EGU2020-3449
https://doi.org/10.5194/egusphere-egu2020-3449
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Econometric methods for empirical climate modelling

David Hendry1 and Jennifer Castle2
David Hendry and Jennifer Castle
  • 1Nuffield College Oxford University, Oxford, UK (david.hendry@nuffield.ox.ac.uk)
  • 2Magdalen College Oxford University, Oxford, UK (jennifer.castle@magd.ox.ac.uk)

To understand the evolution of climate time series, it is essential to take account of their non-stationary nature with both stochastic trends and distributional shifts: see e.g., . Using the novel approach of saturation estimation, explained in the presentation, we model observational records on evolving climate processes that also shift, undertaking empirical studies that are complementary to analyses based on laws of conservation of energy and physical process-based models. Despite saturation estimation creating more candidate variables than observations in the initial general formulation, our machine learning model selection algorithm has seen many successful applications, illustrated here by modelling the highly non-stationary data on UK CO2 emissions annually 1860-2018 with strong upward then downward trends, punctuated by large outliers from world wars, national coal strikes and stringent legislation.

How to cite: Hendry, D. and Castle, J.: Econometric methods for empirical climate modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3449, https://doi.org/10.5194/egusphere-egu2020-3449, 2020

Display materials

Display file

Comments on the display material

AC: Author Comment | CC: Community Comment | Report abuse

Display material version 1 – uploaded on 01 May 2020, no comments