Climate aware andresilient national
security: Challenges forthe 21st Century

Author Team
Eirini Malliaraki, The Alan Turing InstituteDr. Jesse F. Abrams, University of Exeter
Dr. Emma Boland, British Antarctic Survey
Dr. Erik Mackie, Cambridge Zero, University of Cambridge
Alyssa Gilbert, Grantham Institute, Imperial College London
Prof. Weisi Guo, Cranfield University and Turing Fellow
Prof. Tim Lenton, University of Exeter and Turing Fellow
Dr. Emily Shuckburgh, Cambridge Zero, University of Cambridge
Dr. Mark Workman, Grantham Institute, Imperial College London

We would like to thank a number of individuals who generously
contributed their time and expertise:
Dr. Ajay Gambhir, Advanced Research Fellow, Grantham Institute Climate Change and the Environment
Stephen Davison, Deputy Director at Cambridge Zero, University of Cambridge
Prof. Neil Adger, Professor of Human Geography at the University of Exeter
Dr. Edward Pope, Security Team, Met OfficeDr. Nathanial Matthews, Program Director of the Global Resilience Partnership
Jan Selby, Professor of Politics and International Relations, University of Sheffield
Dr. Cristina Rumbaitis del Rio, Action Track Co-Manager for the Global Commission on Adaptation
Prof. Jonathan Bamber, Professor of Physical Geography, University of Bristol
Dr. Tugba Basaran, Deputy Director, Centre for the Study of Global Human Movement, University of Cambridge
Janot Mendler de Suarez, Technical Consultant on Climate Resilience, World Bank
Prof. Stephen Briggs, Chair at Global Climate Observing System, Visiting
Scientist, Dept. of Chemistry, University of Cambridge & Dept. of Meteorology, Reading University

The report was commissioned by GCHQ.

The Alan Turing InstituteContents
ES Executive summary

4

1

Introduction

6

2

Current and future climate change risks

92.1. The characteristics of climate risk
3

11

Climate change and national and human security
3.1. Types of climate security risks
3.2 Climate security stakeholder landscape

423

Developing early warning systems for climate
security
4.1. Improved climate security risk knowledge
4.2 Improved resilience monitoring and warning
Case study: Human Migration

5

Enablers to climate risk decision-making

34

6

Recommendations

40

7

Glossary of terms

49

A

Appendix

52

Appendix Y4: Global landscape of climate
security - initiatives and stakeholders

Figures & Tables
Figure 1. Overview of the synthesis process of this report				Figure 2. This diagram illustrates the connected nature of climate security risk 		
Figure 3. Overview of the components of early warning systems and the identified
research challenges around improving climate risk knowledge and resilience monitoring
Figure 4. Projected population shift due to temperature and sea level changes 			Figure 5: Stylised process of Structured Decision Making
		

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The Alan Turing InstituteExecutive summary
Climate change has been identified as one of the most serious threats to national security
around the world, as it puts pressures on populations, economies, livelihoods and natural
resources. Increases in the incidence of extreme weather events and long-term changes inclimate are already challenging water and food security and exacerbate state fragility andconflict. Additionally, transitioning to renewable energy will not just influence the power
balance between countries, but will also reconfigure trade flows and create new interdependencies around renewables and commodities.
These climate-induced risks directly affect the UK’s defence and security capabilities due tofactors ranging from increased demand for humanitarian and disaster relief operations; supporting civil authorities and international peace-keeping efforts; infrastructure and equipment
planning for operations in increasingly harsh environments; and disruptions to global foodproduction. From a security perspective, the UK needs to find a way to protect a high qualityof life for its citizens, work closely with allies and respond to global needs, especially at timesof crisis.
International structures have been developed over recent years to discuss climate change
and security specifically, including the Climate Security Mechanism and recent discussions
at the United Nations Security Council (UNSC). Although climate change has been recognised as a national security threat, the wide range of global physical and socio-economic
risks associated with climate change are not fully accounted for in the decisions of the security community.
There are a variety of reasons why action on climate change and security has been challenging. One of these reasons is the lack of appropriate, fit-for-purpose data, and another is thechallenge of integrating climate change into decision-making. To integrate climate change
models into existing security planning, both at national and international levels the UK needs
to be able to develop timely and decisive decision-making regarding threats and hazards aswell as plan for contingencies – across all risks.
As the UK’s place in geopolitics shifts, it now has an opportunity to cast itself as a `smart’
global power. The UK is a world-leader in a number of academic and analytical disciplines
essential to quantifying and acting upon climate risks and making decisions in the light of
uncertainties. Areas of strength include climate science, machine learning, AI, data analysis,complexity science, and social sciences associated with decision-making under uncertainty and war studies. This is the right moment for the UK to lead the next steps in developing
state-of-the-art decision-making tools and processes for the defence and security community
on climate change.
This report is one of the first by a cross-disciplinary panel of experts - providing a detailed
background of the risks and opportunities that climate change poses to the UK’s security,
providing evidence based on recent events, and future expectations based on the latest available models. Looking ahead, the report describes an opportunity for the UK to develop moresophisticated and useful forecasting and strategic insight tools that can identify and measureclimate risks, provide early warnings for climate security-related tipping points and also help
policymakers carry out climate security risk assessments to identify where resilience needs
to be built in and/or safeguarded.
4

The Alan Turing InstituteExecutive summary
The proposed methodologies and tools include data-driven as well as qualitative and future-thinking approaches to ensure that the assessment includes extrapolation of known
trends, as well as consideration of outlier possibilities. A key output will be an improved
climate security risk sensing capability. This could take the form of data-driven assessments of the changing resilience of different factors influencing climate security - spanningclimate changes, impacts on resources (e.g. food), and impacts on people (e.g. migration).
A further goal would be to issue early warnings of potential climate security tipping points building on existing work that has demonstrated early warning signals exist for climate andecological tipping points. Lastly, the research frontier is the development of new causal
models that explicitly link climate and socio-economic processes together and can help policymakers create scenarios of future
It is vital that methods and tools for the UK’s defence and security community be developedin a way that aligns to existing risk assessment and risk management tools used by organisations such as the National Security Council. This alignment should consider both theresearch outputs, as well as the decision-making chain in national security to ensure thatclimate change risks can be truly understood and integrated with other key risks as assessedby the UK security community. We anticipate that the novel methodologies we describe here
will be of interest to GCHQ, MoD, and Dstl as well as the Foreign and Commonwealth Office-DFID, the Home Office and the Cabinet Office.
We thus recommend an urgent uplift of the research and decision-making capacity aroundthe anticipated impact of climate-related threats on UK National Security interests in a threeto five-year timeframe. This decision-making capacity should be informed by the best
available information and appropriate data-driven and complex systems methods. The scale
and interdisciplinary nature of the challenge mean that defence and security should incorporate a range of experts in relation to its changing role and operational requirements including
data scientists, climate scientists, designers, futurists and economists. These expert groups,
currently organised in subject-oriented academic departments, should be appropriately incentivised to work together and produce focused challenge-oriented research.
One way to undertake this is via a Climate Security Research Centre whose overarching
objective will be to improve the ability of the UK’s security community to integrate an understanding of climate change risks into decision-making processes. This research initiative
should be funded appropriately over five years to design and deliver three, highly-integrated
work streams: applied research in climate security risk and resilience, capacity building anddevelopment and testing of climate security tools. The research will support the governmentand relevant agencies by providing targeted, user-driven, policy-relevant evidence on climate-related security risk. The close collaboration between policymakers and academics, will
also ensure that this advice is truly integrated in existing decision-making, has a strong data
science core and gets to the heart of understanding the increasing climate security risks as
the outcome of complex, interconnected, dynamic systems.

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The Alan Turing Institute

1. Introduction
Purpose and scope of the report
In the current geological epoch, theAnthropocene, humans are a major driving
force behind changes that have pushed theEarth system out of the stable dynamics
of the Holocene1. Global pressures are
increasing with the increasing demand forfood, water, and energy. Recent work has
shown that humanity is at risk of moving
the planet beyond the boundaries of a safe
operating space2.
Climate change will increasingly lead tohuman and national security challenges.
Between 2009-2019 approximately
250 million people were displaced by asudden onset of disasters in more than
140 countries3. In 2019 alone, a total of
24.9 million people around the world wererecorded as displaced, forced from theirhomes by disaster4. Climate change is
projected to leave billions of people living
outside of the ‘climate niche’ we live in now
and have done for thousands of years.

economic, environmental risks and security
risks such as conflict, migration, foodinsecurity, and health. These climatenduced risks are reducing the resilience (thecapacity to deal with change and return tofunctioning at a similar level) of both socialand ecological systems and cause them tocross thresholds leading to a rapid reduction
in function which may be difficult to reverse5.
The ability of Britain to maintain its global
leadership and to recast its role outside of
the EU is likely to be predicated on the ability
to develop timely and decisive decisionmaking regarding threats and hazards aswell as plan for contingencies. This will also
allow it to develop the capacity to be resilient
to global turbulence and uncertainty in an
increasingly hyperconnected world.

“Climate change is the ultimate‘threat multiplier’, worsening existingsocial, economic, environmental
risks and security risks such as aconflict, migration, food insecurity,and health.”

Climate change is the ultimate ‘threat
multiplier’, worsening existing social,
1

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3
4
5

Steffen, W., Rockström, J., Richardson, K., Lenton, T. M., Folke, C., Liverman, D., ... & Donges, J. F. (2018). Trajectories of the Earth System in the Anthropocene. Proceedings of the National Academy of
Sciences, 115(33), 8252-8259.
Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin III, F. S., Lambin, E., ... & Nykvist, B. (2009). Planetary boundaries: exploring the safe operating space for humanity. Ecology and society, 14(2).
iMDC (2020). Global Report on Internal Displacement 2020
ibid.
Lin, B. B., & Petersen, B. (2013). Resilience, regime shifts, and guided transition under climate change: examining the practical difficulties of managing continually changing systems. Ecology andSociety, 18(1).

The Alan Turing Institute

1. Introduction - purpose and scope of the report
It is noteworthy that an area where the UK
has demonstrated global leadership has
been on climate change, both throughdiplomatic brokering at the COPs (annual
UNFCCC Climate Summits) and theimplementation of national policies aroundemissions reductions and net-zero. The UK
is also hosting the next COP in Glasgow
in November 2021. It is in this context that
GCHQ and other security bodies who set the
UK’s national and foreign security agenda,
develop their policymaking.
It is broadly acknowledged that the capacity
to support decisions which integrate theeffects of climate change is limited6. Criticalpolicy questions about climate change
mitigation, adaptation7, and planning require
better situational intelligence and an
improved forecasting capacity among otherelemenIn building the transdisciplinary tools andassessments required to answer security
policy questions, we need to: characterise
and anticipate climate security risks;
represent different socio-economic paths,
and forecast when systems show increased
sensitivity to climate change and begin to
break down. Recent developments in data
science, statstical modelling and machinelearning can help us better model, predict,and forecast climate change risks andimpacts relevant to national and international
security. We, thus, recommend that the
National Security community improves
its decision making capacity around theanticipated impact of climate related threatson UK National Security interests. We
propose a programme of methodologies andtools that aims to assist the development of
insights in an integrated fashion.

We undertook a 4-month scoping study toassess the feasibility and resources needed
to develop a new generation of models
that include social processes and give us
risk forecasting capability for timescales
up to 3-5 years as well as indicators andearly warnings to support D&S policies
and societal resilience. Our study involves
several independent but mutually supportive
phases:• A rapid evidence assessment ofliterature on the security implications ofclimate change, from various sources
such as journals, reports, and proceedingsthat include evidence on climate change
and security impacts on individuals,
communities and nations.
• Expert consultation: We conductedsemi-structured interviews with experts
across a wide range of relevant disciplines—including climate science, conflict,migration, resilience and security studies—
who need to collaborate to capturechanges in socio-ecological systems. We
also created a repository of key national andinternational climate security stakeholders(see Appendix Y4).
• Stakeholder engagement: In June
2020, we convened senior stakeholders
from academia, government and the
National intelligence community to discuss
emerging research directions and how tobest translate them into tools and policies.
A summary of the Climate Security Idea Lab
can be found here.
• Synthesis: We synthesised the above
in order to guide future strategic project
directions and identify key projectstakeholders. We further assessed theopportunities, enablers and resources suchas structures and research partnerships
needed for the development of a UK
research centre toward the goals outlined
above.

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Chris Lambert 2020 - A proposal to consider establishing a UK Office of Strategic Climate Threat Intelligence
Please refer to the glossary of terms at the end of this document.

The Alan Turing Institute

1. Introduction - purpose and scope of the report

Figure 1. Overview of the synthesis process of this report.

The report has five parts: Chapter 2
describes the characteristics of climate
risk. Chapter 3 gives an overview of thetypes and characteristics of climate
security risks. These include risks toresources, livelihoods and social cohesion
and transition risks from adaptation andmitigation. We also highlight notablestakeholders and national and international
initiatives. Chapter 4 describes theopportunities that exist to anticipate andmanage climate security risks through8

data-driven climate risk assessments, thedevelopment of causal models for security
and the identification of climate security
tipping points. In chapter 5 we discuss
related barriers and enablers which aresurfaced through a review of the literatureand expert consultation. Finally, in chapter
6 we provide a set of recommendations
about challenge prioritisation, as well as thecoordination, delivery and the scale of the
proposed research initiative.

The Alan Turing Institute

2. Current and future climate change
Climate change is already having asubstantial impact on natural and humansystems around the world. Greenhousegas emissions from human activities todate have caused more than 1ºC of global
warming above pre-industrial levels, andwarming is likely to exceed 1.5ºC between
2030 and 2052 at the current rate ofincrease8. The resulting increased frequencyof extreme weather events and otherclimate change-induced risks will havewide-reaching impacts on socioeconomic
and social-ecological systems. The
global socioeconomic impacts could besubstantial, will likely be nonlinear and haveknock-on effects.
Climate risks, in turn, interact with a variety
of other sociopolitical, structural, andeconomic factors and threaten humansecurity, increase the risk of violent conflict9,
affect vital water, energy and transportation
infrastructure, and increasingly shapeconditions of security and national security
policies.

Defining risk and resilience• In this report, risk is used to designate thepotential of shocks and stresses to affect thestate of a system.
• Risk results from the interaction of hazard,
exposure and vulnerability.
• To operationalise risk, we define it as the
combination of the likelihood of a hazardous
event occurring, multiplied by the impact ofthat event and the vulnerability of the system
of interest to the event.
• Vulnerability is the propensity or
predisposition of a system to be adversely
affected.
• Resilience is the ability of a system toanticipate, absorb, or recover from theeffects of a hazardous event in a timely
manner.
• The risk-assessment process often begins
with a risk profile, which is developed by
identifying the types of events that could
occur, the likelihood that events of varying
severity will occur, and the impact of those
events, including economic, infrastructure,and socio-cultural.
• Risks can be managed by reducing themagnitude of any of the three elements:
hazard, exposure and vulnerability.

“The resulting increased frequencyof extreme weather events andother climate change-induced risks
will have wide-reaching impacts
on socioeconomic and socialecological systems.”
899• We refer to the reduction of likelihood as
mitigation and the reduction of the impact ofan event as adaptation.
• Mitigation includes actions that are taken
to reduce greenhouse gas emissions andadaptation is the process of adjusting toactual or expected climate change effects.

IPCC, 2018: Summary for Policymakers. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas
emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O.
Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T.
Waterfield (eds.)]. World Meteorological Organization, Geneva, Switzerland, 32 pp.
Hsiang, S.M., Burke, M. and Miguel, E., 2013. Quantifying the influence of climate on human conflict. Science, 341(6151), p.1235367.

The Alan Turing Institute2.1. The characteristics of climate risk
Climate risks are multifaceted, multidimensional, involve both rapid and slow onset disasters
and range from local to global; and short-medium-long term. Climate risks are:
Increasing: The physical risks andsocioeconomic impacts of climate change
are increasing across the globe10. Climaterelated risks to human and natural systems
will be greater for warming of 1.5ºC than atpresent, and even greater for warming of
2.0ºC11.
Non Linear: Nearly all modelling of futureclimate risks is based on an assumption thatclimate impacts are proportional to theirdrivers. Yet, there are non-linear changes
in weather and climate variables, suchas weather extremes12, and responses ofhuman and natural systems which are notcaptured in climate risk assessments.
Context-dependent: The impacts ofclimate change are context dependent assome societies have the capacity to adapt
to significant levels of stress, while others
suffer severe impacts from lesser pressures.
Climate change should be understoodas increasing the risk of insecurity3
contextually, rather than inevitably causing it
Networked: Climate risk is transmitted
across time and space due to the linked
nature of climates across different regions of
the world.

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Examples of this mechanism include
spatial teleconnections such as the El
Niño–Southern Oscillation (ENSO). Theselarge-scale climatic events may occur
simultaneously (e.g. see the linked Russian
heatwave14 and Pakistan flooding of 2010).
Climate risk can also be transmitted acrosssectors and international boundaries16 anda combination of interacting processes canresult in extreme impacts.
Cascading: Risks to one sector or to one
region, can cascade through networks andacross multiple regions. Climate risks havemultiple direct and indirect pathways thatcascade through complex social–ecologicalsystems17. The mechanisms of transmission
include flows of material, movement ofpeople, and economic and trade linkages18.
Compounding: Climate risk accumulates
leading to gradual build-up of disasterrisk in specific locations, often due to a
combination of processes, some persistentand/or gradual, such as inadequate water
management, land use changes, rural-urban
migration, and unplanned urban growth19.
Security policymakers need to pay attention
to how these interactions affect any
particular region or, indeed, any particular
generic problem.

Woetzel, J., Pinner, D., Samandari, H., Engel, H., Krishnan, M., Boland, B. and Powis, C., 2020. Climate risk and response: Physical hazards and socioeconomic impacts. McKinsey Global Institute.
IPCC, 2018
Ebi, K.L., Ziska, L.H. and Yohe, G.W., 2016. The shape of impacts to come: lessons and opportunities for adaptation from uneven increases in global and regional temperatures. Climatic change,
139(34), pp.341-349.
Adger,W.N., J.M. Pulhin, J. Barnett, G.D. Dabelko, G.K. Hovelsrud, M. Levy, Ú. Oswald Spring, and C.H. Vogel, 2014: Human security. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability.
Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J.
Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA, pp. 755-791.
Klare, M.T., 2019. All Hell Breaking Loose: The Pentagon’s Perspective on Climate Change, page 60-79, Metropolitan Books, Henry Holt.
Jacobs et al., 2016; Lung, T., Füssel, H.‐M., & Eichler, L. (2017). Europe’s vulnerability to climate change impacts outside Europe. In Climate Change, Impacts and Vulnerability in Europe 2016: An
indicator‐based report. EEA Report No 1/2017 (pp. 288–293). Luxembourg: European Environment Agency; Harrison, P. A., Dunford, R. W., Holman, I. P., & Rounsevell, M. D. A. (2016). Climate change
impact modelling needs to include cross‐sectoral interactions. Nature Climate Change, 6, 885–890.
Liu, J., Mooney, H., Hull, V., Davis, S. J., Gaskell, J., Hertel, T., Lubchenco, J., Seto, K. C., Gleick, P., Kremen, C., & Li, S. (2015). Systems integration for global sustainability. Science, 347. 1258832; Pidgen,N., Kasperson, R. E., & Slovic, P. (2003). The Social Amplification of Risk. Cambridge: Cambridge University Press.
Challinor, A.J., Adger, W.N., Benton, T.G., Conway, D., Joshi, M. and Frame, D., 2018. Transmission of climate risks across sectors and borders. Philosophical Transactions of the Royal Society A:
Mathematical, Physical and Engineering Sciences, 376(2121), p.20170301.
Adger, W.N., Eakin, H. and Winkels, A., 2009. Nested and teleconnected vulnerabilities to environmental change. Frontiers in Ecology and the Environment, 7(3), pp.150-157.
Maskrey, 1993b; Lavell, 1994

The Alan Turing Institute

3. Climate change and national andhuman security
Given the diverse effects of climate change
in the biosphere and human societies, thesecurity implications of climate change
have attracted increasing attention inpolicymaking, national intelligence andresearch circles since the early 2000s20.
Researchers and policy actors employ
different approaches in their analyses ofthe broad and complex security risks posedby climate change ranging from human tocommunity, state and international security.
We follow the approach taken in the IPCC
(Intergovernmental Panel on Climate
Change) 5th assessment report and will use
a comprehensive understanding of security.
This approach is centred on human security,
defined as the absence of threat or want, butalso addresses the links between different
dimensions of security. Human security
remains central while the other security
dimensions of national interest related to
trade, migration, instability, and conflict arelikely to have negative effects on humansecurity too.
Climate change directly affects the securityof humans and states, both as a matter ofdisasters that require emergency assistanceand logistical aid support, but also dealing

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with the threats of sea level rise and storms
to infrastructure and the possible disruptionof social cohesion and mobility patterns due
to abrupt changes in food, energy and water
resources.
Without action the impacts of climatechange are predicted to:- Impact 80% of the world’s poorest who
will be living in fragile contexts by 203021 andincrease food prices by 20% for billions of
low-income people22.
- Put 100 million people at risk of being
pushed into extreme poverty by 2030, and
720 million by 2050 23.
- Increase the number of people who lacksufficient water from 3.6 billion today to 5
billion by 205024.
- Force hundreds of millions out of coastal
cities, with a cost to coastal urban areasof more than GBP 800 billion every year by
205025.
- Increase the cost of climate-related
disasters to a GBP 2.17 trillion by 2040.

“The World Climate and Security Report 2020.” Product of the Expert Group of the International Military Council on Climate and Security. Authors: Steve Brock (CCS), Bastien Alex (IRIS), Oliver
Leighton Barrett (CCS), Francesco Femia (CCS), Shiloh Fetzek (CCS), Sherri Goodman (CCS), Deborah Loomis (CCS), Tom Middendorp (Clingendael), Michel Rademaker (HCSS), Louise van Schaik(Clingendael), Julia Tasse (IRIS), Caitlin Werrell (CCS). Edited by Francesco Femia & Caitlin Werrell. Published by the Center for Climate and Security, an institute of the Council on Strategic Risks.
Feb 2020.; Campbell, K.M. ed., 2009. Climatic cataclysm: The foreign policy and national security implications of climate change. Brookings Institution Press.; Campbell, K.M. ed., 2009. Climatic
cataclysm: The foreign policy and national security implications of climate change. Brookings Institution Press; Gemenne, F., Barnett, J., Adger, W.N. and Dabelko, G.D., 2014. Climate and security:evidence, emerging risks, and a new agenda.; Rüttinger, L., Smith, D.F., Stang, G., Tänzler, D. and Vivekananda, J., 2015. A new climate for peace: Taking action on climate and fragility risks: An
Independent Report Commissioned by the G7 Members. Adelphi.; Board, C.M.A., 2014. National security and the accelerating risks of climate change. CNA Corporation.; Louise van Schaik et al,
March 2020, Ready for take-off? Military responses to climate change, Clingendael Report, Available at: https://www.clingendael.org/sites/default/files/2020-03/Report_Military_Responses_to_
Climate_Change_March_2020 pdf Accessed: 22 June 2020
OECD (2018), States of Fragility 2018, OECD Publishing, Paris, https://doi.org/10.1787/9789264302075-en.
Nelson, G.C., Valin, H., Sands, R.D., Havlík, P., Ahammad, H., Deryng, D., Elliott, J., Fujimori, S., Hasegawa, T., Heyhoe, E. and Kyle, P., 2014. Climate change effects on agriculture: Economic responses
to biophysical shocks. Proceedings of the National Academy of Sciences, 111(9), pp.3274-3279.
Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D. and Vogt-Schilb, A., 2015. Shock waves: managing the impacts of climate change on poverty. The
World Bank.
Water, U.N., 2018. Nature-based solutions for water. The United Nations World water development Report.
Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D. and Vogt-Schilb, A., 2015. Shock waves: managing the impacts of climate change on poverty. The
World Bank.

The Alan Turing Institute

3. Climate change and national and human security
Additionally, transitioning to renewableenergy will not just influence the power
balance between countries, but will also
reconfigure trade flows26, create newinterdependencies around renewables
and commodities and could change theincidence of certain kinds of conflict that are
driven by oil, gas, and water rivalries.
Climate impacts will directly affect the UK’s
Defence and Security capabilities due tofactors ranging from increased demand forhumanitarian and disaster relief operations;
supporting civil authorities and international
peace-keeping efforts; disruptions to global
food production; and shifts in geopolitics

due to conflicts triggered by climate change
and associated migration which could haveknock on effects on the UK. In fact, it isestimated that double the number of people
in non-conflict areas will need humanitarianassistance by 2050 costing USD20 billion27.
Additionally, the UK’s banking sector isparticularly exposed to extreme climate
events elsewhere in the world due tobanking’s centrality in the UK’s economy
and its dependence on global assets28. As
such, extreme events elsewhere could leadto increased unrest in the UK as a result ofabrupt shocks to the UK economy.

“Given the diverse effects of climatechange in the biosphere and humansocieties, the security implicationsof climate change have attracted
increasing attention in policymaking,national intelligence and research
circles since the early 2000s.”

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Dellink, R., Hwang, H., Lanzi, E. and Chateau, J., 2017. International trade consequences of climate change.
United Nations. Office for the Coordination of Humanitarian Affairs. Policy Analysis and Innovation Section, 2019. World humanitarian data and trends 2018. UN.
Mandel, A.(2020). Economics of Climate Change and Green Finance. http://www.bachelier-paris.fr/cours/source/ressources/2020-mandel.pdf

The Alan Turing Institute3.1. Types of climate security risks
Climate change affects the biosphereand human societies in various ways, so
there are different approaches to security
analysis. Human security risks spill over
into higher-order security risks, such aspolitical instability, intra-state and inter-statetensions, major natural disasters requiring
military responses, mass displacementsof people as well as threats to criticalresources and infrastructure.
We distinguish 3 types of security risks
that are relevant for the national security:– Physical risks to resources andinfrastructure which include direct security
risks to ecosystems, resources such as food,water and energy and critical infrastructure.
To understand the direct physical risks on
resources we will use the nexus approach,which considers the interconnected natureof water, land and energy resources andassociated infrastructure.

– Risks to human mobility and social
cohesion that occur when people’s
livelihoods are threatened and can resultin increased violence, conflict and shifts inmigratory patterns as a response to socialand environmental changes.
– Transition risks which include theimpacts from the shift towards a lowcarbon economy and may entail policy,
legal, technology, and market changes toaddress both climate change mitigation andadaptation requirements. Depending on thenature, speed, and focus of these changes,
transition risks may pose varying levels ofnational and human security risks.

Figure 2. This diagram illustrates the connected nature of climate security risks

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The Alan Turing Institute

3.1.1 Physical Risks to resources and infrastructure
Water, energy, and food are vital for humanwellbeing. The three resources are strongly
linked and any climate impact on one affects
the other two. Climate change and extreme
events are also projected to damage arange of critical infrastructure, with waterand sanitation, energy, and transportation
infrastructure being particularly vulnerable29.
Agriculture is highly vulnerable to climate
impacts with climate shocks being theleading causes of food crises in 201730.
Climate variability impacts food security
by making production less reliable, which
increases food price volatility and reduces
access to food31. Increases in pests, weeds
and diseases put further pressure on foodproduction32. The UK imports 40% of thefood it consumes, thus impacts of climatechange on agriculture across the globe
has direct implications for UK food security
through trade networks33.

“Climate change and extreme
events are projected to damage arange of criticial infrastructure, withwater and sanitation, energy, andtransportation infrastructure being
particularly vulnerable .”

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32
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37

A key constraint on crop production will
be water availability34 which will decline inregions such as southern Europe, China
and the USA35 affecting irrigation systems
and food production. Climate change willaffect the availability, quality and quantity ofwater for potentially billions of people. Water
scarcity, exacerbated by climate change,
could cost some regions up to 6% of theirgross domestic product, while spurringmigration and sparking conflict36.
The energy requirements for irrigationand drinking water further increase when
the water has to be brought from greater
distances or from deeper groundwater
bodies. Conversely, energy production also
requires water. Although this is probably
most evident for growing biofuels or formining fossil fuels (e.g. hydraulic fracturing,or ‘fracking’) hydropower reservoirs anddams can affect the availability and quality ofwater too.
Extreme weather events will directly impact
energy security. The demand for energy iscorrelated with increases in temperature
which will limit our capacity of power
generation and the ability to reliably deliverelectricity. The links between food andenergy have also become quite apparent
in recent years as increases in the priceof oil lead very quickly to increases in theprice of food37. Food security can also beendangered by energy demand shocks,
for instance, with aggressive bioenergy
subsidies and quota policies.

Habitat, U.N., 2011. Cities and climate change. Global report on human settlements.
Food and Agriculture Organization of the United Nations (FAO) (2018). The State of Food Security and Nutrition in the World
Myers, S. S. et al. (2017). Climate Change and Global Food Systems: Potential Impacts on Food Security and Undernutrition. Annu. Rev. Public Health, Vol 38, 259–277.
InterAcademy Partnership (2018). Opportunities for future research and innovation on food and nutrition security and agriculture: The InterAcademy Partnership’s global perspective
Foresight (2011). International Dimensions of Climate Change. The Government Office for Science, London; UK Committee on Climate Change (2017). UK Climate Change Risk Assessment 2017
Synthesis Report.
Myers, S.S., Smith, M.R., Guth, S., Golden, C.D., Vaitla, B., Mueller, N.D., Dangour, A.D. and Huybers, P., 2017. Climate change and global food systems: potential impacts on food security andundernutrition. Annual review of public health, 38, pp.259-277.
Kent, C., Pope, E., Thompson, V., Lewis, K., Scaife, A.A. and Dunstone, N., 2017. Using climate model simulations to assess the current climate risk to maize production. Environmental Research
Letters, 12(5), p.054012.
World Bank Group, 2016. High and dry: Climate change, water, and the economy. World Bank.
Taghizadeh-Hesary, F., Rasoulinezhad, E. and Yoshino, N., 2019. Energy and food security: Linkages through price volatility. Energy policy, 128, pp.796-806.

The Alan Turing Institute

3.1.2. Risks to human mobility and social cohesion
Migration and displacement
The dynamics between mobility and climatechange are multifaceted. Sea level rise andnatural disasters and food insecurity canlead to migration but it’s difficult to establish
direct causation. Climate change affects
several drivers of human movement38, suchas conflict, the (perceived) opportunity fora better life in another country and factors
affecting governance and the resilience ofsocieties. The uncertainties in the responseof global and regional precipitation patterns
to climate change and the level of future
greenhouse gas emissions make it difficult
to accurately predict the increase in climate
induced migration.
The type of migration varies according
to the type of environmental event39. Sea
level rise, which makes low-lying coastal
regions uninhabitable, can cause permanent
migration while extreme weather events
may lead to temporary movements within aregion. More research is needed to establish
which climate impact (e.g. drought, floods,
food and water scarcity) contributes themost to displacement.
Most environmental migration takes placeinternally, as there are typically fewer
physical, institutional, and financial barriers
to mobility. In most cases, the migration
destination is large coastal cities that offer
more employment prospects40 and most

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40
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migrants return to their original residence assoon as practical.
Environmental migration can be linked topolitical instability, but the security literaturewarns us to be wary of cause-effectassumptions41. Migration due to a short-termclimatic event such as a flood is less likely tocause conflict than migration due to a longterm climatic event such as a drought. Thisis because migrants responding to shortterm climatic events are unlikely to competewith locals in the receiving areas for jobs
and public services. The lack of conclusive
evidence linking climatic changes with
migration and conflict is largely due tothe difficulty in adequately modelling thecomplexity of this relationship.
Security risks in relation to migration should
also focus on those who are left behind. The
poorest of the poor, are often concentrated
in locations that are highly exposed toclimate hazards42 and their livelihoods are
directly tied to natural resources43. These
populations typically have few adaptation
options, and may not have the ability to
migrate at all which traps them in poverty44.
More research is needed to compare acrosscountries and populations and forecast
which groups are most likely to be subject toclimate induced migration.

Burrows K., and Kinney P. 2016. “Exploring the Climate Change, Migration and Conflict Nexus”. International Journal of Environmental Research and Public Health 13 (4): 443.
Hunter, L.M., Luna, J.K. and Norton, R.M., 2015. Environmental dimensions of migration. Annual Review of Sociology, 41, pp.377-397.
Fafchamps M., and Shilpi F. 2013. “Determinants of the Choice of Migration Destination”. Oxford Bulletin of Economics and Statistics 75 (3): 388– 409.
Burrows, K. and Kinney, P.L., 2016. Exploring the climate change, migration and conflict nexus. International journal of environmental research and public health, 13(4), p.443.; Fröhlich, C. andBrzoska, M., 2015. Real Risk or Overrated? Environmental Migration and Violent Conflict.; Bernauer, T., Böhmelt, T. and Koubi, V., 2012. Environmental changes and violent conflict. Environmental
Research Letters, 7(1), p.015601.
McLeman, R.A., 2011. Settlement abandonment in the context of global environmental change. Global Environmental Change, 21, pp.S108-S120.
Burgess, R., Deschenes, O., Donaldson, D. and Greenstone, M., 2014. The unequal effects of weather and climate change: Evidence from mortality in india. Cambridge, United States: Massachusetts
Institute of Technology, Department of Economics. Manuscript.
Government Office for Science (UK government). 2011. “Migration and global environmental change: future challenges and opportunities”. https://www. gov.uk/government/publications/migrationand global-environmental-change-future-challengesand-opportunities; Adger, W.N., Arnell, N.W., Black, R., Dercon, S., Geddes, A. and Thomas, D.S., 2015. Focus on environmental risks andmigration: causes and consequences. Environmental Research Letters, 10(6), p.060201. McLeman, R.A. and Hunter, L.M., 2010. Migration in the context of vulnerability and adaptation to climatechange: insights from analogues. Wiley Interdisciplinary Reviews: Climate Change, 1(3), pp.450-461.

The Alan Turing Institute

3.1.2 Risks to human mobility and social cohesion
Lastly, there has been a tendency to focus
on the negative consequences of climate
induced migration, but mobility is a widelyused strategy to maintain livelihoods in
response to climate and environmental
changes45. Governments are using planned
relocation as a potential policy option toprotect affected populations46. Even thoughdata on internal environmental migrationand planned relocation have improvedin recent years47 we lack comparable

quantitative, longitudinal, disaggregatedand georeferenced data needed to assess
the benefits and risks of different forms ofmobility as adaptation strategy.

Conflict

Most research to date, has focused on
the links between climate variability andintrastate conflicts. Shocks to agricultural
production in fragile settings, where
droughts or floods may disrupt production
can lead to localised violence and foodinsecurity. Climate climate also affects foodprices and higher food prices can increase
conflict risks50. Other analyses have foundthat with each one-degree increase intemperature interpersonal conflict increasedby 2.4% and intergroup conflict by 11.3%.

In the past decade, despite the improved
data and analytical sophistication, scholars
haven’t converged on a single robust
association between climate and conflict.
Several patterns have been reported indetailed syntheses of the literature48 but
more nuanced and context specific analyses
are required to firmly establish the security
dimensions of climate change49.
While the impacts of climate change are
expected to increase over time, many
climate-related disasters are seasonal andaffect the dynamics of conflicts differently.
For instance, farmers may take to thestreets in sporadic protests in response
to a weather shock that damages crops.
However, when faced with long-term decline
in rural incomes as the result of aridity,
pressure to move to the cities may mount,possibly leading to different types of social
tension.

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50

McLeman, R.A. and Hunter, L.M., 2010. Migration in the context of vulnerability and adaptation to climate change: insights from analogues. Wiley Interdisciplinary Reviews: Climate Change, 1(3),pp.450-461.
Ionesco, D., Mokhnacheva, D. and Gemenne, F., 2016. The atlas of environmental migration. Taylor & Francis; Benton 2017, https://georgetown.app.box.com/s/
v1496c75f0saouevj5yfump4k5am8shz; Ferris, E., 2017. A toolbox: Planning relocations to protect people from disasters and environmental change. Institute for the Study of International Migration,
UNHCR, The UN Migration Agency: Georgetown University, Washington DC.
Norwegian Refugee Council/Internal Displacement Monitoring Centre (NRC/IDMC), Global Report on Internal Displacement - 2020, available at: https://www.internal-displacement.org/
publications/2020-global-report-on-internal-displacement
Bernauer, T., Böhmelt, T. and Koubi, V., 2012. Environmental changes and violent conflict. Environmental Research Letters, 7(1), p.015601; Buhaug, H., 2015. Climate–conflict research: somereflections on the way forward. Wiley Interdisciplinary Reviews: Climate Change, 6(3), pp.269-275; Gemenne, F., Barnett, J., Adger, W.N. and Dabelko, G.D., 2014. Climate and security: evidence,
emerging risks, and a new agenda.; Gleditsch, N.P., 2012. Whither the weather? Climate change and conflict.; Hsiang, S.M., Burke, M. and Miguel, E., 2013. Quantifying the influence of climate on
human conflict. Science, 341(6151), p.1235367; Simmons, E., 2013. Harvesting Peace: Food security, conflict, and cooperation. Environmental Change and Security Program Report, 14(3), p.02.
Adam Day and Jessica Caus, Conflict Prevention in an Era of Climate Change: Adapting the UN to Climate-Security Risks (United Nations University: New York, 2020)
Katharine J. Mach et al., “Climate as a Risk Factor for Armed Conflict,” Nature 571 (2019): 195.

The Alan Turing Institute

3.1.2 Risks to human mobility and social cohesion

Moreover, the risk for armed conflict
increases immediately after climaterelated disasters which trigger rebels to act
violently. For instance, a drought affecting
Mali in June 2009 helped Al-Qaeda recruit
fighters and extend its area of operation in
the country.
Climate change has the potential to increase
rivalry between countries over sharedresources such as land, water and food51
but this is unlikely to lead directly to warfare
between states. The links between climatechange and conflict are mediated by anumber of contextual factors, such as thelevel of urbanization, poverty, the distributionof land and governance structures52. Climatechange, thus, represents a challenge to theinstitutions responsible for addressing acuteresource shortages and disputes in a nonviolent manner53.

Given these contextual variations, more
empirical research is needed to establish
the localized ways in which climate change
is driving violence. Quantitative climateconflict studies need to focus on spatial
and temporal disaggregation of data at
a subnational level.This can reduce thechances of missing important conflict
related events or causal mechanisms at
a local scale. Future research also needs
to provide comparability of findings asmost climate-conflict analyses are often
conducted at different geographic, temporaland social scales, making comparisonsacross studies quite challenging. 1“While the impacts of climate change
are expected to increase over time,
many climate-related disasters areseasonal and affect the dynamics ofconflicts differently.”

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52
53

von Uexkull N. Sustained drought, vulnerability and civil conflict in Sub-Saharan Africa. Polit Geogr. 2014;43(0):16–26.; Halvard Buhaug et al., “Climate Variability, Food Production Shocks, andViolent Conflict in Sub-Saharan Africa,” Environmental Research Letters 10 (2015); Benjaminsen TA, Alinon K, Buhaug H, Buseth JT. Does climate change drive land-use conflicts in the Sahel? J
Peace Res. 2012;49(1):97–111; Jaroslav Tir and Douglas M. Stinnett, “Weathering Climate Change: Can Institutions Mitigate International Water Conflict?,” Journal of Peace Research 49, 1 (2012):
211-225.
Nina von Uexküll, “Sustained Drought, Vulnerability and Civil Conflict in Sub-Saharan Africa,” Political Geography 43 (2014): 16–26.
von Uexkull N, Croicu M, Fjelde H, Buhaug H. Civil conflict sensitivity to growing season drought. Proc Natl Acad Sci U S A. 2016;113(44):12391–6; Jaroslav Tir and Douglas M. Stinnett, “Weathering
Climate Change: Can Institutions Mitigate International Water Conflict?,” Journal of Peace Research 49, 1 (2012): 211-225.; Linke AM, Witmer FDW, O’Loughlin J, McCabe JT, Tir J. Drought, local
institutional contexts, and support for violence in Kenya. J Conflict Resolut. 2017

The Alan Turing Institute

3.1.3. Transition Security Risks
Risks from climate change adaptation
A safe and just climate transition requires
governments to create plans to addressresilience and disaster risk54. Adaptation is
not necessarily or always a benign or risk
free process55 and the security risks arisingfrom adaptation are much less studied by
the national intelligence community.
Policy responses that claim to support
climate change adaptation can be an
additional source of human insecurity56.
For example57 in Gambella, Ethiopia, landis being made available to national andforeign investors for agricultural investment
as part of national plans for economic
modernisation and perceived benefits ofincreased food security through connection
to global food markets. These policies inturn, have increased insecurity by devaluing
the capacity of traditional means ofresilience (e.g. shifting cultivation) towardhydro-climatic changes58.
Actions59 taken in response to climatechange can aggravate existing inequalities
or grievances over resources and limit
access to land and other resources required
to maintain livelihoods. For example, inTahoua, Niger, unreliable rainfall, loss ofsoil quality and growing populations has
prompted agro-pastoralist farmers to adapt
by expanding arable farming into areas
reserved for pasture, which puts stress on
the livelihoods of pastoralists.
Social unrest and violence can also be a
result of adaptation policies60.

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Social unrest can develop as a result of
involuntary land-use changes, often framed
as “land grabbing” by governments. For
example, countries dependent on foodimports, and which are particularly affectedby food shortages made worse by climate
stresses such as Saudi Arabia, Japan andSouth Korea, are increasingly looking for
fertile farmland in developing countries like
Uganda, Brazil and Kazakhstan61.
Security risks arise as various governments
are moving settlements as part of theiradaptation strategies. For example, whenpeople have to leave a forest in order topreserve it as a carbon sink under REDD+ or
when people move away from coastal areas
in anticipation of sea-level rise or floods.
Last but not least, poorly planned relocationof communities that does not adequately
consider people’s housing needs has beenfound to create significant new insecurities
for those relocated, reduce social capitaland trust62.
There is currently no robust, evidence based
framework for assessing and anticipatingthese climate maladaptation risks that canhelp policy makers determine the potential
side effects of an initiative before it is
implemented. There is thus a need to movefrom an exclusively ex post assessment ofobserved side effects to an ex ante approachof the expected side effects. Climate risk
assessments require better data on leadtimes required to convert policy plans into
implementation, as this translation may bedelayed by institutional or societal barriers,
and may have residual security risks.

van Schaik, L., Born, C., Sellwood, E. and de Bruin, S., 2019. MAKING PEACE WITH CLIMATE ADAPTATION.
Barnett J, O’Neill S. Maladaptation. Glob Environ Change 2010, 20:211–213.; Juhola S, Glaas E, Linnér B-O, Neset T-S. Redefining maladaptation. Environ Sci Policy 2016, 55:135–140
Macintosh A. Coastal climate hazards and urban planning: how planning responses can lead to maladaptation. Mitig Adapt Strat Glob Change 2013, 18:1035–1055; Zografos, C., Goulden, M.C. andKallis, G., 2014. Sources of human insecurity in the face of hydro-climatic change. Global environmental change, 29, pp.327-336.
Milman, A. and Arsano, Y., 2012. Climate adaptation in highly vulnerable regions: The politics of human security in Gambella, Ethiopia. CLICO case study (on file with authors).
http://www.clico.org/
Marino, E. and Ribot, J., 2012. Special issue introduction: adding insult to injury: climate change and the inequities of climate intervention; Snorek, J., Renaud, F.G. and Kloos, J., 2014. Divergent
adaptation to climate variability: a case study of pastoral and agricultural societies in Niger. Global Environmental Change, 29, pp.371-386
Hegre, H., Buhaug, H., Calvin, K.V., Nordkvelle, J., Waldhoff, S.T. and Gilmore, E., 2016. Forecasting civil conflict along the shared socioeconomic pathways. Environmental Research Letters, 11(5),p.054002
Cotula, L., 2009. Land grab or development opportunity?: agricultural investment and international land deals in Africa. Iied.
Gebert, N., Kloos, J., Birkmann, J. and Rosenfeld, T., 2012. Emerging Risks: sea level rise and potentially forced and planned relocation-Case study from Greater Alexandria, Egypt. Institute forEnvironment and Human Security, United Nations University, Final Report of CLICO project.

The Alan Turing Instituteworld’s transition to cleaner energy forms
will change the geopolitical map and create

new alliances and commercial routes ascountries are beginning to rethink their

Risks from climate change mitigation

The widespread adoption of renewableenergy technologies, such as solar panelsand electric vehicles is expected to increase
the demand for a range of minerals andmetals required for their production and it
could potentially increase intrastate conflict.
For example, in Colombia, a country in which
the longest-running interna armed conflicthas taken place, various armed groups havecontrolled and exploited illegal tin, tungsten,tantalum and gold mining resources.

The intelligence community has mostly
focused on the direct physical risks andhas overlooked the security implications ofclimate change mitigation which are likely
to bring the biggest geopolitical changes in
the next 10 years. This ongoing transition
involves a much deeper transformationof the world’s energy systems that will
have major social, economic and political
implications which go well beyond theenergy sector63 .
Nationally, dams often built for hydropowerthat substitutes fossil-fuel powered energy
production are likely to be impacted bychanges in water resources affected byclimate change. While the damming of majorrivers may increase the energy securityof upstream states, it could harm watersupplies, agricultural productivity and fishstocks in downstream states.
Negative emissions technologies suchas bioenergy with carbon capture andstorage, enhanced weathering of minerals,
afforestation and reforestation can havepotential security risks64. For example therapid expansion of biofuels production leads
to trade-offs between water use, energy
security and food security and is connectedto land grabbing, land dispossession65, andsocial conflict that reproduces existing
inequalities.

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The widespread adoption of renewables
drives among other factors the increasing
digitalisation of the energy grids whichraises additional security and privacy risks.
Criminal groups, terrorists, or the security
services of hostile countries may hack into
the digitized systems that control utilities
and grids, either for criminal purposes suchas fraud and theft, or to commit military orindustrial espionage.
The energy transformation may also deepen
existing national political divisions. For
example, in many of the countries in whichgovernments have attempted to phase out
fossil fuel consumption subsidies, protesters
have frequently taken to the streets to
oppose these reforms66.

Cavanagh, C. and Benjaminsen, T.A., 2014. Virtual nature, violent accumulation: The ‘spectacular failure’ of carbon offsetting at a Ugandan National Park. Geoforum, 56, pp.55-65.
Smith, P., Davis, S.J., Creutzig, F., Fuss, S., Minx, J., Gabrielle, B., Kato, E., Jackson, R.B., Cowie, A., Kriegler, E. and Van Vuuren, D.P., 2016. Biophysical and economic limits to negative CO 2 emissions.
Nature climate change, 6(1), pp.42-50.
Benjaminsen, T.A. and Bryceson, I., 2012. Conservation, green/blue grabbing and accumulation by dispossession in Tanzania. Journal of Peasant Studies, 39(2), pp.335-355; Lund, J.F., Sungusia, E.,
Mabele, M.B. and Scheba, A., 2017. Promising change, delivering continuity: REDD+ as conservation fad. World Development, 89, pp.124-139.
https://www.iisd.org/gsi/subsidy-watch-blog/how-reforming-fossil-fuel-subsidies-can-go-wrong-lesson-ecuador; Rentschler, J., & Bazilian, M. (2017). Reforming fossil fuel subsidies: drivers, barriers
and the state of progress. Climate Policy, 17(7), 891-914.

The Alan Turing Institute

3.1.3 Transition Security Risks
Internationally, the energy transformation is
expected to increase the vulnerability of oil,gas or coal producing countries. It will put
pressure on fossil fuel prices and oil rents aswell as reconfigure the global supply chainsof oil. The loss of oil rents in countries withweak governance could lead to fracturesin society and political instability. Countries
that are highly dependent on fossil fuelrents are also highly exposed to stranded
assets. 12 trillion US dollars of financial value
could be lost in the form of stranded assets
and international bodies leaving certain
countries like Qatar and UAE more exposedto transition risk.
The reduced flow of oil rents could
subsequently affect non-oil producing
countries and increase the energy insecurity
for fossil fuel importers67 such as Lebanon,
Egypt and Jordan. Fossil fuel importing
countries are vulnerable to risks of supply
disruption and price volatility caused bypolitical instability, terrorist attacks, or armed
conflicts that may occur in oil- and gasexporting nations. Smaller energy-importing
countries may also be subject to pressure
or coercion with regard to their energy
supply68.

Abrupt changes in climate policy would
introduce ‘shocks’ to the global financial
system, with wide-ranging impacts69. Most
financial institutions have large direct andindirect exposure to climate-related sectors,
although standard risk assessments do nottake these into account.
Last but not least, we have to consider new
risk conceptualisations which might evolvein possible dynamic and disruptive futures.
For example, will geoengineering pose asignificant security risk? Will solar radiation
management be weaponised? Will coercive
power and therefore potentially military
operations be undertaken to protect natural
capital or climate global public goods ? (asis effectively already done by `Blue-Water’Navies that protect global maritime shipping
routes to ensure the free flow of international
trade). The security risks of energy transition
and geoengineering are relatively uncharted
territory and we lack evidence of theintelligence community considering theserisks extensively.

“Actions taken in response to climatechange can aggravate existing
inequalities or grievances overresources and limit access to land andother resources required to maintain
livelihoods.”

67

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69

The secretariat of the Global Commission on the Geopolitics of Energy Transformation en Thjis Van de Graaf, “A New World: The Geopolitics of the Energy Transformation” (Abu Dhabi: IRENA, 2019), http://hdl.
handle.net/1854/LU-8588274.
Expert Working Group on Climate-related Security Risks, “Iraq Climate-related security risk assessment”, August 2018, https://www.eastwest.ngo/sites/default/files/iraq-climate-relatedsecurity-riskassessment.pdf.
Battiston, S., Mandel, A., Monasterolo, I. et al. A climate stress-test of the financial system. Nature Clim Change 7, 283–288 (2017). https://doi.org/10.1038/nclimate3255

The Alan Turing Institute3.2. Climate security stakeholder landsape
Several military and intelligence organizations around the globe now recognize the security
dimensions of climate change. Many find themselves increasingly confronted with extreme
weather or its impact as a threat multiplier. Organisations, including the UN, NATO and theEU70 are working on this issue in an attempt to foster cooperation by military organizations inaddressing climate change at multiple scales. These stakeholders have different capabilities
and the UK National intelligence community should identify the best mechanisms to engage
with them.

National
British security and defence strategies
already identify climate change as a factor
impacting the armed forces, in particularin terms of emergency operations, both athome and overseas. The National Security
Council, which consists of 4 ministerial
sub- committees considers the threats,
hazards, resilience and contingencies,
nuclear deterrence and security mattersrelating to implementing the Strategic
Defence and Security Review (SDSR) andthe National Security Strategy (including
cyber matters) cross-government funds.
The Ministry of Defence is supported by the
Defence Infrastructure Organisation andthe Development, Concepts and Doctrine
Centre for assessing future trends.
The climate security landscape in the UK
includes various other institutions who haveadopted climate security and resilience intheir practices. These are the Department
for International Development, the Overseas
Development Institute and the Foreign
and Commonwealth Office. Through the
Climate Change Act 2008, the UK introduced
legislation that requires the government
to undertake a cyclical Climate Change
Risk Assessment (CCRA) and National
Adaptation Programme.

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On the research front, the UK Researchand Innovation, the National Environmental
Research Council & the Met Office arefinancing large scale research programmes,
such as the Strategic Priorities Fund’s
Climate Resilience Programme, which aim
to better quantify climate risk and design
adaptive strategies for the UK. These efforts
address national and international security
risks but they are not linked to the strategic
priorities of the national intelligencecommunity.
Regional
Climate-related security risks have become
increasingly mainstream among policy
makers across regional organisations. At
the regional level there are organizations
such as the AU, ASEAN and the EU,
which address climate change from theperspectives of both human and state
security.

Fetzek, S. and van Schaik, L., 2018. Europe’s Responsibility to Prepare: Managing climate security risks in a changing world. Center for Climate and Security, Clingendael & Planetary Security
Initiative.

The Alan Turing Institute3.2. Climate security stakeholder landsape
Intergovernmental
The scale, depth and transnational natureof climate-related security risks challenges
the capacity of national and regional
governments to respond adequately.
Intergovernmental organizations forclimate security —such as the United
Nations (UN), the European Union (EU), theOrganization for Security and Cooperationin Europe (OSCE), the African Union (AU),the Association of Southeast Asian Nations(ASEAN) and Comunidad Andina (CAN)—
frame and incorporate climate-related
security risks in their agendas
The United Nations Security Council
since early 2017 have stressed the needfor adequate climate risk assessmentand management strategies. At present,
there are several initiatives dedicatedto the assessment and coordination ofclimate-related security risks including
the formation of a Group of Friends on
climate security, the Climate Security
Mechanism which is the UN’s response
to climate-related security risks (by UNDP,
UNEP, UNSC) and the independent Climate
Security Expert Network establishedwith support from Sweden and developed
further by Germany which helps inform UN
responses to climate risk management.

Notable research organisations working
on climate security include: The Centerfor Climate and Security (CCS), a nonpartisan institute of the Council on Strategic
Risks, The Climate Diplomacy Initiativeby the German Federal Foreign Office incooperation with adelphi, The Planetary
Security Initiative (PSI) and The Stockholm
Climate Security Hub which provides
evidence-based insights on building security
and prosperity and strengthening resiliencein the face of a changing climate.
Currently, there is no equivalent global
hub of standardised, authoritative climate
security information that reflects the security
and/or social science consensus on theissue, that ranks the confidence of certain
relationships in the climate security nexus,
or that presents credible climate security
futures.
The institutional fragmentation and thetransnational nature of climate risk creates
difficulties in sharing of data and adopting
common data standards and methodologies
when hazard monitoring is spread acrossmultiple jurisdictions. There is thus a needfor increased multi-disciplinary, multi-agency
coordination and collaboration in order toimprove forecasting tools.

“Organisations, including the UN, NATOand the EU are working on this issue
in an attempt to foster cooperation by
military organizations in addressing
climate change at multiple scales.”22

The Alan Turing Institute

4. Developing early warning systems forclimate security
Could we have predicted the risks we
describe above? Could we have anticipatedthe political impacts of climate changearound the world and in the UK? Could wehave prepared and helped our allies preparetoo? How can we best embed climate risk inour strategic decision-making processes?
This section addresses these questions.

It is estimated that implementing effective
disaster-risk actions can result in a 90%
decrease in people needing international
humanitarian assistance after climaterelated disasters by 2050 and that spending
USD 4 billion on early warning systems indeveloping countries can have a benefit cost
ratio between 4 to 36.

Climate risk decision-making is a complex,
multi-scale, often nested, process. It takes
place on a range of time and spatial scales
and involves complex connections (i.e. social
networks, governance structures, multiple
sectors) that are increasingly important.
Importantly it requires the developmentof expert judgement to be used in everchanging systems where past data is not areliable predictor of future data.

Effective early warning systems embrace
all aspects of emergency management,such as: risk knowledge and assessment;
monitoring and predicting the locationand intensity of events; disseminating andcommunicating warnings; and planninga response. In this chapter we will focus
on the opportunities for future work on a)
improving our risk knowledge about climate
security, and b) developing novel resilience
monitoring and early warning systems, while
we address the complexity of the decisionmaking process around climate change inthe following chapter.

Decision support tools like early warning
systems for climate security, are criticalcomponents of the decision-making chain
for climate risk.

Figure 3. Overview of the components of early warning systems and the identified researchchallenges around improving climate risk knowledge and resilience monitoring.

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The cost of doing nothing: The humanitarian price of climate change and how it can be avoided, International Federation of Red Cross and Red Crescent Societies, Geneva, 2019
Hallegatte, S., 2012. A cost effective solution to reduce disaster losses in developing countries: hydro-meteorological services, early warning, and evacuation. The World Bank.
Grasso, V.F. and Singh, A., 2011. Early warning systems: State-of-art analysis and future directions. Draft report, UNEPThe Alan Turing Institute

4.1 Improved climate security risk knowledge
Risk analysis has been the centrepiece of
security planning for many years. Assessing
the risk of climate-related impacts onnatural and human systems requires anunderstanding of changes in the climate
system (including natural variability andanthropogenic climate change), as well
as changes in socio-economic processes(including climate change mitigation andadaptation actions). Both elements, and thecomplex interactions between them, aredrivers of hazard, exposure and vulnerabilityof natural and human systems.
The severity of climate change impacts
depends not only on the nature of climate
hazards and the resilience of natural
ecosystems but also on human factors
such as the degree of socio-economic
development, social inequalities, humanadaptive capacities, health status and healthservices, demographic characteristics,
economic livelihood alternatives, etc.
The most fundamental components forunderstanding and analysing climaterelated risks are clear, verifiable, timely, andcomparable climate and socio-economic
data. These data sources consist ofmeasurements of past and current changes
based on historical data (see Appendix
Y1), as well as projections of what futurechanges may look like (see Appendix
Y2), informed by modelling and scenario
approaches (see Appendix Y3).
Any new climate risk model will rely on
current climate modelling capabilities, either
explicitly as data sources, or as an integrated
part of a larger model. Indeed, the first stepof a predictive climate security model will
be to predict the change in the climate.
However, the predictability of a given climate
variable can vary widely, depending on how
far in advance you are looking and on whatscale.

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For example, the weather tomorrow is highly
predictable at a very local scale, whereas
we can only make projections of the averagetemperature in 50 years at a regional scale.
Seasonal climate forecasts for the next 6
months are commonly produced (Appendix
Y2.1), and there is a reasonably good skill in
forecasting the El Nino Southern Oscillation(ENSO), which plays a substantial role onthese timescales.
There is currently limited skill at
forecasting climate data on a multi-yeartimescale (Appendix Y2.2), although
average temperature, and to lesser extent
precipitation, have some predictability oversome regions and periods. Due to internalvariability of the climate system, decadal
predictions can only be made in probableranges, not absolute values. For longer-term
projections, e.g. to the end of the century,model and scenario variability are thedominant sources of uncertainty (Appendix
Y3.2).
Process-based modelling of the physicalclimate with so-called general circulation
models (GCMs) is a mature field, albeit
subject to continual improvement. Wellestablished methods are used to assimilate
data into these models to improve forecasts
on hourly to decadal timescales (seeappendix Y3 for more details). However,such physical models rarely exist for socioeconomic systems. Only in specific cases
have socio-economic data been assimilated
into such models to improve forecasting - forexample, forecasting cholera in Haiti after
Hurricane Matthew74.

Pasetto D, Finger F, Camacho A, Grandesso F, Cohuet S, Lemaitre JC, et al. (2018) Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew. PLoS Comput Biol 14(5): e1006127.
https://doi.org/10.1371/journal.pcbi.1006127

The Alan Turing Institute

4.1 Improved climate security risk knowledge
A different class of process-based models,
known as integrated assessment models(IAMs), are commonly used to model how
human development and societal choices
affect each other and the natural world, andto explore the costs, benefits and impacts
of different climate policy and mitigation
options. IAMs rely on economic theories
and evidence from historical data to modelsocio-economic systems.
IAMs typically include several separate
but interconnected modules which aresimplified representations of the economy,
the energy system, land use and agriculture,and the climate system. Simplifying these
individual systems makes IAMs morecomputationally feasible, allowing them to
run the modules together in a reasonable
time and explore how they interact. IAMs
form the basis for the development of the
Shared Socioeconomic Pathways (SSPs see Appendix Y3.4).
Where a process-based model does notexist, perhaps because our understandingof a complex system is not good enoughto make one, statistical methods can still
be used to draw out useful information
about the underlying dynamics of thesystem. This works because whatever thecomplex system, a change in the balanceof feedbacks (closed loops of causality)
causes generic changes in resilience. The
major caveat here is that other changes in asystem that are not related to feedbacks andresilience, can sometimes give rise to thesame statistical signals.
Much of our current uncertainty inforecasting climate security risks stems
from the fact that, although we may have areasonable model for the future behaviourof physical climate systems, even on
a seasonal or regional basis to someextent, and while IAMs allow us to explore
the interactions between physical andsocio-economic systems, there remains
considerable uncertainty about the outcome
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of modelling the impact of climate change
on real-world systems.
This is because we do not have a fully
capable model of the overall system.
Hence the driving force for change may beunderstood, but the response of the system(agriculture, water resources, populations,etc.) to change is not. This is exacerbated inmany cases where the factors which govern
the response are not only physical but
economic, social and of other nature. Also,
global models often focus on predicting
general patterns at regional scales, but
are not designed to address dynamics at
local scales, while at the same time, thenecessary local scale information may
often not be available. There is thus a needto downscale (provide higher resolution)
environmental information, and to upscale
the information on societies75.
This is an ideal opportunity to explore how
AI and machine learning tools could helpby combining different kinds of information.
These tools could also help by seeking tounderstand the system in question throughthe analysis of previous climate forcing,which produced evidence-based responses.
This, in turn, could allow the prediction ofoutcomes under changed climate forcing.
AI and machine learning can thus be a
powerful tool to make predictions about
unmodeled outcomes. We outline two
possible approaches below:
Firstly, taking existing historical climate andsocio-economic data and linking them witha machine learning model, that can thenuse either long-term climate projections or
decadal climate predictions to predict future
security risk
Secondly, building a new causal model that
explicitly links climate and socio-economic
processes together, using a sophisticatedcross-disciplinary approach to create
projections of future security risk.

Verburg, P.H., Dearing, J.A., Dyke, J.G., Van Der Leeuw, S., Seitzinger, S., Steffen, W. and Syvitski, J., 2016. Methods and approaches to modelling the Anthropocene. Global Environmental Change, 39,
pp.328-340.

The Alan Turing Institute

4.1.1 Challenge 1: Integrated Climate Security Risk
Assessment
There is an opportunity to develop
integrated data-driven, multi-hazard
indicators of climate security risks, drawing
on global risks meaningful to decisionmakers at national and local scales andconnecting local and global dynamics.
We can use innovative AI and machinelearning approaches to combine physicalclimate parameters with economic, social,and other non-climate datasets. Such a datadriven statistical approach, incorporatingmachine learning techniques, is alreadybeing applied in certain sectors, for example
in the development of a city-based climatechange business risk index at the Universityof Cambridge76 , or the development ofcountry-specific food insecurity, healthand urban climate change risk indices77, or
location-specific indices of crop yield, heat/cold risks in the built environment, and floodrisk 78.
A climate risk index translates long-term
projections from process-based climate
models onto policy-relevant timescales(e.g. 5-20 years) and regional geographies,
by applying innovative machine learningtechniques to perform the downscaling
and bias-correction of the process-basedmodel output (see appendix Y2.3 for moredetail on downscaling). Alternatively, therisk index could be based on probabilistic
decadal climate forecasts. We canassimilate recent historical climate andweather measurements (from observations
or reanalysis products, see appendix Y3.3)
to train the machine learning model, andto form a recent historical baseline for thecurrent level of risk.

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The index can then calculate the future
likelihood of extreme events that exceed
climate and weather thresholds specific tothe security context, which would be definedin collaboration with security experts andmay be informed by data using AI/ML
techniques.
If we take temperature as an example
variable, the index could be based on certain
temperature thresholds (e.g. 25ºC, 30ºC,
35ºC etc.), and return levels of likelihood of alimit being exceeded in any given year (90%,50%, 10%, 5%, 1% exceedance probability),on multiple time horizons (e.g. 2025 and2040). These thresholds and timescales
can be adapted to suit the relevant regional
context of the climate security threat inquestion.
Different levels of risk can then be analysedand presented. For example, in the
Cambridge business climate risk index,
Level 1 is the recent historical baseline fromobservations (or current level of risk). Level
2 is the (downscaled and bias-corrected)
climate change modelled view, taken as themulti-model mean of different climate model
projections over the period 2018-2059. Level
3 is a stress test view accounting for the tailrisk, taking the maximum model pathway
plus one standard deviation.

Cambridge Climate Change Business Risk Index, in prep: https://www.jbs.cam.ac.uk/fileadmin/user_upload/research/centres/risk/downloads/CCBR_5_OliverCarpenter.pdf
NERC EMERGENCE project (Cambridge/Exeter/MetOffice), see e.g.: https://www.metoffice.gov.uk/food-insecurity-index/
Cambridge AI4ER CDT, in prep.: https://ai4er-cdt.esc.cam.ac.uk

The Alan Turing Institute

4.1.1 Challenge 1: Integrated Climate Security Risk
Assessment
Such an index, which describes thelikely future hazard and its probability ofoccurrence, can then be combined withspecific exposure and vulnerability metrics.
These include other environmental andsocio-economic data and can provide acomprehensive understanding of the likely
current and future climate security risks.
For example, an index quantifying heatwave
hazard from climate model outputs canbe combined with scenarios of societal
development based on the Shared SocioEconomic Pathways, to derive a combined
index that describes the impacts of different
future levels of warming for a range of
scenarios of societal development79. These
indices and metrics must be co-designedwith security policy stakeholders to address
the needs of the security sector specifically.
There are many opportunities in this space
to also apply existing AI and machinelearning tools in innovative ways to use
new kinds of data to assess the nonclimate variables (i.e. human exposure andvulnerability to hazards), as well as using AI
to map relationships with the climate data.

There is significant potential to make more
use of remotely sensed satellite data forthis purpose, and improve on existingmachine learning methods to automate
detection processes, for example byusing unsupervised deep feature learning
to detect and characterise unplanned
settlements80, applying machine learning tocount the number of structures in refugee
camps81, or detecting and monitoring thenumber of vehicles on roads82.
Data from other novel sources can also
be incorporated into this process, suchas crowdsourced data from social media
platforms or mobile phones, for example byusing data from Twitter to detect and locate
flooding events in the UK83.
The most significant improvements in themodelling of climate security risks are likely
to come from improvements in connecting
the elements of security risk with theproperties of the climate. However, there
is also the opportunity to improve decadal
climate forecasts by integrating machinelearning techniques while the field is in its
infancy84.

“There is an opportunity to develop
integrated data-driven, multi-hazard
indicators of climate security risks,drawing on global risks meaningful todecision-makers at national and local
scales and connecting local and global
dynamics.”

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Russo, S., Sillmann, J., Sippel, S. et al. Half a degree and rapid socioeconomic development matter for heatwave risk. Nat Commun., 10, 136 (2019). https://doi.org/10.1038/s41467-018-08070-4
Li et al, 2017, Unsupervised Deep Feature Learning for Urban Village Detection from High-Resolution Remote Sensing Images
Quinn et al, 2018, Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping
Cao et al, 2016, Vehicle detection from highway satellite images via transfer learning
Arthur R, et al., 2018, Social sensing of floods in the UK. PLoS ONE 13(1): e0189327.
Stephan Rasp, Michael S. Pritchard, Pierre Gentine, “Deep learning to represent subgrid processes in climate models.” Proceedings of the National Academy of Sciences Sep 2018, 115 (39) 9684-9689;
DOI: 10.1073/pnas.1810286115

The Alan Turing Institute

4.2.2 Challenge 2: Develop the next generation of crossdisciplinary causal models for climate security
Society can have multiple stable points,
where well-being and development andsecurity are established. These can beeither sustained peace and prosperity(e.g Europe in the last few decades) or
protracted violence (e.g Mexico NarcoWar, Sudan Civil War). These points arestable in the respect that usual approaches
that push them out of that state, such as
diplomacy for peace, subversion to create
war, etc., do not work.
A single catastrophe in any one sector
can be mitigated by the others, but a
combination (coincidence) or cascade(causal) of catastrophes can lead to thecollapse of global order. Instead, it requires
a compound set of catastrophes - causal
or coincidental- to create a chain effect.
These are self-amplifying phenomena, justas are tipping points in climate or ecologicalsystems.
For example, the person who starts apolitical protest makes it incrementallyeasier for the next person to join them,and so on. Such mathematically positive
feedback can propel abrupt change.
Beforehand the incumbent regime may
show loss of resilience, i.e. slowing
recovery from perturbations. In our
example, this could take the form of
incipient protests decaying (or being
quashed) more slowly.

Threats to our ecosystem must consider
a wide context consisting of food andwater security, rare minerals and carbon
fuels, new disruptive technologies, aswell as arable land and smooth coastal
access. We often take these foundational
civilisation cornerstones for granted in thecurrent quasi-static environment. Still, as
we step into a new century of acceleratedchange, we must think of how they all cometogether to create compound effects andpush us over social tipping points.
Some societies can be resilient to thesechanges due to socio-economic equality
or advanced technologies. Others arevulnerable due to previous historical rifts
or reliance on imports. Whatever thecase, we must recognise that it is not justa cocktail of factors and disturbances thatcan drive societies past tipping points andlose resilience. The dynamics of the shocks
may also be important (e.g. resonance), andthese are not easily modelled or understoodusing data science and machine learning
alone.

“We must recognise that it is not just a
cocktail of factors and disturbances thatcan drive a societies past tipping points
and lose resilience.”

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The Alan Turing Institute

4.2.2 Challenge 2: Develop the next generation of crossdisciplinary causal models for climate security
Currently, very few research efforts
consider all of these effects in an explicit
modelling framework. European research
centres heavily focus on the socioeconomic and political aspects of stability
and well-being (e.g Uppsala UCDP, Oslo
Peace Institute, German PREVIEW). Other
international research hubs focus onregional specific security (ECOWARN) or
are methodologically specific (e.g. annual
survey of experts or dialogue-based
prediction). Few efforts, like the new DARPAWorld Modeler (2019-), are building multidisciplinary causal models of the world asa whole. All of these explicitly lack bothclimate change and social tipping point
integration. Many remain pseudo models,rather than the much-needed data-driven
sophisticated engines to inform policies
and design decisions.
Thus, the research frontier requires across-disciplinary modelling programmethat examines interdependent causal risks
resulting from climate change, migration,and conflict. A collaborative group of
experts that span both quantitative causal
detection (e.g. statistics, data science) andqualitative mechanics (e.g. political science,
psychologists, strategists) should cometogether and build causal graph models
and embed them with actual behaviourof decision dynamics. Together they cancreate a global engine to feed in climatechange data and understand current oreven predict future societal impacts (e.g.
civil war, migration, famine) for defence andsecurity.

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These future scenarios (rather than predictions) will be critical at the 30, 50, and 100year time scales. Why? Because many of
our critical decisions and investments are
made in those time scales. Examples range
from investment in allies to build a newgeneration weapon capability (30 years),investment in new national infrastructure(50-100), the development of human-based
diplomatic and information networks inemerging strategic regions (30), developing
strategic alliances (30-50), invest in R&D
that can protect UK’s national interests inthe century to come (30-100). All of theseexamples require discrete scenario-based
foresight.

The Alan Turing Institute4.2. Improved resilience monitoring and warning
Climate risk management starts witha continuous and comprehensive risk
analysis where the broad risks must be
identified and assessed - as described in theprevious section. By identifying risks andassessing their impact on people, assets andecosystems, it is possible to develop a rangeof possible responses.
The second step is the development of amanagement strategy that generally aims to
improve adaptation or enhance resilience.
Enhancing resilience requires understanding
the ability of a system to absorb shocks.

“By identifying risks and assessing
their impact on people, assets andecosystems, it is possible to develop a
range of possible responses.”

30

Enhancing resilience requires understanding
the ability of a system to absorb shocks.
However, the occurrence of shocks is notcertain: either their nature, or the nature or
size of the impacts, can be uncertain, or theiroccurrence in time is generally not known.
Our ability to anticipate within uncertainty
these shocks - within the system, or acrossscales- is critical for taking timely action. It isalso essential for preventing tipping points(also referred to as ‘critical transitions’ or‘regime shifts’) where a small perturbation
can cause a profound shift in a system’s
state. These changes are often abrupt anddifficult to reverse.

The Alan Turing Institute

4.2.1. Challenge 3: Resilience monitoring and early
warnings of climate security tipping points
Recently there has been a large amount ofresearch into early warning signals of tipping
points. This has revealed the existence ofgeneric early-warning signals that indicate awide class of systems if a critical threshold
is approaching. The basic idea is that before
a tipping point, a system loses resilience,
which means it recovers more slowly fromperturbations (and often is more sensitive toa given perturbation). Existing early warning
systems for environmental shocks rarely
extend beyond the seasonal timescale andrarely attempt to forecast tipping points.
There is a live opportunity to apply resilience
monitoring and tipping point early warning
methods to monitor changing risks to
climate security in near-real-time. For this,we envision a global ‘dashboard’ of changing
resilience related to climate security, whichdraws on a range of data sources (detailed
below) and issues warnings of potential
tipping points, based on early warning
indicators, when a set of statistical criteria
are met.
We are not referring here to tipping points inthe climate system per se (important though
they are), but rather to climate-related
security tipping points, such as the triggeringof mass protests (e.g. the ‘Arab Spring’) or
mass migration. Climate data and weather/climate model forecasts in this context area ‘forcing’ of the social-ecological systems
that may exhibit changing resilience andtipping points. Specific predictions of
future weather/climate perturbations - e.g.
a hurricane making landfall, or seasonal
forecasts of drought in parts of sub-SaharanAfrica - will be flagged up. Alongside this,changes in the statistical properties ofweather - i.e. the climate - will be monitored
as this also affects the underlying socialecological systems behaviour.

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More targeted and specific early warning
systems could then be designed forparticular sectors and/or regions whereclimate security risks are already known
to be high, and nested within the ‘global
dashboard’ approach. An establishedexample in a related domain is FEWSNET, the famine early-warning system
for sub-Saharan Africa. This is based on
seasonal climate forecast skill but could be
augmented with other resilience monitoring
and tipping point early warning methods,
and additional data sources to turn it into a
climate security early warning system.
Identifying pertinent data
The essence of the proposed resilience
monitoring and early warning system is toanalyse ‘big data’ sources in an innovative,
and as-far-as-possible automated, way. A
global resilience dashboard for climate
security would necessarily draw on global
datasets - including global weather/climate
projections, remotely sensed data, market
data, and social media.
When it comes to social-ecological systems
and the resources they provide, changes
in vegetation and its resilience can bemonitored from continuous, remotelysensed time-series data. This includes
arable croplands as well as the pastures andother landscapes used for cattle grazing.
Animal movement can be inferred fromremotely sensed data85 suggesting thathuman movement too can be inferred if notdirectly observed86.

Neumann, W., Martinuzzi, S., Estes, A. B., Pidgeon, A. M., Dettki, H., Ericsson, G., & Radeloff, V. C. (2015). Opportunities for the application of advanced remotely-sensed data in ecological studies ofterrestrial animal movement. Movement ecology, 3(1), 8.
Li, D., Zhao, X., & Li, X. (2016). Remote sensing of human beings–a perspective from nighttime light. Geo-spatial Information Science, 19(1), 69-79; Sirmacek, B., & Reinartz, P. (2013). Feature analysis
for detecting people from remotely sensed images. Journal of Applied Remote Sensing, 7(1), 073594.

The Alan Turing Institute

4.2.1. Challenge 3: Resilience monitoring and early
warnings of climate security tipping points
Remote sensing of other key environmental
variables such as soil moisture is mature,and its resilience (e.g. recovery rate of watertable depth) can be monitored. Fires andrecovery rate from them can be monitored.
Disease vectors and their transmission canbe inferred. Dangerous oil spills and algal
blooms can be detected, and overall water
quality can be monitored87 . Other resources
and indicators of their resilience include lessvisible properties such as food commodity
prices.
However, these are available from market
price data, which could be analysed forchanging recovery rate of fluctuations as apotential forewarning of price spikes. One
approach would be to data-mine past events,
to identify further sources of data that are
pertinent to resilience monitoring and early
warning of climate security tipping points.
Refining methods of analysis
Methods of detecting changing resilienceand warning of tipping points have alreadybeen successfully tested on data prior topast abrupt climate and ecosystem changes.
This approach could be extended to testing
the methods on data prior to past security
tipping points.

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A complementary approach would be
to use process-based models to identify
what the best resilience indicators are andwhere to monitor them. This approachhas been successfully applied to tipping
points in the climate system88, but not yet toclimate security tipping points because of ashortage of such models. A statistical caveat
here is the need to avoid selection bias - the‘prosecutor’s fallacy’89 - by also examining
non-tipping-point cases. Ideally what isneeded is a dataset of known past climate
security ‘events’ ideally including somethought to have tipping point dynamics (i.e.
where internal feedbacks propelled abruptchange). This might include past famines
triggering conflict or large-scale migration(or both).
There is potential benefit from research touse AI to improve tipping point early warning
methods. This could be carried out ascurrently, using artificial data generated fromgeneric models, but more sophisticatedapproaches are available. There is also
the potential to improve seasonal weather
forecasts by, for example, the integration ofdifferent types of models90, the elimination ofsubjective steps within the climate models
via machine learning91, and the global
application of regional best practice92.

Mishra, Deepak R., Eurico J. D’Sa, and Sachidananda Mishra (2016). Special Issue “Remote Sensing of Water Resources”. Remote Sensing
Boulton, C. A., Allison, L. C., & Lenton, T. M. (2014). Early warning signals of Atlantic Meridional Overturning Circulation collapse in a fully coupled climate model. Nature communications, 5(1), 1-9.
Boettiger, C., & Hastings, A. (2012). Early warning signals and the prosecutor’s fallacy. Proceedings of the Royal Society B: Biological Sciences, 279(1748), 4734-4739.
Strazzo, S., D.C. Collins, A. Schepen, Q.J. Wang, E. Becker, and L. Jia, 2019: Application of a Hybrid Statistical–Dynamical System to Seasonal Prediction of North American Temperature and Precipitation.
Mon. Wea. Rev., 147, 607–625, https://doi.org/10.1175/MWR-D-18-0156.1
Zhou, K., Zheng, Y., Li, B. et al. Forecasting Different Types of Convective Weather: A Deep Learning Approach. J Meteorol Res 33, 797–809 (2019). https://doi.org/10.1007/s13351-019-8162-6
Ault, T. R. (2020). On the essentials of drought in a changing climate. Science, 368(6488), 256-260.
Barrett, S., & Dannenberg, A. (2014). Sensitivity of collective action to uncertainty about climate tipping points. Nature Climate Change, 4(1), 36-39.

The Alan Turing Institute

4.2.1. Challenge 3: Resilience monitoring and early
warnings of climate security tipping points
Turning warning into useful action
A salient lesson from existing research isthat tipping point warnings are often not
met with corresponding action, especially ifthat action is costly, unless the uncertainty
around the warning (i.e. a bad outcome)is reduced below a critical level93. This
reinforces the need for the statistical work
mentioned above. Users would need to
be fully informed of the statistical natureof the warnings and know how to read the
proposed dashboard, as there would always
be false positives and false negatives, which
research would seek to minimise.

Hence warnings would benefit from being
accompanied by scientific advisors that
could translate new information for decision
makers (in policy or the military) who
are fully informed of the corresponding
governance principles. This type of riskresponse management is already mature in
the military - frontline decisions are made on
limited and imperfect information - the key
offering here is to improve the information.

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Barrett, S., & Dannenberg, A. (2014). Sensitivity of collective action to uncertainty about climate tipping points. Nature Climate Change, 4(1), 36-39.

The Alan Turing InstituteCase study: Human migration
As large-scale human migration has been
identified as a specific climate-related
security concern, a resilience-monitoringand early warning system could be designed
combining recent and real time data miningof human movement and its triggers/
correlates, together with future projectionsof population growth, climate change andassociated potential for displacement, andmodelling of human movement.
Human movement in a migration sense
can be regarded in terms of different
resources, familiarity (socio-economic,
ethnolinguistic), and safety factors (climate,security, political). Extreme heat is already adirect trigger for human movement, whichis escalating. Shortage of weather stationsin the tropics means satellite infrared land
surface emission temperatures are crucial
to reconstructing temperature extremes.
Analysis of this data and UK official climatechange prediction data (MET Office) shows
rapidly expanding areas and durations of hot
extremes in India and parts of sub-SaharanAfrica, consistent with areas identified infuture climate projections.

“Human movement in a
migration sense can be
regarded in terms of different
resources, familiarity (socioeconomic, ethnolinguistic), andsafety factors (climate, security,
political).”34This already indicates where climatetriggered migration may be expected to
escalate. Under a UK Defence & Security
funded initiative such as GUARD (2017-21,
we were able to predict migration of 4-5bn
people under RCP8.5 by 2100, mostly in
India and China towards the Himalayan
mountains, Manchuria, and Inner Mongolia.
Significant migration in Europe will beaway from the Mediterranean, and in North
America will be towards northern Canada.
This significant shift in global populationand demographics not only creates new
potential political fractionalization, but also
new frontiers of contention (Himalayanfoothills). Negative climate effects on
resource availability, which then trigger
migration add a resource layer of complexity.
Nevertheless climate limits to the productionof particular resources, e.g. staple crops
such as wheat are well established.
We note that due to the transnational
nature of climate change, the quality andthe availability of data can vary between
countries and from event to event: smallscale events or disasters that occur
in isolated and marginalized areas areunder-reported and thus not included in
the available aggregate estimates whichis frequently the case with environmental
migration. Also, we lack data abouthighly vulnerable populations affected byenvironmental degradation and disasters
who may not be able to move due to a lack ofThe Alan Turing Institute

Human migration as a case study

Figure 3. Projected population shift due to temperature and sea level changes.
(Size of Circles) Indicate projected population sizes of new cities - prominent around Himalayan foothills in India and China, as well as Manchuria. (Colour) Indicate likely cooperative
communities that form as a result of new population shifts.

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The Alan Turing Institute

5. Enablers to climate risk decision
making5.1. Introduction
Decision support tools form an integral
part of the policy development process andassociated decision-making in government. Even within the boundaries of the UK’s
security community, the systemic natureof climate risk will mean that issues cut
across multiple departmental jurisdictions
all of which will have different perspectives,cultures and agendas. Therefore, it isimportant that the use of any climate risk
decision support methodologies and tools,
and their integration into these existing
structures, is considered alongside its
design.

• Data-driven tools should be part of awider arsenal of integrative decision
support tools. There will be a temptation
to use the outputs of data-driven tools toprovide definitive choices as to what to do.
However, a wider set of tools should be used
to assist policy makers explore the future
option space and how the variables interact
for any given policy problem.

• Providing transparency about what thedecision-support tool(s) can provide. The
new decision support tool(s) will integratea range of input variables to provide
insights into the climate dimensions ofa set of policy problems. These insights
will be dependent on the tool itself e.g.
the assumptions, the inputs themselves,sensitivities of the interaction of the input
variables in different boundary conditions
etc. Outputs from the tool(s) should beaccompanied by transparent information
about the tool(s)’ limitations. In particular, thetype of uncertainty being encountered in thepolicy problem should be clear at the outset,
and the ability of the tool(s) in dealing withuncertainty need to be stated clearly.

• Any new tool should be integrated withexisting tools and processes. The security
community already uses decision-making
and analytical tools, and anything new
should be designed with this context inmind.

• Nurture a culture of “deliberation withanalysis”. This culture can help decision
makers manage decisions related tosystemic disruptors, like climate change,
whereby conditions of deep uncertainty,
emergence and complexity are prevalent.

The following enabling factors can help
ensure that climate risk considerations, areproperly integrated into national security
policy development:

94

36• Ensure that there is adequate climatechange expertise amongst the key
decision makers. Climate change is not
linear, and there is the potential for cascade
and threshold effects which are not reflected
in historical data, making the past an
unreliable predictor of the future. Therefore,
the security community needs to train,develop and use experts appropriately, as
well as using policy development processes
that includes expert judgement.

EU Better Regulation Guidelines - see link.

The Alan Turing Institute

5.2. Using a decision support tool in a situation of
uncertainty
Limitations of data-driven tools
Decision support tools which employ dataand parametric modelling, whilst powerful,
will not be able to fully capture either thecomplexity of the impacts around climate
risk, nor prescribe solution sets to formulate
policy prescriptions95. Models represent asystem to help integrate disparate inputs
and understand complex phenomena.
Models make simplifications about `the way
the world works’96 and, with an increasingin computing power, some are becoming
increasingly complex and lacking intransparency97.
Sometimes, the inadvertent effect of thislack of transparency is that levels and typesof uncertainty are poorly characterized
when analyzing a policy problem. As aresult, tools may be chosen, or set to deal
with inappropriate levels of uncertainty.
This results in two unintended outcomes:(1) decision support tools are applied
inappropriately to problem sets resulting indistorted decision support analysis; and (2)outputs become used to provide definitive
choices about what to do, rather than being
used to explore a decision.

37

95
96
97
98
99

Understanding uncertainty to optimise
the use of tools
Policy makers need to understand theextent of uncertainty that relates to a given
policy decision so that they can use theappropriate set of decision support tools.
Mapping uncertainty at the beginning of apolicy development process forces decision
makers to explore the implications of theintegration of different input variables on the
problem space.
This type of exercise increases transparencyof the process and, most importantly, it will
avoid the tendency for parametric outputs
to be used to provide definitive outcomesor choices as to what to do. This approach
can help ensure that modelling tools areused appropriately to explore the way thatpolicy should be designed to achieve statedobjectives, rather than as a way to confirm
the realisability of stated national plans 98.
This approach can enable the full explorationof the future option space99.

Gambhir et al., 2019 - Planning a low-carbon energy transition: What can and can’t the models tell us?, Joule, Vol: 3, Pages: 1795-1798, ISSN: 2542-4351
Mercure, J-Francois, 2019. Modelling innovation and the macroeconomics of low-carbon transitions: theory, perspectives and practical use. Climate Policy Volume 19, 2019 - Issue 8 1019-1037
Bankes, S. (1993). Exploratory Modeling for Policy Analysis. Operations Research, 41(3), 435–449. doi: 10.1287/opre.41.3.435
Mercure, J-Francois, 2019. Modelling innovation and the macroeconomics of low-carbon transitions: theory, perspectives and practical use. Climate Policy Volume 19, 2019 - Issue 8 1019-1037;
Workman et al., 2020 Ibid; van Dorsser et al., 2018. Improving the link between the futures field and policymaking. In Futures 104 (2018) 75–84
Gambhir et al., 2019, Using futures analysis to develop resilient climate change mitigation strategies, Grantham Briefing Paper, Publisher: Imperial College London

The Alan Turing Institute

5.3. Capacity building in the use of decision support tools
The capacity to recognise uncertainty
and make decisions under uncertainty
requires training, time and practice, but
this is a worthwhile investment, especiallyin the case of climate change100. There isan urgent need to increase the skills ofdecision makers in using decision support
tool101 which would help policy makers inthe security space make good use of thedata-driven tool(s) proposed earlier in thisdocument.

Capacity building could cover:– the characteristics and nature of climatechange risk;– identification of the level and type of
uncertainty being encountered in a policy
problem;– Selecting the appropriate decision
support analysis to engage with that
uncertainty;– the implications of the inherent
assumptions and inputs to decision support
tools on a policy problem;– expert judgement102 capacity in relation toclimate risk; and– expertise in using structured decisionmaking (SDM) approaches.

“The capacity to recognise
uncertainty and make decisions
under uncertainty requires training,time and practice, but this is aworthwhile investment, especially inthe case of climate change.”

38

100
101
102

Klien, G. and Wight, C., 2016 - Macro-cognition From Theory to Toolbox. In Front. Psychol., pp5 - 29 January 2016
French, S., Maule, J. and Papamichail, N., 2009. Decision behaviour, analysis and support. Cambridge University Press.
Burgman, M.A., 2016. Trusting judgements: how to get the best out of experts. Cambridge University Press.

The Alan Turing Institute

5.4. Structured decision-making processes and climatechange risk
A structured decision-making (SDM)
approach is a preferred framework forclimate risk policy development as it isdesigned to aid logical and transparent
decision-making 103 compensating for
cognitive biases. The SDM framework
process is shown in Figure 5.2. To apply
such a framework to climate change in the
security context, each component needs
to be mapped onto the existing national
security policy processes.
This approach will enable insight into
the governance mechanisms within andbetween each stage and input metrics /analysis which would be required in making
decisions on climate change.
The SDM framework should be designedwith a range of considerations, including themany psychological barriers to, and enablers
for, climate decision-making104 the need forsocietal buy-in, possibly through the useof narratives105 , how uncertainty is visuallycommunicated and its impact on decisionmaking106 and institutional architecture,
incentives and clear lines of responsibility.
For systemic issues such as climate change
the capacity to generate policy solution
sets is compromised by the fact thatthe responsibility for components of the
problem and policy sets often cut acrossmultiple departmental jurisdictions.

103
104
105

39

106

107
108

The types of decisions required to address
climate and national security policy arelikely to involve multiple objectives, multiple
stakeholders with different perspectives,
uncertainty about actions and outcomesas well as different departmental models of
cause-and-effect.
For example, the Department forInternational Development (DfiD) will havea more sociological perspective on their
theories of change relative to the moremechanistic constructs in the MOD. These
institutional challenges can lead to highly
distorted decision-making, as has been
exemplified in the way that the UK developed
strategies in the expeditionary Afghan and
Iraq campaigns107 and indeed its ongoing
response to C-19108.
Collaboration is essential – not just at
the decision-making stage, but also theanalytical stage. The National Security
Council (NSC) has a substantive analyticalresource which it draws upon and therefore
any new methodology or tool should be
used with a strong connection to this team.
The present suite of analysis effectively
establishes the culture by which thosedecision-makers see the world and design
policy.

Gregory et al 2012. Structured Decision Making: A Practical Guide to Environmental Management Choices
Lewis, M, 2017. The Undoing Project: A Friendship that Changed the World; The Cognitive Bias Codex: A Visual Of 180+ Cognitive Biases - see link
S. Bushell, T. Colley, M. Workman, A unified narrative for climate change, Nat.Clim. Chang. 5 (2015) 971–973 https://doi.org/10.1038/nclimate2726; Parliament is undertaking a Citizens Assembly
around Net Zero and nascent work on Participatory Futures is being pioneered - see: Nesta 2019 - Our Futures: By the people, for the people.
it impacts decision-making as follows: (1) Decision outcomes; (2) Correctness of decisions; (3) 3. Kinds of errors made; (4) Decision time; (5) Confidence in a decision; (6) Willingness to make adecision; (7) How much workload decision-making causes; (8) How a decision is made; and aligned with this is the fact that quantitative outputs more broadly in their very nature will invariably beinterpreted by decision makers as `predictive’ and `optimal’ as per Whitehead’s observation in the “Fallacy of Misplaced Concreteness” (1917) when tables, graphs, etc are presented to decision
makers. ; Levontin, P. et al., Visualising Uncertainty: A Short Introduction. Publisher: AU4DM ISBN: 978-1-912802-05-0
Elliott, C. 2015. High Command: British Military Leadership in the Iraq and Afghanistan Wars. `He reveals how the Service Chiefs were set at odds by the system, almost as rivals in the making, withresponsibility diffuse and authority ambiguous. The MoD concentrated on making things work, rather than questioning whether what they were being asked to do was practicable.’
Sir Paul Nurse interview on Radio 4 Today Programme dated 22nd May ` Do we have a proper government system in here that can combine tentative knowledge, scientific knowledge, with politicalaction? And the question I’m constantly asking myself is: Who is actually in charge of the decisions? Who is developing the strategy and the operation and implementation of that strategy? Is it
ministers? Is it Public Health England? The National Health Service? The Office for Life Scientists, Sage? I don’t know, but more importantly, do they know?”

The Alan Turing Institute

5.4 Structured decision-making processes and climatechange risk

Figure 5: Stylised process of SDM

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The Alan Turing Institute

5.5. Nurturing greater “deliberation with analysis” in policy
designIn addition to using an SDM structure, it isalso helpful to develop greater `deliberation
with analysis’ around the decision-making
process, generating dialogue between theactors involved in decision-making, policy
design and decision support analysis.
Greater deliberation can break theentrenched `tribal’ axioms that may
influence policy decisions acrossdepartments. The iterative nature ofdeliberation with analysis encourages
transparency around both individual tools
and also how they are used in the policy
development process. Uncertainties would
be explicitly discussed and narratives cogenerated between analytical and policy
decision making communities within an
ongoing iterative process articulated
between the analytical community,
decisionmakers and policy makers along thedecision value chain.

The practical implications of moving froman advocacy-approval process between
the decision support analysis and policydecision maker to one with greater`deliberation with analysis’ is articulatedin Spetzler et al., 2016109. This allows
development of the analytical frameworkaround the problem so as to ensure thatthe policy /decision makers questions areanswered and work with lead-in times to
identify decision points and which elementsof any high dimensional policy space isclosed-off as a function of short-term
decisions110.

“The capacity to recognise
uncertainty and make decisions
under uncertainty requires training,time and practice, but this is aworthwhile investment, especially inthe case of climate change.”

41
109
110

Spetzler, C., Winter, H. and Meyer, J., 2016. Decision quality: Value creation from better business decisions. John Wiley & Sons.
Haasnoot, M. et al., 2013. Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world. In Global Environmental Change Volume 23, Issue 2, April 2013,
Pages 485-498

The Alan Turing Institute
The Alan Turing Institute

6. Recommendations‘Plans are worthless, but planning is everything...The reason it is so important to plan is
to keep yourself steeped in the character of the problem that you may one day be called
upon to solve’.
— President Dwight Eisenhower

Human-driven processes are pushing the Earth system beyond the boundaries of a safe
operating space111. In the coming decades humanity will face the decisive challenge of stewarding planetary metabolism and mitigating these Anthropocene risks that extend beyond
the nation-state. Climate-related disasters such as recurring floods, droughts, desertification,or wildfires will not only cause direct damage but also lead to chains of catastrophic events
such as migration, sociopolitical instability and potential armed conflicts that will cost trillionsof dollars and millions of lives. If left unchecked climate change could have a severe impact
on the economic and social gainsmade in countries around the world and jeopardise many of
the UN Sustainable Development Goals.
For defence and security, climate change has implications on the preparedness of forces to
respond, future resource planning, operational commitments and training associated withincreased demands for disaster relief, humanitarian assistance and peacekeeping operations. Therefore, defence and security actors must embed climate change and sustainability
impacts into its leadership, processes and policies. Based on the analysis of this report, werecommend the following actions for consideration by the National Security community:

We recommend an urgent uplift of the research and decision-making capacity aroundthe anticipated impact of climate-related threats on UK National Security interests in athree- to five-year timeframe. This includes promoting an understanding of the characteristics
and nature of climate change risk across government departments; selecting the appropriate
decision support analysis to engage with this uncertainty; and ensuring that policymakers arewell equipped to mitigate the security impacts of climate change proactively.

The best available information should inform the capacity to make better decisions aroundthe anticipated impact of climate-related threats. While climate change is already included
in some early warning systems and risk assessments, more could be done to assess the future
effects using data-driven and complex systems methods.

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111

F. S., Lambin, E., ... & Nykvist, B. (2009). Planetary boundaries: exploring the safe operating space for humanity. Ecology and society, 14(2).

The Alan Turing Institute
The Alan Turing InstituteTo create this evidence base, a substantive research programme is required which getsto the heart of understanding changing climate security as the outcome of complex,
interconnected, dynamical systems. In chapter four, we describe three research challenges
that can be tackled by such a programme:— New data driven tools and methods need to be developed that will determine where theaccumulation of climatic stresses is interacting with human populations and how these
dynamics are changing over time. This will provide an indication of how many people will be
impacted by environmental and climatic stresses. Working with international partners will thenhelp assess how these hotspots overlap with other structural risks to drive fragility, conflict,migration or maladaptation.
— Tools and methods are needed to identify, and to issue early warnings of, potential climate
security tipping points, which can inform the development of proactive and area-specific risk
reduction and resilience planning responses.
— The development of new causal models that explicitly link climate and socio-economic
processes together and can help policymakers create scenarios of future security risks. These
scenarios can help define the boundaries for ‘safe and just operating spaces’ and shape policy
and programme recommendations that reduce environmental stresses and their negative
impacts on populations.

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The Alan Turing Institute

Conditions for success
1. Bringing together disparate researchers
The complexity and interdisciplinary natureof the challenges described in chapter
four mean that Defence and Security
should incorporate a wide range ofexperts in relation to its changing role andoperational requirements: data scientists,climate scientists, designers, futurists andeconomists would form the vanguard of anew platform prepared to devise climateproof resilience plans

While the problem demands aninterdisciplinary group underpinned by solid
theory and data science, academic life iscurrently structured in subject-oriented
departments. The Defence and Security
community needs to bring together andincentivise a thriving network of researchers
around this challenge.

Delivery options– The proposed initiative requires establishing collaborations between the disciplines thatare currently too sparse and discontinuous to address this grand challenge. This can beachieved by establishing a lean Climate Security Centre that brings together experts fromacross disciplines to focus on the shared problem with minimal distraction. Centres and institutes can be an effective way to organise around a challenge. (indicative resource: 50k peryear for 5 years)

44The Alan Turing Institute

2. Flexible research projects and programmes designedwith user groups
Key research projects should be delivered bya scientific leadership team which will be led
by a combination of a high-level official fromthe security decision-making community
and a senior academic, to ensure theintegration of the user community and theresearch community.
The research and delivery teams should
work alongside GCHQ, DSTL, MoD, theNational Security Council and alliedorganisations nationally and internationallyto understand how partner organisations
make sense of information about climaterelated risks. It is vital that any tools for the
UK’s security community be developed in a
way that aligns to existing risk assessment
and risk management tools. Importantly,
given the nature of the challenge, thecharacterisation of the research challenges
themselves requires a two-way engagement.

We recommend that key decision makers
from the security community, acrossdifferent departments, work closely withthe research teams to help develop ashared understanding of the problem set
and to guide the research further. They
will generate a “challenge book”, a shareddocument that is updated frequently inan agile way and will assess the research
results and future plans in a coordinatedfashion within government.
Some questions and challenges might
require short-term respones and others
might have a longer lead time. The
programme of research and choice ofdisciplines, experts and teams needs to beflexible and agile, whilst also grounded witha core team that can ensure relevance andquality.

Delivery options– Multiple research projects will be delivered by the scientific leadership teams (indicative
resource: £5m over 5 years to develop 4 projects per year distributed across teams at £250k
each)– The latest research outputs will be transformed into demonstrators and products by a Development and Testing team. (indicative resource: £200k per year for 5 years)

45

The Alan Turing Institute

3. Building capacity amongst decision-makers
A strong community of best practice on datafor climate security is required. Scoping
and capacity building meetings should
bring together international and UK thought
leaders, policy makers, domain experts,
academics and practitioners from different
fields and parts of the security ecosystem tohelp build the communities capacity to use
exiting tools, and to identify additional gaps
and challenges.
The latest research outputs would betransformed into relevant user-friendly
demonstrators and products that support
effective climate risk communication.

A team of research software engineers anddesigners would also map, catalogue andconnect existing datasets and systems;
develop and trial the technical componentsof the research; and ensure data ethics
principles as well as data and model
integrity.
These outputs can be shared and tested at
the gatherings of stakeholders, allowing fortheir evolution whilst building skills at thesame time, both of researchers and usergroups.

Delivery options– A yearly Climate Futures Retreat for national and international academics and key decision-makers from the security community can help develop a shared understanding of the
problem set and guide the research further. This meeting would inform the direction of theresearch projects the following year, identify the need for any shorter, intensive research
projects, and it would be an opportunity for the researchers to share their findings and get
feedback.
– This annual retreat aims to build capacity within the security community and establish a
new kind of deliberative approach to decision making. These activities would guide the workand ensure that the scientific leadership is delivering on the ambition to make this approach
truly integrated with decision-making, linked to culture change, interdisciplinarity, but alsowith a strong data science core. Smaller “constellation” workshops will be delivered throughout the year by various government departments. (indicative resource: £30k per year for 5
years.)

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The Alan Turing Institute

4. Creating international networks
Building a coalition of international partners,
including those mentioned in chapter
3.2, is a fundamental building block of the
proposed research programme. It requires
international community-building, events
and workshops with allies around the world,such as participating in the upcoming
COP26.

We recommend that the UK drives thisinternational effort with the view to become
a global hub of authoritative climate security
information that reflects the security andsocial science consensus on the topic andpresents credible climate security futures.

Delivery options– Establishing and supporting a community of practice around data for climate security andresilience requires programme management, events, communication, participation in international workshops (indicative resouce £100k over 5 years).
–

5. Making the best use of UK skills and resources
We recommend that any new
interdisciplinary research initiative is funded
appropriately over five years to design atruly holistic climate security research
programme.
The UK is in a strong position to lead thisambitious piece of research, which canbring together its national capabilities andexpertise in climate change research (e.g.
Cambridge, Exeter, Oxford, Reading, Leeds,
Imperial College, Met Office), complex
systems (e.g. Exeter, Cranfield, QMUL,
Warwick, Cambridge), conflict (Essex, UCL,
KCL, Oxford), and data science and machinelearning (Alan Turing Institute).

47

The exact shape of this research effort
should be further defined through a
consultation phase with more stakeholders
from the security community as well as arange of academics who are already working
on these topics. The evidence producedby the research community can bridge
the short-term and long-term timeframes
adopted across and within academic
disciplines as well as policy communities.
The cost of inaction will be in the trillionsof pounds and millions of lives. In a rapidlychanging world, the UK now has a uniqueglobal advantage to demonstrate leadershipin the field and develop the analytical
capacity that can provide answers to theplanetary questions raised by climatechange in order to improve its resilience -and
that of its allies-, with all of the uncertainty
attendant to this grand challenge.

The Alan Turing Institute

Glossary
Abrupt change/abrupt climate change
Abrupt change refers to a change that is substantially faster than the rate of change in the
recent history of the affected components of a system. Abrupt climate change refers to alarge-scale change in the climate system that takes place over a few decades or less, persists
for at least a few decades and causes substantial disruptions in human and natural systems.
Some changes may be truly unexpected, resulting from a strong, rapidly changing forcing of acomplex system.
Adaptation
The process of adjustment to actual or expected climate and its effects, in order to moderate
harm or exploit beneficial opportunities.
Adaptive capacity
The combination of the strengths, attributes, and resources available to an individual, community, society, or organization that can be used to prepare for and undertake actions to
reduce adverse impacts, moderate harm, or exploit beneficial opportunities
Anthropocene
The most recent period in the Earth’s history, starting in the 18th century, when the activitiesof humans first began to have a significant global impact on the Earth’s climate and ecosystems.
Anthropogenic emissions
Emissions of greenhouse gases, greenhouse gas precursors, and aerosols associated withhuman activities. These include all activities that result in a net increase in emissions.
Climate change
Refers to any change in climate over time, whether due to natural variability or as a result of
human activity.
Climate variability
Variations in the climate at any temporal and/or spatial scale beyond that of individual weather events. Variability may be due to natural internal processes within the climate system (internal variability) or to variations in natural or anthropogenic external forcing (external variability)(IPCC AR4, 2007).
Complex Adaptive Systems (CAS)Any natural system or artificial system that is characterized by apparently complex behaviors
that emerge as a result of interactions among a large number of component systems at different levels of organization. Simple rules of cause and effect do not apply, they are complex,
unpredictable and constantly adapting to their environments.
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The Alan Turing InstituteDownscaling
Downscaling is a method that derives higher resolution (in time or space) information fromlarger-scale models or data analyses. See appendix Y2.3 for further details.
Early warning system
The set of capacities needed to generate and disseminate timely and meaningful warning
information to enable individuals, communities, and organizations threatened by a hazard
to prepare and to act appropriately and in sufficient time to reduce the possibility of harm orloss.
Earth system
The Earth system consists of the land, oceans, atmosphere and poles and the interacting
physical, chemical, and biological processes within them. It includes the planet’s natural cycles — the carbon, water, nitrogen, phosphorus, sulphur and other cycles — and deep Earthprocesses.
Earth System Model (ESM)
A coupled atmosphere–ocean general circulation model in which a representation of the carbon cycle is included, allowing for interactive calculation of atmospheric CO2 or compatible
emissions. Additional components (e.g., atmospheric chemistry, ice sheets, dynamic vegetation, nitrogen cycle, crop models) may be included. See appendix Y3 for further details.
Emissions scenarios
Quantitative illustrations of how the release of different amounts of climate altering gases andparticles into the atmosphere from human and natural sources may change in the future. Scenarios are developed using a wide range of assumptions about population growth, economic
and technological development, and other factors. See also Socio-Economic Pathway.
Ensemble
A collection of model simulations used for climate projections. Differences in the results
across the ensemble members gives an estimate of uncertainty. Ensembles made with thesame model but different initial conditions only characterize the uncertainty associated withinternal climate variability, whereas multi-model ensembles including simulations by several
models also include the impact of model differences.
Feedback
The process through which a system changes in response to its own output. Positive feedback results in amplification of the system output; negative feedback reduces the output of asystem.
Forcing
Factors that affect the Earth’s climate. For example, natural factors such as volcanoes andhuman factors such as the emission of heat-trapping gases and particles through fossil fuelcombustion.

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The Alan Turing Institute

Forecast
A best-estimate of the most likely future conditions in a system, based on past information
only. Distinct from Prediction or Projection.
Global Climate Model (GCM)
Mathematical model that simulates the physics, chemistry, and biology that influence theclimate system. See appendix Y3 for further details. IPCC
The Intergovernmental Panel on Climate Change: the United Nations body for assessing thescience related to climate change. See www.ipcc.ch
Human security
Human security has two main aspects. Firstly, safety from chronic threats such as hunger,
disease, and repression. Secondly, protection from sudden and hurtful disruptions in thepatterns of daily life –whether in homes, in jobs, or in communities.
Mitigation
The lessening of the potential adverse impacts of physical hazards through actions that reduce hazard, exposure, and vulnerability.
Prediction
A best estimate of the most likely future conditions in a system, based on past information
and best estimates of the future forcings of the system. Distinct from Forecast or Projection.
Projection
Climate projections are based on simulations by climate models, but are designed to show arange of possible futures based on the emissions scenario/socio-economic pathway used,rather than provide a best-estimate of the most likely future. Distinct from Forecast or Prediction.
Resilience
The ability of a system and its component parts to anticipate, absorb, accommodate or recover from the effects of a hazardous event in a timely and efficient manner while retaining
the same basic structure and ways of functioning, the capacity for self-organization and thecapacity to adapt to stress and change.
Risk
The potential for consequences where something of value is at stake and where the outcome
is uncertain, recognizing the diversity of values. Risk is often represented as probability or
likelihood of occurrence of hazardous events or trends multiplied by the impacts if these
events or trends occur.

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The Alan Turing InstituteSocial-ecological
Linked systems of people and nature. The term emphasizes the view that humans must be
considered as part of, and not apart from, nature.
Socio-Economic Pathway
Global development pathways that depict plausible alternative future states of the society andthe environment, developed by the IPCC, see appendix Y3.4.
Threshold
May refer to a boundary between two different states of a system, or a point beyond which
the system changes rapidly. See also Tipping Point.
Tipping point
A level of change in the properties of a system beyond which a system reorganizes, often
abruptly, and does not return to the initial state even if the drivers of the change are abated. A
tipping point event may be irreversible.
Vulnerability
The degree to which a system is susceptible to, or unable to cope with, adverse effects ofclimate change, including climate variability and extremes.

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Appendix
A1. Overview of models/reanalyses and their limitations/errors

Appendix Y1: Types of historical data and their biases
Table Y1.1: Summary of sources of historical climate data depending on their aims. These are
direct observations, gridded global datasets, and global reanalyses. Table Y1.1 summarises
the availability, sources, advantages, disadvantages and common uses of each data type.
Generally speaking, as one moves from direct measurements to reanalyses, the biases in thedata increase, but the temporal and spatial resolution improve.
Data Type

Advantages

Heading

Common uses

DirectMeasurements

Low bias

Concentrated indeveloped regions

Localised studies-Process studies

High time resolution

Small radius of
relevance
Varying qc methods

Gridded products

Global coverage
Comparable at all
points in time and
space

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Data validity varies by Global studies
location
AggregatedMissing data cananalysis eg regionalcause aliasing
comparisons

The Alan Turing Institute

Direct measurements are the most accurate, but are localised by nature. Organisations thatmanage direct measurement systems often release data close to real-time after processing by quality control systems, which account for random errors such as drift in a particular
instrument. Direct measurements tend to be used for studies focusing on a local region or aparticular physical process that requires high time resolution data. There is an inherent risk
in basing any model on real-time direct measurements, and that is that a given measurement
station can be highly reliant on local infrastructure to remain operational. This is confoundedby the fact that regions which are relatively socio-economically unstable are also measured
sparsely, and so the most at-risk measurements are likely to be in under-sampled regions andso of relatively high importance to any model assessing local systemic risk. Thus the benefits of using accurate data must be weighed against the risk of relying on a network of local
observational infrastructure.
Gridded products are observations on a global spatial grid at regular time intervals (normallymonthly). They are generated by a handful of organisations such as the U.K. Met Office andU.S. National Oceanic and Atmospheric Administration and as such are more operationallystable than individual direct measurements. Direct measurements are first homogenized,
to remove non-climate signals, and then gridded by estimating missing data using neighbouring points. The exact methodology used by each organisation is different and produces
different results. The resulting datasets produce more accurate global results, and allow for
more meaningful comparisons between different regions, than using the direct measurements alone. Any remaining instrumentation bias not removed in the quality control process
is reduced by the homogenisation process, however the gridded data still has coverage bias,
meaning the accuracy of the data varies by location.
Renalyses are synthesised estimates of the climate system, which use models to fill the gaps
in the observational record. The results are high time and spatial resolution datasets of arange of climate variables, spanning decades. Their workings and biases are discussed further in appendix Y3.3.

Appendix Y2: Climate forecasts and their biases
If we want to understand and potentially predict future risks, we need forecasts of climate
data. As with observational data, any predictions are reliant on the quality of the forecast data.
In this section we outline the type and quality of forecasts available by time scale.

Figure Y2.1. Qualitative estimate of forecast skill against lead time of the forecast.
Credit: Elisabeth Gawthrop, iri.columbia.edu.
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The weather (day-to-day changes in climate) is largely determined by changes in the atmosphere. Given accurate measurements of the atmosphere today, weather forecasts can makehighly skillful predictions on timescales of days to weeks. The land and ocean change more
slowly and therefore have a slower influence on the climate, and so land and ocean measurements become important for predictions further into the future. Fig Y2.1 gives an overview of
how forecast skill varies with time into the future, and which kinds of data are important forthat skill.
Y2.1. Seasonal forecasts
Seasonal predictions of climate variables for the next six months are regularly released byseveral organisations. These predictions often come from the same centres which produce
historic climate reanalyses and often based on the same models. The uncertainty in suchforecasts is dominated by the uncertainty in initial conditions (observational bias), with uncertainty due the model also a significant factor.
Dynamical seasonal forecasts are produced by running a physics-based climate model andgive a probabilistic range of possible outcomes. Currently, the probable range of weather only
varies slightly year to year, although the tropical regions can be more predictable, usually inthe case of strong El Nino/La Nina forcing112.
Statistical seasonal forecasts are produced by finding statistical relationships between different climate variables in the past and then using them to predict upcoming seasonal variables.
Statistical models can be simple or complex, but are generally much faster to run than adynamical model.
Combinations of different kinds of seasonal forecasts have been found to have greater skill
than the separate methods113, largely because the different forecasts tend to have skill in
different regions or seasons. Such hybrid forecasts are normally generated for a particularregion or purpose, and are not widely produced at an operational level.
Extreme events can often be derived from the output of seasonal forecasts. Thus, specialistseasonal forecasts exist for events such as tropical storms, drought, sea ice extent. These
are often based on combinations of outputs from seasonal forecasts, but may also involve
additional specialist models (e.g. Arctic sea ice extent); or a degree of subjectivity (e.g. tropicalstorm risk114).
However, in general, as the scale of the risk becomes more regional, the time scale of meaningful predictions also becomes smaller. Regional quantities vary much more than global
quantities, and as such regional predictions have much larger uncertainty. Regional variability
is often dominated by completely different processes than global variability, and smaller scale
processes are more difficult to model well on a global scale.
By their nature, local operational forecasts are produced by a wide range of organisations,using a wide range of tools. This diversity means that the quality of these forecasts varies
widely, and makes them difficult to compare or synthesise. For example, a recent review ofEuropean operational ocean modelling found only 23% of models assimilated observations,despite uncertainty in initial conditions being the largest source of uncertainty in such models; a number of models not using best practices in model implementation; and a range ofmodelling approaches, from running global models at high resolution to running regional
specialised models.115

154

112 Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., and MongeSanz, B. M.: SEAS5: the new ECMWF seasonal forecast system, Geosci. Model Dev., 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019, 2019.
113 Yan, H., Moradkhani, H., & Zarekarizi, M. (2017). A probabilistic drought forecasting framework: A combined dynamical and statistical approach. Journal of Hydrology, 548, 291-304.;
Schepen, A., and Wang, Q. J. ( 2015), Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia, Water Resour. Res., 51, 1797– 1812,
doi:10.1002/2014WR016163.
114 https://www.cpc.ncep.noaa.gov/products/precip/CWlink/ghazards/
115 Capet, Arthur, et al. “Operational Modeling Capacity in European Seas—An EuroGOOS Perspective and Recommendations for Improvement.” Frontiers in Marine Science 7 (2020): 129. https://doi.
org/10.3389/fmars.2020.00129

The Alan Turing Institute

There is large potential to improve on the wide range of local operational forecasts by implementing best practice techniques116, and by optimally combining a variety of forecasts117. Usingan ensemble of models could also improve estimates of model uncertainty118.
Y2.2. Decadal forecasts
Decadal forecasts, defined as climate predictions on the scale of years to a decade, are currently only produced on an experimental basis due to their high uncertainty. As the length of
the forecast increases, dependence on initial conditions decreases, whilst dependence on
the forcing of the climate system increases, see figure Y2.2. The choice of model has a large
impact on predictions of the next decade, whereas the exact choice of future socio-economic
scenario has very little impact119.
Assessing the state of the art climate models in 2013, the IPCC found there was high confidence that temperature could be skillfully predicted on a global or large regional average forup to a decade ahead. There was also skill in predicting precipitation over some land areas
on the same time scale120. Recent studies have found that decadal forecasts have similar skill
as seasonal forecasts at predicting temperature, precipitation, and there is also skill in forecasting the frequency of extreme events such as tropical storms or heatwaves121 . However,predictions of other climate variables, such as sea level, remain deeply uncertain, and multiple
probability distributions should be considered122.
The latest generation of climate models are likely to prove more skillful due to advances in thepast decade, and results from these models are starting to be published123. An assessment of
the decadal prediction skill of these models is expected in the next assessment report of the
IPCC Working Group 1, due in 2021.

1

55

Figure Y2.2. From IPCC AR5 WG1Chapter 11

116 Md Safat Sikder & Faisal Hossain (2019) Improving operational flood forecasting in monsoon climates with bias-corrected quantitative forecasting of precipitation, International Journal of River BasinManagement, 17:4, 411-421, DOI: 10.1080/15715124.2018.1476368
117 Schepen, A., and Wang, Q. J. ( 2015), Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia, Water Resour. Res., 51, 1797– 1812,
doi:10.1002/2014WR016163.
118 Kauffeldt, A., Wetterhall, F., Pappenberger, F., Salamon, P., & Thielen, J. (2016). Technical review of large-scale hydrological models for implementation in operational flood forecasting schemes oncontinental level. Environmental Modelling & Software, 75, 68-76.
119 Hawkins, E. and R. Sutton, 2009: The Potential to Narrow Uncertainty in Regional Climate Predictions. Bull. Amer. Meteor. Soc., 90, 1095–1108, https://doi.org/10.1175/2009BAMS2607.1
120 IPCC AR5 WG1 Chapter 11
121 Kushnir, Y., Scaife, A.A., Arritt, R. et al. Towards operational predictions of the near-term climate. Nature Clim Change 9, 94–101 (2019). https://doi.org/10.1038/s41558-018-0359-7
122 Kopp, R.E., DeConto, R.M., Bader, D.A., Hay, C.C., Horton, R.M., Kulp, S., Oppenheimer, M., Pollard, D. and Strauss, B.H. (2017), Evolving Understanding of Antarctic Ice‐Sheet Physics and Ambiguity in
Probabilistic Sea‐Level Projections. Earth’s Future, 5: 1217-1233. doi:10.1002/2017EF000663
123 Borchert, L. F., Pohlmann, H., Baehr, J., Neddermann, N.‐C., Suarez‐Gutierrez, L., & Müller, W. A. ( 2019). Decadal predictions of the probability of occurrence for warm summer temperature extremes.
Geophysical Research Letters, 46, 14042– 14051. https://doi.org/10.1029/2019GL085385

The Alan Turing InstituteY2.3. Downscaling
Downscaling is the term used to describe the technique of taking a climate forecast at a particular spatial and time scale, and using it to predict a quantity at a higher spatial or time scale. It
has a wide range of applications, from regional flood risk assessments for the next few days124
to predicting regional temperature variations in 100 years time125.

Fig Y2.3: Visual example of statistical downscaling from https://www.meteo.unican.es/downscaling/intro
Machine learning is very well suited to statistical downscaling tasks and therefore has thepotential to provide the kind of local climate data required to assess local security risks fromthe kind of large scale climate forecasts currently produced.
Dynamical downscaling involves taking the results of a larger model (such as the temperature
on a very coarse grid) and using it as the boundary conditions of a regional model (such as amodel of the UK). The results are therefore consistent with our understanding of the physics
of the climate system, and can adapt to a changing climate, although they are computationallyexpensive to run and will include any biases from the larger model.
Statistical downscaling involves finding statistical relationships between large and small scalevariables in historic datasets, and assuming the same relationship holds in the future. They
are comparatively cheaper to run and can incorporate observational data, but they assume
that statistical relationships from the past will hold for the future and are also limited by biases
in the larger model. However, this assumption is likely to hold well for the near future, i.e. thenext 3-5 years.

124 See, e.g., the UK Flood Forecast Centre ffc-environment-agency.metoffice.gov.uk
125 Hayhoe, K., Wake, C., Anderson, B. et al. Regional climate change projections for the Northeast USA. Mitig Adapt Strateg Glob Change 13, 425–436 (2008). https://doi.org/10.1007/s11027-007-9133-2

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Advantages have recently been found in combining statistical and dynamical downscaling
methods126. The World Climate Research Programme (WCRP) - Coordinated Regional Downscaling Experiment (CORDEX)127 is coordinating the production of ensembles of dynamicaland statistical regional projections at high resolution using a range of models and observational datasets - this data will produce better regional predictions and allow for a more accurate estimate of its uncertainty.

Appendix Y3: Climate modelY3.1. Overview of model types
When considering the type of model to use
for joining different systems, such as theclimate system and the socio-economic
system, it is important to consider themodel’s purpose. Broadly speaking, model
purposes fall into five broad categories:128
Prediction: produce estimations ofquantitative or qualitative features of asystem based at a specific time, based onother measurements at the same time.
Forecasting: estimating features in the future
based on current measurements.
Management and decision-making under
uncertainty: to support decisions orformulate problems, in ‘what-if’ or ‘best
option’ type scenarios.
Social learning: help people understanda system, and thereby improve
communication, learning from past
behaviour, and perform collective action.
Develop system understanding/experimentation: summarise or bring
together many aspects to help improveunderstanding of the entire system, or
understand how it might react to changes.

There are a variety of approaches thatmodels can take to realise their purposes,
which can depend on the type of data
available and the type of output required,as well as the purpose itself. The maincategories of model approaches for
combining different systems are outlined
below, although the definitions are notprecise and a given model may fall into morethan one category:
System dynamics: a set of concepts andnumerical techniques for understanding
complex systems, designed to include nonlinearities and feedbacks.
Bayesian networks: systems described by
combinations of probabilistic relationships.
Coupled component models: made bycombining models from different sectors,
which may themselves have differing
approaches.
Agent based models: represents interactions
between autonomous individuals that are
most often humans, but can be biophysicalentities.
Knowledge based models: infer outcomesbased on a collection of knowledge, which
can take the form of rules or logic.

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126 Verdin, A., Rajagopalan, B., Kleiber, W., Podestá, G., & Bert, F. (2018). A conditional stochastic weather generator for seasonal to multi-decadal simulations. Journal of Hydrology, 556, 835-846.
127 http://cordex.org
128 Kelly (Letcher), R. A., Jakeman, A. J., Barreteau, O., Borsuk, M. E., ElSawah, S., Hamilton, S. H., … Voinov, A. A. (2013). Selecting among five common modelling approaches for integrated environmental
assessment and management. Environmental Modelling & Software, 47, 159–181. doi:10.1016/j.envsoft.2013.05.005

The Alan Turing Institute

Table 3.1 summarises how one might choose a model approach based on the purpose of the model and other relevant
considerations. Appropriate use of integrated modelling approaches (X = common feature, * = possible feature). From129

Reason formodelling / type of
application

Type of data
available topopulate model

Model focus
on a complex
description of
specific processes
or greater breathof coverage ofinteractions insystem?

Prediction

System Bayesian Coupled Agent
based
dynamics networks
models*X
X*

Forecasting

X

X
X

Decision*making under
uncertainty

X

*

*

SystemX
understanding

X

X

X

Social learningX

X

Qualitativeandquantitative
data

*

X

Quantitative
data mainly

X

X
*

*

X

X

X

X

X

Depth ofspecific
processes

*

X

X

X

Breadth ofsystem

X

X

*

X

X

1

129

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Kelly (Letcher), R. A., Jakeman, A. J., Barreteau, O., Borsuk, M. E., ElSawah, S., Hamilton, S. H., … Voinov, A. A. (2013). Selecting among five common modelling approaches for integrated
environmental assessment and management. Environmental Modelling & Software, 47, 159–181. doi:10.1016/j.envsoft.2013.05.005

The Alan Turing InstituteY3.2. Global climate models and their biases
Projections of future climate change are made using two variants of climate models: General
Circulation Models (GCMs) and Earth System Models (ESMs). Both types of models represent the global climate system through mechanistic, mathematical equations describing
thermodynamics and fluid dynamics. These models divide Earth into a three-dimensional grid
representing latitude, longitude and a vertical component (altitude of the atmosphere, ocean
depth). At the start of a model run, each grid cell is assigned a value for each of the model’sstate variables (e.g., atmospheric temperature, ocean salinity). These initial conditions are
based on global mean observations.
While GCMs focus on the physical climate system, representing atmosphere, ocean, and sea
ice physics and dynamics, ESMs aim to also capture chemical, and biological processes interrestrial and marine ecosystems and allow for these ecosystems to have feedback on thecirculation. ESMs are global climate models with the added capability to explicitly represent
biogeochemical processes that interact with the physical climate. These more complex ESMs
are actually what most people mean when they talk about “climate models”.
In ESMs major physical and biogeochemical processes in the Earth System are represented
by mathematical equations. To calculate historical projections, models are tuned to reproduce
historical observational data such as atmospheric gas composition and temperature changewith reasonable parameter estimates. To calculate future projections, emission scenarios areprescribed to the models.
The Coupled Model Intercomparison Project (CMIP) coordinates the comparison of comprehensive climate models and has its roots in earlier model intercomparisons, such as the
Atmospheric Model Intercomparison Project (AMIP; Gates 1992; Gates et al. 1999). There areapproximately 33 modeling groups in 16 countries taking place in the latest model intercomparison project, CMIP6, for contribution to the IPCC’s sixth Assessment Report. There are
four main experiments each group must contribute, and a broad range of optional extra experiments, designed to answer three broad scientific questions:1.
How does the Earth System respond to forcing?
2.
What are the origins and consequences of systematic model biases?
3.
How can we assess future climate changes given climate variability, predictability anduncertainties in scenarios?
Earth system models have three main sources of uncertainty: scenario uncertainty, due tounknown future emissions; internal variability, natural fluctuations in the climate system; andmodel uncertainty, due to the fact that different models produce different predictions.
Internal variability is roughly constant in time, but the other sources grow in time, at different
rates that can depend on time, location, and quantity. As an example, figure Y3.2 shows therelative contributions from the three types of uncertainty for global and regional temperatureand precipitation from the CMIP5 models. In the near-term, scenario uncertainty is unimportant for all quantities. Internal variability is the largest source of near-term uncertainty, withmodel uncertainty a significant but quantity-dependent contributor. For example, internalvariability is much more important for European decadal mean winter precipitation than for
global mean annual temperature.
Thus, decadal predictions can be improved by reducing model uncertainty, which can beachieved by a variety of means, including improved initialisation, and direct model improvements such as increased resolution and improved parameterizations. Whilst internal variability cannot be eliminated, it can be better estimated using larger ensemble sizes.

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The Alan Turing Institute

Figure Y3.2: Relative contributions of sources of uncertainty in CMIP5 models from https://www.climate-lab-book.ac.uk/2013/sources-of-uncertainty/. Orange is internal variability, blue
is model uncertainty and green is scenario uncertainty.
Model bias can also be understood as the combination of parametric and structural bias, as
discussed further in section Y3.3.

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The Alan Turing InstituteY3.3. Reanalyses and their biases
Reanalysis products are based on a
combination of observations and model
results, designed to ‘fill-in-the-gaps’ of theobservational record in order to provide
a wide range of variables at high spatial
and temporal resolution. These can beinvaluable for comparing with resultsof models for a wider range of variables
than those available in the observational
record. There are currently a handful of wellestablished products based on different
models available from different institutions.
To produce atmospheric reanalysis
products, a model is run for a shortperiod of 6 hours to produce a ‘forecast’.
Observations are then assimilated througha variety of methods that nudge the‘forecast’ towards the measurements,
which produces the ‘analysis’. This is thenthe basis of the next ‘forecast’, and theprocess is repeated continuously for thelength of the reanalysis.
The results of renalyses are therefore
subject to the combination of observationaland model biases. Some observationalbiases may be reduced due to theassimilation, but others will remain. Biases
will be much higher in regions where there
are sparse observations, such as the polar
regions, and further in the past.
Y3.4. Future Socio-Economic Pathways
In order to produce projections of the
future, climate models require predictionsof future socio-economic states as wellas emissions. The CMIP6 models (seeabove) use a set of standardised scenarios
called Shared Socioeconomic Pathways(SSPs), which take into account possible
future social as well as climate dimensions.
These SSPs are the latest set of global
socioeconomic development trends
designed by the IPCC to guide climatechange research. They are made up of fiveglobal development pathways that depict
plausible alternative future states of thesociety and the environment. They include
key socioeconomic variables such as
demographics130, and economic growth131.
They have been designed to span the wide
range of socioeconomic challenges
161Comparing the results of different
reanalysis products can give an estimateof the model bias, which results from the
combination of parametric bias (uncertainty
in what the best parameters to use in themodel) and structural bias (the failure of
the model to accurately represent theunderlying physics of the real climate).
Some methods use an ensemble of model
forecasts to create their analysis, and the
spread inthese forecasts can give some estimateof the random errors (perhaps due toparametric bias or uncertainty in the initialconditions) for an individual reanalysis.
Similar reanalysis products are available forocean variables, although the time rangeof these is much more limited due to thepaucity of historical ocean observationsat depth. In addition to reanalyses, ocean‘state estimate’ products are also available,
which work by adjusting model parameters
to best match observations, rather thandirectly modifying the forecast.

to adaptation and mitigation. There is alarge body of literature documenting (i)their development132; (ii) their quantificationof key socioeconomic variables ; and (iii)their integration with climate change andgreenhouse gas emissions134.
The different scenarios for forcing theclimate models are determined by a
combination of socioeconomic pathway
and climate forcings, see figure Y3.3 forillustration. The four main pathways are
named SSP1-2.6, SSP2-4.5, SSP3-7.0
and SSP5-8.5, where the first number
indicates the socioeconomic pathway,
and the second number the strength ofclimate forcing at 2100. These are the same
magnitude climate forcings as the previous
CMIP5 RCPs.

130 Samir, K. C., & Lutz, W. (2014). Demographic scenarios by age, sex and education corresponding to the SSP narratives. Population and Environment, 35(3), 243-260.
131 Cuaresma, J. C. (2017). Income projections for climate change research: A framework based on human capital dynamics. Global Environmental Change, 42, 226-236.
132 Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., Van Vuuren, D. P., ... & Meehl, G. A. (2010). The next generation of scenarios for climate change research and assessment. Nature,463(7282), 747-756.; Ebi, K. L., Kram, T., van Vuuren, D. P., O’Neill, B. C., & Kriegler, E. (2014). A new toolkit for developing scenarios for climate change research and policy analysis. Environment:
Science and Policy for Sustainable Development, 56(2), 6-16.; Schweizer, V. J., & O’Neill, B. C. (2014). Systematic construction of global socioeconomic pathways using internally consistent element
combinations. Climatic Change, 122(3), 431-445.
133 IIASA. Shared Socioeconomic Pathways Database, Version 1.1. Available online: https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about.
134 Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O’neill, B. C., Fujimori, S., ... & Lutz, W. (2017). The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions
implications: an overview. Global Environmental Change, 42, 153-168; Marangoni, G., Tavoni, M., Bosetti, V., Borgonovo, E. M. A. N. U. E. L. E., Capros, P., Fricko, O., ... & Johnson, N. (2017). Sensitivityof projected long-term CO 2 emissions across the Shared Socioeconomic Pathways. Nature Climate Change, 7(2), 113-117.

The Alan Turing InstituteFig Y3.3: SSP-RCP scenario matrix illustrating ScenarioMIP simulations. Each cell in the matrix indicates a combination of socioeconomic development pathway (i.e., an SSP) and climate
outcome based on a particular forcing pathway that current IAM runs have shown to be feasible (Riahi et al., 2016). Dark blue cells indicate scenarios that will serve as the basis for climate
model projections in Tier 1 of ScenarioMIP; light blue cells indicate scenarios in Tier 2. CMIP5
RCPs, which were developed from previous socioeconomic scenarios rather than SSPs, areshown for comparison.

Appendix Y4: Global Landscape of Climate Security-Initiatives and stakeholders
The repository can be accessed here62

turing.ac.uk
turing.ac.uk@turinginst@turinginst