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The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range of feedbacks, offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes. Estimating the magnitude of radiative and non-radiative feedbacks is key for understanding the climate dynamics of polar regions. Here the authors propose an inclusive methodology to quantify the influence of all those feedbacks, stimulating more systematic analyses in observational and model ensembles.

Empirical orthogonal functions (EOFs) applied to gridded Earth system data enables users to diagnose modes of variability with relative ease. Yet, many challenges to interpretation exist such that they must be used with awareness and intention when applied to gridded climate data, especially with large ensembles. Utilizing data from two different Earth system modelling large ensemble frameworks, the Energy Exoscale Earth System Model and the Community Earth System Model, as well as reanalysis data, common EOF pitfalls are summarized and discussed. Challenges include erroneous mode swapping, sign flipping, and the temporal variability of the centers of action. For modes of variability with similar contribution to variance, mode swapping is not uncommon. Sign flipping can occur with almost any mode where the pattern is correct, but the sign is arbitrary. Although the variability of the center of action is not necessarily problematic, it potentially complicates interpretation over multi-century timescales. A wide variety of alternative methods to EOFs exist, but fitness-for-purpose must be evaluated. Additionally, illustrations of alternative methods and examples of proper use are provided. Alternative methods fit into three categories: EOF variants, linear methods, and multilinear methods.

Are equilibrium climate sensitivity and the associated radiative feedbacks a constant property of the climate system, or do they change with forcing magnitude and base climate? Using the radiative kernel technique, feedbacks and climate sensitivity are evaluated in a fully coupled general circulation model (GCM) for three successive doublings of carbon dioxide starting from present-day concentrations. Climate sensitivity increases by 23% between the first and third CO₂ doublings. Increases in the positive water vapor and cloud feedbacks are partially balanced by a decrease in the positive surface albedo feedback and an increase in the negative lapse rate feedback. Feedbacks can be decomposed into a radiative flux change and a climate variable response to temperature change. The changes in water vapor and Planck feedbacks are due largely to changes in the radiative response with climate state. Higher concentrations of greenhouse gases and higher temperatures lead to more absorption and emission of longwave radiation. Changes in cloud feedbacks are dominated by the climate response to temperature change, while the lapse rate and albedo feedbacks combine elements of both. Simulations with a slab ocean model (SOM) version of the GCM are used to verify whether an SOM-GCM accurately reproduces the behavior of the fully coupled model. Although feedbacks differ in magnitude between model configurations (with differences as large as those between CO₂ doublings for some feedbacks), changes in feedbacks between CO₂ doublings are consistent in sign and magnitude in the SOM-GCM and the fully coupled model.

Climate feedbacks vary strongly among climate models and continue to represent a major source of uncertainty in estimates of the response of climate to anthropogenic forcings. One method to evaluate feedbacks in global climate models is the radiative kernel technique, which is well suited for model intercomparison studies because of its computational efficiency. However, the usefulness of this technique is predicated on the assumption of linearity between top-of-atmosphere (TOA) radiative fluxes and feedback variables, limiting its application to simulations of small climate perturbations, where nonlinearities can be neglected. This paper presents an extension of the utility of this linear technique to large forcings, using global climate model simulations forced with CO₂ concentrations ranging from 2 to 8 times present-day values. Radiative kernels depend on the model’s radiative transfer algorithm and climate base state. For large warming, kernels based on the present-day climate significantly underestimate longwave TOA flux changes and somewhat overestimate shortwave TOA flux changes. These biases translate to inaccurate feedback estimates. It is shown that a combination of present-day kernels and kernels computed using a large forcing climate base state leads to significant improvement in the approximation of TOA flux changes and increased reliability of feedback estimates. While using present-day kernels results in a climate sensitivity that remains constant, using the new kernels shows that sensitivity increases significantly with each successive doubling of CO₂ concentrations.

As atmospheric carbon dioxide concentrations rise, some regions are expected to warm more than others. Research suggests that whether warming will intensify or slow down over time also depends on location.

For adaptation and mitigation planning, stakeholders need reliable information about regional precipitation changes under different emissions scenarios and for different time periods. A significant amount of current planning effort assumes that each K of global warming produces roughly the same regional climate change. Here using 25 climate models, we compare precipitation responses with three 2 K intervals of global ensemble mean warming: a fast and a slower route to a first 2 K above pre-industrial levels, and the end-of-century difference between high-emission and mitigation scenarios. We show that, although the two routes to a first 2 K give very similar precipitation changes, a second 2 K produces quite a different response. In particular, the balance of physical mechanisms responsible for climate model uncertainty is different for a first and a second 2 K of warming. The results are consistent with a significant influence from nonlinear physical mechanisms, but aerosol and land-use effects may be important regionally. It is often assumed that each additional degree of global warming impacts regional climate equally. Here, Good et al . use the CMIP5 archive to show different precipitation changes arise from 0–2 K versus 2–4 K of warming above pre-industrial levels, partly from nonlinearity in underlying physical mechanisms.

Coupled fire-atmosphere models are increasingly being used to study low-intensity fires, such as those that are used in prescribed fire applications. Thus, the need arises to evaluate these models for their ability to accurately represent fire spread in marginal burning conditions. In this study, wind and fuel data collected during the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiments (RxCADRE) fire campaign were used to generate initial and boundary conditions for coupled fire-atmosphere simulations. We present a novel method to obtain fuels representation at the model grid scale using a combination of imagery, machine learning, and field sampling. Several methods to generate wind input conditions for the model from eight different anemometer measurements are explored. We find a strong sensitivity of fire outcomes to wind inputs. This result highlights the critical need to include variable wind fields as inputs in modeling marginal fire conditions. This work highlights the complexities of comparing physics-based model results against observations, which are more acute in marginal burning conditions, where stronger sensitivities to local variability in wind and fuels drive fire outcomes.

As atmospheric carbon dioxide concentrations rise, some regions are expected to warm more than others. Now research suggests that whether warming will intensify or slow down over time also depends on location.

Dimethyl sulfide (DMS) is a significant source of marine sulfate aerosol and plays an important role in modifying cloud properties. Fully coupled climate simulations using dynamic marine ecosystem and DMS calculations are conducted to estimate DMS fluxes under various climate scenarios and to examine the sign and strength of phytoplankton-DMS-climate feedbacks for the first time. Simulation results show small differences in the DMS production and emissions between pre-industrial and present climate scenarios, except for some areas in the Southern Ocean. There are clear changes in surface ocean DMS concentrations moving into the future, and they are attributable to changes in phytoplankton production and competition driven by complex spatially varying mechanisms. Comparisons between parallel simulations with and without DMS fluxes into the atmosphere show significant differences in marine ecosystems and physical fields. Without DMS, the missing subsequent aerosol indirect effects on clouds and radiative forcing lead to fewer clouds, more solar radiation, and a much warmer climate. Phaeocystis, a uniquely efficient organosulfur producer with a growth advantage under cooler climate states, can benefit from producing the compound through cooling effects of DMS in the climate system. Our results show a tight coupling between the sulfur and carbon cycles. The ocean carbon uptake declines without DMS emissions to the atmosphere. The analysis indicates a weak positive phytoplankton-DMS-climate feedback at the global scale, with large spatial variations driven by individual autotrophic functional groups and complex mechanisms. The sign and strength of the feedback vary with climate states and phytoplankton groups. This highlights the importance of a dynamic marine ecosystem module and the sulfur cycle mechanism in climate projections.