The statistical challenges in tackling persistent climate model uncertainty through model-observation comparisons.

Add to your list(s) Download to your calendar using vCal

• Dr Jill S Johnson; School of Mathematical and Physical Sciences, University of Sheffield, UK
• Tuesday 24 June 2025, 14:00-15:00
• Chemistry Dept, Unilever Lecture Theatre and Teams.

If you have a question about this talk, please contact Yao Ge.

https://teams.microsoft.com/l/meetup-join/19%3ameeting_OWJjY2ViNjktOWZjMS00NGJmLWI5MTUtNTYxM2E5MTgyMTQ1%40thread.v2/0?context=%7b%22Tid%22%3a%2249a50445-bdfa-4b79-ade3-547b4f3986e9%22%2c%22Oid%22%3a%228b208bd5-8570-491b-abae-83a85a1ca025%22%7d

Abstract: The effects of aerosols on the Earth’s energy balance since pre-industrial times (aerosol radiative forcing) has significantly and repeatedly dominated the uncertainty in reported estimates of global temperature change from the IPCC . The magnitude of aerosol radiative forcing of climate over the industrial period is estimated to lie between about -2 and -0.4 W m-2, compared to a much better understood forcing of 1.6 to 2.0 W m-2 due to CO2 . In this seminar, past efforts to quantify the range of possible aerosol forcings predicted from an aerosol-climate model that are caused by parametric uncertainty, and to constrain that forcing uncertainty through model-observation comparison using extensive aerosol and cloud-based measurements from ships, flight campaigns, satellites and ground stations, will be discussed. We find that despite a very large reduction in plausible parameter space and reasonable constraint on observable properties, the observational constraint based on this comprehensive set of measurements only partially reduces the range of aerosol radiative forcings from our model. In the NERC project ‘Towards Maximum Feasible Reduction in Aerosol Forcing Uncertainty’ (Aerosol-MFR), several key statistical challenges highlighted from this work are being addressed in order to improve the model-observation comparison process for uncertainty constraint. This includes optimising the way observational constraints are applied, designing new approaches for reducing error compensation effects and using the PPE to identify and characterise model structural errors. Preliminary results from the project so far will be outlined, along with further plans to tackle this important problem.

Biography: Dr Jill Johnson is a Lecturer in Statistics in the School of Mathematical and Physical Sciences at the University of Sheffield. Her research interests are in the development and practical application of statistical methods to quantify, assess and then reduce uncertainty in large-scale complex models of real-world systems, with a focus on problems in environmental science. Prior to joining Sheffield in August 2021, Jill worked as an applied statistician / research associate for over 8 years in the aerosol research group at the Institute for Climate and Atmospheric Science, University of Leeds, where her work focussed on the quantification and constraint of key uncertainties in models of the atmosphere and climate. Her current research builds on this work, including the NERC research project ‘Towards Maximum Feasible Reduction in Aerosol Forcing Uncertainty (Aerosol-MFR)’.

This talk is part of the Centre for Atmospheric Science seminars, Chemistry Dept. series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• CamBridgeSens
• Cambridge Environment
• Cambridge talks
• Centre for Atmospheric Science seminars, Chemistry Dept.
• Chemistry Dept, Unilever Lecture Theatre and Teams
• Climate Science Seminars within Cambridge
• Department of Chemistry
• Featured lists
• LCLU Departmental Talks
• Life Science Interface Seminars
• Queens' College Politics Society
• School of Physical Sciences
• Talks related to atmosphere and ocean dynamics and climate science
• Trust & Technology Initiative - interesting events

Note that ex-directory lists are not shown.