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In this paper we present and evaluate the skill of the EC-Earth3.3 decadal prediction system contributing to the Decadal Climate Prediction Project - Component A (DCPP-A). This prediction system is capable of skilfully simulating past global mean surface temperature variations at interannual and decadal forecast times as well as the local surface temperature in regions such as the Tropical Atlantic, the Indian Ocean and most of the continental areas, although most of the skill comes from the representation of the externally forced trends. A benefit of initialisation in the predictive skill is evident in some areas of the Tropical Pacific and North Atlantic Oceans in the first forecast years, an added value that gets mostly confined to the south-east Tropical Pacific and the eastern Subpolar North Atlantic at the longest forecast times (6–10 years). The central Subpolar North Atlantic shows poor predictive skill and a detrimental effect of the initialisation due to the occurrence of an initialisation shock, itself related to a collapse in Labrador Sea convection by the third forecast year that leads to a rapid weakening of the Atlantic Meridional Overturning Circulation (AMOC) and excessive local sea ice growth. The shutdown in Labrador Sea convection responds to a gradual increase in the local density stratification in the first years of the forecast, ultimately related to the different paces at which surface and subsurface temperature and salinity drift towards their preferred mean state. This transition happens rapidly in the surface and more slowly in the subsurface, where, by the tenth forecast year, the model is still far from the typical mean states in the corresponding ensemble of historical simulations with EC-Earth3. Our study thus highlights the importance of the Labrador Sea for initialisation, the relevance of reducing model bias by model tuning or, preferably, model improvement when using full-field initialisation, and the need to identify optimal initialisation strategies. [ABSTRACT FROM AUTHOR]

This paper presents a novel data set of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. The data set provides a unique and physically consistent data set, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100 × 10-year simulations and 25 × 10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. The changes in four climate indices for temperature targets of 1.5 °C and 2.0 °C global warming are quantified: number of days per year with daily mean near-surface apparent temperature of > 28 °C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-yr return period (RI50yr); and the annual Consecutive Dry Days (CDD). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at a policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 °C. [ABSTRACT FROM AUTHOR]

The concept of independence has been frequently raised in climate science, but has rarely been defined and discussed in a theoretically robust and quantifiable manner. Improved understanding of this topic is critical to better understanding of climate change. In this paper, we introduce a unifying approach based on the statistical definition of independence, and illustrate with simple examples how it can be applied to practical questions. [ABSTRACT FROM AUTHOR]

The science of climate security and conflict is replete with controversies. Yet the increasing vulnerability of politically fragile countries to the security consequences of climate change is widely acknowledged. Although climate conflict reflects a continuum of conditional forces that coalesce around the notion of vulnerability, how different portrayals of vulnerability influence the discursive formation of climate conflict relations remains an exceptional but under-researched issue. This paper combines a systematic discourse analysis with a vulnerability interpretation diagnostic tool to explore (i) how discourses of climate conflict are constructed and represented, (ii) how vulnerability is communicated across discourse lines, and (iii) the strength of contextual vulnerability against a deterministic narrative of scarcity-induced conflict, such as that pertaining to land. Systematically characterising climate conflict discourses based on the central issues constructed, assumptions about mechanistic relationships, implicit normative judgements and vulnerability portrayals, provides a useful way of understanding where discourses differ. While discourses show a wide range of opinions “for” and “against” climate conflict relations, engagement with vulnerability has been less pronounced – except for the dominant context centrism discourse concerned about human security (particularly in Africa). In exploring this discourse, we observe an increasing sense of contextual vulnerability that is oriented towards a concern for complexity rather than predictability. The article concludes by illustrating that a turn towards contextual vulnerability thinking will help advance a constructivist theory-informed climate conflict scholarship that recognises historicity, specificity, and variability as crucial elements of contextual totalities of any area affected by climate conflict. [ABSTRACT FROM AUTHOR]