P1.16 Prediction of Global Tropical SST Using a Markov Model and... Yan Xue INTRODUCTION

Prediction of Global Tropical SST Using a Markov Model and Comparison with NCEP’s CFS Forecast
P1.16
Yan Xue
Climate Prediction Center/NCEP/NOAA, [email protected]
INTRODUCTION
Although the forecast skill of the tropical Pacific SST is moderate due to the largest
interannual signal associated with ENSO, the forecast skill of the tropical Indian and
Atlantic SST is still poor. This might be due to the fact that the interannual signal is weak
and observations are sparse there. However, ENSO has significant impacts on the SST
variability outside the tropical Pacific, which can be forecast indirectly through forecast of
ENSO. The Markov model, based upon observations in the tropical Pacific, has a
competitive forecast skill for ENSO. Here is an attempt to extend the Markov model to the
global tropics so that it not only forecast the tropical Pacific SST associated with ENSO
but also ENSO’s impacts on SST variability outside of the tropical Pacific.
DATA
•
ERSST version 2, 1979-2000, monthly, 2ox2o, 20oS-20oN
•
Sea level from SODA version 1.2, 1979-2000, monthly, 2ox2o, 20oS-20oN
•
CMAP precipitation, 1979-2000, monthly, 2ox2o, 20oS-20oN
•
Reanalysis-2 wind stress, 1979-2000, monthly, 2ox2o, 20oS-20oN
METHODOLOGY
MEOF SPACE REDUCTION
With 22 degrees of freedom (1979-2000),
MEOF 1 and 2 are significant, describing
the mature and onset phases of ENSO;
MEOF 3 and 4 are mixed, describing
asymmetry between warm and cold events
and the Atlantic Nino; MEOF 5 and 6 are
significant, describing the Atlantic
meridional mode, and Southern Ocean
variability.
Six MEOFs together account for 90% of
SST variability in the central-eastern
Pacific, 60% in the tropical western
Pacific, 50% in the tropical Indian Ocean
(IO) except in the southern-eastern IO, and
60% in the north-western and south-eastern
Atlantic Ocean.
Six MEOFs together account for 80-90%
of sea level variability in the equatorial
Pacific, 50% in the southern-western
Indian Ocean, and 20-30% in the equatorial
Atlantic Ocean, which is attributed to the
small variability there.
 Remove annual cycle in 1979-2000
 Calculate multiple EOFs of SST and sea level with equal weight
 Calculate associated patterns of precipitation and wind stress with MEOFs
 Use Principal Components of MEOFs to construct 12 transition matrixes --Markov model (Xue et al. 2000)
Although the SST and sea level
variability in the southern Indian Ocean
are moderate, they are not represented
well by six MEOFs.
 Determine number of PCs to retain in Markov model with cross validation
•
Take one year data out sequentially
•
Build Markov model with remaining data, and forecast the year that is taken
out
 Compare hindcast skill in 1982-2000 with NCEP’s Climate Forecast System
(CFS)
COMPARISON with NCEP’s CFS FORECAST
CONCLUSIONS
• The warm biases in the Global Ocean Data Assimilation System (GODAS, see
poster P2.9) before 1990 hurts the CFS’s hindcast skill in the western Pacific,
Indian and Atlantic oceans significantly.
• The hindcast skill of the Markov model is superior to that of CFS when the
model’s climatology in 1982-2000 is removed, while it is comparable to that of
CFS when the model’s means in 1982-1990 and 1991-2000 are removed in
addition to removing the model’s climatology in 1982-2000.
• The hindcast skill of the tropical Pacific SST is the lowest in summer due to
spring barrier. CFS simulates the recovery of skill in fall in the central-eastern
Pacific, but it fails to simulate the recovery of skill in the north-western Pacific.
• The hindcast skill of the tropical Indian SST is excellent in later winter and
spring, and that of the north-western Atlantic is modest in spring, while that of
the tropical Atlantic is generally poor.
• CFS’s forecast has too strong variability in the equatorial western and northwestern Pacific, and has significant cold biases since 1999.
Due to the pre-1990 warm biases in the Global Ocean Data Assimilation System (see poster P2.9) that is used to initialize
the oceanic component of CFS, the CFS’s hindcast skill is evaluated in two ways. One way is to remove the model’s
climatology in 1982-2000 (referred as CFS) and another is to remove the model’s means in 1982-1990 and 1991-2000 in
addition to removing the climatology in 1982-2000 (referred as CFS_biascorr). Markov model has a superior hindcast skill
compared to CFS outside of the tropical Pacific, but its superiority diminishes significantly compared to CFS_biascorr.
NINO3.4 (170oW-120oW, 5oS-5oN), WSST (120oE-170oW, 5oS-5oN), NWSST(120oE-160oE, 5oN-20oN), IND (48oE104oE, 6oS-6oN), TNA(80oW-40oW,7oN-20oN),ATL (40oW-8oE, 6oS-6oN).
• Six MEOFs appear capture the most significant air-sea coupled modes in the
global tropics.
Xue, Y., A. Leetmaa, and M. Ji, 2000: ENSO prediction with Markov models:
The impact of sea level J. Climate, 13, 849-871.