methods were selected for their proven high accuracy, missing data tolerance and ability to complete time series via the infilling of missing values before the trend calculations. Metadata were used within the Climatol method. To facilitate this analysis the study area was split into smaller climatic regions by using the Ward clustering method. Five climatic zones consistent with the known spatial patterns of precipitation in Ireland were established. Linear regression fitting and the Mann-Kendall test were applied. Low frequency fluctuations were also examined by applying a Gaussian filter. The results show that the precipitation amount generally increases in the study area, particularly in the northwestern region. The most significant increasing trends for the whole study period (1941–2010) are found for late winter and spring precipitation, as well as for the annual totals. In the period from the early 1970s the increase of precipitation is general in all seasons of the year except in winter, but the statistical significance of this increase is weak.

Time series homogenization for 299 of the available precipitation records for the island of Ireland (IENet) was performed. Four modern relative homogenization methods, that is, HOMER, ACMANT, CLIMATOL and AHOPS were applied to this network of station series where contiguous intact monthly records range from 30 to 70 years within the base period 1941–2010. Break detection results are compared between homogenization methods, and coincidences with available documentary information (metadata) were analysed. The lowest (highest) number of breaks were detected with HOMER (ACMANT). Large differences of break frequency were found, namely ACMANT and AHOPS detected 8 times as many breaks than HOMER, while the break frequency with CLIMATOL was intermediate. Also, the ratio of series classified to be homogeneous varies widely between the methods. It is 85% with HOMER, 60% with CLIMATOL, 31% with AHOPS, while only 22% with ACMANT. In a further experiment, all the available time series for Ireland and Northern Ireland, (910 series) were used with ACMANT and CLIMATOL to explore the stability of break frequency for the same 299 series examined in the base experiment. While overall break frequency slightly increased (by 6–13%), the break positions notably changed for individual time series. The number of breaks changed for 59% (23%) of the series with ACMANT (CLIMATOL). For the breaks detected coincidentally by at least three methods including ACMANT and CLIMATOL in the base experiment, the second experiment confirmed the break positions in 86–87% of the breaks. The consequences of these results in relation to the reliability of statistical homogenization are discussed. Sometimes markedly different step functions provide comparable good approaches. However, the accuracy of homogenized time series cannot be related directly to the instability of break detection results., We found 116 concordant breaks indicated with at least three homogenization methods including metadata analysis. This together with some other aspects of the results suggests that the break frequency is higher than in several other European precipitation datasets. However, spatial correlations are generally higher in IENet than in many other precipitation datasets. Additionally, the ratio of series found to be homogeneous and the frequency of detected breaks are strongly method dependent. In the figure, ratios of homogeneous series for the four programmes. Homogeneous series are denoted in blue; series with one detected break in yellow and series with multiple breaks detected in red.

This article presents an exploratory methodology to co-create climate services for the tourism sector together with local stakeholders, emphasizing focus groups as an essential step for obtaining relevant data throughout the process. The article describes the user engagement for defining the optimal conditions for tourism in four different types of destinations in terms of tourist specialisation in Spain (Jacetania Council in the Aragon Pyrenees, the city of Calvia on the island of Majorca; the city of Barcelona and the Barcelona Coast) and Italy (Sila National Park) This methodology involves a sequence of steps to extract and validate such information through engagement, with destination stakeholders along the value chain (from accommodation managers to destination planners as well as final users). The process facilitates the design of numerical indices based on the information collected (from qualitative to quantitative data). Our methodology is suitable for application in other contexts and tourism activities where the subjective perception of weather and climate plays a role, as well as in other sectors.

Drought variability and change was assessed across the Iberian Peninsula over more than 100 years expanding through the twentieth century and the first decade of the twenty-first century. Daily temperature and precipitation data from 24 Iberian time series were quality controlled and homogenized to create the Monthly Iberian Temperature and Precipitation Series (MITPS) for the period 1906–2010. The Standardized Precipitation Index (SPI), driven only by precipitation, and the Standardized Precipitation Evapotranspiration Index (SPEI), based on the difference between the precipitation and the reference evapotranspiration (ET0), were computed at annual and seasonal scale to describe the evolution of droughts across time. The results confirmed that a clear temperature increase has occurred over the entire Iberian Peninsula at the annual and seasonal scale, but no significant changes in precipitation accumulated amounts were found. Similar drought variability was provided by the SPI and SPEI, although the SPEI showed greater drought severity and larger surface area affected by drought than SPI from 1980s to 2010 due to the increase in atmospheric evaporative demand caused by increased temperatures. Moreover, a clear drying trend was found by the SPEI for most of the Iberian Peninsula at annual scale and also for spring and summer, although the SPI did not experience significant changes in drought conditions. From the drying trend identified for most of the Iberian Peninsula along the twentieth century, an increase in drought conditions can also be expected for this region in the twenty-first century according to future climate change projections and scenarios.

Long-term drought variability and trends were assessed in Barcelona at annual and seasonal scale for the period 1787-2014 and sub-periods 1851-2014, 1901-2014 and 1951-2014 to identify changes in drought patterns across time. High quality and adjusted monthly temperature and precipitation series were required for this purpose. The Standardized Precipitation Index (SPI), based on precipitation, and the Standardized Precipitation Evapotranspiration Index (SPEI), based on the difference between precipitation and reference evapotranspiration (ET0), were calculated to describe temporal drought fluctuations. Therefore, major droughts and wet events were identified and an accurate analysis of drought severity, magnitude and duration were also carried out.Both drought indices provided similar results related to drought variability and trends in Barcelona across time, although the SPEI showed larger drought severity than SPI especially during the second half of the 20th century. Trends analysis revealed a significant drying trend at annual scale according to the SPEI since mid-19th century while the SPI did not show changes in drought patterns. At seasonal scale, both the SPI and SPEI found a clear drying trend only for summer (JJA) during the current period (1951-2014), although the SPEI was indicating the trend towards drier conditions for the whole period (1787-2014). Drought severity in SPEI series increased 13% during the second half of the 20th century compared with the whole period under study while drought magnitude and duration did not present significant changes in both the SPI and SPEI series. The increasing atmospheric evaporative demand associated to the large temperature rising experienced in Barcelona during the last decades could have played a substantial role in explaining the increase of drought severity and trends found in the SPEI series.

This work was supported by the projects “Creacion de una base de datos climatica de calidad para el estudio del cambio climatico de las montanas del Peru-COOPB20042” and ‘Test multisectorial y actividades demostrativas sobre el potencial desarrollo de sistemas de monitorizacion de sequias en tiempo real en la region del oeste de Sudamerica-I-COOP H2O 2013CD0006’, both funded by the CSIC (Spanish Research Council). We thank the Ecuador National Institute for Meteorology and Hydrology (Instituto Nacional de Meteorologia e Hidrologia, INAMHI) and the International Research Center on EL Nino (Centro Internacional para la Investigacion del Fenomeno de El Nino, CIIFEN) for providing and pre-processing the climate data used in this work. ASL was supported by the postdoctoral fellowship JCI-2012-12508.

This work was supported by the projects ‘Creacion de una base de datos climatica de calidad para el estudio del cambio climatico de las montanas del Peru-COOPB20042’ and ‘Test multisectorial y actividades demostrativas sobre el potencial desarrollo de sistemas de monitorizacion de sequias en tiempo real en la region del oeste de Sudamerica- I-COOP H2O 2013CD0006’, both funded by the CSIC (Spanish Research Council). C. A-M was supported by the JCI-2011-10263 grant. A. S-L was supported as a postdoctoral fellow (2011 BP-B 00078 and JCI-2012-12508). Both funded by the Spanish Ministry of Economy.

Extreme heat poses significant risks to the world’s growing urban population, and the heat stress to human health is likely to escalate with the anthropogenically increased temperatures projected by climate models. Thus, the additional heat from the urban heat island (UHI) effect needs to be quantified, including the spatial pattern. This study focuses on the city of Valencia (Spain), investigating the intensity and spatial pattern of UHI during three consecutive hot summer days accompanying a heat record. For the analysis, long-term in situ measurements and remote sensing data were combined. The UHI effect was evaluated using two approaches: (a) based on air temperature (AT) time-series from two meteorological stations and (b) using land surface temperature (LST) images from MODIS products by NASA with 1 km resolution. The strongest nighttime UHI estimated from AT was 2.3 °C, while the most intense surface UHI calculated as the difference between the LST of urban and rural regions (defined by NDVI) was 2.6 °C—both measured during the night after the record hot day. To assess the human thermal comfort in the city the Discomfort Index was applied. With the increasing number of tropical nights, the mitigation of nighttime UHI is a pressing issue that should be taken into consideration in climate-resilient urban planning.

48 Pags.- 1 Tabl.- 18 Figs. The definitive version is available at: http://link.springer.com/journal/382, In this study, we analyzed the influence of El Niño–Southern Oscillation (ENSO) on the spatio-temporal variability of droughts in Ecuador for a 48-year period (1965–2012). Droughts were quantified from 22 high-quality and homogenized time series of precipitation and air temperature by means of the Standardized Precipitation Evapotranspiration Index. In addition, the propagation of two different ENSO indices (El Niño 3.4 and El Niño 1 + 2 indices) and other atmospheric circulation processes (e.g., vertical velocity) on different time-scales of drought severity were investigated. The results showed a very complex influence of ENSO on drought behavior across Ecuador, with two regional patterns in the evolution of droughts: (1) the Andean chain with no changes in drought severity, and (2) the Western plains with less severe and frequent droughts. We also detected that drought variability in the Andes mountains is explained by the El Niño 3.4 index [sea surface temperature (SST) anomalies in the central Pacific], whereas the Western plains are much more driven by El Niño 1 + 2 index (SST anomalies in the eastern Pacific). Moreover, it was also observed that El Niño and La Niña phases enhance droughts in the Andes and Western plains regions, respectively. The results of this work could be crucial for predicting and monitoring drought variability and intensity in Ecuador., This work was supported by the EPhysLab (UVIGO-CSIC Associated Unit) and the research projects I-COOP H2O 2013CD0006: “Test multisectorial y actividades demostrativa sobre el potencial desarrollo de sistemas de monitorización de sequías en tiempo real en la región del oeste de Sudamérica” financed by the Spanish National Research Council, CGL2011-27574-CO2-02, CGL2014-52135-C03-01 and Red de variabilidad y cambio climático RECLIM (CGL2014-517221-REDT), financed by the Spanish Commission of Science and Technology and FEDER, and “LIFE12 ENV/ES/000536-Demonstration and validation of innovative methodology for regional climate change adaptation in the Mediterranean area (LIFE MEDACC)” financed by the LIFE programme of the European Commission. Cesar Azorin-Molina was supported by the JCI-2011-10263 Grant. Arturo Sanchez-Lorenzo was supported by the JCI-2012-12508 Grant. Miquel Tomas-Burguera was supported by a doctoral grant by the Ministry of Economy and Competitiveness and Natalia Martin-Hernandez was supported by a doctoral grant by the Aragón Regional Government. E. Aguilar was funded by the Grant CCI-009-ATN/OC-12439-RG-2012 from the Banco Iberoamericano de Desarrollo.

In this study, the spatio-temporal variability and trends of droughts across Bolivia between 1955 and 2012 were investigated using two climate drought indices: the Standardized Precipitation Index (SPI), which is based on precipitation data, and the Standardized Precipitation Evapotranspiration Index (SPEI), which is based on the difference between the precipitation and the reference evapotranspiration (ETo). We found that the average drought conditions across the country showed a temporal behaviour mainly characterized by decadal variations. The spatial pattern of drought evolution showed marked differences between the Amazonian region and the Bolivian Altiplano. Both regions showed different drought periods, a lower frequency of drought variability in the Amazon region and trends towards drier conditions in the Altiplano, mainly due to a higher atmospheric water demand as a consequence of increased ETo. We also showed that inclusion of ETo, obtained from maximum and minimum temperature records, increased the spatial heterogeneity of the drought evolution in relation to the evolution observed when only precipitation droughts were considered. The SPEI, the calculation of which includes precipitation and ETo, indicated intensification in drought severity in the last years analysed relative to the pattern found when precipitation droughts alone were considered, and also indicated an increase in the magnitude and duration of drought events. The potential for increasing drought conditions under various climate change scenarios is discussed.

Una de las principales novedades que aportará el proyecto MULTITEST consiste en la comparación sistemática de procedimientos automáticos de homogenización aplicados a redes de series de precipitación representativas de climas muy diferentes, algo que no se había hecho hasta ahora. Para la generación de series de temperatura con inhomogeneidades prescritas se han desarrollado una cantidad importante de metodologías en los proyectos previos de intercomparación de procedimientos de homogeneización, como en la pasada Acción COST ES0601. Pero con la precipitación surgen complicaciones evidentes: la distribución (salvo en determinados climas y/o épocas del año) no es normal, sino que tiene habitualmente un sesgo positivo que puede llegar a ser muy pronunciado en climas secos; en estos climas es frecuente la presencia de numerosos ceros de precipitación mensual lo que requiere un tratamiento especial. La ausencia de normalidad complica además la deseable replicación en la red simulada de la estructura de covarianza espacial de los datos observados. En este trabajo presentamos una nueva metodología que consigue, en principio, por una parte reproducir en la red de puntos simulados las características univariantes de las series que cabría esperar a partir de la climatología observada, y por otra conservar la estructura de dependencia espacial. Se muestran y analizan resultados de tres redes simuladas de clima mediterráneo, atlántico y monzónico. One of the main innovations that MULTITEST introduces is that it will allow the systematic comparison of automatic homogenization procedures over networks representative of very disparate climates, something undone up to now. For temperature series generation with inhomogeneities added several methods have been developed under the past COST Action ES0601. But with precipitation there appear a number of complications: non-normality of the underlying distribution (except for some climates and/or year times) but instead it has a positive bias that can be quite great in dry climates; in these last climates it is also fairly common that one encounters null values which requires a special treatment. The absence of normality also disturbs the desirable replication of the spatial covariance structure of the observations. In this work a novel methodology is presented that achieves in principle both the reproduction on simulated points of the univariate characteristics according to the observed climatology and to conserve the spatial structure of dependence. Results of three simulated networks with Mediterranean, Atlantic and monsoon climates are presented and discussed. El proyecto MULTITEST está financiado por el Ministerio de Economía y Competitividad (CGL2014-52901-P). Enric Aguilar y Manola Brunet agradecen el apoyo del proyecto europeo titulado: Uncertainties in Ensembles of Regional Reanalyses (UERRA. FP7-SPACE-2013-1, No. de referencia: 607193).

This paper shows the results obtained for the calculation of annual and seasonal trends of maximum and minimum temperature and precipitation from three climatic series of the Principality of Andorra (Pyrenees), and located between 1140 and the 1645 m. The estimated trends have been obtained for the period 1935-2008 corresponding to the whole length of the series, and for the sub-period 1950-2008. From the same series, at a daily resolution, the indices of change defined by the ETCCDMI (Expert Team on Climate Change Detection Monitoring and Indices) have been also calculated. The results represent a solid description of the Andorran climate dynamics over the past eight decades and provide insight into the variability and climate change experienced in the Pyrenean country, characterized by its high altitude. The results confirm the trends identified regionally by other agencies, both French and Spanish. This confirms the significant increase in air temperature for the annual mean maximum temperature (0.13 to 0.15 ºC/decade), for the summer maximum temperature (0.22 ºC/decade), and the summer minimum temperature (0.11 ºC/ decade). These trends are reinforced when the analysis focuses on the sub-period 1950-2008. For precipitation and for the period 1935-2008, only significant decreasing trends are obtained in the station Central and in summer (-5.4%/decade), while this behaviour is generalized and strengthened if we stick to the period 1950-2008. Finally, the results for the calculation of the ETCCDMI daily indices confirm and detail the results obtained at coarser resolution. Thus, the indices computed from the temperature series show an increase in extreme warm days and a decrease of their opposites. For precipitation rates, significant trends were not found for the period 1935-2008, while several indices did show statistically significant decreasing values for the sub-period 1950-2008.En este trabajo se muestran los resultados obtenidos del cálculo de las tendencias anuales y estacionales de la temperatura máxima, temperatura mínima y la precipitación en el Principado de Andorra (Pirineos) a partir del análisis de tres series climáticas situadas entre los 1140 y los 1645 m. Los cálculos de tendencia se han obtenido para el periodo 1935-2008 correspondiente a la longitud total de la series, así como para el sub-periodo 1950-2008. A partir de las mismas series a resolución diaria se han calculado los índices de cambio climático definidos por el ETCCDMI (Expert Teamfor Climate Change Detection Monitoring and Indices). Los resultados confirman las tendencias detectadas regionalmente por otros organismos, tanto franceses como españoles. Así, se confirma el incremento significativo de la temperatura del aire para la temperatura máxima media anual (0.13 a 0.15 ºC / década), para la máxima estival (0,22 ºC / década), y la mínima estival (0.11 ºC / década). Dichas tendencias al incremento térmico se refuerzan cuando el análisis se centra en el sub-periodo 1950-2008. En cuanto a la precipitación, se obtienen tendencias decrecientes y estadísticamente significativas para el periodo 1935-2008 en la estación de Central y en verano (-5.4 %/década), mientras que este comportamiento se generaliza y refuerza si nos ceñimos al periodo 1950-2008. Finalmente, los resultados del cálculo de los índices diarios del ETCCDMI confirman y detallan lo ya obtenido a más baja resolución. Así, los índices computados a partir de las series de temperatura indican un claro incremento de extremos asociados a días cálidos y un descenso de sus opuestos. En cuanto a los índices de precipitación, no se detecta ninguno con tendencia significativa para el periodo 1935-2008, mientras que varios de ellos sí que presentan valores decrecientes con significación estadística para el sub-periodo 1950-2008.

The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.

Here we present an exploratory statistical analysis aimed at the minimization of the ‘screen bias’ from affected ancient air temperature time series over the Western Mediterranean. Our approach lies in the statistical analysis of about 6 years of daily paired temperature observations taken using the ancient Montsouri shelter and the modern Stevenson screen for daily maximum (Tx) and minimum (Tn) temperature data recorded at two experimental sites: the meteorological gardens of La Coruna and Murcia, Spain (locations under the influence of the Oceanic/Atlantic/Galician and Mediterranean arid and semi-arid climate types, respectively), where ongoing field trials have been carried out. Descriptive statistical analysis of the paired series shows pre-sheltered temperatures tended to induce a strong warm bias in Tx data (of about 1 °C at the annual scale but with a clear seasonal cycle with higher values in summer and lower in winter), while Tn readings have a small (∼0.2 °C, and sustained all year round) cold bias compared to the modern period. Statistical relationships between the screen bias and other related meteorological variables show the highest correlation coefficients between the ‘screen bias’ and Tx, Tn and the diurnal temperature range (DTR) recorded under the replicated ancient shelters at both locations and point to the reliability of these variables as potential predictors of the ΔTx. We generate a parsimonious regression model based on the data from both experimental sites, which takes into account polynomial terms of lower order for the predictor variables (Tx and DTR recorded under the ancient shelter) and harmonic terms, in order to represent the seasonal cycle of the screen bias. The goodness-of-fit of the model is satisfactory, as it explains up to 51.7% of the additional ΔTx variability. Copyright © 2010 Royal Meteorological Society

26 páginas.- Los datos puros del artículo pueden consultarse en el siguiente enlace: https://digital.csic.es/handle/10261/103342., This study presents monthly and annual climate maps for relevant hydroclimatic variables in Bolivia. We used the most complete network of precipitation and temperature stations available in Bolivia, which passed a careful quality control and temporal homogenization procedure. Monthly average maps at the spatial resolution of 1 km were modeled by means of a regression-based approach using topographic and geographic variables as predictors. The monthly average maximum and minimum temperatures, precipitation and potential exoatmospheric solar radiation under clear sky conditions are used to estimate the monthly average atmospheric evaporative demand by means of the Hargreaves model. Finally, the average water balance is estimated on a monthly and annual scale for each 1 km cell by means of the difference between precipitation and atmospheric evaporative demand. The digital layers used to create the maps are available in the digital repository of the Spanish National Research Council.

The International Surface Temperature Initiative (ISTI) is striving towards substantively improving our ability to robustly understand historical land surface air temperature change at all scales. A key recently completed first step has been collating all available records into a comprehensive open access, traceable and version-controlled databank. The crucial next step is to maximise the value of the collated data through a robust international framework of benchmarking and assessment for product intercomparison and uncertainty estimation. We focus on uncertainties arising from the presence of inhomogeneities in monthly surface temperature data and the varied methodological choices made by various groups in building homogeneous temperature products. The central facet of the benchmarking process is the creation of global scale synthetic analogs to the real-world database where both the "true" series and inhomogeneities are known (a luxury the real world data do not afford us). Hence algorithmic strengths and weaknesses can be meaningfully quantified and conditional inferences made about the real-world climate system. Here we discuss the necessary framework for developing an international homogenisation benchmarking system on the global scale for monthly mean temperatures. The value of this framework is critically dependent upon the number of groups taking part and so we strongly advocate involvement in the benchmarking exercise from as many data analyst groups as possible to make the best use of this substantial effort.

The International Surface Temperature Initiative (ISTI) is striving towards substantively improving our ability to robustly understand historical land surface air temperature change at all scales. A key recently completed first step has been collating all available records into a comprehensive open access, traceable and version-controlled databank. The crucial next step is to maximise the value of the collated data through a robust international framework of benchmarking and assessment for product intercomparison and uncertainty estimation. We focus on uncertainties arising from the presence of inhomogeneities in monthly mean land surface temperature data and the varied methodological choices made by various groups in building homogeneous temperature products. The central facet of the benchmarking process is the creation of global-scale synthetic analogues to the real-world database where both the "true" series and inhomogeneities are known (a luxury the real-world data do not afford us). Hence, algorithmic strengths and weaknesses can be meaningfully quantified and conditional inferences made about the real-world climate system. Here we discuss the necessary framework for developing an international homogenisation benchmarking system on the global scale for monthly mean temperatures. The value of this framework is critically dependent upon the number of groups taking part and so we strongly advocate involvement in the benchmarking exercise from as many data analyst groups as possible to make the best use of this substantial effort.

Here we show and discuss the results of an assessment of changes in both area-averaged and station-based climate extreme indices over South America (SA) for the 1950–2010 and 1969–2009 periods using high-quality daily maximum and minimum temperature and precipitation series. A weeklong regional workshop in Guayaquil (Ecuador) provided the opportunity to extend the current picture of changes in climate extreme indices over SA. Our results provide evidence of warming and wetting across the whole SA since the mid-20th century onwards. Nighttime (minimum) temperature indices show the largest rates of warming (e.g. for tropical nights, cold and warm nights), while daytime (maximum) temperature indices also point to warming (e.g. for cold days, summer days, the annual lowest daytime temperature), but at lower rates than for minimums. Both tails of night-time temperatures have warmed by a similar magnitude, with cold days (the annual lowest nighttime and daytime temperatures) seeing reductions (increases). Trends are strong and moderate (moderate to weak) for regional-averaged (local) indices, most of them pointing to a less cold SA during the day and warmer night-time temperatures. Regionally-averaged precipitation indices show clear wetting and a signature of intensified heavy rain events over the eastern part of the continent. The annual amounts of rainfall are rising strongly over south-east SA (26.41 mm/decade) and Amazonia (16.09 mm/decade), but north-east Brazil and the western part of SA have experienced non-significant decreases. Very wet and extremely days, the annual maximum 5-day and 1-day precipitation show the largest upward trends, indicating an intensified rainfall signal for SA, particularly over Amazonia and south-east SA. Local trends for precipitation extreme indices are in general less coherent spatially, but with more general spatially coherent upward trends in extremely wet days over all SA.

The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies. The algorithms were validated against a realistic benchmark dataset. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including i) the centered root mean square error relative to the true homogeneous values at various averaging scales, ii) the error in linear trend estimates and iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones. © 2013 AIP Publishing LLC.

Título del postprint: The last two centuries of environmental history in Picos de Europa National Park as assessed from sediments of a mountain lake (Lago Enol, N Iberia), We present a study of two short sediment cores recovered from Lago Enol, in the Picos de Europa National Park, Cantabrian Mountains, northern Iberia. We inferred past climate conditions and anthropogenic impacts using geochemical and biological (pollen and diatoms) variables in the dated sequences, in conjunction with temperature and precipitation data collected since 1871 at meteorological stations in the region. The record provides evidence of environmental changes during the last 200 years. At the end of the Little Ice Age (~1800-1875 AD) the region was characterized by an open landscape. Long-term use of the area for mixed livestock grazing in the mountains, and cultivation of rye during the nineteenth century, contributed to the expansion of grassland at the expense of forest. Warmer temperatures since the end of the nineteenth century are inferred from a change in diatom assemblages and development of the local forest. Socioeconomic transformation during the twentieth century, such as livestock changes related to dairy specialization, planting of non-native trees, mining activities, and management of the national park since its creation in 1918, caused profound changes in the catchment and in the lake ecology. The last several decades (~1970-2007 AD) of the Lago Enol sediment record are strikingly different from previous periods, indicating lower runoff and increasing lake productivity, particularly since AD 2000. Today, the large number of tourists who visit the area cause substantial impacts on this ecosystem. © 2011 Springer Science+Business Media B.V.

[1] A workshop on climate change indices was held at the Mauritius Meteorological Services in October 2009 to produce the first analysis of climate trends for the countries of the western Indian Ocean. Scientists brought their long-term daily temperature and precipitation for a careful assessment of data quality and homogeneity, and for the preparation of climate change indices. This paper reports on the trends in daily and extreme temperature and precipitation indices for 1961–2008. The results indicate a definitive warming of surface air temperature at land stations. Annual means of the daytime and nighttime temperatures have increased at a similar rate, leading to no discernible change in the diurnal temperature range. Significant increasing trends were found in the frequency of warm days and warm nights, while decreasing trends were observed in the frequency of cold days and cold nights. Moreover, it seems that the warm extremes have changed more than the cold extremes in the western Indian Ocean region. Trends in precipitation indices are generally weak and show less spatial coherence. Regionally, a significant decrease was found in the annual total rainfall for the past 48 years. The results also show some increase in consecutive dry days, no change in daily intensity and consecutive wet days, and a decrease in extreme precipitation events. Temperature indices are highly correlated with sea surface temperatures of the region, whereas weak correlations are found with the precipitation indices.

The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random break-type inhomogeneities were added to the simulated datasets modeled as a Poisson process with normally distributed breakpoint sizes. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones.

[1] Understanding how extremes are changing globally, regionally, and locally is an important first step for planning appropriate adaptation measures, as changes in extremes have major impacts. The Intergovernmental Panel on Climate Change's synthesis of global extremes was not able to say anything about western central Africa, as no analysis of the region was available nor was there an adequate internationally exchanged long-term daily data set available to use for analysis of extremes. This paper presents the first analysis of extremes in this climatically important region along with analysis of Guinea Conakry and Zimbabwe. As per many other parts of the world, the analysis shows a decrease in cold extremes and an increase in warm extremes. However, while the majority of the analyzed world has shown an increase in heavy precipitation over the last half century, central Africa showed a decrease. Furthermore, the companion analysis of Guinea Conakry and Zimbabwe showed no significant increases.

[1] Changes in indices of climate extremes are analyzed on the basis of daily maximum and minimum surface air temperature and precipitation at 71 meteorological stations with elevation above 2000 m above sea level in the eastern and central Tibetan Plateau (TP) during 1961–2005. Twelve indices of extreme temperature and nine indices of extreme precipitation are examined. Temperature extremes show patterns consistent with warming during the studied period, with a large proportion of stations showing statistically significant trends for all temperature indices. Stations in the northwestern, southwestern, and southeastern TP have larger trend magnitudes. The regional occurrence of extreme cold days and nights has decreased by −0.85 and −2.38 d/decade, respectively. Over the same period, the occurrence of extreme warm days and nights has increased by 1.26 and 2.54 d/decade, respectively. The number of frost days and ice days shows statistically significant decreasing at the rate of −4.32 and −2.46 d/decade, respectively. The length of growing season has statistically increased by 4.25 d/decade. The diurnal temperature range exhibits a statistically decreasing trend at a rate of −0.20°C per decade. The extreme temperature indices also show statistically significant increasing trends, with larger values for the index describing variations in the lowest minimum temperature. In general, warming trends in minimum temperature indices are of greater magnitude than those for maximum temperature. Most precipitation indices exhibit increasing trends in the southern and northern TP and show decreasing trends in the central TP. On average, regional annual total precipitation, heavy precipitation days, maximum 1-day precipitation, average wet days precipitation, and total precipitation on extreme wet days show nonsignificant increases. Decreasing trends are found for maximum 5-day precipitation, consecutive wet days, and consecutive dry days, but only the last is statistically significant.

We analyze temporal and spatial patterns of temperature change over Spain during the period 1850–2005, using daily maximum (T max), minimum (T min), and mean (T mean) temperatures from the 22 longe ...