Meteorology: Climate trends, coverage bias and rainfall

Meteorology: Climate trends, coverage bias and rainfall

Date: Wednesday 26 May 2021, 2.30PM - 3.30PM
Location: Online
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Local Group Meeting
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14:30
Prof Kevin Cowtan,
York Structural Biology Laboratory, University of York

"Coverage bias in global sea temperatures"


15:10
Mr Donald Cummins, Department of Mathematics, University of Exeter

"How reliable is detection and attribution of climate change trends?"


15:50
Prof Douglas Parker,
School of Earth and Environment & School of Mathematics, University of Leeds

"Predicting African rainfall, from hours to decades"

 

14:00-14:15 Login

 

14:15-14:30 Chair of the Leeds/Bradford Group

Introductions

 

14:30-15:10 Prof Kevin Cowtan,York Structural Biology Laboratory, University of York

KC_grey.jpg
Coverage bias in global sea temperature

Abstract coming soon.


 

15:10-15:50 Mr Donald Cummins, Department of Mathematics, University of Exeter

How reliable is detection and attribution of climate change trends?

Since the 1970s, climate scientists have developed statistical methods intended to formalize detection of changes in global climate, and attribute such changes to relevant causal factors, natural and anthropogenic. Detection and attribution of climate change trends is commonly performed using a variant of Klaus Hasselmann's "optimal fingerprinting" method, which involves a linear regression of historical climate observations on simulated output from numerical climate models. However, it is well known in time series analysis that regressions of non-stationary variables are in general inconsistent and liable to produce spurious results.

This study has shown, using an idealized linear-response-model framework, that if standard assumptions about the inputs to the numerical climate model hold, then the optimal fingerprinting estimator is consistent. In the case of global surface temperature, parameterizing abstract linear response models in terms of planetary energy balance and ocean heat storage provides this result with physical interpretability. Hypothesis tests have been conducted using global temperature output from 16 of the latest generation of numerical climate models, to assess whether the assumptions required for consistency hold in practice. Each of the 16 tests yielded strong evidence that the assumptions hold.

 
 
15:50-16:30 Prof Douglas Parker, School of Earth and Environment & School of Mathematics, University of Leeds

DP_grey.jpg
Predicting African rainfall, from hours to decades

Abstract coming soon.
 

Prof Kevin Cowtan, York Structural Biology Laboratory, University of York
Mr Donald Cummins, Department of Mathematics, University of Exeter
Prof Douglas Parker, School of Earth and Environment & School of Mathematics, University of Leeds

 

For more information, contact Leeds-Bradford Local Group at rssleedsbradfordgroup@gmail.com

Chair: John Bates
Secretary: Ciarán McInerney

 
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