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Cooperative Institute for Climate and Satellites (CICS)

Reconstruction of Near-Global Precipitation Variations Based on Gauges and Correlations with SST and SLP Thomas Smith 1 Phillip Arkin 2. 1. NOAA/NESDIS/STAR SCSB and CICS, College Park, Maryland 2. CICS/ESSIC/University of Maryland, College Park, Maryland.

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Cooperative Institute for Climate and Satellites (CICS)

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  1. Reconstruction of Near-Global Precipitation Variations Based on Gauges and Correlations with SST and SLPThomas Smith1Phillip Arkin2 1. NOAA/NESDIS/STAR SCSB and CICS, College Park, Maryland2. CICS/ESSIC/University of Maryland, College Park, Maryland Cooperative Institute for Climate and Satellites (CICS) Earth System Science Interdisciplinary Center (ESSIC)

  2. Outline • All analyses are of Precipitation Anomalies • Base Satellite Data • IR (from 1979), MW (from mid 1980s) • Need global satellite analyses for reconstruction statistics • Direct Reconstructions: fitting data to Empirical Orthogonal Functions (REOF) • EOF (or PC) analysis, for covariance maps • Fit available gauge-station data to a set of covariance maps • Monthly gauge-based 5-degree analyses available beginning 1900 • Indirect Reconstructions: using Canonical Correlation Analysis (RCCA) • Correlate fields of sea-surface temperature (SST) and sea-level pressure (SLP) with fields of precipitation • Both SST and SLP analyzed for the 20th century • Merged Direct & Indirect Reconstructions • Direct Recons for over land and interannual and shorter variations over oceans • Indirect Recons more reliable for multi-decadal variations over oceans

  3. Anomaly S.D. • CAMS/OPI ocean S.D. more concentrated in tropics • OIP best for convective precipitation • Both GPCP and OI S.D. have more extra-tropical variations • GPCP uses mix of satellite estimates + gauges • OI uses only microwave estimates, stronger variations than GPCP

  4. Reconstruction Based on EOFs (REOF) • EOF spatial covariance modes • 3 regions: 80S-20S, 30S-30N, 20N-80N • Separate so tropics do not dominate • Only large-scale modes used • In each region, fit available gauge anomalies to the set of modes • 3 areas merged with smoothing at boundaries • Cross-validation testing to find the best set of modes for each region • For S.H. 5 EOFs, for Tropics 15 EOFs, for N.H. 10 EOFs

  5. Gauge Sampling of 5-deg Regions • Gauge-based analyses, annual averages of monthly % sampling • Global Historical Climate Network (GHCN) • Global Precipitation Climatology Center (GPCC) • Climate Research Unit (CRU) • CRU gives best sampling of 5-deg areas in historical period • Differences due to data processing & how many stations needed to form a 5-deg area • Test all & use CRU based analysis

  6. REOF Spatial Statistics • Global spatial standard deviation (upper) • Similar interannual changes • GHCN low before 1940 (low sampling) • CRU strong most of record • Filtered GPCP strong at the end of record • Global spatial correlation between analyses • High GHCN, CRU for high-sampling period, lower values before 1940

  7. Regression Against SOI • SOI represents ENSO interannual variability (annual averages) • Shows typical ENSO precipitation patterns • GHCN-based recon gives slightly weaker regression

  8. Ocean Comparisons • RCCA & REOF ocean averages, filtered • RCCA & REOF differ before 1980 • 1970s climate shift in RCCA • REOF does not resolve trend in RCCA & in AR4 ensemble • RCCA & REOF(GPCP) similar • REOF(GPCP) can be used for updates

  9. Merged Reconstructions • REOF reliable over land where gauges are available • Interannual REOF reliable over oceans, but multi-decadal REOF less reliable over oceans • Multi-decadal RCCA appears to be more reliable over oceans • Merge by replacing ocean multi-decadal REOF with ocean multi-decadal from RCCA • For recent period, use REOF(GPCP)

  10. Merged Recon Averages • Filtered Recons for All Areas and Ocean Areas • Ocean average changes most • Including land removes the 1970s climate shift and most interannual variations

  11. Normalized Joint EOF • Merged Recon and AR4 Ensemble • Both annual averaged and filtered before JEOF • First mode indicates joint trend-like variations • Tropical ENSO-like increase • Mid-latitude decrease • High-latitude increase • Pattern differences may reflect model biases

  12. Summary & Conclusions • EOF-based reconstructions resolve oceanic interannual variations through the 20th century • Direct reconstruction using the available gauge data • Over land REOF does best for all variations • CCA-based reconstructions resolve oceanic multi-decadal variations through the 20th century • Indirect method using correlations with better sampled variables • Merged analysis takes advantage of the best qualities of both • Future improvements possible with new data or refined reconstruction methods • Extended reanalyses may yield independent precipitation information Data available at http://cics.umd.edu/~tsmith/recpr/

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