Process Based Earth System Model (ESM) Evaluation
This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/K016008/1 - led by Professor Mathew Evans (University of York).
Climate change and air pollution are two of the biggest challenges facing humanity today. Ozone and particulate matter are pollutants that are particularly harmful to human health. Recent studies have suggested that in the UK alone they cause 50,000 extra deaths and result in a financial burden of 8-22 billion pounds per year. Both ozone and particulate matter also play an important role in climate change. Ozone absorbs infra-red radiation resulting in a warming of the climate. Particles scatter and absorb incoming solar radiation and alter the properties of clouds. This results in complex interactions with the Earth's climate, with some types of aerosol pollution warming climate whereas others cool climate. Future air quality depends both on changes to emissions of pollutants and to changes in climate. Furthermore, a warming climate can result in worsened air pollution, which in turn can drive additional warming, meaning that complex feedbacks are possible between air pollution and climate.
To help understand these complex interactions and feedbacks scientists have developed Earth System Models that include a description of the important physical and biogeochemical processes. These models are increasingly being used by policy makers to make predictions about future air quality and climate and to guide policy decisions. It is therefore important that the models are rigorously tested.
This testing involves using detailed observations of atmospheric composition that have been made over the past few decades at locations around the world. Most model evaluation to date has involved testing whether the models simulate current average concentrations of atmospheric pollutants. Whilst this is a useful and necessary first step in model evaluation it does not test whether the model accurately simulates the change in concentration of a pollutant under changing emissions or changing climate. For example, does the model capture the real-world change in concentrations of a pollutant given a particular change in emission or under a future climate change scenario? This is particularly important as these predictions under-pin policy recommendations for air quality abatement.
This project synthesised long-term (multi-decadal) observations of ozone and particulate matter and their atmospheric precursors. They used these observations to explore trends and variability that have been observed over the past few decades. A model-observation framework was developed that can be used to evaluate how well models simulate observed variability and trends. The project tested state-of-the-art Earth System Models using existing model output from model intercomparison exercises. Finally, they explored the model processes that are driving simulated variability and trends.
The results inform the scientific community as to the fidelity of Earth System Models. This project helped to improve our models and give us more confidence in our predictions.
The overall objective of this project was to develop and implement a framework capable of evaluating the sensitivity of atmospheric composition simulated by ESMs to changing climate and emissions.
Our scientific objectives were to:
O1. Develop observationally-based metrics and relationships with which to evaluate variability and trends in atmospheric composition and its drivers in ESMs.
O2. Understand the sensitivity of observed and simulated atmospheric composition to environmental drivers.
O3. Quantify the ability of ESMs to capture observed temporal variability and trends in atmospheric composition.
O4. Improve our understanding of the processes driving observed variability in atmospheric composition.
|Keywords:||Ozone, Climate Change, Air Pollution, Particulate matter, Atmospheric Composition, Earth System Models|
|Previously used record identifiers:||
No related previous identifiers.
|Gateway to Research - NE/K016008/1|