Dietze (CMS 2020): Multisensor data assimilation to support terrestrial carbon cycle and disturbance Monitoring, Reporting, Verification, and Forecasting
Michael Dietze, Boston University, mcdietze@gmail.com (Presenter)
Dongchen Zhang, Boston University, zhangdc@bu.edu
Qianyu Li, Brookhaven National Laboratory, qli1@bnl.gov
Alexis Helgeson, Boston University, ahelgeso@bu.edu
Shawn Paul Serbin, Brookhaven National Laboratory, sserbin@bnl.gov
This proposal builds on the successes of our current CMS project, “A prototype data assimilation system for the terrestrial carbon cycle to support Monitoring, Reporting, and Verification” which has developed a CONUS-scale carbon cycle reanalysis product spanning from 1986-2019 that assimilates CMS aboveground biomass estimates and MODIS LAI into a terrestrial ecosystem model. In this proposal we aim to extend this system to all of North America (to allow easier use of our product in atmospheric inversions) but focus on 2015-present, which is more directly relevant to managers and has seen the addition of many new remote sensing platforms that are important for carbon monitoring. We also aim to refine our temporal grain, moving toward a rolling analysis on at least a weekly basis. We also propose three major thrusts to improve the quality and monitoring/management utility of our products: (1) Nowcasting and near-term forecasting; (2) Disturbance assimilation; (3) Multisensor assimilation.
Thrust 1 (Nowcast/forecast) proposed to merge our CMS system with our NSF-funded site-scale automated land C forecast system, which is already running at a number of Ameriflux and NEON towers. This will produce a continental-scale C budget with a seamless transition from reanalysis (best harmonized estimate of past carbon pools and fluxes) to nowcast (current state) to forecasts on the weather (35-day) and subseasonal-to-seasonal (9mo) timescales. Such a system would more directly meet the needs of land managers in a number of sectors, who desire information on daily to seasonal scales and make decisions based on what they expect to happen in the future.
Thrust 2 (disturbance assimilation) would build on and scale up the site-scale proof-of-concept from our current CMS proposal, which developed a novel data assimilation algorithm that demonstrated we could assimilate discrete data on land use, land use change, forestry (LULUCF) and natural disturbance. Work across CMS has demonstrated that these transitions are critically important to carbon monitoring, but conventional data assimilation only handles continuous, Gaussian carbon pools and responds incorrectly to disturbance by gradually “nudging” those pools down over multiple years. Our system is uniquely capable of assimilating LULUCF/disturbance and correctly attributing the carbon implications of these transitions. Furthermore, by coupling disturbance assimilation with near-term forecasting (Thrust 1) we will be able to make recovery forecasts that could help guide management and restoration efforts.
Thrust 3 (multi-sensor assimilation) aims to extend our assimilation system to a range of new sensors that were not considered in our prototype system. This will start with a straightforward extension of our current multispectral capability (Landsat, MODIS) to VIIRS and Sentinel, including an increased emphasis on assimilating LULC and disturbance. Second, we will extend the assimilation to GEDI lidar biomass, SMAP soil moisture and VOD, ECOSTRESS evapotranspiration, and solar-induced fluorescence from multiple platforms (OCO-2, OCO-3, TROPOMI). Beyond improving the constraint on overall carbon pools and fluxes, a particular focus is on improving our ability to detect (nowcast), monitor (reanalysis), and forecast drought stress impacts on vegetation productivity and carbon sequestration. The focus on drought is also synergistic with Thrust 2 (disturbance) as drought is both a disturbance and interacts strongly with other disturbances (e.g. fire and pest risk).
Overall, the proposed additions to our prototype system will provide an unprecedented level of timely and synthetic information about the state of the terrestrial C cycle and its near-term trajectory. This will make our system more directly relevant to managers while simultaneously accelerating the pace of carbon cycle research.
Associated Project(s):
Poster Location ID: 3
Presentation Type: Poster
Session: Poster Session 1
Session Date: Wednesday (9/27) 1:15 PM