Uncertainty In Climate Sensitivity Research Articles

Vertical Profile Analysis of Cloud Feedbacks

AbstractCloud feedback is the largest source of uncertainty in climate sensitivity. This feedback is generally discussed in terms of radiative flux at the top of the atmosphere, but the vertical distribution of the contribution of cloud changes to the top‐of‐atmosphere cloud feedback should be discussed to understand the feedback in greater detail. We have developed a simple analysis method called “vertical profile analysis of cloud feedback,” which simply uses radiative flux data from each level of the model. The analysis is applied for typical cloud regimes and the results are discussed together with cloud fraction change profiles. The advantages and disadvantages of the vertical profile analysis, compared with the commonly used International Satellite Cloud Climatology Project (ISCCP) histogram kernel method, are discussed. Our analysis has the advantage of resolving the detailed vertical profiles of the contribution to the top‐of‐atmosphere cloud feedback from the changes in cloud regimes associated with warming, such as reduced cloud cover and upward shifts of the cloud layer in subtropical low‐cloud regions as well as the increased height of the melting layer in tropical deep convection. The main disadvantage is that the vertical profile analysis cannot represent the feedback components sorted by cloud optical thickness as in the ISCCP histogram kernel method.

Prudent carbon dioxide removal strategies hedge against high climate sensitivity

Uncertainty in climate sensitivity has been shown to warrant early-on mitigation to limit global warming while anticipating future carbon dioxide removal creates mitigation deterrence. Here we use an integrated assessment model to quantify the impacts of under- or overestimating the cost and availability (feasibility) of carbon dioxide removal when limiting warming to 1.5 °C by 2100 under uncertain climate sensitivity. If climate sensitivity uncertainty is disregarded, initial assumptions on the feasibility have only minor effects on mitigation costs. However, the climate sensitivity risk compounds the impact of prior assumptions. Wrong assumptions on carbon dioxide removal feasibility can lead to lower costs under extreme realizations of climate sensitivity. Moreover, scenarios considering uncertainty in climate sensitivity rely less on carbon dioxide removal. A prudential strategy assuming low feasibility for carbon dioxide removal reduces the “double whammy” risk of overestimating carbon dioxide removal in combination with a realization of high climate sensitivity.

Opinion: Can uncertainty in climate sensitivity be narrowed further?

Abstract. 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.

KNMI'23 Climate Scenarios for the Netherlands: Storyline Scenarios of Regional Climate Change

AbstractThis paper presents the methodology for the construction of the KNMI'23 national climate scenarios for the Netherlands. We have developed six scenarios, that cover a substantial part of the uncertainty in CMIP6 projections of future climate change in the region. Different sources of uncertainty are disentangled as much as possible, partly by means of a storyline approach. Uncertainty in future emissions is covered by making scenarios conditional on different SSP scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5). For each SSP scenario and time horizon (2050, 2100, 2150), we determine a global warming level based on the median of the constrained estimates of climate sensitivity from IPCC AR6. The remaining climate model uncertainty of the regional climate response at these warming levels is covered by two storylines, which are designed with a focus on the annual and seasonal mean precipitation response (a dry‐trending and wet‐trending variant for each SSP). This choice was motivated by the importance of future water management to society. For users with specific interests we provide means how to account for the impact of the uncertainty in climate sensitivity. Since CMIP6 GCM data do not provide the required spatial detail for impact modeling, we reconstruct the CMIP6 responses by resampling internal variability in a GCM‐RCM initial‐condition ensemble. The resulting climate scenarios form a detailed storyline of plausible future climates in the Netherlands. The data can be used for impact calculations and assessments by stakeholders, and will be used to inform policy making in different sectors of Dutch society.

Climate sensitivity controls global precipitation hysteresis in a changing CO2 pathway

The responses of the Earth’s climate system to positive and negative CO2 emissions are not identical in magnitude, resulting in hysteresis. In particular, the degree of global precipitation hysteresis varies markedly among Earth system models. Based on analysis of Earth’s energy budget, here we show that climate sensitivity controls the degree of global precipitation hysteresis. Using an idealized CO2 removal scenario, we find that the surface available energy for precipitation continues to increase during the initial negative CO2 emission period following a positive CO2 emission period, leading to a hysteresis of global precipitation. This feature is more pronounced in Earth System Models with a high climate sensitivity. Our results indicate that climate sensitivity is a key factor controlling the hysteresis behavior of global precipitation in a changing CO2 pathway. Therefore, narrowing the uncertainty of climate sensitivity helps improve the projections of the global hydrological cycle.

Climate Models Underestimate Dynamic Cloud Feedbacks in the Tropics

AbstractCloud feedbacks are the leading cause of uncertainty in climate sensitivity. The complex coupling between clouds and the large‐scale circulation in the tropics contributes to this uncertainty. To address this problem, the coupling between clouds and circulation in the latest generation of climate models is compared to observations. Significant biases are identified in the models. The implications of these biases are assessed by combining observations of the present day with future changes predicted by models to calculate observationally constrained feedbacks. For the dynamic cloud feedback (i.e., due to changes in circulation), the observationally constrained values are consistently larger than the model‐only values. This is due to models failing to capture a nonlinear minimum in cloud brightness for weakly descending regimes. Consequently, while the models consistently predict that these regimes increase in frequency in association with a weakening tropical circulation, they underestimate the positive cloud feedback associated with this increase.

Effects of Circulation on Tropical Cloud Feedbacks in High‐Resolution Simulations

AbstractUncertainty in the response of clouds to global warming remains a significant barrier to reducing uncertainty in climate sensitivity. A key question is the extent to which the dynamic component—that which is due to changes in circulation rather than changes in the thermodynamic properties of clouds—contributes to the total cloud feedback. Here, simulations with a range of cloud‐resolving models are analyzed to quantify the impact of circulation changes on tropical cloud feedbacks. The dynamic component of the cloud feedback is substantial for some models and is controlled both by sea surface temperature (SST) induced changes in circulation and nonlinearity in the climatological relationship between clouds and circulation. We find notable inter‐model differences in the extent to which ascending regions narrow or expand in response to a change in SST, which we link to differences in the longwave and shortwave dynamic components across models. The diversity of changes in ascent area is coupled to intermodel differences in non‐radiative diabatic heating in ascending regions.

Quantifying uncertainty about global and regional economic impacts of climate change

The economic impacts of climate change are highly uncertain. Two of the most important uncertainties are the sensitivity of the climate system and the so-called damage functions, which relate climate change to economic costs and benefits. Despite broad awareness of these uncertainties, it is unclear which of them is most important, especially at the regional level. Here we construct regional damage functions, based on two different global damage functions, and apply them to two climate models with vastly different climate sensitivities. We find that uncertainty in both climate sensitivity and aggregate economic damages per degree of warming are of similar importance for the global economic impact of climate change, with the decrease in global economic productivity ranging between 4% and 24% by the end of the century under a high-emission scenario. At the regional level, however, the effects of climate change can vary even more substantially, depending both on a region’s initial temperature and the amount of warming it experiences, with some regions gaining in productivity and others losing. The ranges of uncertainty are therefore potentially much larger at a regional level. For example, at the end of the century, under a high-emission scenario, we find that India’s productivity decreases between 13% and 57% and Russia’s increases between 24% and 74%, while Germany’s change in productivity ranges from an increase of 8% to a decrease of 4%. Our findings emphasise the importance of including these uncertainties in estimates of future economic impacts, as they are vital for the resulting impacts and thus policy implications.

Constraining Clouds and Convective Parameterizations in a Climate Model Using Paleoclimate Data

AbstractCloud and convective parameterizations strongly influence uncertainties in equilibrium climate sensitivity. We provide a proof‐of‐concept study to constrain these parameterizations in a perturbed parameter ensemble of the atmosphere‐only version of the Goddard Institute for Space Studies Model E2.1 simulations by evaluating model biases in the present‐day runs using multiple satellite climatologies and by comparing simulated δ18O of precipitation (δ18Op), known to be sensitive to parameterization schemes, with a global database of speleothem δ18O records covering the Last Glacial Maximum (LGM), mid‐Holocene (MH) and pre‐industrial (PI) periods. Relative to modern interannual variability, paleoclimate simulations show greater sensitivity to parameter changes, allowing for an evaluation of model uncertainties over a broader range of climate forcing and the identification of parts of the world that are parameter sensitive. Certain simulations reproduced absolute δ18Op values across all time periods, along with LGM and MH δ18Op anomalies relative to the PI, better than the default parameterization. No single set of parameterizations worked well in all climate states, likely due to the non‐stationarity of cloud feedbacks under varying boundary conditions. Future work that involves varying multiple parameter sets simultaneously with coupled ocean feedbacks will likely provide improved constraints on cloud and convective parameterizations.

Evaluating Observational Constraints on Intermodel Spread in Cloud, Temperature, and Humidity Feedbacks

AbstractUncertainty in climate feedbacks is the primary source of the spread in projected surface temperature responses to anthropogenic forcing. Cloud feedback persistently appears as the main source of disagreement in future projections while the combined lapse‐rate plus water vapor (LR + WV) feedback is a smaller (30%), but non‐trivial source of uncertainty in climate sensitivity. Here we attempt to observationally constrain the feedbacks in an effort to reduce their intermodel uncertainties. The observed interannual variation provides a useful constraint on the long‐term cloud feedback, as evidenced by the consistency of global‐mean values and regional contributions to the intermodel spread on both interannual and long‐term timescales. However, interannual variability does not serve to constrain the long‐term LR + WV feedback spread, which we find is dominated by the varying tropical relative humidity (RH) response to interhemispheric warming differences under clear‐sky conditions and the RH‐fixed LR feedback under all‐sky conditions.

Observational evidence that cloud feedback amplifies global warming

Global warming drives changes in Earth's cloud cover, which, in turn, may amplify or dampen climate change. This "cloud feedback" is the single most important cause of uncertainty in Equilibrium Climate Sensitivity (ECS)-the equilibrium global warming following a doubling of atmospheric carbon dioxide. Using data from Earth observations and climate model simulations, we here develop a statistical learning analysis of how clouds respond to changes in the environment. We show that global cloud feedback is dominated by the sensitivity of clouds to surface temperature and tropospheric stability. Considering changes in just these two factors, we are able to constrain global cloud feedback to 0.43 ± 0.35 W⋅m-2⋅K-1 (90% confidence), implying a robustly amplifying effect of clouds on global warming and only a 0.5% chance of ECS below 2 K. We thus anticipate that our approach will enable tighter constraints on climate change projections, including its manifold socioeconomic and ecological impacts.

Underestimated marine stratocumulus cloud feedback associated with overly active deep convection in models

Cloud feedback remains the largest source of uncertainty in equilibrium climate sensitivity (ECS). Many studies have attempted to narrow uncertainties in cloud feedback and ECS by proposing observable metrics with high skill at predicting future climate, referred to as emergent constraints. These constraints are often associated with clouds, convection, and circulation, and are interrelated. However, physical explanations for these connections remain unclear. Here, we propose a new mechanism relating convection and clouds across multiple climate models. Some models show overly active deep convection on daily timescales in the subtropical low cloud regions, which contributes to weaker subsidence inversion and smaller amounts of low-level clouds. Such models predict smaller shortwave (SW) cloud feedback. Using precipitation frequency in these regions as an emergent constraint, encapsulating this mechanism, models with lower SW cloud feedback (<0.50 W m−2 °C−1) are found to exhibit erroneously frequent convection. Our results suggest that further improvements in understanding and better modeling of cloud and convective systems are necessary for accurate climate predictions.

Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity

Marine low clouds strongly cool the planet. How this cooling effect will respond to climate change is a leading source of uncertainty in climate sensitivity, the planetary warming resulting from CO2 doubling. Here, we observationally constrain this low cloud feedback at a near-global scale. Satellite observations are used to estimate the sensitivity of low clouds to interannual meteorological perturbations. Combined with model predictions of meteorological changes under greenhouse warming, this permits quantification of spatially resolved cloud feedbacks. We predict positive feedbacks from midlatitude low clouds and eastern ocean stratocumulus, nearly unchanged trade cumulus and a near-global marine low cloud feedback of 0.19 ± 0.12 W m−2 K−1 (90% confidence). These constraints imply a moderate climate sensitivity (~3 K). Despite improved midlatitude cloud feedback simulation by several current-generation climate models, their erroneously positive trade cumulus feedbacks produce unrealistically high climate sensitivities. Conversely, models simulating erroneously weak low cloud feedbacks produce unrealistically low climate sensitivities.

The effect of climate change on agro-climatic indicators in the UK

The effect of climate change on agriculture in the UK is here assessed using a comprehensive series of policy-relevant agro-climate indicators characterising changes to climate resources and hazards affecting productivity and operations. This paper presents projections of these indicators across the UK with gridded observed data and UKCP18 climate projections representing a range of greenhouse gas emissions scenarios. The projections can be used to inform climate change mitigation and adaptation policy. There will be substantial changes in the climate resource and hazard across the UK during the twenty-first century if emissions continue to follow a high trajectory, and there will still be some changes if emissions reduce to achieve international climate policy targets. Growing seasons for certain crops will lengthen, crop growth will be accelerated, and both drought and heat risks (for some types of production) will increase. Soils will become drier in autumn, although there will be less change in winter and spring. The longer growing seasons and warmer temperatures provide opportunities for new crops, subject to the effects of increasing challenges to production. Most of the changes are relatively consistent across the UK, although drought risk and heat stress risk increase most rapidly in the south and east. The climate change trend is superimposed onto considerable year to year variability. Although there is strong consensus across climate projections on the direction of change, there is considerable uncertainty in the rate and magnitude of change for a given emissions scenario. For the temperature-based indicators, this reflects uncertainty in climate sensitivity, whilst for the precipitation-based indicators largely reflects uncertainty in projected changes in the weather systems affecting the UK.

The risk-adjusted carbon price

We use perturbation methods to derive a rule for the optimal risk-adjusted social cost of carbon (SCC) that incorporates the effects of uncertainties associated with climate and the economy from a calibrated DSGE model. We allow for different aversions to risk and intertemporal fluctuations, convex damages, uncertainties in economic growth, atmospheric carbon, climate sensitivity and damages, their correlations, and distributions that are skewed in the longer run to capture long-run climate feedbacks. Our non-certainty-equivalent rule for the SCC incorporates precaution, risk insurance, and climate sensitivity and damage rate hedging effects to deal with future economic and climatic and damage risks.

Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models

Abstract. Climate sensitivity to CO2 remains the key uncertainty in projections of future climate change. Transient climate response (TCR) is the metric of temperature sensitivity that is most relevant to warming in the next few decades and contributes the biggest uncertainty to estimates of the carbon budgets consistent with the Paris targets. Equilibrium climate sensitivity (ECS) is vital for understanding longer-term climate change and stabilisation targets. In the IPCC 5th Assessment Report (AR5), the stated “likely” ranges (16 %–84 % confidence) of TCR (1.0–2.5 K) and ECS (1.5–4.5 K) were broadly consistent with the ensemble of CMIP5 Earth system models (ESMs) available at the time. However, many of the latest CMIP6 ESMs have larger climate sensitivities, with 5 of 34 models having TCR values above 2.5 K and an ensemble mean TCR of 2.0±0.4 K. Even starker, 12 of 34 models have an ECS value above 4.5 K. On the face of it, these latest ESM results suggest that the IPCC likely ranges may need revising upwards, which would cast further doubt on the feasibility of the Paris targets. Here we show that rather than increasing the uncertainty in climate sensitivity, the CMIP6 models help to constrain the likely range of TCR to 1.3–2.1 K, with a central estimate of 1.68 K. We reach this conclusion through an emergent constraint approach which relates the value of TCR linearly to the global warming from 1975 onwards. This is a period when the signal-to-noise ratio of the net radiative forcing increases strongly, so that uncertainties in aerosol forcing become progressively less problematic. We find a consistent emergent constraint on TCR when we apply the same method to CMIP5 models. Our constraints on TCR are in good agreement with other recent studies which analysed CMIP ensembles. The relationship between ECS and the post-1975 warming trend is less direct and also non-linear. However, we are able to derive a likely range of ECS of 1.9–3.4 K from the CMIP6 models by assuming an underlying emergent relationship based on a two-box energy balance model. Despite some methodological differences; this is consistent with a previously published ECS constraint derived from warming trends in CMIP5 models to 2005. Our results seem to be part of a growing consensus amongst studies that have applied the emergent constraint approach to different model ensembles and to different aspects of the record of global warming.

Impact of Cloud Ice Particle Size Uncertainty in a Climate Model and Implications for Future Satellite Missions

AbstractIce particle size is pivotal to determining ice cloud radiative effect and precipitating rate. However, there is a lack of accurate ice particle effective radius (Rei) observation on the global scale to constrain its representation in climate models. In support of future mission design, here we present a modeling assessment of the sensitivity of climate simulations to Rei and quantify the impact of the proposed mission concept on reducing the uncertainty in climate sensitivity. We perturb the parameters pertaining to ice fall speed parameter and Rei in radiation scheme, respectively, in National Center for Atmospheric Research CESM1 model with a slab ocean configuration. The model sensitivity experiments show that a settling velocity increase due to a larger Rei results in a longwave cooling dominating over a shortwave warming, a global mean surface temperature decrease, and precipitation suppression. A similar competition between longwave and shortwave cloud forcing changes also exists when perturbing Rei in the radiation scheme. Linearity generally holds for the climate response for Rei related parameters. When perturbing falling snow particle size (Res) in a similar way, we find much less sensitivity of climate responses. Our quadrupling CO2 experiments with different parameter settings reveal that Rei and Res can account for changes in climate sensitivity significantly from +12.3% to −6.2%. By reducing the uncertainty ranges of Rei and Res from a factor of 2 to ±25%, a future satellite mission under design is expected to improve the climate state simulations and reduce the climate sensitivity uncertainty pertaining to ice particle size by approximately 60%. Our results highlight the importance of better observational constraints on Rei by satellite missions.

A framework for experimental scenarios of global change in marine systems using coral reefs as a case study.

Understanding the consequences of rising CO2 and warming on marine ecosystems is a pressing issue in ecology. Manipulative experiments that assess responses of biota to future ocean warming and acidification conditions form a necessary basis for expectations on how marine taxa may respond. Although designing experiments in the context of local variability is most appropriate, local temperature and CO2 characteristics are often unknown as such measures necessitate significant resources, and even less is known about local future scenarios. To help address these issues, we summarize current uncertainties in CO2 emission trajectories and climate sensitivity, examine region-specific changes in the ocean, and present a straightforward global framework to guide experimental designs. We advocate for the inclusion of multiple plausible future scenarios of predicted levels of ocean warming and acidification in forthcoming experimental research. Growing a robust experimental base is crucial to understanding the prospect form and function of marine ecosystems in the Anthropocene.

Determining the Social Cost of Carbon: Under Damage and Climate Sensitivity Uncertainty

This article quantifies the impact on optimal climate policy, of both damage elasticity and equilibrium climate sensitivity uncertainty, under separable preferences for risk and intergenerational inequality. The primary findings are as follows. (1) Such preferences can depress the social cost of carbon (SCC) when calibration aims at matching actual economic outcomes, countering the prevailing view that the SCC is greater with separable than with conventional entangled preferences. (2) Damage elasticity uncertainty has larger effects on climate policy than equilibrium climate sensitivity uncertainty, even under high impact tail risk of the latter. (3) Risk aversion decisively strengthens optimal climate policy under joint damage and climate sensitivity uncertainty, than with a single source of uncertainty alone. Indeed, failing to account for the interaction between damage and climate sensitivity uncertainty underestimates the cost of climate change by more than US dollars 1 trillion.

Exploring the 2 °C target with 100 percent renewable energy under uncertainty in climate sensitivity

Renewable energy has gained main issues in energy policy globally due to its rapid expansion after the Fukushima daiichi nuclear accident. The Paris agreement triggered to publish numerous studies to meet the 2 °C or well-below target. However, little studies had carried out in achieving 100% renewable energy up to 2100 simultaneously aiming the target, to which we addressed in this study by using our cost-minimizing resource balance models. The models are consisted from production of resources, land use and land use changes, inter-regional transportation, energy conversion (power, liquid fuels, gas), production of materials, final demand, wood products, disposal of used products, and materials recycling. In order to address uncertainty in climate sensitivity to meet the target, we integrated the latest version of simplified climate model by Nordhaus to our models. We clarified that the temperature rise calculated by the simplified climate model ranged widely due to uncertainty in climate sensitivity, resulted in the energy structure changes to meet the target as well.