• Aguilar, E., M. Brunet, J. Sigró, F. S. Rodrigo, Y. L. Rico, and D. R. Alvarez, 2008: Homogenization of Spanish temperature series on a daily resolution: A step forward towards an analysis of extremes in the Iberian Peninsula. Proc. Seventh European Conf. on Applied Climatology, Amsterdam, Netherlands, European Meteor. Soc., A-00-697.

    • Search Google Scholar
    • Export Citation
  • Auchmann, R., and S. Brönnimann, 2012: A physics-based correction model for homogenizing sub-daily temperature series. J. Geophys. Res., 117, D17119, https://doi.org/10.1029/2012JD018067.

    • Search Google Scholar
    • Export Citation
  • Banerjee, S., A. E. Gelfand, A. O. Finley, and H. Sang, 2008: Gaussian predictive process models for large spatial datasets. J. Roy. Stat. Soc., B70, 825848, https://doi.org/10.1111/j.1467-9868.2008.00663.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bessembinder, J., 2016: User requirement specification for product design. EUSTACE Deliverable 4.1, 13 pp., www.eustaceproject.org/eustace/static/media/uploads/Deliverables/eustace_d4-1.pdf.

    • Search Google Scholar
    • Export Citation
  • Bessembinder, J., N. Rayner, D. Ghent, K. Madsen, and J. Mitchelson, 2017: Report on user requirements: results from second round of user consultations. EUSTACE Deliverable 4.9, 58 pp., www.eustaceproject.org/eustace/static/media/uploads/Deliverables/eustace_d4-9.pdf.

    • Search Google Scholar
    • Export Citation
  • Bolin, D., and F. Lindgren, 2011: Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping. Ann. Appl. Stat., 5, 523550, https://doi.org/10.1214/10-AOAS383.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandsma, T., and G. P. Können, 2006: Application of nearest-neighbor resampling for homogenizing temperature records on a daily to sub-daily level. Int. J. Climatol., 26, 7589, https://doi.org/10.1002/joc.1236.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brönnimann, S., and Coauthors, 2018: Observations for reanalyses. Bull. Amer. Meteor. Soc., 99, 18511866, https://doi.org/10.1175/BAMS-D-17-0229.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brugnara, Y., R. Auchmann, S. Brönnimann, A. Bozzo, D. Cat Berro, and L. Mercalli, 2016: Trends of mean and extreme temperature indices since 1874 at low-elevation sites in the Southern Alps. J. Geophys. Res. Atmos., 121, 33043325, https://doi.org/10.1002/2015JD024582.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brugnara, Y., E. Good, A. A. Squintu, G. van der Schrier, and S. Brönnimann, 2019: The EUSTACE global land station daily air temperature dataset. Geosci. Data J., 6, 189204, https://doi.org/10.1002/gdj3.81.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brunet, M., and Coauthors, 2006: The development of a new dataset of Spanish Daily Adjusted Temperature Series (SDATS) (1850–2003). Int. J. Climatol., 26, 17771802, https://doi.org/10.1002/joc.1338.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., and Coauthors, 2018: The EU-FP7 ERA-CLIM2 project contribution to advancing science and production of Earth system climate reanalyses. Bull. Amer. Meteor. Soc., 99, 10031014, https://doi.org/10.1175/BAMS-D-17-0199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bulgin, C., C. Merchant, D. Ghent, L. Klüser, T. Popp, C. Poulsen, and L. Sogacheva, 2018: Quantifying uncertainty in satellite-retrieved land surface temperature from cloud detection errors. Remote Sens ., 10, 616, https://doi.org/10.3390/rs10040616.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caesar, J., L. Alexander, and R. Vose, 2006: Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. J. Geophys. Res., 111, D05101, https://doi.org/10.1029/2005JD006280.

    • Search Google Scholar
    • Export Citation
  • Carrea, L., and Coauthors, 2019: Lake surface temperature [in “State of the Climate 2018”]. Bull. Amer. Meteor. Soc., 100 (9), S13S14, https://doi.org/10.1175/2019BAMSStateoftheClimate.1.

    • Search Google Scholar
    • Export Citation
  • Caussinus, H., and O. Mestre, 2004: Detection and correction of artificial shifts in climate series. J. Roy. Stat. Soc., 53, 405425, https://doi.org/10.1111/j.1467-9876.2004.05155.x.

    • Search Google Scholar
    • Export Citation
  • Cornes, R. C., G. van der Schrier, E. J. M. Besselaar, and P. D. Jones, 2018: An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. Atmos., 123, 93919409, https://doi.org/10.1029/2017JD028200.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cristóbal, J., M. Ninyerola, and X. Pons, 2008: Modeling air temperature through a combination of remote sensing and GIS data. J. Geophys. Res., 113, D13106, https://doi.org/10.1029/2007JD009318.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunn, R. J. H., K. M. Willett, D. E. Parker, and L. Mitchell, 2016: Expanding HadISD: Quality-controlled, sub-daily station data from 1931. Geosci. Instrum. Methods Data Syst ., 5, 473491, https://doi.org/10.5194/gi-5-473-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • EUMETSAT, 2014: CORE-CLIMAX system maturity matrix instruction manual. EUMETSAT Doc. CC/EUM/MAN/13/002 v4, 34 pp., www.eumetsat.int/website/wcm/idc/idcplg?IdcService=GET_FILE&dDocName=PDF_CORE_CLIMAX_MANUAL&RevisionSelectionMethod=LatestReleased&Rendition=Web.

    • Search Google Scholar
    • Export Citation
  • Furrer, R., M. G. Genton, and D. Nychka, 2006: Covariance tapering for interpolation of large spatial datasets. J. Comput. Graph. Stat., 15, 502523, https://doi.org/10.1198/106186006X132178.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghent, D., K. Veal, T. Trent, E. Dodd, H. Sembhi, and J. Remedios, 2019: A new approach to defining uncertainties for MODIS land surface temperature. Remote Sens ., 11, 1021, https://doi.org/10.3390/rs11091021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Good, E. J., 2016: An in situ-based analysis of the relationship between land surface “skin” and screen-level air temperatures. J. Geophys. Res. Atmos., 121, 88018819, https://doi.org/10.1002/2016JD025318.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. Rev. Geophys., 48, RG4004, https://doi.org/10.1029/2010RG000345.

  • Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister, 2013: 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
  • Hartmann, D. L., and Coauthors, 2013: Observations: Atmosphere and surface. Climate Change 2013: The Physical Science Basis, Stocker, T. F. et al., Eds., Cambridge University Press, 159254.

    • Search Google Scholar
    • Export Citation
  • Høyer, J. L., E. Good, P. Nielsen-Englyst, K. S. Madsen, I. Woolway, and J. Kennedy, 2018: Report on the relationship between satellite surface skin temperature and surface air temperature observations for oceans, land, sea ice and lakes. EUSTACE Deliverable 1.5, 101 pp., www.eustaceproject.org/eustace/static/media/uploads/d1.5_revised.pdf.

    • Search Google Scholar
    • Export Citation
  • Hunziker, S., and Coauthors, 2017: Identifying, attributing, and overcoming common data quality issues of manned station observations. Int. J. Climatol., 37, 41314145, https://doi.org/10.1002/joc.5037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, G. S., and J. J. Kennedy, 2017: Sensitivity of attribution of anthropogenic near-surface warming to observational uncertainty. J. Climate, 30, 46774691, https://doi.org/10.1175/JCLI-D-16-0628.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, P. D., D. H. Lister, T. J. Osborn, C. Harpham, M. Salmon, and C. P. Morice, 2012: Hemispheric and large-scale land surface air temperature variations: An extensive revision and an update to 2010. J. Geophys. Res., 117, D05127, https://doi.org/10.1029/2011JD017139.

    • Search Google Scholar
    • Export Citation
  • Kent, E. C., N. A. Rayner, D. I. Berry, M. Saunby, B. I. Moat, J. J. Kennedy, and D. E. Parker, 2013: Global analysis of night marine air temperature and its uncertainty since 1880: The HadNMAT2 data set. J. Geophys. Res. Atmos., 118, 12811298, https://doi.org/10.1002/JGRD.50152.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kilibarda, M., T. Hengl, G. B. M. Heuvelink, B. Gräler, E. Pebesma, M. Perčec Tadić, and B. Bajat, 2014: Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. J. Geophys. Res. Atmos., 119, 22942313, https://doi.org/10.1002/2013JD020803.

    • 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
  • Kuglitsch, F. G., A. Toreti, E. Xoplaki, P. M. Della-Marta, J. Luterbacher, and H. Wanner, 2009: Homogenization of daily maximum temperature series in the Mediterranean. J. Geophys. Res., 114, D15108, https://doi.org/10.1029/2008JD011606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kushnir, Y., and Coauthors, 2019: Towards operational predictions of the near-term climate. Nat. Climate Change, 9, 94101, https://doi.org/10.1038/s41558-018-0359-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindgren, F., H. Rue, and J. Lindström, 2011: An explicit link between Gaussian fields and Gaussian Markov random fields: The stochastic partial differential equation approach. J. Roy. Stat. Soc., B73, 423498, https://doi.org/10.1111/j.1467-9868.2011.00777.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menne, M. J., I. Durre, R. S. Vose, B. E. Gleason, and T. G. Houston, 2012: An overview of the global historical climatology network-daily database. J. Atmos. Oceanic Technol., 29, 897910, https://doi.org/10.1175/JTECH-D-11-00103.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merchant, C. J., and Coauthors, 2013: The surface temperatures of Earth: Steps towards integrated understanding of variability and change. Geosci. Instrum. Methods Data Syst., 2, 305321, https://doi.org/10.5194/gi-2-305-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merchant, C., and Coauthors, 2014: sea surface temperature datasets for climate applications from phase 1 of the European space agency climate change initiative (SST CCI). Geosci. Data J., 1, 179191, https://doi.org/10.1002/gdj3.20.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merchant, C., D. Ghent, J. Kennedy, E. Good, and J. Høyer, 2015: Common approach to providing uncertainty estimates across all surfaces. EUSTACE Deliverable 1.2, 20 pp., www.eustaceproject.org/eustace/static/media/uploads/Deliverables/eustace_d1-2.pdf.

    • Search Google Scholar
    • Export Citation
  • Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset. J. Geophys. Res., 117, D08101, https://doi.org/10.1029/2011JD017187.

    • Search Google Scholar
    • Export Citation
  • Nielsen-Englyst, P., J. L. Høyer, K. S. Madsen, R. Tonboe, and G. Dybkjær, 2019a: Deriving Arctic 2-m air temperatures from satellite. Cryosphere Discuss ., https://doi.org/10.5194/tc-2019-126.

    • Search Google Scholar
    • Export Citation
  • Nielsen-Englyst, P., J. L. Høyer, K. S. Madsen, R. Tonboe, G. Dybkjær, and E. Alerskans, 2019b: In situ observed relationships between snow and ice surface skin temperatures and 2 m air temperatures in the Arctic. Cryosphere, 13, 10051024, https://doi.org/10.5194/tc-13-1005-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Osborn, T. J., P. D. Jones, and M. Joshi, 2017: Recent United Kingdom and global temperature variations. Weather, 72, 323329, https://doi.org/10.1002/wea.3174.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., and Coauthors, 1998: Homogeneity adjustments of in situ atmospheric climate data: A review. Int. J. Climatol., 18, 14931517, https://doi.org/10.1002/(SICI)1097-0088(19981115)18:13<1493::AID-JOC329>3.0.CO;2-T.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., 2019: Final verified products delivered to CEMS for verification. EUSTACE Deliverable 2.6, 6 pp., www.eustaceproject.org/eustace/static/media/uploads/d_2.6_final.pdf.

    • Search Google Scholar
    • Export Citation
  • Rennie, J. J., and Coauthors, 2014: The international surface temperature initiative global land surface databank: Monthly temperature data release description and methods. Geosci. Data J., 1, 75102, https://doi.org/10.1002/gdj3.8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rohde, R., and Coauthors, 2013a: A new estimate of the average Earth surface land temperature spanning 1753 to 2011. Geoinfor. Geostat: Overview, 1 (1), http://doi.org/10.4172/2327-4581.1000101.

    • Search Google Scholar
    • Export Citation
  • Rohde, R., and Coauthors, 2013b: Berkeley Earth temperature averaging process. Geoinfor. Geostat: Overview, 1, 2, https://doi.org/10.4172/GIGS.1000103.

    • Search Google Scholar
    • Export Citation
  • Rue, H., S. Martino, F. Lindgren, D. Simpson, and A. Riebler, 2013: R-INLA: Approximate Bayesian inference using integrated nested Laplace approximations. www.r-inla.org/.

  • Sánchez-Lugo, A., P. Berrisford, C. Morice, and J. P. Nicolas, 2019: Global surface temperature [in “State of the Climate 2018”]. Bull. Amer. Meteor. Soc., 100 (9), S11S12., https://doi.org/10.1175/2019BAMSStateoftheClimate.1.

    • Search Google Scholar
    • Export Citation
  • Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land–ocean surface temperatures analysis (1880–2006). J. Climate, 21, 22832296, https://doi.org/10.1175/2007JCLI2100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sohrabinia, M., P. Zawar-Reza, and W. Rack, 2014: Spatio-temporal analysis of the relationship between LST from MODIS and air temperature in New Zealand. Theor. Appl. Climatol., 119, 567583, https://doi.org/10.1007/s00704-014-1106-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Squintu, A. A., G. van der Schrier, Y. Brugnara, and A. Klein Tank, 2019a: Homogenization of daily temperature series in the European climate assessment & dataset. Int. J. Climatol., 39, 12431261, https://doi.org/10.1002/joc.5874.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Squintu, A. A., G. van der Schrier, E. J. M. van den Besselaar, R. C. Cornes, A. Klein Tank, 2019b: Building long homogeneous temperature series across Europe: A new approach for the blending of neighboring series. J. Appl. Meteor. Climatol., 59, 175189, https://doi.org/10.1175/JAMC-D-19-0033.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stickler, A., and Coauthors, 2014: ERA-CLIM: Historical surface and upper-air data for future reanalyses. Bull. Amer. Meteor. Soc., 95, 14191430, https://doi.org/10.1175/BAMS-D-13-00147.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toreti, A., F. G. Kuglitsch, E. Xoplaki, and J. Luterbacher, 2012: A novel approach for the detection of inhomogeneities affecting climate time series. J. Appl. Meteor. Climatol., 51, 317326, https://doi.org/10.1175/JAMC-D-10-05033.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trewin, B., 2013: A daily homogenized temperature data set for Australia. Int. J. Climatol., 33, 15101529, https://doi.org/10.1002/joc.3530.

  • Veal, K., 2019a: Validation report for the final in-filled EUSTACE surface air temperature product. EUSTACE Deliverable 3.5, 41 pp., https://www.eustaceproject.org/eustace/static/media/uploads/d3-5_final.pdf.

    • Search Google Scholar
    • Export Citation
  • Veal, K., 2019b: Intercomparison report for the final in-filled EUSTACE surface air temperature product. EUSTACE Deliverable 3.4, 39 pp., https://www.eustaceproject.org/eustace/static/media/uploads/d3-4_final.pdf.

    • Search Google Scholar
    • Export Citation
  • Venema, V., 2012: Detecting and repairing inhomogeneities in datasets: Assessing current capabilities. Bull. Amer. Meteor. Soc., 93, 951954, https://doi.org/10.1175/1520-0477-93.7.947.

    • Search Google Scholar
    • Export Citation
  • Vincent, L., X. Zhang, B. R. Bonsall, and W. D. Hogg, 2002: Homogenization of daily temperatures over Canada. J. Climate, 15, 13221334, https://doi.org/10.1175/1520-0442(2002)015<1322:HODTOC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vose, R. S., and Coauthors, 2012: NOAA’s Merged Land–Ocean Surface Temperature analysis. Bull. Amer. Meteor. Soc., 93, 16771685, https://doi.org/10.1175/BAMS-D-11-00241.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walters, D., and Coauthors, 2019: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations. Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X. L., 2008: Accounting for autocorrelation in detecting mean shifts in climate data series using the penalized maximal t or f test. J. Appl. Meteor. Climatol., 47, 24232444, https://doi.org/10.1175/2008JAMC1741.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weedon, G. P., and Coauthors, 2011: Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J. Hydrometeor., 12, 823848, https://doi.org/10.1175/2011JHM1369.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wikle, C. K., 2010: Low-rank representations for spatial processes. Handbook of Spatial Statistics, Gelfand, A. E. et al., Eds., Chapman and Hall/CRC Press, 107118.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woodruff, S. D., and Coauthors, 2011: ICOADS release 2.5: Extensions and enhancements to the surface marine meteorological archive. Int. J. Climatol., 31, 951967, https://doi.org/10.1002/joc.2103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woolway, R. I., and C. Merchant, 2017: Amplified surface temperature response of cold, deep lakes to inter-annual air temperature variability. Sci. Rep., 7, 4130, https://doi.org/10.1038/S41598-017-04058-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woolway, R. I., and C. Merchant, 2018: Intra-lake heterogeneity of thermal responses to climate change: A study of large Northern Hemisphere lakes. J. Geophys. Res. Atmos., 123, 30873098, https://doi.org/10.1002/2017JD027661.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woolway, R. I., and Coauthors, 2016: Lake surface temperatures [in “State of the Climate in 2015”]. Bull. Amer. Meteor. Soc., 97 (8), S17S18, https://doi.org/10.1175/2016BAMSStateoftheClimate.1.

    • Search Google Scholar
    • Export Citation
  • Woolway, R. I., and Coauthors, 2017a: Lake surface temperature [in “State of the Climate in 2016”]. Bull. Amer. Meteor. Soc., 98 (8), S13S14, https://doi.org/10.1175/2017BAMSStateoftheClimate.1.

    • Search Google Scholar
    • Export Citation
  • Woolway, R. I., and Coauthors, 2017b: Latitude and lake size are important predictors of over-lake atmospheric stability. Geophys. Res. Lett., 44, 88758883, https://doi.org/10.1002/2017GL073941.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woolway, R. I., M. T. Dokulil, W. Marszelewski, M. Schmid, D. Bouffard, and C. J. Merchant, 2017c: Warming of Central European lakes and their response to the 1980s climate regime shift. Climatic Change, 142, 505520, https://doi.org/10.1007/s10584-017-1966-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woolway, R. I., P. Meinson, P. Nõges, I. D. Jones, and A. Laas, 2017d: Atmospheric stilling leads to prolonged thermal stratification in a large shallow polymictic lake. Climatic Change, 141, 759773, https://doi.org/10.1007/s10584-017-1909-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woolway, R. I., and Coauthors, 2018a: Lake surface temperature [in “State of the Climate in 2017”]. Bull. Amer. Meteor. Soc., 99 (8), S13S15, https://doi.org/10.1175/2018BAMSStateoftheClimate.1.

    • Search Google Scholar
    • Export Citation
  • Woolway, R. I., and Coauthors, 2018b: Geographic and temporal variation in turbulent heat loss from lakes: A global analysis across 45 lakes. Limnol. Oceanogr., 63, 24362449, https://doi.org/10.1002/lno.10950.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, W., Q. Li, X. L. Wang, S. Yang, L. Cao, and Y. Feng, 2013: Homogenization of Chinese daily surface air temperatures and analysis of trends in the extreme temperature indices. J. Geophys. Res. Atmos., 118, 97089720, https://doi.org/10.1002/JGRD.50791.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, X., and Coauthors, 2019: The effects of temperature on human mortality in a Chinese city: Burden of disease calculation, attributable risk exploration, and vulnerability identification. Int. J. Biometeor., 63, 13191329, https://doi.org/10.1007/s00484-019-01746-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 155 155 53
PDF Downloads 155 155 54

The EUSTACE Project: Delivering Global, Daily Information on Surface Air Temperature

View More View Less
  • 1 Met Office Hadley Centre, Exeter, United Kingdom
  • 2 Oeschger Centre for Climate Change Research and Institute of Geography, University of Bern, Bern, Switzerland
  • 3 Royal Netherlands Meteorological Institute (KNMI), AE De Bilt, Netherlands
  • 4 Oeschger Centre for Climate Change Research and Institute of Geography, University of Bern, Bern, Switzerland
  • 5 Met Office Hadley Centre, Exeter, United Kingdom
  • 6 Department of Meteorology, University of Reading, Reading, United Kingdom
  • 7 National Centre for Earth Observation, University of Leicester, Leicester, United Kingdom
  • 8 Met Office Hadley Centre, Exeter, United Kingdom
  • 9 Danish Meteorological Institute, Copenhagen, Denmark
  • 10 Met Office Hadley Centre, Exeter, United Kingdom
  • 11 National Oceanography Centre, Southampton, United Kingdom
  • 12 Met Office Hadley Centre, Exeter, United Kingdom
  • 13 School of Mathematics, University of Edinburgh, Edinburgh, United Kingdom
  • 14 Danish Meteorological Institute, Copenhagen, Denmark
  • 15 National Centre for Earth Observation, and Department of Meteorology University of Reading, Reading, United Kingdom
  • 16 Met Office Hadley Centre, Exeter, United Kingdom
  • 17 Danish Meteorological Institute, Copenhagen, Denmark
  • 18 National Centre for Earth Observation, University of Leicester, Leicester, United Kingdom
  • 19 National Centre for Earth Observation, University of Leicester, Leicester, United Kingdom
  • 20 Royal Netherlands Meteorological Institute (KNMI), AE De Bilt, Netherlands
  • 21 Science and Technology Facilities Council, Didcot, United Kingdom
  • 22 Department of Geography, National University of Ireland Maynooth, Maynooth, Ireland
  • 23 Danish Meteorological Institute, Copenhagen, Denmark
  • 24 National Centre for Earth Observation, University of Leicester, Leicester, United Kingdom
  • 25 Science and Technology Facilities Council, Didcot, United Kingdom
  • 26 Met Office Hadley Centre, Exeter, United Kingdom
  • 27 Department of Meteorology, University of Reading, Reading, United Kingdom
© Get Permissions
Restricted access

Abstract

Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.

CURRENT AFFILIATIONS: Capponi—Sky, Osterley, Greater London, United Kingdom; Ortiz—Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom; Woolway—Centre for Freshwater and Environmental Studies, Dundalk Institute of Technology, Dundalk, Ireland

Corresponding author: Nick A. Rayner, nick.rayner@metoffice.gov.uk

Abstract

Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.

CURRENT AFFILIATIONS: Capponi—Sky, Osterley, Greater London, United Kingdom; Ortiz—Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom; Woolway—Centre for Freshwater and Environmental Studies, Dundalk Institute of Technology, Dundalk, Ireland

Corresponding author: Nick A. Rayner, nick.rayner@metoffice.gov.uk
Save