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Challenges in Quantifying Changes in the Global Water Cycle

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  • 1 School of GeoSciences, University of Edinburgh, Grant Institute, United Kingdom
  • | 2 Department of Meteorology, University of Reading/National Centre for Atmospheric Science Climate, Reading, United Kingdom
  • | 3 Met Office Hadley Centre, Exeter, and Department of Physics, Oxford University, Oxford, United Kingdom
  • | 4 School of GeoSciences, University of Edinburgh, Grant Institute, United Kingdom
  • | 5 National Center for Atmospheric Research,* Boulder, Colorado
  • | 6 Met Office Hadley Centre, Exeter, United Kingdom
  • | 7 Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland
  • | 8 Department of Meteorology, University of Reading/National Centre for Atmospheric Science Climate, Reading, United Kingdom
  • | 9 Deutscher Wetterdienst, Offenbach, Germany
  • | 10 Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York, and National Center for Atmospheric Research,* Boulder, Colorado
  • | 11 Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, and Commonwealth Scientific and Industrial Research Organisation, Hobart, Tasmania, Australia
  • | 12 NOAA/National Climatic Data Center, Asheville, North Carolina
  • | 13 School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, United Kingdom
  • | 14 Met Office Hadley Centre, Exeter, United Kingdom
  • | 15 NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 16 Department of Meteorology, University of Reading/National Centre for Atmospheric Science Climate, Reading, United Kingdom
  • | 17 Ocean and Earth Science, University of Southampton, Southampton, United Kingdom
  • | 18 University of Cape Town, Rondebosch, Cape Town, South Africa
  • | 19 Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norfolk, United Kingdom
  • | 20 Ocean and Earth Science, University of Southampton, Southampton, United Kingdom
  • | 21 Met Office Hadley Centre, Exeter, United Kingdom
  • | 22 Department of Meteorology, University of Reading/National Centre for Atmospheric Science Climate, Reading, United Kingdom
  • | 23 Commonwealth Scientific and Industrial Research Organisation, Hobart, Tasmania, Australia
  • | 24 Department of Meteorology, University of Reading/National Centre for Atmospheric Science Climate, Reading, United Kingdom
  • | 25 Met Office Hadley Centre, Exeter, United Kingdom
  • | 26 Climate Research Division, Environment Canada, Toronto, Ontario, Canada
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Abstract

Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time series over land, but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols and because of the large climate variability presently limits confidence in attribution of observed changes.

Publisher’s Note: This article was modified on 14 August 2015 to correct latitudinal labels on the x-axis in Fig. 3.

The National Center For Atmospheric Research is sponsored by the National Science Foundation.

CORRESPONDING AUTHOR: Gabriele Hegerl, GeoSciences, Grant Institute, Kings Buildings, James Hutton Rd, Edinburgh EH9 3FE, United Kingdom, E-mail: gabi.hegerl@ed.ac.uk

A supplement to this article is available online (10.1175/BAMS-D-13-00212.2)

Abstract

Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time series over land, but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols and because of the large climate variability presently limits confidence in attribution of observed changes.

Publisher’s Note: This article was modified on 14 August 2015 to correct latitudinal labels on the x-axis in Fig. 3.

The National Center For Atmospheric Research is sponsored by the National Science Foundation.

CORRESPONDING AUTHOR: Gabriele Hegerl, GeoSciences, Grant Institute, Kings Buildings, James Hutton Rd, Edinburgh EH9 3FE, United Kingdom, E-mail: gabi.hegerl@ed.ac.uk

A supplement to this article is available online (10.1175/BAMS-D-13-00212.2)

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