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Climate change impacts on water availability and hydrological extremes are major concerns as regards the Sustainable Development Goals. Impacts on hydrology are normally investigated as part of a modelling chain, in which climate projections from multiple climate models are used as inputs to multiple impact models, under different greenhouse gas emissions scenarios, which result in different amounts of global temperature rise. While the goal is generally to investigate the relevance of changes in climate for the water cycle, water resources or hydrological extremes, it is often the case that variations in other components of the model chain obscure the effect of climate scenario variation. This is particularly important when assessing the impacts of relatively lower magnitudes of global warming, such as those associated with the aspirational goals of the Paris Agreement. In our study, we use ANOVA (analyses of variance) to allocate and quantify the main sources of uncertainty in the hydrological impact modelling chain. In turn we determine the statistical significance of different sources of uncertainty. We achieve this by using a set of five climate models and up to 13 hydrological models, for nine large scale river basins across the globe, under four emissions scenarios. The impact variable we consider in our analysis is daily river discharge. We analyze overall water availability and flow regime, including seasonality, high flows and low flows. Scaling effects are investigated by separately looking at discharge generated by global and regional hydrological models respectively. Finally, we compare our results with other recently published studies. We find that small differences in global temperature rise associated with some emissions scenarios have mostly significant impacts on river discharge-however, climate model related uncertainty is so large that it obscures the sensitivity of the hydrological system.

Climate projection uncertainty can be partitioned into model uncertainty, scenario uncertainty and internal variability. Here, we investigate the different sources of uncertainty in the projected frequencies of daily maximum temperature and precipitation extremes, which are defined as events that exceed the 99.97th percentile. This is done globally using large initial-condition ensembles. For maximum temperature extremes, internal variability that generates deviations about the ensemble average, dominates in the next 2 decades. Around the middle of the twenty-first century model and scenario uncertainty become the dominant contribution in the tropics but internal variability remains dominant in the extra-tropics. Towards the end of the century, model and scenario uncertainty increase to near equal contributions of ∼ 40% each globally with large regional fluctuations. For precipitation extremes, internal variability dominates throughout the twenty-first century, except for some tropical regions, for example, West Africa. In regions where internal variability constitutes the major source of uncertainty, the potential impact of reducing model uncertainty on the signal-to-noise ratio of the climate projection is estimated to be small. We discuss the caveats of the methodology used and impact of our findings for the design of future climate models. The importance of internal variability found here emphasizes that large ensembles are a vital tool for understanding climate projections.

Multi-model ensembles are commonly used in climate prediction to create a set of independent estimates, and so better gauge the likelihood of particular outcomes and better quantify prediction uncertainty. Yet researchers share literature, datasets and model code—to what extent do different simulations constitute independent estimates? What is the relationship between model performance and independence? We show that error correlation provides a natural empirical basis for defining model dependence and derive a weighting strategy that accounts for dependence in experiments where the multi-model mean would otherwise be used. We introduce the “replicate Earth” ensemble interpretation framework, based on theoretically derived statistical relationships between ensembles of perfect models (replicate Earths) and observations. We transform an ensemble of (imperfect) climate projections into an ensemble whose mean and variance have the same statistical relationship to observations as an ensemble of replicate Earths. The approach can be used with multi-model ensembles that have varying numbers of simulations from different models, accounting for model dependence. We use HadCRUT3 data and the CMIP3 models to show that in out of sample tests, the transformed ensemble has an ensemble mean with significantly lower error and much flatter rank frequency histograms than the original ensemble.

Today, a major challenge for climate science is to overcome what is called the “usability gap” between the projections derived fromclimate models and the needs of the end-users. Regional Climate Models (RCMs) are expected to provide usable information concerning a variety of impacts and for a wide range of end-users. It is often assumed that the development of more accurate, more complex RCMs with higher spatial resolution should bring process understanding and better local projections, thus overcoming the usability gap. In this paper, I rather assume that the credibility of climate information should be pursued together with two other criteria of usability, which are salience and legitimacy. Based on the Swiss climate change scenarios, I study the attempts at meeting the needs of end-users and outline the trade-off modellers and users have to face with respect to the cascade of uncertainty. A conclusion of this paper is that the trade-off between salience and credibility sets the conditions under which RCMs can be deemed adequate for the purposes of addressing the needs of end-users and gearing the communication of the projections toward direct use and action.

We assess future flood hazard in view of climate change at pan‐European scale using a large ensemble of climate projections. The ensemble consists of simulations from 12 climate experiments conducted within the ENSEMBLES project, forced by the SRES A1B emission scenario for the period 1961–2100. Prior to driving the hydrological model LISFLOOD, climate simulations are corrected for bias in precipitation and temperature using a Quantile Mapping (QM) method. For time slices of 30 years, a Gumbel distribution is fitted by the maximum likelihood method through the simulated annual maximum discharges. Changes in extreme river flows, here exemplified by the 100‐year discharge (Q100), are then analyzed with respect to a control period (1961–1990). We assess the uncertainty arising from using alternative climate experiments to force LISFLOOD and from the fitting of extreme value distributions. Results show large discrepancies in the magnitude of change in Q100among the hydrological simulations for different climate experiments, with some regions even showing an opposite signal of change. Due to the low signal‐to‐noise ratio in some areas the projected changes showed not all to be statistically significant. Despite this, western Europe, the British Isles and northern Italy show a robust increase in future flood hazard, mainly due to a pronounced increase in extreme rainfall. A decrease inQ100, on the other hand, is projected in eastern Germany, Poland, southern Sweden and, to a lesser extent, the Baltic countries. In these areas, the signal is dominated by the strong reduction in snowmelt induced floods, which offsets the increase in average and extreme precipitation. Key Points Future flood hazard using high‐resolution bias‐corrected climate simulations Large uncertainties bound to projected changes of future flood hazard Increasing trend in future flood hazard, shifting to rainfall‐dominated floods

The assessment of climate change and its impact relies on the ensemble of models available and/or sub-selected. However, an assessment of the validity of simulated climate change impacts is not straightforward because historical data is commonly used for bias-adjustment, to select ensemble members or to define a baseline against which impacts are compared—and, naturally, there are no observations to evaluate future projections. We hypothesize that historical streamflow observations contain valuable information to investigate practices for the selection of model ensembles. The Danube River at Vienna is used as a case study, with EURO-CORDEX climate simulations driving the COSERO hydrological model. For each selection method, we compare observed to simulated streamflow shift from the reference period (1960–1989) to the evaluation period (1990–2014). Comparison against no selection shows that an informed selection of ensemble members improves the quantification of climate change impacts. However, the selection method matters, with model selection based on hindcasted climate or streamflow alone is misleading, while methods that maintain the diversity and information content of the full ensemble are favorable. Prior to carrying out climate impact assessments, we propose splitting the long-term historical data and using it to test climate model performance, sub-selection methods, and their agreement in reproducing the indicator of interest, which further provide the expectable benchmark of near- and far-future impact assessments. This test is well-suited to be applied in multi-basin experiments to obtain better understanding of uncertainty propagation and more universal recommendations regarding uncertainty reduction in hydrological impact studies.

In parts of the global ocean, large internal variability continues to mask the detection of externally forced sea surface temperature (SST) trends in observations and climate models. Such regions of large internal variability are typically where wind driven ocean dynamical processes contribute heavily to SST variability. Through analysis of two climate model ensembles, we find that internal wind driven ocean circulation variability delays the time of emergence of SST signals nearly everywhere, but the delay is longest (>10 years) in dynamically active regions like the tropical oceans. We also find that internal wind driven ocean circulation variability is the dominant contributor to changes in the amplitude of internal SST variability over the historical period. Results suggest that inter‐model differences in wind driven SST variability may be a key contributor to inter‐model differences in the time of emergence of externally forced SST signals in climate change scenarios.

This paper analyzes the effectiveness of agriculture-led versus non-agriculture-led development strategies under climate-induced economic uncertainty. Utilizing Malawi as a case study, we introduce the application of Stochastic Dominance (SD) analysis, a tool from decision analysis theory, and compare the two strategies in the context of weather/climate-associated economic uncertainty. Our findings suggest that an agriculture-led development strategy consistently surpasses its non-agriculture-led antagonist in poverty and undernourishment outcomes across almost all possible weather/climate scenarios. This underscores that, despite increasing exposure of the entire economy to weather/climate uncertainty, agriculture-led development remains the optimal strategy for Malawi to reduce poverty and undernourishment. The study also endorses the broader use of SD analysis in policy planning studies, promoting its potential to integrate risk and uncertainty into policymaking.

Climate adaptation decisions are difficult because the future climate is deeply uncertain. Combined with uncertainties concerning the cost, lifetime, and effectiveness of adaptation measures, this implies that the net benefits of alternative adaptation strategies are ambiguous. On one hand, a simple analysis that disregards uncertainty might lead to near-term choices that are later regretted if future circumstances differ from those assumed. On the other hand, careful uncertainty-based decision analyses can be costly in personnel and time and might not make a difference. This paper considers two questions adaptation managers might ask. First, what type of analysis is most appropriate for a particular adaptation decision? We answer this question by proposing a six-step screening procedure to compare the usefulness of predict-then-act analysis, multi-scenario analysis without adaptive options, and multi-scenario analysis incorporating adaptive options. A tutorial application is presented using decision trees. However, this procedure may be cumbersome if managers face several adaptation problems simultaneously. Hence, a second question is how can managers quickly identify problems that would benefit most from thorough decision analysis? To address this question, we propose a procedure that ranks multiple adaptation problems in terms of the necessity and value of comprehensive analysis. Analysis can then emphasize the highest-ranking problems. This procedure is illustrated by a ranking of adaptation problems in the Chesapeake Bay region. The two complementary procedures proposed here can help managers focus analytical efforts where they will be most useful.

Aim: Pyrogeographical theory suggests that fire is controlled by spatial gradients in resources to burn (fuel amount) and climatic conditions promoting combustion (fuel moisture). Examining trade-offs among these environmental constraints is critical to understanding future fire activity. We evaluate constraints on fire frequency in modern fire records over the entire Mediterranean biome and identify potential shifts in fire activity under an ensemble of global climate projections. Location: The biome encompassing the Mediterranean-type ecosystems (MTEs). Methods: We evaluate potential changes in fire over the 21st century in MTEs based on a standardized global framework. Future fire predictions are generated from statistical fire–climate models driven by ensembles of climate projections under the IPCC A2 emissions scenario depicting warmer–drier and warmer–wetter syndromes. We test the hypothesis that MTEs lie in the transition zone discriminating fuel moisture versus fuel amount as the dominant constraint on fire activity. Results: Fire increases reported in MTEs in recent decades may not continue throughout the century. MTEs occupy a sensitive portion of global fire–climate relationships, especially for precipitation-related variables, leading to highly divergent fire predictions under drier versus wetter syndromes. Warmer–drier conditions could result in decreased fire activity over more than half the Mediterranean biome by 2070-2099, and the opposite is predicted under a warmer–wetter future.MTEs encompass, however, a climate space broad and complex enough to include spatially varied fire responses and potential conversions to non-MTE biomes. Main conclusions: Our results strongly support the existence of both fuel amount and fuel moisture constraints on fire activity and show their geographically variable influence throughout MTEs. Climatic controls on fire occurrence in MTEs lie close to 'tipping points', where relatively small changes in future climates could translate into drastic and divergent shifts in fire activity over the Mediterranean biome, mediated by productivity alterations.