• Energy Research

AbstractGlobal warming is a non‐uniform process across space and time. This opens the door to a heterogeneous relationship between and temperature that needs to be explored going beyond the standard analysis based on mean temperature. We revisit this topic through the lens of a new class of factor models for high‐dimensional panel data, called quantile factor models. This technique extracts quantile‐dependent factors from the distributions of temperature across a wide range of stable weather stations in the northern and southern hemispheres over 1959–2018. In particular, we test whether the (detrended) growth rate of concentrations helps to predict the underlying factors of the different quantiles of the distribution of (detrended) temperatures in the time dimension. We document that predictive association is greater at the lower and medium quantiles than at the upper quantiles of temperature in all stations, and provide some conjectures about what could be behind this non‐uniformity. These findings complement recent results in the literature documenting steeper trends in lower temperature levels than in other parts of the spatial distribution.

Windstorms are one of the most important natural hazards affecting Europe. This article investigates the potential impacts of climate change on windstorm losses in Europe employing the Loss Index (LI) method. A large EURO-CORDEX multi-model ensemble at 12 km resolution with 20 different general circulation model to regional climate model (GCM-RCM) chains following the historical plus RCP8.5 scenario is considered. A comparison between the simulated historical 10 m wind gusts and ERA5 reanalysis reveals substantial model biases. An Empirical Quantile Mapping method is employed to bias-correct the daily wind gust speeds, leading to the effective reduction of these biases. Considering different global warming levels (GWLs), our results show an increase in windstorm intensity for Western, Central and Eastern Europe in a warming world, and a general decrease in windstorm frequency for large parts of Europe. While the ensemble mean changes are mostly moderate for +2°C world, signals are more pronounced for +3°C. The projected changes in windstorm losses are small and mostly non-robust, with negative trends for Central Europe and positive trends for Eastern Europe. For the most extreme loss events, the EURO-CORDEX ensemble projects shorter return periods for Eastern Europe independent of the GWL, while no clear trends for Core Europe emerge. Our results show a large spread between the individual ensemble members, without a clear dominance of a single GCM or RCM. In summary, the projected changes in windstorm losses are subtle, but important particularly for Central and Eastern Europe, which should be considered in the mid- and long-term planning of the insurance industry.

AbstractRainfall is considered the most important physical process for landslide triggering in Portugal. It is expected that changes in the precipitation regimes in the region, as a direct consequence of climate change, will have influence in the occurrence of extreme rainfall events that will be more frequently, throughout the century. The aim of this study relied on the assessment of the projected future changes in the extreme precipitation over Portugal mainland and quantifying the correlation between extreme rainfall events and landslide events through Rainfall Triggering Thresholds (RTTs). This methodology was applied for two specific locations within two Portuguese areas of great geomorphological interest. To analyze the past frequency of landslide events, we resorted to the DISASTER database. To evaluate the possible projected changes in the extreme precipitation, we used the Iberia02 dataset and the EURO-CORDEX models’ runs at a 0.11° spatial resolution. It was analyzed the models’ performance to simulate extreme values in the precipitation series. The simulated precipitation relied on RCM-GCM models’ runs, from EURO-CORDEX, and a multimodel ensemble mean. The extreme precipitation assessment relied on the values associated to the highest percentiles, and to the values associated to the RTTs’ percentiles. To evaluate the possible future changes of the precipitation series, both at the most representative percentiles and RTTs’ percentiles, a comparison was made between the simulated values from EURO-CORDEX historical runs (1971–2000) and the simulated values from EURO-CORDEX future runs (2071–2100), considering two concentration scenarios: RCP 4.5 and RCP 8.5. In the models’ performance, the multimodel ensemble mean appeared to be within the best representing models. As for the projected changes in the extreme precipitation for the end of the century, when following the RCP 4.5 scenario, most models projected an increase in the extreme values, whereas, when following the RCP 8.5 scenario, most models projected a decrease in the extreme values.

<p>Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (a) <em>preconditioned</em>, (b) <em>multivariate</em>, (c) t<em>emporally compounding</em>, and (d) <em>spatially compounding</em> events. However, guidelines on how to study these types of events are still lacking. Here, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (a) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (b) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non-stationary multivariate process. For instance, future mean sea-level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (c) In Portugal, deep-landslides are often caused by temporal clusters of moderate precipitation events. Finally, (d) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors.</p>

This study deals with the question of how winegrowing in Spain may be altered by anthropogenic climate change. The present state and expected future development of three bioclimatic indices relevant for winegrowing were assessed by observation, and four regional climate models from the EU-ENSEMBLES project were investigated. When comparing the 2061–2090 scenario period to the 1961–1990 reference period, the models unanimously indicate a significant increase in the mean of the two considered thermal indices over the entire study region. However, for the index based on temperature and precipitation, the models are heavily biased when verified against observations and generally disagree on the size of the projected future change. For this index, unanimous model agreement was only found for northwestern Spain where all models indicated a significant decrease in the mean. From these results, regional climate change is expected to negatively affect the quality of wine in the growing regions of central and southern Spain, and the Ebro valley, whereas positive effects should be expected in the northwest. No significant changes in the risk of mildew infestation are to be expected except for the northwest, where this risk is projected to decrease. This work was supported by the Xunta de Galicia under Research Grant No. 10PXIB383169PR and co-financing by European Regional Development Fund (FEDER). Alexandre M. Ramos was supported by the Portuguese Foundation for Science and Technology (FCT) through Grant FCT/DFRH/SFRH/BPD/84328/2012. S. Brands would like to thank the Consejo Superior de Investigaciones Científicas JAE-PREDOC programme for financial support. Peer Reviewed

AbstractThe Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is a community effort to systematically assess how the uncertainties from AR detectors (ARDTs) impact our scientific understanding of ARs. This study describes the ARTMIP Tier 2 experimental design and initial results using the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 multi‐model ensembles. We show that AR statistics from a given ARDT in CMIP5/6 historical simulations compare remarkably well with the MERRA‐2 reanalysis. In CMIP5/6 future simulations, most ARDTs project a global increase in AR frequency, counts, and sizes, especially along the western coastlines of the Pacific and Atlantic oceans. We find that the choice of ARDT is the dominant contributor to the uncertainty in projected AR frequency when compared with model choice. These results imply that new projects investigating future changes in ARs should explicitly consider ARDT uncertainty as a core part of the experimental design.