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The intensity of the heaviest extreme precipitation events is known to increase with global warming. How often such events occur in a warmer world is however less well established, and the combined effect of changes in frequency and intensity on the total amount of rain falling as extreme precipitation is much less explored, in spite of potentially large societal impacts. Here, we employ observations and climate model simulations to document strong increases in the frequencies of extreme precipitation events occurring on decadal timescales. Based on observations we find that the total precipitation from these intense events almost doubles per degree of warming, mainly due to changes in frequency, while the intensity changes are relatively weak, in accordance to previous studies. This shift towards stronger total precipitation from extreme events is seen in observations and climate models, and increases with the strength – and hence the rareness – of the event. Based on these results, we project that if historical trends continue, the most intense precipitation events observed today are likely to almost double in occurrence for each degree of further global warming. Changes to extreme precipitation of this magnitude are dramatically stronger than the more widely communicated changes to global mean precipitation.

Better preparedness for summer heatwaves could mitigate their adverse effects on society. This can potentially be attained through an increased understanding of the relationship between heatwaves and one of their main dynamical drivers, atmospheric blocking. In the 1979-2015 period, we find that there is a significant correlation between summer heatwave magnitudes and the number of days influenced by atmospheric blocking in Northern Europe and Western Russia. Using three large global climate model ensembles, we find similar correlations, indicating that these three models are able to represent the relationship between extreme temperature and atmospheric blocking, despite having biases in their simulation of individual climate variables such as temperature or geopotential height. Our results emphasize the need to use large ensembles of different global climate models as single realizations do not always capture this relationship. The three large ensembles further suggest that the relationship between summer heatwaves and atmospheric blocking will not change in the future. This could be used to statistically model heatwaves with atmospheric blocking as a covariate and aid decision-makers in planning disaster risk reduction and adaptation to climate change.

We present a new method that allows a separation of the attribution of human influence in extreme events into changes in atmospheric flows and changes in other processes. Assuming two data sets of model simulations or observations representing a natural, or 'counter-factual' climate, and the actual, or 'factual' climate, we show how flow analogs used across data sets can provide quantitative estimates of each contribution to the changes in probabilities of extreme events. We apply this method to the extreme January precipitation amounts in Southern UK such as were observed in the winter of 2013/2014. Using large ensembles of an atmospheric model forced by factual and counterfactual sea surface temperatures, we demonstrate that about a third of the increase in January precipitation amounts can be attributed to changes in weather circulation patterns and two thirds of the increase to thermodynamic changes. This method can be generalized to many classes of events and regions and provides, in the above case study, similar results to those obtained in Schaller et al (2016 Nat. Clim. Change 6 627-34) who used a simple circulation index, describing only a local feature of the circulation, as in other methods using circulation indices (van Ulden and van Oldenborgh 2006 Atmos. Chem. Phys. 6 863-81).

Complexity and resolution of global climate models are steadily increasing, yet the uncertainty of their projections remains large, particularly for precipitation. Given the impacts precipitation changes have on ecosystems, there is a need to reduce projection uncertainty by assessing the performance of climate models. A common way of evaluating models is to consider global maps of errors against observations for a range of variables. However, depending on the purpose, feature‐based metrics defined on a regional scale and for one variable may be more suitable to identify the most accurate models. We compare three different ways of ranking the CMIP3 climate models: errors in a broad range of climate variables, errors in global field of precipitation, and regional features of modeled precipitation in areas where pronounced future changes are expected. The same analysis is performed for temperature to identify potential differences between variables. The multimodel mean is found to outperform all single models in the global field‐based rankings but performs only averagely for the feature‐based ranking. Selecting the best models for each metric reduces the absolute spread in projections. If anomalies are considered, the model spread is reduced in a few regions, while the uncertainty can be increased in others. We also demonstrate that the common attribution of a lack of model agreement in precipitation projections to different model physics may be misleading. Agreement is similarly poor within different ensemble members of the same model, indicating that the lack of robust trends can be attributed partly to a low signal‐to‐noise ratio. Key Points Multimodel mean does not outperform individual models for the defined metrics Uncertainty in precipitation projections is not reduced by selecting best models Internal variability contributes partly to lack of agreement in projections

Background Physical functioning is a crucial factor for independence and quality of life in old age. The aim of the \"bestform—Best function of range of motion\" trial is to investigate the effects of a 6 months multimodal machine-based strength, coordination and endurance training on physical function, risk of falls and health parameters in older adults. Methods Bestform is a cluster-randomised trial including older adults  ≥ 65 years living in senior care facilities in Southern Germany. Senior care facilities are randomly allocated to the control group with usual care ( n  ≥ 10 care facilities) and to the intervention group ( n  ≥ 10 care facilities), overall including  ≥ 400 seniors. Residents belonging to the intervention group are offered a supervised machine-based exercise training programme twice weekly over 45–60 min over six months in small groups, while those in the usual care facilities will not receive active intervention. The primary outcome is the change in Short Physical Performance Battery over six months between groups. Secondary outcomes are change in risk of falling, fear of falling, number of falls and fall-related injuries, physical exercise capacity, handgrip strength, body composition, cardiac function, blood parameters, quality of life, risk of sarcopenia, activities of daily living, and cognition over three and six months. Discussion The bestform study investigates the change in physical function between seniors performing exercise intervention versus usual care over six months. The results of the study will contribute to the development of effective physical activity concepts in senior care facilities. Trial registration ClinicalTrials.gov: NCT04207307. Registered December 2019.

Within the last decade, extreme weather event attribution has emerged as a new field of science and garnered increasing attention from the wider scientific community and the public. Numerous methods have been put forward to determine the contribution of anthropogenic climate change to individual extreme weather events. So far nearly all such analyses were done months after an event has happened. Here we present a new method which can assess the fraction of attributable risk of a severe weather event due to an external driver in real-time. The method builds on a large ensemble of atmosphere-only general circulation model simulations forced by seasonal forecast sea surface temperatures (SSTs). Taking the England 2013/14 winter floods as an example, we demonstrate that the change in risk for heavy rainfall during the England floods due to anthropogenic climate change, is of similar magnitude using either observed or seasonal forecast SSTs. Testing the dynamic response of the model to the anomalous ocean state for January 2014, we find that observed SSTs are required to establish a discernible link between a particular SST pattern and an atmospheric response such as a shift in the jetstream in the model. For extreme events occurring under strongly anomalous SST patterns associated with known low-frequency climate modes, however, forecast SSTs can provide sufficient guidance to determine the dynamic contribution to the event.

Purpose: Mobility is a crucial factor for independence and quality of life in old age. Nevertheless, many old people in retirement homes do not meet the physical activity recommendations. The aim of the Bestform-F--Best Function of Range of Motion feasibility study (bestform-F) was to evaluate the feasibility of implementing a machine-based multimodal exercise training program in older residents in retirement homes. Materials and Methods: The participants (n = 77) were recruited from two retirement homes and took part in a six-month multimodal exercise training program (2x/week, 45 minutes) on pneumatic strength training machines, a balance platform and bicycle ergometers. Feasibility criteria were recruitment number [greater than or equal to] 35 participants within six months, dropout rate < 40% of participants within six months of exercise, and training adherence [greater than or equal to] 50% of participants taking part in at least 50% of offered training sessions. Additionally, physical performance, fear of falling, cognitive function, and quality of life were assessed at baseline and after six months. Results: For the bestform-F study, 77 (85.6 [+ or -] 6.6 years; 78% women) out of 215 eligible residents from two senior residences were recruited. The dropout rate over six months was 10% (8/77 participants). The training adherence rate for the finishing participants was 77% (53/69 participants). In addition to the achieved feasibility criteria, significant improvements were recorded in the Chair Stand Test, Six-Minute Walk Test, and fear of falling after six months. Conclusion: All feasibility criteria have been fulfilled. The high number of recruited participants, the low dropout rate, and high adherence to the training program confirm the feasibility of a multimodal machine-based exercise training program offered to residents in retirement homes. The results provide a basis for a cluster-randomized controlled trial aimed at further investigating the efficacy of the bestform-F program. Keywords: machine-based exercise, seniors, sarcopenia, mobility, resistance training, balance

The reversibility of the Atlantic meridional overturning circulation (AMOC) is investigated in multi-model experiments using global climate models (GCMs) where CO₂ concentrations are increased by 1 or 2 % per annum to 2× or 4× preindustrial conditions. After a period of stabilisation the CO₂ is decreased back to preindustrial conditions. In most experiments when the CO₂ decreases, the AMOC recovers before becoming anomalously strong. This \"overshoot\" is up to an extra 18.2Sv or 104 % of its preindustrial strength, and the period with an anomalously strong AMOC can last for several hundred years. The magnitude of this overshoot is shown to be related to the build up of salinity in the subtropical Atlantic during the previous period of high CO₂ levels. The magnitude of this build up is partly related to anthropogenic changes in the hydrological cycle. The mechanisms linking the subtropical salinity increase to the subsequent overshoot are analysed, supporting the relationship found. This understanding is used to explain differences seen in some models and scenarios. In one experiment there is no overshoot because there is little salinity build up, partly as a result of model differences in the hydrological cycle response to increased CO₂ levels and partly because of a less aggressive scenario. Another experiment has a delayed overshoot, possibly as a result of a very weak AMOC in that GCM when CO₂ is high. This study identifies aspects of overshoot behaviour that are robust across a multi-model and multi-scenario ensemble, and those that differ between experiments. These results could inform an assessment of the real-world AMOC response to decreasing CO₂.

Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

Cullin (Cul)-based E3 ubiquitin ligases are activated through the attachment of Nedd8 to the Cul protein. In yeast, Dcn1 (defective in Cul neddylation 1 protein) functions as a scaffold-like Nedd8 E3-ligase by interacting with its Cul substrates and the Nedd8 E2 Ubc12. Human cells express 5 Dcn1-like (DCNL) proteins each containing a C-terminal potentiating neddylation domain but distinct amino-terminal extensions. Although the UBA-containing DCNL1 and DCNL2 are likely functional homologues of yeast Dcn1, DCNL3 also interacts with human Culs and is able to complement the neddylation defect of yeast dcn1Δ cells. DCNL3 down-regulation by RNAi decreases Cul neddylation, and overexpression of a Cul3 mutant deficient in DCNL3 binding interferes with Cul3 function in vivo. Interestingly, DCNL3 accumulates at the plasma membrane through a conserved, lipid-modified motif at the N terminus. Membrane-bound DCNL3 is able to recruit Cul3 to membranes and is functionally important for Cul3 neddylation in vivo. We conclude that DCNL proteins function as nonredundant Cul Nedd8-E3 ligases. Moreover, the diversification of the N termini in mammalian Dcn1 homologues may contribute to substrate specificity by regulating their subcellular localization.