3B.8 A Tropical Ecological Forecasting Strategy for ENSO Based on a Global Modeling Framework

Monday, 8 January 2018: 3:45 PM
616 AB (Hilton) (Austin, Texas)
Forrest M. Hoffman, ORNL, Oak Ridge, TN; and M. Xu, N. O. Collier, P. Levine, and J. T. Randerson

Strong drying conditions in the Asia-Pacific region and western South America during El Niño lead to reduced ecosystem productivity and increased mortality and fire risk. The intensity of the 2015–2016 ENSO event rivaled or exceeded that of the 1997–1998 event, which was the strongest well-observed El Niño on record. We performed a set of simulations using the U.S. Department of Energy’s Accelerated Climate Modeling for Energy (ACME) model, forced with prescribed sea surface temperatures, to study the responses and feedbacks of drought effects on terrestrial ecosystems induced by both of these events. The ACME model was configured to run with active atmosphere and land models alongside the “data” ocean and thermodynamic sea ice models. The Community Atmosphere Model used the Spectral Element dynamical core (CAM-SE) operating on the ne30 (~1°) grid, and the ACME Land Model (ALM) was equivalent to the Community Land Model with prognostic biogeochemistry (CLM4.5-BGC). We used Optimal Interpolation SSTs (OISSTv2) and predicted SST anomalies from NCEP’s Climate Forecast System (CFSv2) as forcing. We conducted two transient simulations from 1995 to 2020, following a spin up simulation, and analyzed the ENSO impacts on tropical terrestrial ecosystems for the 5-year periods centered on these two strong ENSO events. During the transient simulation, we saved the resulting atmospheric forcing, which included prognostic biosphere–atmosphere interactions, every three hours for use in future offline simulation for model development and testing. We will present simulation results, focusing on hydroclimatic and CO2 anomalies as compared with observations and the accompanying terrestrial ecological responses, including changes in primary productivity, soil moisture, and stomatal conductance. In addition, we will discuss the potential utility of this modeling framework for ecological forecasting on seasonal to decadal scales.
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