Preprints
https://doi.org/10.5194/gmd-2022-63
https://doi.org/10.5194/gmd-2022-63
Submitted as: model description paper
07 Apr 2022
Submitted as: model description paper | 07 Apr 2022
Status: this preprint is currently under review for the journal GMD.

ICLASS 1.0: a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model: description, validation and application

Peter J. M. Bosman1 and Maarten C. Krol1,2 Peter J. M. Bosman and Maarten C. Krol
  • 1Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
  • 2Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, The Netherlands

Abstract. This paper provides a description of ICLASS 1.0: a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model. This framework can be used to study the atmospheric boundary layer, surface layer or the exchange of gases, moisture, heat and momentum between the land surface and the lower atmosphere. The general aim of the framework is to allow to assimilate various streams of observations (fluxes, mixing ratios at multiple heights, ...) to estimate model parameters, thereby obtaining a physical model that is consistent with a diverse set of observations. The framework allows to retrieve parameters in an objective manner, and enables to estimate information that is difficult to obtain directly by observations, for example free-tropospheric mixing ratios or stomatal conductances. Furthermore it allows to estimate possible biases in observations. Modelling the carbon cycle at ecosystem level is one of the main intended fields of application. The physical model around which the framework is constructed is relatively simple, yet contains the core physics to model the essentials of a well-mixed boundary layer and of land–atmosphere exchange. The model includes an explicit description of the atmospheric surface layer, a region where scalars show relatively large gradients with height. An important challenge is the strong non-linearity of the model, which complicates the estimation of best parameter values. The constructed adjoint of the tangent linear model can be used to mitigate this challenge. The adjoint allows for an analytical gradient of the objective cost function, used for minimisation of this function. An implemented Monte-Carlo way of running ICLASS can further help to handle non-linearity, and provides posterior statistics on the estimated parameters. The paper provides a technical description of the framework, includes a validation of the adjoint code, as well as tests for the full inverse modelling framework and a successful example application for a grassland in the Netherlands.

Peter J. M. Bosman and Maarten C. Krol

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-63', Anonymous Referee #1, 12 May 2022
  • RC2: 'Comment on gmd-2022-63', Anonymous Referee #2, 30 May 2022
    • AC1: 'Authors' response to all reviewers', Peter Bosman, 04 Aug 2022
  • RC3: 'Comment on gmd-2022-63', Anonymous Referee #3, 01 Jun 2022
  • AC1: 'Authors' response to all reviewers', Peter Bosman, 04 Aug 2022

Peter J. M. Bosman and Maarten C. Krol

Model code and software

The code of ICLASS, inlcuding the code and the data used for creating the plots with optimisation results in this paper Peter Bosman and Maarten Krol https://doi.org/10.5281/zenodo.6408051

Peter J. M. Bosman and Maarten C. Krol

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Short summary
We describe an inverse modelling framework constructed around a simple model for the atmospheric boundary layer. This framework can be fed with various observations types to study the boundary layer and land-atmosphere exchange. With this framework it is possible to estimate model parameters and the associated uncertainties. Some of these parameters, such as the free-tropospheric CO2 mixing ratio, are difficult to get directly by observations. An example application to a grassland is included.