TY - JOUR
T1 - Large uncertainties in observed daily precipitation extremes over land
AU - Herold, Nicholas
AU - Behrangi, Ali
AU - Alexander, Lisa V.
N1 - Funding Information:
This work contributes to the WCRP Grand Challenge on Extremes. N.H. and L.V.A. are supported by ARC grant CE110001028. L.V.A. is also supported by ARC grant DP160103439. Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. A.B. was supported by NASA Energy and Water Cycle Study (NEWS) and NASA WEATHER awards. The authors would like to thank the anonymous reviewers for their insightful comments and suggestions that have contributed to improve this paper. Data were processed and plots were created by using the National Center for Atmospheric Research Command Language [UCAR/NCAR/CISL/VETS,] and the R statistical language [Team, R. C.,]. ETCCDI indices were calculated by using the climdex.pcic R software package [Consortium, D. B. for the P. C. I.,], which represents the official implementation of the ETCCDI indices. GPCP-1DD was downloaded from Climate and Global Dynamics (ftp://ftp.cgd.ucar.edu/archive/PRECIP/, accessed 24 March 2015). GPCC-FDD was downloaded from Deutscher Wetterdienst (ftp://ftp.dwd.de/pub/data/gpcc/full_data_daily_V1/, accessed 15 July 2015). PERSIANN-CDR was downloaded from the National Center for Environmental Information [ftp://eclipse.ncdc.noaa.gov/pub/cdr/persiann/files/, accessed 10 August 2015]. CHIRPS was downloaded from the Climate Hazards Group (ftp://ftp.chg.ucsb.edu/pub/org/chg/products/CHIRPS-2.0/global_daily/netcdf/p25/, accessed 10 August 2015). T3B42 was downloaded from Goddard Earth Sciences Data and Information Services Center (http://disc.sci.gsfc.nasa.gov/TRMM, accessed 10 August 2015).
Publisher Copyright:
© 2016. American Geophysical Union. All Rights Reserved.
PY - 2017
Y1 - 2017
N2 - We explore uncertainties in observed daily precipitation extremes over the terrestrial tropics and subtropics (50°S–50°N) based on five commonly used products: the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset, the Global Precipitation Climatology Centre-Full Data Daily (GPCC-FDD) dataset, the Tropical Rainfall Measuring Mission (TRMM) multi-satellite research product (T3B42 v7), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and the Global Precipitation Climatology Project’s One-Degree Daily (GPCP-1DD) dataset. We use the precipitation indices R10mm and Rx1day, developed by the Expert Team on Climate Change Detection and Indices, to explore the behavior of “moderate” and “extreme” extremes, respectively. In order to assess the sensitivity of extreme precipitation to different grid sizes we perform our calculations on four common spatial resolutions (0.25° × 0.25°, 1° × 1°, 2.5° × 2.5°, and 3.75° × 2.5°). The impact of the chosen “order of operation” in calculating these indices is also determined. Our results show thatmoderate extremes are relatively insensitive to product and resolution choice, while extreme extremes can be very sensitive. For example, at 0.25° × 0.25° quasi-global mean Rx1day values vary from 37mmin PERSIANN-CDR to 62mm in T3B42. We find that the interproduct spread becomes prominent at resolutions of 1° × 1° and finer, thus establishing a minimum effective resolution at which observational products agree. Without improvements in interproduct spread, these exceedingly large observational uncertainties at high spatial resolution may limit the usefulness of model evaluations. As has been found previously, resolution sensitivity can be largely eliminated by applying an order of operation where indices are calculated prior to regridding. However, this approach is not appropriate when true area averages are desired (e.g., for model evaluations).
AB - We explore uncertainties in observed daily precipitation extremes over the terrestrial tropics and subtropics (50°S–50°N) based on five commonly used products: the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset, the Global Precipitation Climatology Centre-Full Data Daily (GPCC-FDD) dataset, the Tropical Rainfall Measuring Mission (TRMM) multi-satellite research product (T3B42 v7), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and the Global Precipitation Climatology Project’s One-Degree Daily (GPCP-1DD) dataset. We use the precipitation indices R10mm and Rx1day, developed by the Expert Team on Climate Change Detection and Indices, to explore the behavior of “moderate” and “extreme” extremes, respectively. In order to assess the sensitivity of extreme precipitation to different grid sizes we perform our calculations on four common spatial resolutions (0.25° × 0.25°, 1° × 1°, 2.5° × 2.5°, and 3.75° × 2.5°). The impact of the chosen “order of operation” in calculating these indices is also determined. Our results show thatmoderate extremes are relatively insensitive to product and resolution choice, while extreme extremes can be very sensitive. For example, at 0.25° × 0.25° quasi-global mean Rx1day values vary from 37mmin PERSIANN-CDR to 62mm in T3B42. We find that the interproduct spread becomes prominent at resolutions of 1° × 1° and finer, thus establishing a minimum effective resolution at which observational products agree. Without improvements in interproduct spread, these exceedingly large observational uncertainties at high spatial resolution may limit the usefulness of model evaluations. As has been found previously, resolution sensitivity can be largely eliminated by applying an order of operation where indices are calculated prior to regridding. However, this approach is not appropriate when true area averages are desired (e.g., for model evaluations).
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U2 - 10.1002/2016JD025842
DO - 10.1002/2016JD025842
M3 - Article
AN - SCOPUS:85012960689
VL - 122
SP - 668
EP - 681
JO - Journal of Geophysical Research: Atmospheres
JF - Journal of Geophysical Research: Atmospheres
SN - 2169-897X
IS - 2
ER -