Temperature Variations in Spain Since 1901: A Preliminary Analysis

Oñate, Juan J.; Pou, Antonio

doi:10.1002/(sici)1097-0088(199607)16:7<805::aid-joc48>3.0.co;2-z

Cited by 48 publications

(19 citation statements)

References 16 publications

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“…These components define three spatial patterns that explain the bulk of the long-term Spanish temperature change on an annual, spring, summer, and autumn basis: Northern Spain pattern (NS), Southeastern and Eastern Spain pattern (SEES), and Southwestern Spain pattern (SWS). This result is in complete agreement with the study of Oñate and Pou [1996] where the same spatial patterns are defined and in partial agreement with the findings of the study of Esteban-Parra et al [2003a] on long-term temperature variability and trends over Spain. These authors employed a nonrotated PCA approach, which identified a simpler spatial pattern of Spanish temperature variability (western and eastern parts of Spain) across the year.…”

Section: Spatial Patterns Of Long-term Temperature Changesupporting

confidence: 91%

“…[3] Within the last 10 years, many studies, focused on Spanish temperature change on a monthly basis, have shown evidence of warming over the country by analyzing data from groups of stations [e.g., Oñate and Pou, 1996;Esteban-Parra et al, 2003a] or by developing regional time series for peninsular Spain [Brunet et al, 2001a[Brunet et al, , 2001bBrunet et al, 2002;Rodríguez-Puebla et al, 2002;Brunet et al, 2005;Brunet et al, 2006] or for different subregions [Esteban-Parra et al, 1995;Abaurrea et al, 2001;Brunet et al, 2001cBrunet et al, , 2001dGalan et al, 2001;Horcas et al, 2001;Staudt, 2004;Staudt et al, 2005;Morales et al, 2005]. Even though these studies have used different spatial and temporal scales or diverse analytical approaches for assessing data quality and homogeneity, consistent and coherent temporal patterns of warming have been highlighted mainly for the second half of the twentieth century.…”

Section: Introductionmentioning

confidence: 99%

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Temporal and spatial temperature variability and change over Spain during 1850–2005

et al. 2007

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[1] 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 longest and most reliable Spanish records. Over mainland Spain, a significant (at 0.01 level) warming of 0.10°C/decade is found for the annual average of T mean . Autumn and winter contributed slightly more than spring and summer to the annual warming over the 1850-2005 period. The overall warming is also associated with higher rates of change for T max than T min (0.11°versus 0.08°C/decade for . This asymmetric diurnal warming increased in the twentieth century (0.17°versus 0.09°C/decade during 1901 -2005). Nevertheless, at many (few) individual stations, the difference between T max and T min is not statistically significant over 1850-2005 (1901-2005). Principal Component Analysis has been carried out to identify spatial modes of Spanish long-term temperature variability . Three principal spatial patterns are found, Northern Spain, Southeastern and Eastern Spain, and Southwestern Spain. All three patterns show similar significant warming trends. The overall warming has been more associated with reductions in cold extremes, as opposed to increases in warm extremes. Estimated trends in the number of moderately extreme cold days (T max < 10th percentile) and moderately extreme cold nights (T min < 10th percentile) show significant reductions of 0.74 and 0.54 days/decade, respectively, over 1850-2005. Moderately extreme warm days and nights (T max and T min > 90th percentile) increased significantly but at lower rates of 0.53 and 0.49 days/decade.

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Section: Spatial Patterns Of Long-term Temperature Changesupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Temporal and spatial temperature variability and change over Spain during 1850–2005

et al. 2007

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“…Plantico et al, 1990;Horton, 1995;Türkeş et al, 1996). The trends vary not only in their magnitude but also in sign even over relatively small areas such as central Europe or individual countries (Brázdil et al, 1996;Oñ ate and Pou, 1996;Ben-Gai et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Disaggregating climatic trends by classification of circulation patterns

Huth

2001

Intl Journal of Climatology

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The trends in occurrence frequencies of circulation types over Europe and in nine climate variables in the Czech Republic conditioned by the types are examined for period . The circulation types are determined by an objective procedure from daily 500 hPa heights. Both in summer and winter, anticyclonic types have become more frequent at the expense of cyclonic types. The circulation changes are shown to be unrelated to the trends in surface climate elements in summer, whereas in winter, trends in circulation explain a part of the observed warming and strengthening of southerly winds.The trends in climate elements are not uniform among circulation types. In summer, the trend pattern consisting of decreasing maximum and daily mean temperatures, daily temperature range (DTR) and sunshine duration, and increasing cloudiness and relative humidity is observed under the cyclonic types and the types with a well-pronounced jet, but is missing under types with a blocking anticyclone over Europe. Two possible mechanisms causing this trend pattern are proposed: increasing cloudiness, and a process responsible for the reduction of sunshine without a concurrent increase of cloudiness. The latter mechanism can possibly be identified with increasing aerosol concentrations. In winter, the degree of warming is governed by changes in zonal wind. The mechanism of change in DTR seems to vary with elevation: at the lowland station (Prague-Klementinum), the increase in DTR is related to the warming trend, and consequently with zonal wind changes, while at the mountain station (Milešovka), the increase in DTR reflects the increase in precipitating clouds.The changes in DTR are related much more to mid-tropospheric circulation than to cloud cover in summer, whereas in winter, cloud cover plays a more important role in affecting DTR trends.

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“…Advantages of station observation include long time records and high observation frequencies. The longest records of surface air temperature at some station can be over one hundred years [10]. At manually operated stations, observations are recorded four times per day (00:00, 06:00, 12:00 and 18:00 in UTC time) while the observation frequency at automatic weather stations can be up to one minute.…”

Section: Introductionmentioning

confidence: 99%

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

et al. 2017

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Near surface air temperature (Ta) is one of the key input parameters in land surface models and hydrological models as it affects most biogeophysical and biogeochemical processes of the earth surface system. For distributed hydrological modeling over glacierized basins, obtaining high resolution Ta forcing is one of the major challenges. In this study, we proposed a new high resolution daily Ta estimation scheme under both clear and cloudy sky conditions through integrating the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and China Meteorological Administration (CMA) land data assimilation system (CLDAS) reanalyzed daily Ta. Spatio-temporal continuous MODIS LST was reconstructed through the data interpolating empirical orthogonal functions (DINEOF) method. Multi-variable regression models were developed at CLDAS scale and then used to estimate Ta at MODIS scale. The new Ta estimation scheme was tested over the Langtang Valley, Nepal as a demonstrating case study. Observations from two automatic weather stations at Kyanging and Yala located in the Langtang Valley from 2012 to 2014 were used to validate the accuracy of Ta estimation. The RMSEs are 2.05, 1.88, and 3.63 K, and the biases are 0.42, −0.68 and −2.86 K for daily maximum, mean and minimum Ta, respectively, at the Kyanging station. At the Yala station, the RMSE values are 4.53, 2.68 and 2.36 K, and biases are 4.03, 1.96 and −0.35 K for the estimated daily maximum, mean and minimum Ta, respectively. Moreover, the proposed scheme can produce reasonable spatial distribution pattern of Ta at the Langtang Valley. Our results show the proposed Ta estimation scheme is promising for integration with distributed hydrological model for glacier melting simulation over glacierized basins.

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Temperature Variations in Spain Since 1901: A Preliminary Analysis

Cited by 48 publications

References 16 publications

Temporal and spatial temperature variability and change over Spain during 1850–2005

Temporal and spatial temperature variability and change over Spain during 1850–2005

Disaggregating climatic trends by classification of circulation patterns

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

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