Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

Postal Subscription Code 80-963

2018 Impact Factor: 1.205

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Earth Sci.    2015, Vol. 9 Issue (4) : 594-604    https://doi.org/10.1007/s11707-014-0489-9
RESEARCH ARTICLE
Sensitivity study of subgrid scale ocean mixing under sea ice using a two-column ocean grid in climate model CESM
Meibing JIN1,*(),Jennifer HUTCHINGS2,Yusuke KAWAGUCHI3
1. International Arctic Research Center, University of Alaska Fairbanks, AK 99775, USA
2. College of Earth, Ocean and Atmospheric Sciences, Oregon State University, OR 97331, USA
3. Japan Agency for Marine-Earth Science and Technology, Yokosuka 237-0061, Japan
 Download: PDF(2930 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Brine drainage from sea ice formation plays a critical role in ocean mixing and seasonal variations of halocline in polar oceans. The horizontal scale of brine drainage and its induced convection is much smaller than a climate model grid and a model tends to produce false ocean mixing when brine drainage is averaged over a grid cell. A two-column ocean grid (TCOG) scheme was implemented in the Community Earth System Model (CESM) using coupled sea ice-ocean model setting to explicitly solve the different vertical mixing in the two sub-columns of one model grid with and without brine rejection. The fraction of grid with brine rejection was tested to be equal to the lead fraction or a small constant number in a series of sensitivity model runs forced by the same atmospheric data from 1978 to 2009. The model results were compared to observations from 29 ice tethered profilers (ITP) in the Arctic Ocean Basin from 2004 to 2009. Compared with the control run using a regular ocean grid, the TCOG simulations showed consistent reduction of model errors in salinity and mixed layer depth (MLD). The model using a small constant fraction grid for brine rejection was found to produce the best model comparison with observations, indicating that the horizontal scale of the brine drainage is very small compared to the sea ice cover and even smaller than the lead fraction. Comparable to models using brine rejection parameterization schemes, TCOG achieved more improvements in salinity but similar in MLD.
Keywords climate model      sea ice      mixed-layer depth      ocean mixing      brine drainage     
Corresponding Author(s): Meibing JIN   
Online First Date: 26 January 2015    Issue Date: 30 October 2015
 Cite this article:   
Meibing JIN,Yusuke KAWAGUCHI,Jennifer HUTCHINGS. Sensitivity study of subgrid scale ocean mixing under sea ice using a two-column ocean grid in climate model CESM[J]. Front. Earth Sci., 2015, 9(4): 594-604.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-014-0489-9
https://academic.hep.com.cn/fesci/EN/Y2015/V9/I4/594
Fig.1  (a) Horizontal section of sea ice with major brine channels shown by arrows; (b) Schematic drawing of a brine drainage channel and its feed arms from Lake and Lewis (1970); (c) Side view of sinking brine plume dye in water (the scales in the left are 1 cm interval) from Wakatsuchi and Ono (1983); (d) ‘Brinicle’ ice finger in Antarctic by BBC nature news (http://www.bbc.co.uk/nature/15835017).
Fig.2  Schematic plot of brine drainage distribution in one ocean model grid (a) in climate models, (b) in real ocean. The density is homogenous in the upper mixed layer but jumps in the halocline due to increase salinity in the Arctic Ocean.
Case name Control LE F2 F3 F4 F5 F6 F7 NA JM
Fraction p0 Lead fraction g0 10−2 10−3 10−4 10−5 10−6 10−7 Nguyen et al. (2009) Jin et al. (2012)
Ice volume /(1012m3) 15.87 15.92 15.93 15.96 15.99 16.01 16.04 16.14 15.95 15.93
Tab.1  Model cases in the sensitivity study and their corresponding modeled northern hemisphere sea ice volume averaged over 2005 to 2009
Fig.3  (a) Comparison of the monthly modeled (Control case) and NSIDC sea ice extent in the northern hemisphere; (b) location of stations A, B and C of ice thickness measurements. Comparison of the monthly modeled (Control and LE case) and observed; (c) ice thickness (m) and (d) lead fraction (%). Stations A, B and C are from left to right in (c) and (d).
Fig.4  Ice Tethered Profiler (ITP) tracks from 2005 to 2009. The Arctic Basin are divided into region 1 (Canada Basin), 2 (Makarov Basin) and 3 (Eurasian Basin) as the tracks in them are colored in purple, green and red.
ITP number Region Starting time Endingtime ?ITP number Region Starting time Endingtime
1 1 2005.08.16 2007.01.06 ?19 3 2008.04.08 2008.11.21
3 1 2005.08.24 2006.09.08 ?21 1 2008.08.04 2009.09.23
4 1 2006.09.03 2007.08.17 ?23 1 2008.08.05 2010.07.06
5 1 2006.09.08 2007.09.06 ?24 3 2008.10.03 2009.09.25
6 1 2006.09.05 2008.06.23 ?25 1 2008.09.22 2009.07.07
7 3 2007.04.28 2007.10.24 ?26 2 2008.09.11 2009.03.11
8 1 2007.08.13 2009.03.23 ?27 2 2008.09.10 2009.01.20
9 2 2007.09.12 2008.10.03 ?28 2 2008.09.01 2008.12.18
10 2 2007.09.10 2008.05.15 ?29 2 2008.08.31 2010.09.15
11 1 2007.09.09 2009.09.03 ?32 1 2009.10.04 2010.01.13
12 3 2007.09.15 2007.12.23 ?33 1 2009.10.07 2011.01.24
13 1 2007.08.14 2008.07.16 ?34 1 2009.10.11 2010.11.26
15 2 2007.09.11 2008.10.05 ?35 1 2009.10.09 2010.03.30
16 2 2007.09.03 2008.01.01 ?37 3 2009.08.30 2010.12.24
Tab.2  List of ITP data. The ITP number is the number counted in the order the ITP was implemented in the Arctic Ocean. The missing numbers are those with few or no data due to instrument failure. The region is defined in Fig. 4. The starting and ending time of ITPs are in the format of ‘year.month.day’
Variableregion Case name Control LE F2 F3 F4 F5 F6 F7 NA JM
S 1 RMSE 1.18 1.04 1.09 1.04 1.02 0.98 0.96 1.21 1.04 1.04
RI 11.6 7.9 11.7 13.8 16.3 18.6 −2.9 11.8 11.7
S 2 RMSE 1.12 0.96 1.01 0.95 0.93 0.90 0.89 1.06 0.94 0.94
RI 14.4 10.2 14.8 17.1 19.8 20.6 5.8 16.4 15.8
S 3 RMSE 1.10 0.95 1.00 0.94 0.91 0.87 0.84 0.97 0.89 0.89
RI 13.8 9.1 14.1 17.5 21.2 23.4 11.4 19.2 18.6
S All 3 RMSE 1.15 1.00 1.05 1.00 0.97 0.94 0.92 1.13 0.99 0.99
RI 12.7 8.7 12.9 15.3 18.1 19.9 1.8 14.3 14.0
T 1 RMSE 0.46 0.41 0.43 0.41 0.40 0.39 0.40 0.46 0.39 0.39
RI 9.89 6.20 10.82 12.63 14.23 11.97 −1.27 14.99 14.74
T 2 RMSE 0.25 0.29 0.26 0.30 0.31 0.32 0.28 0.21 0.37 0.36
RI −17.91 −6.93 −20.30 −27.29 −29.87 −13.08 13.99 −50.76 −43.86
T 3 RMSE 0.44 0.48 0.46 0.48 0.49 0.49 0.48 0.49 0.51 0.50
RI −7.69 -4.68 −7.87 −9.55 −10.61 −8.10 −11.45 −14.72 −13.70
T All 3 RMSE 0.40 0.39 0.39 0.39 0.39 0.39 0.38 0.40 0.41 0.40
RI 1.75 1.86 1.90 1.53 1.90 3.82 −0.61 −1.98 −0.76
MLD 1 RMSE 30.77 8.09 22.43 17.04 13.55 12.44 13.90 16.79 12.28 13.01
RI 41.19 27.09 44.61 55.97 59.55 54.81 45.42 60.10 57.71
MLD 2 RMSE 24.81 15.14 18.10 14.13 11.42 10.73 11.10 12.53 10.93 11.78
RI 8.97 27.06 43.04 53.95 56.73 55.26 49.48 55.96 52.51
MLD 3 RMSE 21.55 3.99 6.12 12.84 11.43 10.92 10.93 11.58 10.72 11.09
RI 5.05 5.17 40.43 46.97 49.33 49.25 46.24 50.23 48.54
MLD RMSE 27.53 6.57 0.15 15.51 12.60 11.71 12.62 14.72 11.64 12.34
All 3 RI 9.81 6.82 43.66 54.25 57.47 54.17 46.54 57.74 55.18
Tab.3  Root mean square errors (RMSE) of modeled S, T in the upper 150 m and MLD as compared with the ITP data. Relative improvements (%) over the Control case are calculated as R I = R M S E C o n t r o l − R M S E C a s e R M S E C o n t r o l × 100 % . The numbers in bold font denote the best performer of all cases in each line
Fig.5  Comparison of MLD from model simulation and ITP data along ITP track in region 1.
Fig.6  The same as Fig. 5 but in region 2.
Fig.7  The same as Fig. 5 but in region 3.
Fig.8  March mean MLD of (a) climatology observation from PHC3.0, (b) Control case, (c) F6 case, and (d) F6 case minus Control case of year 2009.
1 Bitz  C M, Holland  M M, Weaver  A J, Eby  M (2001). Simulating the ice-thickness distribution in a coupled climate model. J Geophys Res, 106(C2): 2441–2463
https://doi.org/10.1029/1999JC000113
2 Danabasoglu  G, Bates  S, Briegleb  B P, Jayne  S R, Jochum  M, Large  W G, Peacock  S, Yeager  S G (2012). The CCSM4 ocean component. J Clim, 25(5): 1361–1389
https://doi.org/10.1175/JCLI-D-11-00091.1
3 Duffy  P, Eby  M, Weaver  A (1999). Effects of sinking of salt rejected during formation of sea ice on results of an ocean-atmosphere-sea ice climate model. Geophysical Research Letter, 26(12), 1739–1742
4 Fetterer  F, Knowles  K, Meier  K, Savoie  M (2002). Updated 2009. Sea Ice Index [ice extent]. Boulder: National Snow and Ice Data Center.
5 Hunke  E C, Lipscomb  W H, Turner  A K, Jeffery  N, Elliott  S (2013). CICE: The Los Alamos Sea Ice Model Documentation and Software User’s Manual Version 5.0 LA-CC-06–012, Los Alamos National Laboratory, USA
6 Jin  M, Hutchings  J, Kawaguchi  Y, Kikuchi  T (2012). Ocean mixing with lead-dependent subgrid scale brine rejection parameterization in climate model. J Ocean Univ China, 11(4): 473–480
https://doi.org/10.1007/s11802-012-2094-4
7 Kantha  L H (1995). A numerical model of Arctic leads. J Geophys Res, 100(C3): 4653–4672
https://doi.org/10.1029/94JC02348
8 Lake  R A, Lewis  E L (1970). Salt rejection by sea ice during growth. J Geophys Res, 75(3): 583–597
https://doi.org/10.1029/JC075i003p00583
9 Large  W, Danabasoglu  G, Doney  S, McWilliams  J (1997). Sensitivity to surface forcing and boundary layer mixing in the NCAR CSM ocean model: annual-mean climatology. J Phys Oceanogr, 27(11): 2418–2447
https://doi.org/10.1175/1520-0485(1997)027<2418:STSFAB>2.0.CO;2
10 Large  W G, McWilliams  J C, Doney  S C (1994). Oceanic vertical mixing: a review and a model with a vertical K-profile boundary layer parameterization. Rev Geophys, 32(4): 363–403
https://doi.org/10.1029/94RG01872
11 Large  W G, Yeager  S G (2009). The global climatology of an interannually varying air-sea flux data set. Clim Dyn, 33(2-3): 341–364
https://doi.org/10.1007/s00382-008-0441-3
12 Matsumura  Y, Hasumi  H (2008). Brine-driven eddies under sea ice leads and their impact on the Arctic Ocean mixed layer. Journal of Physical Oceanography, 38: 146–163
https://doi.org/10.1175/2007JPO3620.1
13 Morison  J H (1993). The lead experiment. Eos Trans AGU, 74(35): 393–397
https://doi.org/10.1029/93EO00341
14 Nguyen  A T, Menemenlis  D, Kwok  R (2009). Improved modeling of the Arctic halocline with a subgrid-scale brine rejection parameterization. J Geophys Res, 114(C11): C11014
https://doi.org/10.1029/2008JC005121
15 Steele  M, Morley  R, Ermold  W (2001). PHC: a global ocean hydrography with a high quality Arctic Ocean. J Clim, 14(9): 2079–2087
https://doi.org/10.1175/1520-0442(2001)014<2079:PAGOHW>2.0.CO;2
16 Toole  J M, Timmermans  M L, Perovich  D K, Krishfield  R A, Proshutinsky  A, Richter-Menge  J A (2010). Influences of the ocean surface mixed layer and thermohaline stratification on Arctic Sea ice in the central Canada Basin. J Geophys Res, 115(C10): C10018
https://doi.org/10.1029/2009JC005660
17 Wakatsuchi  M, Ono  N (1983). Measurements of salinity and volume of brine excluded from growing sea ice. J Geophys Res, 88(C5): 2943–2951
https://doi.org/10.1029/JC088iC05p02943
18 Wettlaufer  J S, Worster  M C, Huppert  H E (1997). The phase evolution of young ice. Geophys Res Lett, 24(10): 1251–1254
https://doi.org/10.1029/97GL00877
19 Zhang  J, Steele  M (2007). Effect of vertical mixing on the Atlantic Water layer circulation in the Arctic Ocean. J Geophys Res, 112(C4): C04S04
https://doi.org/10.1029/2006JC003732
[1] Lei HUANG, Hui ZHAO, Jiayi PAN, Adam DEVLIN. Remote sensing observations of phytoplankton increases triggered by successive typhoons[J]. Front. Earth Sci., 2017, 11(4): 601-608.
[2] Xueyuan WANG,Jianping TANG,Xiaorui NIU,Shuyu WANG. An assessment of precipitation and surface air temperature over China by regional climate models[J]. Front. Earth Sci., 2016, 10(4): 644-661.
[3] Huijie DONG,Xiaolei ZOU. Variations of sea ice in the Antarctic and Arctic from 1997–2006[J]. Front. Earth Sci., 2014, 8(3): 385-392.
[4] Shuyan LIU, Wei GAO, Min XU, Xueyuan WANG, Xin-Zhong LIANG. China summer precipitation simulations using an optimal ensemble of cumulus schemes[J]. Front Earth Sci Chin, 2009, 3(2): 248-257.
[5] JIN Liya, CHEN Fahu. Progress in rapid climate changes and their modeling study in millennial and centennial scales[J]. Front. Earth Sci., 2008, 2(2): 187-198.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed