Snow avalanches are one of the most impactful natural hazards in mountainous areas. Thus, the assessment and forecasting of avalanche danger are of great importance for the protection of life and property. A changing climate may lead to changes in avalanche danger, although the manifestation is unclear. Since climate change is regionally different, an assessment of potential avalanche-danger changes should be conducted on a regional basis. Here the focus is on avalanche danger in the Troms region in northern Norway, i.e. a region in the Arctic. To estimate the linkage between avalanche danger and weather conditions in this region, we utilise expert assessments of regional avalanche-danger level (ADL), the 3 km Norwegian Reanalysis (NORA3), and snow-cover information from the snow model seNorge. Random forest (RF) models are trained and optimised for a binary case and for a four-level case. The binary-case RF model exhibits a much higher overall accuracy (76 %) than the four-level case RF model (57 %), which is due to the latter model often misclassifying ADL 1 as ADL 2 and ADL 4 as ADL 3. Still, the misclassification difference is seldom larger than one ADL, and the distribution of the frequencies of the different ADLs is reproduced. The most important predictive features are related to new snow and wind accumulated and averaged over several days. The binary-case RF model is used to hindcast avalanche-day frequency (ADF) from 1970 to 2024. In this period, the spring season (March–May) shows a small increase in ADF, whereas the winter season (December–February) exhibits negative trends. Moreover, the ADF is found to be correlated with the Arctic Oscillation (AO) index especially in winter, although this correlation appears to have deteriorated in recent years. Given recent advances in skill of representing the AO in decadal prediction systems, this is an encouraging result for the predictability of future avalanche-danger tendencies in northern Norway.

Climate sensitivity changes over time in numerical global climate models (GCMs) due to a so-called “pattern effect”. That is, surface-warming patterns evolve over time to favour different geographical regions giving rise to different climate feedbacks, thus changing climate sensitivity over time. One of the most important climate feedbacks is the cloud feedback and it has been shown that the pattern effect may strongly impact the strength of this feedback in GCMs. Here we perform slab-ocean model simulations with different versions of the Community Earth System Model (CESM). Different patterns of ocean heat transport convergence (Q-flux) are prescribed, inducing different patterns of surface warming. Notably, the prescribed Q-flux changes average to zero in the global mean, thus introducing no net forcing. We show that (1) net-zero forcing Q-flux changes can have surprisingly large effects on the climate, (2) that the impact strongly depends on the geographic pattern of the Q-flux change and, (3) that different cloud parametrisations may imply different impacts of the same patterns. While these results may have important implications for the quantification of the pattern effect and climate sensitivity in climate models, we caution against overinterpretation, as preliminary experiments with fully coupled models indicate a weaker sensitivity to similar pattern changes.

Numerical climate model simulations suggest that global warming is enhanced or hampered by the spatial pattern of the warming itself. This phenomenon is known as the ``pattern effect'' and has in recent years become the most promising explanation for the change over time of climate sensitivity in climate models. Under historical global warming, different patterns of surface-temperature change have emerged, notably a yet unexplained cooling in the Southern Ocean and the East Pacific. Historical climate model simulations notoriously fail to reproduce this cooling, which may contribute to the deviation of the simulated global-mean warming from the observed record.Here we qualitatively investigate the potential impact of historical and other surface-temperature pattern changes by changing the ocean heat transport convergence (Q-flux) in a slab-ocean model. The Q-flux changes are always implemented such that in the global mean they impose no net forcing. Consistent with earlier studies we find that the impact of a negative Q-flux change in the Southern Ocean has a stronger effect than in other regions because of a feedback loop between sea-surface temperatures (SSTs) and clouds in the Southern Ocean and the stably stratified regions in the tropics. The SST-cloud feedback loop facilitates the expansion of the Antarctic sea ice, indeed taking the model into a Snowball-Earth state. The intensity of this effect is found to be model dependent, especially due to differences in the cloud parametrisation. In experiments with deactivated sea ice the impact of the negative Q-flux change is much weaker.

Global temperature responses from different abrupt CO2 change experiments participating in Coupled Model Intercomparison Project Phase 6 (CMIP6) and LongRunMIP are systematically compared in order to study the linearity of the responses. For CMIP6 models, abrupt-4xCO2 experiments warm on average 2.2 times more than abrupt-2xCO2 experiments. A factor of about 2 can be attributed to the differences in forcing, and the rest is likely due to nonlinear responses. Abrupt-0p5xCO2 responses are weaker than abrupt-2xCO2, mostly because of weaker forcing. CMIP6 abrupt CO2 change experiments respond linearly enough to well reconstruct responses to other experiments, such as 1pctCO2, but uncertainties in the forcing can give uncertain responses. We derive also a generalised energy balance box model that includes the possibility of having oscillations in the global temperature responses. Oscillations are found in some models, and are connected to changes in ocean circulation and sea ice. Oscillating components connected to a cooling in the North Atlantic can counteract the long-term warming for decades or centuries and cause pauses in global temperature increase.

It has recently been established that in numerical model experiments climate sensitivity and feedback change over time and that this time dependence may result from a so-called “pattern effect”, i.e., changing patterns of surface warming. The Atlantic Meridional Overturning Circulation (AMOC) influences surface warming patterns as it redistributes energy across the globe. Thus, it may be an important factor for climate feedback change over time.In this study, members of the Coupled Model Intercomparison Project (CMIP) phases 5 and 6 are investigated and two groups of models distinguished, one with weak and one with strong feedback change over time. It is found that the model groups differ significantly in the AMOC response to quadrupling of CO2. To investigate if the difference in AMOC development between the two groups may be responsible for the differences in feedback, experiments with a slab-ocean model (SOM) are performed where the AMOC change is mimicked by changing the ocean heat uptake pattern. Especially in the Northern-Hemisphere Extra-Tropics the differences between the CMIP model groups are found to be qualitatively reproduced but other factors are needed to explain differences in the Southern Hemisphere and the Tropics.

The climate sensitivity of Earth and the radiative climate feedback both change over time as a result of a so-called “pattern effect,” i.e., changing patterns of surface warming. This is suggested by numerical climate model experiments. The Atlantic meridional overturning circulation (AMOC) influences surface warming patterns as it redistributes energy latitudinally. Thus, this ocean circulation may play an important role for climate-feedback change over time. In this study, two groups of members from the abrupt4xCO2 experiment of phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP) are distinguished: one group showing weak feedback change and the other showing strong feedback change over time. It is found that both groups differ significantly in the AMOC response to 4xCO 2 . Therefore, experiments with a slab ocean model (SOM) with quadrupling of the CO 2 concentration are performed in which the AMOC change is mimicked by changing the ocean heat transport. It is found that in the Northern Hemisphere extratropics the CMIP model group differences can be qualitatively reproduced by the SOM experiments, indicating that the AMOC plays an important role in setting the surface warming pattern. However, in the tropics and especially in the Southern Hemisphere other explanations are necessary.

Climate sensitivity – the response of the Earth’s surface temperature to radiative forcing – and climate feedbacks are important and widely used metrics to gauge global climate change. In recent years it has become clear that climate sensitivity and feedback change over time in numerical climate model experiments but the reasons for this change are not yet well understood. We investigate the abrupt4xCO2 experiment as simulated by multiple members of the Coupled Model Intercomparison Project (CMIP) phases 5 and 6 and apply a radiative kernel method to decompose climate feedback into contributions from physical processes. We extract two groups of models, one with small (G1) and one with large (G2) global mean lapse-rate feedback change over time. It is found that the model groups differ with respect to warming and feedback patterns and that the Arctic stands out as the region with the biggest between-group differences. We retrace these Arctic changes to the different evolution of Arctic sea ice in both model groups. A further finding is that G1 members exhibit much more warming over the simulation period than G2s members. This appears to result from a more positive early cloud feedback in G1 than in G2. Further investigation is needed to uncover possible cause-effect relationships between Arctic changes and global feedbacks.

Robust estimates of climate sensitivity are important for decision-making on mitigation of climate change. However, climate sensitivity and its governing processes are still subject to large uncertainty. Recently it has been established that climate sensitivity changes over time in numerical climate model experiments with abrupt quadrupling of the CO 2 concentration. Here we conduct an analysis of such experiments from a range of climate models from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP). Climate feedbacks associated with clouds, lapse rate, Planck radiation, surface albedo, and water vapor and their changes over time are diagnosed based on a radiative kernel method. We find two clearly distinct model groups, one with weak and one with strong lapse-rate feedback change. The Arctic is the region showing the largest differences between these two model groups, with respect to both warming change and individual feedback changes. We retrace this change to the development over time of the Arctic sea ice, which impacts both the surface-albedo and lapse-rate feedbacks. Generally, models that warm quickly, both globally and in the Arctic, also quickly lose their Arctic sea ice and change their total global-mean climate feedback only little, and vice versa. However, it remains unclear if the Arctic changes are a cause or rather a by-product of the total global-mean feedback change. Finally, we find support for the results of previous studies finding that the relative warming in the tropical Indo-Pacific region may control the change of total climate feedback over time.

A remaining carbon budget (RCB) estimates how much CO2 we can emit and still reach a specific temperature target. The RCB concept is attractive since it easily communicates to the public and policymakers, but RCBs are also subject to uncertainties. The expected warming levels for a given carbon budget has a wide uncertainty range, which increases with less ambitious targets, i.e., with higher CO2 emissions and temperatures. Leading causes of RCB uncertainty are the future non-CO2 emissions, Earth system feedbacks, and the spread in the climate sensitivity among climate models. The latter is investigated in this paper, using a simple carbon cycle model and emulators of the temperature responses of the Earth System Models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble. Driving 41 CMIP6 emulators with 127 different emission scenarios for the 21st century, we find almost perfect linear relationship between maximum global surface air temperature and cumulative carbon emissions, allowing unambiguous estimates of RCB for each CMIP6 model. The range of these estimates over the model ensemble is a measure of the uncertainty in the RCB arising from the range in climate sensitivity over this ensemble, and it is suggested that observational constraints imposed on the transient climate response in the model ensemble can reduce uncertainty in RCB estimates.

A remaining carbon budget (RCB) estimates how much CO2 we can emit and still reach a specific temperature target. The RCB concept is attractive since it easily communicates to the public and policymakers, but RCBs are also subject to uncertainties. The expected warming levels for a given carbon budget has a wide uncertainty range, which we show here to increase with less ambitious targets, i.e., with higher CO2 emissions and temperatures. Leading causes of RCB uncertainty are the future non-CO2 emissions, Earth system feedbacks, and the spread in the climate sensitivity among climate models. The latter is investigated in this paper, using simple emulators of Earth System Models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble. It is shown that the transient climate response to cumulative emissions of carbon (TCRE) is approximately proportional to the effective equilibrium climate sensitivity (ECS). For temperature targets between 1.5-3.0 degrees C, the models exhibiting low ECS increase RCB by a factor two compared to those with high sensitivity, suggesting that observational constraints imposed on the ECS in the model ensemble also will reduce uncertainty in the RCB estimates.

Climate sensitivity is a measure for the global mean temperature change of the earth in response to a given radiative forcing. In an experiment with an instantaneous forcing by e.g. a doubling of the atmospheric CO2 content the radiative imbalance at the top of the atmosphere can be regarded as a function of the global mean temperature change. In such an experiment the climate sensitivity can be approximated by linearly extrapolating to zero the TOA imbalance where equilibrium is obtained. The thus derived value is usually referred to as effective climate sensitivity. It has been established however, that the effective climate sensitivity changes over time. While the reason for this change is not clear, most recent investigations of the abrupt4xCO2 experiments of multiple members of the CMIP5 archive point to a delay in warming of the eastern tropical Pacific region relative to the global average in the multi model mean. Due to high stability in this region the heat is trapped there close to the surface which reduces the local lower tropospheric stability. The trapping of the warming close to the surface implies that the longwave cooling is less efficient in this region and its delayed warming relative to the global average increases global climate sensitivity over time. The decrease in lower tropospheric stability furthermore reduces low cloud cover leading to less negative low cloud feedback which causes additional warming.We investigate the delayed warming in the eastern Pacific region in more detail in terms of its effects on stability as well as clouds for individual members and multi model means of both the CMIP5 and CMIP6 archives. We find that in the multi model mean, the CMIP6 members show an even larger delayed warming than the CMIP5 members. Furthermore, the individual members of both archives generally exhibit the same pattern of delayed eastern tropical Pacific warming and a corresponding decrease in lower tropospheric stability in the same region, which indicates robustness of the earlier results based on the CMIP5 multi model mean. Additionally, there is a decrease in liquid water content in the lower atmospheric layers, confirming the influence of reduced lower tropospheric stability on low clouds. However, there are several further regions such as the Southern Ocean with a consistent delayed warming and reduced stability, which might influence climate sensitivity as well.