4 research outputs found
Using the past to constrain the future: how the palaeorecord can improve estimates of global warming
Climate sensitivity is defined as the change in global mean equilibrium
temperature after a doubling of atmospheric CO2 concentration and provides a
simple measure of global warming. An early estimate of climate sensitivity,
1.5-4.5{\deg}C, has changed little subsequently, including the latest
assessment by the Intergovernmental Panel on Climate Change.
The persistence of such large uncertainties in this simple measure casts
doubt on our understanding of the mechanisms of climate change and our ability
to predict the response of the climate system to future perturbations. This has
motivated continued attempts to constrain the range with climate data, alone or
in conjunction with models. The majority of studies use data from the
instrumental period (post-1850) but recent work has made use of information
about the large climate changes experienced in the geological past.
In this review, we first outline approaches that estimate climate sensitivity
using instrumental climate observations and then summarise attempts to use the
record of climate change on geological timescales. We examine the limitations
of these studies and suggest ways in which the power of the palaeoclimate
record could be better used to reduce uncertainties in our predictions of
climate sensitivity.Comment: The final, definitive version of this paper has been published in
Progress in Physical Geography, 31(5), 2007 by SAGE Publications Ltd, All
rights reserved. \c{opyright} 2007 Edwards, Crucifix and Harriso
A review of spatial causal inference methods for environmental and epidemiological applications
The scientific rigor and computational methods of causal inference have had
great impacts on many disciplines, but have only recently begun to take hold in
spatial applications. Spatial casual inference poses analytic challenges due to
complex correlation structures and interference between the treatment at one
location and the outcomes at others. In this paper, we review the current
literature on spatial causal inference and identify areas of future work. We
first discuss methods that exploit spatial structure to account for unmeasured
confounding variables. We then discuss causal analysis in the presence of
spatial interference including several common assumptions used to reduce the
complexity of the interference patterns under consideration. These methods are
extended to the spatiotemporal case where we compare and contrast the potential
outcomes framework with Granger causality, and to geostatistical analyses
involving spatial random fields of treatments and responses. The methods are
introduced in the context of observational environmental and epidemiological
studies, and are compared using both a simulation study and analysis of the
effect of ambient air pollution on COVID-19 mortality rate. Code to implement
many of the methods using the popular Bayesian software OpenBUGS is provided