Intraseasonal and Interannual Variability of Carbon and Water Cycles in Terrestrial Ecosystem Models from a Multi-Model Ensemble Experiment
Weile
Wang, CSUMB & NASA Ames Research Center, weile.wang@gmail.com
(Presenting)
Jennifer
L.
Dungan, NASA Ames Research Center, jennifer.l.dungan@nasa.gov
Hirofumi
Hashimoto, CSUMB & NASA Ames Research Center, hirofumi.hashimoto@gmail.com
Andrew
Michaelis, CSUMB & NASA Ames Research Center, amac@hyperplane.org
Cristina
Milesi, CSUMB & NASA Ames Research Center, cristina.milesi@gmail.com
Kazuhito
Ichii, Fukushima University, Japan, kazuhito.ichii@gmail.com
Ramakrishna
R.
Nemani, NASA Ames Research Center, rama.nemani@nasa.gov
This study applies linear difference equations and statistical time-series techniques to analyze intraseasonal and interannual variability of key ecosystem variables simulated by an ensemble model experiment conducted under the Terrestrial Observation and Prediction System (TOPS). In particular, we examine the sensitivity of GPP, soil moisture, and biomass to variations of temperature, precipitation, as well as their own status from preceding time steps (i.e., autocorrelations). For monthly anomalies, a simple ARMA model can capture 40~50% of GPP variability in Biome-BGC and LPJ, and 20~30% of GPP variability in CASA and TOPS-BGC. The estimated regression coefficients of the lagged GPP anomalies indicate weak oscillatory behavior in most of the models, particularly in arid/semiarid climate regions. In a similar fashion, the ARMA model captures 80~90% of variability in soil moisture anomalies, in which weak oscillatory characteristics are also detected. Such intraseasonal oscillations may result from interactions between vegetation and soil moisture. At interannual time scales, the memory of soil moisture is reduced while that of GPP almost disappears. On the other hand, annual biomass anomalies are persistent in Biome-BGC and CASA, as 95% of its variability can be explained by the statistical model. However, biomass anomalies in LPJ are much more variable: its variability is one order higher than that in Biome-BGC and CASA, and only 40% of such variability can be explained by lagged GPP and biomass anomalies. This sharp discrepancy indicates the diverse approaches in simulating biomass allocation, turnover, and ecosystem disturbances in these models.
Presentation Type: Poster
Poster Session: Carbon Cycle Science
NASA TE Funded Awards Represented:
NONE: Related Activity or Previously Funded TE Award