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Quantification of the relationship between uncertainties in carbon cycle prediction and uncertainties in climate variables
Junjie
Liu, JPL, junjie.liu@jpl.nasa.gov
(Presenter)
Better understanding of the relationship between carbon cycle and the other physical processes (i.e., radiation, water cycle) is crucial to improve carbon cycle prediction. In this study, we quantify the relationship between uncertainties in Gross Primary Production (GPP)/surface CO2 flux and the uncertainties in other climate variables, such as radiation and precipitation, by running an ensemble predication using a carbon-climate model. We also quantify the CO2 concentration uncertainties from the uncertainties in biosphere CO2 flux. The ensemble prediction is driven by ensemble meteorology states that were generated by assimilating meteorology observations into the same carbon-climate model with one type of Ensemble Kalman filter (EnKF). Based on the quantified relationship, we further pinpoint the model parameters that drive the variability in Gross Primary Production (GPP)/surface CO2 flux. The preliminary results show that the relationship between climate factors and GPP variability is spatiotemporal dependant. GPP is closely related to the variability of incident solar radiation in Amazon region. This study is a first step to estimate model parameters prediction by using EnKF and satellite observations to improve carbon cycle, and further attribute the total flux estimation into biosphere and other sources (mainly anthropogenic emissions). Presentation Type: Poster Session: Coupled Processes at Land-Atmosphere-Ocean Interfaces (Mon 4:00 PM) Associated Project(s):
Poster Location ID: 50
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