Interannual variability of Black Sea's hydrodynamics and connection to atmospheric patterns

Transcription

1

2 P. Alechinsky,The Black Sea, 1990

3 Interannual variability of Black Sea's hydrodynamics and connection to atmospheric patterns Capet Arthur, Barth Alexander, Beckers Jean-Marie, Grégoire Marilaure

4 Context Quasi-enclosed basin. Strong stratification. >Highly sensitive to external forcings.

5 Context Severe environmental shifts occurs in late 80s Combination of pressure on ecosystem and hydrodynamic changes. Open basin Chl Yunev, 2003

6 Objectives To detail interannual variability of Black Sea hydrodynamics. To assess the driving role of atmospheric conditions on this variability.

7 Tools Combining satellite observations to model results : Connect surface observations to subsurface dynamics. Extend period of investigation.

8 The Satellite data SST : Pathfinder AVHRR ( ), Optimal interpolation (Nardelli et al, JMS, 2010) SLA : Ssalto/Duacs Gridded Sea Level Anomalies (

9 The GHER 3D Model Double-sigma vertical coordinates. Atmospheric conditions from ERA40. No data assimilation and no relaxation. Long term run

10 Model Variables and Diagnostics SLA : Sea level anomaly. Satellite AVISO

11 Model Variables and Diagnostics SLA : Sea level anomaly SST : Sea suface temperature Satellite AVISO AVHRR

12 Model Variables and Diagnostics Satellite SLA : Sea level anomaly SST : Sea suface temperature MKE : Mean kinetik energy (0-100 m). AVISO AVHRR

13 Model Variables and Diagnostics Satellite SLA : Sea level anomaly SST : Sea suface temperature MKE : Mean kinetik energy (0-100 m). MLD : Mixed layer depth. AVISO AVHRR

14 Model Variables and Diagnostics Satellite SLA : Sea level anomaly AVISO SST : Sea suface temperature MKE : Mean kinetik energy (0-100 m). MLD : Mixed layer depth. CCC : Cold intermediate layer Cold content AVHRR

15 Model Variables and Diagnostics Satellite Summer Temperature profile SLA : Sea level anomaly CIL SST : Sea suface temperature AVISO AVHRR MKE : Mean kinetik energy (0-100 m). HALOCLINE MLD : Mixed layer depth. CCC : Cold intermediate layer Cold content

16 Model Variables and Diagnostics Satellite Summer Temperature profile SLA : Sea level anomaly cold content SST : Sea sufacecil temperature MKE : Mean kinetik energy (0-100 m). AVISO AVHRR HALOCLINE MLD : Mixed layer depth. CCC : Cold intermediate layer Cold content

17 Model Variables and Diagnostics Satellite SLA : Sea level anomaly AVISO SST : Sea suface temperature MKE : Mean kinetik energy (0-100 m). MLD : Mixed layer depth. CCC : Cold intermediate layer Cold content AVHRR

18 Model Variables and Diagnostics Satellite SLA : Sea level anomaly AVISO SST : Sea suface temperature MKE : Mean kinetik energy (0-100 m). MLD : Mixed layer depth. CCC : Cold intermediate layer Cold content AVHRR Interannual anomalies = interannual model results climatological seasonal cycle

19 Model Variables and Diagnostics Satellite SLA : Sea level anomaly SST : Sea suface temperature MKE : Mean kinetik energy (0-100 m). MLD : Mixed layer depth. CCC : Cold intermediate layer Cold content Interannual anomalies EOF decomposition AVISO AVHRR

20 Results

21 SST anomalies EOFs intercomparison : Model VS AVHRR ( ) ( Gridded AVHRR products provided by Nardelli et al, 2010 )

22 SST anomalies EOFs intercomparison : Model VS AVHRR SST-1 ( ) ( Gridded AVHRR products provided by Nardelli et al, 2010 )

23 SST anomalies EOFs intercomparison : Model VS AVHRR SST-1 ( ) ( Gridded AVHRR products provided by Nardelli et al, 2010 )

24 SST-1 : Long Term ( )

25 SST-1 : Atmopsheric drivers Air Temperature Basin average

26 SST-1 : Atmopsheric drivers

27 SST-1 : Atmopsheric drivers East Atlantic / West Russia North Atlantic Oscillation

28 SST-1 : Atmopsheric drivers East Atlantic / West Russia North Atlantic Oscillation

29 SST-1 : Atmopsheric drivers East Atlantic / West Russia North Atlantic Oscillation

30 SST-1 : relation to other variables

31 Validation : SLA EOFs of anomalies intercomparison Model VS AVISO ( Gridded SLA provided by AVISO website )

32 SLA-1 : Long term

33 SLA-1 : Long term MKE-1 = The Rim current Korotaev, 2003

34 SLA-1 : Long term MKE-1 = The Rim current Korotaev, 2003

35 Winds regimes 38 years = 468 monthly anomalies distributed among 6 patterns identified trough SOM analysis

36 Winds regimes 38 years of monthly anomalies are distributed among 6 patterns identified trough SOM analysis

37 Winds regimes 38 years of monthly anomalies are distributed among 6 patterns identified trough SOM analysis

38 Rim current & winds regime Cyclonic patterns AntiCyclonic patterns Negative curls patterns with brder of the same colors than bars..

39 Rim current & winds regime Cyclonic patterns AntiCyclonic patterns Negative curls patterns with brder of the same colors than bars..

40 Rim current & winds regime Cyclonic patterns AntiCyclonic patterns Negative curls patterns with brder of the same colors than bars..

41 Rim current & winds regime Cyclonic patterns AntiCyclonic patterns Negative curls patterns with brder of the same colors than bars..

42 Rim current & winds regime

43 Rim current & other variables

44 Environmental Implications Open basin Chl Yunev, 2003 Daskalov et al., 1998.

45 Conclusion SST respond to Air temperature and is linked to EA/WR patterns trough meridional wind component, NAO for long-term trends. The Rim current regulates the Black Sea hydrodynamics and responds to the wind curl. Enhanced duration of atmospheric anomalies regimes leads to an increasing variability of the Black Sea hydrodynamics over the period. Ecosystem resilience (biodiversity) should not only be maintained but promoted to maintain marine GES in the context of increasing hydrodynamic variability.

46 Thanks for your attention More details in: Deep Sea research II, Arthur Capet,

47 EOF interpretation : SLA-1

48 EOF interpretation : SLA-1 Effect of the Rim current on the vertical structure Surface fresh waters Deep salted waters

49 Validation : SLA EOFs of anomalies intercomparison Model VS AVISO Interannual variability ~ Seasonal variability Seasonal signal is keeped for comparison ( Gridded SLA provided by AVISO website )

50 Temperature profile validation

51

52 P. Alechinsky,The Black Sea, 1990

53 Interannual variability of Black Sea's hydrodynamics and connection to atmospheric patterns Capet Arthur, Barth Alexander, Beckers Jean-Marie, Grégoire Marilaure 3

54 Context Quasi-enclosed basin. Strong stratification. >Highly sensitive to external forcings.

55 Context Severe environmental shifts occurs in late 80s Combination of pressure on ecosystem and hydrodynamic changes. Open basin Chl Yunev, 2003

56 Objectives To detail interannual variability of Black Sea hydrodynamics. To assess the driving role of atmospheric conditions on this variability.

57 Tools Combining satellite observations to model results : Connect surface observations to subsurface dynamics. Extend period of investigation.

58 The Satellite data SST : Pathfinder AVHRR ( ), Optimal interpolation (Nardelli et al, JMS, 2010) SLA : Ssalto/Duacs Gridded Sea Level Anomalies (

59 The GHER 3D Model Double-sigma vertical coordinates. Atmospheric conditions from ERA40. No data assimilation and no relaxation. Long term run

60 Model Variables and Diagnostics SLA : Sea level anomaly. Satellite AVISO

61 Model Variables and Diagnostics SLA : Sea level anomaly SST : Sea suface temperature Satellite AVISO AVHRR

62 Model Variables and Diagnostics Satellite SLA : Sea level anomaly SST : Sea suface temperature MKE : Mean kinetik energy (0-100 m). AVISO AVHRR

63 Model Variables and Diagnostics Satellite SLA : Sea level anomaly SST : Sea suface temperature MKE : Mean kinetik energy (0-100 m). MLD : Mixed layer depth. AVISO AVHRR

64 Model Variables and Diagnostics Satellite SLA : Sea level anomaly AVISO SST : Sea suface temperature MKE : Mean kinetik energy (0-100 m). MLD : Mixed layer depth. CCC : Cold intermediate layer Cold content AVHRR

65 Model Variables and Diagnostics Satellite Summer Temperature profile SLA : Sea level anomaly CIL SST : Sea suface temperature AVISO AVHRR MKE : Mean kinetik energy (0-100 m). MLD : Mixed layer depth. CCC : Cold intermediate layer Cold content HALOCLINE

66 Model Variables and Diagnostics Satellite Summer Temperature profile SLA : Sea level anomaly AVISO cold content SST : Sea sufacecil temperature MKE : Mean kinetik energy (0-100 m). MLD : Mixed layer depth. CCC : Cold intermediate layer Cold content AVHRR HALOCLINE

67 Model Variables and Diagnostics Satellite SLA : Sea level anomaly AVISO SST : Sea suface temperature MKE : Mean kinetik energy (0-100 m). MLD : Mixed layer depth. CCC : Cold intermediate layer Cold content AVHRR

68 Model Variables and Diagnostics Satellite SLA : Sea level anomaly AVISO SST : Sea suface temperature MKE : Mean kinetik energy (0-100 m). MLD : Mixed layer depth. CCC : Cold intermediate layer Cold content AVHRR Interannual anomalies = interannual model results climatological seasonal cycle

69 Model Variables and Diagnostics Satellite SLA : Sea level anomaly SST : Sea suface temperature MKE : Mean kinetik energy (0-100 m). MLD : Mixed layer depth. CCC : Cold intermediate layer Cold content Interannual anomalies EOF decomposition AVISO AVHRR

70 Results

71 SST anomalies EOFs intercomparison : Model VS AVHRR ( ) ( Gridded AVHRR products provided by Nardelli et al, 2010 )

72 SST anomalies EOFs intercomparison : Model VS AVHRR SST-1 ( ) ( Gridded AVHRR products provided by Nardelli et al, 2010 )

73 SST anomalies EOFs intercomparison : Model VS AVHRR SST-1 ( ) ( Gridded AVHRR products provided by Nardelli et al, 2010 )

74 SST-1 : Long Term ( )

75 SST-1 : Atmopsheric drivers Air Temperature Basin average

76 SST-1 : Atmopsheric drivers

77 SST-1 : Atmopsheric drivers East Atlantic / West Russia North Atlantic Oscillation

78 SST-1 : Atmopsheric drivers East Atlantic / West Russia North Atlantic Oscillation

79 SST-1 : Atmopsheric drivers East Atlantic / West Russia North Atlantic Oscillation

80 SST-1 : relation to other variables

81 Validation : SLA EOFs of anomalies intercomparison Model VS AVISO ( Gridded SLA provided by AVISO website ) Only Quite short period to correctly extract a good seasonnal cycle as interannual changes are of the same order of magnitudes than seasonal

82 SLA-1 : Long term

83 SLA-1 : Long term MKE-1 = The Rim current Korotaev, 2003

84 SLA-1 : Long term MKE-1 = The Rim current Korotaev, 2003

85 Winds regimes 38 years = 468 monthly anomalies distributed among 6 patterns identified trough SOM analysis

86 Winds regimes 38 years of monthly anomalies are distributed among 6 patterns identified trough SOM analysis

87 Winds regimes 38 years of monthly anomalies are distributed among 6 patterns identified trough SOM analysis

88 Rim current & winds regime Cyclonic patterns Negative curls patterns with brder of the same colors than bars.. AntiCyclonic patterns The middle panel gives the MKE-1 temporal mode ( Rim Current intensity), as well as the occurences of atmospheric patterns by year. Cyclonic patterns are on the upper part, and anticyclonic on the lower part. What can be seen on this figure is the important intensification of the Rim current that occurs during and how it was caused by the predominence of Cyclonic patterns durig these years. Similiarly the drop in the Rim current intensity from 1996 is due to important South-West winds.

89 Rim current & winds regime Cyclonic patterns Negative curls patterns with brder of the same colors than bars.. AntiCyclonic patterns The middle panel gives the MKE-1 temporal mode ( Rim Current intensity), as well as the occurences of atmospheric patterns by year. Cyclonic patterns are on the upper part, and anticyclonic on the lower part. What can be seen on this figure is the important intensification of the Rim current that occurs during and how it was caused by the predominence of Cyclonic patterns durig these years. Similiarly the drop in the Rim current intensity from 1996 is due to important South-West winds.

90 Rim current & winds regime Cyclonic patterns Negative curls patterns with brder of the same colors than bars.. AntiCyclonic patterns The middle panel gives the MKE-1 temporal mode ( Rim Current intensity), as well as the occurences of atmospheric patterns by year. Cyclonic patterns are on the upper part, and anticyclonic on the lower part. What can be seen on this figure is the important intensification of the Rim current that occurs during and how it was caused by the predominence of Cyclonic patterns durig these years. Similiarly the drop in the Rim current intensity from 1996 is due to important South-West winds.

91 Rim current & winds regime Cyclonic patterns Negative curls patterns with brder of the same colors than bars.. AntiCyclonic patterns The middle panel gives the MKE-1 temporal mode ( Rim Current intensity), as well as the occurences of atmospheric patterns by year. Cyclonic patterns are on the upper part, and anticyclonic on the lower part. What can be seen on this figure is the important intensification of the Rim current that occurs during and how it was caused by the predominence of Cyclonic patterns durig these years. Similiarly the drop in the Rim current intensity from 1996 is due to important South-West winds.

92 Rim current & winds regime

93 Rim current & other variables

94 Environmental Implications Open basin Chl Yunev, 2003 Daskalov et al., The correspondances with environemntal changes evidences the role of the Rim current in driving the export of nutrient and/or organic matter from the shelf to the open basin, sustaining primary production in the deep basin (in accord also with Yunev 2003). To evidence this using the biogeocemical model is our task for the coming months. A potential effect on resilience time and modification of the horizontal advections pathways is also expected to affect fish populations. We will work on this with Georgi Daskalov in the framework of Perseus but I'm delighted to learn that some work is already in course.

95 Conclusion SST respond to Air temperature and is linked to EA/WR patterns trough meridional wind component, NAO for long-term trends. The Rim current regulates the Black Sea hydrodynamics and responds to the wind curl. Enhanced duration of atmospheric anomalies regimes leads to an increasing variability of the Black Sea hydrodynamics over the period. Ecosystem resilience (biodiversity) should not only be maintained but promoted to maintain marine GES in the context of increasing hydrodynamic variability.

96 Thanks for your attention More details in: Deep Sea research II, Arthur Capet,

97 EOF interpretation : SLA-1

98 EOF interpretation : SLA-1 Effect of the Rim current on the vertical structure Surface fresh waters Deep salted waters

99 Validation : SLA EOFs of anomalies intercomparison Model VS AVISO Interannual variability ~ Seasonal variability Seasonal signal is keeped for comparison ( Gridded SLA provided by AVISO website ) Only Quite short period to correctly extract a good seasonnal cycle as interannual changes are of the same order of magnitudes than seasonal

100 Temperature profile validation