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© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Dec 2018

Research article | 10 Dec 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Calibration of a multi-physics ensemble for greenhouse gas atmospheric transport model uncertainty estimation

Liza I. Díaz-Isaac1,a, Thomas Lauvaux1, Marc Bocquet2, and Kenneth J. Davis1 Liza I. Díaz-Isaac et al.
  • 1Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, USA
  • 2CEREA, joint laboratory École des Ponts ParisTech and EDF R&D, Université Paris-Est, Champs-sur-Marne, France
  • anow at: Scripps Institution of Oceanography, University of California, San Diego, CA 92093, USA

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 spatio-temporal structures, we present an objective method to generate a calibrated ensemble adjusted with meteorological measurements collected across a region, here the US upper 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. 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 calibration 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 errors in the full ensemble, and that transport ensembles calibrated with relevant meteorological variables provide a promising path forward for improving the treatment of transport errors in atmospheric inverse flux estimates.

Liza I. Díaz-Isaac et al.
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Liza I. Díaz-Isaac et al.
Liza I. Díaz-Isaac et al.
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Publications Copernicus
Short summary
We demonstrate that transport model errors, one of the main contributors to the uncertainty in regional CO2 inversions, can be represented by a small-size ensemble carefully calibrated with meteorological data. Our results also confirm transport model errors represent a significant fraction of the model-data mismatch in CO2 mole fractions, hence in regional inverse CO2 fluxes.
We demonstrate that transport model errors, one of the main contributors to the uncertainty in...