Avijit Gangopadhyay With Bisagni, Gifford and Batcheldar GLOBEC meeting, WHOI 2 nd October, 2006

1. Impacts of Climate and basin-scale variability on the seeding and production of Calanus finmarchicus in the Gulf of Maine and Georges Bank Avijit Gangopadhyay With Bisagni, Gifford and Batcheldar GLOBEC meeting, WHOI 2nd October, 2006

2. List of Investigators • Avijit Gangopadhyay (PI – Basin-scale physical modeling) • Jim Bisagni (UMass Dartmouth) – Satellite SST field, Hydrographic analysis • Dian Gifford (URI) – Zooplankton data analysis • Hal Batchelder (OSU) – IBM modeling

3. Goals and Objectives • to probe the connections between Calanus finmarchicus distributions and the physical oceanographic properties, climate variability, and basin-scale circulation changes that are likely to affect the copepod’s transport onto Georges Bank. • We will do this using a combination of numerical model simulations and observational data.

4. Ongoing NASA project • Basin scale modeling for North Atlantic • High and Low NAO simulations • Focus on Gulf Stream and Labrador Sea • Nutrient Dynamics – Depletion vs. Dilution • Physics and nutrient flux experiments • (with Ayan Chaudhuri)

5. NA Basin-scale Simulations North Atlantic Model Simulation January 6. by McGillicuddy et. al Source: www,whoi.edu High NAO Model Simulation using SOC Climatology (1980-1993), January 6 Low NAO Model Simulation using NCEP Climatology (1963-1971), January 6 • Low NAO simulation looses excess heat as compared to High NAO simulation • Is the NCEP climatology overestimating the heat loss in the system ?

6. NCEP vs SOC -- January JANNCEP JAN SOC JAN SOC-NCEP Comparison of Monthly Mean Climatology of SOC and NCEP during 1980-1993 for January shows differences on the order of 100 W/m2

7. NCEP vs. SOC -- Annual ANNUAL SOC ANNUAL SOC-NCEP ANNUAL NCEP Comparison of Annual Mean Climatology of SOC and NCEP during 1980-1993 show similar differences as January on the order of 100 W/m2

8. Net Heat Flux Components Net Heat Flux is given as Qnet = QH+QE+QLW+QSW where, QH = Sensible Heat Loss QE = Latent Heat Loss QLW = Longwave Loss QSW = Shortwave Heat Gain Is the NCEP climatology underestimating/overestimating any of these components?

9. Latent Heat Loss JAN Latent NCEP JAN Latent SOC JAN Latent SOC-NCEP NCEP overestimates the Latent Heat Loss (QE) when compared to SOC Climatology on the order of ~50 W/m2 for January

10. Sensible Heat Loss JAN Sensible NCEP JAN Sensible SOC JAN Sensible SOC-NCEP NCEP overestimates the Sensible Heat Loss (QH) when compared to SOC Climatology on the order of ~25 W/m2 for January

11. Shortwave Heat Gain JAN Shortwave NCEP JAN Shortwave SOC JAN Shortwave SOC-NCEP NCEP underestimates the Shortwave Heat Gain (QSW) when compared to SOC Climatology on the order of ~25 W/m2 for January

12. Longwave Heat Flux JAN Longwave NCEP JAN Longwave SOC JAN Longwave SOC-NCEP NCEP overestimates the Longwave Heat Loss (QLW) when compared to SOC Climatology on the order of ~10 W/m2 for January which is negligible.

13. Calibrating NCEP against SOC • The NCEP Climatology thus overestimates the Net Heat Loss for the North Atlantic Region due to overestimation of Latent and Sensible Heat Loss terms and underestimation of Shortwave Gain term. • This overestimation is leading to spurious results in the Low NAO Model simulation. • Functional regression is used resolve the overestimation in NCEP Climatology as follows: • Slope (m) and Intercept (y) are determined for each month using the SOC and NCEP climatologies for 1980-1993 (High NAO) • SOC(high NAO) = m*NCEP(high NAO) + y • m and y are used to adjust the NCEP Climatology for 1958-1971 (Low NAO) • Predicted NCEP (low NAO) = m*NCEP(low NAO) y, also • Predicted NCEP (high NAO) = [SOC(high NAO)-y] / m

14. Calibrating NCEP with SOC January Example SOC(high NAO) = 0.9139 * NCEP(high NAO) + 26.19 Predicted(low NAO) = 0.9139 * NCEP(low NAO) + 26.19

15. Readjusting NCEP High NAO 1980-1993 Results ANNUAL NCEP ANNUAL SOC ANNUAL SOC-NCEP ANNUAL ADJUSTED NCEP ANNUAL SOC ANNUAL SOC- ADJUSTED NCEP

16. Improved Results

17. Improved Results

18. Present and Future • 1/6 degree simulations will be completed by December 2006 • High-resolution field generation (ROMS + FORMS) • Biological model simulations • Computational platforms – SGI Altix 350 (8p) at SMAST; NOAA (FSL) at Colorado (64p); NASA Columbia and Palm Clusters

19. Possible high resolution fields • Use Feature oriented regional modeling system (FORMS) for GOMGB (Gangopadhyay et al., 2003) • 270 non-dimensional structure functions for temperature and salinity along and across seven features in the Gulf/Bank • Calibrate with SST 5-day composite (Bisagni’s lab) • Use basin-scale simulations as background • Multiscale Objective analysis will meld basin-to-regional scale fields • Use these high-resolution fields for biological simulations

20. ROMS & FORMS – A Synthesis

21. ROMS + FORMSBasin-Scale + Regional Synoptic fields GOMGB Western North Atlantic

22. Proposed Biological simulations • Individual-based models (HPB) • Lagrangian pathways • Zooplankton data as initial and validation fields (DG) • Seeding vs. production hypothesis testing • Impact of Labrador water inflow on Slope sea and GOMGB regions

23. Summary • NASA-funded Basin-scale simulation is in progress • Wind forcing fields during 1988-1999 are ready • Will use this set-up to start GLOBEC period simulations • Biological IBM towards understanding impact of climate and BSV on calfin seeding and production

24. Bio-physical Hypotheses • Hypothesis: The occurrence of large populations of Calanus finmarchicus in the coupled GB/GoM system REQUIRES (1) high seed stocks (supply) of diapausing C.finmarchicus in the deeper ocean regions nearby (GOM basins and the Slope Sea), (2) that the deep C. finmarchicus stocks terminate diapause at the appropriate time to be synchronous with continental shelf spring blooms, and (3) a nutrient enriched, highly productive ecosystem in the GB/GoM to sustain high growth and survival rates of Calanus that will provide seed for the subsequent year. • Prediction A: Overwintering Calanus finmarchicus seed stocks are LOW and GB/GoM productivity is HIGH when the water masses of the Slope Sea have little influence (input) from Labrador-Irminger Gyre (Labrador Slope Water) water masses (due to the relatively nutrient replete bottom waters and low Calanus supply in Warm Slope Waters), but C. finmarchicus recruitment is good because of a near-perfect match between the time of diapause awakening and the time of the spring bloom, the latter of which is large because of the higher concentration of nutrients in deep warm slope waters. • Prediction B: Overwintering C. finmarchicus seed stocks are HIGH and GB/GoM productivity is LOW when the water masses of the Slope Sea have a large proportion of Labrador Sea water (due to the relatively nutrient-depleted bottom waters and high C. finmarchicus supply in cold Labrador Slope Water), but recruitment and productivity are poor because of the generally low springtime productivity (low nutrients) and a timing mismatch between diapause awakening, ascent and reproduction and the NW Atlantic spring bloom.

25. Methodology • Set up and run an individual based model (IBM) for the Northwest Atlantic, using the high-NAO (1980-1993) and low-NAO (1962-1971) forced physical fields from an ongoing eddy-resolving North Atlantic simulation. • Perform a set of eddy-resolving basin-scale model simulations during 1988-1999 starting from already existing high-NAO simulations (from the ongoing NASA project) and run the IBM to study the interannual variability of C. finmarchicus seeding and production in this region. • Analyze long-term in-situ physical and biological datasets and satellite-derived sea surface temperature (SST) along with in-situ physical, biological, and chemical data collected during the GLOBEC core-measurement period (1995-1999), and validate the basin-scale physical and biological fields to develop a broader understanding of C. finmarchicus seeding and production. • Generate four-dimensional high-resolution (5-km) physical fields using basin-scale fields and available data during 1993-1999, and run a series of IBM simulations at higher resolution in order to address questions relating ecosystem variability on the Scotian Shelf, on Slope Sea and within the Gulf of Maine and on Georges Bank to the large-scale fluctuations of the NAO.