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Concurrent extreme events, i.e. multi-variate extremes, can be associated with strong impacts. Hence, an understanding of how such events are changing in a warming climate is helpful to avoid some associated climate change impacts and better prepare for them. In this article, we analyse the projected occurrence of hot, dry, and wet extreme events' clusters in the multi-model ensemble of the 6th phase of the Coupled Model Intercomparison Project (CMIP6). Changes in 'extreme extremes', i.e. events with only 1% probability of occurrence in the current climate are analysed, first as univariate extremes, and then when co-occurring with other types of extremes (i.e. events clusters) within the same week, month or year. The projections are analysed for present-day climate (+1 °C) and different levels of additional global warming (+1.5 °C, +2 °C, +3 °C). The results reveal substantial risk of occurrence of extreme events' clusters of different types across the globe at higher global warming levels. Hotspot regions for hot and dry clusters are mainly found in Brazil, i.e. in the Northeast and the Amazon rain forest, the Mediterranean region, and Southern Africa. Hotspot regions for wet and hot clusters are found in tropical Africa but also in the Sahel region, Indonesia, and in mountainous regions such as the Andes and the Himalaya.

Social scientists have argued that good communication around risks in climate hazards requires information to be presented in a user-relevant way, allowing people to better understand the factors controlling those risks. We present a potentially useful way of doing this by explaining future UK winter precipitation in terms of changes in the frequency, and associated average rainfall, of local pressure patterns that people are familiar with through their use in daily weather forecasts. We apply this approach to a perturbed parameter ensemble (PPE) of coupled HadGEM3-GC3.05 simulations of the RCP8.5 emissions scenario, which formed part of the UK Climate Projections in 2018. The enhanced winter precipitation by 2050–99 is largely due to an increased tendency towards westerly and south-westerly conditions at the expense of northerly/easterly conditions. Daily precipitation is generally more intense, most notably for the south-westerlies. In turn, we show that the changes in the frequency of the pressure patterns are consistent with changes in larger scale drivers of winter circulation and our understanding of how they relate to each other; this should build user confidence in the projections. Across the PPE, these changes in pressure patterns are largely driven by changes in the strength of the stratospheric polar vortex; for most members the vortex strengthens over the twenty-first century, some beyond the CMIP6 range. The PPE only explores a fraction of the CMIP6 range of tropical amplification, another key driver. These two factors explain why the PPE is skewed towards exploring the more westerly side of the CMIP6 range, so that the PPE’s description of UK winter precipitation changes does not provide a full picture.

A model for source‐limited dust emission is proposed. The model accounts for the evolution of the supply of soil dust depleted by dust emission and enriched by the process of surface renewal, together with several other new developments. The model is tested with a field dataset. The impact of source limitation to dust emission is profound. Our tests show that by considering source limitation, the model predicted dust emission can reduce by one order of magnitude in a real‐case simulation period of less than 20 days. We show that the process of dust emission is much more complex and variable than considered in previous dust models. Our findings have far‐reaching implications, for example, to the global dust emission estimates. Because source‐limited dust emission has so far not been represented in global dust models, the model estimated dust emission is only its potential and may be a substantial overestimate. Plain Language Summary Airborne dust is important to climate change. Models exist to estimate how much dust is emitted from the surface every year. But these models do not account for source limited dust emission. This lack of capacity may cause serious errors in the estimated dust emission. Here, we develop a new model to overcome this problem, which simulates the evolution of dust availability on the surface. We test our model using observed data and found that the impact of source limited dust emission is very large. This is the first source‐limited dust emission model we know. Our findings have far‐reaching implications to climate research. We believe that existing global dust emission estimates may be too large. Key Points A new model for source‐limited dust emission is proposed Impact of source limitation to dust emission is profound and model predicted dust emission can reduce by order of magnitude Global dust model estimated dust emission may be a substantial overestimate

A significant warming is projected in Antarctic climate change under high CO2 forcing, involving complex interactions between ocean and land surfaces. While previous studies have emphasized the seasonal mechanism driving Antarctic ocean surface warming, the processes governing land surface warming remain less explored. Here we show that, under abrupt quadrupled CO2 forcing, Antarctic land surface experiences uniform warming throughout the year, primarily driven by poleward atmospheric heat transport, with latent energy transport playing a dominant role. This moisture‐related transport not only delivers energy but also amplifies the water vapor feedback, significantly contributing to the warming. Our findings suggest that the discrepancies in representing these atmospheric processes across models, contribute substantially to the uncertainties in Antarctic land surface warming projections. The result emphasizes the need for improved understanding of the atmospheric dynamics in polar regions to reduce model uncertainties under future climate scenarios. Plain Language Summary To project the surface warming of Antarctic under high‐emission scenarios, we utilize the pre‐industrial and abrupt quadrupled CO2 (abrupt‐4 × CO2) experiments from 18 Coupled Model Intercomparison Project Phase 6 models. The surface temperature response is analyzed using the climate feedback‐response analysis method. Our research highlights significant warming differences between the ocean and land surfaces: the ocean surface exhibits strong winter warming and weak summer warming attributable to the seasonal energy transfer mechanism; in contrast, land surface warming is relatively weak and more uniform throughout the year, primarily driven by poleward atmospheric heat transport (AHT), with latent energy (LE) transport playing a central role in both energy delivery and the amplification of water vapor (WV) feedback. The inter‐model spread in land warming is closely tied to the variations in the strength of AHT, particularly the transport of LE, which influences regional WV content. Understanding these mechanisms is crucial for advancing future climate changes in the Antarctic region. Key Points Meridional Atmospheric heat transport (AHT), particularly latent energy (LE) transport, serves as the primary driver of Antarctic land warming LE transport delivers heat to Antarctic land surface, boosting moisture and amplifying the regional water vapor feedback The uncertainty in Antarctic land warming projections is closely linked to variations in the strength of AHT

Internal climate variability co‐exists with anthropogenic climate change and places limits on the accuracy of regional climate projections due to its inherent unpredictability. This “certain” uncertainty in regional projections introduced by internal variability contrasts with uncertainty resulting from structural differences amongst climate models, which is potentially reducible as climate models improve. Initial‐condition “Large Ensembles” of simulations with individual climate models provide a new perspective on the expected range of future climate change outcomes. Their value for climate risk assessment, adaptation management, and decision‐making has yet to be fully realized. Key Points Internal climate variability introduces unavoidable uncertainty in regional climate projections due to its limited predictability Initial‐condition \"Large Ensembles\" conducted with global coupled climate models provide an expected range of future climate outcomes Their value for climate risk assessment, adaptation management, and decision‐making has yet to be fully realized

Increasing climate change risks in Africa, emerging from global warming and their interaction with non‐climate risks such as market, have increased the need for comprehensive Climate Risk Management (CRM) that considers both climatic and non‐climatic risks and enables actions that address the underlying drivers of vulnerability to these risks. However, comprehensive CRM requires holistic Climate Information Services (CIS), that is, CIS that balances between components focused on provision of short‐ and long‐term climate information for both local and non‐local decision makers. In this article, we ask: to what extent is CIS in Africa holistic? We review recent and ongoing CIS interventions in Africa to determine whether a balance of components between short‐ and long‐term CIS for local and non‐local decision makers is achieved for holistic CIS. We find a focus on provision of short‐term, that is, Sub‐Seasonal‐to‐Seasonal (S2S) CIS for local and non‐local decision makers, with limited focus on medium‐ to long‐term (MLT) CIS, particularly MLT CIS for local decision makers which represents the biggest gap to achieving holistic CIS in Africa. We present a supported case that ensuring holistic CIS in Africa will require a portfolio‐based approach to CIS development, particularly focusing on MLT CIS for local decision makers. We further highlight the need for integration of this MLT CIS for local decision makers into existing CIS. Achieving this will require: (a) experimentation and innovation with different CIS formats, products and timeframes to enable learning and flexibility to achieve desired goals; (b) capacity development of producers and consumers of climate information to ensure that they have the skills and expertise to understand and generate, and articulate needs and consume MLT CIS respectively; and (c) coordinated allocation of financial resources to ensure that all components of the holistic CIS are advanced.

Wetlands provide multiple ecosystem services of local and global importance, but currently there exists no comprehensive, high‐quality wetland map for the Arctic region. Improved information about Arctic wetland extents and their vulnerability to climate change is essential for adaptation and mitigation efforts, including for indigenous people dependent on the ecosystem services that wetlands provide, as inadequate planning could result in dire consequences for societies and ecosystems alike. Synthesizing high‐resolution wetland databases and datasets on soil wetness and soil types from multiple sources, we created the first high‐resolution map with full coverage of Arctic wetlands. We assess the vulnerability of Arctic wetlands for the years 2050, 2075, and 2100, using datasets on permafrost extent, soil types, and projected mean annual air temperature from the HadGEM2‐ES climate model for three change scenarios (RCP2.6, RCP4.5, and RCP8.5). Our mapping shows that wetlands cover approximately 3.5 million km2 or roughly 25% of Arctic landmass and 99% of these wetlands are in permafrost areas, indicating considerable vulnerability to future climate change. Unless global warming is limited to scenario RCP2.6, robust results show that large areas of Arctic wetlands are vulnerable to ecosystem regime shifts. If scenario RCP8.5 becomes a reality, at least 50% of the Arctic wetland area would be highly vulnerable to regime shifts with considerable adverse impacts on human health, infrastructure, economics, ecosystems, and biodiversity. The developed wetland and vulnerability maps can aid planning and prioritization of the most vulnerable areas for protection and mitigation of change. Plain Language Summary Wetlands play an important role in the Arctic; they cool the global climate, hold freshwater for animals and plants, regulate water, carbon, and nutrient cycling, and are biologically diverse and of great importance for indigenous human activities. Large areas lie on frozen ground (permafrost), but this could thaw out under expected future global and regional warming. Permafrost thaw can lead to wetland change leading to ecosystem as well as societal problems since the permafrost also acts as the foundation for roads and buildings in the Arctic. Through combined multi‐variable mapping, we estimate that wetlands cover 25% of land in the Arctic region, almost entirely on permafrost areas. Air temperatures above −2 degrees Celsius may lead to permafrost thaw and associated wetlands drainage, and this is enhanced by higher temperatures and longer durations of temperature elevation. If global temperatures increase by more than 2 degrees Celsius from preindustrial levels to year 2100, 30%–50% of Arctic wetlands are vulnerable to change. Limiting global warming is critical for preserving Arctic wetlands and reducing societal and ecosystem impacts of their changes. Key Points Multi‐data synthesis shows that wetlands cover 25% of the Arctic landmass Around 50% of Arctic wetlands are vulnerable to permafrost thaw by the year 2100 in climate scenario RCP8.5 Much of the permafrost underlying Arctic wetland areas can remain stable up until the year 2100 under climate scenario RCP2.6

The energy sector is the largest contributor to global greenhouse gas emissions, but could also be seriously affected by climate change, calling into question society’s current consumption patterns. In this communication, climate projections based on a set of numerical models of global circulation are used to simulate the climate until the end of the century and keep in mind the alternative scenarios of pollutant emissions. Apart from solar energy, the results for the Azores region show a negative impact on the production and consumption of renewable energies. In the regional context, this issue assumes special relevance, given the geographical constraints, such as territorial discontinuity and insularity. Based on these assumptions, measures and recommendations are pointed out for the sectors that most penalize greenhouse gas emissions, considering the energy sustainability in the Azores and the commitments and goals assumed under international agreements.

We analyze data of 27 global climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), and examine projected changes in temperature and precipitation over the African continent during the twenty-first century. The temperature and precipitation changes are computed for two future time slices, 2030–2059 (near term) and 2070–2099 (long term), relative to the present climate (1981–2010), for the entire African continent and its eight subregions. The CMIP6 multi-model ensemble projected a continuous and significant increase in the mean annual temperature over all of Africa and its eight subregions during the twenty-first century. The mean annual temperature over Africa for the near (long)-term period is projected to increase by 1.2 °C (1.4 °C), 1.5 °C (2.3 °C), and 1.8 °C (4.4 °C) under the Shared Socioeconomic Pathways (SSPs) for weak, moderate, and strong forcing, referenced as SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The future warming is not uniform over Africa and varies regionally. By the end of the twenty-first century, the largest rise in mean annual temperature (5.6 °C) is projected over the Sahara, while the smallest rise (3.5 °C) is over Central East Africa, under the strong forcing SSP5-8.5 scenario. The projected boreal winter and summer temperature patterns for the twenty-first century show spatial distributions similar to the annual patterns. Uncertainty associated with projected temperature over Africa and its eight subregions increases with time and reaches a maximum by the end of the twenty-first century. On the other hand, the precipitation projections over Africa during the twenty-first century show large spatial variability and seasonal dependency. The northern and southern parts of Africa show a reduction in precipitation, while the central parts of Africa show an increase, in future climates under the three reference scenarios. For the near (long)-term periods, the area-averaged precipitation over Africa is projected to increase by 6.2 (4.8)%, 6.8 (8.5)%, and 9.5 (15.2)% under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The median warming simulated by the CMIP6 model ensemble remains higher than the CMIP5 ensemble over most of Africa, reaching as high as 2.5 °C over some regions, while precipitation shows a mixed spatial pattern.

In spite of the chaotic nature of the atmosphere and involvement of complex nonlinear dynamics, forecasting climate fluctuations over different timescales is feasible due to the interaction between the atmosphere and the slowly varying underlying surfaces. This review provides insights into climate predictions across subseasonal to decadal timescales and into making projections of future climate change. Different sources of uncertainty in climate predictions are discussed, including internal variability uncertainty, which is large for short-term predictions of up to a decade or two, model uncertainty for predictions at all timescales, and scenario uncertainty for climate change projections at the end of this century. Climate models have been significantly improved in recent decades, mostly through improved parameterization of unresolved processes and enhancement of the spatial resolution, while ensemble forecasting has also been developed to capture strong predictable signals. Future research should aim to reduce uncertainty in climate predictions, for example, through the application of high-resolution climate models. However, sub-grid-scale features would still be parameterized, underlining the need for further improvements in physical parameterizations to account for sub-grid-scale processes. There is also a need for improvement and extension of the current observing system, which will greatly advance understanding of the key processes and features in the climate system. The advanced observing system in the future will also be beneficial for more accurate representation of the initial state of the components of the climate system in order to obtain more accurate climate predictions. In spite of progress in model development, the spread of projected precipitation by different models under a specific radiative forcing of greenhouse gases is still large at the regional scale. Improving future projections of regional precipitation requires better accounting for internal variability and model uncertainty, which can be partly achieved by improvement and extension of the observing system.