Articles | Volume 9, issue 2
Earth Syst. Sci. Data, 9, 511–527, 2017
Earth Syst. Sci. Data, 9, 511–527, 2017

Review article 25 Jul 2017

Review article | 25 Jul 2017

Uncertainty information in climate data records from Earth observation

Christopher J. Merchant1,2, Frank Paul3, Thomas Popp4, Michael Ablain5, Sophie Bontemps6, Pierre Defourny6, Rainer Hollmann7, Thomas Lavergne8, Alexandra Laeng9, Gerrit de Leeuw10, Jonathan Mittaz1,11, Caroline Poulsen12, Adam C. Povey13, Max Reuter14, Shubha Sathyendranath15, Stein Sandven16, Viktoria F. Sofieva10, and Wolfgang Wagner17 Christopher J. Merchant et al.
  • 1Department of Meteorology, University of Reading, Reading RG6 6AL, UK
  • 2National Centre for Earth Observation, University of Reading, Reading RG6 6AL, UK
  • 3Department of Geography, University of Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland
  • 4Deutsches Zentrum für Luft-und Raumfahrt e. V., Deutsches Fernerkundungsdatenzentrum, 82234 Oberpfaffenhofen, Germany
  • 5Collecte Localisation Satellites, 11 Rue Hermès, 31520 Ramonville-Saint-Agne, France
  • 6Earth and Life Institute, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
  • 7Deutscher Wetterdienst, Frankfurterstr. 135, 63500 Offenbach, Germany
  • 8Norwegian Meteorological Institute, 0313 Oslo, Norway
  • 9Karlsrhue Institute for Technology, Institut für Meteorologie und Klimaforschung, 76021 Karlsruhe, Germany
  • 10Finnish Meteorological Institute, 00101 Helsinki, Finland
  • 11National Physical Laboratory, Teddington TW11 0LW, UK
  • 12Science and Technology Facilities Council, Rutherford Appleton Laboratory, Didcot OX11 0QX, UK
  • 13National Centre for Earth Observation, University of Oxford, Oxford OX1 3PU, UK
  • 14Institute of Environmental Physics, University of Bremen, 28359 Bremen, Germany
  • 15Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
  • 16Nansen Environmental and Remote Sensing Center, Thormohlensgate 47, 5006 Bergen, Norway
  • 17Department of Geodesy and Geoinformation, Vienna University of Technology, 1040 Wien, Austria

Abstract. The question of how to derive and present uncertainty information in climate data records (CDRs) has received sustained attention within the European Space Agency Climate Change Initiative (CCI), a programme to generate CDRs addressing a range of essential climate variables (ECVs) from satellite data. Here, we review the nature, mathematics, practicalities, and communication of uncertainty information in CDRs from Earth observations. This review paper argues that CDRs derived from satellite-based Earth observation (EO) should include rigorous uncertainty information to support the application of the data in contexts such as policy, climate modelling, and numerical weather prediction reanalysis. Uncertainty, error, and quality are distinct concepts, and the case is made that CDR products should follow international metrological norms for presenting quantified uncertainty. As a baseline for good practice, total standard uncertainty should be quantified per datum in a CDR, meaning that uncertainty estimates should clearly discriminate more and less certain data. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence in the uncertainty estimate provided or indicators of conditions violating the retrieval assumptions). The paper discusses the many sources of error in CDRs, noting that different errors may be correlated across a wide range of timescales and space scales. Error effects that contribute negligibly to the total uncertainty in a single-satellite measurement can be the dominant sources of uncertainty in a CDR on the large space scales and long timescales that are highly relevant for some climate applications. For this reason, identifying and characterizing the relevant sources of uncertainty for CDRs is particularly challenging. The characterization of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation when possible. These principles are quite general, but the approach to providing uncertainty information appropriate to different ECVs is varied, as confirmed by a brief review across different ECVs in the CCI. User requirements for uncertainty information can conflict with each other, and a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight concrete recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.

Short summary
Climate data records (CDRs) contain data describing Earth's climate and should address uncertainty in the data to communicate what is known about climate variability or change and what range of doubt exists. This paper discusses good practice for including uncertainty information in CDRs for the essential climate variables (ECVs) derived from satellite data. Recommendations emerge from the shared experience of diverse ECV projects within the European Space Agency Climate Change Initiative.