2003: ARCS/IRI

ARCS/IRI Regional ConsortiumJ. Roads, S. Chen, J. Chen ECPC

D. Lettenmaier, E. Salathe, E. Miles UWH. Juang, J. Han, S. Lord NCEP

S. Cocke, T. Larow FSUA. Robertson, J. -H. Qian, S. Zebiak IRI

• Previous Work• Background (current work)• Examples (Roads)

– ECPC fireweather and model development– FSU crops – NCEP regional ensembles– IRI statistical downscaling and model development

• Examples (Lettenmaier)– Landsurface hydrology– Ecology

2003: ARCS/IRI

ARCs/IRI S. A. Reg. Mod. Domain Roads et al. 2003, J. Geophys. Res. (in press)

(1) Scripps Experimental Climate Prediction Center regional spectral model (RSM)

(2) Florida State Univ. nested regional spectral model (FSUNRSM)

(3) Goddard Institute for Space Studies regional climate model (RCM)

(4) IRI regional climate model (RegCm2)

2003: ARCS/IRI

IRI/ARCs Regional Model ComparisonModel Reanalysis Scripps RSM FSUNRSM GISS RCM IRI

RegCM2Levels 28 28 27 16 14Type spectral spectral spectral grid gridSW. Rad. Lacis and

Hansen(1974)

Chou and Lee(1996)

CCM 3.6 (Kiehlet al. 1998)

Davies(1982)

CCM 3.3(Kiehl etal. 1998)

LW Rad. GFDL(Schwartzkopfet al. 1994)

GFDL(Schwartzkopfet al. 1994)

CCM 3.6 (Kiehlet al. 1998)

Harshvardhanand Corsetti(1984)

CCM 3.6(Kiehl etal. 1998)

ConvectiveParam.

SAS (Kalnayet al. 1996)

SAS* (Hongand Pan 1996)

Zhang-McFarlane(1995)

modified Kuo(Krishnamurtiet al., 1990)

Grell(1993)

PBL Louis et al.(1982)

Hong and Pan(1996)

Holtslag andBoville (1993)

Krishnamurtiet al. (1990)

Holtslag etal. 1990

LandSurface

NOAA(Kalnay et al.1996)

NOAA* (seeChen andRoads 2002)

FSU (Cocke andLaRow 2000)

Fulakeza etal. (2002)

BATS(Dickinsonet al.1993)

–Part of the problem may be due to a bias in high rainfall events.–All regional models apear to have this bias, which is only partially ameliorated in the ensemble forecast.

–In short, regional models provide many advantages, including better control over local conditions, which have yet to be taken advantage of, but at the same time they are still noticeably influenced by large-scale physical parameterizations, which need to be improved in future regional models.

–Most regional models were able to adequately simulate the new Xie and Arkin .5 deg land precipitation climatology and interannual variability, although they added little skill to the driving R1.–The regional model ensemble mean systematic errors were somewhat smaller than the driving NCEP/NCAR reanalysis systematic error–However, the ensemble reduction in systematic error did not increase the correlations–Threat scores also indicated that the regional model ensemble was not noticeably better than the driving reanalysis.

2003: ARCS/IRI

New Regional Application Work• Basic regional model development is still taking place over US, Asia, Brazil • However, as part of the IRI/ARCs regional consortium, we were subsequently requested to

work with the applications community to better apply regional forecasting methodologies. • Basic applications we are now investigating include seasonal forecasts of:

– Hydrology, Fire Danger, Crops, Ecology

HydrologyIRI, ECPC, UW

FireECPC

CropFSU

EcologicalUW

RegionalIRI, NCEP, ECPC, FSU

GlobalIRI,NCEP,ECPC,FSU

2003: ARCS/IRI

ECPC Regional Consortium Work

• ECPC is developing and evaluating the Regional Spectral Model, which is being used to drive various application models.

– Regional RSM forecasts (CA, SW, US, BZ) are being evaluated.– Various improvements to the RSM are being implemented.

• ECPC has acquired the National Fire Danger Code, and is attempting to develop useful long range forecasts over the US and elsewhere.

– This fire danger code supplements its current simplified fire weather index, which is only influenced by wind speed, relative humidity, and temperature.

• ECPC has transferred its regional modeling methodology to Taiwan, Hong Kong, and Helinjong, and is currently driving their regional forecasts with ECPC global forecasts (4 month forecasts once a week).

– Taiwan and Hong Kong are concerned with hydrologic applications and Helinjong is concerned with fire danger forecasts.

• ECPC has recently acquired the VIC hydrologic model to develop land surface forecasts, including streamflow for the US.

– Comparisons will be made to current Noah model– We have also used a river network model, and have since found that for

certain heavily managed basins that we will need to also include an engineering model.

2003: ARCS/IRI

Site Description

Fuel ModelSlope Class

Live Fuel TypesClimate Class

AverageAnnual

Precipitation

1300 LSTObservation

24-HourObservations

Carryover FuelMoistures (FM)

Relative HumidityTemperatureCloudiness

Wind Speed

Fuel StickMoisture

Max/Min RH

Min Temp

PrecipitationDuration

PrecipitationAmount

100-Hour

1000-Hour

Live Woody

FM

1-hr FM 10-hr FM 100-hr FM 1000-hr FMKBDI

Live FM

Drought Fuel

MaximumTemperature

PeriodicMeasurements

Season Code &Greenness Factor

SpreadComponent

SC

Burning Index BI

Energy Release Component

ERC

Ignition Component

IC

Cal

cula

ted

Inp

ut

Ou

tpu

t

September 19,2000

Contribution of dead FM to SC

Contribution of dead FM to ERC

(88)

(88)

(88)

National Fire Danger Rating System Structure

Optional pathway

Latitude

Summer validation correlations with AC (cf.

A. Westerling): (a) FWI; (b) IC; (c) BI; (d) ER;(e) KB; (f) SC; (g) CN; (h) AC.

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RSM97

RSM CVS

sDIFF pDIFF

OBS

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FSU• FSU is assessing the skill of both global (T63L17) and regional (20km) models

for driving crop models.• One of the crop models being used to simulate maize yield is the CERES-Maize

simulation model (Ritchie et al., 1998). – This model is a dynamic process based crop model that simulates plant response

to soil, weather, water stress and management practices. – The model calculates development, growth and partitioning processes on a daily

basis, beginning with planting and ending at harvest maturity. – Input into the crop model are the Regional Models daily values of:

• Max. Temperatures• Min. Temperatures• Precipitation • Surface Solar Radiation

– Crop model appears sensitive to all four input parameters. • One measure of the skill will be the crop yields determined by the crop models

and verified against the observed yields for selected locations in Florida and Georgia. – Preliminary results for selected locations in Florida are encouraging and show

greater skill of the FSU regional model to predict Maize yields when compared to the FSU global model (Jagtap et al 2002).

Spring/Summer growing season (MAMJJA) 180 days from 1987 to 1999

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NCEP• NCEP currently makes an ensemble of 20 7-month global forecasts,

which are initialized every 12 hours 5 days before the start of the month and continuing to 5 days after. – These forecasts are supplemented by hindcasts of 10 hindcasts for

the same month but for each year for the previous 21 years. In all there are 230 7-month forecasts made every month.

• This strategy allows the model to be changed monthly if needed.• NCEP is now developing a corresponding ensemble of 5 4-month

regional US forecasts, initialized every 24 hours 2 days prior and continuing to 2 days after the start of the month. – These forecasts are being supplemented by hindcasts, made the

previous month, of 1 4-month hindcast for the same month but for each year of the previous 21 years. In all there will be 26 forecasts made each month. Depending upon computer time, these hindcasts will be supplemented by up to 4 additional ensemble members, started every 24 hours 2 days prior and 2 days after the start of the month.

– Additional regions, like Brazil may could also be added (depending on computer resources).

4 month GSM forecast 4 month RSM forecast

R2 Analysis

• http://wwwt.emc.ncep.govv.gov/mmb/RSM

• Last year’s experiments: http://nomad2.ncep.noaa.gov/cgi-bin/web_rsm.sh

– Download monthly mean climate average, and ensemble forecast average

– Plot variables of climate average and ensemble forecast average from web by users

2003: ARCS/IRI

Future Experiments

• GSM: NCEP GFS 28LT62• Parallel RSM (MPI version): new GFS physics

– Forecast range: 4 month forecast– Domain: 30km

• lon: 228.908 -295.832 lat: 20.823 - 50.994– 3 member ensemble hindcasts, – 5 members ensemble forecasts

• Data to be stored:– flx, sig, sfc, r_sig, r_sfc, monthly mean of flx, pgb,

r_pgb

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Statistical downscaling of daily rainfall occurrence at IRI

• Construct a statistical transformation of atmospheric GCM predictions from the IRI two-tier system

• Predict local daily rainfall characteristics (e.g., occurrence frequency, dry-spell frequency) over Ceará up to several seasons in advance

• Train on observed station data• Application to IRI seasonal forecast• Comparison with dynamical downscaling

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Statistical downscaling of daily rainfall occurrence over NE Brazil from a GCM with a Hidden Markov Model

ECHAM4.5 run with observed SSTs 1975–2002

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Interannual Changes in NHMM Rainfall Frequency (10-station average)

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Precipitation and low-level winds

Feb 1999

(a) NCEP/NCAR reanalysis

(b) SG1: uniform high resolution (50km grid)

© SG3: stretched grid from 150km to 50km

(d) SG5: stretched grid from 250km to 50km

2003: ARCS/IRI

ARCS/IRI Regional Model Consortium Summary (Part I)

• The IRI/ARCS regional model consortium has now moved beyond simply comparing regional simulations (and forecasts) to connecting these regional models to the application community

• Firedanger, Crops, Hydrology, Ecology• In fact, the application community has already connected directly to the

global modeling community, in part because this community has larger forecast ensembles available.– For example, the IRI is currently attempting to statistically downscale from its

current multi-model global model ensemble rather than develop corresponding multi-model regional model ensembles.

• To provide additional regional model ensembles, – NCEP has begun to develop ensembles of regional model forecasts for the

US, which are freely available to interested researchers• The regional consortium is also still attempting to improve and further

develop regional models– ECPC RSM CVS with pressure diffusion will soon replace current RSM96/97

versions and thus be more compatible with the ECPC SFM– NCEP is replacing RSM97 physics with new GFS physics– Variable resolution global models are being investigated by the IRI

2003: ARCS/IRI

ARCS/IRI Regional Applications Project2. Hydrology and water resources projects

Dennis P. Lettenmaier

with contributions from

John Roads (Scripps Institution of Oceanography), and Eric Salathe and Ed Miles (University of Washington)

Climate Diagnostics and Prediction Workshop

Sparks, NV

October 23, 2003

2003: ARCS/IRI

1. Downscaling climate predictions for the Pacific Northwest using RCM (Regional Climate Model)Project leads: Eric Salathe and Ed MilesClimate Impacts GroupUniversity of Washington

Background:

• Mesoscale models must resolve below 20 km in order to simulate meteorology of the Puget Sound region

• The interaction of mesoscale processes with climate change and variability may yield a different response than interpolating from coarse resolution simulations

• MM5 will be applied as a Regional Climate Model forced by Global Climate Model simulations (PCM CCSM)

2003: ARCS/IRI

RCM Details

• 10-year MM5 model runs nested in PCM. (1990-2000; 2045-2055; 2090-2100).• MM5 nests at 108km, 36km, and 12 km horizontal resolution. • WSU-EPA air pollution study over Chicago – 4th nest at 12km.• To be determined:

1. Nudging: on interior? How frequently?2. Re-initializing MM5: How frequently? How much time overlap?

2003: ARCS/IRI

Applications of MM5 Results for Climate Change

1. Water resources in small basins West of the Cascades

• Seattle hydropower and water supply

2. Air quality -- collaboration with EPA-STAR project at Washington State

• Biogenic and forest fire emissions• CMAQ air quality model• Puget Sound and Northern

Midwest

2003: ARCS/IRI

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Precipitation PressureRadiation(Shortwaver, Longwaver)Wind HumidityAir temperature

Sensible heat fluxLatent heat fluxesMomentum Flux

Subgrids representsland use/cover heterogeneity

VIC Land Surface

Variable infiltration capacitymodels the spatial and temporal variabilities of runoff-prediction

RSMRegionalSpectralModel

Initial Conditions reanalysis data

Boundary Conditions climatology reanalysis data

RSM and VIC Physical processes

RSM models precipitation Process

VIC simulates surface runoff and baseflow

VIC energy balance modesimulates snow processsimulates surface energy fluxes

2. COUPLING RSM with VIC land surface hydrology

Project lead: John Roads, SIO

Nonlinear baseflow based on the third layer soil moisture

2003: ARCS/IRI

RSM sea/land mask and Orography from the fixed fields

RSM climatology data for initializing sea/land parameters and variablesVIC land soil and vegetation fixed fields

Initializing analysis fieldsusing climatology datathen, reading RSM analysis fields for updating sea/land variables

Merging the analysis fields and forecast fields

Initializing forecast sea/land fields using analysis fieldsthen, reading RSM forecast fields using provided base field

Initializing VIC parameters and variablesusing VIC land soil and vegetation fixed fields and RSM forecast fields

Check the consistence of RSM land surface mask with VIC land grids

Output of VIC and RSM surface fields

Process of Initializing the RSM and VIC System

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Preliminary Results using VIC and RSM

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3. S/I Hydrologic Forecasting ProjectProject lead: Dennis Lettenmaier, University of Washington

www.hydro.washington.edu/Lettenmaier/Projects/fcst/index.htm

2003: ARCS/IRI

Project Goals

1. Produce real-time seasonal ensemble hydrologic forecasts:• based on experimental climate model forecast products• based on established methods (such as ESP)• of streamflow, for selected large river basins (primarily

in the West)• of snowpack / soil moisture anomalies

2. Assess experimental product skill relative to established forecast products

3. Evaluate relative hydrologic prediction skill due to ICs - initial land surface conditions (soil moisture, snow) and due to climate forecast skill.

2003: ARCS/IRI

Forecasting Approach

forecast ensemblemeteorological sequences

local scale weather inputs

streamflow, soil moisture,snowpack,runoff

General

VIC Hydrology Model

• 1/8 degree resolution• daily P, Tmin, Tmax

NASA NSIPP-I Forecasts• 2-2.5 degree resolution• monthly total P, avg T

NCEP GSM Forecasts• 1.9 degree resolution• monthly total P, avg T

Experimental forecast applications

downscaling process *

hydrologic simulation

* for climate model forecasts

2003: ARCS/IRI

Overview: VIC Simulations

Forecast Productsstreamflow soil moisture

runoffsnowpack

derived products

VIC model spin-upforecast ensemble(s)

climate forecast

information

climatology ensemble

1-2 years back start of month 0 end of mon 6-12

NCDC met. station obs. up to 2-4

months from current

LDAS/other real-time met. forcings for remaining

spin-up

data sources

snow state information

2003: ARCS/IRI

Overview: Spin-up approach, Index Stn Method

1. interpolate monthly percentiles from sparse index stations to 1/8 degree grid

2. find percentiles’ matching amounts in the dense station-derived climatology

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Current Columbia River basin forecasts

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Current Forecasts: Initial Soil Moisture CPC Percent of Normal Precip

Jun-Jul-Aug

Sep

%

September 25, 2003

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Current forecasts: Snake River locations

Upper Snake Storage Forecast

2003: ARCS/IRI

Current forecasts: Reservoir system storage

ensemble mean

Unconditional ESP

Full Pool

System Storage Forecast from SnakeSim includes:

Jackson LakePalisadesIsland ParkRirieAmerican FallsLake Walcott