dynamical downscaling of climate for the southeast united states

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1 of 56 Dynamical Downscaling of Climate for the Southeast United States With contributions from Tim LaRow (FSU/COAPS) and Michelle M. Irizarry-Ortiz and Jayantha Obeysekera (SFWMD) Lydia Stefanova Center for Ocean-Atmosphere Prediction Studies (COAPS) Florida State University

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Dynamical Downscaling of Climate for the Southeast United States. Lydia Stefanova Center for Ocean-Atmosphere Prediction Studies (COAPS) Florida State University. With contributions from Tim LaRow (FSU/COAPS) and Michelle M. Irizarry-Ortiz and Jayantha Obeysekera (SFWMD). Outline. - PowerPoint PPT Presentation

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Dynamical Downscaling of Climate for the Southeast United States

With contributions from Tim LaRow (FSU/COAPS) and Michelle M. Irizarry-Ortiz and Jayantha Obeysekera (SFWMD)

Lydia Stefanova

Center for Ocean-Atmosphere Prediction Studies (COAPS)Florida State University

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Outline

• La Florida climate downscaling experiments• Available data• Regional model configuration • Results from downscaling of reanalysis: CLARReS

data set• Preliminary results from a climate change scenario

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Downscaling Experiments

20 century 21 century (A2)

Historic veg

Current veg

Future veg

Historic veg

Current veg

Future veg

R2

ERA-40

CCSM

GFDL

HadCM3

• 20 century: 1979-2000 for reanalyses (R2 and ERA-40), 1969-2000 for models (CCSM, GFDL, HadCM3)

• 21 century: A2 scenario 2039-2070• Blue (reanalyses) and Green (climate scenarios): completed• Yellow: pending

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Vegetation Scenarios

PresentHistorical

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What is a Reanalysis?A retroactive "best guess" for the state of the atmosphere based on all

available observations and knowledge.

The observations (relatively sparse, and possibly containing errors and thus not necessarily mutually consistent) are assimilated into a dynamical forecast model which is run for a very short time (essentially creating a now-cast) to produce a best estimate for the state of the atmosphere (including variables that are not available from observations, but are derived through the model equations (based on fluid dynamics and physics)) as a spatially and physically consistent gridded data set.

In summary, a reanalysis is our best guess about the atmospheric fields of the recent weather and climate, based on a combination of a) observations and b) modeling knowledge of the physics and dynamics of the atmosphere.

To obtain dynamically downscaling regional reanalysis, the global reanalysis is used to force a regional model

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Model, Input, and Output

• Regional Model: National Centers for Environmental Prediction (NCEP)/Experimental Climate Prediction Center (ECPC) Regional Spectral Model (RSM)

• Input: Global reanalysis (NCEP’s R2 or ECMWF’s ERA-40), atmospheric winds, temperature, humidity, and surface pressure at 6-hourly intervals as lateral boundary conditions.

• Output: Regionally downscaled reanalysis: COAPS Land-Atmosphere Regional Reanalysis for the Southeast (CLARReS-R2 or CLARReS-ERA40), hourly 2-D variables, 3-hourly 3-D variables

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Available Data• ftp://ftp.coaps.fsu.edu/pub/Southeast/CLARReS10/• Subdirectories: Documentation; ERA-40; R2• Data uploaded in netCDF format• Request additional variables by emailing [email protected]

Date Frequency Field Name Units10/21/2010

Hourly

Precipitation pratescf kg m-2 s-1

10/26/2010 Specific humidity at 2m spfh2m kg kg-1

10/29/2010

U, V winds at 10m ugrd10m, vgrd10m m s-1

Temperature 2m tmp2m KDownward shortwave flux at surface dswrfsfc W m-2

Latent heat flux at surface lhtflsfc W m-2

Snowfall rate water equivalent srwegsfc kg m-2 s-1

11/17/2010

Ground heat flux gfluxsfc W m-2

Surface roughness sfcrsfc mSensible heat flux shtflsfc W m-2

Surface pressure pressfc Pa

1/25/2011 DailyDaily maximum temperature at 2m tmin2m KDaily minimum temperature at 2m tmax2m K

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Chattanooga

Melbourne

West Palm Beach

Miami

Tampa

Daytona

Tallahassee

Savannah

Charleston

MontgomeryMacon

Augusta

Domain and Model ConfigurationDynamics: hydrostatic primitive equations

with spectrally transformed onto Fourier basis functions, Juang and Kanamitsu (1994)

10-km horizontal resolution; 28 vertical layers; 4-min resolution orography

Planetary boundary layer processes, Hong and Pan (1996)

Shortwave and longwave radiation, Chou and Lee (1996)

Shallow convection, Slingo (1987)

Deep convection: Simplified Arakawa-Schubert Scheme, Pan and Wu (1995)

Boundary forcing: scale selective bias correction, Kanamaru and Kanamitsu (2007)

Land surface: Noah; 4 soil layers, Ek et al (2003)

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Precipitation

• Mean: wet bias outside of Florida; wet bias in CLARReS-ERA40 over Everglades; Dry bias over southeast Florida

• Seasonal cycle: summer precipitation overestimated outside Florida

• Interannual variability: generally proper sign• JJA frequency of rainy days: frequency of precipitation days

is generally underestimated in Florida, and overestimated for Georgia, Alabama and South Carolina. The frequency of light events is generally underestimated, while that of heavy events is generally overestimated. Precipitation from tropical cyclones is realistic, provided the storms are present in the global model.

• Diurnal cycle: Good agreement with observations

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Climatology

How well is the AVERAGE year represented?

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Annual Precipitation Rate

mm/day

Global Reanalysis (R2)

Downscaled Reanalysis (CLARReS-R2)

Observations (PRISM)

• Note grid size of global vs downscaled reanalysis: amount of detail

• Comparable magnitudes. Note different spatial structure and bias.

[PRISM: Oregon State Climate Group data set, gauge-based, uses intelligent interpolation, 4km resolution]

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Summer Precipitation Rate

mm/day

R2 CLARReS-R2 PRISM

Very wet bias in the global reanalysis; reduced but not eliminated in downscaled version; Florida bias less than remainder of domain;

Note that rainfall is not directly downscaled; Instead, the regional model produces its own rain from the atmospheric circulation generated from the winds/temperature/humidity forcing

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Interannual Variability

How well is the DIFFERENCE between two years captured?

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Difference of dry and wet winter month, (Jan 1988-Jan1987) (mm/day)

R2

ERA-40

CLARReS-R2

CLARReS-ЕRА40

OBS (UDel)

OBS (PRISM)

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Difference of dry and wet summer month (Jun 1988-Jun1987) (mm/day)

R2

ERA-40

CLARReS-R2

CLARReS-ЕRА40

OBS (UDel)

OBS (PRISM)

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North Georgia

0

0.05

0.1

0.15

0.2

0.25

1 2 3 4 5 6 7 8 9 10 11 12

NE Florida

0

0.05

0.1

0.15

0.2

0.25

1 2 3 4 5 6 7 8 9 10 11 12

Central Florida

0

0.05

0.1

0.15

0.2

0.25

1 2 3 4 5 6 7 8 9 10 11 12

South Florida

0

0.05

0.1

0.15

0.2

0.25

1 2 3 4 5 6 7 8 9 10 11 12

Normalized Annual Cycle (Area averages)

0

0.05

0.1

0.15

0.2

0.25

1 2 3 4 5 6 7 8 9 10 11 12

CLARReS10/R2

CLARReS10/ERA40

R2

ERA40

PRISM

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Normalized Annual Cycle (Stations)

Tallahassee

0

0.05

0.1

0.15

0.2

0.25

1 2 3 4 5 6 7 8 9 10 11 12

Tampa

0

0.05

0.1

0.15

0.2

0.25

1 2 3 4 5 6 7 8 9 10 11 12

Miami

0

0.05

0.1

0.15

0.2

0.25

1 2 3 4 5 6 7 8 9 10 11 12

0

0.05

0.1

0.15

0.2

0.25

1 2 3 4 5 6 7 8 9 10 11 12

CLARReS10/R2

CLARReS10/ERA40

R2

ERA40

PRISMCOOP Station

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Frequency of JJA Precipitation

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Percent JJA days with Prate > 20mm/dayCLARReS-R2 CLARReS-ERA40

Observations (CPC Unified)

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Probability of exceeding a given precipitation threshold on any summer day

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Diurnal Cycle of JJA Precipitation

How is precipitation distributed throughout a typical summer day?

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CLARReS-R2 CLARReS-ЕRА40

OBS

Time of diurnal maximum, GMT(EDT=GMT-4)

• Earlier maxima along the coasts (~18-20 GMT = ~2-4PM EDT); later inland (~22-24 GMT=6-8pm EDT)

• Sea breeze convergence in peninsular Florida

• Land breeze signature over the water

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Correlation of modeled mean daily cycle with observations

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

AS

H

AU

G

TA

M

MA

C

MO

N

TA

L

CH

AT

ME

L

SA

V

CH

AR

DA

Y

MIA

WE

S

station

CLARReS10-R2

CLARReS10-ERA40

MERRA

CFSR

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Tropical and Extratropical Storms

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Tropical Cyclone Associated Precipitation (inches)

Global Reanalysis CLARReS Observation

Global Reanalysis CLARReS Observation

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Tropical Cyclone Associated Precipitation (inches)

ObservationCLARReSGlobal Reanalysis

ObservationCLARReSGlobal Reanalysis

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Hurricane Andrew [1992] Associated Precipitation (inches)

ObservationCLARReSGlobal Model

Observations from HPC

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Opal [1995] Wind Field (kts)CLARReS-ERA40 (21UTC04Oct1995)

10m Wind Speed

850hPa Wind Speed

Observed H*Wind

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Satellite Image

12-15 March 1993 “Storm of the Century”

Observed Snow Cover

… was an extratropical storm of unusual intensity affecting the Eastern US. Areas as far south as central Alabama and Georgia received 6 to 8 inches of snow. The Florida Penninsula recorded hurricane-force wind gusts and record-low barometric pressure.

CLARReS-R2 (12UTC 13 March 1993)

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Seasonal Cycle for Daily Average, Minimum and Maximum 2-m Temperatures(Assessment limited to Florida)

Daily average temperature (Tave) is captured very well in both models.Daily maximum temperature (Tmax):• Both models overestimate Tmax across the state especially from Feb-Oct.• In both models Lake Okeechobee and areas very close to the coast show

lower Tmax than interior areas (not evident in gridded observations). The models are probably correct.

Daily minimum temperature (Tmin):• In general, Tmin is overestimated across the state, especially in northern

Florida and in the Panhandle area.• In both models LOK and areas very close to the coast show higher Tmin

than interior areas (higher Tmin over LOK not seen inPRISM or USGS). Models are probably correct.

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Interannual Signal in Daily Tmin and Tmax

• Interannual variability in the statistical distribution of daily minimum and maximum temperatures

• Large-scale control from El Nino and La Nina: On average, El Nino winters are colder and wetter than La Nina years.

• Standard deviation and anomalies

• Skewness and kurtosis

• Comparison with COOP station data

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Daily Tmin

El Nino (avg. cold and wet) La Nina (avg. warm and dry)

CLARReS

Station Observations

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Daily Tmax

El Nino (avg. cold and wet) La Nina (avg. warm and dry)

CLARReS

Station Observations

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Higher moments, daily Tmin

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Higher moments, daily Tmax

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Downscaling of Regional Reanalises: Summary

Precipitation• Downscaled reanalysis precipitation has wet bias outside of Florida; wet

bias in CLARReS-ERA40 over Everglades; Dry bias over southeast Florida.• In Florida, the frequency of heavy rainfall is generally overestimated.• The shapes of annual and diurnal cycles are simulated well.• The interannual variability is simulated well.• Tropical storm precipitation is simulated well provided the storm is well

inside the regional domain. Temperature• Tmax and Tmin generally overestimated; Average T generally OK. • Interannual variability and higher statistical moments simulated well. Hourly downscaled data available from

ftp://ftp.coaps.fsu.edu/pub/Southeast/CLARReS10/

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Preliminary results from downscaling of climate projections

• Regional model forced with boundary conditions from the National Center from Environmental Prediction (NCEP) Community Climate System Model (CCSM) simulations.

• Seasonal means for 1969-1999 subtracted from the seasonal means for 2039-2069.

• Maximum and minimum daily temperatures (Tmin and Tmax), average daily temperature (Tave), precipitation rate (Prate, mm/day)

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Downscaling of Climate ProjectionsSummary

Compared to the 20th century, the 21st century Florida is warmer and drier.

• Tmax is up by 2-3⁰C in winter, ~3⁰C and more the rest of the year• Tmin is up by 1.5-2⁰C• Prate is down by 0.25-1 mm/day (3-12 inches/month). (NB: The regionally

downscaled CCSM has a strong dry bias, therefore probably underestimates the drying)