High-Resolution Temperature Datasets in Portugal from a Geostatistical Approach: Variability and Extremes

A. R. Fonseca Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Universidade de Trás-os-Montes e Alto Douro, Vila Real, Portugal

Search for other papers by A. R. Fonseca in
Current site
Google Scholar
PubMed
Close
and
J. A. Santos Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Universidade de Trás-os-Montes e Alto Douro, Vila Real, Portugal

Search for other papers by J. A. Santos in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Climate research in Portugal is often constrained by the lack of homogeneous, temporally and spatially consistent, and long-term climatic series. To overcome this limitation, the authors developed new high-resolution gridded datasets (~1 km) of daily mean, minimum, and maximum air temperatures over Portugal (1950–2015, 66 yr), based on gridded daily temperatures (E-OBS) at ~25-km spatial resolution. A two-step approach was followed, under the assumption that daily temperature variability in Portugal is mainly controlled by atmospheric large-scale forcing, while local processes are mostly expressed as strong spatial gradients. First, monthly baseline (1971–2000) patterns were estimated at 1-km grid resolution by applying multivariate linear regressions (exploratory variables: elevation, latitude, and distance to coastline). A kriging of residuals from baseline normals of 36 weather stations was applied for bias corrections. Second, bilinearly interpolated daily temperature anomalies were then added to the daily baseline patterns to obtain the final datasets. The method performance was evaluated using fivefold cross-validations. The datasets were also validated using daily temperatures from 23 stations not incorporated in E-OBS. A climatological analysis based on these datasets was carried out, highlighting spatial heterogeneities, seasonality, long-term trends, interannual variability, and extremes. The spatial and temporal variability is generally coherent with previous studies at coarser resolutions. An overall warming trend is apparent for all variables and indices, but showing different strengths and spatial variability. These datasets show important advantages over preexisting data, including more detailed and accurate information on trends and interannual variability of precipitation extremes, and can thus be applied to several areas of research in Portugal, such as hydrology, ecology, agriculture, and forestry.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10/1175/JAMC-D-17-0215.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: André Fonseca, andre.fonseca@utad.pt

Abstract

Climate research in Portugal is often constrained by the lack of homogeneous, temporally and spatially consistent, and long-term climatic series. To overcome this limitation, the authors developed new high-resolution gridded datasets (~1 km) of daily mean, minimum, and maximum air temperatures over Portugal (1950–2015, 66 yr), based on gridded daily temperatures (E-OBS) at ~25-km spatial resolution. A two-step approach was followed, under the assumption that daily temperature variability in Portugal is mainly controlled by atmospheric large-scale forcing, while local processes are mostly expressed as strong spatial gradients. First, monthly baseline (1971–2000) patterns were estimated at 1-km grid resolution by applying multivariate linear regressions (exploratory variables: elevation, latitude, and distance to coastline). A kriging of residuals from baseline normals of 36 weather stations was applied for bias corrections. Second, bilinearly interpolated daily temperature anomalies were then added to the daily baseline patterns to obtain the final datasets. The method performance was evaluated using fivefold cross-validations. The datasets were also validated using daily temperatures from 23 stations not incorporated in E-OBS. A climatological analysis based on these datasets was carried out, highlighting spatial heterogeneities, seasonality, long-term trends, interannual variability, and extremes. The spatial and temporal variability is generally coherent with previous studies at coarser resolutions. An overall warming trend is apparent for all variables and indices, but showing different strengths and spatial variability. These datasets show important advantages over preexisting data, including more detailed and accurate information on trends and interannual variability of precipitation extremes, and can thus be applied to several areas of research in Portugal, such as hydrology, ecology, agriculture, and forestry.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10/1175/JAMC-D-17-0215.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: André Fonseca, andre.fonseca@utad.pt

Supplementary Materials

    • Supplemental Materials (PDF 4.58 MB)
Save
  • Ahmed, K., S. Shahid, and S. Bin Harun, 2015: Statistical downscaling of rainfall in an arid coastal region: A radial basis function neural network approach. Appl. Mech. Mater., 735, 190194, https://doi.org/10.4028/www.scientific.net/AMM.735.190.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andrade, C., S. M. Leite, and J. A. Santos, 2012: Temperature extremes in Europe: Overview of their driving atmospheric patterns. Nat. Hazards Earth Syst. Sci., 12, 16711691, https://doi.org/10.5194/nhess-12-1671-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andrade, C., H. Fraga, and J. A. Santos, 2014: Climate change multi-model projections for temperature extremes in Portugal. Atmos. Sci. Lett., 15, 149156, https://doi.org/10.1002/asl2.485.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beldring, S., T. Engen-Skaugen, E. J. Førland, and L. A. Roald, 2008: Climate change impacts on hydrological processes in Norway based on two methods for transferring regional climate model results to meteorological station sites. Tellus, 60A, 439450, https://doi.org/10.1111/j.1600-0870.2008.00306.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CDO, 2015: Climate data operators. Max Planck Institute for Meteorology, http://www.mpimet.mpg.de/cdo.

  • Chen, J., F. P. Brissette, D. Chaumont, and M. Braun, 2013: Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resour. Res., 49, 41874205, https://doi.org/10.1002/wrcr.20331.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, J.-L., H. Kang, C.-Y. Tam, C.-K. Park, and C.-T. Chen, 2008: Seasonal forecast for local precipitation over northern Taiwan using statistical downscaling. J. Geophys. Res., 113, D12118, https://doi.org/10.1029/2007JD009424.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Costa, R., H. Fraga, P. M. Fernandes, and J. A. Santos, 2017: Implications of future bioclimatic shifts on Portuguese forests. Reg. Environ. Change, 17, 117127, https://doi.org/10.1007/s10113-016-0980-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daly, C., W. P. Gibson, G. H. Taylor, G. L. Johnson, and P. Pasteris, 2002: A knowledge-based approach to the statistical mapping of climate. Climate Res., 22, 99113, https://doi.org/10.3354/cr022099.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Smith, M. J., M. F. Goodchild, and P. A. Longley, 2007: Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools. Troubador Publishing, 394 pp.

  • Di Luzio, M., G. L. Johnson, C. Daly, J. K. Eischeid, and J. G. Arnold, 2008: Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States. J. Appl. Meteor. Climatol., 47, 475497, https://doi.org/10.1175/2007JAMC1356.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dobler, C., S. Hagemann, R. L. Wilby, and J. Stötter, 2012: Quantifying different sources of uncertainty in hydrological projections in an Alpine watershed. Hydrol. Earth Syst. Sci., 16, 43434360, https://doi.org/10.5194/hess-16-4343-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duchon, C. E., 1979: Lanczos filtering in one and two dimensions. J. Appl. Meteor., 18, 10161022, https://doi.org/10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Espírito Santo, F., M. I. P. de Lima, A. M. Ramos, and R. M. Trigo, 2014: Trends in seasonal surface air temperature in mainland Portugal, since 1941. Int. J. Climatol., 34, 18141837, https://doi.org/10.1002/joc.3803.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fonseca, A., C. Botelho, R. A. R. Boaventura, and V. J. P. Vilar, 2015: Global warming effects on faecal coliform bacterium watershed impairments in Portugal. River Res. Appl., 31, 13441353, https://doi.org/10.1002/rra.2821.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fraga, H., and J. A. Santos, 2017: Daily prediction of seasonal grapevine production in the Douro wine region based on favourable meteorological conditions. Aust. J. Grape Wine Res., 23, 296304, https://doi.org/10.1111/ajgw.12278.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fraga, H., A. C. Malheiro, J. Moutinho-Pereira, G. V. Jones, F. Alves, J. G. Pinto, and J. A. Santos, 2014: Very high resolution bioclimatic zoning of Portuguese wine regions: Present and future scenarios. Reg. Environ. Change, 14, 295306, https://doi.org/10.1007/s10113-013-0490-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fraga, H., I. García de Cortázar Atauri, A. C. Malheiro, and J. A. Santos, 2016a: Modelling climate change impacts on viticultural yield, phenology and stress conditions in Europe. Global Change Biol., 22, 37743788, https://doi.org/10.1111/gcb.13382.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fraga, H., J. A. Santos, A. C. Malheiro, A. A. Oliveira, J. Moutinho-Pereira, and G. V. Jones, 2016b: Climatic suitability of Portuguese grapevine varieties and climate change adaptation. Int. J. Climatol., 36, 112, https://doi.org/10.1002/joc.4325.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fraga, H., J. A. Santos, J. Moutinho-Pereira, C. Carlos, J. Silvestre, J. Eiras-Dias, T. Mota, and A. C. Malheiro, 2016c: Statistical modelling of grapevine phenology in Portuguese wine regions: Observed trends and climate change projections. J. Agric. Sci., 154, 795811, https://doi.org/10.1017/S0021859615000933.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frías, M. D., E. Zorita, J. Fernández, and C. Rodríguez-Puebla, 2006: Testing statistical downscaling methods in simulated climates. Geophys. Res. Lett., 33, L19807, https://doi.org/10.1029/2006GL027453.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and L. O. Mearns, 1999: Introduction to special section: Regional climate modeling revisited. J. Geophys. Res., 104, 63356352, https://doi.org/10.1029/98JD02072.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Graham, L. P., J. Andréasson, and B. Carlsson, 2007: Assessing climate change impacts on hydrology from an ensemble of regional climate models, model scales and linking methods—A case study on the Lule River basin. Climatic Change, 81, 293307, https://doi.org/10.1007/s10584-006-9215-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gutiérrez, J. M., D. San-Martín, S. Brands, R. Manzanas, and S. Herrera, 2013: Reassessing statistical downscaling techniques for their robust application under climate change conditions. J. Climate, 26, 171188, https://doi.org/10.1175/JCLI-D-11-00687.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister, 2014: Updated high-resolution grids of monthly climatic observations—The CRU TS3.10 dataset. Int. J. Climatol., 34, 623642, https://doi.org/10.1002/joc.3711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones, and M. New, 2008: A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res., 113, D20119, https://doi.org/10.1029/2008JD010201.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis, 2005: Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol., 25, 19651978, https://doi.org/10.1002/joc.1276.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hofstra, N., M. Haylock, M. New, and P. D. Jones, 2009: Testing E-OBS European high-resolution gridded data set of daily precipitation and surface temperature. J. Geophys. Res., 114, D21101, https://doi.org/10.1029/2009JD011799.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein Tank, A. M. G., and G. P. Können, 2003: Trends in indices of daily temperature and precipitation extremes in Europe, 1946–99. J. Climate, 16, 36653680, https://doi.org/10.1175/1520-0442(2003)016<3665:TIIODT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein Tank, A. M. G., and Coauthors, 2002: Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. Climatol., 22, 14411453, https://doi.org/10.1002/joc.773.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laflamme, E. M., E. Linder, and Y. Pan, 2016: Statistical downscaling of regional climate model output to achieve projections of precipitation extremes. Wea. Climate Extremes, 12, 1523, https://doi.org/10.1016/j.wace.2015.12.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laslett, G. M., 1994: Kriging and splines: An empirical comparison of their predictive performance in some applications. J. Amer. Stat. Assoc., 89, 391400, https://doi.org/10.1080/01621459.1994.10476759.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lenderink, G., A. Buishand, and W. van Deursen, 2007: Estimates of future discharges of the river Rhine using two scenario methodologies: Direct versus delta approach. Hydrol. Earth. Syst. Sci., 11, 11451159, https://doi.org/10.5194/hess-11-1145-2007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lewis, S. C., and A. D. King, 2017: Evolution of mean, variance and extremes in 21st century temperatures. Wea. Climate Extremes, 15, 110, https://doi.org/10.1016/j.wace.2016.11.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, H., J. Sheffield, and E. F. Wood, 2010: Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J. Geophys. Res., 115, D10101, https://doi.org/10.1029/2009JD012882.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maraun, D., and Coauthors, 2010: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Rev. Geophys., 48, RG3003, https://doi.org/10.1029/2009RG000314.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maraun, D., and Coauthors, 2015: VALUE: A framework to validate downscaling approaches for climate change studies. Earth’s Future, 3, 114, https://doi.org/10.1002/2014EF000259.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Markatou, M., H. Tian, S. Biswas, and G. Hripcsak, 2005: Analysis of variance of cross-validation estimators of the generalization error. J. Mach. Learn. Res., 6, 11271168.

    • Search Google Scholar
    • Export Citation
  • Martin-Vide, J., 2004: Spatial distribution of a daily precipitation concentration index in peninsular Spain. Int. J. Climatol., 24, 959971, https://doi.org/10.1002/joc.1030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menzel, L., and G. Bürger, 2002: Climate change scenarios and runoff response in the Mulde catchment (southern Elbe, Germany). J. Hydrol., 267, 5364, https://doi.org/10.1016/S0022-1694(02)00139-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merino, A., M. Fernández-Vaquero, L. López, S. Fernández-González, L. Hermida, J. L. Sánchez, E. García-Ortega, and E. Gascón, 2016: Large-scale patterns of daily precipitation extremes on the Iberian Peninsula. Int. J. Climatol., 36, 38733891, https://doi.org/10.1002/joc.4601.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monteiro-Henriques, T., M. J. Martins, J. O. Cerdeira, P. Silva, P. Arsénio, Á. Silva, A. Bellu, and J. C. Costa, 2016: Bioclimatological mapping tackling uncertainty propagation: Application to mainland Portugal. Int. J. Climatol., 36, 400411, https://doi.org/10.1002/joc.4357.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • New, M., D. Lister, M. Hulme, and I. Makin, 2002: A high-resolution data set of surface climate over global land areas. Climate Res., 21, 125, https://doi.org/10.3354/cr021001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ninyerola, M., X. Pons, and J. M. Roure, 2005: Atlas Climático Digital de la Península Ibérica. Metodología y Aplicaciones en Bioclimatología y Geobotánica. Universidad Autónoma de Barcelona, 44 pp.

  • Pareeth, S., and Coauthors, 2017: Warming trends of perialpine lakes from homogenised time series of historical satellite and in-situ data. Sci. Total Environ., 578, 417426, https://doi.org/10.1016/j.scitotenv.2016.10.199.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peixoto, J. P., and A. H. Oort, 1992: Physics of Climate. American Institute of Physics, 520 pp.

  • Price, D. T., D. W. McKenney, I. A. Nalder, M. F. Hutchinson, and J. L. Kesteven, 2000: A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agric. For. Meteor., 101, 8194, https://doi.org/10.1016/S0168-1923(99)00169-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santos, J. A., and J. Corte-Real, 2006: Temperature extremes in Europe and wintertime large-scale atmospheric circulation: HadCM3 future scenarios. Climate Res., 31, 318, https://doi.org/10.3354/cr031003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santos, J. A., M. F. Carneiro, A. Correia, M. J. Alcoforado, E. Zorita, and J. J. Gómez-Navarro, 2015a: New insights into the reconstructed temperature in Portugal over the last 400 years. Climate Past, 11, 825834, https://doi.org/10.5194/cp-11-825-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santos, J. A., S. Pfahl, J. G. Pinto, and H. Wernli, 2015b: Mechanisms underlying temperature extremes in Iberia: A Lagrangian perspective. Tellus, 67A, 26032, https://doi.org/10.3402/tellusa.v67.26032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santos, M., J. A. Santos, and M. Fragoso, 2017: Atmospheric driving mechanisms of flash floods in Portugal. Int. J. Climatol., 37, 671680, https://doi.org/10.1002/joc.5030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schamm, K., M. Ziese, A. Becker, P. Finger, A. Meyer-Christoffer, U. Schneider, M. Schröder, and P. Stender, 2014: Global gridded precipitation over land: A description of the new GPCC First Guess Daily product. Earth Syst. Sci. Data, 6, 4960, https://doi.org/10.5194/essd-6-49-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schuurmans, J. M., M. F. P. Bierkens, E. J. Pebesma, and R. Uijlenhoet, 2007: Automatic prediction of high-resolution daily rainfall fields for multiple extents: The potential of operational radar. J. Hydrometeor., 8, 12041224, https://doi.org/10.1175/2007JHM792.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Solman, S. A., 2013: Regional climate modeling over South America: A review. Adv. Meteor., 2013, 504357, https://doi.org/10.1155/2013/504357.

  • Vrac, M., M. L. Stein, K. Hayhoe, and X.-Z. Liang, 2007: A general method for validating statistical downscaling methods under future climate change. Geophys. Res. Lett., 34, L18701, https://doi.org/10.1029/2007GL030295.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Washington, W. M., and C. L. Parkinson, 2005: An Introduction to Three-Dimensional Climate Modeling. University Science Books, 353 pp.

  • Willmott, C. J., and S. M. Robeson, 1995: Climatologically aided interpolation (CAI) of terrestrial air temperature. Int. J. Climatol., 15, 221229, https://doi.org/10.1002/joc.3370150207.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1736 536 51
PDF Downloads 1375 409 37