Climate projections and predictions: Challenges and possible solutions

Transcription

1 United Nations Open-ended Informal Consultative Process on Oceans and the Law of the Sea Eighteenth meeting Climate projections and predictions: Challenges and possible solutions Fangli Qiao First Institute of Oceanography, SOA, China May 2017 Collaborators: Zhenya Song, Guansuo Wang, Chuanjiang Huang

2 Outline 1. Challenges of ocean and climate models 2. Surface wave induced mixing and ocean model development 3. Surface wave in climate models 4. Summary

3 1. Challenges of ocean and climate models

4 Monsoon 2015 ends with 14% shortfall Indonesian Forest Fires Out of Control Flooding 'worst in 50 years', as 150,000 flee in Paraguay, Argentina, Brazil and Uruguay

5 Great challenge: Sea surface temperature difference between MMM of 45 CMIP5 models and observation

6 Tropical biases: a common problem for all climate models Song et al, 2012, JGR

7 Global oceans have absorbed more that 90% heat due to climate change, and about 1/3 of anthropogenic CO 2 IPCC-AR5

8 MLD in CMIP5 models Huang et al, 2014, JGR

9 MLD in OGCM Observation Model from POM 9

10 Lack of mixing in the upper ocean As the mixing process is essentially an energy balance problem, waves, as the most energetic motions at the ocean surface, should play a controlling role. Surface wave: 60 TW, Circulation: 4 TW

11 Motivation for model development Two of the common problems nearly all models faced: OGCMs: Simulated SST is overheating in summertime, and mixed layer depth is too shallow while the thermocline is too weak (Martin 1985, Kantha 1994, Ezer 2000, Mellor 2003). Climate Models: Tropical bias for all CGCMs, such as too cold tongue, wrong Atlantic SST gradient Is the surface wave a low-lying fruit?

12 2. Surface wave induced mixing and ocean model development

13 How surface waves affect OGCM? Breaking wave induced stress and energy flux (Craig and Banner, 1994; He and Chen, 2011) Coriolis-Stokes force (Polton et al., 2005) Langmuir circulation (Kantha and Clayson, 2004) Wave-induced shear (Pleskachevsky et al., 2011) Wave-turbulence interaction enhanced mixing (Qiao et al, 2004, 2010,2016). The non-breaking wave induced vertical mixing is the key

14 E(K) is the wave number spectrum which can be calculated from a wave numerical model. It will change with (x, y, t), so Bv is the function of (x, y, z, t). v ( ) 2 B exp{ 2 } ( ) V = α E k kz dk ω E k exp{ 2kz} dk z k k Qiao et al, GRL, 2004; OD, 2010; RS, 2016 If we regard surface wave as a monochramatic wave, B = αa kω e = αau e 3 ( 3 kz) ( 3 kz) s Bv is wave motion related vertical mixing instead of wave breaking., 1 2 Stokes Drift

15 Laboratory experiments reveals that the non-breaking surface wave can generate strong turbulence: To generate temperature gradient through bottom cooling of refrigeration tubes. Top of wave tank Temperature sensor Refrigeration tube Bottom of wave tank Dai and Qiao et al, JPO, 2010

16 Experiment results without and with waves

17 Observation evidences Vertical profiles of the measured dissipation rates ε m (dots), and those predicted by wave ε wave (black lines) and the law of the wall ε wall (pink lines) at Station S1~S12 (in m 2 s 3 ). Observation is conducted in SCS during October 29 to November 10, Huang and Qiao et al, 2012, JGR

18 Blue line Osborn, 1980 Green line Terray et al. (1996) Sutherland et al., 2013, OS Red line Huang and Qiao (2010)

19 Wave effects: MLD in summer (Qiao et al, OD, 2010) MLD of the Southern Pacific in Feb. MLD of the Northern Atlantic in Aug. With waveinduce mixing Without waveinduce mixing World Ocean Atlas

20 Bv in NEMO: Prof Adrian New of NOC, UK 2 1 1/4 No Bv Bv effect With Bv Simulated temperature difference at 50m in February

21 Bv in FESOM: Prof G Lohmann of AWI, Germany Temperature Difference of 30S Temperature Difference of 30N (a) without Bv - WOA09 (b) with Bv - without Bv (c) with Bv - WOA09 black line - zero line (a) without Bv - WOA09 (b) with Bv - without Bv (c) with Bv - WOA09 black line - zero line

22 3. Surface wave in climate models In tropical area, Bv has no much improvements for the ocean circulation model compared with mid- and high latitudes. For full coupled climate model, it is a different story because of the feedback and nonlinearity. FGCM0 of LASG and CCSM3 of NCAR

23 50a averaged SST ( a). Up: Exp1-Levitus, Down: Exp2-Exp1 Exp1: CCSM3 without Bv Exp2: with Bv

24 Water vapor transport in Australian-Asian Monsoon area No Bv With Bv Bv effect Data Song and Qiao et al, 2012, JAS

25 Yalin Fan, and Stephen M. Griffies, 2014, JC (Fig 3) Summertime oceanic mixed layers are biased shallow in both the GFDL and NCAR climate models (Bates et al. 2012; Dunne et al. 2012, 2013). This scheme (Qiao et al., 2004) has most impact in our simulations on deepening the summertime mixed layers, yet it has minimal impact on wintertime mixed layers.

26 FIO-ESM for CMIP5 Wave-induced mixing, Qiao et al., 2004 Land carbon CASA Ocean carbon OCMIP-2 Atmosphere CO2 transport Framework of FIO-ESM version1.0

27 Descriptions of the FIO-ESM Physical models ATM: CAM3.0, T42L26 LND: CLM3, T42 OCN: POP2, 1.1 o *0.3~0.5 o, 40 vertical layers ICE: CICE4, 1.1 o *0.3~0.5 o WAV: MASNUM wave model, 2 o *2 o Wave-circulation coupling: based on the wave-inducing mixing

28 SST absolute mean errors for 45 CMIP5 models

29 Centurial Future Projections 3.92

30 2.2 x x x x RCP2.6 RCP4.5 RCP6.0 RCP January 1.4 February 1.4 March 1.4 April x x x x May June 6 4 July 0 August 12 x x x x September 5 October 0.5 November 1 December Time series of Arctic sea ice extent from RCP run. Unit: m 2

31 12 x September

32 Conclusions P R China is one of the countries suffering mostly from climate change. For example, the sea level rise due to climate change is about 1.8 mm/year globally, while it is 3.2 mm/year for China during 1980 and 2016, which is about double of the global average, and the stronger Typhoons in the NW Pacific. We would like to contribute more for climate change research on observation and projection.

33 Conclusions (1)Climate prediction and projection still have large uncertainty. (2) We find that the omitted surface wave, which plays dominant role in upper ocean mixing, can greatly improve our prediction and projection ability of climate models.

34 Thank you for your attention