1. Global Precipitation Analyses and Reanalyses Phil Arkin, Cooperative Institute for Climate Studies Earth System Science Interdisciplinary Center, University of Maryland based on work by Matt Sapiano, Ching-Yee Chang and John Janowiak of CICS/ESSIC and Tom Smith, NOAA/NESDIS/STAR and CICS

2. Scientific Issues Precipitation matters! Precipitation matters! Fresh water for people, agriculture and industry Fresh water for people, agriculture and industry Extremes, both droughts and floods, have great impact on societies Extremes, both droughts and floods, have great impact on societies One of the most anticipated manifestations of global change One of the most anticipated manifestations of global change Precipitation is an index of the vigor of the hydrological cycle – generally expected to change with global temperature increases Precipitation is an index of the vigor of the hydrological cycle – generally expected to change with global temperature increases We can “measure” (estimate quantitatively) precipitation over the globe We can “measure” (estimate quantitatively) precipitation over the globe Fundamental questions: Fundamental questions: How much precipitation occurs? (i.e. What is the strength of the global hydrological cycle?) How much precipitation occurs? (i.e. What is the strength of the global hydrological cycle?) How does precipitation vary with time and space? (i.e. How is the hydrological cycle changing?) How does precipitation vary with time and space? (i.e. How is the hydrological cycle changing?)

3. Observing Precipitation Not uniformly well defined – generally speaking we attempt to obtain spatial and/or temporal means, but rigorous definitions are not typical Not uniformly well defined – generally speaking we attempt to obtain spatial and/or temporal means, but rigorous definitions are not typical Gauges – point values with relatively well understood errors Gauges – point values with relatively well understood errors Remote Sensing – radars (surface and space), passive radiometers (space-based) Remote Sensing – radars (surface and space), passive radiometers (space-based) All of these are inferences All of these 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 – simulations, short-range forecasts Models – simulations, short-range forecasts Derived from observations to varying degree Derived from observations to varying degree Extensive validation, especially for forecasts, which provides some information on errors; but model changes go on continuously so that information is constantly being outdated Extensive validation, especially for forecasts, which provides some information on errors; but model changes go on continuously so that information is constantly being outdated Quantitative, but dependent on reality of model physical processes Quantitative, but dependent on reality of model physical processes

4. Integrating/Analyzing Precipitation Observations Analysis – creating complete (in time and space) fields from varying and incomplete observations Analysis – creating complete (in time and space) fields from varying and incomplete observations Satellite-derived estimates have complementary characteristics (geostationary IR is more complete but has poor accuracy, low Earth orbit PMW is more accurate but has sparse sampling) so combining them makes sense (CMAP, GPCP, CMORPH, TMPA, GSMaP…) Satellite-derived estimates have complementary characteristics (geostationary IR is more complete but has poor accuracy, low Earth orbit PMW is more accurate but has sparse sampling) so combining them makes sense (CMAP, GPCP, CMORPH, TMPA, GSMaP…) CMAP and GPCP use gauges to reduce bias over land, leading to complexities regarding homogeneity CMAP and GPCP use gauges to reduce bias over land, leading to complexities regarding homogeneity GPCP mean annual cycle (left) and global mean precipitation (below) Monthly/5-day; 2.5° lat/long global CMAP has similar characteristics

5. CMAP and GPCP have some shortcomings: CMAP and GPCP have some shortcomings: Resolution – too coarse for many applications that require finer spatial/temporal resolution Resolution – too coarse for many applications that require finer spatial/temporal resolution Obsolescent - based on products and techniques available some time ago Obsolescent - based on products and techniques available some time ago Short records - limited to period since 1979 (or later) Short records - limited to period since 1979 (or later) Incomplete error characterization Incomplete error characterization Particular problems with high latitude and orographic precipitation Particular problems with high latitude and orographic precipitation Goals of our current work: Goals of our current work: Experiment with new approaches to analyzing precipitation during the modern era (1979 – present) Experiment with new approaches to analyzing precipitation during the modern era (1979 – present) Using reanalysis precipitation and optimal interpolation to improve global analyses Using reanalysis precipitation and optimal interpolation to improve global analyses Combine different satellite-derived precipitation estimates to produce high time/space resolution precipitation analyses Combine different satellite-derived precipitation estimates to produce high time/space resolution precipitation analyses Develop and verify methods to extend global precipitation analyses to earlier years Develop and verify methods to extend global precipitation analyses to earlier years

6. Longer time series of global precipitation analyses is needed: Longer time series of global precipitation analyses is needed: To validate global climate models To validate global climate models To describe long-term trends in global, particularly oceanic, precipitation To describe long-term trends in global, particularly oceanic, precipitation To describe interdecadal variability in phenomena such as ENSO, the NAO, the PDO and others To describe interdecadal variability in phenomena such as ENSO, the NAO, the PDO and others Approach: reconstruct/reanalyze global precipitation back to 1900 using 2 methods Approach: reconstruct/reanalyze global precipitation back to 1900 using 2 methods EOF-based reconstruction using GPCP and other global precipitation analyses, combined with historical coastal and island rain gauge observations EOF-based reconstruction using GPCP and other global precipitation analyses, combined with historical coastal and island rain gauge observations CCA reanalysis using SST and SLP, based on modern era analyses CCA reanalysis using SST and SLP, based on modern era analyses Compare to GHCN gauge observations, NOAA/ESRL 20 th Century SLP-based reanalysis and IPCC AR4 C20C products Compare to GHCN gauge observations, NOAA/ESRL 20 th Century SLP-based reanalysis and IPCC AR4 C20C products

7. Climate Modes from EOF Reconstructions Good representation of climate modes Good representation of climate modes Better in NH and tropics; OI possibly better in mid- and high northern latitudes Better in NH and tropics; OI possibly better in mid- and high northern latitudes Global time series of EOF reconstructions not realistic Global time series of EOF reconstructions not realistic

8. CCA Reanalyses CCA nearly independent of GHCN observations, although GPCP uses gauge data to remove bias (CCA based on gauge-free version of GPCP gives similar results) CCA nearly independent of GHCN observations, although GPCP uses gauge data to remove bias (CCA based on gauge-free version of GPCP gives similar results) Top panel shows comparison over land areas where gauges are found – small areal coverage Top panel shows comparison over land areas where gauges are found – small areal coverage Decadal-scale signal looks reasonable Decadal-scale signal looks reasonable Ability to resolve finer scale phenomena like ENSO is limited – yearly, 5°, bigger errors on short time scales Ability to resolve finer scale phenomena like ENSO is limited – yearly, 5°, bigger errors on short time scales See Smith et. al. 2008, JGR See Smith et. al. 2008, JGR Fig 1: DJF means.

9. +/- 1 and 2 SD plotted for AR4 runs +/- 1 and 2 SD plotted for AR4 runs Compo reanalysis above AR4 range – similar to modern reanalyses, which are 0.5-0.8 mm/dy > GPCP and CMAP Compo reanalysis above AR4 range – similar to modern reanalyses, which are 0.5-0.8 mm/dy > GPCP and CMAP GPCP and CCA in lower part of AR4 range GPCP and CCA in lower part of AR4 range

10. Note scale changed by factor of 10 Note scale changed by factor of 10 Biases removed so means are the same for all time series Biases removed so means are the same for all time series AR4 ensemble mean exhibits much less variability since it is an average of many (20 or so) runs AR4 ensemble mean exhibits much less variability since it is an average of many (20 or so) runs

11. Re-scale AR4 ensemble mean so variance is about same as a single realization Re-scale AR4 ensemble mean so variance is about same as a single realization CCA and AR4 ensemble mean show similar centennial-scale changes, but interannual variations rather different CCA and AR4 ensemble mean show similar centennial-scale changes, but interannual variations rather different

12. Conclusions/Issues EOF-based Reconstruction back to 1900 exhibits skill in capturing seasonal-to-decadal variations EOF-based Reconstruction back to 1900 exhibits skill in capturing seasonal-to-decadal variations GPCP-based CCA reanalysis matches 20 th Century variations from IPCC AR4 model simulations GPCP-based CCA reanalysis matches 20 th Century variations from IPCC AR4 model simulations Best historical analysis probably is combination of low frequency from CCA and finer scales from filtered EOF reconstructions Best historical analysis probably is combination of low frequency from CCA and finer scales from filtered EOF reconstructions Significant biases still present between models and observed datasets Significant biases still present between models and observed datasets

13. Opportunities for Collaboration Creating/improving data sets Creating/improving data sets Estimating precipitation from satellite observations Estimating precipitation from satellite observations Improved solid precipitation algorithms Improved solid precipitation algorithms Orographic precipitation Orographic precipitation Light oceanic precipitation Light oceanic precipitation Combining information from multiple sources to improve regional and global precipitation analyses Combining information from multiple sources to improve regional and global precipitation analyses Analysis technique development Analysis technique development Validation and verification Validation and verification Extending analyses into the past Extending analyses into the past Improved historical period products Improved historical period products Link to precipitation proxies in earlier periods Link to precipitation proxies in earlier periods Diagnostic analyses Diagnostic analyses Improved descriptions of climate phenomena like ENSO, NAO,… Improved descriptions of climate phenomena like ENSO, NAO,… Better characterization of long-term changes in the global hydrological cycle Better characterization of long-term changes in the global hydrological cycle Model validation Model validation