1. ECPC’s Forecast System
There
is a major change underway at the Scripps Experimental Climate Prediction
Center (ECPC). Previously, the ECPC used the reanalysis I version (Kalnay et
al. 1996) of the National Centers for Environmental Prediction’s (NCEP’s)
medium range forecast (MRF) model or global spectral model (GSM; Roads et al.
2001a) to make routine experimental global forecasts. These global forecasts
(daily out to 7days and weekly out to 16-weeks) start from the NCEP operational
00UTC global analysis and use persisted SST anomalies (+climatology) as a lower
boundary condition.
These
GSM forecasts (e.g. Roads et al. 2003a) have been augmented recently by an
updated version of NCEP's seasonal forecast model (SFM; Kanamitsu et al.
2002a), which is based on updated physics from the NCEP/DOE reanalysis II
(Kanamitsu et al. 2002b). The SFM has a nominal (a reduced grid technique is
used near the poles) horizontal resolution of T62 (about 2o). There
are 28 levels in the vertical sigma coordinate system. ECPC's SFM is run in a
different fashion from the GSM. Starting from slightly perturbed initial
conditions, and forced with observed SST anomalies, 10 simulations are made up
to present. Then, persisted SSTs or
forecast SSTs are used to generate a forecast ensemble. The forecast SSTs come from a simplified
model for the tropical Pacific and are produced by the IRI. This new SFM is
being coupled to an ocean model and sometime in the future we hope to
demonstrate that such a coupled system will be demonstrably better than current
persisted or forecast SSTs as well as our current ocean forecasts, which use
forecast GSM anomalies to drive a Pacific Ocean model (Auad et al. 2003).
A
major advantage of the SFM over the GSM is that the computer code of the SFM
was completely rewritten to run on multiple platforms with single and/or multiple
shared memory machines. The code was improved further to run on massively
parallel processor (MPP) machines using Message Passing Interface (MPI)
routines. The SFM is now running on the
COMPAS cluster at the Scripps Institution of Oceanography. Normally the SFM runs on 64 processors and
takes 2 hours to make a 7-month forecast. Depending upon the number of ensemble
members, a normal 7-month forecast takes between 1-2 days. During the rest of
the month background runs are being made to augment the growing ensemble
climatology. In addition, as changes are made in the model new climatologies
have to be developed. In fact, there are a few physical parameterization
differences between the ECPC SFM and the NCEP SFM. The ECPC SFM has an updated set of land physics state as well as
revised formulation of land surface evaporation. However, it should be noted
that the NCEP SFM does start from observed initial conditions unlike the ECPC
SFM, which is starting from previous simulations. Ignoring the initial conditions
is generally thought to be reasonable when considering long-lead forecasts
(greater than a month) although there are certainly times and places when
initial conditions can be important even for seasonal forecasts (Reichler and
Roads 2003a,b,c,d). Another difference
between ECPC SFM and NCEP SFM is the initial condition of the soil
moisture. In the NCEP SFM,
climatological soil moisture is used while in the ECPC SFM; the simulated soil
moisture is used. This may impact the
forecast skill particularly in warm seasons.
The
ECPC SFM has not yet fully replaced the ECPC GSM in part because the GSM is
currently tightly linked to a number of additional models and applications. The
GSM forces a regional spectral model (RSM; Juang et al. 1997; Anderson and Roads
2002, Roads et al. 2003b, c, Roads 2003, Chen and Roads 2003) in order to gain
increased spatial resolution (50-25 km resolution) for several selected regions
(US, CA, SW, Brazil). The GSM and RSM are based upon the same physics used in
the GSM (and SFM) and can, in principle, be updated as the GSM (SFM) is
updated. We are attempting to implement these updates and to replace the GSM
with the SFM but this process may take some time due to lack of personnel.
Current output products from the GSM/RSM include a fire weather index (FWI, see
Roads et al. 1997) and associated variables such as 2m-temperature, relative
humidity and 10m-windspeed as well as precipitation and soil moisture.
Additional GSM products are provided to drive US National Fire Danger Rating
System Indices (Roads et al. 2003) and surface hydrologic models.
2. Forecast Skill Evaluations
Five
years worth of forecasts (260 forecasts) were previously used to develop
GSM/RSM forecast climatologies, which are dependent upon season as well as forecast
lead-time. Both means and standard deviations were derived in order to provide
normalized (by their respective standard deviation) anomalies. As discussed by
Roads et al. (2001a,b), Roads and Brenner (2002), Roads et al. (2003a,b); Roads
(2003), Chen et al. (2001), Chen and Roads (2003), the GSM/RSM provides
skillful forecasts of temperature, precipitation, soil moisture and fire danger
indices at long forecast ranges. Although the greatest skill occurs initially
and then rapidly decays, monthly and seasonal averages can still demonstrate
significant skill (Reichler and Roads 2003a,b,c,d), which may be comparable to
empirical long-range forecast methodologies.
ECPC
SFM forecast skill evaluations are underway in collaboration with the IRI and
will be reported upon later. Suffice it to say that the ECPC SFM has skill
comparable to other forecast models used by IRI, namely ECHAM models, NCAR CCM
and COLA GCM. ECPC SFM forecast skill apparently exceeds that of others in some
areas and in some seasons, and thus contributes to making a better multi-model
ensemble forecast.
As
a preliminary evaluation of the SFM we compare the GSM anomaly forecasts for
MJJ with the corresponding SFM forecasts. Whereas the GSM was initialized on
May 3, 2003 the SFM ensemble members were initialized one month earlier (April)
from continuous simulations. The GSM and SFM also have different base
climatologies (5 years for the GSM and 52 years for the SFM). Nonetheless,
there are some remarkable agreements. Fig. 1 shows that during MJJ, forecast
temperature anomalies were especially high over equatorial and South Africa,
India and Siberia. Temperature anomalies were low over the US and western
Pacific Ocean, Asia and the Middle East.
The major differences between the GSM and SFM appear to be mainly
quantitative. Although one can find differences it is not clear how significant
these differences are.
There
are larger differences for the precipitation anomaly forecasts. Fig. 2 shows that during MJJ both
models forecast drought over India and equatorial Africa. Both models forecast
above normal precipitation in the Indian Ocean and eastern US. However, there
are clearly large differences in the Caribbean and western equatorial
Pacific. However, given the much lower
skills in forecasting precipitation it is not clear how significant these
differences are.
3. Global seasonal GSM forecasts and US
monthly RSM forecasts
Fig.s 3,4 show the SFM seasonal forecast anomalies. Below normal
seasonal temperatures (Fig. 3) are
being forecast for the US and most of the NH middle latitudes for the next 7
months. By contrast northern Canada and
other NH high latitude regions indicate above normal temperatures will be
prevalent in the late summer and early fall.
This zonal character of the temperature anomalies continues to the
equatorial region where above normal land temperatures over Africa, India and
perhaps Central America are indicated.
Australia begins warm but then changes in late summer to below normal.
Precipitation
(Fig. 4) also shows this remarkably
persistent character but has much greater geographic variability. Much of the tropical land mass appears to be
tending toward a dry state in contrast to the adjacent oceans where the
precipitation is forecast to be above normal. The major exception appears to be
the north equatorial dateline where precipitation will be below normal,
consistent with the demise of the previous season El Nino. Over the Western US the precipitation is
forecast to be above normal although this is occurring during the dry
season. Perhaps more significant is the
below normal precipitation in the southeast and Caribbean.
References
Anderson, B.T., J. O.
Roads, 2002: Regional Simulation of of
Summertime Precipitation over the Southwestern United States. Journal of Climate, 15, 3321-3342.
Auad, G., A. Miller, J. Roads 2003: Ocean Forecasts. J.
Marine Res. (submitted)
Chen, S-C. J. O. Roads, and M. Wu, 2001:
ECPC’s Asia forecasts. Journal of
Terrestrial-Atmosphere-Oceanography, 12, 377-400.
Chen, S. and J. Roads, 2003: Regional Spectral Model
Simulations for South America. J. Hydrometeor. (submitted)
Juang, H. -M. H., S. -Y. Hong and M. Kanamitsu,
1997: The NCEP regional spectral model: an update. Bulletin Amer. Meteor.
Soc., 78, 2125-2143.
Kalnay, E. et al., 1996: The
NMC/NCAR reanalysis project, Bull. Am.
Meteor. Soc., 77, 437- 471.
Kanamitsu, M., A. Kumar,
H.-M. H. Juang, W. Wang, F. Yang, J. Schemm, S.-Y. Hong, P. Peng, W. Chen and
M. Ji, 2002a: NCEP Dynamical Seasonal Forecast System 2000. Bull. Amer. Met.
Soc., 83, 1019-1037.
Kanamitsu,
M., W. Ebisuzaki, J. Woolen, J. Potter and M. Fiorino, 2002b: NCEP/DOE AMIP-II
Reanalysis (R-2). Bull. Amer. Met. Soc. 83, 1631-1643.
Kanamitsu, M.,
Cheng-Hsuan Lu, Jae Schemm and W. Ebisuzaki, 2003a: The predictability of soil moisture and near surface temperature
in hindcasts of NCEP Seasonal Forecast Model. J. Climate, 16,
510-521.
Kanamitsu, M. and
Kingtse, Mo, 2003b: Dynamical Effect of Land Surface Processes on Summer
precipitation over the Southwestern United States. J. Climate, 16,
496-509.
Reichler, T. J. and J. O.
Roads, 2003: The Role of Boundary and Initial Conditions for Dynamical Seasonal
Predictability. Nonlinear Processes in
Geophysics, 10 (3) May/June 2003, 1-22.
Reichler, T. and J. O.
Roads , 2003: Time-space distribution of long-range atmospheric predictability.
J. Atmos. Sci., (submitted).
Reichler, T. and J. O.
Roads, 2003: Long-range predictability in the tropics. Part I: monthly
averages. J. Climate, (submitted).
Reichler, T. and J. O. Roads, 2003:
Long-range predictability in the tropics. Part II: 30-60 days
variability. J. Climate, (submitted).
Roads, J.O., S. -C. Chen, F. M.
Fujioka, H. Juang, and M. Kanamitsu. 1997. Global to Regional Fire Weather
Forecasts. Int. Forest Fire News, 33-37.
Roads,
J.O., S-C. Chen and F. Fujioka, 2001a:
ECPC’s Weekly to Seasonal Global Forecasts. Bull. Amer. Meteor. Soc., 82, 639-658.
Roads,
J., B. Rockel, E. Raschke, 2001b: Evaluation of ECPC’s Seasonal Forecasts Over
the BALTEX Region and Europe. Meteorologische
Zeitschrift Vol. 10 (4) p. 283-294.
Roads,
J. and S. Brenner, 2002: Global Model Seasonal Forecasts for the Mediterranean
Region. Israel Journal of Earth Sciences.
51 (1), 1-16.
Roads,
J. 2003: Experimental Weekly to Seasonal, Global to Regional US Precipitation
Forecasts J. Hydrology (submitted)
Roads, J., S. -C. Chen, J. Ritchie, 2003a: ECPC’s
Weekly to Seasonal U.S. Forecasts of FWI, Soil Moisture, and Precipitation.
ELLFB bulletin, Mar. 2003.
Roads, J., S.-C. Chen, M. Kanamitsu, 2003b: US
Regional Climate Simulations and Seasonal Forecasts. Journal of Geophysical
Research-Atmospheres (in press).
Roads, J., S. Chen, S. Cocke, L. Druyan, M. Fulakeza,
T. LaRow, P. Lonergan, J.-H. Qian, S. Zebiak, 2003c: The IRI/ARCs Regional
Model Intercomparison Over S. America. J. Geophys. Res. (in press).
Roads, J. et al. 2003: Seasonal Fire Danger
Forecasts (to be submitted).
