The Scientific Foundation of the GEWEX Americas Prediction Program

(GAPP)

Dennis P. LettenmaierDepartment of Civil and Environmental

EngineeringUniversity of Washington

CGU/AGU/SEG/EEGS Joint AssemblyMontreal

May 20, 2004

Continental-Scale Experiments (CSEs)Continental-Scale Experiments (CSEs)

““CATCH”>>AMMACATCH”>>AMMA

GAPP

MDB

MAGS overall goals

• To understand and model the high-latitude energy and water cycles that play roles in the climate system

• To improve the ability to assess the changes to Canada’s water resources that arise from climate variability and anthropogenic climate change.

MAGS Focus Areas

Atmospheric Research• Large-scale Forcings and Interactions• Regional Processes• Upscale Influence• Warm-season ProcessesLand Surface Processes• Snow Processes• Surface Energy Balance• Lake Processes• Large Lake Characteristics and BehaviourHydrologic Processes• Flow Production• Flow Routing• Flow Integration• Flow PredictionCoupled Modelling• The Canadian Regional Climate Model (CRCM)• The Mesoscale Compressible Community (MC2) Model• WATCLASSData and Information Support

MAGS priorities• Process studies (storm generation, high latitude precipitation and

snow, atmospheric and surface energy fluxes, lake dynamics, river ice breakup mechanisms, frost and moisture interactions) and associated algorithm development and improvement

• Enhancement of parameterization through enhanced spatial

resolution, remote sensing and ground based quantification of parameters, scaled and optimized parameter aggregation

• Stand-alone testing of algorithms and parameter applications to land surface schemes such as CLASS and ISBA

• Linkages of models (e.g. MC2 with CLASS, radiation inputs to WATFLOOD)

• Continued updating of models by partner institutions (e.g. MC2,

CLASS)

• Coupling of model feedback mechanisms (e.g. CRCM, CLASS and WATFLOOD) as part of the effort to create a fully coupled model for energy and water flux investigations at various scales.

Demonstration Projects (selected)

Hydroelectricity – application of MAGS models to improve streamflow forecasts for NWT Power Corporation;

River Engineering – developing river ice breakup and flood forecasting tools for the Town of Hay River;

Great Bear Lake – determining changes in ice cover of lake. Potential for significant publicity and outreach activities with community involvement

GAPP Overall Objectives • To develop and demonstrate a capability to

make reliable monthly to seasonal predictions of precipitation and land-surface hydrologic variables through improved understanding and representation of land surface and related hydrometeorological and boundary layer processes in climate prediction models

• interpret and transfer the results of improved seasonal predictions for the optimal management of water resources.

GAPP Priority Areas

• Predictability in Land Surface Processes • Hydrometeorology of Orographic Systems • Predictability in Monsoonal Systems • Integration of Predictability Into Prediction

Systems• Testing of Models in Special Climate Regimes • CEOP: Data and Studies for Model

Development• Use of Predictions for Water Resource

Management

Combined Frozen Soil Algorithm

Snow thickness, air and soil temperature,land cover and soil type

snow

Provide timing, duration, and extentof frozen soils w/o snow

Research outputs:

Seasonal and inter-annualvariations of frozen soils and their

relationship with

environmentalconditions`

Run the validated algorithm

using SSM/I data

Validate freeze/thaw algorithm

using available data

NO

Validate 1-dimensional numerical model

using available data

Run the validated one-dimensionalnumerical model

YES

Activities/Research: Diagnostic Studies

composites (positive-negative) on LLJ indices using observations and results of 10-year RSM runs (Mo)

GCLLJ GPLLJ

• Inverse relationship between rainfalls over the Great Plains and the southwest is due to inverse relationship between GCLLJ and GPLLJ

NCEP-MRF9 GSFC-NSIPP CCM 3.2

Correlations between GCM simulations and observations for JJA 65-97

GCMs with prescribed SST show some descriptive/predictive capability at least in the core monsoon region, indicating the potential predictability given SST

In general, state_of_the art GCMs can’t simulate warm season precipitation well.

Activities/Research: GCM Studies

- soil moisture signal dominant; - snow signal dominant in W in summer- climate signal strong in SE in winter

Most of hydrological predictability comes from initial boundary conditions

=> importance of LDAS

(Maurer)

Hydrologic Predictability

TRANSFERABILITY: LEARNING AND SHARING THROUGH MODEL APPLICATIONS IN OTHER REGIONS

The Eta model with the same Parameters is being run in the Mississippi and la Platin Basins (H. Berbery)

Issues to be assessed:•Effects of Pantanal on land surface parameterizations.•Assess model ability in an area of low data availability.

1. Downscaling

2. VIC hydrologic simulations

University of Washington experimental west-wide hydrologic prediction system

ESP as baseline fcst

Real-timeEnsemble Forecasts

Ensemble Hindcasts(for bias-correction

and preliminaryskill assessment) West-wide forecast products

streamflow

soil moisture, snowpack

tailored to application sectors

fire, power, recreation

* ESPextended streamflow prediction(unconditional climate forecastsrun from current hydrologic state)

climate model output

(NCEP,NSIPP)

CPC official forecasts

Land Data Assimilation System (LDAS)

Soil

Canopy

rsoil

rara

r c

rd

rsurf

rplant

EH

ea

e*(Ts)

MPr

ABL

BARE SOIL: 15%

10%

GRASSLAND:50%

SHRUBS:

NEEDLELEAFTREES: 25%

SVAT Model

in Mosaic Form

2-D Array

Forcing Data

ModeledMet.Fields

MergedGauge

& RadarPrecip.

RemotelySensed

Radiation

RemotelySensed

SoilMoisture

?

““LDAS” concept: LDAS” concept: - Optimal integration of land surface

observations and models to operationally obtain high quality land surface conditions and fluxes.

- Continuous in time&space; multiple scales; retrospective, realtime, and forecast

Conduct a pilot 3.5 (2.5? 2?) year synoptic climatological case study of regional and global water and energy budgets as a guide to the interpretation of longer-term past and future global and regional analyses and observations.

4DDA

MPIM

GLDAS

Goddard

In Situ

UCAR

Remote Sensing/Data Integration

U. Tokyo

User

500 TB 0.1 TB 50 TB 100 TB?

CEOP WESP(Water & Energy Simulation and Prediction)

(AFTER JOHN ROADS)

REGIONAL REANALYSIS

Regional Reanalysis will be run in real time at NCEP/CPC

Two key advancements in RR over the Global Reanalysis1) Assimilation of observed precipitation2) Improved land surface model (Noah LSM)

High resolution, dynamically consistent historical NA analysis

NCEP/ETA MODEL 32 KM Spatial Resolution;3 Hourly Temporal Resolution;1979 through 2003

(REGIONAL REANALYSIS DOMAIN)

Some key differences GAPP vs MAGS• GAPP – much larger program (~$US 5-6M/yr, vs ~US 1-

2 for MAGS)• MAGS – selected science team (in MAGS-2) based on

targeted proposal, rather than annual solicitation and selection

• MAGS – stronger focus on land surface hydrology• MAGS – field campaign (CAGES), GAPP no field

campaign (although NAME is partially a GAPP activity)• GAPP – core project assures connection with operating

agencies (NCEP in particular) – MAGS has no such formal connection (especially MAGS-2)

• MAGS – formal “community model” (for land – WATCLASS) – no such for GAPP (although NCEP NOAH model has been used by part of the GAPP community)

• GAPP – more focus on water resources applications (although still a weak link)

• MAGS – IAP offers annual assessments; GAPP has more formal advisory panel, but less frequent advice

Conclusions

• Need for focus

• Role of field campaigns

• Role of the CSEs in GEWEX (Global Energy and Water)