Calibration of a multi-physics ensemble for estimating the uncertainty of a greenhouse gas atmospheric transport model
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Published:2019-04-30
Issue:8
Volume:19
Page:5695-5718
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ISSN:1680-7324
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Container-title:Atmospheric Chemistry and Physics
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language:en
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Short-container-title:Atmos. Chem. Phys.
Author:
Díaz-Isaac Liza I., Lauvaux ThomasORCID, Bocquet MarcORCID, Davis Kenneth J.ORCID
Abstract
Abstract. Atmospheric inversions have been used to assess biosphere–atmosphere
CO2 surface exchanges at various scales, but variability among
inverse flux estimates remains significant, especially at continental scales.
Atmospheric transport errors are one of the main contributors to this
variability. To characterize transport errors and their spatiotemporal
structures, we present an objective method to generate a calibrated ensemble
adjusted with meteorological measurements collected across a region, here the
upper US Midwest in midsummer. Using multiple model configurations of the
Weather Research and Forecasting (WRF) model, we show that a reduced number
of simulations (less than 10 members) reproduces the transport error
characteristics of a 45-member ensemble while minimizing the size of the
ensemble. The large ensemble of 45 members was constructed using different
physics parameterization (i.e., land surface models (LSMs), planetary
boundary layer (PBL) schemes, cumulus parameterizations and microphysics
parameterizations) and meteorological initial/boundary conditions. All the
different models were coupled to CO2 fluxes and lateral boundary
conditions from CarbonTracker to simulate CO2 mole fractions.
Observed meteorological variables critical to inverse flux estimates, PBL
wind speed, PBL wind direction and PBL height are used to calibrate our
ensemble over the region. Two optimization techniques (i.e., simulated
annealing and a genetic algorithm) are used for the selection of the optimal
ensemble using the flatness of the rank histograms as the main criterion. We
also choose model configurations that minimize the systematic errors (i.e.,
monthly biases) in the ensemble. We evaluate the impact of transport errors
on atmospheric CO2 mole fraction to represent up to 40 % of the
model–data mismatch (fraction of the total variance). We conclude that a
carefully chosen subset of the physics ensemble can represent the
uncertainties in the full ensemble, and that transport ensembles calibrated
with relevant meteorological variables provide a promising path forward for
improving the treatment of transport uncertainties in atmospheric inverse
flux estimates.
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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