Variability in Oceanic Variability in Oceanic Precipitation: Methods and Precipitation: Methods and

Results Results

Phil Arkin, Cooperative Institute for Climate Phil Arkin, Cooperative Institute for Climate StudiesStudies

Earth System Science Interdisciplinary Center, Earth System Science Interdisciplinary Center, University of MarylandUniversity of Maryland

Why should we care where/how Why should we care where/how much precipitation occurs over much precipitation occurs over

oceans? oceans? Associated condensation heating Associated condensation heating

drives large-scale atmospheric drives large-scale atmospheric circulation - critical to weather circulation - critical to weather forecastingforecasting

Effects are crucial to atmosphere-Effects are crucial to atmosphere-ocean interactions in climate ocean interactions in climate variability - critical to climate variability - critical to climate monitoring and predictionmonitoring and prediction

Key to understanding global signals of Key to understanding global signals of ENSO, NAO, PDO, etc.ENSO, NAO, PDO, etc.

Essential to validation of climate Essential to validation of climate models used in IPCC projections of models used in IPCC projections of future climatefuture climate

Before satellite observations, two main Before satellite observations, two main methods were based on island methods were based on island measurements and ship observationsmeasurements and ship observations

Island rain gauge observations Island rain gauge observations interpolated over the oceansinterpolated over the oceans

Ship observations of precipitation Ship observations of precipitation frequency or present weather converted frequency or present weather converted to accumulationto accumulation

None of these approaches agreed, None of these approaches agreed, leading to some entertaining discussions leading to some entertaining discussions in the literature about the merits of the in the literature about the merits of the various methods (especially considering various methods (especially considering that there was virtually nothing in the that there was virtually nothing in the way of validating information)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 Current state of the art depends upon combining information from many sourcescombining information from many sources Rain gauges - land only, with the obvious sampling Rain gauges - land only, with the obvious sampling

problemsproblems Surface-based radars - not used for global Surface-based radars - not used for global

analyses so faranalyses so far Satellite observations: TRMM radar, passive Satellite observations: TRMM radar, passive

microwave, visible and infrared from geostationary microwave, visible and infrared from geostationary satellitessatellites

Atmospheric observations – through atmospheric Atmospheric observations – through atmospheric general circulation modelsgeneral circulation models

GPCP (Global Precipitation Climatology GPCP (Global Precipitation Climatology Project and CMAP (CPC Merged Analysis of Project and CMAP (CPC Merged Analysis of Precipitation) are examples on global scale – Precipitation) are examples on global scale – details to followdetails to follow Global, 2.5° latitude/longitude gridGlobal, 2.5° latitude/longitude grid Monthly (and pentad, but with larger errors) since Monthly (and pentad, but with larger errors) since

January 1979, continuing through the present January 1979, continuing through the present (slightly behind real time)(slightly behind real time)

(see Xie and Arkin, BAMS, 1997 for CMAP, Adler et al, JHM, 2003 for (see Xie and Arkin, BAMS, 1997 for CMAP, Adler et al, JHM, 2003 for GPCP v.2)GPCP v.2)

Satellite-derived estimatesSatellite-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 data30 minute temporal sampling, many years (20-30) of data Highly empirical (cloud top temperature), but many approaches Highly empirical (cloud top temperature), but many approaches

workwork Not sensitive to nature of surface – land/oceanNot sensitive to nature of surface – land/ocean

Passive microwave - emissionPassive microwave - emission At lower frequencies, raindrops emit like blackbodies over colder-At lower frequencies, raindrops emit like blackbodies over colder-

appearing ocean surfaceappearing ocean surface Most physically direct, but ocean only, cold surface a problemMost physically direct, but ocean only, cold surface a problem Thought to be most accurate over oceans, but sampling is limitedThought to be most accurate over oceans, but sampling is limited

Passive microwave - scatteringPassive microwave - scattering At higher frequencies, large ice particles scatter radiation At higher frequencies, large ice particles scatter radiation

upwelling from the surface – works over land and ocean, but not upwelling from the surface – works over land and ocean, but not as direct as emissionas direct as emission

Other satellite methodsOther satellite methods Rain radar (TRMM, GPM) – most accurate, in principle, but worst Rain radar (TRMM, GPM) – most accurate, in principle, but worst

samplingsampling Inversion (GPROF) – takes advantage of all frequenciesInversion (GPROF) – takes advantage of all frequencies

January 1994January 1994

Model-derived estimatesModel-derived estimates Precipitation is not a random occurrence - other Precipitation is not a random occurrence - other

atmospheric observations contain relevant atmospheric observations contain relevant informationinformation Atmospheric winds, temperature, moisture largely Atmospheric winds, temperature, moisture largely

determine where precipitation falls and how much occursdetermine where precipitation falls and how much occurs Physically based dynamical models of the Physically based dynamical models of the

atmosphere predict/specify precipitation in various atmosphere predict/specify precipitation in various waysways Numerical Weather Prediction models forecast precipitationNumerical Weather Prediction models forecast precipitation Assimilation of radiances can yield cloud, hydrometeor Assimilation of radiances can yield cloud, hydrometeor

distributionsdistributions These can be used as “estimates” of precipitationThese can be used as “estimates” of precipitation

Best where models best – mid and high latitudesBest where models best – mid and high latitudes Accuracy strongly dependent on validity of modeled Accuracy strongly dependent on validity of modeled

physical processesphysical processes Examples: atmospheric reanalysesExamples: atmospheric reanalyses

TMPA 3-Hrly CMORPH 3-Hrly

MERRA 3-Hrly MERRA 3-Hrly

First 7 days of January 2004

How are the varied sources How are the varied sources combined to get precipitation over combined to get precipitation over

the oceans?the oceans? This is an “analysis” problem (in the NWP This is an “analysis” problem (in the NWP

sense: getting a complete gridded field sense: getting a complete gridded field from disparate irregularly distributed from disparate irregularly distributed observations) observations)

Microwave-based estimates are most Microwave-based estimates are most accurate, but their spatial and temporal accurate, but their spatial and temporal sampling is mediocresampling is mediocre

Geostationary IR provides much better Geostationary IR provides much better sampling, but poor accuracysampling, but poor accuracy

Gauge observations might be useful for Gauge observations might be useful for calibration and validation, but unclear how calibration and validation, but unclear how best to use them over oceansbest to use them over oceans

GPCP uses a compositing technique: at any GPCP uses a compositing technique: at any location where more than one value is location where more than one value is available, use the “best” (in this case, available, use the “best” (in this case, determined a priori)determined a priori) Emission microwave over oceans, scattering over Emission microwave over oceans, scattering over

land (both corrected for diurnal sampling errors land (both corrected for diurnal sampling errors using geostationary IR), IR-based cloud index from using geostationary IR), IR-based cloud index from HIRS assimilation over high latitudesHIRS assimilation over high latitudes

CMAP uses a weighted average (of inputs similar to CMAP uses a weighted average (of inputs similar to GPCP)GPCP) Weights are proportional to errors, which are Weights are proportional to errors, which are

estimated over land from comparison with gauge estimated over land from comparison with gauge observations and over ocean from earlier observations and over ocean from earlier validation studiesvalidation studies

To ensure spatial completeness, CMAP uses an IR-To ensure spatial completeness, CMAP uses an IR-based product derived from anomalies in OLR, and based product derived from anomalies in OLR, and one version uses precipitation from the NCEP one version uses precipitation from the NCEP reanalysis as an additional inputreanalysis as an additional input

Both GPCP and CMAP combine the initial product Both GPCP and CMAP combine the initial product with a gauge-based analysis over land to reduce with a gauge-based analysis over land to reduce systematic errorssystematic errors

Global Precipitation ClimatologiesGlobal 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 Resolution – too coarse for many applications that

require finer spatial/temporal resolutionrequire finer spatial/temporal resolution Aging - based on products and techniques available Aging - based on products and techniques available

some time agosome time ago Short records - limited to period since 1979 (or later)Short records - limited to period since 1979 (or later) Incomplete error characterizationIncomplete error characterization

Some current work at CICS (Matt Sapiano/Tom Some current work at CICS (Matt Sapiano/Tom Smith):Smith): Experiment with new approaches to analyzing Experiment with new approaches to analyzing

precipitation during the modern era (1979 – present)precipitation during the modern era (1979 – present) Using reanalysis precipitation and optimal interpolation to Using reanalysis precipitation and optimal interpolation to

improve global analyses improve global analyses Combine different satellite-derived precipitation estimates Combine different satellite-derived precipitation estimates

to produce high time/space resolution precipitation analysesto produce high time/space resolution precipitation analyses Develop and verify methods to extend oceanic Develop and verify methods to extend oceanic

precipitation analyses to the entire 20precipitation analyses to the entire 20thth Century Century

Multi-Source Analysis of Precipitation Multi-Source Analysis of Precipitation (MSAP)(MSAP)

Used OI to produce Used OI to produce blend of ERA-40 (now blend of ERA-40 (now includes ERA-I) and includes ERA-I) and SSM/I (GPROF & Wentz)SSM/I (GPROF & Wentz)

Relies on satellite Relies on satellite estimates in tropics, estimates in tropics, reanalysis in high reanalysis in high latitudes, mix in latitudes, mix in betweenbetween

Results of initial OI in Results of initial OI in Sapiano et al., 2008, Sapiano et al., 2008, JGRJGR

Extensions of the OI AnalysisExtensions of the OI AnalysisMSAP 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, The new OI analyses are promising, particularly since both reanalyses and particularly since both reanalyses and satellite-derived estimates should improve satellite-derived estimates should improve in the futurein the future

Longer time series of global precipitation Longer time series of global precipitation analyses is needed:analyses is needed: To validate global climate modelsTo validate global climate models To describe long-term trends in global, To describe long-term trends in global,

particularly oceanic, precipitationparticularly oceanic, precipitation To describe interdecadal variability in To describe interdecadal variability in

phenomena such as ENSO, the NAO, the PDO phenomena such as ENSO, the NAO, the PDO and othersand others

Approach: reconstruct/reanalyze global Approach: reconstruct/reanalyze global precipitation back to 1900 using 2 methodsprecipitation back to 1900 using 2 methods Empirical Orthogonal Function (EOF)-based Empirical Orthogonal Function (EOF)-based

reconstruction using GPCP and other global reconstruction using GPCP and other global precipitation analyses, combined with historical precipitation analyses, combined with historical coastal and island rain gauge observationscoastal and island rain gauge observations

Canonical Correlation Analysis (CCA) reanalysis Canonical Correlation Analysis (CCA) reanalysis using SST and SLP, based on modern era using SST and SLP, based on modern era analyses analyses

Goal: Reconstruct/reanalyze global Goal: Reconstruct/reanalyze global precipitation back to 1900precipitation back to 1900

Use 2 methods, both for the period 1900 - 1998Use 2 methods, both for the period 1900 - 1998 Empirical Orthogonal Function (EOF)-based reconstruction Empirical Orthogonal Function (EOF)-based reconstruction

Use GPCP and other global precipitation analyses to determine Use GPCP and other global precipitation analyses to determine dominant modes of variabilitydominant modes of variability

Compare filtered modes to coastal and island rain gauge observations Compare filtered modes to coastal and island rain gauge observations to derive specification relationsto derive specification relations

Use those relations with historical gauge observations to create fieldsUse those relations with historical gauge observations to create fields Monthly, 2.5Monthly, 2.5° x° x2.52.5°° Can’t capture longer time scale variations wellCan’t capture longer time scale variations well

Canonical Correlation Analysis (CCA) Canonical Correlation Analysis (CCA) Compare variability in modern precipitation using GPCP and other Compare variability in modern precipitation using GPCP and other

global products to sea surface temperature (SST) and sea level global products to sea surface temperature (SST) and sea level pressure (SLP) during same period – SST and SLP known to exhibit pressure (SLP) during same period – SST and SLP known to exhibit correlation with precipitationcorrelation with precipitation

Use derived relations to specify historical precipitation reanalysis using Use derived relations to specify historical precipitation reanalysis using SST and SLP fields from the periodSST and SLP fields from the period

Can’t provide spatial/temporal detail that EOF method can – annual, 5Can’t provide spatial/temporal detail that EOF method can – annual, 5° ° xx55°°

CCA ReanalysesCCA Reanalyses

Anomalies relative to 1979 – 2007 base periodAnomalies relative to 1979 – 2007 base period Decadal-scale signal looks reasonable (although who knows what is Decadal-scale signal looks reasonable (although who knows what is

correct?)correct?) Ability to resolve finer scale phenomena like ENSO is limited due to coarse Ability to resolve finer scale phenomena like ENSO is limited due to coarse

resolution (yearly, 5resolution (yearly, 5°x°x55°); bigger errors on short time scales°); bigger errors on short time scales See Smith et. al. 2009 (in press), JGRSee Smith et. al. 2009 (in press), JGR EOF-based reconstructions (not shown here) offer finer time/space EOF-based reconstructions (not shown here) offer finer time/space

resolution but fail to capture the decadal signal (Smith et. al. 2008, JGR)resolution but fail to capture the decadal signal (Smith et. al. 2008, JGR)

XX X X X X X X X X X X

Southern Oscillation IndexX X X X X X XX X X

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 20th Century• EOF (based on MSAP, which is short base period) does not

Warm Phase Cool Phase

Pacific Decadal Oscillation (PDO)

From http://jisao.washington.edu/pdo

(1930-1945) (1978-1998)

(1950-1975)

• 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 Datasets based on observations (GPCP, CMAP) give about 2.6 mm/day (AR4 range is about 2.5-3.2 mm/day)mm/day (AR4 range is about 2.5-3.2 mm/day)

Data assimilation products average about 3 mm/day; also Data assimilation products average about 3 mm/day; also have larger mean annual cycle and greater interannual have larger mean annual cycle and greater interannual variability than observation-based productsvariability than observation-based products

ESRL-Compo/Whittaker SLP-based reanalysis is about 3.3 mm/dayESRL-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 Global Mean Precipitation from Reanalyses and Reconstructions (differences largest over oceans)Reconstructions (differences largest over oceans)

All plots are anomalies relative to the mean of the CCA reanalysis All plots are anomalies relative to the mean of the CCA reanalysis (same as GPCP)(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 Compo reanalysis above AR4 range – at the high end of modern

reanalyses, which are wetter than GPCP and CMAPreanalyses, which are wetter than GPCP and CMAP GPCP and CCA in lower part of AR4 rangeGPCP and CCA in lower part of AR4 range

Re-scale AR4 ensemble mean so variance is about same as a single Re-scale AR4 ensemble mean so variance is about same as a single realizationrealization

CCA and AR4 ensemble mean show similar centennial-scale changes, CCA and AR4 ensemble mean show similar centennial-scale changes, but interannual variations are quite differentbut interannual variations are quite different

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

Conclusions/IssuesConclusions/Issues OI analysis offers potential, but still plenty of things to work onOI 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 varietyOther reanalyses – take advantage of variety

Reconstruction back to 1900 is encouragingReconstruction back to 1900 is encouraging EOF-based product shows skill in capturing seasonal-to-decadal variationsEOF-based product shows skill in capturing seasonal-to-decadal variations Decadal-to-centennial variations well-represented in CCADecadal-to-centennial variations well-represented in CCA A combined approach will be tried nextA 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 rainfallMagnitude of tropical rainfall Light precipitation – drizzle, fog, cloud liquid waterLight 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 changeTemporal stability – critical to understanding global climate change Sustainability of integrated global precipitation data setsSustainability of integrated global precipitation data sets Sustainability of critical observations – both satellite and in situSustainability of critical observations – both satellite and in situ

Bottom line: Observations and theory disagree dramatically – Bottom line: Observations and theory disagree dramatically – not a satisfactory state of affairsnot a satisfactory state of affairs