Global Precipitation Analyses Global Precipitation Analyses and Reanalysesand Reanalyses

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

based on work by Matt Sapiano, Ching-Yee based on work by Matt Sapiano, Ching-Yee Chang and John Janowiak of CICS/ESSICChang and John Janowiak of CICS/ESSIC

andandTom Smith, NOAA/NESDIS/STAR and CICSTom Smith, NOAA/NESDIS/STAR and CICS

Scientific IssuesScientific Issues Precipitation matters!Precipitation matters!

Fresh water for people, agriculture and industryFresh water for people, agriculture and industry Extremes, both droughts and floods, have great impact Extremes, both droughts and floods, have great impact

on societieson societies One of the most anticipated manifestations of global One of the most anticipated manifestations of global

changechange Precipitation is an index of the vigor of the Precipitation is an index of the vigor of the

hydrological cycle – generally expected to hydrological cycle – generally expected to change with global temperature increaseschange with global temperature increases

We We cancan “measure” (estimate quantitatively) “measure” (estimate quantitatively) precipitation over the globe precipitation over the globe

Fundamental questions:Fundamental questions: How much precipitation occurs? (i.e. What is the How much precipitation occurs? (i.e. What is the

strength of the global hydrological cycle?)strength of the global hydrological cycle?) How does precipitation vary with time and space? How does precipitation vary with time and space?

(i.e. How is the hydrological cycle changing?)(i.e. How is the hydrological cycle changing?)

Observing PrecipitationObserving Precipitation Not uniformly well defined – generally speaking Not uniformly well defined – generally speaking

we attempt to obtain spatial and/or temporal we attempt to obtain spatial and/or temporal means, but rigorous definitions are not typicalmeans, but rigorous definitions are not typical

Gauges – point values with relatively well Gauges – point values with relatively well understood errorsunderstood errors

Remote Sensing – radars (surface and space), Remote Sensing – radars (surface and space), passive radiometers (space-based)passive radiometers (space-based) All of these are inferencesAll of these are inferences Errors vary in time and space and are poorly Errors vary in time and space and are poorly

known/understoodknown/understood Models – simulations, short-range forecastsModels – simulations, short-range forecasts

Derived from observations to varying degreeDerived from observations to varying degree Extensive validation, especially for forecasts, which Extensive validation, especially for forecasts, which

provides some information on errors; but model provides some information on errors; but model changes go on continuously so that information is changes go on continuously so that information is constantly being outdatedconstantly being outdated

Quantitative, but dependent on reality of model Quantitative, but dependent on reality of model physical processesphysical processes

Integrating/Analyzing Precipitation Integrating/Analyzing Precipitation ObservationsObservations Analysis – creating complete (in time and space) Analysis – creating complete (in time and space)

fields from varying and incomplete observationsfields from varying and incomplete observations Satellite-derived estimates have complementary Satellite-derived estimates have complementary

characteristics (geostationary IR is more complete characteristics (geostationary IR is more complete but has poor accuracy, low Earth orbit PMW is more but has poor accuracy, low Earth orbit PMW is more accurate but has sparse sampling) so combining accurate but has sparse sampling) so combining them makes sense (CMAP, GPCP, CMORPH, TMPA, them makes sense (CMAP, GPCP, CMORPH, TMPA, GSMaP…)GSMaP…)

CMAP and GPCP use gauges to reduce bias over CMAP and GPCP use gauges to reduce bias over land, leading to complexities regarding homogeneityland, 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 Resolution – too coarse for many applications that

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

available some time agoavailable some 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 Particular problems with high latitude and orographic Particular problems with high latitude and orographic

precipitationprecipitation Goals of our current work:Goals of our current work:

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 to Combine different satellite-derived precipitation estimates to produce high time/space resolution precipitation analysesproduce high time/space resolution precipitation analyses

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

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

oceanic, precipitationoceanic, precipitation To describe interdecadal variability in phenomena such as To describe interdecadal variability in phenomena such as

ENSO, the NAO, the PDO and othersENSO, the NAO, the PDO and others Approach: reconstruct/reanalyze global precipitation Approach: reconstruct/reanalyze global precipitation

back to 1900 using 2 methodsback to 1900 using 2 methods EOF-based reconstruction using GPCP and other global EOF-based reconstruction using GPCP and other global

precipitation analyses, combined with historical coastal precipitation analyses, combined with historical coastal and island rain gauge observationsand island rain gauge observations

CCA reanalysis using SST and SLP, based on modern era CCA reanalysis using SST and SLP, based on modern era analyses analyses

Compare to GHCN gauge observations, NOAA/ESRL Compare to GHCN gauge observations, NOAA/ESRL 2020thth Century SLP-based reanalysis and IPCC AR4 C20C Century SLP-based reanalysis and IPCC AR4 C20C productsproducts

Climate Modes from EOF Climate Modes from EOF ReconstructionsReconstructions

Good representation of climate modes Good representation of climate modes Better in NH and tropics; OI possibly better in mid- and high northern Better in NH and tropics; OI possibly better in mid- and high northern

latitudeslatitudes Global time series of EOF reconstructions not realisticGlobal time series of EOF reconstructions not realistic

CCA ReanalysesCCA Reanalyses

CCA nearly independent CCA nearly independent of GHCN observations, of GHCN observations, although GPCP uses although GPCP uses gauge data to remove gauge data to remove bias (CCA based on bias (CCA based on gauge-free version of gauge-free version of GPCP gives similar GPCP gives similar results)results)

Top panel shows Top panel shows comparison over land comparison over land areas where gauges are areas where gauges are found – small areal found – small areal coveragecoverage

Decadal-scale signal Decadal-scale signal looks reasonablelooks reasonable

Ability to resolve finer Ability to resolve finer scale phenomena like scale phenomena like ENSO is limited – yearly, ENSO is limited – yearly, 55°, bigger errors on short °, bigger errors on short time scalestime scales

See Smith et. al. 2008, See Smith et. al. 2008, JGRJGR

+/- 1 and 2 SD plotted for AR4 runs+/- 1 and 2 SD plotted for AR4 runs Compo reanalysis above AR4 range – similar to modern Compo reanalysis above AR4 range – similar to modern

reanalyses, which are 0.5-0.8 mm/dy > GPCP and CMAPreanalyses, which are 0.5-0.8 mm/dy > GPCP and CMAP GPCP and CCA in lower part of AR4 rangeGPCP and CCA in lower part of AR4 range

Note scale changed by factor of 10Note scale changed by factor of 10 Biases removed so means are the same for all time seriesBiases removed so means are the same for all time series AR4 ensemble mean exhibits much less variability since it AR4 ensemble mean exhibits much less variability since it

is an average of many (20 or so) runsis an average of many (20 or so) runs

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

CCA and AR4 ensemble mean show similar centennial-scale CCA and AR4 ensemble mean show similar centennial-scale changes, but interannual variations rather differentchanges, but interannual variations rather different

Conclusions/IssuesConclusions/Issues EOF-based Reconstruction back to 1900 EOF-based Reconstruction back to 1900

exhibits skill in capturing seasonal-to-decadal exhibits skill in capturing seasonal-to-decadal variationsvariations

GPCP-based CCA reanalysis matches 20GPCP-based CCA reanalysis matches 20 thth Century variations from IPCC AR4 model Century variations from IPCC AR4 model simulationssimulations

Best historical analysis probably is combination Best historical analysis probably is combination of low frequency from CCA and finer scales of low frequency from CCA and finer scales from filtered EOF reconstructionsfrom filtered EOF reconstructions

Significant biases still present between models Significant biases still present between models and observed datasetsand observed datasets

Opportunities for CollaborationOpportunities for Collaboration Creating/improving data setsCreating/improving data sets

Estimating precipitation from satellite observationsEstimating precipitation from satellite observations Improved solid precipitation algorithmsImproved solid precipitation algorithms Orographic precipitationOrographic precipitation Light oceanic precipitationLight oceanic precipitation

Combining information from multiple sources to improve regional Combining information from multiple sources to improve regional and global precipitation analysesand global precipitation analyses

Analysis technique developmentAnalysis technique development Validation and verificationValidation and verification

Extending analyses into the pastExtending analyses into the past Improved historical period productsImproved historical period products Link to precipitation proxies in earlier periodsLink to precipitation proxies in earlier periods

Diagnostic analysesDiagnostic 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 Better characterization of long-term changes in the global

hydrological cyclehydrological cycle Model validationModel validation