Understanding Oceans Sustaining Future. Shaoqing Zhang

Transcription

1 Understanding Oceans Sustaining Future Shaoqing Zhang

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3 OUTLINE 1. Background: Problem in AMOC reconstruction of GFDL ECDA 2. Hypothesis Importance of tropical high-frequency information to maintain the balanced meridional transportation 3. Model studies to understand the role of optimal assimilation windows in coupled data assimilation on AMOC analysis 4. Discussions: Ongoing project on 2C climate reanalysis at QNLM of China

4 Limitation of Model & Importance of Coupled Data Assimilation (CDA) CDA is good for ocean & climate studies All coupled components adjusted by observed data through instantaneously-exchanged fluxes Model Does a good job: Simulate the interactions of multi-scale components Assess global changes due to the changes of GHG-NA But: Different climate features Different climate variability Sea-Ice model GHGNA forcings Atmosphere model u, v, T, q, ps u obs, v obs, T obs, q obs, p s obs Ocean model T,S,U,V,η T obs,s obs Land mode l CDA Goal: Understanding climate variability to better estimate and predict climate on seasonal-interannual to decadal scales

5 Ensemble CDA (ECDA) ECDA is OPTIMAL for ocean & climate studies An ensemble of model integrations establishing the background error statistics to extract obs information, addressing the probabilistic nature of Ocn & Clim evolution Balanced: Ensemble statistics provides multivariate relationships, such as temperature-salinity relationship and geostrophic balance. Prio r PDF obs PDF Coherent: A set of self-balanced and coherent initial coupled states generates optimal ensemble initialization of coupled model with minimum initial shocks. Data Assim x b y o Analysis PDF Extendable/Renewable: Ensemblebased CDA is naturally and easily to be extended/renewed as the model becomes more comprehensive and includes more and more components. x a

6 Ocean Heat Content Estimation and AMOC Reconstruction in GFDL_CM2.1_ECDA km Model Control CDA Product (Chang et al. 212CD) (Karspeck et al. 21CD)

7 AMOC Standard Deviation in Model Control and in ECDA Standard deviation of model AMOC 4S 2S 2N 4N 6N Standard deviation of ECDA AMOC (Karspeck et al. 21CD)

8 Problem and Question in AMOC Reconstruction of ECDA Problem: In ECDA AMOC, the Southern Hemisphere transportation becomes strong while the Northern Hemisphere transportation becomes weak. AMOC stream functions show continuities at the equator Question: What causes the distorted feature of ECDA AMOC between 2 Hemispheres and the discontinuity at the equator?

9 AMOC: the Atlantic branch of global heat and salt convey belt

10 AMOC time mean features: the balance of 3 terms term balance: Ts - Tn = Tu (Gnanadesikan 1999Sci.)

11 Data Assimilation Scheme in GFDL_CM2.1_ECDA GHGNA forcings Atmosphere model u, v, T, q, ps Atmosphere assimilation frequency:6 hours RE2 Land u obs, v obs, T obs, q obs, p s obs mode l Sea-Ice model Ocean model T,S,U,V,η T obs,s obs Ocean assimilation frequency:1 day with a -day obs window (Zhang et al 27MWR; Zhang 2MWR)

12 Are tropical high-frequency signals important for formation of the AMOC feature? It includes high frequency signals at tropics, but ECDA doesn t have the ability to resolve them. Given the role of tropical high frequency variability in the formation of AMOC feature and variability: Does the lack of tropical high frequency distort the AMOC feature?

13 Model Study:Importance of tropical diurnal cycle in formation of the AMOC feature 4 model experiments to examine the hypothesis: 1 Model Control experiment (CTL) 2 Smooth atmosphere (SMATM) 3 Smooth ocean only (SMOCN) ºN ºS smooth range 4 Smooth both (SMOCN+SMATM) All experiments run the model for years starting from identical ICs

14 Smoothing Effect: Zonal Wind Stress zonal wind stress (N/m 2 ) timeseries CTL SMATM CTL SMATM (14 W, ) (3 W, )

15 Smoothing Effect: SST response in coupled model SST time series CTL SMATM CTL SMATM (14 W, ) (3 W, )

16 Influence of filtering out diurnal cycle of tropical Atm fluxes on AMOC time-mean AMOC features (-year integrations) Depth/m S 2S 2N 4N 6N 4S 2S 2N 4N 6N 4S 2S 2N 4N 6N CTL SMATM SMATM - CTL

17 Influence of filtering out sub-daily variability of tropical Ocn states on AMOC Depth/m time-mean AMOC features (-year integrations) S 2S 2N 4N 6N 4S 2S 2N 4N 6N 4S 2S 2N 4N 6N CTL SMATM SMOCN - CTL

18 Influence of filtering out sub-daily variability of tropical Atm fluxes and Ocn states on AMOC time-mean AMOC features (-year integrations) Depth/m S 2S 2N 4N 6N 4S 2S 2N 4N 6N 4S 2S 2N 4N 6N CTL SMOCN+SMATM SMOCN+SMATM - CTL

19 Summary and Discussions Tropical sub-daily high-frequency variability is very important to maintain the balanced northward transportation of AMOC. In coupled data assimilation, optimal observational time windows and assimilation frequency should be pursued to resolve diurnal cycles in the tropical atmosphere and ocean. We plan to re-run the ECDA system with 1-hour Atm (6-hour Ocn) obs time window and assimilation frequency with GFDL CM2.1 and NCAR CESM (1 o Ocn + 2 o Atm) for 2C climate reanalysis.

20 Thank You! Thank you very much!

21 Ocean University of China College of Oceanic and Atmospheric Sciences 1 2 Model study 3 Atmosphere smoothing strategy S-8S, 8N-N: 3 time steps mean 8S-S, N-8N: 6 time steps mean S-N: 12 time steps mean Smoothed variables:wind stress, sensible heat flux, specific humidity, long wave radiation, salt flux Atmosphere smooth Ocean

22 Ocean University of China College of Oceanic and Atmospheric Sciences 1 2 Model study 3

23 Ocean University of China College of Oceanic and Atmospheric Sciences 1 2 Model study 3 EOF region: 12E~18W, S~N

24 Ocean University of China College of Oceanic and Atmospheric Sciences SST EOF modes 1 2 Model study 3

25 Ocean University of China College of Oceanic and Atmospheric Sciences 1 2 Model study 3 SST PC1 and PC2 Standard deviation=3.2 Standard deviation=2.97 CTL PC1 (3.82) % SMATM PC1 (3.6) % Year 1978 CTL PC2 (29.64) % Year Year 1978 SMATM PC2 (29.83) % Year 1978

26 Ocean University of China College of Oceanic and Atmospheric Sciences 1 2 Model study 3 Ocean smoothing strategy 12 time steps mean time steps mean 12 time steps mean between S-N Smoothed variables:sst, SSS

27 Ocean University of China College of Oceanic and Atmospheric Sciences 1 2 Model study 3 SST timeseries CTL SMOCN CTL SMOCN (14 W, ) (3 W, )

28 Ocean University of China College of Oceanic and Atmospheric Sciences 1 2 Model study 3 SMOCN SST EOF mode

29 Ocean University of China College of Oceanic and Atmospheric Sciences SST PC1 and PC2 Timeries 1 2 Model study 3 Standard deviation=3.2 Standard deviation=1.29 CTL PC1 (3.82) % SMOCN PC1 (44.84) % Year 1978 CTL PC2 (29.64) % Year Year 1978 SMOCN PC2 (3.2) % Year 1978

30 Ocean University of China College of Oceanic and Atmospheric Sciences 1 2 Model study 3 SMOCN_SMATM SST EOF mode

31 Ocean University of China College of Oceanic and Atmospheric Sciences SST PC1 and PC2 Timeries 1 2 Model study 3 CTL PC1 (3.82) % SMATM PC1 (3.6) % SMOCN PC1 (44.84) % Year 1978 CTL PC2 (29.64) % Year Year 1978 SMATM PC2 (29.83) % Year Year 1978 SMOCN PC2 (3.2) % Year SMOCN_SMATM PC1 (44.93) % Year 1978 SMOCN_SMATM PC2 (34.8) % Year CTL SMATM SMOCN SMOCN_SMATM Standard deviation of timeseries

32 Ocean University of China College of Oceanic and Atmospheric Sciences CTL Standard deviation of model AMOC 4S 2S 2N 4N 6N SMOCN SMATM SMOCN_SMATM 1 2 Model study 3