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\"Meet Marvel Boy--A.K.A. Noh-Varr of the Kree Empire, last survivor of a doomed starship. He's seen good friends killed by sheer ignorance and hate, and his welcome to Earth consisted of imprisonment and torture. Now he's angry. And if necessary, he'll take on our entire planet--in the name of love, justice and the freedom to ride in his spaceship! but who are Doctor Midas and the Exterminatrix--and what do they want with Marvel Boy?\"--Back cover.

The results of a large ensemble of regional climate models lead to two contrasting but plausible scenarios for the precipitation change over West Africa, one where mean precipitation is projected to decrease significantly over the Gulf of Guinea in spring and the Sahel in summer, and the other where summer precipitation over both regions is projected to increase. Dry and wet models show similar patterns of the dynamic and thermodynamic terms of the moisture budget, although their magnitudes are larger in the dry models. The largest discrepancies are found in the strength of the land-atmosphere coupling, with dry models showing a marked decrease in soil moisture and evapotranspiration. Some changes in precipitation characteristics are consistent for both sets of models. In particular, precipitation frequency is projected to decrease in spring over the Gulf of Guinea and in summer over the Sahel, but precipitation is projected to become more intense.

Climate model ensembles, such as the Coupled Model Intercomparison Project, phase 3 (CMIP3), are used to characterize broadscale ranges of projected regional climate change and their impacts. The 17-member Hadley Centre perturbed physics GCM ensemble [Quantifying Uncertainty in Model Predictions (“QUMP”)] extends this capability by including data enabling dynamical downscaling of these ranges, and similar data are now being made available from the CMIP phase 5 (CMIP5) GCMs. These raise new opportunities to provide and apply high-resolution regional climate projections. This study highlights the importance of employing a well-considered sampling strategy from available ensembles to provide scientifically credible information on regional climate change while minimizing the computational complexity of ensemble downscaling. A subset of the QUMP ensemble is selected for a downscaling program in Vietnam using the Providing Regional Climates for Impacts Studies (PRECIS) regional climate modeling system. Multiannual mean fields from each GCM are assessed with a focus on the Asian summer monsoon, given its importance to proposed applications of the projections. First, the study examines whether any model should be eliminated because significant deficiencies in its simulation may render its future climate projections unrealistic. No evidence is found to eliminate any of the 17 GCMs on these grounds. Second, the range of their future projections is explored and five models that best represent the full range of future climates are identified. The subset characterizes the range of both global and regional responses, and patterns of rainfall response, the wettest and driest projections for Vietnam, and different projected Asian summer monsoon changes. How these ranges of responses compare with those in the CMIP3 ensemble are also assessed, finding differences in both the signal and the spread of results in Southeast Asia.

We employ a large ensemble of Regional Climate Models (RCMs) from the COordinated Regional-climate Downscaling EXperiment to explore two questions: (1) what can we know about the future precipitation characteristics over Africa? and (2) does this information differ from that derived from the driving Global Climate Models (GCMs)? By taking into account both the statistical significance of the change and the models’ agreement on its sign, we identify regions where the projected climate change signal is robust, suggesting confidence that the precipitation characteristics will change, and those where changes in the precipitation statistics are non-significant. Results show that, when spatially averaged, the RCMs median change is usually in agreement with that of the GCMs ensemble: even though the change in seasonal mean precipitation may differ, in some cases, other precipitation characteristics (e.g., intensity, frequency, and duration of dry and wet spells) show the same tendency. When the robust change (i.e., the value of the change averaged only over the land points where it is robust) is compared between the GCMs and RCMs, similarities are striking, indicating that, although with some uncertainty on the geographical extent, GCMs and RCMs project a consistent future. Potential added value of downscaling future climate projections (i.e., non-negligible fine-scale information that is absent in the lower resolution simulations) is found for instance over the Ethiopian highlands, where the RCM ensemble shows a robust decrease in mean precipitation in contrast with the GCMs results. This discrepancy may be associated with the better representation of topographical details that are missing in the large scale GCMs. The impact of the heterogeneity of the GCM–RCM matrix on the results has been also investigated; we found that, for most regions and indices, where results are robust or non-significant, they are so independently on the choice of the RCM or GCM. However, there are cases, especially over Central Africa and parts of West Africa, where results are uncertain, i.e. most of the RCMs project a statistically significant change but they do not agree on its sign. In these cases, especially where results are clearly clustered according to the RCM, there is not a simple way of subsampling the model ensemble in order to reduce the uncertainty or to infer a more robust result.

Multimodel ensembles, whereby different global climate models (GCMs) and regional climate models (RCMs) are combined, have been widely used to explore uncertainties in regional climate projections. In this study, the extent to which information can be enhanced from sparsely filled GCM–RCM ensemble matrices and the way in which simulations should be prioritized to sample uncertainties most effectively are examined. A simple scaling technique, whereby the local climate response in an RCM is predicted from the large-scale change in the GCM, is found to often show skill in estimating local changes for missing GCM–RCM combinations. In particular, scaling shows skill for precipitation indices (including mean, variance, and extremes) across Europe in winter and mean and extreme temperature in summer and winter, except for hot extremes over central/northern Europe in summer. However, internal variability significantly impacts the ability to determine scaling skill for precipitation indices, with a three-member ensemble found to be insufficient for identifying robust local scaling relationships in many cases. This study suggests that, given limited computer resources, ensembles should be designed to prioritize the sampling of GCM uncertainty, using a reduced set of RCMs. Exceptions are found over the Alps and northeastern Europe in winter and central Europe in summer, where sampling multiple RCMs may be equally or more important for capturing uncertainty in local temperature or precipitation change. This reflects the significant role of local processes in these regions. Also, to determine the ensemble strategy in some cases, notably precipitation extremes in summer, better sampling of internal variability is needed.

The climate science and applications communities need a broad and demand‐driven concept to assess physical climate conditions that are relevant for impacts on human and natural systems. Here, we augment the description of the “climatic impact‐driver” (CID) approach adopted in the Working Group I (WGI) contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. CIDs are broadly defined as “physical climate system conditions (e.g., means, events, and extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions.” We give background information on the IPCC Report process that led to the development of the 7 CID types (heat and cold, wet and dry, wind, snow and ice, coastal, open ocean, and other) and 33 distinct CID categories, each of which may be evaluated using a variety of CID indices. This inventory of CIDs was co‐developed with WGII to provide a useful collaboration point between physical climate scientists and impacts/risk experts to assess the specific climatic phenomena driving sectoral responses and identify relevant CID indices within each sector. The CID Framework ensures that a comprehensive set of climatic conditions informs adaptation planning and risk management and may also help prioritize improvements in modeling sectoral dynamics that depend on climatic conditions. CIDs contribute to climate services by increasing coherence and neutrality when identifying and communicating relevant findings from physical climate research to risk assessment and planning activities. Plain Language Summary Climatic impact‐drivers (CIDs) are climate conditions that affect the things we care about in nature and society. We deepen the motivation and definitions that allowed the Intergovernmental Panel on Climate Change to identify 33 distinct CID categories including extreme heat, hydrological drought, severe wind storm, permafrost, relative sea level, marine heatwaves, and air pollution weather. Each CID category may be analyzed with specific indices that inform adaptation, mitigation and risk management. The CID Framework allows us to avoid universally labeling a climate condition as a “hazard,” recognizing that the same physical condition may be detrimental for some and beneficial or inconsequential for others. This approach allows climate scientists to engage with impacts and risk experts to target specific tolerance thresholds that are system‐ and sector‐dependent. This more comprehensive description of the CID Framework provides a practical foundation for climate research, climate and impact model development, risk assessments and climate service product creation. Key Points Deepens explanation of Climatic Impact‐Driver (CID) Framework utilized in Intergovernmental Panel on Climate Change Sixth Assessment Reports Distinguishes practical CID types and categories that allows climate information to target conditions that affect the things we care about Neutral Framework does not pre‐judge beneficial, detrimental or neutral outcomes which are system‐ and sector‐dependent

Reliable projections of future changes in local precipitation extremes are essential for informing policy decisions regarding mitigation and adaptation to climate change. In this paper, the extent to which the natural variability of the climate affects one’s ability to project the anthropogenically forced component of change in daily precipitation extremes across Europe is examined. A three-member ensemble of the Hadley Centre Regional Climate Model (HadRM3H) is used and a statistical framework is applied to estimate the uncertainty due to the full spectrum of climate variability. In particular, the results and understanding presented here suggest that annual to multidecadal natural variability may contribute significant uncertainty. For this ensemble projection, extreme precipitation changes at the grid-box level are found to be discernible above climate noise over much of northern and central Europe in winter, and parts of northern and southern Europe in summer. The ability to quantify the change to a reasonable level of accuracy is largely limited to regions in northern Europe. In general, where climate noise has a significant component varying on decadal time scales, single 30-yr climate change projections are insufficient to infer changes in the extreme tail of the underlying precipitation distribution. In this context, the need for ensembles of integrations is demonstrated and the relative effectiveness of spatial pooling and averaging for generating robust signals of extreme precipitation change is also explored. The key conclusions are expected to apply more generally to other models and forcing scenarios.

East African aridification during the past 8 million years is frequently invoked as a driver of large-scale shifts in vegetation 1 and the evolution of new animal lineages, including hominins 2 – 4 . However, evidence for increasing aridity is debated 5 and, crucially, the mechanisms leading to dry conditions are unclear 6 . Here, numerical model experiments show that valleys punctuating the 6,000-km-long East African Rift System (EARS) are central to the development of dry conditions in East Africa. These valleys, including the Turkana Basin in Kenya, cause East Africa to dry by channelling water vapour towards Central Africa, a process that simultaneously enhances rainfall in the Congo Basin rainforest. Without the valleys, the uplift of the rift system leads to a wetter climate in East Africa and a drier climate in the Congo Basin. Results from climate model experiments demonstrate that the detailed tectonic development of Africa has shaped the rainfall distribution, with profound implications for the evolution of African plant and animal lineages. Numerical model experiments show that deep valleys cutting across the East African Rift System dry out East Africa by channelling water vapour towards Central Africa, leading to elevated rainfall in the Congo Basin rainforest.

We isolate the contribution of warming, other large-scale changes and soil moisture decline and feedbacks in driving future projected changes in daily precipitation across Europe. Our confidence in each of these mechanisms differs, so this analysis then allows us to determine an overall confidence (or reliability) in the projected changes. In winter, increases in extreme precipitation over Europe as a whole are judged to be reliable, dominated by increased atmospheric moisture with warming. At scales less than about 2,000 km changing circulation patterns could enhance or offset this increase. Additionally, over the Scandinavian mountains warming-induced circulation changes do offset the effect of increased moisture and the overall change is unreliable. In summer, increases in extreme precipitation over northern Scandinavia and decreases over the Mediterranean are reliable in the absence of considerable circulation change. Over central Europe, an increase in the proportion of summer rainfall falling as extreme events is reliable.

IUPAC recommendations on source-based nomenclature for single-strand polymers have so far addressed its application mainly to copolymers, non-linear polymers and polymer assemblies, and within generic source-based nomenclature of polymers. In this document, rules are formulated for devising a satisfactory source-based name for a polymer, whether homopolymer or copolymer, which are as clear and rigorous as possible. Thus, the source-based system for naming polymers is presented in a totality that serves as a user-friendly alternative to the structure-based system of polymer nomenclature. In addition, because of their widespread and established use, recommendations for the use of traditional names of polymers are also elaborated.