CFSv2 Research at COLA: Understanding the effect of air-sea

CFSv2 Research at COLA:
Understanding the effect of air-sea coupling
in Asian-Pacific monsoon prediction and
improving sea ice simulation for climate study
Bohua Huang1,2 and Jieshun Zhu2
1Department
of Atmospheric, Oceanic, and Earth Sciences, George Mason University
2Center
for Ocean-Land-Atmosphere Studies (COLA)
Acknowledgments: J. Shukla, J. Kinter, L. Marx, C.-S. Shin, Z.-Z. Hu, X. Wu, A. Kumar
The Second Taiwan West Pacific Global Forecast System Planning Workshop
CWB, Taipei, Taiwan, May 7-8, 2014
1
An Overview
• Multiple Analyses Ensemble Ocean Initialization
ENSO prediction skill and reliability (Zhu et al. 2012, 2013a)
US summer monsoon precipitation (Zhu et al. 2013b)
Indian Ocean SST and Asian monsoon prediction (in progress)
• Effects of air-sea coupling in Asian-Pacific
monsoon prediction
• Decadal prediction: reducing climate drift by
improving sea-ice simulation
• Diagnostic analyses
Simulation/hindcast of South Pacific dipole mode (Guan et al. 2014a,b)
2
Fast annual cycle in monsoon (Shin and Huang, in preparation)
Air-sea coupling in monsoon prediction
A comparison of One- and Two-Tier Approaches
Zhu, J. and J. Shukla, 2013: The Role of Air–Sea Coupling in Seasonal Prediction of Asia–Pacific Summer
Monsoon Rainfall. J. Climate, 25, 5689-5697, doi:10.1175/JCLI-D-13-00190.1.
One-Tier: CGCM for seasonal prediction
•
•
•
Relatively new: operational since 2000s (Palmer et al. 2004; Saha et al. 2006;…)
CGCM bias affects quality of prediction (e.g., tele-connection)
Computationally expensive (trade-off between resolution and physics)
Two-Tier:
Tier-1: SST prediction
Tier-2: AGCM with predicted SST forcing (Bengtsson et al 1993)
•
•
•
•
Yielding boundary-forced predictability
Avoid CGCM bias
Achieving computational efficiency (or higher resolution)
A useful research tool (e.g., climate downscaling, signal/noise separation, etc)
An issue with Tier-2: Lack of air-sea interaction
• In particular, atmospheric feedback to SST is vital over monsoon regions.
Without feedback, SST forcing is excessive in Tier-2 (e.g., Fu et al. 2002; Wu and
Kirtman 2004, 2005, 2007; Wu et al. 2006; Wang et al. 2005; Cherchi and Navarra 2007; Chen et al.
2012; Hendon et al. 2012; Hu et al. 2012…)
• Few studies have compared the one tier and two tier systems in a prediction
mode (Kug et al. 2008; Kumar et al. 2008): Less controlled experiments.
3
Model and Experiments
One-Tier vs. Two-Tier: 6-month hindcasts starting from April (1982-2009)
• Forecast Model: NCEP CFS version 2
1)
Atmosphere: T126, L64
2)
Ocean (MOM4): 0.5°x0.5° (0.25° lat, 10°S-10°N), L40
3)
Coupling: every half hour
• Experiment design (Coupled vs. Uncoupled)
1) An identical AGCM is used in Tier-1 and Tier-2 predictions
2) Tier-1: Coupled run based on CFSv2
Tier-2: Daily mean SSTs from Tier-1 are prescribed as boundary conditions
• Initial Conditions (1982-2009)
1)
Tier-1: Ocean IC: Instantaneous states from ECMWF ORA-S4
2)
Tier-1/2: Atmosphere/Land IC: CFSR (4-member, Apr 1-4)
4
(1982-2009)
mm/day
5
Averaging first,
Then Calculating
Sdev.
Standard Deviation (1)
(1982-2009)
mm/day
6
Calculating Sdev
first,
Standard Deviation (2)
(1982-2009)
Then averaging.
mm/day
7
(1982-2009)
mm/day
8
9
Large differences occur in monsoon regions
because of large mean rainfall? Then why not in Atlantic?
Is warm pool temperature a factor? (a warm water
phenomenon?)
CMAP
25.0%
10.8%
8.8%
CGCM
44.7%
13.6%
5.6%
AGCM
Leading Modes of Precipitation
27.7%
16.7%
10.5%
O-A: 0.77
O-C: 0.53
O-A: 0.51
O-C: 0.25
PC
O-C: 0.80
A-C: 0.88
A-C: 0.60
A-C: 0.38
O-A: 0.41
CMAP
25.0%
10.8%
8.8%
CGCM
44.7%
13.6%
5.6%
AGCM
Leading Modes of Precipitation
27.7%
16.7%
10.5%
O-A: 0.77
O-C: 0.53
O-A: 0.51
O-C: 0.25
PC
O-C: 0.80
A-C: 0.88
A-C: 0.60
A-C: 0.38
O-A: 0.41
A-C
AGCM
CGCM
Surface Heat Balance
15
A-C
AGCM
CGCM
Atmosphere forces ocean
16
AGCM
CGCM
Atmosphere forces ocean
A-C
A Measure to Lack of SST feedback
17
An Example
18
mm/day
(1982-2009)
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20
21
(1982-2009)
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Summary I
1) In the absence of air–sea coupling, Tier-2 predictions produce higher
rainfall biases and unrealistically high rainfall interannual variations.
2) The prediction skill, as measured by anomaly correlation, does not show
significant differences between the two types of predictions. Observed leading
modes can be reproduced by both to a certain degree.
3) RMSEs are significantly larger for the AGCM (Tier-2) predictions
compared to the CGCM (Tier-1) predictions.
4) The reduced RMSE skills in the Tier-2 predictions are due to a lack of
coupled surface feedback to the prescribed SST anomalies, which, particularly,
results in an unrealistic SST-rainfall relationship over the TWNP region.
Incorporating regional ocean–atmosphere feedback is important for
rainfall prediction over the Asia–Pacific region.
23
Improving sea ice simulation for climate study
Huang, B., J. Zhu, L. Marx, X. Wu, A. Kumar, Z.-Z. Hu, M.A. Balmaseda, S. Zhang, J. Lu,
E.K. Schneider and J.L. Kinter, 2014: Climate Drift of AMOC, North Atlantic Salinity and
Arctic Sea Ice in CFSv2 Decadal Predictions. Clim. Dyn. submitted.
• In the framework of seamless prediction, there is merit in
extending operational seasonal prediction systems, such as
CFSv2, to predicting decadal variability and climate
change (The extension is not as straightforward as it seems)
• Long-term integration/prediction tests CFSv2 behavior on
wider ranges with new targets. The resulted model
improvements may benefit seasonal prediction
• Achieving both goals simultaneously is not an easy task.
Research institutes, such as COLA, can play a useful role
in bridging the two
24
30-year simulations
Red contours: zonal stress provided by AGCM
Blue contours: zonal stress “seen” by OGCM
Black curves: true sea ice range
The discrepancy in some ice-free regions is due to a misidentification
of sea-ice index in coupler
Corrected sea ice cover reduces warm SST bias in summer
AMOC is enhanced
but still weaker

than observations
Sea Ice Concentration
Sensitivity Experiment
Adjusting
(1) Sea-ice albedo
(2) Dry-wet ice
transition temperature
35
Summary II
1) A major climate drift occurs in the CFSv2 decadal hindcasts, with the
weakening of AMOC, reduction of North Atlantic salinity and thinning of
the Arctic sea ice.
2) Among other factors, the melting sea ice generates excessive freshwater
in the Arctic Ocean that can be transported to the North Atlantic.
3) Adjusting sea ice albedo parameters produces a sustainable ice cover
with realistic thickness distribution, enhancing AMOC moderately.
4) Improved CFSv2 sea ice simulation also reduces the warm SST bias in
the North Pacific during summer.
A more realistic freshwater balance may lead to a major improvement of CFSv2
How well is the Slow Annual Cyrcle
simulated by CSFv2?
OLR in the East Asian Monsoon Region (120E-140E)
OBS
CFSv2
OBS
CFSv2
target period & area for FAC
Total Annual Cycle
Slow Annual Cycle
*Removed the model bias in (b)
FAC of OLR (120E-140E)
Mei-yu front
Pre Mei-yu
Subtropical
Wet Phase
ridge
OBS
Pre Monsoon
Dry Phase
CFSv2
Monsoon Gyre
Western North
Pacific Monsoon
How is the Fast Annual Cycle
simulated by CSFv2?
Repeating dry and wet phases are very well
simulated in the CFSv2, i.e., Pre-Monsoon
Dry Phase, Pre-Meiyu Phase, Grand Onset,
and Monsoon Gyre.
Northward propagating signal for both dry
and wet phases are also well simulated in
the CSFv2.
However, the intensity is much weaker in the
CFSv2 and the timing of both phases are
later than observation.
Zhu et al. (GRL, 2012)
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