Past Earth Network

Lead Research Organisation: University of Leeds
Department Name: Statistics

Abstract

Forecasts of climate rely on model projections, but derivation of
sophisticated climate models from first principles is not currently
feasible. Therefore, evaluating climate models with observations is
essential. The development and improvement of global climate models
is currently only based on comparison with and tuning to historical
observations of climate (the instrumental record). Model simulations
of the present climate are well-tuned and are in general agreement
with each other. However, there is no clear relationship between
model performance for present day and model behaviour for projections.
Models show a range of sensitivities when predicting the future
climate response to the emission of greenhouse gases. This indicates
that the evaluation of models using observations of historical climate
is insufficient. It is very difficult to reduce uncertainties on
projections based on the instrumental period only and the use data
from earlier periods is critical.

A wide variety of different climate states are recorded in the
geological record (spanning greenhouse to icehouse scenarios). The
modelling of past climates, in combination with data from the
geological record, provides a unique laboratory to evaluate the
ability of models to forecast global change. While data is available
from numerous intervals in Earth
history, analysis is often constrained by the availability of material
of the correct age and data collection is often very time consuming
and expensive (e.g. for marine sediment cores). For this reasons, it
is important that data on past climate and environments is utilised
optimally and that challenges resulting from sparsity of the data as
well as from temporal and spatial uncertainties are addressed in the
best way possible.

The earth system modelling and proxy reconstruction communities often
have little contact with professional statisticians. Even in
publications, ad-hoc methods are used instead of established
statistical "best practice". If inappropriate statistical methods
are used, inference about models and the earth system will be weakly
supportable or plainly wrong. To avoid these problems and to realise
the opportunity of improved earth system forecasting, sound
statistical methods as advised by statisticians must be used. On the
other hand, use of appropriate statistical methodology is often made
difficult due to sparsity of data or lack of resources, and
statisticians are not always aware of the resulting restrictions on
the applicability of methods. Statisticians need to develop awareness
of the restrictions and requirements caused by the sparsity of
palaeoclimate data and the high complexity or climate models.

The Past Earth Network will develop a shared, multi-disciplinary
vision for addressing the challenges encompassed by the following four
network themes. (1) Quantification of error and uncertainty of data:
The uncertainties inherent in different forms of climate data must be
well-understood. This is particularly challenging for palaeoclimate
data, since uncertainties are often large and varied. (2)
Quantification of uncertainty in complex models: The uncertainties in
the output of the (complex and high-dimensional) models in use must be
well-understood. (3) Methodologies which enable robust model-data
comparison: Appropriate methods for model-data comparison must be
used, taking into account the nature and sparsity of data. (4)
Forecasting and future climate projections: This theme synthesizes the
results from the first three themes in order to assess and ultimately
improve the ability of climate models to forecast climate change.

By addressing these four challenges, results produced by the Past
Earth Network will help to better understand and reduce the
uncertainties in climate forecasts and ultimately will contribute to
the development of better climate forecasts.

Planned Impact

The Past Earth Network strives to obtain a better understanding and a
quantification or reduction of uncertainties, both in Earth system
models and in environmental forecasts. Such efforts will have a broad
impact on society and policy.

Policy makers are required to meet legally binding climate targets.
For example, the Climate Change Act 2008 requires the UK to reduce
green house gas emissions to 80% of the 1990 levels by 2050. The
responsibility of implementing this policy lies with the Department of
Energy & Climate Change (DECC), the Committee on Climate Change
(CCC), the Department for Environment, Food & Rural Affairs (DEFRA),
the Department for Transport, and the Environment Agency (EA) and the
UK research funding councils. Reductions of the uncertainties in
earth system forecasts, achieved from the results of theme~4 in the
Past Earth Network, will allow for better management of risk for
policy makers, thus assisting in the planning and implementation of
required changes. Ultimately, the Past Earth Network aspires to
reduce the forecasting error in climate projections, allowing for more
accurate planning and resource allocation.

National policy decisions are informed by scientific activities like
the Intergovernmental Panel on Climate Change (IPCC). We have
discussed our network proposal with project partner Valerie
Masson-Delmotte (Coordinating Lead Author of Chapter 5 of Working
Group 1 of the IPCC 5th Assessment Report), and Valerie was very
positive about our proposal. While the corresponding section of the
IPCC has now concluded, future IPCC-type activities will benefit from
the results of the network.

One of the main aims of the network is identification of the main
challenges which must be overcome to obtain better climate forecasts.
Knowledge about these challenges will allow the UK research councils
to make better decisions about where to focus future research funding
in this area.

Economic impact of the network will come from the ability of
geosciences-based industries to build on better knowledge of
uncertainties related to the earth system, thus reducing economic
risk. For example, in the petroleum and mineral exploration
industries, results of climate model experiments can help to reduce
risk by helping to better predict pertinent aspects of the petroleum
system. Andrew Davies (Head of Innovation at Neftex Petroleum
Consultants Ltd.) is a member of our steering committee and has helped
to shape the network to achieve maximal impact of the network to
related industries.

Institutions like the British Geological Survey address climate change
issues through a variety of channels, with the aims of observing past
and present climate, understanding those observations, and ultimately
forecasting future climates and the environmental responses to those
climates. Michael Ellis (Head of Climate Change Science at the BGS)
is a member of our steering committee and has helped to shape the
network to fully realise impacts from network activities for the BGS.

Finally, the network will inform public discussion with the help of
outreach activities. The public will benefit from better awareness of
the challenges faced and results achieved by the Past Earth Network,
by being able to make better choices about their own life styles and
about what the UK should do to achieve its climate targets.

Publications

10 25 50
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Fairman J (2017) Build Your Own Earth: A Web-Based Tool for Exploring Climate Model Output in Teaching and Research in Bulletin of the American Meteorological Society

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Dowsett H (2016) The PRISM4 (mid-Piacenzian) paleoenvironmental reconstruction in Climate of the Past

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Haywood A (2019) What can Palaeoclimate Modelling do for you? in Earth Systems and Environment

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Holloway M (2017) The Spatial Structure of the 128 ka Antarctic Sea Ice Minimum in Geophysical Research Letters

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Aquino-López M (2018) Bayesian Analysis of $$^{210}$$ 210 Pb Dating in Journal of Agricultural, Biological and Environmental Statistics

 
Description Some trends have become visible during the EPSRC funded period: 1) The network aims to bring together statisticians and climate scientists. We have found that we have a much larger involvement from climate scientists than from statisticians. A challenge will be to make the area more attractive to statisticians. 2) We found that there is still large potential for the use of statistical methods to bring improvements in Palaeo-climate research, ranging from more efficient use of (typically very sparse) data to improvements in computational efficiency (e.g. by using emulators to replace/augment computationally expensive runs of climate simulations).
Exploitation Route Both the areas of statistics and of climate science should benefit from closer collaboration.
Sectors Other

URL https://www.pastearth.net/feasibility_studies.html
 
Description The Past Earth Network has funded various outreach activities, with the aim to bring the research done inside the network to a wider audience. As part of this, we have run a talks in schools series, have funded two "People Like Me" workshops (aiming to encourage female pupils to study STEM subjects), and were represented at the Yorkshire Fossil Festival (Scarborough) in 2016 and 2017.
First Year Of Impact 2016
Sector Education,Culture, Heritage, Museums and Collections,Other
Impact Types Societal

 
Title Surface elevation of 69 Greenland Ice Sheet morphologies and associated d18O anomalies (with respect to Pre-industrial) simulated by HadCM3 
Description The text file (.csv) contains d18O changes simulated at six Greenland deep ice cores (NEEM, NGRIP, GRIP, GISP2, Camp Century and DYE3) from 69 simulations performed using the isotope-enabled HadCM3 climate model forced with mid last interglacial boundary conditions, centred at 125,000 years ago. HadCM3 is used to reproduce the d18O response to 69 modified Last Interglacial (LIG) Greenland Ice Sheet (GIS) morphologies at the ice-core sites. To parameterise the set of 69 GIS morphologies, we undertake a Principal Component Analysis (PCA) approach. The text file also contains the 8PC coefficients for each of the 69 morphologies. The netcdf file (.nc) contains the 8PC shapes and the average shape. To obtain any of the 69 GIS morphologies: (1) store the 8 PC coefficients of a specific GIS morphology and, (2) take a linear combination of the PC shapes (according to those coefficients) and add the average shape. Funding was provided by the following grants: EPSRC-funded Past Earth Network (Grant number EP/M008363/1); NERC funding through grants NE/P009271/1, NE/P013279/1, NE/J004804/1, and Irene Malmierca's PhD studentship. 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://data.bas.ac.uk/full-record.php?id=GB/NERC/BAS/PDC/01283