The seasonal burden of Dimethyl sulphide-derived aerosols in the Arctic and the impact on global warming

Transcription

1 The seasonal burden of Dimethyl sulphide-derived aerosols in the Arctic and the impact on global warming Bo Qu *, Albert Gabric, Patricia Matrai 3 and T. Hirst. Lecturer, Nantong University, Science Faculty, China;. Associate Professor, School of Australian Environmental Studies, Griffith University, Australia; 3. Principal Scientist, Bigelow laboratory for Ocean Sciences, USA;. Principal Scientist, CSIRO atmospheric Research, Australia

2 Global climate changes have led to remarkable environmental changes in the Arctic. On the other hand, Dimethyl sulphide (DMS) emission in Arctic Ocean plays an important role for the global warming. The ice cover as the special feature of Arctic Ocean has significant effect on regulation of the large distribution tion of phytoplankton production. Chlorophyll-a a (CHL), as the primary production of phytoplankton, has its strong relationship with DMS derived aerosol in the ocean surface.

3 Arctic Ocean North Pole

4 Arctic Current The sunlight goes some way towards heating the Arctic, but heat also comes from the south with ocean currents and airstreams. One branch of the Gulf Stream, called the North Atlantic Current, flows along the coast of Norway and continues all the way to the Arctic Ocean. The area of the Barents Sea where the cold, relatively fresh, Arctic water meets the warm, saline Atlantic water is called the polar front. The polar front does not lie in a specific geographical position, but may move somewhat from year to year.

5 Study region: Barents Sea, 3-35E, 7-8N Three Cruises collected DMSP data from Biglow Laboratory for Ocean Sciences, ME, USA

6 MLD (m) Monthly Mean MLD (m) Month Ice Cover (%) Ice cover (%) (988-) Month Study region: 3-35E, 7-8N SST (Celsius) Monthly Mean SST (98-993) Cloud Cover (Octas) 8 Monthly Mean Cloud Cover (98-993) Month Month Speed (m/s) Monthly Mean Wind Speed (98-993) Month PAR (W/m^) 998- Mean PAR (998-) Day of year

7 The comparisons for the calibrated model results and the original SeaWiFS data for 998. Due to the fitness function is calculated by using negative value with maximum optimization rule applied, hence, the closer to zero, the better the fitness. Year 998 had its best fitness of -.758, while the 5 years mean (from year 998 to ) also achieved quite high fitness (-.98). CHL (mg/m^3) GA calibration for CHL Comparison ( k=., k3=.357, Ik=7.888, no=5.73, k9=.8, k=.) fitness=-.758 CHL(SeaWiFS) CHL(Model) 3/ 7 3/ 3 / 5/ 8 / 7/ 5 7/ 9 8/ 9/ 5

8 CHL and DMS in 999 in the study region CHL DMS CHL (mg/m^3) 3 8 DMS (nm) Day of year CHL and DMS in in the study region CHL CHL (mg/m^3) DMS 8 DMS (nm) Day of year CHL and DMS in in the study region CHL DMS 5 CHL (mg/m^3) 3 5 DMS (nm) Day of year

9 CHL(mg/m^3) CHL and DMS in 998 (5 days later than the first DMS bloom, 5 days to the peak DMS) CHL DMS(nmol/l / /8 /7 5/5 7/ 8/9 / /3..8. DMS(nmol/L) CHL(mg/m^3) 8 CHL and DMS in ( days later than the first DMS bloom and 8 days to the peak DMS) CHL DMS(nmol/l / /8 /7 5/5 7/ 8/9 / /3.. DMS(nmol/L)

10 The spring blooms of phytoplankton (CHL peaks) were gradually shifted ahead from 7th May to 5th May during the 5 years. This is due to the increased SST and the earlier ice melting each year happened in the study region. The time lags between the CHL blooms and DMS peak blooms are increased from 5 days to 8 days in the 5 years. It is interesting to see that year 998 had its DMS first blooms in spring a few days before CHL bloom while in other years, DMS had its first bloom a few days after the CHL spring bloom. The reason could be that the higher ice cover in year 998 in south part of the study region could generate more and earlier DMS from the ice algal when ice started melting in spring.

11 5 GA Calibration for 98- mean CHL for zonal 7-8N : k=.55, k3=.9, ik=.3997, no=53.88, k9=., k=.38, fitness= CHL (mg/m^3) 3 CHL Model SeaWiFS Month Before the transit climate data prediction, another CHL GA calibration was carried out using average CHL SeaWiFS data in zonal 7-8N during the 5 years period: The excellent fitness value (-.7598) gives better model parameter values. There was a second smaller bloom in September due to the shallower MLD and increased wind speed from August to September.

12 The transit climate data from GCM simulations for the period of 9-97 (pre-industry level: CO) and 78-8 (tripled equivalent CO: 3CO) were obtained in the zonal 7-8N. SST (Celsius) CO 3CO Cloud Cover (%) 8 CO 3CO

13 The transit climate data from GCM simulations for the period of 9-97 (pre-industry level: CO) and 78-8 (tripled equivalent CO: 3CO) in the zonal 7-8N. Ice Cover (%) 8 CO 3CO MLD (m) 5 3 CO 3CO

14 Table : Mean Transit Climate Data for CO and 3 CO in zonal 7-8N SST Wind Speed Cloud Cover Ice Cover MLD DMS DMS Flux CO % 8.8% CO % 5.3% Increased % 3% -9% -7% -3% 8% 7%

15 CHL 3 CHL(CO) CHL(3CO) Day of year Z Z(CO) Z(3CO) Day of year DMS DMS(CO) DMS(3CO) Day of year 3 umole/m^/d xco Flux 3xCO Flux m onth

16 DMS (nm) 5 years DMS (nm) comparison in the study region Day of year Monthly Mean Ice Cover and DMS in 998 in the study region Ice Cover (%) Ice Cover DMS Month DMS (nm) umole/d^/d Monthly Mean DMS Flux in Ice Free Water for Year 998- in the Study Region Month

17 Ice Cover (%) 5 3 Annual mean ice cover in the study region during 988- Error bar is the Standard Error of the Mean y = -.55x + 5. R = Year Average Ice cover in winter-sping (Nov.-Mar.) in southern 7-75N, 3-35E Ice Cover (%) Year

18 We can conclude that the significant decrease of ice cover and increase of SST are the main reason of increasing DMS flux to more than % by year 8. This significant change in the northern belt would cause large impact on global warming.