Mean temperature evolution on the Spanish mainland 1916-2015

An analysis of the evolution of annual and seasonal mean temperatures on the Spanish mainland (western Mediterranean basin) was carried out, using the new MOnthly TEmperature Dataset of Spain century (MOTEDAS-century) data set. This data set was developed by combining archives from the National Meteorological Agency and newly digitised legacy data from the Annual Books published between 1916 and 1949. Both the annual and seasonal regional mean temperature series experienced increasing trends during the study period. However, the in-crement was neither constant in time (monotonic) nor homogeneous between seasons. Four main periods were identified in the annual mean regional series. The first 3 corresponded to those previously confirmed in global temperature series (rise-pause-rise); a second pause or hiatus was also detected at the end of the period of analysis. The seasonal regional series followed specific patterns: Winter mean temperature only increased in the second rising period, autumn in the first and spring and summer during the 2 rising periods. Also, negative trends were found in extended areas in the first pause in spring, and to a lesser extent in summer. From the middle of the 1980s, the trends in annual and seasonal mean values were not significant up until 2015. Furthermore, spatial variations were found in the significance of the trends, revealing regional differences in the intensity of warming through the seasons. In comparison with other versions of secular mean temperature series developed for the Spanish mainland, MOTEDAS-century seems to better capture the spatial variability

Spatial variability of maximum and minimum monthly temperature in Spain during 1981–2010 evaluated by correlation decay distance (CDD)

The spatial variability of monthly diurnal and nocturnal mean values of temperature in Spain has been analysed to evaluate the optimal threshold distance between neighbouring stations that make a meteorological network (in terms of stations’ density) well representative of the conterminous land of Spain. To this end, the correlation decay distance has been calculated using the highest quality monthly available temperature series (1981–2010) from AEMet (National Spanish Meteorological Agency). In the conterminous land of Spain, the distance at which couples of stations have a common variance above the selected threshold (50 %, r Pearson ~0.70) for both maximum and minimum temperature on average does not exceed 400 km, with relevant spatial and temporal differences, and in extended areas of Spain, this value is lower than 200 km. The spatial variability for minimum temperature is higher than for maximum, except in cold months when the reverse is true. Spatially, highest values are located in both diurnal and nocturnal temperatures to the southeastern coastland and lower spatial variability is found to the inland areas, and thus the spatial variability shows a clear coastland-to-inland gradient at annual and monthly scale. Monthly analyses show that the highest spatial variability in maximum and minimum temperatures occur in July and August, when radiation is maximum, and in lowland areas, (<200 m o.s.l.), which coincide with the mostly transformed landscapes, particularly by irrigation and urbanization. These results highlight local factors could play a major role on spatial variability of temperature. Being maximum and minimum temperature interstation correlation values highly variable in Spanish land, an average of threshold distance of about 200 km as a limit value for a well representative network should be recommended for climate analyses,

A new climatology of maximum and minimum temperature (1951–2010) in the Spanish mainland: a comparison between three different interpolation methods

This study presents a new climatology of monthly temperature for mainland Spain (1951–2010), performed with the highest quality and spatially dense, up-to-date monthly temperature dataset available in the study area (MOTEDAS). Three different interpolation techniques were evaluated: the Local Weighted Linear Regression (LWLR), the Regression-Kriging (RK) and the Regression-Kriging with stepwise selection (RKS), a modification of RK. The performances of the different models were evaluated by the leave-one-out validation procedure, comparing the results from the models with the original data and calculating different error measurements. The three techniques performed better for Tmax than for Tmin, and for the cold, rather than warmer months, also at lower altitude than highland areas. The best results were achieved with LWLR applied for the first time on temperatures in the Spanish mainland. This method improved the accuracy of the temperature reconstruction with respect to RK and RKS. We present a collection of Tmax and Tmin monthly charts, using the same temperature legend to prevent any visual bias in the interpretation of the results. The dataset is available upon request

Variability of maximum and minimum monthly mean air temperatures over mainland Spain and their relationship with low-variability atmospheric patterns for period 1916–2015

The analysis of monthly air temperature trends over mainland Spain during 1916–2015 shows that warming has not been constant over time nor generalized among different months; it has not been synchronous for maximum and minimum air temperatures; and it has been heterogeneous in space. Temperature rose during two characteristic pulses separated by a pause around the middle of the 20th century in some months. In other months, only the second rising period is identified, or no warming can be found. In all months, and both for maximum and minimum air temperatures, a stagnation of the increasing trend is observed in the last two decades of the study period. High spatial variability exists in trend signal and significance, and two contrasting temporal patterns of advance over the study area are identified for maximum and minimum air temperatures. These patterns can be related to prevalent flow directions and relief disposition with respect to the flows associated with low-variability meteorological patterns North Atlantic Oscillation (NAO) and Western Mediterranean Oscillation (WEMO). The results show that warming is a complex phenomenon at regional and sub-regional scales that can only be analysed using high-spatial-resolution data and considering global and local factors

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MOPREDAS_century database and precipitation trends in mainland Spain, 1916–2020

Due to its geographical location in the western Mediterranean region, the Iberian Peninsula involves a challenge for current climatic conditions and future projections. In this study we analysed monthly precipitation trends over mainland Spain from 1916 to 2020 by using the new MOPREDAS_century database. This database combines information from the Spanish Meteorological Agency's archives, as well as data retrieved from Annual Summaries between 1916 and 1950. A combination of both sources produced the largest amount of original information ever collected and researched in mainland Spain between 1916 and 2020

MOPREDAScentury: a long-term monthly precipitation grid for the Spanish mainland

This article describes the development of a monthly precipitation dataset for the Spanish mainland (western Mediterranean basin), covering the period between December 1915 and December 2020. The dataset combines ground observational data from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET) and new data rescued from meteorological yearbooks published prior to 1951 that was never incorporated into the NCDB. The yearbooks data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century, the period when the dataset was more scarce. The final dataset contains records from 11,312 stations, although the number of stations with data in a given month varies largely between 674 in 1939 and a maximum of 5,234 in 1975. Spatial interpolation was used on the resulting dataset to create monthly precipitation grids. The process involved a two-stage process: estimation of the probability of zero-precipitation (dry month), and estimation of precipitation magnitude. Interpolation was carried out using universal kriging, using anomalies (ratios with respect to the 1961&ndash;2000 monthly climatology) as dependent variable and several geographic variates as independent variables. Cross-validation results showed that the resulting grids are spatially and temporally unbiased, although the mean error and the variance deflation effect are highest during the first decades of the 20th century, when the observational dataset was more scarce. The dataset is available at https://doi.org/10.20350/digitalCSIC/15136 under an open license, and can be cited as Beguer&iacute;a et al. (2023).</p

Variaciones temporales de las tendencias en la serie de temperatura de inglaterra central

Variations in trend rates of annual values of the Central England Temperature series (CET) over the period 1659-2017 were analysed using moving windows of different length, to identify the minimum period in which the trend expresses a climate signal not hidden by the noise produced by natural variability. Trend rates exhibit high variability and irregular shifting from positive to negative values unless very long window lengths (of 100 years or more) are used. In general, as the duration of the length of the temporal window analysed increases, the absolute range of the trend rates decreases and the signal-to-noise (S/N) ratio increases. The relationship between the S/N ratio and the window length also depended on the total length of the series, so high S/N values are achieved faster when shorter time series are considered. This prevents suggesting a minimum window length for undertaking trend analyses. A comparison between CET and the average continental series in the Berkeley Earth Surface Temperature (BEST) database in their common period (17532017) repeats the patterns described for 1659-2017, although the average values of the rates, ranges and the “threshold period” in years change, and are more variable in CET than in BEST. Analysis of both series suggests that the recent warming started early and can be linked to the recovery of temperatures after the Little Ice Age. This process has characterised by progressively increasing trend rates, but also includes periods of deceleration or even negative trends spanning less than 50 years. The behaviour of the two long-term temperature records analysed agrees with a long-term persistence (LTP) process. We estimated the Hurst exponent of the CET series to be around 0.72 and 0.8, which reinforces the LTP hypothesis. This implies that the currently widespread statistical framework assuming a stationary, short-memory process in which departures from the norm can be easily assessed by monotonic trend analysis should not be accepted for long climatic series. In brief, relevant questions relative to the recent evolution of temperatures such as the distinction between natural variability and departures from stationarity; attribution of the causes of variability at different time scales; determination of the shortest window length to detect a trend; and other similar ones have still not been answered and may require adoption of an alternative analytical framework

The potential of using climate indices as powerful tools to explain mortality anomalies: An application to mainland Spain

Changes in the frequency and magnitude of extreme weather events represent one of the key indicators of climate change and variability. These events can have an important impact on mortality rates, especially in the ageing population. This study assessed the spatial and seasonal distributions of mortality rates in mainland Spain and their association with climatic conditions over the period 1979–2016. The analysis was done on a seasonal and annual basis using 79 climatic indices and regional natural deaths data. Results indicate large spatial variability of natural deaths, which is mostly related to how the share of the elderly in the population varied across the studied regions. Spatially, both the highest mortality rates and the largest percentage of elders were found in the northwest areas of the study domain, where an extreme climate prevails, with very cold winters and hot summers. A strong seasonality effect was observed, winter shows more than 10% of natural deaths compared to the rest of the seasons. Also, results suggest a strong relation between climatic indices and natural deaths, albeit with a high spatial and seasonal variability. Climatic indices and natural deaths show a stronger correlation in winter and summer than in spring and autumn. © 2021 The Author