Model Study of Coupled Physical-Biogeochemical Variability in the Labrador Sea

Transcription

1 Model Study of Coupled Physical-Biogeochemical Variability in the Labrador Sea Hakase Hayashida M.Sc. Thesis (Physical Oceanography) Memorial University of Newfoundland January 14, 214

2 Global carbon cycle: the ocean is the largest carbon reservoir. [Denman et al., 27]

3 Atmosphere-ocean CO2 exchange [PMEL website]

4 Spatial variability in the sea-air CO2 flux 8-19 % of global carbon sequestration [Tait, et al., 21] [Takahashi et al., 29]

5 Deep Convection & Spring bloom a b Chlorophyll a concentration (mg/m 3 ) c d [NASA Earth Observatory] [SeaWiFS climatology]

6 Objectives Despite the importance of the Labrador Sea in the global carbon cycle, no ocean carbon studies using 3-D eddy-resolving coupled physical-biogeochemical models have yet been published. Thus, the present study takes the initiative towards the implementation of such a model for this region. The goals of my thesis are to: Examine the performance of the model implemented for three distinct locations in the Labrador Sea. Analyze the structure of seasonal variability of the ecosystem and carbon dynamics and their relations to coupled physicalbiogeochemical processes.

7 Coupled physical-biogeochemical ocean model [NEMO] Atmospheric Forcing (NCEP 6-hour reanalysis) Physical component (OPA) Dynamical Fields (T, S, U, D, MLD, surface winds, insolation, water flux, and sea-ice cover) Biogeochemical component (PISCES)

8 OPA physical model [Madec, 28] Implemented for the North Atlantic domain. 6 Depth (m) 5 Resolutions: 46 z-levels, 1/12 degree (~4.5km) ---> Eddy-resolving! Latitude Coupled to the LIM2 sea-ice model Spin-up: 3 years with climatological forcing Longitude

9 PISCES biogeochemical model [Aumont et al, 26]

10 Experimental Design Model simulations with PISCES at the 3 monitoring stations in the Labrador Sea. The simulations are done in offline 1-D mode. Offline dynamics: Dailymean model output for Year Vertical 1-D configuration: vertical diffusion and sourceminus-sink (SMS) equations.

11 Validation 1: Vertical profiles (a) (b) (c) BioChem dataset (BIO): Insitu measurements along AR7W Sparse temporally (one cruise per year) Temperature ( C) (d) Salinity (PSU) (e) Oxygen (mol/l) (f) Period: (NAO+ years) Model (black line): Averaged for the month of June. Strong physical and biogeochemical variability in the data (scatter) Nitrate (mol/l) (g) DIC (mol/l) Ammonium (mol/l) Data Model Phosphate (mol/l)

12 Validation 2: Surface chlorophyll Satellite-derived sea surface chlorophyll-a (SeaWiFS) weeklycomposite data for Model (line): Sum of weeklyaveraged nanophytoplankton and diatoms. The magnitude of the phytoplankton blooms (seasonal maxima) fit in the range of observed values. The bloom timings however are delayed. Chlorophyll Concentration (g/l) Station Station Station Days

13 Sensitivity Analysis There are >8 model parameters in PISCES... which of these have a great impact on diatoms bloom? Re-run the model with a ±1% variation in each of the 8 parameters. Compare the newly-obtained chlorophyll concentration in diatoms with the value from the standard run. Change in DCHL (%) (a) conc3m (d) chlcdm (g) wchld (b) xksi (e) fecdm (h) grazrat (c) pislope Change in parameter value (%) (f) grosip 1 1

14 Identification of key parameters Station 8 the mean Si/C ratio, the slope of photosynthesis-irradiance (PI) curve, and the half-saturation constant for silicate uptake of diatoms! Station 15 grazing preference for diatoms and growth efficiency of microzooplankton! Station 25 grazing preference for particulate organic carbon, half-saturation constant for grazing, and growth efficiency of mesozooplankton nca kdca ligand oxymin xlam1 xsilab xsiremlab xsirem nitrif xremip xremik unass sigma1 epsher xkgraz xthresh xthreshpoc xthreshphy xthreshdia xpref2d xpref2p xpref2c mzrat resrat grazrat part grazflux unass2 sigma2 epsher2 xkgraz2 xthresh2 xthresh2poc xthresh2phy xthresh2dia xthresh2zoo xprefpoc xprefz xprefp xprefc mzrat2 resrat2 grazrat2 part2 mprat2 mprat wchld wchl grosip fecdm fecnm chlcmin chlcdm chlcnm bresp excret2 excret pislope2 pislope caco3r qdfelim qnfelim concfebac xkdoc xksi2 xksi1 concdnh4 concnnh4 xsizephy xsizedia conc3m conc3 conc2m conc2 conc1 conc wsbio2 ferat3 xkmort wsbio Standard deviation

15 Simulated seasonal cycle in the central Labrador Sea (a) T ( C) (b) S (PSU) (c) DIC (mol/l) (a) N (mol/l) (b) D (mol/l) (c) Z (mol/l) (d) NO 3 (mol/l) (e) NH 4 (mol/l) (f) PO 4 (mol/l) (d) M (mol/l) (e) DOC (mol/l) (f) POC (mol/l) (g) Fe (mol/l) (h) Si (mol/l) (g) GOC (mol/l (h) CaCO3 (mol/l) (i) GSi (mol/l) x

16 Air-sea CO2 flux The overall seasonal trends of low-pco2 primarily driven by bloom drawdown and high-pco2 during fall and winter are consistent with previous model and observational studies of the central Labrador Sea. The net annual CO2 sink at Station 15 is 7.65 mol/ m2, which is stronger than the estimates from previous studies. pco 2 (µatm) Jan.1 Feb.1 Mar.1 Apr.1 May.1 Jun.1 Jul.1 Aug.1 Sep.1 Oct.1 Nov.1 Dec.1 Chlorophyll (g/l) Jan.1 Feb.1 Mar.1 Apr.1 May.1 Jun.1 Jul.1 Aug.1 Sep.1 Oct.1 Nov.1 Dec.1 (a) (b) Station 8 Station 15 Station 25

17 Conclusions The results from the model simulations show the model s capability in resolving the general structures of observed vertical nutrients and oxygen profiles, as well as the seasonal cycle of primary production at different regions of the Labrador Sea. The model sensitivity to parameter variations differ regionally. The model depicts the characteristics of the seasonal cycles of biogeochemical tracers specific to the simulated regions. The seasonal cycle of surface pco2 is reasonably reproduced by the model.

18 Future Work This study has shown the applicability of the PISCES biogeochemical model for regional coupled physicalbiogeochemical modeling. Furthermore, it has demonstrated that the offline coupling of the 1-D PISCES model and the 3-D eddyresolving OPA model can be used as a tool for model testing and improvement with relatively low computational resources. VITALS - Ventilation, Interactions and Transports Across the Labrador Sea. 1-D parameter optimization based on data assimilation. Implementation of the coupled 3-D model. Study of interannual variability.