Variability in Oceanic Precipitation: Methods and Results

1. Variability in Oceanic Precipitation: Methods and Results Phil Arkin, Cooperative Institute for Climate Studies Earth System Science Interdisciplinary Center, University of Maryland

2. Why should we care where/how much precipitation occurs over oceans? • Associated condensation heating drives large-scale atmospheric circulation - critical to weather forecasting • Effects are crucial to atmosphere-ocean interactions in climate variability - critical to climate monitoring and prediction • Key to understanding global signals of ENSO, NAO, PDO, etc. • Essential to validation of climate models used in IPCC projections of future climate

3. Before satellite observations, two main methods were based on island measurements and ship observations • Island rain gauge observations interpolated over the oceans • Ship observations of precipitation frequency or present weather converted to accumulation • None of these approaches agreed, leading to some entertaining discussions in the literature about the merits of the various methods (especially considering that there was virtually nothing in the way of validating information)

4. Wright and Reed, 1981, NOAA Tech Memo (frequency); results similar to Tucker, 1961 (present weather) TRMM Composite Climatology (mm/day; Adler et al., JMSJ, 2009 (interpolated island gauges) Iowa State University website

5. Current state of the art depends upon combining information from many sources • Rain gauges - land only, with the obvious sampling problems • Surface-based radars - not used for global analyses so far • Satellite observations: TRMM radar, passive microwave, visible and infrared from geostationary satellites • Atmospheric observations – through atmospheric general circulation models • GPCP (Global Precipitation Climatology Project and CMAP (CPC Merged Analysis of Precipitation) are examples on global scale – details to follow • Global, 2.5° latitude/longitude grid • Monthly (and pentad, but with larger errors) since January 1979, continuing through the present (slightly behind real time) • (see Xie and Arkin, BAMS, 1997 for CMAP, Adler et al, JHM, 2003 for GPCP v.2)

6. Satellite-derived estimates • Visible and/or infrared (IR) • Geostationary coverage nearly global (up to 60° latitude) • 30 minute temporal sampling, many years (20-30) of data • Highly empirical (cloud top temperature), but many approaches work • Not sensitive to nature of surface – land/ocean • Passive microwave - emission • At lower frequencies, raindrops emit like blackbodies over colder-appearing ocean surface • Most physically direct, but ocean only, cold surface a problem • Thought to be most accurate over oceans, but sampling is limited • Passive microwave - scattering • At higher frequencies, large ice particles scatter radiation upwelling from the surface – works over land and ocean, but not as direct as emission • Other satellite methods • Rain radar (TRMM, GPM) – most accurate, in principle, but worst sampling • Inversion (GPROF) – takes advantage of all frequencies

7. January 1994

8. Model-derived estimates • Precipitation is not a random occurrence - other atmospheric observations contain relevant information • Atmospheric winds, temperature, moisture largely determine where precipitation falls and how much occurs • Physically based dynamical models of the atmosphere predict/specify precipitation in various ways • Numerical Weather Prediction models forecast precipitation • Assimilation of radiances can yield cloud, hydrometeor distributions • These can be used as “estimates” of precipitation • Best where models best – mid and high latitudes • Accuracy strongly dependent on validity of modeled physical processes • Examples: atmospheric reanalyses

9. TMPA 3-Hrly CMORPH 3-Hrly First 7 days of January 2004 MERRA 3-Hrly MERRA 3-Hrly

10. How are the varied sources combined to get precipitation over the oceans? • This is an “analysis” problem (in the NWP sense: getting a complete gridded field from disparate irregularly distributed observations) • Microwave-based estimates are most accurate, but their spatial and temporal sampling is mediocre • Geostationary IR provides much better sampling, but poor accuracy • Gauge observations might be useful for calibration and validation, but unclear how best to use them over oceans

11. GPCP uses a compositing technique: at any location where more than one value is available, use the “best” (in this case, determined a priori) • Emission microwave over oceans, scattering over land (both corrected for diurnal sampling errors using geostationary IR), IR-based cloud index from HIRS assimilation over high latitudes • CMAP uses a weighted average (of inputs similar to GPCP) • Weights are proportional to errors, which are estimated over land from comparison with gauge observations and over ocean from earlier validation studies • To ensure spatial completeness, CMAP uses an IR-based product derived from anomalies in OLR, and one version uses precipitation from the NCEP reanalysis as an additional input • Both GPCP and CMAP combine the initial product with a gauge-based analysis over land to reduce systematic errors

12. Global Precipitation Climatologies • 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

13. CMAP and GPCP have some shortcomings: • Resolution – too coarse for many applications that require finer spatial/temporal resolution • Aging - based on products and techniques available some time ago • Short records - limited to period since 1979 (or later) • Incomplete error characterization • Some current work at CICS (Matt Sapiano/Tom Smith): • Experiment with new approaches to analyzing precipitation during the modern era (1979 – present) • Using reanalysis precipitation and optimal interpolation to improve global analyses • Combine different satellite-derived precipitation estimates to produce high time/space resolution precipitation analyses • Develop and verify methods to extend oceanic precipitation analyses to the entire 20th Century

14. Multi-Source Analysis of Precipitation (MSAP) • Used OI to produce blend of ERA-40 (now includes ERA-I) and SSM/I (GPROF & Wentz) • Relies on satellite estimates in tropics, reanalysis in high latitudes, mix in between • Results of initial OI in Sapiano et al., 2008, JGR

15. Extensions of the OI Analysis MSAP 1.1 uses ERA-I – better model precipitation MSAP-G adjusts to GPCC gauge analysis – much less bias over land MSAP-OPI uses IR-based OPI – longer record

16. Pronounced annual cycles in extratropics • MSAP-OPI has tropical artifacts related to orbital drift of NOAA satellites • Noise in tropics similar in all; large relative to signal

17. The new OI analyses are promising, particularly since both reanalyses and satellite-derived estimates should improve in the future • Longer time series of global precipitation analyses is needed: • To validate global climate models • 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

18. Approach: reconstruct/reanalyze global precipitation back to 1900 using 2 methods • Empirical Orthogonal Function (EOF)-based reconstruction using GPCP and other global precipitation analyses, combined with historical coastal and island rain gauge observations • Canonical Correlation Analysis (CCA) reanalysis using SST and SLP, based on modern era analyses

19. Goal: Reconstruct/reanalyze global precipitation back to 1900 • Use 2 methods, both for the period 1900 - 1998 • Empirical Orthogonal Function (EOF)-based reconstruction • Use GPCP and other global precipitation analyses to determine dominant modes of variability • Compare filtered modes to coastal and island rain gauge observations to derive specification relations • Use those relations with historical gauge observations to create fields • Monthly, 2.5° x2.5° • Can’t capture longer time scale variations well • Canonical Correlation Analysis (CCA) • Compare variability in modern precipitation using GPCP and other global products to sea surface temperature (SST) and sea level pressure (SLP) during same period – SST and SLP known to exhibit correlation with precipitation • Use derived relations to specify historical precipitation reanalysis using SST and SLP fields from the period • Can’t provide spatial/temporal detail that EOF method can – annual, 5° x5°

20. CCA Reanalyses • Anomalies relative to 1979 – 2007 base period • Decadal-scale signal looks reasonable (although who knows what is correct?) • Ability to resolve finer scale phenomena like ENSO is limited due to coarse resolution (yearly, 5°x5°); bigger errors on short time scales • See Smith et. al. 2009 (in press), JGR • EOF-based reconstructions (not shown here) offer finer time/space resolution but fail to capture the decadal signal (Smith et. al. 2008, JGR) Fig 1: DJF means.

21. Southern Oscillation Index X X X X X X X X X X X X X X X X X X X X X X ENSO Signal: Warm (top), Cold (Bottom); CCA (Left), EOF (Right) 1900 – 1998; Annual Anomalies X

22. (mm/day units) Sensitivity of ENSO Signal to EOF Base Data Set

23. (mm/day units) • CCA preserves ENSO signal well throughout 20th Century • EOF (based on MSAP, which is short base period) does not

24. Pacific Decadal Oscillation (PDO) Cool Phase Warm Phase (1978-1998) (1930-1945) (1950-1975) From http://jisao.washington.edu/pdo

25. CCA captures similarity between early and late warm periods • EOF-MSAP loses detail in early period, but provides more spatial detail in later two periods

26. Global Mean Precipitation from Reanalyses and Reconstructions (differences largest over oceans) • 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 than observation-based products • ESRL-Compo/Whittaker SLP-based reanalysis is about 3.3 mm/day • (figure courtesy Junye Chen, NASA/GMAO-MERRA)

27. All plots are anomalies relative to the mean of the CCA reanalysis (same as GPCP) • +/- 1 and 2 SD plotted for AR4 runs • Compo reanalysis above AR4 range – at the high end of modern reanalyses, which are wetter than GPCP and CMAP • GPCP and CCA in lower part of AR4 range

28. 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 are quite different • Still an open question: is the precipitation trend really independent of the SST trend?

29. Conclusions/Issues • OI analysis offers potential, but still plenty of things to work on • Use other satellite products (IR, Wilheit/Chang, TRMM PR) • Other reanalyses – take advantage of variety • Reconstruction back to 1900 is encouraging • EOF-based product shows skill in capturing seasonal-to-decadal variations • Decadal-to-centennial variations well-represented in CCA • A combined approach will be tried next • Many issues related to satellite-derived precipitation estimates: • Solid precipitation – snow, etc. • Magnitude of tropical rainfall • Light precipitation – drizzle, fog, cloud liquid water • Broader issues related to global precipitation data sets: • Temporal stability – critical to understanding global climate change • Sustainability of integrated global precipitation data sets • Sustainability of critical observations – both satellite and in situ • Bottom line: Observations and theory disagree dramatically – not a satisfactory state of affairs