TY - JOUR
T1 - Examining CO2 Model Observation Residuals Using ACT-America Data
AU - Gerken, Tobias
AU - Feng, Sha
AU - Keller, Klaus
AU - Lauvaux, Thomas
AU - DiGangi, Joshua P.
AU - Choi, Yonghoon
AU - Baier, Bianca
AU - Davis, Kenneth J.
N1 - Funding Information:
The Atmospheric Carbon and Transport‐America (ACT) project was sponsored by the National Aeronautics and Space Administration (NASA) under Awards NNX15AG76G and NNX15AJ06G. T. Lauvaux was also supported by the French research program Make Our Planet Great Again (Project CIUDAD‐CNRS). We thank NASA's Airborne Sciences program, NASA Headquarters and staff, in particular, Kenneth W. Jucks and Jennifer R. Olson for their support of our mission. We would like to acknowledge the contributions of ACT collaborators, in particular, NASA project managers, scientists, and engineers and our colleagues at NOAA, Colorado State University for their excellent cooperation during the field campaign. Thanks are also due to the flight crews and aircraft facility groups from Wallops Flight Facility, Langley Research Center, and Duncan Aviation for their outstanding work supporting these flights and measurements. We also thank Hannah Halliday and John B. Novack for their contributions in data collection.
Funding Information:
The Atmospheric Carbon and Transport-America (ACT) project was sponsored by the National Aeronautics and Space Administration (NASA) under Awards NNX15AG76G and NNX15AJ06G. T. Lauvaux was also supported by the French research program Make Our Planet Great Again (Project CIUDAD-CNRS). We thank NASA's Airborne Sciences program, NASA Headquarters and staff, in particular, Kenneth W. Jucks and Jennifer R. Olson for their support of our mission. We would like to acknowledge the contributions of ACT collaborators, in particular, NASA project managers, scientists, and engineers and our colleagues at NOAA, Colorado State University for their excellent cooperation during the field campaign. Thanks are also due to the flight crews and aircraft facility groups from Wallops Flight Facility, Langley Research Center, and Duncan Aviation for their outstanding work supporting these flights and measurements. We also thank Hannah Halliday and John B. Novack for their contributions in data collection.
Publisher Copyright:
© 2021. American Geophysical Union. All Rights Reserved.
PY - 2021/9/27
Y1 - 2021/9/27
N2 - Atmospheric (Formula presented.) inversion typically relies on the specification of prior flux and atmospheric model transport errors, which have large uncertainties. Here, we used ACT-America airborne observations to compare (Formula presented.) model observation mismatch in the eastern U.S. and during four climatological seasons for the mesoscale WRF(-Chem) and global scale CarbonTracker/TM5 (CT) models. Models used identical surface carbon fluxes, and CT was used as (Formula presented.) boundary condition for WRF. Both models showed reasonable agreement with observations, and (Formula presented.) residuals follow near symmetric peaked (i.e., non-Gaussian) distribution with near-zero bias of both models (CT: (Formula presented.) ppm; WRF: (Formula presented.) ppm). We also found large magnitude residuals at the tails of the distribution that contribute considerably to overall bias. Atmospheric boundary-layer biases (1–10 ppm) were much larger than free tropospheric biases (0.5–1 ppm) and were of same magnitude as model-model differences, whereas free tropospheric biases were mostly governed by (Formula presented.) background conditions. Results revealed systematic differences in atmospheric transport, most pronounced in the warm and cold sectors of synoptic systems, highlighting the importance of transport for (Formula presented.) residuals. While CT could reproduce the principal (Formula presented.) dynamics associated with synoptic systems, WRF showed a clearer distinction for (Formula presented.) differences across fronts. Variograms were used to quantify spatial correlation of residuals and showed characteristic residual length scales of approximately 100–300 km. Our findings suggest that inclusion of synoptic weather-dependent and non-Gaussian error structure may benefit inversion systems.
AB - Atmospheric (Formula presented.) inversion typically relies on the specification of prior flux and atmospheric model transport errors, which have large uncertainties. Here, we used ACT-America airborne observations to compare (Formula presented.) model observation mismatch in the eastern U.S. and during four climatological seasons for the mesoscale WRF(-Chem) and global scale CarbonTracker/TM5 (CT) models. Models used identical surface carbon fluxes, and CT was used as (Formula presented.) boundary condition for WRF. Both models showed reasonable agreement with observations, and (Formula presented.) residuals follow near symmetric peaked (i.e., non-Gaussian) distribution with near-zero bias of both models (CT: (Formula presented.) ppm; WRF: (Formula presented.) ppm). We also found large magnitude residuals at the tails of the distribution that contribute considerably to overall bias. Atmospheric boundary-layer biases (1–10 ppm) were much larger than free tropospheric biases (0.5–1 ppm) and were of same magnitude as model-model differences, whereas free tropospheric biases were mostly governed by (Formula presented.) background conditions. Results revealed systematic differences in atmospheric transport, most pronounced in the warm and cold sectors of synoptic systems, highlighting the importance of transport for (Formula presented.) residuals. While CT could reproduce the principal (Formula presented.) dynamics associated with synoptic systems, WRF showed a clearer distinction for (Formula presented.) differences across fronts. Variograms were used to quantify spatial correlation of residuals and showed characteristic residual length scales of approximately 100–300 km. Our findings suggest that inclusion of synoptic weather-dependent and non-Gaussian error structure may benefit inversion systems.
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U2 - 10.1029/2020JD034481
DO - 10.1029/2020JD034481
M3 - Article
AN - SCOPUS:85115803659
VL - 126
JO - Journal of Geophysical Research: Atmospheres
JF - Journal of Geophysical Research: Atmospheres
SN - 2169-897X
IS - 18
M1 - e2020JD034481
ER -