ALBERT

Description: Worldwide bees provide an important ecosystem service of plant pollination. Climate change and land-use changes are among drivers threatening bee survival with mounting evidence of species decline and extinction. In developing countries, rural areas constitute a significant proportion of the country's land, but information is lacking on how different habitat types and weather patterns in these areas influence bee populations. This study investigated how weather variables and habitat-related factors influence the abundance, diversity, and distribution of bees across seasons in a farming rural area of Zimbabwe. Bees were systematically sampled in five habitat types (natural woodlots, pastures, homesteads, fields, and gardens) recording ground cover, grass height, flower abundance and types, tree abundance and recorded elevation, temperature, light intensity, wind speed, wind direction, and humidity. Zero-inflated models, censored regression models, and PCAs were used to understand the influence of explanatory variables on bee community composition, abundance, and diversity. Bee abundance was positively influenced by the number of plant species in flower (p 〈 .0001). Bee abundance increased with increasing temperatures up to 28.5°C, but beyond this, temperature was negatively associated with bee abundance. Increasing wind speeds marginally decreased probability of finding bees. Bee diversity was highest in fields, homesteads, and natural woodlots compared with other habitats, and the contributions of the genus Apis were disproportionately high across all habitats. The genus Megachile was mostly associated with homesteads, while Nomia was associated with grasslands. Synthesis and applications. Our study suggests that some bee species could become more proliferous in certain habitats, thus compromising diversity and consequently ecosystem services. These results highlight the importance of setting aside bee-friendly habitats that can be refuge sites for species susceptible to land-use changes.

Description: Background Although effects on labour is one of the most tangible and attributable climate impact, our quantification of these effects is insufficient and based on weak methodologies. Partly, this gap is due to the inability to resolve different impact channels, such as changes in time allocation (labour supply) and slowdown of work (labour productivity). Explicitly resolving those in a multi-model inter-comparison framework can help to improve estimates of the effects of climate change on labour effectiveness. Methods In this empirical, multi-model study, we used a large collection of micro-survey data aggregated to subnational regions across the world to estimate new, robust global and regional temperature and wet-bulb globe temperature exposure-response functions (ERFs) for labour supply. We then assessed the uncertainty in existing labour productivity response functions and derived an augmented mean function. Finally, we combined these two dimensions of labour into a single compound metric (effective labour effects). This combined measure allowed us to estimate the effect of future climate change on both the number of hours worked and on the productivity of workers during their working hours under 1·5°C, 2·0°C, and 3·0°C of global warming. We separately analysed low-exposure (indoors or outdoors in the shade) and high-exposure (outdoor in the sun) sectors. Findings We found differentiated empirical regional and sectoral ERF's for labour supply. Current climate conditions already negatively affect labour effectiveness, particularly in tropical countries. Future climate change will reduce global total labour in the low-exposure sectors by 18 percentage points (range −48·8 to 5·3) under a scenario of 3·0°C warming (24·8 percentage points in the high-exposure sectors). The reductions will be 25·9 percentage points (–48·8 to 2·7) in Africa, 18·6 percentage points (–33·6 to 5·3) in Asia, and 10·4 percentage points (–35·0 to 2·6) in the Americas in the low-exposure sectors. These regional effects are projected to be substantially higher for labour outdoors in full sunlight compared with indoors (or outdoors in the shade) with the average reductions in total labour projected to be 32·8 percentage points (–66·3 to 1·6) in Africa, 25·0 percentage points (–66·3 to 7·0) in Asia, and 16·7 percentage points (–45·5 to 4·4) in the Americas. Interpretation Both labour supply and productivity are projected to decrease under future climate change in most parts of the world, and particularly in tropical regions. Parts of sub-Saharan Africa, south Asia, and southeast Asia are at highest risk under future warming scenarios. The heterogeneous regional response functions suggest that it is necessary to move away from one-size-fits-all response functions to investigate the climate effect on labour. Our findings imply income and distributional consequences in terms of increased inequality and poverty, especially in low-income countries, where the labour effects are projected to be high.

Description: Code for coupling the Parallel Ice Sheet Model PISM with the Modular Ocean Model MOM

Description: In the Paris Agreement, just about all of the world’s nations pledged to “pursue efforts to limit the temperature increase to 1.5 degrees Celsius above pre-industrial levels”. On Saturday, the top climate diplomats from the U.S. and China, John Kerry and Xie Zhenhua, reiterated in a joint statement that they want to step up their climate mitigation efforts to keep that goal “within reach”.

Description: The W5E5 dataset was compiled to support the bias adjustment of climate input data for the impact assessments carried out in phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). Version 2.0 of the W5E5 dataset covers the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2019. Data sources of W5E5 are version 2.0 of WATCH Forcing Data methodology applied to ERA5 data (WFDE5; Weedon et al., 2014; Cucchi et al., 2020), ERA5 reanalysis data (Hersbach et al., 2020), and precipitation data from version 2.3 of the Global Precipitation Climatology Project (GPCP; Adler et al., 2003). Variables (with short names and units in brackets) included in the W5E5 dataset are Near Surface Relative Humidity (hurs, %), Near Surface Specific Humidity (huss, kg kg-1), Precipitation (pr, kg m-2 s-1), Snowfall Flux (prsn, kg m-2 s-1), Surface Air Pressure (ps, Pa), Sea Level Pressure (psl, Pa), Surface Downwelling Longwave Radiation (rlds, W m-2), Surface Downwelling Shortwave Radiation (rsds, W m-2), Near Surface Wind Speed (sfcWind, m s-1), Near-Surface Air Temperature (tas, K), Daily Maximum Near Surface Air Temperature (tasmax, K), Daily Minimum Near Surface Air Temperature (tasmin, K), Surface Altitude (orog, m), and WFDE5-ERA5 Mask (mask, 1).

Description: Die USA ächzen unter eisiger Kälte bis hinunter in den Süden von Texas. Mindestens dreißig Menschen kamen ums Leben, Millionen sind ohne Strom. Auch bei uns in Deutschland ist gerade eine Kältewelle vorübergegangen, und im Januar gab es einen historischen Schneesturm in Madrid. Was ist da los?

Description: In seinem Umweltgutachten 2020 greift der SRU umweltpolitische Themenfelder auf, in denen großer Handlungsbedarf besteht: Klimapolitik, Kreislaufwirtschaft, Gewässerschutz, Lärmschutz, städtische Mobilität und nachhaltige Quartiersentwicklung. Gleichzeitig zeigt der SRU auf, wie ein Umsteuern mit zielgerichteten Maßnahmen möglich ist. Vor dem Hintergrund der deutschen EU-Ratspräsidentschaft analysiert das Gutachten zudem anstehende Weichenstellungen in Europa. In Deutschland wie in der EU muss die Politik unter Beweis stellen, dass sie angesichts der enormen ökologischen und wirtschaftlichen Herausforderungen entschlossen handeln kann. Auf der europäischen Ebene werden ambitionierte Umweltziele, eine fokussierte und zugleich flexible Arbeitsmethodik und klare Vorgaben für Umsetzung und Monitoring benötigt. Auch bislang nicht ausreichend ökologisch ausgerichtete Wirtschaftsbereiche müssen jetzt den Umwelt- und Klimaschutz stärker in den Vordergrund stellen.

Description: Sauerstoff ist lebenswichtig. Wälder produzieren ihn, sind aber stark bedroht. Die Auswirkungen auf das Lebenselixier von Mensch und Tier ist erstaunlich gering – ein anderes Problem umso gravierender.

Description: Atlantic hurricane activity varies substantially from year to year and so do the associated damages. Longer-term forecasting of hurricane risks is a key element to reduce damages and societal vulnerabilities by enabling targeted disaster preparedness and risk reduction measures. While the immediate synoptic drivers of tropical cyclone formation and intensification are increasingly well understood, precursors of hurricane activity on longer time-horizons are still not well established. Here we use a causal network-based algorithm to identify physically motivated late-spring precursors of seasonal 15Atlantic hurricane activity. Based on these precursors we construct seasonal forecast models with competitive skill compared to operational forecasts. We present a skillful model to forecast July to October cyclone activity at the beginning of April.Earlier seasonal hurricane forecasting provides a multi-month lead time to implement more effective disaster risk reduction measures. Our approach also highlights the potential of applying causal effects network analysis in seasonal forecasting