Cooperative Institute for Climate and Satellites (CICS)

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. Satellite-Based Analyses • Monthly analyses, 1979-Present • Several analyses available • Global precipitation climatology project (GPCP), multiple inputs, begins 1979, developed for climate studies • CAMS/OPI, several inputs, begins 1979, developed for interannual studies • Optimum Interpolation (OI) of MW and ERA-40 reanalysis, begins 1987, data problems over land before 1992 • New OI analyses being tested in an attempt to obtain longer record using OI methods

4. 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

5. Examples of spatial modes: Joint EOFs of OI and GPCP Anomalies • EOF (PC) analysis jointly of 2 fields • Shows common variations by mode • Mode 1: Main ENSO

6. Joint EOF 2: Secondary ENSO Mode • Mostly variance associated with the very strong 1997-1998 episode • Lags mode 1 • Both analyses have similar ocean variations • Tested using different analyses, use GPCP for final REOF

7. 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

8. Anomaly Distribution • Relative frequency distribution for common months and locations (1992-2001) • Both anomalies approximately normal • reconstruction methods should work

9. 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

10. REOF Methods • Space points, x, and times, t • Data: Anomalies, a, minus a first guess • Can sometimes estimate a first guess; otherwise can use 0 first guess • Anomaly reconstruction: First guess plus weighted sum of the modes

11. Best Fit Weights for the Modes • Reconstruction fit, F, using modes • Modes function of space • Weights function of time • Squared error of the fit over all areas with sampling

12. System of Equations For Weights • Differentiate error with each weight and set to 0 for system of equations to minimize error • Equations to minimize error • Weights affected by data and sampling • System of equations solved each time for the set of weights • Note: with complete data and orthogonal modes, computing the weights is simplified • EOF (or PC) time series

13. Sampling and Screening Modes • Data must be sufficient to sample a mode’s variations • Mode variance proportional to the square of the mode value • Compute the fraction of sampled variance of each mode • Use only modes with a critical fraction of variance sampled • Use cross-validation testing to find the critical sampling fraction

14. Cross Validation • Simulate historical periods • Modes computed from data excluding the time to be analyzed, and enough surrounding time to be independent • Data sub-sampled to simulate historical sampling • Reconstruct using simulated historical conditions and compare to full data to find errors • Used for tuning the reconstruction and for finding its errors

15. Cross-Validation For Tuning and Skill Evaluation • Independent modes • Delete base data for each test analysis year • Station sampling for 3 periods • Here GHCN sampling used • Anomaly correlation with base full data • S.H. extra-tropics: low skill • Tropics: highest skill • N.H. extra-tropics some skill over oceans Ocean Avgs 1912-21 1952-61 1992-01 0.37 0.40 0.39

16. 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

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

18. Regression Against NAO • Dec-March Regression • Similar patterns, especially in Northern Hemisphere • GPCC tropical Pacific different from the others

19. REOF Conclusions • Satellite-based analysis are needed for historical reconstruction statistics • Major interannual variations can be reconstructed for the 20th century • REOF oceanic multi-decadal variations may be less reliable • Different base data & analysis data change multi-decadal results much more than interannual results • REOF multi-decadal inconsistent with theoretical modeling results • Improvements may be obtained from improved satellite base data • Ocean-area sampling too sparse to use more EOF modes

20. Reconstructions Based on CCA (RCCA), and Comparisons to REOFs • Canonical Correlation Analysis (CCA) • Fields of predictors correlated with a predictand field • Data smoothed and condensed using EOFs before CCA computed • Training Data: GPCP, SST, SLP (1979-2004) • Annual average anomalies, GPCP satellite based • SST and SLP give ocean observations correlated with precipitation on long time scales • Analysis Data: SST, SLP (1900-2004) • SST & SLP global analyses available

21. Canonical Correlation Analysis (CCA) • Compress anomaly predictor (SST, SLP) and predictand (GPCP) fields using separate EOFs • All fields normalized • One joint predictor EOF, separate predictand EOF • Predictor CCA equations: to relate compressed predictor fields to compressed predictand field • CCA modes developed • CCA eigenvalue used to weight analysis • For reconstruction, predictor data used to find weights for predictand field • CCA predicting annual precipitation from annual SST and SLP • 8 modes used, most oceanic variations from the first 3 modes CCA is described in detail by Barnett and Preisendorfer (1987, Mon. Wea. Rev., 115, 1825-1850)

22. Correlations Between Analyses • Computed for period when all are available (1900-1998) • Averages over oceans, land, & areas with CRU gauge sampling • Annual-spatial averages correlated • Each individual reconstruction correlates well with CRU gauges • ENSO and other major modes allow interannual variations to be resolved • REOF(Blend) has the best fit over land, but the nearly-independent RCCA is almost as good • AR4 ensemble averages out interannual variations, leaving in multi-decadal variations • RCCA has same oceanic multi-decadal tendency as AR4, REOF has opposite tendency Correlations between averages over the given areas Oceans Land Gauge-sampling CRU, REOF(Blend) ---- ---- 0.88 CRU, RCCA ---- ---- 0.74 REOF(Blend), RCCA 0.64 0.81 0.83 REOF(Blend), AR4 -0.06 -0.01 -0.07 RCCA, AR4 0.32 -0.02 0.00

23. Land Comparisons • RCCA & REOF & CRU data land averages, filtered • REOF(Blend) from REOF(CRU) and REOF(GPCP) • RCCA & REOF similar for most of period • RCCA & REOF(GPCP) similar for the GPCP period

24. 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

25. Trends • Computed for period when all are available (1900-1998) • Averages over oceans, land, & land areas with CRU gauge sampling • Annual and low-pass filtered (as in figures) • In each individual reconstruction, opposite trends over ocean & land • May be from use of ENSO modes to analyze ENSO-like multi-decadal • ENSO has opposite land-sea anomalies • Gauge data make land trends positive for REOF, no gauge data in RCCA Trends in mm/mon per 100 years for averages over the given areas Oceans Land Gauge-sampling CRU Gauges ---- ---- 1.2 REOF(Blend) -0.4 0.4 0.4 RCCA 1.6 -0.5 -1.1 AR4 0.7 -0.1 -0.5

26. Spatial Standard Deviation of Recons • RCCA underestimates interannual signals • REOFs give consistent level of signal over analysis period • GPCP resolves variations filtered by REOF modes

27. RCCA Conclusions • Multi-decadal variations over oceans can be reconstructed from SST and SLP using RCCA • Over land, RCCA large-scale multi-decadal & interannual variations are consistent with independent observations over the 20th century • Over oceans, RCCA large-scale multi-decadal & interannual variations are consistent with model variations 20th century

28. 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)

29. 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

30. Recon Trends • Ocean tropical trend greatest • Land trends weaker & tend to be opposite to ocean trends • Similar to ENSO land-sea differences, suggests ENSO-like processes

31. 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

32. Possible Uses of Reconstructed Precipitation • Climate-dynamics studies of global precipitation on interannual to multi-decadal time scales • Developing ocean-land changes over the 20th century can be better understood and diagnosed • Oceanic influence on dry and wet regimes can be more clearly shown • To validate and improve coupled-climate model precipitation • Both improvement of the models and statistical adjustment of model output possible with global reconstructions Data available at http://cics.umd.edu/~tsmith/recpr/

33. 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/