narccap users meeting april 2011 results from ncep-driven rcms overview based on mearns et al....

NARCCAP Users MeetingNARCCAP Users Meeting April 2011April 2011

Results from NCEP-driven RCMsResults from NCEP-driven RCMs

Overview Based on Mearns et al. (BAMS, 2011)Overview Based on Mearns et al. (BAMS, 2011)

William J. Gutowski, Jr. William J. Gutowski, Jr. Iowa State University Iowa State University

and and The NARCCAP TeamThe NARCCAP Team


Simulations AnalyzedSimulations Analyzed

• DomainDomain

- Most of North America- Most of North America

• PeriodPeriod

- 1980-2004- 1980-2004

• Boundary ConditionsBoundary Conditions

- NCEP/DOE reanalysis- NCEP/DOE reanalysis

• ResolutionResolution

- 50 km- 50 km

MM5MM5Iowa Iowa

State/State/PNNLPNNL

RegCM3RegCM3UC Santa CruzUC Santa Cruz

ICTPICTP

CRCMCRCMQuebec,Quebec,OuranosOuranos

HADRM3HADRM3Hadley CentreHadley Centre

RSMRSMScrippsScripps

WRFWRFNCAR/NCAR/PNNLPNNL


Analysis RegionsAnalysis Regions

Comparison with 0.5Comparison with 0.5oo gridded observations from Univ. Delaware gridded observations from Univ. Delaware


1. Means & Variability1. Means & Variability


Temperature Bias - DJFTemperature Bias - DJFTemp.Temp.BiasBias

DJFDJF-10 10


Temperature BiasTemperature BiasTemp.Temp.BiasBias

JJAJJA-10 10


Interannual Variance

0.45 2.23


Temperature BiasTemperature Bias


0.45 2.23



-60 60


Pattern CorrelationPattern Correlation


Regional Regional Annual Annual CyclesCycles

-10

35

0

6

-10

35

35

35

- 5

- 5 0

8

0

0

6

6


Regional Regional Annual Annual CyclesCycles(bias)(bias)

-10

35

0

6


TemperatureTemperature--

PrecipitationPrecipitationCorrelationsCorrelations


0

3

6

9

12

15

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

mm

/day

RCM3 MM5IECPC CRCMWRFP HRM3Observed (GPCC) Observed (UDEL)Observed (CRUT) Ensemble

1982-83 El Nino

1997-98 El Nino

multi-year drought

Monthly time series of precipitation in coastal California

small spread, high skillSubstantial annual cycle


Correlation with Observed Precipitation - Coastal California

Model Correlation

HadRM3 0.857

RegCM3 0.916

MM5 0.925

RSM 0.945

CRCM 0.946

WRF 0.918

Ensemble 0.947

Ensemble mean has a higher correlation than any model

All models have high correlations with observed monthly time series of precipitation.


Monthly Time Series - Deep South

Model Correlation

HadRM3 0.489

RegCM3 0.231

MM5 0.343

RSM 0.649

CRCM 0.649

WRF 0.513

Ensemble 0.640

RSM+CRCM 0.727

A “mini ensemble” of RSM and CRCM performs best in this region.

0

3

6

9

12

15

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

mm

/da

y

RCM3 MM5I ECPCCRCM WRFP HRM3Observed (GPCC) Observed (UDEL) Observed (CRUT)Ensemble

Ensemble (black curve)


2. Precipitation Extremes - Daily2. Precipitation Extremes - Daily


Region AnalyzedRegion Analyzed

Boreal forest

Pacific coast

California coast

Great LakesMaritimes

Upper Mississippi

River

Deep South


Precipitation Frequency vs. IntensityPrecipitation Frequency vs. Intensity

99.5%


99.5%

Precipitation Frequency vs. IntensityPrecipitation Frequency vs. IntensityNARCCAP - JJA


Precipitation Frequency vs. IntensityNARCCAP - JJA

(OBS = Co-op Stations)


Precipitation Frequency vs. IntensityPrecipitation Frequency vs. Intensity

99.5%


Days with Simultaneous Extremes on Days with Simultaneous Extremes on ““NN”” Grid Points Grid Points


Composite Structure of Extreme Events - DJF

NARRm/s



NARR

MM5

GFDL

CCSM + 9 hr

NARCCAPNARCCAP



MM5

GFDL

CCSMNARR 2-m specific humidity anomaly



NARCCAPNARCCAP

NARR

GFDL

HadRM3

CCSM + 9 hr2-m specific humidity anomaly


Thank You!Thank You!

(www.narccap.ucar.edu)(www.narccap.ucar.edu)


Higher resolution is necessary, but not sufficient, for Higher resolution is necessary, but not sufficient, for simulating short-term (e.g., daily) precipitation simulating short-term (e.g., daily) precipitation extremes.extremes.

Coarser models (and nudged regional models) tend to Coarser models (and nudged regional models) tend to have daily extremes covering a wider area than have daily extremes covering a wider area than observed extremes. observed extremes.

Focusing on environments conducive to extremes Focusing on environments conducive to extremes yields relevant climatic behavior, even in relatively yields relevant climatic behavior, even in relatively coarse models.coarse models. This conclusion rests on the assumption that important This conclusion rests on the assumption that important

small-scale features are not missing (e.g., low-level small-scale features are not missing (e.g., low-level jets).jets).


Part I: Interannual Variability

• Results shown for 1981-2002Results shown for 1981-2002

• Comparison with 0.5Comparison with 0.5oo gridded gridded precipitation analysis from the University precipitation analysis from the University of Delawareof Delaware


Precipitation analysis for two regionsPrecipitation analysis for two regions

Coastal California Deep

South


Monthly Time Series - Deep South

Model Correlation

HadRM3 0.489

RegCM3 0.231

MM5 0.343

RSM 0.649

CRCM 0.649

WRF 0.513

Ensemble 0.640

Two models (RSM and CRCM) perform much better. These models inform the domain interior about the large scale.

0

3

6

9

12

15

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

mm

/da

y

RCM3 MM5I ECPCCRCM WRFP HRM3Observed (GPCC) Observed (UDEL) Observed (CRUT)Ensemble

Ensemble (black curve)


Full ensembleFull ensemble RSM + Canadian RCMRSM + Canadian RCM

The "mini-ensemble" has better correlation than the full The "mini-ensemble" has better correlation than the full ensemble in the southern and eastern parts of the domain.ensemble in the southern and eastern parts of the domain.

Other measures of forecast skill (such as bias) are not Other measures of forecast skill (such as bias) are not necessarily better.necessarily better.

Correlation of Monthly Time SeriesCorrelation of Monthly Time SeriesCorrelation of Monthly Time SeriesCorrelation of Monthly Time Series


Ensemble error and spread (January)Ensemble error and spread (January)

BiasBias Ensemble spreadEnsemble spread

There are hints of a spread-skill relation but it is There are hints of a spread-skill relation but it is not consistent.not consistent.


ensemble July minus Juneensemble July minus June observed July minus Juneobserved July minus June

The ensemble reproduces the dipole of June-The ensemble reproduces the dipole of June-July precipitation change, but the monsoon does July precipitation change, but the monsoon does not extend as far north as observed.not extend as far north as observed.


Part 2: Extreme Monthly PrecipitationPart 2: Extreme Monthly Precipitation

• ObservationsObservations Precip: University of Washington VIC retrospective analysisPrecip: University of Washington VIC retrospective analysis

500 hPa Heights: North American Regional Reanalysis500 hPa Heights: North American Regional Reanalysis

• Comparison period: 1982 -1999Comparison period: 1982 -1999 1979-1981 omitted - spinup1979-1981 omitted - spinup UW data end in mid-2000UW data end in mid-2000

• AnalysisAnalysis Cold season (Oct-Mar)Cold season (Oct-Mar) 10 wettest months (top 10%)10 wettest months (top 10%)


Regions AnalyzedRegions Analyzed

Boreal forest

Pacific coast

California coast

Deep South

Great LakesMaritimes

Upper Mississippi

River


Frequency – Coastal CAFrequency – Coastal CA


Ranked Precipitation – Coastal CARanked Precipitation – Coastal CA

Ensemble Ensemble average of average of top 10 top 10 = 9% smaller = 9% smaller than UWthan UW


Interannual Variability – Coastal CAInterannual Variability – Coastal CA

59 of 60 (98%) 59 of 60 (98%) simulated simulated extremes occur extremes occur in cold seasons in cold seasons with an with an observed observed extreme.extreme.

(random (random chance: 27) chance: 27)


Composite Composite 500 hPa Height 500 hPa Height Anomalies Anomalies

Top 10 ExtremesTop 10 Extremes

Coastal CACoastal CA


Frequency – Deep SouthFrequency – Deep South


Ranked Precipitation – Deep SouthRanked Precipitation – Deep South



Interannual Variability – Deep SouthInterannual Variability – Deep South

27 of 60 (45%) 27 of 60 (45%) simulated simulated extremes occur extremes occur in cold seasons in cold seasons with an with an observed observed extreme.extreme.

(random (random chance: 27) chance: 27)


500 hPa Height Anomalies – 500 hPa Height Anomalies – Deep South ExtremeDeep South Extreme


Summary Summary Monthly PrecipitationMonthly Precipitation

Where there is a substantial periodic cycle:Where there is a substantial periodic cycle:- Models simulate well the interannual variability- Models simulate well the interannual variability- Models simulate well monthly, regional extremes- Models simulate well monthly, regional extremes

Where there is no substantial periodic cycle:Where there is no substantial periodic cycle:- Models simulate poorly the interannual var. & - Models simulate poorly the interannual var. & extremesextremes- Interior nudging improves interannual variability- Interior nudging improves interannual variability- Interior nudging does not help extremes- Interior nudging does not help extremes


Hydrologic Analysis (Takle et al.)Hydrologic Analysis (Takle et al.)

SWAT model domainSimulation period: last 2 decades of 20C



Streamflow Interannual Variability



Precipitation Annual Cycle



Streamflow Annual Cycle


Summary Summary MONTHLY PRECIPITATIONMONTHLY PRECIPITATIONWhere there is a substantial periodic cycle:Where there is a substantial periodic cycle:- Models simulate well the interannual variability- Models simulate well the interannual variability- Models simulate well monthly, regional extremes- Models simulate well monthly, regional extremes

Where there is no substantial periodic cycle:Where there is no substantial periodic cycle:- Models simulate poorly the interannual var. & - Models simulate poorly the interannual var. & extremesextremes- Interior nudging improves interannual variability- Interior nudging improves interannual variability-Interior nudging does not help extremesInterior nudging does not help extremes

UPPER MISSISSIPPI STREAMFLOWUPPER MISSISSIPPI STREAMFLOWEnsemble replicates well the interannual variabilityEnsemble replicates well the interannual variabilityAnnual cycle simulated less wellAnnual cycle simulated less well


2b. Precipitation Extremes - Monthly2b. Precipitation Extremes - Monthly


Ranked Precipitation – Coastal CARanked Precipitation – Coastal CA



Ranked Precipitation – Upper MSRanked Precipitation – Upper MS



Ranked Precipitation – Deep SouthRanked Precipitation – Deep South



Regional Regional Annual Annual CyclesCycles

-10

35

0

6




0.45 2.23


Composite Structure of Extreme Events - DJFComposite Structure of Extreme Events - DJF

narccap users meeting april 2011 results from ncep-driven rcms overview based on mearns et al....

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