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- 2The 2nd phase of the
Global Land-Atmosphere Coupling Experiment
Randal KosterGMAO, NASA/GSFCrandal.d.koster@nasa.gov
GLACE-1 was a successful international modeling project that looked at soil moisture impacts on precipitation...
Where precipitation responds to variations in soil moisture, according to the 12 GLACE models.
Motivation for GLACE-2
For soil moisture initialization to add to subseasonal or seasonal forecast skill, two criteria must be satisfied:
1. An initialized anomaly must be “remembered” into the forecast period, and
2. The atmosphere must be able to respond to the remembered anomaly.
Addressedby GLACE
Addressedby GLACE2:the fullinitializationforecast problem
GLACE-2:Experiment Overview
Perform ensembles of retrospective
seasonal forecasts
Initialize land stateswith “observations”,
using GSWP approach
Prescribed, observed SSTs or the use of a coupled ocean model
Initialize atmosphere with “observations”, via
reanalysis
Evaluate forecasts against
observations
Step 1:
GLACE-2:Experiment Overview
Perform ensembles of retrospective
seasonal forecasts
Initialize land stateswith “observations”,
using GSWP approach
Prescribed, observed SSTs or the use of a coupled ocean model
Initialize atmosphere with “observations”, via
reanalysis
Evaluate forecasts against
observations
Step 2:
“Randomize” land
initializatio
n!
GLACE-2:Experiment Overview
Step 3: Compare skill; isolate contribution of realistic land initialization.
Forecast skill obtain in experiment using
realistic land initialization
Forecast skill obtained in identical experiment,
except that land is not initialized to realistic
values
Forecast skill due to land initialization
FORECAST START DATES
Apr 1
19861987198819891990
100 different 10-member forecast ensembles.
Each ensemble consists of 10 simulations, each running for 2 months.
19911992199319941995
Apr 1
5
May
1M
ay 1
5Ju
n 1
Jun
15
Jul 1
Jul 1
5
Aug 1
Aug 1
5
Observedprecipitation
Wind speed, humidity, air
temperature, etc.from reanalysis
Observedradiation
LSM
A decade of LSM initial conditions
for seasonal forecasts
The resulting LSM initial conditions reflect observed antecedent atmospheric forcing.
LAND STATE INITIALIZATIONvia offline simulations, a la GSWP
A decade of offline
integration
Two goals of project:
1. Calculate “potential predictability” in each forecast system – where does atmospheric noise overwhelm any potential signal?
2. Calculate skill in P and T prediction associated with the accurate initialization of land surface states.
Progress to date…
Integration into WCRP/TFSP project(TFSP = Task Force on Seasonal Prediction)
{
Updated Participant List
Group/Model Points of Contact
1. NASA/GSFC (USA): GMAO seasonal forecast system (old and new)
2. COLA (USA): COLA GCM, NCAR/CAM GCM
3. Princeton (USA): NCEP GCM
4. IACS (Switzerland): ECHAM GCM
5. KNMI (Netherlands): ECMWF
6. ECMWF
7. GFDL (USA): GFDL system
8. U. Gothenburg (Sweden): NCAR
9. CCSR/NIES/FRCGC (Japan): CCSR GCM
10. FSU/COAPS
# models
S. Seneviratne, A. Roesch
E. Wood, L. Luo
P. Dirmeyer, Z. Guo
R. Koster, T. Yamada2
B. van den Hurk
T. Gordon
J.-H. Jeong
T. Yamada
2
1
1
1
1
1
1
12 models
1 G. Balsamo
M. Boisserie1
“Persisted” SST boundary conditions have been constructed and are now available online. (T. Yamada)
Fcst. Model Points of Contact Progress to Date
COLA GCM ; NCAR/CAM GCM, via COLA
Paul Dirmeyer,Zhichang Guo
-- Forcing data interpolated to proper resolution; offline land simulations proceeding.-- Completed 10 years of COLA runs-- NCAR runs being set up.
Three analyses performed here at GLACE-2 Central on COLA results:
1. Analysis of potential predictability.
2. Analysis of forecast skill.
3. Analysis of forecast skill, using statistical enhancements.
For a given ensemble forecast, assume that the first ensemble member represents “nature”.
STEP 1:
Assume that the remaining ensemble members represent the “forecast”.
STEP 2:
STEP 3: Determine the degree to which the “forecast” agrees with the assumed “nature”.
Precipitationanomaly(mm/day)
01234
-1-2-3-4 Ensemble member
1 2 3 4 5 6 7 8 9
To what extent does this anomaly…
… agree with the average of these anomalies?
10
Potential predictability is the maximum predictability possible in the forecasting system.
Potential Predictability
(r2),Precipitation
(COLA model,Series 1)
(July 1 start, land initialized)
1st half of July 2nd half of July
1st half of August 2nd half of August
Potential Predictability
(r2),Precipitation
(COLA model,Series 2)
(July 1 start, land initialized)
1st half of July 2nd half of July
1st half of August 2nd half of August
Net land impact on Potential
Predictability (r2),
Precipitation(COLA model,
Ser1 – Ser2)
(July 1 start, land initialized)
1st half of July 2nd half of July
1st half of August 2nd half of August
Forecast Skill: COLA model
Notes:
Skill is averaged separately for four leads:a. 1-15 days after forecast start dateb. 16-30 days after forecast start datec. 31-45 days after forecast start dated. 46-60 days after forecast start date
For a given forecast start date and lead, the forecasts from the 10 ensemble members are averaged into a single field.
Skill for a given period is compared to observations during that period. (Measured as r2.) Analysis focuses on US first, out of convenience: we have access to a high quality multi-decade observational dataset there (Higgins et al., 2000), and besides, most models show some coupling strength there (GLACE-1).
Prior to computing the skill scores (observations), all 15-day forecasts are standardized (using relevant means and standard deviations for given start date and lead), as are all the observations.
Forecast Skill: COLA model
Notes (continued):
The skill analysis is supplemented with an analysis of transformed forecasts:
Suppose we can predict P here with the forecast system (high potential
predictability)…
Suppose, though, that P in these two areas are correlated in the
real world.
Then we can combine the model forecasts with the observed correlations to derive a forecast here.
…but not here
Using these ideas, we can compute a “transformation matrix” A that improves a forecast:
x = A x~
Vector holding original forecasts
Vector holding transformed forecasts
For details, see Koster et al., Monthly Weather Review, 136, p. 1923-1939.
Forecast skill (COLA): PrecipitationAll start dates (100 standardized values going into r2 calculation).
Lead: Days 1-15
Series 2:no land information
Series 1:land information
Differences:impact of land
RawResults
TransformedResults
(colors go from 0. to 0.5) (colors go from -0.5 to 0.5)
Forecast skill (COLA): PrecipitationAll start dates (100 standardized values going into r2 calculation).
Lead: Days 15-30
Series 2:no land information
Series 1:land information
Differences:impact of land
RawResults
TransformedResults
(colors go from 0. to 0.5) (colors go from -0.5 to 0.5)
Forecast skill (COLA): PrecipitationAMJ start dates (60 standardized values going into r2 calculation).
Lead: Days 1-15
Series 2:no land information
Series 1:land information
Differences:impact of land
RawResults
TransformedResults
(colors go from 0. to 0.5) (colors go from -0.5 to 0.5)
Forecast skill(COLA):
Temperature
Raw results,all start dates
Series 2:no land information
Series 1:land information
Differences:impact of land
Lead:1-15 days
(colors go from 0. to 0.5) (colors go from -0.5 to 0.5)
Lead:16-30 days
Lead:31-45 days
Lead:46-60 days
Fcst. Model Points of Contact Progress to Date
NCAR (USA, via
U. Gothenburg, Sweden)
Jee-Hoon Jeong -- Baseline set of simulations for the period 1986-1995 is finished (Series 1 and Series 2).-- Performing additional forecasts with modified initialization strategy.
We found some unusual aspects of the results that need clearing up – we need to talk to Jee-Hoon. Currently, the results for NCAR are indeterminate.
Fcst Model Points of Contact Progress to Date
GEOS5 GCM; NSIPP GCM(NASA/GSFC)
Randal Koster, Tomohito Yamada
-- Simulated 50 years of land surface conditions for initialization-- Ran GEOS5 GCM 10 years to generate climatology-- July 1 forecasts finished.
Model: GEOS5Variable: SWOIStart date: July 1(potential predictability)
Ser1 Ser2
Ser1-2
Model: GEOS5Variable: PRCPStart date: July 1(potential predictability)
Ser1 Ser2
Ser1-2
Model: GEOS5Variable: PRCPStart date: July 1(potential predictability)
Ser1 Ser2
Ser1-2
These runs used an early, unfinished version of the NASA free-running climate model, a version known to be deficient in land-atmosphere coupling strength.
Fcst. Model Points of Contact Progress to Date
KNMI Bart van den Hurk,Helio Camargo,Gianpaolo Balsamo
-- GSWP2 forcings regridded to their GCM’s resolution.-- 10-yr climatology run with the GCM, to allow for soil moisture scaling.-- Land model incorporated into LIS, for efficient offline simulation.-- 1+ years of Series 1 forecasts, 1 set of Series 2 forecasts. (Forecasts are ongoing.)
First results showing forecasted soil moisture’s agreement with “truth” across the globe:-- decrease of agreement with time -- agreement differs amongst ensemble members.-- longer apparent memory in mid-summer
Fcst Model Points of Contact Progress to Date
GFDL (USA) Tony Gordon -- AMIP style control run performed for atmospheric initial conditions and for scaling of land variables. -- 10 years (1st of each of month, 10 ensemble members) completed, for both Series 1 and Series 2.-- All Series 1 runs done; scaled and unscaled; Series 2 done two ways: with pdf, and with average.
NCEP (via Princeton, USA)
Eric Wood, Lifeng Luo
-- Simulated 50 years of land surface conditions for initialization.-- Ready to go; waiting for time on NCEP machine.
ECHAM (via IACS, Switzerland)
Sonia Seneviratne,Roesch Andreas
-- Series 2 simulations for GSWP2 period are finished for most start dates in 10-year period.
ECMWF Gianpaulo Balsamo
CCSR/NIES/FRCGC (Japan)
Tomohito Yamada -- Simulated 50 years of land surface conditions for initialization.
FSU/COAPS Marie Boisserie (New to project)
Fcst Model Points of Contact Progress to Date
Discovered: Problem with GLACE2 SST data(Thanks to Tony Gordon for spotting this.)
• Original data: Hadley monthly mean state• Two types of data
1. start date: 1st (interpolated by two months, e.g., (March+April)/2=April 1st)
2. start date: 15th (directly from Hadley monthly mean sate at a same month)
1995, Sea Surface TemperatureGLACE2 (black line) & Hadley (Green line)
15th simulation1st simulation
GLACE2: April 1st
Hadley: (March+April)/2
GLACE2: April 1st
Hadley: (April+May)/2
Main point: Somehow, the SST data meant to refer to March 1 actually refers to observations on April 1.
The problem only applies to start dates on the first of the month. SST data for start dates on the 15 of the month are not in error.
New SST datasets are being constructed now.
1995, Sea Surface TemperatureGLACE2 (black line) & Hadley (Green line)
15th simulation1st simulation
GLACE2: April 1st
Hadley: (March+April)/2
GLACE2: April 1st
Hadley: (April+May)/2
Main point: Somehow, the SST data meant to refer to March 1 actually refers to observations on April 1.
The problem only applies to start dates on the first of the month. SST data for start dates on the 15 of the month are not in error.
New SST datasets are being constructed now.
Problem has been fixed!
Summary: Aside from that glitch, GLACE-2 is proceeding nicely!
Back-up slides
Computational requirement:
100forecast
startdates
10ensemblemembers
perforecast
1/6years
(2 months)per
simulation
2experiments
(includingcontrol)
X XX
= 333 years of simulation
Required output diagnostics (to be provided to GLACE data center):
1. For each of the 4 15-day periods of each forecast simulation, provide global fields of:
15-day total precipitation
15-day average near-surface air temperature (in the lowest AGCM level)
15-day total evaporation
15-day average net radiation
15-day average vertically-integrated soil moisture content.
15-day average near-surface relative humidity
Required output diagnostics (to be provided to GLACE data center):
2. For each day of the first 30 days of certain forecast simulations, provide global fields of:
vertically-integrated soil moisture content.
This will allow us to analyze the decay with lead time of the information provided by soil moisture initialization.
The forecast simulations chosen for this output are:Apr. 1, May 1, June 1, July 1, and Aug. 1 of 1986, 1988, 1990, 1992, and 1994.
3. Generation of SST Boundary Conditions
Needed: SST conditions for forecasts that do not include measurements of SSTs during the forecast period.
Approach: Determine persistence timescales from observational record (with data exclusion) and reduce initial (measured) SST anomalies with time into the forecast, using those timescales.
“Persisted” SST boundary conditions have been constructed and are now available online. (T. Yamada)
GSWP2 Princeton
June 1988 Soil Moisture Anomaly (against 1986-1995)
4. Examined forcing datasets (T. Yamada)
1. Determine maximum level of precipitation (and air temperature) predictability associated with land initialization.
Results from pilot study of
Koster et al. 2004
Regress full forecast against actual observations to retrieve r2.
2. Determine forecast skill for precipitation (and air temperature) associated with land initialization.
Contribution to skill: P forecasts
Contributions of land moisture initialization to the skill of subseasonal (one-month) forecasts of P and T
Contribution to skill: T forecasts
Koster et al., Journal of Hydrometeorology,5, pp. 1049-1063, 2004
Results from pilot study of
Koster et al. 2004
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