| |
| |
|
|
|
|
| |
 |
Pushing the frontiers of marine ecological modeling: where are we now and how can we move forward?
|
| |
|
|
Cascading while diminishing: modeling biological-physical responses to Southern Annular Modes in the Southern Ocean
Monday 7th @ 1450-1510, Conference Room 5
Meibing Jin* , University of Alaska Fairbanks and Nanjing University of Information, Science and Technology
Rubao Ji, Woods Hole Oceanographic Institution
Yun Li, University of South Florida
Presenter Email: mjin@alaska.edu |
| Southern Annual Modes (SAM), the dominant atmospheric variability in the Southern Hemisphere, appears to be linked to large-scale anomalies of both physical and biogeochemical variables, most evident along a zonally asymmetric belt. How is the SAM signal propagated along the bottom-up cascading pathway from physics to biology? What are the relationships between SAM and variability of multiple environmental factors including sea ice, water column properties, nutrients and primary productivity? Addressing those questions in a highly dynamic system is important yet challenging. We used the reanalysis data-forced run of coupled ice-ocean-ecosystem modules of the global Community Earth System Model (CESM) to investigate the different responses of ocean physics and biology to the SAM variations in the Southern Ocean (SO). Pelagic and sea ice algal biological modules are embedded in the coupled sea ice model (CICE) and Parallel Ocean Program (POP). The model was validated with in-situ, Argo and remote sensing data. The modeled mixed layer depth (MLD) and sea surface temperature (SST) showed similar seasonal and spatial patterns to the observed responses to SAM, whereas the nutrients and primary production responded to SAM in different ways. The macronutrients (e.g. nitrate) showed direct response to variations of MLD, but the response of iron (a limiting micronutrient) and chlorophyll did not show clear large-scale spatial patterns related to the SAM-induced MLD variations. Our model results revealed that the SAM signal, represented by its spatial and temporal variability, diminishes before it reaches the primary producer level, largely due to the additional sources of variability affecting the availability of iron. Additionally, the strong and nonlinear Fe-phytoplankton interactions could prevent the detection of relationship among the associated biophysical variables. The findings have important implication for a better understanding of the impact of large-scale forcing. |
|
|
|
|
|
|
|
|