This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
Emulators are computationally inexpensive representations of complex model output as a function of uncertain inputs. They provide drastic computational savings, and may thus greatly expand the possibilities for statistical inference using state-of-the-art, spatially-explicit plankton ecosystem models - yet they have never previously been utilized in this context. The multidimensional nature of plankton models requires significant extension of current emulator methods. Research in this area will therefore be entirely novel and require a close collaboration between plankton ecologists and mathematicians.
The proposed research focuses on three specific applications, all of which are beyond the scope of modern computational resources without the use emulators or alternative fast approximations.
First, emulators will be used to test simple plankton ecosystem models in terms of their ability to fit the Bermuda Atlantic Time Series (BATS) observations in a one-dimensional spatial setting. This problem provides an ideal testbed for developing emulator methods for multidimensional output. This will begin with an application in which the forcing is assumed to be known. In this context, the dynamical model is relatively inexpensive, allowing the inferences drawn using the emulator to be compared with those drawn using the dynamical model. The computational advantage of the emulators will then be exploited in the context of forcing that is not perfectly known, facilitating joint inference of biological processes and uncertainty in physical forcing. This will allow the investigators to address a long-standing question in plankton ecosystem modeling: How much physical and biological complexity is necessary to explain the BATS data?
The second application aims to develop an emulator for a 3D plankton model of the entire North Atlantic, and use it to infer model parameter values from satellite data under the assumption of known physical forcing. This will permit inference of basin-wide seasonal variability in plankton abundance, primary production, and carbon export.
Third, emulation methods will be used together with the basin-wide model to assess uncertainty in forecasts of ecosystem response for various climate change scenarios. The intellectual merit of this effort stems from an interdisciplinary approach to problems at the interface between oceanography and statistics. Conjoining of models with observations using advanced statistical and data assimilative methodologies will result in new insights into plankton ecology on local to basin scales, providing information relevant to oceanic ecosystems and global carbon cycling. In so doing, the proposed research will advance both disciplines as well as their interface.
Broader impacts of this activity include training of graduate and undergraduate students as well as a postdoctoral fellow. Curricular development will include enhancement of an existing course on linking models and observations in planktonic ecosystems. The PIs also plan to produce a short text that will promote broad dissemination of this material.
