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

Why should we care where/how much precipitation occurs over oceans? Associated condensation heating drives large-scale atmospheric circulation - critical to weather forecasting 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 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. Key to understanding global signals of ENSO, NAO, PDO, etc. Essential to validation of climate models used in IPCC projections of future climate Essential to validation of climate models used in IPCC projections of future climate

Before satellite observations, two main methods were based on island measurements and ship observations Before satellite observations, two main methods were based on island measurements and ship observations Island rain gauge observations interpolated over the oceans Island rain gauge observations interpolated over the oceans Ship observations of precipitation frequency or present weather converted to accumulation 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) 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)

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

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

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

January 1994

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

TMPA 3-HrlyCMORPH 3-Hrly MERRA 3-Hrly First 7 days of January 2004

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) 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 Microwave-based estimates are most accurate, but their spatial and temporal sampling is mediocre Geostationary IR provides much better sampling, but poor accuracy 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 Gauge observations might be useful for calibration and validation, but unclear how best to use them over oceans

GPCP uses a compositing technique: at any location where more than one value is available, use the “best” (in this case, determined a priori) 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 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) 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 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 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 Both GPCP and CMAP combine the initial product with a gauge-based analysis over land to reduce systematic errors

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

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 Aging - based on products and techniques available some time ago Aging - 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 Some current work at CICS (Matt Sapiano/Tom Smith): Some current work at CICS (Matt Sapiano/Tom Smith): 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 oceanic precipitation analyses to the entire 20 th Century Develop and verify methods to extend oceanic precipitation analyses to the entire 20 th Century

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

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

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

The new OI analyses are promising, particularly since both reanalyses and satellite-derived estimates should improve in the future 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: 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 Empirical Orthogonal Function (EOF)-based reconstruction using GPCP and other global precipitation analyses, combined with historical coastal and island rain gauge observations 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 Canonical Correlation Analysis (CCA) reanalysis using SST and SLP, based on modern era analyses

Goal: Reconstruct/reanalyze global precipitation back to 1900 Use 2 methods, both for the period Use 2 methods, both for the period Empirical Orthogonal Function (EOF)-based reconstruction Empirical Orthogonal Function (EOF)-based reconstruction Use GPCP and other global precipitation analyses to determine dominant modes of variability 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 Compare filtered modes to coastal and island rain gauge observations to derive specification relations Use those relations with historical gauge observations to create fields Use those relations with historical gauge observations to create fields Monthly, 2.5° x2.5° Monthly, 2.5° x2.5° Can’t capture longer time scale variations well Can’t capture longer time scale variations well Canonical Correlation Analysis (CCA) 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 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 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° Can’t provide spatial/temporal detail that EOF method can – annual, 5° x5°

CCA Reanalyses Anomalies relative to 1979 – 2007 base period Anomalies relative to 1979 – 2007 base period Decadal-scale signal looks reasonable (although who knows what is correct?) 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 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 (in press), JGR See Smith et. al (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) 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.

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

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

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

Warm Phase Cool Phase Pacific Decadal Oscillation (PDO) From ( ) ( ) ( )

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

Datasets based on observations (GPCP, CMAP) give about 2.6 mm/day (AR4 range is about mm/day) Datasets based on observations (GPCP, CMAP) give about 2.6 mm/day (AR4 range is about mm/day) Data assimilation products average about 3 mm/day; also have larger mean annual cycle and greater interannual variability than observation-based products 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 ESRL-Compo/Whittaker SLP-based reanalysis is about 3.3 mm/day (figure courtesy Junye Chen, NASA/GMAO-MERRA) (figure courtesy Junye Chen, NASA/GMAO-MERRA) Global Mean Precipitation from Reanalyses and Reconstructions (differences largest over oceans)

All plots are anomalies relative to the mean of the CCA reanalysis (same as GPCP) All plots are anomalies relative to the mean of the CCA reanalysis (same as GPCP) +/- 1 and 2 SD plotted for AR4 runs +/- 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 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 GPCP and CCA in lower part of AR4 range

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

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 EOF-based product shows skill in capturing seasonal-to-decadal variations EOF-based product shows skill in capturing seasonal-to-decadal variations Decadal-to-centennial variations well-represented in CCA Decadal-to-centennial variations well-represented in CCA A combined approach will be tried next A combined approach will be tried next Many issues related to satellite-derived precipitation estimates: Many issues related to satellite-derived precipitation estimates: Solid precipitation – snow, etc. Solid precipitation – snow, etc. Magnitude of tropical rainfall Magnitude of tropical rainfall 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: 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 Bottom line: Observations and theory disagree dramatically – not a satisfactory state of affairs Bottom line: Observations and theory disagree dramatically – not a satisfactory state of affairs