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Toward Integrated Seasonal Predictions of Land and Ocean Carbon Flux: Lessons from the 2015-16 El NinoSeasonal forecasts made by coupled atmosphere-ocean general circulation models (GCMs) are increasingly able to provide skillful forecasts of climate anomalies. At some centers, the capabilities of these models are being expanded to represent carbon-climate feedbacks including ocean biogeochemistry (OB), terrestrial biosphere (TB) interactions, and fires. These advances raise the question of whether such models can support skillful forecasts of carbon fluxes.Here, we examine whether land and ocean carbon flux anomalies associated with the 2015-16 El Nino could have been predicted months in advance. This El Nino was noteworthy for the magnitude of the ocean temperature perturbation, the skill with which this perturbation was predicted, and the extensive satellite observations that can be used to track its impact. We explore this topic using NASA's Goddard Earth Observing System (GEOS) model, which routinely produces an ensemble of seasonal climate forecasts, and a suite of offline dynamical and statistical models that estimate carbon flux processes. Using GEOS forecast fields from 2015-16 to force flux model hindcasts shows that these models are able to reproduce significant features observed by satellites. Specifically, OB hindcasts are able to predict anomalies in chlorophyll distributions with lead times of 3-4 months. The ability of TB hindcasts to reproduce NDVI anomalies is driven by the skill of the climate forecast, which is greatest at short lead times over tropical landmasses. Statistical fire forecasts driven by ocean climate indices are able to predict burned area in the tropics with lead times of 3-12 months. We also integrate the ocean and land hindcast fluxes into the GEOS GCM to examine the magnitude of the atmospheric carbon dioxide anomaly and compare with satellite and ground-based observations.While seasonal forecasting remains an active area of research, these results demonstrate that forecasts of carbon flux processes can support a variety of applications, potentially allowing scientists to understand carbon-climate feedbacks as they happen and to capitalize on more flexible satellite technologies that allow areas of interest to be targeted with lead times of weeks to months. We also provide a first glimpse at the spring 2019 carbon forecast using the GEOS-based forecasting system.
Document ID
20190000394
Acquisition Source
Goddard Space Flight Center
Document Type
Presentation
Authors
Ott, L. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Chatterjee, A. (Universities Space Research Association (USRA) Greenbelt, MD, United States)
Chen, Y. (California Univ. Irvine, CA, United States)
Chini, L. (Maryland Univ. College Park, MD, United States)
Davis, S. (California Univ. Irvine, CA, United States)
Hubacek, K. (Maryland Univ. College Park, MD, United States)
Hurtt, G. (Maryland Univ. College Park, MD, United States)
Lee, E. (Universities Space Research Association (USRA) Greenbelt, MD, United States)
Ma, L. (Maryland Univ. College Park, MD, United States)
Poulter, B. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Randerson, J. (California Univ. Irvine, CA, United States)
Rousseaux, C. (Universities Space Research Association (USRA) Greenbelt, MD, United States)
Sun, L. (Maryland Univ. College Park, MD, United States)
Woodard, D. (California Univ. Irvine, CA, United States)
Zeng, F. (Science Systems and Applications, Inc. Lanham, MD, United States)
Date Acquired
February 5, 2019
Publication Date
December 10, 2018
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
B51E-1990
GSFC-E-DAA-TN64286
Meeting Information
Meeting: American Geophysical Union (AGU) Fall Meeting
Location: Washington, D.C.
Country: United States
Start Date: December 10, 2018
End Date: December 14, 2018
Sponsors: American Geophysical Union (AGU)
Funding Number(s)
CONTRACT_GRANT: NNG17HP01C
CONTRACT_GRANT: NNG11HP16A
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.

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