1. GLOBAL PRECIPITATION ANALYSES AND REANALYSES: BASIS, METHODS AND APPLICATIONS Phil Arkin, Cooperative Institute for Climate and Satellites Earth System Science Interdisciplinary Center, University of Maryland

2. Background and Motivation Climate models indicate that global temperature increases will be accompanied by changes in water vapor and precipitation: Climate models indicate that global temperature increases will be accompanied by changes in water vapor and precipitation: Water vapor increases to maintain roughly constant relative humidity (about 7% per degree) Water vapor increases to maintain roughly constant relative humidity (about 7% per degree) Precipitation increases but at a slower rate (about 2-3% per degree) Precipitation increases but at a slower rate (about 2-3% per degree) Regionally, precipitation intensifies in climatologically favored regions, decreases at margins (“rich get richer”) Regionally, precipitation intensifies in climatologically favored regions, decreases at margins (“rich get richer”) Observations show: Observations show: Global water vapor has increased recently as temperatures have warmed (but data have limitations) Global water vapor has increased recently as temperatures have warmed (but data have limitations) Global precipitation has increased at 7%/degree since 1990 (Wentz et al., 2007) or at 2.3%/degree since 1979 (Adler et al., 2008), but again the data have shortcomings Global precipitation has increased at 7%/degree since 1990 (Wentz et al., 2007) or at 2.3%/degree since 1979 (Adler et al., 2008), but again the data have shortcomings Rain gauge observations show increases in intense precipitation, but current datasets aren’t adequate to test the rich get richer hypothesis Rain gauge observations show increases in intense precipitation, but current datasets aren’t adequate to test the rich get richer hypothesis What do the models say about projected regional changes in precipitation? What do the models say about projected regional changes in precipitation?

3. IPCC AR4 Summary for Policy Makers (Figure SPM.7) Figure SPM.7. Relative changes in precipitation (in percent) for the period 2090–2099, relative to 1980–1999. Values are multi-model averages based on the SRES A1B scenario for December to February (left) and June to August (right). White areas are where less than 66% of the models agree in the sign of the change and stippled areas are where more than 90% of the models agree in the sign of the change. {Figure 10.9}

4. IPCC AR4 Summary for Policy Makers said “ IPCC AR4 Summary for Policy Makers said “There is now higher confidence in projected patterns of warming and other regional-scale features, including changes in wind patterns, precipitation and some aspects of extremes and of ice.” Specific projections derived from averaging model projections; uncertainty indicated by agreement among models Specific projections derived from averaging model projections; uncertainty indicated by agreement among models The models used in AR4 were judged to have improved representation of precipitation, based on annual mean fields and the time-mean annual cycle. However, these comparisons were based on climatologies compared to 25- year means of observations – not long enough to capture much of the important variability The models used in AR4 were judged to have improved representation of precipitation, based on annual mean fields and the time-mean annual cycle. However, these comparisons were based on climatologies compared to 25- year means of observations – not long enough to capture much of the important variability What observations are used to validate the models, how are they derived, and where do they need improvement? What observations are used to validate the models, how are they derived, and where do they need improvement?

5. Observing Precipitation Gauges – point values with relatively well understood errors (but with atrocious spatial sampling characteristics) Gauges – point values with relatively well understood errors (but with atrocious spatial sampling characteristics) Remote Sensing – radars (surface and space), space-based infrared and microwave radiometers Remote Sensing – radars (surface and space), space-based infrared and microwave radiometers All are inferences All are inferences Errors vary in time and space and are poorly known/understood Errors vary in time and space and are poorly known/understood Models Models Observed/estimated winds, temperature, moisture provide information on where precipitation will occur in near future Observed/estimated winds, temperature, moisture provide information on where precipitation will occur in near future This is done regularly for weather forecasts; can be used in areas where other information is limited This is done regularly for weather forecasts; can be used in areas where other information is limited Using such forecasts in global datasets may improve the product, but at the risk of making its use more difficult Using such forecasts in global datasets may improve the product, but at the risk of making its use more difficult

6. Estimating Precipitation from Satellite Observations Visible and/or infrared (IR) Visible and/or infrared (IR) Essentially an index of cloudiness, usually with embellishment Essentially an index of cloudiness, usually with embellishment Related to precipitation amount statistically, with various calibrations Related to precipitation amount statistically, with various calibrations Passive microwave - emission Passive microwave - emission Ocean only, coarse resolution, mediocre sampling Ocean only, coarse resolution, mediocre sampling Relatively directly related to liquid water amount Relatively directly related to liquid water amount Things like freezing level, cloud non-precipitating liquid have to be determined somehow Things like freezing level, cloud non-precipitating liquid have to be determined somehow No snow signal No snow signal Passive microwave - scattering Passive microwave - scattering Intermediate between IR and emission Intermediate between IR and emission Signal based on precipitation-size ice, so no warm rain signal Signal based on precipitation-size ice, so no warm rain signal Ice/snow on surface looks like heavy precipitation Ice/snow on surface looks like heavy precipitation Resolution better than emission, worse than IR Resolution better than emission, worse than IR Sampling similar to emission Sampling similar to emission

7. Integrating/Analyzing Precipitation Estimates Satellite-derived estimates have complementary characteristics (geostationary infrared is more complete but has poor accuracy, low Earth orbit passive microwave is more accurate but has sparse sampling) Satellite-derived estimates have complementary characteristics (geostationary infrared is more complete but has poor accuracy, low Earth orbit passive microwave is more accurate but has sparse sampling) Satellite-derived estimates have biases that can be reduced/removed by adding information from rain gauges Satellite-derived estimates have biases that can be reduced/removed by adding information from rain gauges Since the input data are not uniformly distributed in time and space, an analysis (method for creating complete in time and space fields from varying and incomplete observations) must be used to create the final dataset Since the input data are not uniformly distributed in time and space, an analysis (method for creating complete in time and space fields from varying and incomplete observations) must be used to create the final dataset Analysis can be statistical combination of inputs, or simply a composite, or include an atmospheric model (data assimilation) Analysis can be statistical combination of inputs, or simply a composite, or include an atmospheric model (data assimilation)

8. Global Precipitation Datasets GPCP (left)/CMAP (right) mean annual cycle and global mean time series Monthly/5-day; 2.5° lat/long global; both based on microwave/IR combined with gauges Both used in IPCC AR4

9. Datasets based on observations (GPCP, CMAP) give about 2.6 mm/day (AR4 range is about 2.5-3.2 mm/day) Datasets based on observations (GPCP, CMAP) give about 2.6 mm/day (AR4 range is about 2.5-3.2 mm/day) Data assimilation products average about 3 mm/day; also have larger mean annual cycle and greater interannual variability Data assimilation products average about 3 mm/day; also have larger mean annual cycle and greater interannual variability Global Mean Precipitation from Data Assimilation Junye Chen, ESSIC and GMAO/MERRA

10. Climate Model-Based Precipitation A number of climate models have been used to simulate the 20 th Century and precipitation from those runs can be compared to global precipitation datasets A number of climate models have been used to simulate the 20 th Century and precipitation from those runs can be compared to global precipitation datasets We would really prefer more than the 30 years available from GPCP and other global precipitation datasets We would really prefer more than the 30 years available from GPCP and other global precipitation datasets

11. Reconstruction of Near-Global Precipitation Variations Back to 1900 Based on Gauges and Correlations with SST and SLP (Tom Smith, NOAA/NESDIS and CICS) Base Satellite Data Base Satellite Data Need global satellite analyses for reconstruction statistics Need global satellite analyses for reconstruction statistics GPCP, CMAP and MSAP tested; GPCP works best GPCP, CMAP and MSAP tested; GPCP works best Direct Reconstructions: fitting data to Empirical Orthogonal Functions (REOF) Direct Reconstructions: fitting data to Empirical Orthogonal Functions (REOF) EOF (or PC) analysis, for covariance maps EOF (or PC) analysis, for covariance maps Fit available gauge-station data to a set of covariance maps Fit available gauge-station data to a set of covariance maps Yields monthly gauge-based 5-degree analyses available beginning 1900 Yields monthly gauge-based 5-degree analyses available beginning 1900 Indirect Reconstructions: using Canonical Correlation Analysis (RCCA) Indirect Reconstructions: using Canonical Correlation Analysis (RCCA) Correlate fields of sea-surface temperature (SST) and sea-level pressure (SLP) with fields of precipitation during satellite era Correlate fields of sea-surface temperature (SST) and sea-level pressure (SLP) with fields of precipitation during satellite era Both SST and SLP analyzed for the 20 th century Both SST and SLP analyzed for the 20 th century

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

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

14. Correlations Between Analyses Computed for period when all are available (1900-1998) Computed for period when all are available (1900-1998) Averages over oceans, land, & areas with CRU gauge sampling Averages over oceans, land, & areas with CRU gauge sampling Annual-spatial averages correlated Annual-spatial averages correlated Each individual reconstruction correlates well with CRU gauges Each individual reconstruction correlates well with CRU gauges ENSO and other major modes allow interannual variations to be resolved 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 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 AR4 ensemble averages out interannual variations, leaving in multi-decadal variations RCCA has same oceanic multi-decadal tendency as AR4, REOF has opposite tendency 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

15. Trends Computed for period when all are available (1900-1998) Computed for period when all are available (1900-1998) Averages over oceans, land, & land areas with CRU gauge sampling Averages over oceans, land, & land areas with CRU gauge sampling Annual and low-pass filtered (as in figures) Annual and low-pass filtered (as in figures) In each individual reconstruction, opposite trends over ocean & land In each individual reconstruction, opposite trends over ocean & land May be from use of ENSO modes to analyze ENSO-like multi-decadal, since ENSO has opposite land-sea anomalies May be from use of ENSO modes to analyze ENSO-like multi-decadal, since ENSO has opposite land-sea anomalies Gauge data make land trends positive for REOF, no gauge data in RCCA 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

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

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

18. Merged Reconstruction Near-Global Averages Filtered Reconstructions for All Areas and Ocean Areas Filtered Reconstructions for All Areas and Ocean Areas Ocean average changes most Ocean average changes most Including land removes the 1970s climate shift and greatly smoothes interannual variations Including land removes the 1970s climate shift and greatly smoothes interannual variations

19. Reconstruction Trends Ocean tropical trend greatest Ocean tropical trend greatest Land trends weaker & tend to be opposite to ocean trends Land trends weaker & tend to be opposite to ocean trends Similar to ENSO land-sea differences Similar to ENSO land-sea differences

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

21. Summary EOF-based reconstructions resolve oceanic interannual variations through the 20 th century EOF-based reconstructions resolve oceanic interannual variations through the 20 th century Direct reconstruction using the available gauge data Direct reconstruction using the available gauge data Over land REOF does best for all variations Over land REOF does best for all variations CCA-based reconstructions resolve oceanic multi-decadal variations through the 20 th century CCA-based reconstructions resolve oceanic multi-decadal variations through the 20 th century Indirect method using correlations with better sampled variables Indirect method using correlations with better sampled variables Merged analysis takes advantage of the best qualities of both Merged analysis takes advantage of the best qualities of both Future improvements possible with new data or refined reconstruction methods Future improvements possible with new data or refined reconstruction methods Extended reanalyses may yield independent precipitation information Extended reanalyses may yield independent precipitation information The merged reconstruction has some important potential applications The merged reconstruction has some important potential applications

22. Potential Uses of Reconstructed Precipitation Diagnostic/descriptive studies of global precipitation variations on interannual to multi- decadal time scales Diagnostic/descriptive studies of global precipitation variations on interannual to multi- decadal time scales Changes in ENSO, PDO, NAO, AMO over the 20 th century can be better described and understood Changes in ENSO, PDO, NAO, AMO over the 20 th century can be better described and understood Oceanic influence on dry and wet regimes, particularly multi-year droughts, can be more clearly diagnosed Oceanic influence on dry and wet regimes, particularly multi-year droughts, can be more clearly diagnosed Validate and improve climate model simulations/projections of precipitation Validate and improve climate model simulations/projections of precipitation Longer baseline of observed precipitation should facilitate improvement of the models Longer baseline of observed precipitation should facilitate improvement of the models And can be used to enable statistical adjustment of model output And can be used to enable statistical adjustment of model output Data available at http://cics.umd.edu/~tsmith/recpr/