Statistical Analyses of Statistical Analyses of Historical Monthly Historical Monthly

Precipitation Anomalies Precipitation Anomalies Beginning 1900Beginning 1900

Phil Arkin, Cooperative Institute for Climate Phil Arkin, Cooperative Institute for Climate and Satellitesand Satellites

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

AndAndTom Smith, Matt Sapiano and Ching-Yee ChangTom Smith, Matt Sapiano and Ching-Yee Chang

Background and MotivationBackground and Motivation Climate models indicate that global temperature Climate models indicate that global temperature

increases will be accompanied by changes in water increases will be accompanied by changes in water vapor and precipitation:vapor and precipitation: Water vapor increases to maintain roughly constant relative Water vapor increases to maintain roughly constant relative

humidity (about 7% per degree)humidity (about 7% per degree) Precipitation increases but at a slower rate (about 2-3% per Precipitation increases but at a slower rate (about 2-3% per

degree) degree) Regionally, precipitation intensifies in climatologically favored Regionally, precipitation intensifies in climatologically favored

regions, decreases at margins (“rich get richer”)regions, decreases at margins (“rich get richer”) Observations show:Observations show:

Global water vapor has increased recently as temperatures have Global water vapor has increased recently as temperatures have warmed (but data have limitations)warmed (but data have limitations)

Global precipitation has increased at 7%/degree since 1990 Global precipitation has increased at 7%/degree since 1990 (Wentz et al., 2007) or at 2.3%/degree since 1979 (Adler et al., (Wentz et al., 2007) or at 2.3%/degree since 1979 (Adler et al., 2008), but again the data have shortcomings2008), but again the data have shortcomings

Rain gauge observations show increases in intense precipitation, Rain gauge observations show increases in intense precipitation, but current datasets aren’t adequate to test the rich get richer but current datasets aren’t adequate to test the rich get richer hypothesishypothesis

What do the models say about projected regional What do the models say about projected regional changes in precipitation?changes in precipitation?

Global Precipitation DatasetsGlobal Precipitation Datasets

• 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

• Both used in IPCC AR4

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 have Data assimilation products average about 3 mm/day; also have larger mean annual cycle and greater interannual variabilitylarger mean annual cycle and greater interannual variability

Global Mean Precipitation from Data Global Mean Precipitation from Data AssimilationAssimilation

Junye Chen, ESSIC and GMAO/MERRA

Climate Model-Based Precipitation Climate Model-Based Precipitation A number of climate models have been A number of climate models have been

used to simulate the 20used to simulate the 20thth Century and Century and precipitation from those runs can be precipitation from those runs can be compared to global precipitation datasetscompared to global precipitation datasets

We would really prefer more than the 30 We would really prefer more than the 30 years available from GPCP and other global years available from GPCP and other global precipitation datasetsprecipitation datasets

Reconstruction of Near-Global Precipitation Variations Reconstruction of Near-Global Precipitation Variations Back to 1900 Based on Gauges and Correlations with Back to 1900 Based on Gauges and Correlations with

SST and SLPSST and SLP(Tom Smith, NOAA/NESDIS and CICS)(Tom Smith, NOAA/NESDIS and CICS)

Base Satellite DataBase Satellite Data Need global satellite analyses for reconstruction statisticsNeed global satellite analyses for reconstruction statistics GPCP, CMAP and MSAP tested; GPCP works bestGPCP, CMAP and MSAP tested; GPCP works best

Direct Reconstructions: fitting data to Empirical Direct Reconstructions: fitting data to Empirical Orthogonal Functions (REOF)Orthogonal Functions (REOF) EOF (or PC) analysis, for covariance mapsEOF (or PC) analysis, for covariance maps Fit available gauge-station data to a set of covariance mapsFit available gauge-station data to a set of covariance maps Yields monthly gauge-based 5-degree analyses available Yields monthly gauge-based 5-degree analyses available

beginning 1900beginning 1900 Indirect Reconstructions: using Canonical Indirect Reconstructions: using Canonical

Correlation Analysis (RCCA)Correlation Analysis (RCCA) Correlate fields of sea-surface temperature (SST) and sea-Correlate fields of sea-surface temperature (SST) and sea-

level pressure (SLP) with fields of precipitation during level pressure (SLP) with fields of precipitation during satellite erasatellite era

Both SST and SLP analyzed for the 20Both SST and SLP analyzed for the 20thth century century

Land ComparisonsLand Comparisons

RCCA & REOF & CRU data land averages, filteredRCCA & REOF & CRU data land averages, filtered REOF(Blend) from REOF(CRU) and REOF(GPCP)REOF(Blend) from REOF(CRU) and REOF(GPCP)

RCCA & REOF similar for most of periodRCCA & REOF similar for most of period RCCA & REOF(GPCP) similar for the GPCP periodRCCA & REOF(GPCP) similar for the GPCP period

Ocean ComparisonsOcean Comparisons RCCA & REOF ocean averages, filteredRCCA & REOF ocean averages, filtered RCCA & REOF differ before 1980RCCA & REOF differ before 1980

1970s climate shift in RCCA1970s climate shift in RCCA REOF does not resolve trend in RCCA & in AR4 ensembleREOF does not resolve trend in RCCA & in AR4 ensemble

RCCA & REOF(GPCP) similarRCCA & REOF(GPCP) similar REOF(GPCP) can be used for updatesREOF(GPCP) can be used for updates

Correlations Between AnalysesCorrelations Between Analyses

Computed for period when all are available (1900-1998)Computed for period when all are available (1900-1998) Averages over oceans, land, & areas with CRU gauge samplingAverages over oceans, land, & areas with CRU gauge sampling Annual-spatial averages correlatedAnnual-spatial averages correlated

Each individual reconstruction correlates well with CRU gaugesEach individual reconstruction correlates well with CRU gauges ENSO and other major modes allow interannual variations to be resolvedENSO and other major modes allow interannual variations to be resolved REOF(Blend) has the best fit over land, but the nearly-independent RCCA is REOF(Blend) has the best fit over land, but the nearly-independent RCCA is

almost as goodalmost as good AR4 ensemble averages out interannual variations, leaving in multi-decadal AR4 ensemble averages out interannual variations, leaving in multi-decadal

variationsvariations RCCA has same oceanic multi-decadal tendency as AR4, REOF has opposite RCCA has same oceanic multi-decadal tendency as AR4, REOF has opposite

tendencytendency

Correlations between averages over the given areas Oceans Land Gauge-samplingCRU, REOF(Blend) ---- ---- 0.88CRU, RCCA ---- ---- 0.74REOF(Blend), RCCA 0.64 0.81 0.83REOF(Blend), AR4 -0.06 -0.01 -0.07RCCA, AR4 0.32 -0.02 0.00

TrendsTrends

Computed for period when all are available (1900-1998)Computed for period when all are available (1900-1998) Averages over oceans, land, & land areas with CRU gauge samplingAverages over oceans, land, & land areas with CRU gauge sampling Annual and low-pass filtered (as in figures)Annual and low-pass filtered (as in figures)

In each individual reconstruction, opposite trends over ocean & landIn each individual reconstruction, opposite trends over ocean & land May be from use of ENSO modes to analyze ENSO-like multi-decadal, May be from use of ENSO modes to analyze ENSO-like multi-decadal,

since ENSO has opposite land-sea anomaliessince ENSO has opposite land-sea anomalies Gauge data make land trends positive for REOF, no gauge data in RCCAGauge data make land trends positive for REOF, no gauge data in RCCA

Trends in mm/mon per 100 years for averages over the given areas Oceans Land Gauge-samplingCRU Gauges ---- ---- 1.2REOF(Blend) -0.4 0.4 0.4 RCCA 1.6 -0.5 -1.1 AR4 0.7 -0.1 -0.5

Spatial Standard Deviation of ReconsSpatial Standard Deviation of Recons RCCA underestimates interannual signalsRCCA underestimates interannual signals REOFs give consistent level of signal over analysis REOFs give consistent level of signal over analysis

periodperiod GPCP resolves variations filtered by REOF modesGPCP resolves variations filtered by REOF modes

Merged ReconstructionsMerged Reconstructions

REOF reliable over land where gauges are REOF reliable over land where gauges are availableavailable

Interannual REOF reliable over oceans, but multi-Interannual REOF reliable over oceans, but multi-decadal REOF less reliable over oceansdecadal REOF less reliable over oceans

Multi-decadal RCCA appears to be more reliable Multi-decadal RCCA appears to be more reliable over oceansover oceans

Merge by replacing ocean multi-decadal REOF Merge by replacing ocean multi-decadal REOF with ocean multi-decadal from RCCAwith ocean multi-decadal from RCCA

For recent period, use REOF(GPCP)For recent period, use REOF(GPCP)

Merged Reconstruction Near-Global Merged Reconstruction Near-Global AveragesAverages

Filtered Reconstructions for All Areas and Ocean AreasFiltered Reconstructions for All Areas and Ocean Areas Ocean average changes mostOcean average changes most Including land removes the 1970s climate shift and Including land removes the 1970s climate shift and

greatly smoothes interannual variationsgreatly smoothes interannual variations

Reconstruction TrendsReconstruction Trends Ocean tropical trend greatestOcean tropical trend greatest Land trends weaker & tend to be opposite to Land trends weaker & tend to be opposite to

ocean trendsocean trends Similar to ENSO land-sea differencesSimilar to ENSO land-sea differences

Normalized Joint Normalized Joint EOFEOF

Merged Reconstruction Merged Reconstruction and AR4 Ensembleand AR4 Ensemble

Both annual averaged Both annual averaged and filtered before and filtered before computing JEOFscomputing JEOFs

First mode indicates First mode indicates joint trend-like variationsjoint trend-like variations Tropical ENSO-like Tropical ENSO-like

increaseincrease Mid-latitude decrease Mid-latitude decrease High-latitude increaseHigh-latitude increase Pattern differences may Pattern differences may

reflect model biasesreflect model biases

SummarySummary

EOF-based reconstructions resolve oceanic EOF-based reconstructions resolve oceanic interannual variations through the 20interannual variations through the 20thth century century Direct reconstruction using the available gauge data Direct reconstruction using the available gauge data Over land REOF does best for all variationsOver land REOF does best for all variations

CCA-based reconstructions resolve oceanic multi-CCA-based reconstructions resolve oceanic multi-decadal variations through the 20decadal variations through the 20thth century century Indirect method using correlations with better sampled Indirect method using correlations with better sampled

variablesvariables Merged analysis takes advantage of the best Merged analysis takes advantage of the best

qualities of bothqualities of both Future improvements possible with new data or Future improvements possible with new data or

refined reconstruction methodsrefined reconstruction methods Extended reanalyses may yield independent precipitation Extended reanalyses may yield independent precipitation

informationinformation The merged reconstruction has some important The merged reconstruction has some important

potential applications potential applications

Potential Uses of Reconstructed Potential Uses of Reconstructed PrecipitationPrecipitation

Diagnostic/descriptive studies of global Diagnostic/descriptive studies of global precipitation variations on interannual to precipitation variations on interannual to multi-decadal time scalesmulti-decadal time scales Changes in ENSO, PDO, NAO, AMO over the 20Changes in ENSO, PDO, NAO, AMO over the 20thth

century can be better described and understood century can be better described and understood Oceanic influence on dry and wet regimes, Oceanic influence on dry and wet regimes,

particularly multi-year droughts, can be more particularly multi-year droughts, can be more clearly diagnosedclearly diagnosed

Validate and improve climate model Validate and improve climate model simulations/projections of precipitationsimulations/projections of precipitation Longer baseline of observed precipitation should Longer baseline of observed precipitation should

facilitate improvement of the models facilitate improvement of the models And can be used to enable statistical adjustment And can be used to enable statistical adjustment

of model outputof model output

Data available at http://cics.umd.edu/~tsmith/recpr/