cooperative research programs (corp) satellite climate studies branch (scsb) 1 1 reconstruction of...

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Cooperative Research Programs (CoRP)Satellite Climate Studies Branch (SCSB)

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Reconstruction of Near-Global Precipitation Variations

Thomas Smith1

Phillip Arkin2

1. NOAA/NESDIS/STAR SCSB and CICS, College Park, Maryland2. CICS/ESSIC/University of Maryland, College Park, Maryland

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Precipitation Reconstructions Beginning 1900• Want to understand precipitation change with climate change

– Need ocean-area precipitation anomalies

• Satellite data for global analyses since 1979– Defines statistical properties of anomalies

• Gauges over land and islands since 1900– For reconstructions with spatial covariance– EOFs define covariance (called REOF)– First regional-monthly REOF, later test global-annual REOF

• Analyses of SST and SLP available since 1900 or earlier– For reconstructions using anomaly correlations between precip. and SST+SLP– CCA used (called RCCA), for global-annual analysis

• Merging REOF and RCCA can give an improved reconstruction

• Other improvements being developed and tested

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REOF Method

M

mmm twxtxF

1

)()(),(

M

mmm twxtxtxA

1

)()(),(),(

),(),(),( txtxatxD guessst1

patternspatialmmodem

• Find historical time series for a set of covariance EOF maps• Covariance maps based on modern data (satellites)• Fit the historical data to the set of modes, minimize the error of the fit

Data, D, at spatial point, x, and time, t, is anomaly, a, minus a first guess

REOF anomaly is first guess + weighted sum of covariance modes

Best fit to get weighted sum,

xx

K

x

txFtxDtE cos),(),()(2

1

2

Minimizes squared error over spatial locations,

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System of Equations For Best-Fit Weights

MnforxtxD

xxtw

K

xxxn

M

m

K

xxxnmm

,,2,1,cos)(),(

cos)()()(

1

1 1

Mmforw

E

m

,,2,1,02

Differentiate squared error w.r.t. each weight

System of equations to solve to get the best-fit weights:

• Screen out modes not adequately sampled

• Cross validation used to tune number of modes & screening parameters

• Can use since monthly anomalies are approximately normal

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REOF DataMonthly Gauge Historical Analyses

• Global Historical Climatology Network (GHCN)– Longest record, least filling

• Global Precipitation Climatology Center (GPCC)– Used in GPCP modern analysis

• Climate Research Unit (CRU)– Shortest record, most filling

Gauge-Satellite: Global Precipitation Climatology Project (GPCP)• Merges GPCC gauges and multi-satellite estimates• Global-monthly analysis since 1979, used for statistics

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Against SOI Against NAO (Dec-Mar)

REOF Climate-Mode Regressions: Consistent Interannual Variations

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REOF Spatial Standard Deviation

• Global spatial standard deviation – Similar interannual changes, REOF(GHCN) lower before 1940 (sampling)– REOF(CRU) strong most of record, blended with REOF(GPCP) and used in

merged analysis

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Reconstructions Using CCA (RCCA)

• REOF interannual variations consistent, but multi-decadal variations less consistent

• Annual Canonical Correlation Analysis (CCA) to reconstruct longer-period variations– Fields of predictors (SST and SLP anomalies) correlated with a

predictand field (precipitation anomalies)– Train CCA using GPCP data and SST+SLP analyses– RCCA to reconstruct annual-average global anomalies

beginning 1900

• RCCA only uses gauges in training period

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Ocean Comparisons (7-Year Filtered)

• RCCA & REOF differ before 1980– 1970s climate shift in RCCA– REOF does not resolve trend in RCCA & in AR4 ensemble

• RCCA & REOF(GPCP) similar

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Merged Reconstructions

• Over land– REOF

• Over oceans– REOF interannual and RCCA

multi-decadal

• Comparison to AR4 Ensemble – 1st mode, compares model and

reconstruction response to climate change

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

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GPCP-Period Trends

• Full GPCP trends (upper) show the most spatial variation

• GPCP filtered with monthly-regional REOF smoother

• RCCA is similar to GPCP, but with larger spatial scales and stronger local trends

• Testing to evaluate differences in trends

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Global-Annual EOF Tests: Ocean Comparisons• 20 mode global-annual EOF

– GHCN only & GHCN + PSST (pseudo data from regression against SST)

– No PSST: Global EOF multi-decadal change similar to RCCA

– With PSST: The trend is stronger and spatial scales of the trend are larger

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Summary

• Regional-monthly REOF resolves oceanic interannual variations, but is less consistent for oceanic multi-decadal variations

• Global-annual RCCA using SST & SLP indicates oceanic multi-decadal increasing precipitation, roughly consistent with climate models

• Merged analysis combining the regional-monthly REOF and the RCCA has been developed and is available at http://cics.umd.edu/~tsmith/recpr/

• Tests using a global-annual EOF show:– Global multi-decadal variations in RCCA are similar to those from a global-annual

EOF reconstruction computed from only gauges– Trends are stronger and spatial scales are larger using SST information

• Reconstruction testing and improvements are continuing

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Extra Slides

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Anomaly Distribution

• Relative frequency distribution for common months and locations (1992-2001)

• Both anomalies approximately normal– reconstruction methods

should work

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Trend Maps: Global EOF and RCCA

• Global EOF using only gauges:– Trend has smaller-scale patterns

• Global EOF using gauges and pseudo-observations from SST:

– Trend has larger-scale spatial features

• RCCA– Large-scale features, roughly

similar to those in EOF with pseudo-observations

• Many large-scale features similar in all

– Use of correlated data (SST or SLP or both) causing trend spatial scales to expand

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Global-Annual EOF Tests: Gauge Area• 20 mode global-annual EOF

– GHCN only & GHCN + PSST– Gauge average: heavy black dashed line

• No PSST: EOF similar to gauge average• With PSST: negative trend before 1950

cooperative research programs (corp) satellite climate studies branch (scsb) 1 1 reconstruction of...

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