Democratising climate science: how climate model emulators add robustness and relevance to IPCC AR6

SIXTH ASSESSMENT REPORT
Working Group I – The Physical Science Basis
9 August 2021
#ClimateReport #IPCC
4 November 2021
Democratising climate science: how
climate model emulators add robustness
and relevance to IPCC AR6

From Nobel prizes to IPCC projections
Piers Forster (University of Leeds)
Coordinating Lead Author AR6 WGI (Chapter 7) and AR4 WGI
Lead Author Special Report on 1.5C and WGI AR5, SYR AR5, TEAP
Contributing Author SAR, TAR, AR5 WGIII, AR6 WGIII

How the 2007 Nobel Peace Prize connects to the 2021 Nobel Prize for Physics

Physically based emulators of climate change
a model for an aspect of climate change through a very
carefully considered very small number of equations
Also known as
● simple climate models
● reduced complexity climate models

Why are physical emulators special?
● Precedent in foundational science
● Project a single variable (e.g. surface temperature)
● Agile/fast
● Tunable
→ probabilistic projections
● Reliant on process understanding from complex models or
theory

Which of the following headlines used reduced
complexity models as part of the assessment?

“It is indisputable that human
activities are causing climate
change
[Credit: Yoda Adaman | Unsplash]

“There’s no going back from
some changes in the climate
system. However, some
changes could be slowed and
others could be stopped by
limiting warming.
[Credit: Shari Gearheard | NSIDC]

“Unless there are immediate,
rapid, and large-scale
reductions in greenhouse gas
emissions, limiting warming to
1.5°C will be beyond reach.
[Credit: Peter John Maridable | Unsplash]

Today’s event
● User needs relating to IPCC projections
● IPCC AR6 science details behind projections
● Democratising emulators

Panel: How are IPCC projections used by
stakeholders?
Rex Barrer (Institute for Climate and Sustainable Cities (ICSC))
Rueanna Haynes (Trinidad and Tobago)
Andy Jarvis (Alliance of Bioversity International and CIAT)
Moderator: Marion Ferrat (Sustainable Development Solutions Network)

How have climate model emulators supported
mitigation efforts
Joeri Rogelj
Contributing Author IPCC AR5 WGI, WGIII, SYR (2013/2014)
Coordinating Lead Author IPCC SR1.5 (2018)
Lead Author IPCC AR6 WGI (2021)
Contributing Author IPCC AR6 WGIII (forthcoming 2022)

The journey from AR5 to AR6 via SR1.5 and
how emulators have informed mitigation
targets
UNFCCC COP26 – IPCC-WMO-MO Pavilion Side Event – 4 November 2021
Joeri ROGELJ

Linking science across IPCC Working Groups

MAGICC emulator based on AR4
knowledge, updated with AR5
climate sensitivity
MAGICC = Model for the Assessment of Greenhouse-gas Induced Climate Change

FaIR = Finite-amplitude Impulse Response model
climate sensitivity
+ FaIR emulator with updated
forcing

climate sensitivity
FaIR = Finite-amplitude Impulse Response model
+ FaIR emulator with updated
forcing
Cross-chapter calibration
effort for
cross-Working-Group
integration of climate
science assessment
WG3

UNEP Emissions Gap Report
annual science-based assessments of pledges since 2010
18

Understanding where current policies and pledges are leading us
Source: UNEP Emissions Gap Report 2021

Climate risk information based on the latest IPCC Physical Science Assessment
(AR6)
Source: UNEP Emissions Gap Report 2021

AR6 headline results informed by climate model
emulators and interpretation for COP
Chris Smith, Contributing Author IPCC AR6 WG1 Chapters 2, 4,
6, 7; WG3 Chapter 3; Editor, WG1 Annex III (Radiative Forcing)

Why use emulators in the first place?
● Support and extend findings based on Earth System models
● Fill in gaps for simulations not run by Earth System models
● Calibrate and constrain Earth System model results to observations
Different types of emulators used in IPCC AR6 Working Group 1:
● Emulators that convert emissions to radiative forcing (Ch. 2, 6, 7; SPM)
● Projections of global mean temperature from radiative forcing - in particular, the climatic
effect of a single species or group of species (Ch. 1, 3, 4, 5, 6, 7; SPM)
● Projections of sea-level rise from temperature and ice-sheet melt (Ch. 9, SPM)
● Regional climate projections from global mean temperatures (Ch. 11)

SPM Fig. 2: Attribution of
present-day warming by emissions.
Emitted methane has contributed
to half of the net warming
SPM Fig. 4: Attribution of future warming. CO2
remains the dominant contributor to climate change
in all headline 21st century scenarios
Summary for Policymakers: Headline results
SPM Fig. 8:
Projections of sea
level rise to 2100
and 2300

Figure 6.12: a more detailed version of SPM Fig. 2
Chapter 6: Climate implications from short-lived pollutants

Figure 3.8
“Chapter 7 emulator” = a two-layer climate
model used extensively throughout the
report
From the Summary for Policymakers
The likely range of total human-caused global
surface temperature increase from 1850–1900 to
2010–2019 is 0.8°C to 1.3°C, with a best estimate of
1.07°C. It is likely that well-mixed GHGs contributed
a warming of 1.0°C to 2.0°C, other human drivers
(principally aerosols) contributed a cooling of 0.0°C
to 0.8°C, natural drivers changed global surface
temperature by –0.1°C to 0.1°C, and internal
variability changed it by –0.2°C to 0.2°C.
Chapter 3: Supporting assessment of human-attributed warming

Figure 4.11
Projections of warming were made using a
combination of constrained climate model
projections (pink, purple, blue bars) and results from
an emulator (green).
The emulator is consistent with assessments of key
climate variables in the rest of the AR6 WG1 report
(equilibrium climate sensitivity, transient climate
response, radiative forcing, …)
We do this because some climate models produce
historical warming outside of the observed range
Chapter 4: Projections of 21st Century warming

Projections from the Chapter 7 emulator are extended beyond 2100 to determine thermal expansion from
ocean heat content; further emulators describing ice sheet loss and other components of the sea level
budget are included.
Low-likelihood, high impact outcomes can be assessed using emulators: cannot rule out 15m sea level rise
by 2300 under high emissions pathway
Chapter 9: Sea level projections

● Chapter 1: 1750 to 1850 human-induced warming
● Chapter 4: Post-2100 warming
● Chapter 5: Non-CO2 contributions to remaining carbon budget
● Chapter 7: Temperature attribution to present-day warming
● Chapter 7: CO2 equivalent metrics for greenhouse gases (e.g. GWP100)
● Chapter 11: Determination of regional climate change at various warming thresholds
In summary, it would have been very difficult to provide such a level of policy-relevant detail without the
assistance of emulators.
Other uses of emulators in the Working Group 1 report

Emulator calibration in IPCC WGI and planned uses
in WGIII
Zebedee Nicholls, Contributing Author IPCC AR6 Chapters 1, 4, 5, 6
and 7

Scope
Emissions-driven emulators for WG3
● Start from emissions
● Calculate atmospheric concentrations, effective radiative forcing and then global-mean
temperature change
● Calibrated to capture the WG1 assessment as closely as possible to facilitate
cross-working group consistency
● Computationally efficient enough to be useable by WG3
Emulator calibration

Challenges
● WG1 produces assessment of multiple metrics (e.g. ECS, TCR, TCRE,
aerosol effective radiative forcing, warming under different scenarios)
● WG3’s tools need to reflect this assessment (all metrics at once)
● We must understand the extent to which the WG3 tools differ from the
WG1 assessment

Today
1. How do we create a tool which reflects a multi-dimensional
assessment?
2. How good are the tools we ended up with?

Multi-dimensional probabilistic calibration
Starting point:
● Assessment* for a single
climate system property
*Here it’s labelled proxy assessment in line with the paper which documented
the method before AR6 (hence didn’t use AR6 ranges), for AR6 we used the
AR6 assessment
Nicholls et al., Earth’s Future 2021 https://doi.org/10.1029/2020EF001900

Then build up
to multiple
climate system
properties

Options for multi-dimensional calibration:
● Monte Carlo Markov Chain plus subsampling (MAGICC,
CICERO-SCM)
● Weighting of parameters by likelihood (FaIR, OSCAR)
● Others?

Monte Carlo Markov Chain
Combine prior knowledge and observations (via likelihood
calculation) to create a multi-dimensional parameter
distribution (posterior)

Combine prior knowledge and
observations (via likelihood
calculation) to create a
multi-dimensional parameter

Combine prior knowledge and
observations (via likelihood
calculation) to create a
multi-dimensional parameter
● Prior (blue): very wide
● Posterior (orange): narrower based
on consistency with observations
● AR6 distribution (green): subsampled
to match AR6
ECS

Subsampling
● Assessed range (blue): target
● Posterior (orange): what comes out of our Monte Carlo Markov Chain
● AR6 distribution (green): set subsampled from the posterior to be as close to the
assessed range as possible

Comparison across multiple models
Compare
multiple models
across multiple
climate system
properties

Performance of emissions-driven emulators in AR6
Cross-chapter Box 7.1, Table 2
● Compare performance
of emulators with
assessment
● White cells ⇒ agreement
to within acceptable bounds
(+/- 5% for median,
+/- 10% for ranges)
● Dark shades ⇒ less close
agreement
Emulator assessment

Performance of
emissions-driven
emulators in AR6
Cross-chapter Box 7.1, Table 2
● Compare performance
of emulators with
assessment
● White cells ⇒ agreement
to within acceptable bounds
(+/- 5% for median,
+/- 10% for ranges)
● Dark shades ⇒ less close
agreement
Emulator assessment

WGIII handover
● WGIII are now running scenarios with the calibrated
emissions-driven emulators
● This will be used as part of their assessment of the
socioeconomic transformations aligned with different
climate outcomes
● Full details will be released alongside WGIII in early 2022
WGIII handover

Potential to go beyond WGI
Informing adaptation and assessing realism of
mitigation pathways with regional Earth System Model
emulators
Sonia Seneviratne, Coordinating Lead Author IPCC AR6 Chapter 11
Lea Beusch, Lukas Gudmundsson (ETH Zurich)

interactive-atlas.ipcc.ch
IPCC WG1: Earth
System Models
(climate physics,
biogeochemistry)
● global
● regional
IPCC WG3:
Integrated
Assessment
Models (IAMs)
global
temperature
IPCC WG2:
Impacts and
adaptation
● global
● regional

IPCC WG1: Earth
System Models
(climate physics,
biogeochemistry)
● global
● regional
IPCC WG3:
Integrated
Assessment
Models (IAMs)
global
temperature
IPCC WG2:
Impacts and
adaptation
● global
● regional
Realistic emissions scenarios?

IPCC WG1: Earth
System Models
(climate physics,
biogeochemistry)
● global
● regional
global
temperature
IPCC WG2:
Impacts and
adaptation
● global
● regional
IPCC WG3:
Integrated
Assessment
Models (IAMs)

IPCC WG1: Earth
System Models
(climate physics,
biogeochemistry)
● global
● regional
global
temperature
IPCC WG2:
Impacts and
adaptation
● global
● regional
IPCC WG3:
Integrated
Assessment
Models (IAMs)
Impacts of regional climate changes including extremes, with implications for emissions scenarios, e.g.:
● forests (afforestation, bioenergy, bioenergy with carbon capture and storage)
● crops, food production

IPCC WG1: Earth
System Models
(climate physics,
biogeochemistry)
● global
● regional
global
temperature
IPCC WG2:
Impacts and
adaptation
● global
● regional
IPCC WG3:
Integrated
Assessment
Models (IAMs)
Impacts of regional climate changes including extremes, with implications for emissions scenarios, e.g.:
● forests (afforestation, bioenergy, bioenergy with carbon capture and storage)
● crops, food production
Regional Earth System Model Emulator

MESMER regional emulator

MAGICC-MESMER coupling
Beusch et al, in review in GMD:
https://gmd.copernicus.org/preprint
s/gmd-2021-252/

Conclusions
● Regional Earth System Model emulators can help bridge the gap
between IPCC WG1, WG2 and WG3 science
● Key questions need to be addressed at this interface, in particular:
○ realism of developed emissions pathways
○ full integration from emissions to impacts, including using
impacts as constraints for emissions (optimization, exclusion of
given pathways)
○ speeding up research in the context of rapidly needed emissions
transitions (-50% by 2030)

Outreach with
emulators and
online tools
A/Prof. Malte Meinshausen
Lead author IPCC WG1 AR6 and SYR
with big thanks to WG1 CCB7.1 emulator teams:
CICERO-SCM, HECTOR, FaIR, MAGICC

Using emulators to
support outreach
IPCC WG1 calibrated emulators can support
the outreach of IPCC assessments to a wider
audience, including:
● classrooms,
● negotiations,
● courtrooms,
● and boardrooms.

62
https://constrain-eu.org/ ZERO IN report

3 Nov Analysis: https://www.climate-resource.com/tools/ndcs

The reduced-complexity emulators used in IPCC AR6
One size does not fit all… There are different strength and weaknesses across the
various emulators.
➔ Simplicity of interface
Online interfaces, github repos, integration.
➔ Degree of complexity
For some emulators, you need forcing inputs, others run with key
emissions, others require full emission scenarios.
➔ OpenSCM
Most emulators are developing towards open-source or are
already. Many share common interfaces
➔ Calibration skill
Emulators can differ on their emulation skill. Mind their limitations.

CICERO-SCM
A simple climate model. Used
in studies of sectoral/regional
contributions to global
warming and observational
based estimates of climate
sensitivity. In Fortran, will be
ported to python and used in
communication projects and
linked to regional emulator.
Online: Web tool in
development (has been
included in OpenSCM)
Example of recent scientiﬁc usage:
From Skeie et al. Environ. Res. Lett. 12 (2017) 024022
Link to model: www.cicero.oslo.no
Contact: Ragnhild Bieltvedt Skeie r.b.skeie@cicero.oslo.no
Contributions to global mean surface temperature in 2012 of total
territorial emissions. Impact of start year of emission for the CO2
fossil fuel only case (left) and for Kyoto gases case (right).

FaIR 1.6
Fully open-source,
emissions-driven,
python-based model,
incorporating simpliﬁed carbon
cycle, atmospheric chemistry
and climate response to
radiative forcing based on
calibrations to CMIP6 models.
Link to model: www.fair-scm.org |
github.com/OMS-NetZero/FAIR
Contact: Chris Smith (c.j.smith1@leeds.ac.uk)
Online example
fair-scm.org
Outreach examples:
● IPCC SR1.5, AR6 WG1 & WG3
● Carbon Brief
● Responsive climate
projections
● Frontiers for Young Minds

Outreach
e.g. Assessing the climate
outcomes of the Global Methane
Pledge
30% global methane reduction =
0.1C avoided warming in 2050
Carbon Brief: assessing near-term mitigation options
67

Online snapshot
Old SR1.5 version (v1.3.6) currently
available at www.fair-scm.org
(new website in development
using forthcoming v2.0)
Interactive Binder launchable from
GitHub project site:
https:/
/github.com/OMS-NetZer
o/FAIR
Full documentation:
https:/
/fair.readthedocs.io

MAGICC7.5
The oldie, around for 30+ years,
in all IPCC Assessment reports
from IPCC 1990. Slightly bigger
model with upwelling-diﬀusion
ocean, permafrost, sea level
rise, clathrates, various
gas-cycles etc.
Online:
live.magicc.org
Outreach examples:
● UNEP Gap Report
● IEA WEO 2021
● Climate Action Tracker
● IPCC WG1 → WG3
● IMAGE, MESSAGE, REMIND...
Link to model: live.magicc.org
Contact: malte.meinshausen@unimelb.edu.au,
zebedee.nicholls@climate-energy-college.org
jared.lewis@climate-resource.com

live.magicc.org
You can run your own emission
scenarios with either IPCC AR6 WG1
calibrated probabilistic settings or play
around with individual parameters.
70

IEA WEO
2021 report
using MAGICC7
to determine
temperatures of
their scenarios
(and probably
also today’s
announcement
of 1.8C warming
due to NDCs +
methane pledge)

HECTOR
Link to model: https://github.com/jgcri/hector
Contact: Kalyn Dorheim, kalyn.dorheim@pnnl.gov
Hector Structure
Woodard et al. 2020
An open source,
object-oriented, simple
global climate carbon-cycle
model that represents critical
global scale Earth system
processes on an annual time
step. Hector is both a
stand-alone model and also
coupled with the integrated
assessment model GCAM.

Hector outreach
● Workshops, seminars and
tutorials (https:/
/youtu.be/EFJS-buvGN8)
● New code development
(including carbon-tracking
mechanism, right) by early
career scientists
● Accessible via command line
(C++), R package, Python
wrapper, and online interface
● Well documented

HectorUI
A web app for:
● Running Hector (no
coding required)
● Downscaling results
● Changing inputs
● Accessing
documentation
● Constructing custom
scenarios
https:/
/jgcri.shinyapps.io/HectorUI/

Outreach with
emulators
Future Direction:
● More detail and even better
calibration
● Smoothen intra-IPCC
consistency even further
● Expanding on regional
information, extremes, sea
levels
● Integration to decision tools
75
Enables:
● IPCC consistent climate information
in the hands of many
● Informed decisions at COPs
● Probabilistic physical risk
information for TCFD (if coupled
with regional tools)
● capacity transfer
● ...

76
Simulating climate impacts of the COVID lockdown
Harriet Forster
Glasgow School of Art
https://www.bbc.co.uk/programmes/p08n7wy
k Forster et al. (2020), Nature Climate

Drop in sulphur dioxide pollution (coal)
Total effect
Drop in
traffic
pollution
CO2 cooling
effect
78
Temperature
change
degrees
C
Decrease in aviation
contrails
Short-term change in emissions create a
small long-term cooling effect:
less than 100th
of a degree
Warming
Cooling

79
We explored possible future pathways with some simple
economic modelling from Climate Action Tracker
https://climateactiontracker.org/publications/addressing-the-climate-and-post-covid-19-economic-crises/
1.2% GDP spending on
green policies, removing
some spending on fossil
fuels

80
How we recover can have a real effect by 2050 and potentially keep
us on track to meet the Paris Long-Term Temperature Goal

81
We explored possible future pathways with some simple
economic modelling from Climate Action Tracker
https://climateactiontracker.org/publications/addressing-the-climate-and-post-covid-19-economic-crises/

The #BlahBlahBlah needs to deliver
82
UNEP GAP Report
2021

Democratising climate science: how climate model emulators add robustness and relevance to IPCC AR6

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