the scientific foundation of the gewex americas prediction program (gapp) dennis p. lettenmaier...
Post on 19-Jan-2016
215 Views
Preview:
TRANSCRIPT
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)
top related