Chris E. Forest

Uncertainties in estimates of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) are influenced by observational temperature data sets. Variability exists not just among the data products, but also within the creation of each one. This includes significant variations among ensemble members within a single data product. Using the optimal fingerprint approach combined with Bayesian updating, we quantify the uncertainties in ECS and TCR estimates arising from both individual data sets and their various groupings. Our methodology, utilizing both spatial and temporal data, shows impacts on the estimates of ECS and TCR. As we assess different groupings of observational data products, we observe that using products sharing identical Sea Surface Temperatures (SST) introduce discernible biases. These results highlight that variations among ensemble members within a single data product are as influential as the disparities across multiple data products.

Uncertainties in estimates of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) are influenced by observational datasets. Variability exists not just among the data products, but also within the creation of individual ones. This includes significant variations among ensemble members within a single data product. Using the optimal fingerprint approach combined with Bayesian updating, we quantify the uncertainties in ECS and TCR estimates arising from both individual datasets and their various groupings. Our methodology, utilizing both spatial and temporal data, reveals impacts on the estimates of ECS and TCR. As we assess different groupings of observational data products, we observe that using products sharing identical Sea Surface Temperatures (SST) introduces a discernible biases. Furthermore, these results highlight that the variations among ensemble members within a single data product are as influential as the disparities across multiple data products.

Uncertainties in estimates of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) are influenced by observational datasets. Variability exists not just among the data products, but also within the creation of individual ones. This includes significant variations among ensemble members within a single data product. Using the optimal fingerprint approach combined with Bayesian updating, we quantify the uncertainties in ECS and TCR estimates arising from both individual datasets and their various groupings. Our methodology, utilizing both spatial and temporal data, reveals impacts on the estimates of ECS and TCR. As we assess different groupings of observational data products, we observe that using products sharing identical Sea Surface Temperatures (SST) introduces a discernible biases. Furthermore, these results highlight that the variations among ensemble members within a single data product are as influential as the disparities across multiple data products.

After many years with little change in community views on equilibrium climate sensitivity (ECS), in 2021 the Intergovernmental Panel on Climate Change (IPCC) concluded that it was much better known than previously. This development underpinned increased confidence in long-term climate changes in that report. Here, we place this development in historical context, briefly assess progress since then, and discuss the challenges and opportunities for further improving our knowledge of this iconic concept. We argue that the probability distributions published in those assessments are still approximately valid; while various subsequent studies have claimed further narrowing, they have omitted important structural uncertainties associated with missing processes, imperfect relationships, or other factors that should be included. The distributions could nonetheless be narrowed in the future, particularly through better understanding of certain climate processes and paleoclimate proxies. Not all touted strategies are truly helpful, however. We also note that ECS does not address risks from the carbon cycle or possible tipping points, and as increasingly strong mitigation (i.e., “net-zero”) scenarios are considered, ECS becomes less informative about future climate change compared to other factors such as aerosol radiative forcing and influences on regional change such as ocean dynamics.

After many years with little change in community views on Equilibrium Climate Sensitivity (ECS), in 2021 the IPCC concluded that it was much better known than previously, supported by a major assessment the previous year. This development underpinned increased confidence in long-term climate changes in that report. Here, we place this development in historical context, briefly assess progress since then, and discuss the challenges and opportunities for further improving our knowledge of this iconic concept. We argue that the probability distributions published in those assessments are still approximately valid; while various subsequent studies have claimed further narrowing, they have omitted important structural uncertainties that should be included. The distributions could nonetheless be narrowed in the future, particularly through better understanding of certain climate processes and paleoclimate proxies. Not all touted strategies are truly helpful, however. We also note that as increasingly strong mitigation (i.e., “net-zero”) scenarios are considered, ECS becomes less informative about future climate change compared to other factors such as aerosol radiative forcing, carbon cycle and vegetation processes, and influences on regional change such as ocean dynamics.

After many years with little change in community views on Equilibrium Climate Sensitivity (ECS), in 2021 the IPCC concluded that it was much better known than previously, supported by a major assessment the previous year. This development underpinned increased confidence in long-term climate changes in that report. Here, we place this development in historical context, briefly assess progress since then, and discuss the challenges and opportunities for further improving our knowledge of this iconic concept. We argue that the probability distributions published in those assessments are still approximately valid; while various subsequent studies have claimed further narrowing, they have omitted important structural uncertainties that should be included. The distributions could nonetheless be narrowed in the future, particularly through better understanding of certain climate processes and paleoclimate proxies. Not all touted strategies are truly helpful, however. We also note that as increasingly strong mitigation (i.e., “net-zero”) scenarios are considered, ECS becomes less informative about future climate change compared to other factors such as aerosol radiative forcing, carbon cycle and vegetation processes, and influences on regional change such as ocean dynamics.

After many years with little change in community views on Equilibrium Climate Sensitivity (ECS), in 2021 the IPCC concluded that it was much better known than previously, supported by a major assessment the previous year. This development underpinned increased confidence in long-term climate changes in that report. Here, we place this development in historical context, briefly assess progress since then, and discuss the challenges and opportunities for further improving our knowledge of this iconic concept. We argue that the probability distributions published in those assessments are still approximately valid; while various subsequent studies have claimed further narrowing, they have omitted important structural uncertainties that should be included. The distributions could nonetheless be narrowed in the future, particularly through better understanding of certain climate processes and paleoclimate proxies. Not all touted strategies are truly helpful, however. We also note that as increasingly strong mitigation (i.e., “net-zero”) scenarios are considered, ECS becomes less informative about future climate change compared to other factors such as aerosol radiative forcing, carbon cycle and vegetation processes, and influences on regional change such as ocean dynamics.

After many years with little change in community views on Equilibrium Climate Sensitivity (ECS), in 2021 the IPCC concluded that it was much better known than previously, supported by a major assessment the previous year. This development underpinned increased confidence in long-term climate changes in that report. Here, we place this development in historical context, briefly assess progress since then, and discuss the challenges and opportunities for further improving our knowledge of this iconic concept. We argue that the probability distributions published in those assessments are still approximately valid; while various subsequent studies have claimed further narrowing, they have omitted important structural uncertainties that should be included. The distributions could nonetheless be narrowed in the future, particularly through better understanding of certain climate processes and paleoclimate proxies. Not all touted strategies are truly helpful, however. We also note that as increasingly strong mitigation (i.e., “net-zero”) scenarios are considered, ECS becomes less informative about future climate change compared to other factors such as aerosol radiative forcing, carbon cycle and vegetation processes, and influences on regional change such as ocean dynamics.

After many years with little change in community views on Equilibrium Climate Sensitivity (ECS), in 2021 the IPCC concluded that it was much better known than previously, supported by a major assessment the previous year. This development underpinned increased confidence in long-term climate changes in that report. Here, we place this development in historical context, briefly assess progress since then, and discuss the challenges and opportunities for further improving our knowledge of this iconic concept. We argue that the probability distributions published in those assessments are still approximately valid; while various subsequent studies have claimed further narrowing, they have omitted important structural uncertainties that should be included. The distributions could nonetheless be narrowed in the future, particularly through better understanding of certain climate processes and paleoclimate proxies. Not all touted strategies are truly helpful, however. We also note that as increasingly strong mitigation (i.e., “net-zero”) scenarios are considered, ECS becomes less informative about future climate change compared to other factors such as aerosol radiative forcing, carbon cycle and vegetation processes, and influences on regional change such as ocean dynamics.

Decisions on how to manage future flood risks are frequently informed by both sophisticated and computationally expensive models. This complexity often limits the representation of uncertainties and the consideration of strategies. Here we use an intermediate complexity model framework that enables us to analyze a richer set of strategies, a wider range of objectives, and greater levels of uncertainty than are typically considered by more sophisticated and computationally expensive models. We find that allowing for more combinations of risk mitigation strategies can help expand the solution set, help explain synergies and trade‐offs, and point to strategies that can improve outcomes.