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  • ddc:551.6  (4)
  • 2020-2023  (4)
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  • 1
    Publication Date: 2022-10-13
    Description: Observation‐based and modeling studies have identified the Eastern Mediterranean and Middle East (EMME) region as a prominent climate change hotspot. While several initiatives have addressed the impacts of climate change in parts of the EMME, here we present an updated assessment, covering a wide range of timescales, phenomena and future pathways. Our assessment is based on a revised analysis of recent observations and projections and an extensive overview of the recent scientific literature on the causes and effects of regional climate change. Greenhouse gas emissions in the EMME are growing rapidly, surpassing those of the European Union, hence contributing significantly to climate change. Over the past half‐century and especially during recent decades, the EMME has warmed significantly faster than other inhabited regions. At the same time, changes in the hydrological cycle have become evident. The observed recent temperature increase of about 0.45°C per decade is projected to continue, although strong global greenhouse gas emission reductions could moderate this trend. In addition to projected changes in mean climate conditions, we call attention to extreme weather events with potentially disruptive societal impacts. These include the strongly increasing severity and duration of heatwaves, droughts and dust storms, as well as torrential rain events that can trigger flash floods. Our review is complemented by a discussion of atmospheric pollution and land‐use change in the region, including urbanization, desertification and forest fires. Finally, we identify sectors that may be critically affected and formulate adaptation and research recommendations toward greater resilience of the EMME region to climate change.
    Description: Key Points: The Eastern Mediterranean and Middle East is warming almost two times faster than the global average and other inhabited parts of the world. Climate projections indicate a future warming, strongest in summers. Precipitation will likely decrease, particularly in the Mediterranean. Virtually all socio‐economic sectors will be critically affected by the projected changes.
    Description: European Union Horizon 2020
    Description: https://esg-dn1.nsc.liu.se/search/esgf-liu/
    Keywords: ddc:551.6
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2022-10-17
    Description: In this paper we coupled a crop growth model to the Weather Research and Forecasting model with its land surface model Noah‐MP and demonstrated the influence of the weather driven crop growth on land‐atmosphere (L‐A) feedback. An impact study was performed at the convection permitting scale of 3 km over Germany. While the leaf area index (LAI) in the control simulation was the same for all cropland grid cells, the inclusion of the crop growth model resulted in heterogeneous crop development with higher LAI and stronger seasonality. For the analyses of L‐A coupling, a two‐legged metric was applied based on soil moisture, latent heat flux and convective available potential energy. Weak atmospheric coupling is enhanced by the crop model, the terrestrial coupling determines the regions with the L‐A feedback. The inclusion of the crop model turns regions with no L‐A feedback on this path into regions with strong positive coupling. The number of non‐atmospherically controlled days between April and August is increased by 10–15 days in more than 50% of Germany. Our work shows that this impact results in a reduction of both cold bias and warm biases and thus improves the metrics of distributed added value of the monthly mean temperatures. The study confirms that the simulation of the weather driven annual phenological development of croplands for the regional climate simulations in mid‐latitudes is crucial due to the L‐A feedback processes and the currently observed and expected future change in phenological phases.
    Description: Key Points: Coupling a crop growth model with the Weather and Research Forecasting model significantly improves the simulation of the leaf area index. Land‐atmosphere coupling strength is enhanced by weather dependent crop growth simulation. The distributed added value metric shows a reduction in temperature biases of up to 80% in croplands throughout the season in Germany.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: https://opendata.dwd.de/climate_environment/CDC/grids_germany/daily/Project_TRY/air_temperature_mean/
    Description: https://doi.org/10.5281/zenodo.6501984
    Description: http://land.copernicus.eu/pan-european/corine-land-cover/clc-2006/view
    Description: https://doi.org/10.1594/WDCC/WRF_NOAH_HWSD_world_TOP_SOILTYP
    Keywords: ddc:551.6
    Language: English
    Type: doc-type:article
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  • 3
    Publication Date: 2022-03-31
    Description: 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) preconditioned, (b) multivariate, (c) temporally compounding, and (d) spatially compounding 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.
    Description: Plain Language Summary: Many societal and environmental impacts from events such as droughts and storms arise from a combination of weather and climate factors referred to as a compound event. Considering the complex nature of these high‐impact events is crucial for an accurate assessment of climate‐related risk, for example to develop adaptation and emergency preparedness strategies. However, compound event research has emerged only recently, therefore our ability to analyze these events is still limited. In practice, studying compound events is a challenging task, which often requires interaction between experts across multiple disciplines. Recently, compound events were divided into four types to aid the framing of research on this topic, but guidelines on how to study these four types are missing. Here, we take a pragmatic approach and—focusing on case studies of different compound event types—illustrate how to address specific research questions that could be of interest to users. The results allow identifying recommendations for compound event analyses. Furthermore, through the case studies, we highlight the relevance that compounding effects have for the occurrence of landslides, flooding, vegetation impacts, and crop failures. The guidelines emerged from this work will assist the development of compound event analysis across disciplines and sectors.
    Description: Key Points: Using case studies representative of four main compound event types we show how compound event‐related research questions can be tackled. We present user‐friendly guidelines for compound event analysis applicable to different compound event types. We demonstrate that compound events cause vegetation impacts, coastal flooding, landslides, and continental‐scale crop yield failures.
    Description: European COST action DAMOCLES
    Description: NERC
    Description: Swiss National Science Foundation
    Description: Helmholtz Initiative and Networking Fund
    Description: Netherlands Organisation for Scientific Research (NWO)
    Description: Fundação para a Ciência e a Tecnologia
    Description: Scientific Employment Stimulus 2017
    Description: Italian Ministry of University and Research
    Description: European Union's Horizon 2020 research and innovation programme
    Description: AXA Research Fund for support
    Description: Portuguese Foundation for Science and Technology
    Keywords: ddc:551.6
    Language: English
    Type: doc-type:article
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  • 4
    Publication Date: 2022-03-25
    Description: Cold-pool-driven convective initiation is investigated in high-resolution, convection-permitting simulations with a focus on the diurnal cycle and organization of convection and the sensitivity to grid size. Simulations of four different days over Germany were performed using the ICON-LEM model with grid sizes from 156 to 625 m. In these simulations, we identify cold pools, cold-pool boundaries and initiated convection. Convection is triggered much more efficiently in the vicinity of cold pools than in other regions and can provide as much as 50% of total convective initiation, in particular in the late afternoon. By comparing different model resolutions, we find that cold pools are more frequent, smaller and less intense in lower-resolution simulations. Furthermore, their gust fronts are weaker and less likely to trigger new convection. To identify how model resolution affects this triggering probability, we use a linear causal graph analysis. In doing so, we postulate a graph structure with potential causal pathways and then apply multi-linear regression accordingly. We find a dominant, systematic effect: reducing grid sizes directly reduces upward mass flux at the gust front, which causes weaker triggering probabilities. These findings are expected to be even more relevant for km-scale, numerical weather prediction models. We thus expect that a better representation of cold-pool-driven convective initiation will improve forecasts of convective precipitation.
    Keywords: ddc:551.6
    Language: English
    Type: doc-type:article
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