Global Precipitation Analyses and Reanalyses Phil Arkin, Cooperative Institute for Climate Studies Earth System Science Interdisciplinary Center, University of Maryland

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which provides some information on errors; but model changes go on continuously so that information is constantly being outdated Extensive validation, especially for forecasts, which provides some information on errors; but model changes go on continuously so that information is constantly being outdated Quantitative, but dependent on reality of model physical processes Quantitative, but dependent on reality of model physical processes

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

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) Develop and verify methods to extend global precipitation analyses to earlier years Develop and verify methods to extend global precipitation analyses to earlier years

New Global Analysis and Reanalysis Back to 1900 (Matt Sapiano, UMD/CICS and Tom Smith, NESDIS/STAR) Concept: combine satellite-based estimates (most accurate in tropics and convective regimes) with model-derived precipitation (most accurate in high latitudes and synoptic situations) using optimal interpolation (permits weighting based on relative errors and provides error estimates) Concept: combine satellite-based estimates (most accurate in tropics and convective regimes) with model-derived precipitation (most accurate in high latitudes and synoptic situations) using optimal interpolation (permits weighting based on relative errors and provides error estimates) Time scale is monthly, spatial resolution of 2.5°, global coverage 1979 – present, although shorter for many combinations Time scale is monthly, spatial resolution of 2.5°, global coverage 1979 – present, although shorter for many combinations Goal is good temporal stability and accurate rendition of oceanic variability on scales from seasonal to decadal Goal is good temporal stability and accurate rendition of oceanic variability on scales from seasonal to decadal Use the new analysis as basis to reconstruct/reanalyze global precipitation back to 1900 Use the new analysis as basis to reconstruct/reanalyze global precipitation back to 1900

DJF/JJA means from 6 Optimum Interpolation analyses Fig 1: DJF means. 3 satellite-derived inputs: SSM/I, AGPI, OPI 3 satellite-derived inputs: SSM/I, AGPI, OPI 2 reanalysis inputs: ERA-40, JRA-25 2 reanalysis inputs: ERA-40, JRA-25 Six OI analyses with records ranging from about 15 years (only 10 usable) to almost 30 years Six OI analyses with records ranging from about 15 years (only 10 usable) to almost 30 years Errors estimated by comparison to GPCP Errors estimated by comparison to GPCP No gauge data used so far No gauge data used so far Initial description in Sapiano et al., in revision with JGR Initial description in Sapiano et al., in revision with JGR

Evaluation: GPCP_ms and OI-SE compared to GPCC gauge analysis JJADJF New OIGPCP MS OI-SEGPCP_ms

The new OI analysis is promising, particularly since both reanalyses and satellite-derived estimates should improve in the future The new OI analysis is promising, particularly since both reanalyses and satellite-derived estimates should improve in the future However, a longer time series of global precipitation analyses is needed: However, a longer time series of global precipitation analyses is needed: To validate global climate models To validate global climate models 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 Goal: reconstruct/reanalyze global precipitation back to 1900 Goal: reconstruct/reanalyze global precipitation back to 1900 EOF-based reconstruction using 6 OI analyses, as well as CMAP, GPCP and GPCP_ms, combined with historical coastal and island rain gauge observations EOF-based reconstruction using 6 OI analyses, as well as CMAP, GPCP and GPCP_ms, combined with historical coastal and island rain gauge observations CCA reanalysis using SST and SLP, based on CMAP, GPCP and GPCP_ms as well as all OIs CCA reanalysis using SST and SLP, based on CMAP, GPCP and GPCP_ms as well as all OIs Previous (EOF) attempts by Xie et al. (2001) and Efthymiadis et al. (2005) yield some modest success; we hope to gain from methodological improvements as well as better base data sets Previous (EOF) attempts by Xie et al. (2001) and Efthymiadis et al. (2005) yield some modest success; we hope to gain from methodological improvements as well as better base data sets Compare to ESRL 20 th Century SLP-based reanalysis Compare to ESRL 20 th Century SLP-based reanalysis

Reanalyses and Reconstructions Compared to GHCN Land Precipitation (Black Line) Reconstructions use these same gauge observations to weight EOFs, so not independent Reconstructions use these same gauge observations to weight EOFs, so not independent CCA Reanalyses are more independent of GHCN observations, although GPCP and CMAP use gauge data to remove bias CCA Reanalyses are more independent of GHCN observations, although GPCP and CMAP use gauge data to remove bias C(MSAT) truly independent of GHCN – represents baseline potential skill C(MSAT) truly independent of GHCN – represents baseline potential skill Areal coverage (only where GHCN stations found) is quite small compared to globe Areal coverage (only where GHCN stations found) is quite small compared to globe Initial results reported in Smith et. al. 2008, JGR (in press); more in preparation Initial results reported in Smith et. al. 2008, JGR (in press); more in preparation Fig 1: DJF means

Correlation CMAP OI GPCP GPCPms MODEL CMAP OI GPCP 0.58 Reconstructions all give global mean similar to modern GPCP, CMAP; not much decadal-scale variability Reconstructions all give global mean similar to modern GPCP, CMAP; not much decadal-scale variability C20R (Compo/NOAA&CIRES) about 18% higher C20R (Compo/NOAA&CIRES) about 18% higher Similar to differences found in modern datasets (figure courtesy Junye Chen, NASA/GMAO-MERRA) Similar to differences found in modern datasets (figure courtesy Junye Chen, NASA/GMAO-MERRA) Global Mean Precipitation from Reanalyses and Reconstructions C20R

C20R uses ensemble Kalman filter, CFS atmosphere and SLP observations; precipitation is 0-6 hour forecasts C20R uses ensemble Kalman filter, CFS atmosphere and SLP observations; precipitation is 0-6 hour forecasts Reconstructions should be nearly (or totally) independent of reanalysis (except OIs, which use other reanalyses) Reconstructions should be nearly (or totally) independent of reanalysis (except OIs, which use other reanalyses) Correlations appear to represent similar depictions of interannual variability; low frequency variations not in agreement Correlations appear to represent similar depictions of interannual variability; low frequency variations not in agreement SH winter not as good; both reconstructions and reanalysis not as good there SH winter not as good; both reconstructions and reanalysis not as good there Other reconstructions give very similar results Other reconstructions give very similar results Correlations Between GPCP-Based Reconstruction and C20R Jan-MarJun-Aug Annual

Modal patterns in GPCP and OI reconstructions Better resemblance here than in the global time series Better resemblance here than in the global time series Some indication that OI-base is better in mid- and high northern latitudes Some indication that OI-base is better in mid- and high northern latitudes

Conclusions/Issues OI analysis offers potential, but still plenty of things to work on OI analysis offers potential, but still plenty of things to work on Use other satellite products (IR, Wilheit/Chang, TRMM PR) Use other satellite products (IR, Wilheit/Chang, TRMM PR) Other reanalyses – take advantage of variety Other reanalyses – take advantage of variety Reconstruction back to 1900 is encouraging Reconstruction back to 1900 is encouraging Some skill in capturing seasonal-to-decadal variations Some skill in capturing seasonal-to-decadal variations Decadal-to-centennial variations still need to be pinned down better Decadal-to-centennial variations still need to be pinned down better Many issues related to satellite-derived precipitation estimates: Many issues related to satellite-derived precipitation estimates: Solid precipitation – snow, etc. Solid precipitation – snow, etc. High latitude and orographic precipitation High latitude and orographic precipitation Light precipitation – drizzle, fog, cloud liquid water Light precipitation – drizzle, fog, cloud liquid water Broader issues related to global precipitation data sets: Broader issues related to global precipitation data sets: Oceanic precipitation magnitude – critical to understanding the global water cycle Oceanic precipitation magnitude – critical to understanding the global water cycle Temporal stability – critical to understanding global climate change Temporal stability – critical to understanding global climate change Sustainability of integrated global precipitation data sets Sustainability of integrated global precipitation data sets Sustainability of critical observations – both satellite and in situ Sustainability of critical observations – both satellite and in situ