# Presentation: 791898

## ARCS/IRI Regional Consortium

*J. Roads, S. Chen, J. Chen* ECPC
*D. Lettenmaier, E. Salathe, E. Miles* UW
*H. Juang, J. Han, S. Lord* NCEP
*S. Cocke, T. Larow* FSU
*A. 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

## 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)

## IRI/ARCs Regional Model Comparison

## 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.

- 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.

## 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

## 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.

## Slide 7

## Summer validation correlations with AC (cf. A. Westerling): (a) FWI; (b) IC; (c) BI; (d) ER;(e) KB; (f) SC; (g) CN; (h) AC.

## Slide 9

**Notes:**
Sensitivity 50-km simulations for May 1998 over East Asia compared to available coarse observations (CMAP and GPCP). The RSM97 simulations have a strong wet bias over land regions, this web bias is reduced in the RSM CVS which has more updated model physics in surface land module as well as the cumulus scheme. The horizontal diffusion scheme on constant pressure level (pDIFF)for  temperature and specific humidity is also improving the simulation over the old scheme which in operated on sigma level (sDIFF). Note the great reduction of spurious precipitation excess over mountainous Taiwan Island. This improvement is more obvious in the even higher resolution simulation. 

## 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

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

## 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).

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

**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**

## 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

## 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**

## 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

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

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

## Precipitation and low-level winds 

**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

## 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

## ARCS/IRI Regional Applications Project2.  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**

## Downscaling climate predictions for the Pacific Northwest using RCM (Regional Climate Model)Project leads:  Eric Salathe and Ed MilesClimate Impacts GroupUniversity 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)**

## 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 – 4****th**** nest at 12km.**
**To be determined:**
**Nudging: on interior? How frequently?**
**Re-initializing MM5: How frequently? How much time overlap?**

## Applications of MM5 Results for Climate Change

**Water resources in small basins West of the Cascades**
**Seattle hydropower and water supply**
**Air quality -- collaboration with EPA-STAR project at Washington State**
**Biogenic and forest fire emissions**
**CMAQ air quality model**
**Puget Sound and Northern Midwest**

## Slide 24

## 2. COUPLING RSM with VIC land surface hydrology

**Precipitation    ****Pressure**
**Radiation**
**(Shortwaver, Longwaver)**
**Wind  ****Humidity**
**Air temperature**

**Sensible heat flux**
**Latent heat fluxes**
**Momentum Flux**

**Subgrids represents**
**land use/cover heterogeneity**

**VIC Land Surface**

**Variable infiltration capacity**
**models the spatial and temporal **
**variabilities of runoff-prediction **

**RSM**
**Regional**
**Spectral**
**Model**

**Initial Conditions**
**    ****reanalysis data**

**Boundary Conditions**
**   ****climatology**
**    ****reanalysis data**

**2. COUPLING RSM with VIC land surface hydrology**
**Project lead:  John Roads, SIO**

**Nonlinear baseflow based on**
** ****the third layer soil moisture**

## Process of Initializing the RSM and VIC System

**RSM**** ****sea/land mask and **
**Orography from **
**the fixed fields**

**RSM**** ****climatology data for **
**initializing sea/land **
**parameters and variables**

**VIC**** ****land soil and **
**vegetation fixed fields**

**Initializing analysis fields**
**using climatology data**
**then, reading RSM analysis fields **
**for updating sea/land variables**

**Merging the analysis fields and**
** ****forecast fields**** **

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

**Initializing VIC parameters and variables**
**using 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**

## Preliminary Results using VIC and RSM

**Preliminary Results using VIC and RSM**

## S/I Hydrologic Forecasting ProjectProject lead:  Dennis Lettenmaier, University of Washington

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

## Project Goals

**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**
**Assess experimental product skill relative to established forecast products**
**Evaluate relative hydrologic prediction skill due to ICs - initial land surface conditions (soil moisture, snow) and due to climate forecast skill.**

## Forecasting Approach

- forecast ensemble
- meteorological sequences

- local scale weather inputs

- streamflow,
- soil moisture,
- snowpack,
- runoff

**General**

**downscaling process ***

**hydrologic simulation**

*** for climate model**
**   ****forecasts**

**Notes:**
important point(s):
the overall forecasting approach involves using forecast model (the global spectral model) T & P output at a coarse timestep & scale as hydrologic model input at a finer timestep and scale.
to make a hydrologic forecast, you need a transformation of the forecasts that first overcomes climate model bias and the scale differences, then simulates the water balance.
also, GSM is really run at very fine timestep (~5-15 minutes) but only the monthly anomalies are archived for our use.  most of the signal is at the monthly scale, however, so this is acceptable. 

## Overview:  VIC Simulations

**Notes:**
important point(s):
after bias correcting and downscaling the climate model forecasts, the procedure for producing hydrologic forecasts is as follows:
we spin up the hydrologic model to the start of the forecast using observed met. data (from 2 sources:  NCDC cooperator stations through 3-4 months before the start of the forecasts, then LDAS 1/8 degree gridded forcings thereafter). 
The GSM forecasts comprise 2 sets of ensembles, one for climatology and one for the forecast.  The climatology ensemble yields a distribution of the conditions we’ve seen over the period 1979-1999, while the forecast ensemble yields the distribution of the conditions we might see for the next 6 months.  Although the climatology ensemble is nominally unbiased against a simulated climatology based on observed met. data (rather than bias-corrected, downscaled GSM met. forcings), we compare the forecast and GSM climatology so that any unforeseen biases (resulting, perhaps, from the downscaling method) occur in both climatology and forecast.  Eventually this cautionary step may be eliminated, and we’ll compare directly to the simulated observed climatology.
at the end of the spin-up period and one month before month 1 (out of 6) of the forecasts, we save the hydrologic model state.  The state is then used for initializing the forecast runs.  Through the first month, the model runs on observed data to the last date possible, then switches to the forecast data.  Usually, we process the observed forcings up through the 15th to 25th of this initialization month, then the forecast forcing data carries the run forward for the remaining days in the month, and throughout the following 6 month forecast period.  Note, the state files used for the climatology runs correspond to the spin-up associated with the particular year (out of 1979-1999) from which the climatology ensemble member is drawn.
 the spin-up period captures the antecedent land surface hydrologic conditions for the forecast period: in the Columbia basin, the primary field of interest is snow water equivalent.
forecast products are spatial (distributed soil moisture, runoff, snowpack (swe), etc.), and spatial runoff + baseflow is routed to produce streamflow at specific points, the inflow nodes for a management model, perhaps. 

## 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

**Notes:**
important point(s):
the approach attempts to make use of forecast skill from 2 sources:
better understanding of synoptic scale teleconnections and the effects of persistence in SSTs on regional climate, as reproduced in coupled ocean-atmosphere models;
the macroscale hydrologic model yields an improved ability to model the persistence in hydrologic states at the regional scale (more compatible with climate model scales than prior hydrologic modeling).

Climate forecasts with monthly and seasonal horizons are now operationally available, and if they can be translated to streamflow, then they may be useful for water management.

## Current Columbia River basin forecasts

**Notes:**
important point(s):
the approach attempts to make use of forecast skill from 2 sources:
better understanding of synoptic scale teleconnections and the effects of persistence in SSTs on regional climate, as reproduced in coupled ocean-atmosphere models;
the macroscale hydrologic model yields an improved ability to model the persistence in hydrologic states at the regional scale (more compatible with climate model scales than prior hydrologic modeling).

Climate forecasts with monthly and seasonal horizons are now operationally available, and if they can be translated to streamflow, then they may be useful for water management.

## Current Forecasts:  Initial Soil Moisture

- CPC Percent of Normal Precip

**Jun-Jul-Aug**

**Sep**

- %

- September 25, 2003

**Notes:**
important point(s):
the approach attempts to make use of forecast skill from 2 sources:
better understanding of synoptic scale teleconnections and the effects of persistence in SSTs on regional climate, as reproduced in coupled ocean-atmosphere models;
the macroscale hydrologic model yields an improved ability to model the persistence in hydrologic states at the regional scale (more compatible with climate model scales than prior hydrologic modeling).

Climate forecasts with monthly and seasonal horizons are now operationally available, and if they can be translated to streamflow, then they may be useful for water management.

## Current forecasts: Snake River locations

## Current forecasts: Reservoir system storage

**Current forecasts: Reservoir system storage**

- ensemble mean

- Unconditional ESP

**Full Pool**

- System Storage Forecast from SnakeSim includes:
**Jackson Lake**
**Palisades**
**Island Park**
**Ririe**
**American Falls**
**Lake Walcott**