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Estimating Monthly Precipitation Reconstruction Uncertainty Beginning in 1900

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  • 1 NOAA/NESDIS/STAR, and ESSIC/CICS, University of Maryland, College Park, College Park, Maryland
  • | 2 San Diego State University, San Diego, California
  • | 3 ESSIC/CICS, University of Maryland, College Park, College Park, Maryland
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Abstract

Uncertainty estimates are computed for a statistical reconstruction of global monthly precipitation that was developed in an earlier publication. The reconstruction combined the use of spatial correlations with gauge precipitation and correlations between precipitation and related data beginning in 1900. Several types of errors contribute to uncertainty, including errors associated with the reconstruction method and input data errors. This reconstruction includes the use of correlated data for the ocean-area first guess, which contributes to much of the uncertainty over those regions. Errors associated with the input data include random, sampling, and bias errors. Random and bias data errors are mostly filtered out of the reconstruction analysis and are the smallest components of the total error. The largest errors are associated with sampling and the method, which together dominate the total error. The uncertainty estimates in this study indicate that (i) over oceans the reconstruction is most reliable in the tropics, especially the Pacific, because of the large spatial scales of ENSO; (ii) over the high-latitude oceans multidecadal variations are fairly reliable, but many month-to-month variations are not; and (iii) over- and near-land errors are much smaller because of local gauge. The reconstruction indicates that the average precipitation increases early in the twentieth century, followed by several decades of multidecadal variations with little trend until near the end of the century, when precipitation again appears to systematically increase. The uncertainty estimates indicate that the average changes over land are most reliable, while over oceans the average change over the reconstruction period is slightly larger than the uncertainty.

Corresponding author address: Thomas Smith, NOAA/NESDIS/STAR/SCSB and ESSIC/CICS, 5825 University Research Court, Suite 4001, College Park, MD 20740. E-mail: tom.smith@noaa.gov

Abstract

Uncertainty estimates are computed for a statistical reconstruction of global monthly precipitation that was developed in an earlier publication. The reconstruction combined the use of spatial correlations with gauge precipitation and correlations between precipitation and related data beginning in 1900. Several types of errors contribute to uncertainty, including errors associated with the reconstruction method and input data errors. This reconstruction includes the use of correlated data for the ocean-area first guess, which contributes to much of the uncertainty over those regions. Errors associated with the input data include random, sampling, and bias errors. Random and bias data errors are mostly filtered out of the reconstruction analysis and are the smallest components of the total error. The largest errors are associated with sampling and the method, which together dominate the total error. The uncertainty estimates in this study indicate that (i) over oceans the reconstruction is most reliable in the tropics, especially the Pacific, because of the large spatial scales of ENSO; (ii) over the high-latitude oceans multidecadal variations are fairly reliable, but many month-to-month variations are not; and (iii) over- and near-land errors are much smaller because of local gauge. The reconstruction indicates that the average precipitation increases early in the twentieth century, followed by several decades of multidecadal variations with little trend until near the end of the century, when precipitation again appears to systematically increase. The uncertainty estimates indicate that the average changes over land are most reliable, while over oceans the average change over the reconstruction period is slightly larger than the uncertainty.

Corresponding author address: Thomas Smith, NOAA/NESDIS/STAR/SCSB and ESSIC/CICS, 5825 University Research Court, Suite 4001, College Park, MD 20740. E-mail: tom.smith@noaa.gov
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