Abstract
ABSTRACTIn this article, we approached the study of spatiotemporal variation in trends for the monthly mean values of maximum and minimum temperatures on the Spanish mainland between 1951 and 2010, in order to find out how length and selected periods affected trends. The trend and significance signals were calculated every month and for each cell individually, in a high spatial resolution grid (Mann–Kendall test) by using decreasing and increasing temporal windows (from 20 to 60 years and vice versa). Finally, the results are presented as a sequence of temporal window trend maps to show the spatiotemporal variability of trends at high resolution over the years. The results of increasing temporal window trends show that temperatures have increased overall on the Spanish mainland, but the impact is different for cold and warm months, maximum and minimum temperatures, and the area affected by significant trends varies depending on the month. The positive and significant trend affecting >20% of the total area extends in a west–east gradient during the cold months, while the reverse is true for the warmest ones. The analyses from decreasing the length of moving windows also vary greatly among months. The areas affected by significant trends are highly variable month‐on‐month, differ for maximum and minimum temperatures, and evolve in different ways over time. Few months show a significant trend during the last 30 years, and spatial distribution differences among trends for the maximum and minimum temperatures are detected. Spatially, a more complex gradient can be observed, but the global east–west and west–east gradient can also be generally seen in the warmest or coldest months. These findings show that a selected period determines the final trend. Furthermore, the results suggest that recent warming processes on the Spanish mainland have high spatial variability that differs among months and maximum and minimum temperatures, and has not been constant.
Highlights
The significance and trend rates of any climate variable depend heavily on the selected period (Soon et al, 2004; Liebmann et al, 2010; Lüdecke et al, 2011; Santer et al, 2011; Mauget and Cordero, 2014; Anderson and Kostinski, 2016; González-Hidalgo et al, 2016)
The complete spatial analyses comprising the whole set of temporal windows is included in Figures S1–S4, Appendix S1 for the maximum (Tmax) and Tmin, with increasing and decreasing temporal windows, respectively), and the main text only presents a discrete sequence of maps at regular time steps of 10 years
The evolution of the percentage of land affected by significant trend of Tmax in successive increasing temporal windows from 1951–1970 to 1951–2010 shows that the months highly affected by significant positive trends are January, February, March, June, and August (Figure 2 and Tables S1–S3 in Appendix S1)
Summary
The significance and trend rates of any climate variable depend heavily on the selected period (Soon et al, 2004; Liebmann et al, 2010; Lüdecke et al, 2011; Santer et al, 2011; Mauget and Cordero, 2014; Anderson and Kostinski, 2016; González-Hidalgo et al, 2016). This is true in climate research, because natural variability and induced forcing may vary at different spatial and temporal scales, depending on global and local factors (Soon et al, 2004; McKitrick, 2014; Marotzke and Forster, 2015). Fischer and Paterson (2014) described an approach to identifying the effect of both climate-related factors and observation/site-related factors on trends in meteorological data series, after noticing that trends in climate time series are often nonlinear and asymmetric in time (Cohen et al, 2012, i.e. the trend is different for different seasons), while Ribes et al (2016) discussed the uncertainty in linear trends, using a white noise assumption on the residuals for detecting temperature behaviour, and suggested that nonlinear estimates should be preferred to describe any climate trend
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