![llustration of how climate prediction information can be used for decision-making [adapted from Jacox et al. (2022) Figure 4 under CC-BY-4.0]. Top panel: hazard intensity (magnitude of a climate extreme) as a function of forecast probability, indicating that more intense extremes are typically predicted with higher probability. Bottom panel: False prediction rates (false positive and false negative predictions) as a function of forecast probability, illustrated for a more skillful and a less skillful prediction (where the skill can differ between different regions, climate variables or prediction systems).](https://www.researchgate.net/profile/Pablo-Borges-De-Amorim/publication/387823666/figure/fig2/AS:11431281302198811@1736352172160/llustration-of-how-climate-prediction-information-can-be-used-for-decision-making_Q320.jpg)
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Frontiers in Climate 01 frontiersin.org
Climate extremes and risks: links
between climate science and
decision-making
JanaSillmann
1,2*, Timothy H.Raupach
3,4,5, KirstenL.Findell
6,
MarkusDonat
7,8, LincolnM.Alves
9, LisaAlexander
5,4,
LeonardBorchert
1, PabloBorgesdeAmorim
10,
CarloBuontempo
11, ErichM.Fischer
12, ChristianL.Franzke
13,14,
BinGuan
15, MarjolijnHaasnoot
16,17, EdHawkins
18,
DanielaJacob
19, RochéMahon
20, DouglasMaraun
21,
MonicaA.Morrison
22, BenjaminPoschlod
1, AlexC.Ruane
23,
Shampa
24, TanneciaStephenson
26, Narellevan der Wel
26,
ZhuoWang
27, XuebinZhang
28 and JosipaŽupanić
1
1 Research Unit Sustainability and Climate Risk, Department of Earth System Sciences, University of
Hamburg, Hamburg, Germany, 2 Centre for International Climate and Environmental Research
(CICERO), Oslo, Norway, 3 UNSW Institute for Climate Risk and Response, University of New South
Wales, Sydney, NSW, Australia, 4 Centre of Excellence for Climate Extremes (CLEX), Sydney, NSW,
Australia, 5 Climate Change Research Centre, Faculty of Science, University of New South Wales,
Sydney, NSW, Australia, 6 NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United
States, 7 Barcelona Supercomputing Center, Barcelona, Spain, 8 Catalan Institution for Research
and Advanced Studies (ICREA), Barcelona, Spain, 9 National Institute for Space Research (INPE),
Sao Jose dos Campos, Brazil, 10 Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ),
Bonn, Brazil, 11 European Centre for Medium-Range Weather Forecasts, Bonn, Germany, 12 ETH
Zürich, Zürich, Switzerland, 13 Center for Climate Physics, Institute for Basic Science, Busan,
Republic of Korea, 14 Pusan National University, Busan, Republic of Korea, 15 Joint Institute for
Regional Earth System Science and Engineering, University of California, Los Angeles,
LosAngeles, CA, United States, 16 Utrecht University, Utrecht, Netherlands, 17 Deltares, Delft,
Netherlands, 18 Department of Meteorology, National Centre for Atmospheric Science, University
of Reading, Reading, United Kingdom, 19 Climate Service Center Germany (GERICS), Hamburg,
Germany, 20 Caribbean Institute for Meteorology and Hydrology, Bridgetown, Barbados, 21 Wegener
Center for Climate and Global Change, University of Graz, Graz, Austria, 22 Climate and Global
Dynamics Laboratory, NSF National Center for Atmospheric Research (NCAR), Boulder, Colorado,
CO, United States, 23 Goddard Institute for Space Studies (NASA), New York, NY, United States,
24 Department of Physics, Bangladesh University of Engineering and Technology, Dhaka,
Bangladesh, 25 Department of Physics, University of the West Indies, Kingston, Jamaica, 26 World
Climate Research Programme, WMO, Geneva, Switzerland, 27 Department of Atmospheric
Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, United States, 28 Pacific
Climate Impacts Consortium, University of Victoria, Victoria, BC, Canada
The World Climate Research Programme (WCRP) envisions a future where
actionable climate information is universally accessible, supporting decision
makers in preparing for and responding to climate change. In this perspective,
weadvocate for enhancing links between climate science and decision-making
through a better and more decision-relevant understanding of climate impacts.
The proposed framework comprises three pillars: climate science, impact science,
and decision-making, focusing on generating seamless climate information from
sub-seasonal, seasonal, decadal to century timescales informed by observed climate
events and their impacts. The link between climate science and decision-making
has strengthened in recent years, partly owing to undeniable impacts arising from
disastrous weather extremes. Enhancing decision-relevant understanding involves
utilizing lessons from past extreme events and implementing impact-based early
warning systems to improve resilience. Integrated risk assessment and management
require a comprehensive approach that encompasses good knowledge about
OPEN ACCESS
EDITED BY
Matthew Collins,
University of Exeter, UnitedKingdom
REVIEWED BY
Karin Van Der Wiel,
Royal Netherlands Meteorological Institute,
Netherlands
*CORRESPONDENCE
Jana Sillmann
jana.sillmann@uni-hamburg.de
RECEIVED 21 September 2024
ACCEPTED 26 November 2024
PUBLISHED 18 December 2024
CITATION
Sillmann J, Raupach TH, Findell KL, Donat M,
Alves LM, Alexander L, Borchert L, Borges de
Amorim P, Buontempo C, Fischer EM,
Franzke CL, Guan B, Haasnoot M, Hawkins E,
Jacob D, Mahon R, Maraun D, Morrison MA,
Poschlod B, Ruane AC, Shampa,
Stephenson T, van der Wel N, Wang Z,
Zhang X and Županić J (2024) Climate
extremes and risks: links between climate
science and decision-making.
Front. Clim. 6:1499765.
doi: 10.3389/fclim.2024.1499765
COPYRIGHT
© 2024 Sillmann, Raupach, Findell, Donat,
Alves, Alexander, Borchert, Borges de
Amorim, Buontempo, Fischer, Franzke, Guan,
Haasnoot, Hawkins, Jacob, Mahon, Maraun,
Morrison, Poschlod, Ruane, Shampa,
Stephenson, van der Wel, Wang, Zhang and
Županić. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The
use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
TYPE Perspective
PUBLISHED 18 December 2024
DOI 10.3389/fclim.2024.1499765
Sillmann et al. 10.3389/fclim.2024.1499765
Frontiers in Climate 02 frontiersin.org
possible impacts, hazard identification, monitoring, and communication of risks
while acknowledging uncertainties inherent in climate predictions and projections,
but not letting the uncertainty lead to decision paralysis. The importance of data
accessibility, especially in the Global South, underscores the need for better
coordination and resource allocation. Strategic frameworks should aim to enhance
impact-related and open-access climate services around the world. Continuous
improvements in predictive modeling and observational data are critical, as is
ensuring that climate science remains relevant to decision makers locally and
globally. Ultimately, fostering stronger collaborations and dedicated investments
to process and tailor climate data will enhance societal preparedness, enabling
communities to navigate the complexities of a changing climate eectively.
KEYWORDS
climate extremes, climate impacts, climate risk, decision making, climate service,
climate change
1 Introduction
Global warming is causing a wide range of weather and climate
extremes to become more frequent, intense and widespread
(Seneviratne etal., 2021). Every region of the world is experiencing
changes in climatic impact-drivers, such as heatwaves, oods,
droughts, re weather, sea level rise, and storm surges (IPCC AR6
2021, 2022). Climate change will continue to cause unprecedented
extreme events, such as record-breaking heat waves (Fischer etal.,
2021), and will lead to increases in the occurrence of compound
events (Raymond etal., 2022). Weare thus challenged with “imagining
the unimaginable” for better risk preparedness and management.
e World Climate Research Programme (WCRP) envisions a
future in which actionable climate information is available for all
regions of the world, with climate services providing decision makers
with tools enabling them to prepare for, respond to, and build
resilience to climate change alongside reducing anthropogenic
emissions. Weather and climate information is crucial for climate risk
management, as well as for adaptation and mitigation, and needs to
bemade (or remain) accessible for everyone. e quickly evolving
eld of climate services is concerned with making climate information
accessible and useful to decision makers (Jacob etal., 2024), but there
is no one-size-ts-all approach to climate services. Context, user
involvement, and collaboration among scientists, practitioners, and
users determine the type and usefulness of climate services for
adaptation, mitigation, and risk management (Ranasinghe etal., 2021).
Around the time of the rst Intergovernmental Panel on Climate
Change (IPCC) report (early 1990s) climate science was a relatively
young eld, with a focus on global scales and far-distant time horizons
(e.g., global mean temperature projections out to 2,100 or 2,300).
While useful for some [e.g., in the context of sea level rise (van
Dorland etal., 2023; van der Wiel etal., 2024)], these temporal and
spatial scales exceed the scope and directives of many decision makers.
In the last decade, climate science has evolved substantially to also
include regional climate change, climate extremes and their
attribution, and has advanced in near-to-medium term climate
predictions (IPCC, 2021). e severe and widespread impacts of
climate extremes in recent years have alerted many decision makers
to the topic of climate change and piqued their interest in becoming
better prepared for (or adapted to) the consequences of climate
change, but actual action in terms of proactive adaptation and
mitigation at the scale needed is limited (e.g., Schleussner etal., 2024;
eokrito etal., 2023; omas etal., 2021).
Here wefocus on aspects that could improve the links between
recent advances in climate science and decision-making through a
better and more decision-relevant understanding of climate-related
impacts (Figure1). Impacts, such as damaged infrastructure, lost lives,
destroyed ecosystems or livelihoods, describe the actual consequences
of climate change that have already occurred or are currently
happening, so basically the realization of risk (IPCC, 2022). e
framework wepresent here is based on three main pillars: climate and
climate science, impacts and impact science, and decision-making.
Weargue that climate-resilient pathways can beestablished through
processes enabling strong connections between, and collaboration
across, these three pillars. is will include seamless climate
information based on learning from past impacts, impact-based early
warning systems, and climate prediction and projections of relevant
climatic impact-drivers. Societal considerations of resilience in the
face of a changing climate imply that decision-making should befront
and center, and that more robust adaptation and mitigation eorts
need to happen than those wesee today.
2 Linking climate science and
decision-making via impact
understanding
Climatic impact-drivers (including climate extremes), or
hazards in the context of risk assessments, must beconnected to
their respective impacts on society and ecosystems (Ruane etal.,
2022). Understanding how climatic changes lead to impacts is
necessary to support decision-making (e.g., heat or ood warnings,
forecasts and warnings for tropical cyclones, agricultural decisions
supported by sub-seasonal to seasonal predictions). A better-
informed risk assessment through advances in climate science and
impact science will contribute to adaptation and response strategies
for climate-resilient development (IPCC, 2022). Depending on the
vulnerability of populations, economic sectors or assets, impacts
can betotally dierent for the same climatic impact-driver (e.g., in
dierent countries or for dierent groups of people) or the same
forecast (e.g., Tradowsky etal., 2023). Multiple climatic impact-
drivers, such as extreme heat, oods, droughts and compounding
Sillmann et al. 10.3389/fclim.2024.1499765
Frontiers in Climate 03 frontiersin.org
events, can aect a specic sector, such as health, agriculture or
infrastructure, but also specic climatic impact-drivers aect
multiple sectors in unique ways (Ranasinghe et al., 2021).
Moreover, impacts can also cascade across sectors and regions
(Simpson etal., 2021, 2023). ese compounding and cascading
events make risks more complex and dicult to manage
(IPCC, 2022).
For better preparedness, wecan gain a lot of knowledge from
current and past impacts of climate extremes and how weresponded
to them [e.g., through disaster forensics (Keating etal., 2016)], thus
informing our decision-making related to possible future impacts.
Historical observations of previous extreme events, their impacts and
the translation into a warmer climate, can provide valuable insights
into the types of event that could beexperienced today and in the near
future (e.g., Hawkins etal., 2023a; Hegdahl etal., 2020). A review of a
variety of approaches to constructing such physical climate storylines
can befound in Baldissera Pacchetti etal. (2023). Also, in the eld of
extreme events attribution, which focuses mainly on the probability
of a hazard in a world with and without human-induced climate
change, the storyline approach can provide some insight in the
thermodynamic and dynamic processes that contributed to a past
extreme event (Shepherd, 2016). However, even for large-scale
heatwaves, for which event attribution seems rather straightforward,
this method is challenged by the limited ability of climate models to
represent relevant local processes (e.g., moisture budget, land
interactions) and natural variability (Shepherd, 2016; van Oldenborgh
etal., 2022). In recent years, event attribution has also been applied to
impacts, with considerable challenges, but also opportunities for this
emerging eld, as elaborated in Perkins-Kirkpatrick etal. (2024).
Ideally, wegenerate seamless information over time, meaning that
we combine what we have learned from the past with climate
predictions or projections to inform impact forecasts tailored to
specic decision makers or contexts. In this way, as new or rened
insights become available, they can inform decision-making as part of
adaptive pathways planning (Haasnoot etal., 2018). Such insights
could include the time of emergence in climate scenarios or the time
that a critical threshold is reached (e.g., Slangen etal., 2022). As time
goes on, the situation can bereviewed and new decisions can bemade.
When this is part of an adaptive pathways plan, decision-relevant
information can beidentied which can bemonitored for signals of
change (e.g., climate changing faster/slower, adaptation thresholds
being reached in the near-term). is includes accounting for and
imagining high-impact low-likelihood outcomes associated with
unprecedented climate extremes or compound events, which need to
beincluded in stress-testing and storylines to support risk assessments
(e.g., Sillmann etal., 2021a; IPCC, 2021).
Climate predictions and projections can provide the information
needed to underpin decision-making or deliver early warning
systems. Climate projections can inform adaptation and mitigation
planning about medium to long-term future climate change (e.g.,
Zhang etal., 2024), while climate predictions can provide useful
information on the sub-seasonal to decadal time scale. In particular,
national climate projections provided by National Meteorological and
Hydrological Services (NMHS) are oen prepared with a focus on
FIGURE1
Illustration of linking climate science and decision-making via the impacts of climate change. Three conceptual pillars form a foundation on which
climate-resilient pathways are built: (i) climate science, which provides a body of knowledge and understanding of the climate system; (ii) impact
sciences and a robust understanding of climatic impact-drivers; and (iii) the decision-making sphere, where a synthesis of relevant climate science data
and understanding of the impacts inform action. The three pillars interact (arrows) via physical eects, transfer of learnings, and implementation of
decisions.
Sillmann et al. 10.3389/fclim.2024.1499765
Frontiers in Climate 04 frontiersin.org
specic use cases of decision-making on adaptation and mitigation
(e.g., Lowe etal., 2018; Fischer etal., 2022). To connect the dierent
timescales, it is thus important to have both early warning systems
(which predict specic events or climate conditions that are oen
context and place specic) and forewarning systems (which indicate
the growing potential of future climate risks that may necessitate
proactive planning; e.g., Haasnoot et al., 2018). Although both
systems may beequipped to inform responses to the same type of
extreme event (e.g., a ood), in the early warning case decision
makers are under immediate pressure to respond based on the
response options available, while in the latter case decision makers
may consider what adaptation measures they could plausibly develop
to beequipped for future responses and to build long-term resilience.
Forewarning may also belinked with foresight analyses that link
changes in climatic impact-drivers with shis in vulnerability and
exposure (via scenarios or storylines; e.g., Findell etal., 2023) for
more comprehensive risk assessments. Ideally, both would go hand-
in-hand with seamless data and information ows and opportunities
for iterations and learning to support decision-making
under uncertainty.
As Earth’s climate is subject to chaotic characteristics and climate
models and prediction systems are not perfect, decisions need to
bemade under uncertainty (e.g., Cross Chapter Box DEEP in New
etal., 2022). e uncertainty and usefulness of climate predictions can
partly bequantied by forecast quality evaluations (e.g., Delgado-
Torres etal., 2022; Meehl etal., 2021). However, the skill of predictions
also varies in time related to the phase of large-scale climate drivers,
which can also provide so-called “windows of opportunity” (Borchert
etal., 2019a; Sgubin etal., 2021; Mariotti etal., 2020; Liu etal., 2023).
For instance, we know that the state of the El Niño-Southern
Oscillation can be predicted several months in advance, and its
consequences, such as changes in rainfall patterns and increases in the
likelihood of extreme events, are well known for many regions, as was
illustrated for the 2022 Pakistan Floods by Dunstone et al. (2023).
Furthermore, skillful predictions of North Atlantic sea surface
temperature may enable predictions of hot European summers up to
a decade ahead (Borchert etal., 2019b).
e impact of any climate impact-driver relates to the driver itself
(the hazard), as well as exposure and vulnerability of the aected
system. Weargue that impact understanding and modeling are the
connector between climate science and decision-making. Using past
impacts and iterative knowledge gained through experience,
combined with predictions and projections of hazard, exposure, and
vulnerability changes, and taking uncertainty into account, decisions
can be tailored to balance risk and impact for each individual
circumstance. We outline challenges in climate sciences, impact
sciences, and decision making below.
3 Key challenges
Several decades of climate science provide a robust understanding
of how and why global climate is changing, and this knowledge can
underpin action and decisions (Forster et al., 2023; IPCC, 2023).
However, numerous challenges related to physical climate and impact
sciences remain, and hinder the ecient production and uptake of
climate information in decision-making. In this section wediscuss
these challenges with respect to the current state of knowledge,
separated into the physical climate science aspects, climate impacts,
and the relevance for decision-making.
3.1 Physical climate science
Key challenges in the physical climate sciences are here divided
into data-related challenges, process understanding and representation
in models, and prediction and projection of changes.
3.1.1 Monitoring and observation of climate
extremes
Data coverage and gaps and access to data remain consistent
challenges, particularly for understanding and analysis of climate
extremes (Sillmann etal., 2017). e nature of the challenge depends
on the climate extreme being investigated, its occurrence frequency,
spatial size and distribution, and ease of observation. Generally, there
is a lack of data for the Global South (e.g., Dunn etal., 2020), with
research resources oen missing for climate risk assessments (Otto
etal., 2020a; Seneviratne etal., 2021). However, space-based data and
technologies have made signicant contributions in addressing these
challenges, providing critical data and insights that can enhance our
understanding and management of climate change impacts (Yang
etal., 2013; Alexander etal., 2020; CEOS, 2023). e latter, however,
should not beseen as a replacement for other forms of observations,
especially in situ measurements. Data rescue and recovery, for
example of non-digitized past records, can play an important role in
calculating and assessing past trends and dening extreme events
(Hawkins et al., 2023b). In addition, human observations (e.g.,
narratives) can complement instrumental records of climate extremes
in regions with sparse data (Savo et al., 2024). Also, Articial
Intelligence methods are showing promising potential in inlling
sparse observational data and revealing past climate extremes
consistent with proxy reports or narratives (e.g., Plésiat etal., 2024).
Historical records are also useful for providing context to
contemporary extremes (Yule etal., 2023). Development of plausible
extreme-event scenarios based on combinations of model outputs and
historical records could increase scientic defensibility for emergency
preparedness and planning (Dettinger et al., 2012; Huang and
Swain, 2022).
3.1.2 Process understanding and representation
in models
Some relevant processes are still insuciently understood, and
climate models have known shortcomings in representing these
relevant processes. Here weconsider process representation in models,
but it is worth noting that some processes related to hazards, such as
surface hydrology or geomorphology, are not represented at all in
meteorological or climate models (Senatore etal., 2015). Some key
processes related to climate extremes have spatial scales smaller than
typical model resolutions and must oen be parameterized
(Seneviratne et al., 2021). Representation of extremes in models
depends on the accuracy of such parameterizations (e.g., Kong etal.,
2022), as well as model resolution (e.g., Rowell and Berthou, 2023)
and physical process understanding (Seneviratne et al., 2021).
Increases in computational power have led to the availability of
higher-resolution, convection-permitting simulations with benets
particularly for precipitation extremes (Kendon etal., 2021; Poschlod,
Sillmann et al. 10.3389/fclim.2024.1499765
Frontiers in Climate 05 frontiersin.org
2021). e ability to run climate models at kilometer resolution
represents a signicant advance with wide-ranging benets but also
challenges (Schär etal., 2020; Prein etal., 2021). is being said,
process understanding gaps related to extremes remain (Seneviratne
etal., 2021) in areas including the scaling with temperature of short-
duration and high-intensity rainfall events (Fowler etal., 2021), severe
storms (Allen, 2018), and compound extreme events which can
exacerbate impacts (Zscheischler etal., 2020; Poschlod etal., 2020).
Increases in computational power have also enabled the creation of
single model initial-condition large ensembles, which allow for a
robust sampling of univariate and compound extremes (Bevacqua
etal., 2023; Lang and Poschlod, 2024) and show the eect of internal
climate variability on climate extremes (Suarez-Gutierrez etal., 2020;
Poschlod etal., 2021). Other recent advances in process understanding
and model representation have covered a wide variety of topics,
including tropical-extratropical interactions (e.g., Zilli etal., 2023),
ocean-land-atmosphere interactions (e.g., Dehondt et al., 2024;
Freisen etal., 2022; Duan etal., 2023), extreme heat and drought in
selected regions (e.g., Baier and Stohl, 2023; Sun etal., 2022), monsoon
rains (e.g., Menon etal., 2022; Vg etal., 2023), wildres (e.g., Charlton
etal., 2022; Son etal., 2024), atmospheric convection (e.g., Bony etal.,
2020; Klein etal., 2023; Nkrumah etal., 2023; Prein etal., 2021), and
jets and eddies in the atmosphere (e.g., Garnkel etal., 2024) and
ocean (Gutierrez-Villanueva et al., 2023). ere have also been
improvements in understanding tipping points, which are rapid and/
or irreversible changes in the climate system (Lee et al., 2021) and
their interactions (Franzke et al., 2022). However, further
understanding of the underlying nonlinear mechanisms is needed
(Boers etal., 2022), and robust assessment of nonlinear processes
requires longer observational data sets than linear processes (Findell
etal., 2015). Overall, linking our understanding of climate extremes,
their characteristics, and their changes, to drivers, feedbacks and
compound characteristics, remains a challenge.
3.1.3 Prediction and projection of changes
Predicted and projected changes in extremes, and scientic
condence in the changes, vary by region and event type, with
thermodynamic changes generally better understood than dynamic
changes (Seneviratne etal., 2021). Projections with relatively higher
condence include those for temperature-related extremes, drought,
and re weather in many regions, while dierences between event
scales and model resolutions and uncertainties in parametrizations
lead to relatively lower condence in extremes including severe
storms, and heavy snowfall (Ranasinghe et al., 2021). Articial
intelligence (AI) and machine learning (ML) have opened new
modeling opportunities, with ML methods used for the prediction
and attribution of extremes (Reichstein etal., 2019; Salcedo-Sanz
etal., 2024), and the skill of some ML-based models in sub-seasonal
prediction comparable to dynamic prediction (Li et al., 2024).
However, traditional Numerical Weather Prediction (NWP) models
still perform better for some extreme events, especially when
considering metrics for issuing warnings (Charlton-Perez etal., 2024).
Ensembles of moderately high-resolution climate simulations
combined with AI/ML, built on domain-specic knowledge, have
been championed as an eective approach to advancing climate
prediction and projection (Schneider etal., 2023) as well as early
warning (Reichstein etal., 2024). One challenge is that wemay need
to anticipate events that have hitherto been considered implausible.
Approaches that go beyond statistical assessments and standard
climate model ensemble projections may berequired. Such approaches
include physical climate storylines (Baldissera Pacchetti etal., 2023;
Doblas-Reyes etal., 2021; Harvey etal., 2023; Maraun etal., 2022;
Sánchez-Benítez etal., 2022; Seneviratne etal., 2021; Hegdahl etal.,
2020), iterative decision-making processes (New etal., 2022), and the
UNSEEN approach (ompson etal., 2017; Kelder etal., 2020).
3.2 Impacts to society and ecosystems
Understanding the full extent of climate change impacts is
complicated due to the complexity and uncertainty inherent in the
climate system and their interactions with ecosystems and human
societies (IPCC, 2022). Climate change impacts vary widely across
regions and contexts, making it challenging to develop generalizable
assessments that accurately capture the diverse range of impacts
experienced globally. Furthermore, limited availability of high-quality
impacts data, particularly in regions with inadequate monitoring
infrastructure, poses challenges for assessing current and future
impacts of climate change (e.g., Vicedo-Cabrera etal., 2021). In the
last decade, there have been substantial advances in impact attribution
(Hansen and Stone, 2016; Lloyd and Shepherd, 2020; Perkins-
Kirkpatrick etal., 2022) and impact assessments highlighting the
widespread and profound impacts of climate change across various
regions and sectors, including ecosystems, water resources,
agriculture, human health, and infrastructure (IPCC, 2022). In their
systematic review of advances in extreme event attribution, Perkins-
Kirkpatrick etal. (2024) conclude that there is a challenge and need
for coordinated interdisciplinary and transdisciplinary eorts to
advance impact attribution assessments and their broader applicability.
Wetake that conclusion one step further, highlighting the need for
tighter collaboration between climate science and impact science for
climate-savvy decision-making.
Using a variety of data sources can help form a better picture of
extreme events and their impacts: for example, observations and
reanalyses can becomplemented by community data (e.g., Fox etal.,
2023) and social media and citizen-run application data can provide
impact information (e.g., Young etal., 2022). Wenote however that
data homogeneity and quality can be adversely aected when
combining (oen temporally short) data from dierent sources and
attention is required to ensure the data are meaningful in a climate
context. Climatic impact-drivers must becombined with vulnerability
and exposure information to properly assess climate risks (IPCC,
2022; Seneviratne etal., 2021), yet collecting such data at sucient
temporal and spatial resolutions is dicult.
Reporting on impacts may bespatially biased (Harrington and
Otto, 2020) or delayed following extreme events, and disentangling
individual impacts becomes challenging when multiple extremes
occur simultaneously. ese complexities underscore the need for
comprehensive, timely, and contextually appropriate impact data to
inform eective adaptation strategies (Otto etal., 2020b). Identifying
and prioritizing adaptation measures to address the impacts of climate
change requires a nuanced understanding of local vulnerabilities and
adaptation capacities, which may vary widely across dierent
communities, sectors and regions. Additional challenges for impact
assessment and decision-making concern equitable and just
distribution of adaptation resources and addressing the
Sillmann et al. 10.3389/fclim.2024.1499765
Frontiers in Climate 06 frontiersin.org
disproportionate impacts of climate change on vulnerable populations
(IPCC, 2022). An example of a practical response to these challenges
is the Brazilian adaptation platform illustrating how climatic and
non-climatic factors are interrelated in generating risks to society,
providing a comprehensive tool for decision makers and the public
(Alves etal., 2022).
Integrating climate change impacts across dierent sectors and
regions to provide comprehensive assessments requires
interdisciplinary collaboration and the synthesis of diverse sources of
information (IPCC, 2022). Generally, impacts depend on individual-,
system- or sector-specic thresholds (e.g., heat tolerance thresholds
in humans, plants or infrastructure) (Ruane et al., 2022). Hence,
impact data need to becontextual, ensuring accurate representation
of systems and the translation of biophysical or engineering impacts
into socioeconomic or health-related impacts (e.g., Schwingshackl
etal., 2021). A further challenge is the myriads of methods and models
used to capture and analyze impacts across dierent sectors and
climatic impact-drivers (e.g., Sillmann etal., 2021b). Including state-
of-the-art climate model data into impact models to assess changes in
impacts due to climate change, particularly related to climate
extremes, is oen not straightforward and requires better and
sustained collaboration between the climate and impact science
communities (e.g., Schewe etal., 2019). Large eorts are made in the
Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to
provide a framework for ensemble modeling of climate change
impacts and impact attribution for various sectors (e.g., health,
agriculture) across the world (Frieler etal., 2024).
3.3 Decision-making
ere is increasing recognition of the urgent need to bridge the
gap between physical climate science and decision-making (e.g., ARC
Centre of Excellence for Climate Extremes, 2023; Galford etal., 2016;
von Winterfeldt, 2013). Here weargue that impact science and a better
understanding of climate change impacts (see section 2 and 3.2) can
bea gateway to bridging this gap. Furthermore, bridging can only
berobustly accomplished with active participation by people on both
sides of the gap: decision makers working to anticipate and respond
to climate impacts on one hand, and climate scientists working to
better understand climatic impact-drivers on the other hand. Both
require close interaction with the climate impact science community
and practitioners. Together they can ensure that climate and impact
information become more decision-relevant. Climate and impact
information can betailored to stakeholders considering the spatial
and temporal scales associated with their decision domains (Fischer
etal., 2024), for example long-term breadbasket-wide drought has a
distinct impact on dierent food system stakeholders (Stuart etal.,
2024). is requires that decision makers provide insight into the
vulnerabilities of their system, the lead time for decision-making and
implementation, and thus the reliability and timing of information
supply and demand (e.g., Haasnoot etal., 2018). Beyond that, however,
information needs to t the needs of the decision makers in terms of
responsiveness to their values and interests. Sustained engagement
with stakeholders is required with sectoral and regional experts
working together with physical climate scientists to co-produce useful
and contextual impact-, risk- and decision-relevant information that
is “t-for-purpose.” is can help to focus science but is oen very
time consuming, requires new skill sets, partnerships and
infrastructure (Mahon etal., 2019), and is usually not covered by
current research funding schemes (Hermansen etal., 2021). In this
context, the role of scientic institutions with both a mandate for
climate services and sustained funding could bestrengthened, for
instance in public-private partnerships (Doblas-Reyes etal., 2024;
Jacob etal., 2024).
Climate predictions for specic climatic impact-drivers can
support decisions to prepare for them and mitigate potential negative
impacts or exploit climate-related opportunities (e.g., weather
conditions ideal for crop sowing or harvesting) (e.g., Dunstone etal.,
2022; Solaraju-Murali etal., 2022). Besides the uncertainties related to
prediction quality, the usefulness of predictions depends on user-
specic contexts regarding their risk tolerance and level of acceptable
uncertainty (e.g., Hinkel etal., 2019). is inherently requires an
understanding of how values operate in the decision-making context.
To optimize decision-making, users and scientists therefore need to
beaware of the specic decision context, which will depend on the
cost of action and the potential losses from inaction. Figure 2
conceptually illustrates how dierent levels (thresholds) of risk
tolerance and prediction skill of the model system can result in
dierent false negative, i.e., misses (how oen the forecast does not
predict an event that does occur), and false positive rates (how oen
the forecast predicts an event that does not occur in the real world),
which will aect decision-making. e skill of the prediction system
depends on the location and specic variable or indicator of relevance,
as depicted by the green and pink lines in Figure2B and discussed for
marine heatwaves at dierent locations in Jacox etal. (2022).
All decisions must be taken under uncertainty, and in such
probabilistic settings decision makers oen optimize the costs and
benets of action or inaction while taking into account the false
positive (overestimation) and false negative (underestimation) rates
of the prediction. In a risk-avoidance approach, action is taken at a low
forecast probability threshold, meaning more events are acted on but
actions risk being unnecessary when predicted events do not occur.
In a risk-acceptance approach, action is taken at a high forecast
probability, limiting actions to more intense events that are better
predicted but risking no action being taken for unpredicted events. In
the presence of deep uncertainty, event-based or physical climate
storylines may bea promising alternative approach (Sillmann etal.,
2021a; IPCC, 2021). To deal with uncertainties in adaptation decision-
making, an adaptive approach can betaken to support decisions that
are low-regret and allow for further adaptation depending on how the
future unfolds (e.g., Haasnoot etal., 2018). Additionally, regarding
actions under high uncertainty, insights from other scientic elds—
such as Decision eory —could provide valuable guidance (Green
and Weatherhead, 2014; Gibbs, 2015; Pope etal., 2017; Delpiazzo
etal., 2022). is helps to overcome decision paralysis and avoid
maladaptation (IPCC, 2022). Projections on when new climate and
impact information may become available can inform such adaptive
plans (e.g., by indicating when and what kind of information may
become available).
While advancements in climate data and models are undoubtedly
necessary, an overemphasis on achieving clear, precise, and reliable
climate information oen also results in decision paralysis (Nissan
etal., 2019). What is needed is the implementation of methodologies
that allow for the achievement of t-for-purpose climate information
for the users and the context in which they are operating—information
Sillmann et al. 10.3389/fclim.2024.1499765
Frontiers in Climate 07 frontiersin.org
that is reliable and usable. For example, participatory methods that
emphasize multi-actor knowledge and utilize climate risk storylines
featuring realistic impact events have the potential to catalyze
informed decisions (see also 3.2). e benets of this approach were
showcased at the 2023 WCRP Open Science Conference (OSC) with
real-world examples (e.g., Jack, 2023; Duba, 2023; Mahon and
Trotman, 2023) and corroborated by several authors (e.g., Taylor etal.,
2021; McClure, 2023; Ndebele-Murisa etal., 2020). Further research
on this approach is recommended in order to develop a comprehensive
framework for its future application (see also Baldissera Pacchetti
etal., 2024).
4 Recommendations and conclusions
Actionable climate information is only attainable with better
integration of climate and impact sciences, and decision-making (cf.
Figure1 pillars). Hence, it is essential to invest in the continuous
improvement of climate models to enhance the accuracy and
robustness of climate predictions. In parallel, a more detailed and
quantitative understanding of climate-related impacts, that can ideally
be applied seamlessly to climate predictions, is needed– as it is
typically the anticipation of certain impacts as opposed to climate
anomalies that will bethe basis for making decisions. Prioritizing the
co-production of data outputs by involving impact scientists and
decision makers will ensure relevance and usefulness, while better
understanding the inherent uncertainties, communicating them
between pillars, and reducing them when possible. Peiderer etal.
(2023) suggest reversing the traditional impact chain (i.e., going from
emissions, to global warming levels, to physical climate impacts) to
make information from climate science more actionable and tangible
to decision makers. is approach includes socio-economic and value
judgment dimensions by starting from a decision-relevant impact or
critical threshold of interest (e.g., heat-related mortality in a city of
interest) and then estimates global emission reductions that are
required to avoid related climate-impact drivers (e.g., exceedance of a
temperature threshold associated with heat stress) at the local level.
Linking risk threshold exceedance directly to global emissions can
further aid the understanding of the benets of stringent emission
reductions for societies and local decision makers (Schleussner
etal., 2024).
A climatic impact-driver approach (Ruane etal., 2022) may help
stakeholders identify climate conditions that elevate or reduce risk or
opportunities, and underpin climate services that can meet
stakeholders’ decision-making needs. Risk reduction and risk
management are heavily dependent on eective early warning systems
and learning from past events. e World Meteorological Organization
is investing in a multi-hazard early warning system based on global
collaboration and supported by foundational nancing mechanisms
(WMO, 2022). However, recent extreme events have shown that
despite the existence of accurate forecasts and early warning systems,
many people lost their lives because not all responsible authorities and
individuals received the alerts, or the warnings were not always
understood or heeded (Tradowsky etal., 2023). Hence, to provide
FIGURE2
Illustration of how climate prediction information can beused for decision-making [adapted from Jacox etal. (2022) Figure4 under CC-BY-4.0]. Top
panel: hazard intensity (magnitude of a climate extreme) as a function of forecast probability, indicating that more intense extremes are typically
predicted with higher probability. Bottom panel: False prediction rates (false positive and false negative predictions) as a function of forecast probability,
illustrated for a more skillful and a less skillful prediction (where the skill can dier between dierent regions, climate variables or prediction systems).
Sillmann et al. 10.3389/fclim.2024.1499765
Frontiers in Climate 08 frontiersin.org
credible, accessible, relevant and actionable information to underpin
decisions in anticipation of certain climatic conditions and related
impacts (risks or opportunities) or adaptation to climate change,
weneed seamless integration of weather and climate information over
dierent time scales and a better understanding of the needs of
decision makers, recognizing their diverse backgrounds, objectives,
capacities and decision contexts. is needs to becombined with a
better integration of information about possible impacts of hazards,
particularly for hazards with intensities and frequencies that are
changing with global warming. In this context, for instance, Geiger
et al. (2024) proposes a strategic, methodological and technical
framework for the development of impact-related services provided
by National Meteorological and Hydrological Services.
A nuanced, multi-evidence approach enables more robust
conclusions by combining insights across observational, theoretical,
and modeled lines of evidence for climatic impact-drivers and their
impacts, even in cases of uncertainty or divergence. Doblas-Reyes
et al. (2021) outlined key strategies to synthesize information
coherently when bringing together dierent types of evidence. ese
strategies include using a systematic approach to quantify and
communicate uncertainty, assessing the physical consistency and
plausibility of evidence within the context of known climate processes
and mechanisms, hierarchical structuring of evidence and transparent
communication of all of these aspects. Furthermore, physical climate
storylines are highlighted in Doblas-Reyes etal. (2021) as a valuable
complementary tool for reconciling dierent types of evidence and
addressing uncertainties in regional climate assessments. is
narrative approach enriches understanding by integrating various
forms of evidence, clarifying physical mechanisms, and supporting
transparent communication and robust climate risk communication.
e WCRP Regional Information for Society (RIfS) Core Project
and the My Climate Risk Lighthouse Activity are exploring
frameworks and methodologies in this context (WCRP, 2024a, 2024b).
ose eorts are complemented by the annual-to-decadal focus of the
WCRP Lighthouse Activity on Explaining and Predicting Earth
System Change (EPESC) (Findell etal., 2023). EPESC is looking to
develop operational methodologies for integrated attribution,
prediction, and projection of changes in the Earth system, particularly
those that aect the frequency and intensity of climatic impact-drivers
in dierent regions of the globe. It will beuseful if this work could
be integrated into the planning of future science priorities across
climate and impact science communities. Since the rst IPCC
Assessment report, wehave come a long way in terms of representation
of extreme events in observations and climate models, improving
prediction systems, climate change and extreme event impact
attribution (Noy etal., 2024), and information available for mitigation
and adaptation decision-making (IPCC, 2023). Nevertheless, there
remains a need to accelerate climate science to expand its relevance
and utility to decision makers, and to do so globally (WCRP, 2024c).
ere are large disparities in data availability and access to training
and technology in some regions of the world, including a shortage of
high-quality, long-term observations (e.g., Harrington and Otto, 2020;
Perkins-Kirkpatrick et al., 2024) and less access to advanced
technologies, such as the skills and resources to access high-
performance computing facilities needed to run computationally
intensive climate models (Forster etal., 2023; UNFCCC, 2023). Recent
initiatives, such as FAIR principles (Findable, Accessible,
Interoperable, and Reusable data), CARE Principles for Indigenous
Data Governance, the Copernicus Climate Change Service
(Buontempo etal., 2022) and the European Open Science Cloud
(EOSC) help to advance knowledge and address societal challenges
through collaborative and transparent data-sharing frameworks.
Particularly, outcomes of publicly funded climate services should
betreated as public goods accessible to all as highlighted in the Global
Framework for Climate Services (Webber and Donner, 2016;
Hewitson etal., 2017; Jacob etal., 2024). However, wealso note that
principles such as FAIR in themselves are not fully sucient for
equitable sharing of data as they might ignore power dierentials (e.g.,
colonialism) and historical contexts (e.g., Indigenous knowledge)
(e.g., Jennings etal., 2023).
Likewise, observational campaigns and modeling eorts need to
bebetter integrated and coordinated, which is one of the aims of the
WCRP Earth System Modeling and Observations (ESMO) Core
Project, to enhance the accuracy, reliability, and accessibility of climate
data and projections. While further improvements to data and models
are still needed (e.g., Lacagnina etal., 2022), it is also important to
focus on the vast amount of climate and impact information already
available that can beused to support or trigger decision-making, as
shown in the recent risk assessment report by the European
Environmental Agency (EEA, 2024). In particular, it is essential to set
research priorities with the scientic community in the Global South
and allocate resources to foster stronger collaboration, shared and
equitable leadership, and alignment with local understanding of
science challenges and opportunities (WCRP, 2024c).
In many cases a substantial investment is needed in behind-the-
scenes work to quality-control, process, and tailor open-access
datasets to facilitate climate services (WMO, 2014). Global observation
systems enhancing the real-time weather observing system, combined
with advanced impact-based early warning systems (e.g., GIEWS-
Global Information and Early Warning System on Food and
Agriculture) (WMO, 2022), near real-time information provision
during ongoing events and insights gained from post-disaster
assessments (e.g., disaster forensics) can beused to sharpen our focus
for seasonal and decadal predictions tailored to the needs of specic
sectors, but also making the oen limited (but denitely non-zero)
information content of these predictions more usable. Future climate
assessments will need to adopt a more risk-facing perspective,
focusing on thresholds, surprises, and the potential for impacts to
cascade (Sherwood etal., 2024) to ensure that society is adequately
prepared and climate-resilient.
Data availability statement
Publicly available datasets were analyzed in this study.
Author contributions
JS: Conceptualization, Funding acquisition, Methodology,
Resources, Visualization, Writing– original dra, Writing– review &
editing. TR: Conceptualization, Visualization, Writing– original dra,
Writing– review & editing. KF: Conceptualization, Visualization,
Writing – original dra, Writing – review & editing. MD:
Conceptualization, Visualization, Writing– original dra, Writing–
review & editing. LMA: Writing – review & editing. LA:
Sillmann et al. 10.3389/fclim.2024.1499765
Frontiers in Climate 09 frontiersin.org
Writing – review & editing. LB: Writing– review & editing. PB:
Writing – review & editing. CB: Writing – review & editing.
EF: Writing– review & editing. CF: Writing – review & editing.
BG: Writing– review & editing. MH: Writing– review & editing. EH:
Writing– review & editing. DJ: Writing – review & editing. RM:
Writing– review & editing. DM: Writing– review & editing. MM:
Writing – review & editing. BP: Writing – review & editing.
AR: Writing– review & editing. Shampa: Writing– review & editing.
TS: Writing– review & editing. NW: Writing– review & editing. ZW:
Writing – review & editing. XZ: Writing – review & editing.
JŽ: Writing– review & editing.
Funding
e author(s) declare that nancial support was received for the
research, authorship, and/or publication of this article. JS, BP, and LB
were funded by the Deutsche Forschungsgemeinscha (DFG, German
Research Foundation) under Germany’s Excellence Strategy—EXC
2037: “CLICCS—Climate, Climatic Change, and Society”—Project
number: 390683824, contribution to the Center for Earth System
Research and Sustainability (CEN) at the University of Hamburg, JS
and JZ further acknowledge funding from the European Union’s
Horizon 2020 research and innovation programmes under Grant
agreement no. 101003687 (PROVIDE), CF was supported by the
Institute for Basic Science (IBS), Republic of Korea, under
IBS-R028-D1, EH was supported by the UK National Centre for
Atmospheric Science. DM acknowledges funding by the Austrian
Climate Research Programme Project CHIANTI (KR19AC0K17553),
AR participation was made possible by NASA Earth Sciences Division
support for the GISS Climate Impacts Group, TR acknowledges
funding support by QBE Insurance since March 2024. LMA was
supported by São Paulo Research Foundation (FAPESP, grant
2022/08622-0), and LA was supported by Australian Research Council
grant CE170100023. MD is supported by the Horizon Europe project
EXPECT (Grant 101137656). CB has been supported by the
Copernicus Climate Change Service, a programme funded by the
European Commission and implemented by ECMWF.
Acknowledgments
We thank Cathy Raphael for help with Figure1. JS would like to
thank OpenAI’s ChatGPT v2 (training data up to October 2023) for
assistance in rening text in this article.
Conflict of interest
TR declares funding received by QBE Insurance since March
2024; this funding provider was not involved in this study.
e remaining authors declare that the research was conducted in
the absence of any commercial or nancial relationships that could be
construed as a potential conict of interest.
e handling editor MC declared a past co-authorship with the
following authors LMA and EH.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their aliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may beevaluated in this article, or claim
that may bemade by its manufacturer, is not guaranteed or endorsed
by the publisher.
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