Fossil fuel-based development has led to climate change and biodiversity loss, threatening ecosystem sustainability. Despite the need for transformative efforts to combat climate change, the complexity of social-ecological systems can hinder climate mitigation and adaptation efforts. Moreover, climate change is always characterised by deep uncertainty, ambiguity, and limited understanding of its pace, consequences, and options for reduction.

The uncertainty and complexity that lies behind climate change create a multitude of challenges for decision-makers. These challenges may become more complex due to the dynamic nature of climate change and intertwined social-ecological systems. Effective climate risk management requires a comprehensive understanding of these interconnected challenges. By integrating scientific research, economic analysis, and social considerations, stakeholders can better manage the risks and opportunities presented by a changing climate, ultimately contributing to more resilient and sustainable societies [1], [2].

Understanding Climate Change Uncertainty

The nature of uncertainty in climate change investigations is a complex and stubborn issue. For instance, climate uncertainties such as rising sea-level projections, temperature variations, and extreme weather events, which interact with each other in complex scenarios of climate science, economics, and ethics [3].

Defining Uncertainty

To fully understand climate change uncertainty, first, we need to comprehend the term ‘uncertainty’ which is a blend of multiple meanings. Uncertainty is often connected with a lack of knowledge and is typically critiqued negatively. However, uncertainty can also refer to unstable, endless, or ambiguous states. There are various types of uncertainty, and the term is conceptualised distinctly in various fields. In research literature, several classifications have been proposed to categorise uncertainty into its multiple dimensions. Uncertainty can be classified to fulfil various purposes, and it may be valuable to customise its classes for specific scenarios. Furthermore, majority of the scholarly literature broadly classified uncertainty in three categories [3], [4]:

• Epistemic Uncertainty: This type of uncertainty arises from lack of knowledge, and can be minimised by actively seeking more knowledge through extensive research, data collection, and improved modelling techniques.

• Aleatory Uncertainty: Uncertainty results from inherent volatility present within the phenomenon being studied. While organisations cannot eliminate it, they can better characterise it through rigorous statistical methods and mathematical modelling.

• Ambiguity: Ambiguity arises when we lack clarity or a precise understanding of a situation. Acknowledging ambiguity, taking adaptive management measures, and carrying out interdisciplinary collaboration allows us to make informed decisions despite incomplete information.

Collectively, these categories form the basis of uncertainty, providing a framework for understanding the underlying reasons for climate change uncertainty in certain circumstances.

Climate Change Uncertainties

Climate change impacts are directly connected with numerous uncertainties, particularly if the predictions are made in the direction of year 2100. These uncertainties are embedded in every stage of the impact assessment process. Climate change uncertainty further influences scenario planning, and climate data projection methodologies. Despite technological progress in forecasting climate change events, there always exists significant uncertainty regarding the exact rate of change and substantial impact due to their dynamic nature. Moreover, the risks associated with climate change may get aggravated by how organisations implement adaptation strategies and policies. Therefore, for devising effective decision-making on climate adaptation it is vital for organisations to understand uncertainty pertaining to climate change [5].

Typology of Uncertainty In Climate Change

It is crucial for stakeholders and policymakers to understand the typology of uncertainty related to climate change; it will not only enrich reporting and disclosure standards with multiple dimensions of uncertainty but will also improve communication among them. Environmental scientists and scholars have explained and classified climate change uncertainties in various distinct types; five most relevant and impactful of them are briefly explained below:

1. Scientific Uncertainty

Scientific uncertainty in climate change happens due to insufficient knowledge about the climate system. As global greenhouse gas (GHG) emissions continue to rise, climate change will become more complex. Climate science is not able to provide quick, precise answers to when, where, and by how much climate change is occurring; which leads to scientific uncertainty. Scientific uncertainty is further segmented into three types: (i) internal variability: it happens because the results of complex climatic models are based upon initial conditions. (ii) model uncertainty: it occurs due to an organisation’s lack of understanding about suitable climate models. (iii) emissions pathway uncertainty: it generally appears due to the incapability of scientific institutions to forecast future GHG emissions [6].

2. Moral Uncertainty

Moral uncertainty is related to the behavioural acceptance, responsibility, and decisions about ethics and values that relates to climate change. Like scientific uncertainty, moral uncertainty may be clustered in various types. One of its types is related to the uncertainty about factual matters. For example, there is ethical concern over how the investments and mitigation resources are allocated to Carbon Capture and Storage (CCS), which wouldn't withdraw any investments away from renewable energy solutions. Another type of moral uncertainty is connected with deficiencies in theories and concepts. For instance, imprecision and vagueness in moral values may raise uncertainty about who should perform moral duties and assessments regarding climate mitigation [3].

3. Observational Uncertainty in Climate Data

Data collection techniques play an important role in investigating various climatic patterns. The quality of observed data is highly dependent on the reliability of measurements. Various measurement errors may become a part of data collection methods, such as the use of uncalibrated tools and instruments. Furthermore, in some instances calibration of climate measuring devices is less practical or even impossible: for example, a meteorological satellite once deployed is unreachable and and couldn’t be easily troubleshooted or recalibrated. Moreover, observational uncertainty in climate data may increase the chance of getting biassed and unreliable data. For instance, samples usually collected for weather measurements may lack large-scale trends and historical measurements; for that climate scientists are mostly dependent on proxy data, which are less reliable than direct measurements [3].

4. Uncertainty Related To Rising GHG Emissions

Despite taking multiple steps in shifting towards a low carbon economy worldwide, the current global carbon emissions are still very high. NOAA’s Global Monitoring Lab reported that the year 2023 saw record-high global average atmospheric CO2 levels. This will create greater complexities and uncertainties in mitigating climate change impacts associated with rising GHG emissions. Climate sensitivity and rate of heat uptake by deep sea waters are the two most uncertain dimensions that elevate GHG emissions by modifying the climatic response system. Climate sensitivity is defined as "the rise in global average temperature as a consequence of doubling carbon dioxide concentrations as compared to pre-industrial levels." The Intergovernmental Panel on Climate Change (IPCC) estimates that the climate sensitivity parameter is generally between 1.5 and 4.5 °C. Moreover, the rate of heat uptake by deep oceans is still ineffectively specified, which further alters the possible range of values for climate sensitivity. the emissions reductions needed to achieve policies aiming to meet maximum warming rates. The above specified range of average temperature clearly represents uncertainties associated with climate change predictions, which can considerably alter the policies and outcome measures taken for climate adaptation [7].

5. Uncertainty From Different Climate Models

Global climate models (GCM) are the central tools for projecting the future climate and weather patterns across the world. For doing projections corresponding to local territories and specific geographical areas, downscaling is implemented. Downscaling includes forecasting local climate conditions by studying comparative change at broader levels. However, downscaling methods can create uncertainty by significantly changing the magnitude and sign of climate signals in relation to original data. Furthermore, currently, more than 20 climate modelling centres are working around the world have developed their own GCM(s), and therefore the disparity that exists among them creates uncertainty in climate predictions. The primary reason behind this uncertainty among GCM(s) is the use of different modelling techniques that leads to differences in climate sensitivity data [8].

The Socio-Economic View On Climate Uncertainty

The socio-economic perspective of climate change is a highly complex dimension, which not only raises major uncertainties but also needs much greater attention than scientific uncertainty. This perspective acknowledges that climate change does not occur in isolation, but is connected with economic systems, social structures, and cultural contexts. The uncertainty in climate change not only includes physical impacts, such as global warming, but also the socio-economic consequences, such as shifts in agricultural productivity, migration, and economic inequality. For instance, regions heavily dependent on agriculture may experience economic uncertainty due to extreme weather events. Moreover, the socio-economic uncertainties are often faced by the most vulnerable populations, worsening existing inequalities [9].

Decision-Making in a Dynamic Environment

Decision-making in a dynamic environment created by climate change involves navigating a complex nature of uncertainties and variables. The dynamic nature of climate systems, influenced by environmental factors like greenhouse gas (GHG) emissions, technological advancements, and policy changes, requires decision-makers to be flexible and adaptive. Traditional decision-making models, which often rely on stable and predictable conditions, are less effective in such a fluid context. Adaptive management strategies are vital for minimising uncertainties in climate change. This allows for continuous learning and adjustment of policies as new information becomes available. This approach acknowledges the limitations of predictive models and the need for robust frameworks that can accommodate unexpected changes. For example, the use of scenario planning, which explores a range of future possibilities, helps policymakers prepare to save multiple contingencies, but reduce the risks associated with uncertain outcomes [10].

Conclusion

Navigating the complexities of climate change requires a precise understanding of the inherent uncertainties and their socio-economic implications. Decision-making in this context must account for the dynamic and interconnected nature of climate systems and human societies. It demands adaptive management and collaborative governance to accommodate scientific knowledge and changing socio-political landscapes. As the world is facing an increasingly uncertain climate future, the integration of diverse perspectives and adaptive strategies becomes crucial for formulating effective and equitable policies. The challenges posed by climate change necessitate an agile approach, allowing for rapid adjustments and continuous learning. By fostering inclusive and flexible decision-making frameworks, we can better manage the risks and opportunities of a changing climate, ensuring resilience and sustainability for future generations.

How can SmartResilience help?

SmartResilience is your dedicated climate partner that helps businesses navigate the challenges of climate change. We provide organisations with the tools and expertise needed to assess and mitigate climate-related risks, supporting them for long-term sustainability and success.

SmartResilience can help your business thrive in a changing world by offering the following services:

Comprehensive Physical Risk Assessments: SmartResilience in-depth assessments pinpoint vulnerabilities to extreme weather events and other climate-related hazards. This comprehensive approach assists you in meeting the Corporate Sustainability Reporting Directive (CSRD) requirements by ensuring transparent disclosure of climate risks and their potential impacts.

Future-Oriented Scenario Analysis: Our scenario analysis tools evaluate how different climate scenarios could impact your business operations. By considering diverse possibilities, you can strategically plan for the future and improving both financial and strategic resilience, aligning perfectly with the CSRD's mandate for future-proof planning.

Tailored Risk Management Strategies: SmartResilience goes beyond simply identifying risks. SmartResilience develops comprehensive risk management strategies tailored to your needs. These solutions reduce identified physical risks and proactively protect your business from climate impacts, directly aligning with the CSRD's focus on proactive risk management and adaptation measures.

Ongoing Compliance Support: We provide ongoing support to ensure your sustainability reporting remains compliant with evolving CSRD requirements. This commitment includes regular updates reflecting the latest regulations, staff training to maintain expertise, and consistent monitoring to guarantee accurate and up-to-date disclosures.

The future may be uncertain, but together, we can proactively build resilience. By partnering with SmartResilience, you can navigate the changing climate with confidence and clarity. Contact us today and embark on a journey towards a sustainable future, together.

References

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[2] G. Heal and A. Millner, “Uncertainty and Decision in Climate Change Economics,” Mar. 2013, National Bureau of Economic Research: 18929. doi: 10.3386/w18929.

[3] J. Hopster, “Climate Change, Uncertainty, and Policy,” in Handbook of Philosophy of Climate Change, G. Pellegrino and M. Di Paola, Eds., Cham: Springer International Publishing, 2020, pp. 1–24. doi: 10.1007/978-3-030-16960-2_16-1.

[4] J. C. Refsgaard et al., “The role of uncertainty in climate change adaptation strategies—A Danish water management example,” Mitig Adapt Strateg Glob Change, vol. 18, no. 3, pp. 337–359, Mar. 2013, doi: 10.1007/s11027-012-9366-6.

[5] L. van Bree and J. van der Sluijs, “Background on Uncertainty Assessment Supporting Climate Adaptation Decision-Making,” in Adapting to an Uncertain Climate: Lessons From Practice, T. Capela Lourenço, A. Rovisco, A. Groot, C. Nilsson, H.-M. Füssel, L. Van Bree, and R. B. Street, Eds., Cham: Springer International Publishing, 2014, pp. 17–40. doi: 10.1007/978-3-319-04876-5_2.

[6] G. Heal and A. Millner, “Uncertainty and Decision in Climate Change Economics,” Mar. 2013, National Bureau of Economic Research: 18929. doi: 10.3386/w18929.

[7] W. Blyth, M. Yang, and R. Bradley, Climate Policy Uncertainty and Investment Risk. France: International Energy Agency (IEA), 2008. doi: 10.1787/9789264042216-1-en.

[8] I. Rangwala et al., “Uncertainty, Complexity and Constraints: How Do We Robustly Assess Biological Responses under a Rapidly Changing Climate?,” Climate, vol. 9, no. 12, Art. no. 12, Dec. 2021, doi: 10.3390/cli9120177.

[9] “Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.,” 2022.

[10] W. E. Walker, M. Haasnoot, and J. H. Kwakkel, “Adapt or Perish: A Review of Planning Approaches for Adaptation under Deep Uncertainty,” Sustainability, vol. 5, no. 3, Art. no. 3, Mar. 2013, doi: 10.3390/su5030955.