Article

Environmental and Ecological Statistics

, Volume 20, Issue 1, pp 129-146

First online:

A constrained least-squares approach to combine bottom-up and top-down CO2 flux estimates

  • Daniel CooleyAffiliated withDepartment of Statistics, Colorado State University Email author 
  • , F. Jay BreidtAffiliated withDepartment of Statistics, Colorado State University
  • , Stephen M. OgleAffiliated withNatural Resources Ecology Laboratory, Colorado State University
  • , Andrew E. SchuhAffiliated withDepartment of Atmospheric Sciences, Colorado State University
  • , Thomas LauvauxAffiliated withDepartment of Meteorology, Pennsylvania State University, State College

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Abstract

Terrestrial CO2 flux estimates are obtained from two fundamentally different methods generally termed bottom-up and top-down approaches. Inventory methods are one type of bottom-up approach which uses various sources of information such as crop production surveys and forest monitoring data to estimate the annual CO2 flux at locations covering a study region. Top-down approaches are various types of atmospheric inversion methods which use CO2 concentration measurements from monitoring towers and atmospheric transport models to estimate CO2 flux over a study region. Both methods can also quantify the uncertainty associated with their estimates. Historically, these two approaches have produced estimates that differ considerably. The goal of this work is to construct a statistical model which sensibly combines estimates from the two approaches to produce a new estimate of CO2 flux for our study region. The two approaches have complementary strengths and weaknesses, and our results show that certain aspects of the uncertainty associated with each of the approaches are greatly reduced by combining the methods. Our model is purposefully simple and designed to take the two approaches’ estimates and measures of uncertainty at ‘face value’. Specifically, we use a constrained least-squares approach to appropriately weigh the estimates by the inverse of their variance, and the constraint imposes agreement between the two sources. Our application involves nearly 18,000 flux estimates for the upper midwest United States. The constrained dependencies result in a non-sparse covariance matrix, but computation requires only minutes due to the structure of the model.

Keywords

Atmospheric inversion Carbon inventory Climate change