• Energy Research
• 2016-2025close
• CN close
• US close
• DE close
• AU close
• FR close

The data comprise Climber3alpha+C simulations created by Matthias Hofmann (PIK) as part of the Work Package 2.1 of the COMFORT project as well as the PyFerret scripts (written by Ralf Liebermann and Matthias Hofmann) used for their evaluation. The simulation data consist of snap_*.nc files and history.nc files for ocean, atmosphere and mixed layer depth (hmxl) performed for different idealized scenarios: CONTROL, double and fourfold atmospheric CO2 (CO2X2 and CO2X4), also with additional Greenland freshwater influx (CO2X2_HOSING and CO2X4_HOSING). Furthermore, tracer simulations (CONTROL, CO2X4, CO2X4_HOSING) and simulations with constant scavenging (CO2X4) are also included. The aim was to analyse the simulations regarding climate change-induced changes in marine biogeochemistry and primary production, which will be published under the title "Shutdown of Atlantic overturning circulation could cause persistent increase of primary production in the Pacific" (see Related Work). Simulation data were generated with Climber3alpha+C (Earth system model of intermediate complexity) and evaluated with PyFerret v7.41. CDO was used to aggregate monthly simulation data into annual means.

This dataset and codebook correspond to the second round of survey data gathered in Denmark in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained. This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort. New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.

Survey data used in a perception study of stalked barnacle harvesters on the effectiveness of fisheries management practices in Spain, Portugal and France. Harvesters from the following six regions along the Atlantic Arc participated: Morbihan in Brittany (France), Asturias-East, Asturias-West and Galicia (Spain), the Reserva Natural das Berlengas (RNB; Portugal) and the Parque Natural do Sudoeste Alentejano e Costa Vicentina (PNSACV; Portugal). We administered 184 surveys from October 2019 to September 2020 and each region was treated as an independent population. The data includes: general demographic data (Region, Age, Gender, Level of Education, Main income source, Years of Experience); perception data of the effectiveness of the currently implemented management strategies in each region (coded: e_name_of_strategy – using Likert Scale with scores ranging from 1 = completely ineffective to 5 = very effective); data of the willingness for change of the currently implemented management (Yes, No, NA); and data of harvesters’ perceptions regarding the most important strategy to achieve sustainability in the fishery. Because the surveys were conducted both before and during the Covid-19 pandemic (the column Covid indicates whether the data was collected before or during the pandemic), we had to make adjustments in our data collection methods. We provided the following options for survey completion (see the Recollection_of_data column): by hand in a written format, online, or via an oral interview conducted with the assistance of a scientist per telephone. Our results indicate that the majority of harvesters in the regions in Portugal and France were willing to make changes to current management strategies, reflecting their awareness of the need for improvement. Based on the AIC model selection analysis results, the model with the single variable region explained 83% of the cumulative model weight. The variable region was the best predictor of the trends in management strategy preferences, and presented a highly significant goodness-of-fit result (p<0.001), suggesting that regional differences play a significant role in shaping these preferences. No clear trend emerged regarding a single "optimal" management strategy preferred by harvesters across regions. Harvesters in less developed co-management systems favored general input and output restrictions and expressed a desire for greater involvement in co-management processes. Conversely, harvesters in highly developed co-management systems with Territorial User Rights for Fishers (TURFs) preferred the most restrictive and spatially explicit management strategies, such as implementing harvest bans and establishing marine reserves. Our findings emphasise that management strategies do not only need to be tailored to each region's particular practices, needs, and characteristics, but that resource users’ readiness for specific strategies also needs to be considered.

These data include population fitness measurements collected for Acartia hudsonica during multigenerational exposure to ocean warming (OW), ocean acidification (OA), and combined ocean warming and acidification (OWA) including a benign ambient condition temperature and CO2 control (AM).

From September of 2017, till August of 2019, water temperatures and A. aegypti larval presence was recorded in nine 19 liter buckets placed in the backyard of Jon Harrison’s home in Tempe, Arizona (33.339, -111.924), as it was known to experience high abundances of A. aegypti. Buckets were 5 – 10 m apart, and so should not be considered ecologically independent. Onset HOBO Pendant® UA-002-08 data loggers (Bourne, Massachusetts) were used to record temperature levels, and larval presence was observed every 1-10 days depending on season (frequently in the summer, less so in winter). If mosquito larvae were observed, they were collected from the bucket with a net and their species identity confirmed with a dissection scope. The data set labeled Figure 2 data provides the water temperatures in one representative bucket from 2017-2019 as shown in Fig. 2 of the manuscript. Larval rearing for mesocosm experiments The parents of larvae used in the mesocosm overwintering experiments were reared from Maricopa County, AZ, origin eggs collected by Maricopa County Vector Control from September to November of 2019. These eggs were placed in a 500 ml beaker, submerged, and hatched in a solution of 0.25 g/L baker’s yeast (Byttebier et al. 2014). As the 1st instar larvae emerged, they were fed TetraMin fish flakes every 1-2 days, making sure that an excess amount of food was visible in the container. The rearing density for the larvae was maintained at fewer than 500 animals per liter of water. As pupae began to appear, the beaker of larvae was placed in a 95-liter polymer-screened cage to contain the expected adults. Cotton balls saturated with 10% sucrose solution were made available for the adults as they began to emerge; these were taken away two days prior to blood feeding. One week after emerging, the adults were blood-fed using mice (IACUC protocol: 18-1662R). After a three-day gestation period, the females were supplied with moist seed-germinating paper to encourage oviposition. Once the females had finished ovipositing, the eggs were kept moist for an additional 48 hours before being dried, and placed in open zip lock sandwich bags which were stored at 100% humidity and 24°C. High humidity in the egg storage containers was achieved by storing damp paper towels along with the opened egg bags within a larger 3.8L bag. These eggs were kept for less than one month before the hatching procedure was repeated to produce the larvae for the experiment. In the lab, across all life stages, the mosquitoes were exposed to a 12:12 L/D photoperiod at 24°C. After hatching, the 2nd instar larvae were moved to their outdoor experimental mesocosms. The larvae were randomly distributed with 20 larvae supplied per each of three ambient mesocosms (Amb1, Amb2, Amb3) and six to warmed mesocosms (W1 – W6), which were warmed by varying amounts (W1 = least warmed, W6 = most warmed). The goal was to achieve a range of warming from very small warming (1-2°C in the least-warmed mesocosm (W1), to near-summer conditions in the most-warmed mesocosm (W6). Each mesocosm was a 150 ml clear plastic container, filled with 125 mL dechlorinated tap water. TetraMin fish flakes were supplied to each mesocosm, with more added every three days or when food was completely consumed. Although the mesocosms were open, we observed no mosquitoes flying in the field, and none were captured in local water buckets, and all A.a. in the mesocosms were of uniform stage, so we believe that this experiment was not affected by oviposition from wild mosquitoes. Manipulation of thermal conditions for larval outdoor rearing All mesocosms were placed on a table one meter above the ground and protected from rain, wind, and sunlight by a roof. The mesocosms were placed within individual lidless pine boxes (10x10x14 cm, 0.95 cm thick walls), and so were exposed to normal fluctuations in air temperature. Each warmed mesocosm was placed on 40mm2 thermoelectric plates with 40mm2 aluminum heatsinks attached using thermally conductive adhesive on each side. The warming orientation of the thermoelectric plate was positioned upwards, towards the mesocosms, to ensure adequate energy transfer from the heating units to the water. Each thermoelectric device was powered by two KORAD KD3005D 30V, 5A power supplies (Shenzhen, China). The thermoelectric plates were wired in parallel. Variable warming was produced by changing the supplied voltage. Temperatures were measured in the cups using HOBO Pendant® UA-002-08 data loggers submerged in the center of each cup. We did not measure temperature gradients within the mesocosms, but believe that they are likely to be small except possibly in the mesocosms that were maximally-warmed, as the mesocosms were small and mostly not strongly warmed above air temperature. Temperatures were logged each hour in each warmed mesocosm, and in one ambient treatment mesocosm. The data file labeled Figure 3 data provides the wate temperatures at hourly intervals during the experiment for one mesocosm at ambient temperature, mesocosm W1 (the least warmed mesocosm) and mesocosm W6 (the most warmed mesocosm) as shown in Fig. 3 of the manuscript. Global warming trends, human-assisted transport, and urbanization have allowed poleward expansion of many tropical vector species, but the specific mechanisms responsible for thermal mediation of range changes and ecological success of invaders remain poorly understood. Aedes aegypti (Diptera: Culicidae) is a tropical mosquito currently expanding into many higher-latitude regions including the urban desert region of Maricopa County, Arizona. Here, adult populations virtually disappear in winter and spring, and then increase exponentially through summer and fall, indicating that winter conditions remain a barrier to development of A. aegypti. To determine whether cold limits the winter development of A. aegypti larvae in Maricopa County, we surveyed for larval abundance, and tested their capacity to develop in ambient and warmed conditions. Aedes aegypti larvae were not observed in artificial aquatic habitats in winter and spring but were abundant in summer and fall, suggesting winter suppression of adults, larvae or both. Water temperatures in winter months fluctuated strongly; larvae were usually cold-paralyzed at night but active during the day. Despite daytime temperatures that allowed activity, larvae reared under ambient winter conditions were unable to develop to adulthood, perhaps due to repetitive cold damage. However, warming average temperature by 1.7°C allowed many larvae to successfully develop to adults. Because daytime highs in winter will often allow adult flight, it is possible that relatively minor additional winter warming may allow A. aegypti populations to develop and reproduce year-round in Maricopa County. # Data for Mesocosm studies suggest climate change may release Aedes aegypti (Diptera:Culicidae) larvae from cold-inhibition and enable year-round development in a desert city [https://doi.org/10.5061/dryad.nzs7h44z7](https://doi.org/10.5061/dryad.nzs7h44z7) Most of the data for this study are provided as supplementary files in the submitted manuscript. Here we provide representative thermal data. One file (Figure 2 data) contains the temperature data for the bucket kept under ambient conditions as shown in Figure 2, which also shows when Aedes aegypti larvae were found in the bucket. From to October 18 -November 29 2017, water temperatures were recorded every 6 minutes. Thereafter, water temperatures were recorded hourly until August 2, 2019. Another file (Figure 3 data) contains water temperatures for three of the mesocosms used in this study, as shown in the manuscript figure 3. This experiment ran from Jan 31, 2020 - March 1, 2020. One column sW1 was and ## Description of the data and file structure Figure 2 data has two columns, column A gives the date and column B the temperature of the ambient bucket in degrees Centigrade. Figure 3 data has four columns; column A gives the hours since the start of the experiment. Column B shows temperatures for an unheated mesocosm kept at ambient conditions. Column C shows temperatures for W6, the most warmed mesocosm (mean temperature 12C higher than the ambient mesocosm, to represent near-summer conditions). Column D shows temperatures for the least-warmed mesocosm (W1, mean temperature 1.8C higher than the ambient mesocosm). All temperatures are in degrees Centigrade.

This database includes more than 100 decarbonisation innovations in Paper, Plastic, Steel and Meat & Dairy sectors, across their value chains, as well as in Finance. For each innovation there is a description, information about its contribution to decarbonisation, actors and collaborators involved, sources of funding, drivers, (co)benefits and disadvantages. More information on the method for selecting innovations for the database is available here. The database was created as part of REINVENT – a Horizon 2020 research project funded by the European Commission (grant agreement 730053). REINVENT involves five research institutions from four countries: Lund University (Sweden), Durham University (United Kingdom), Wuppertal Institute (Germany), PBL Netherlands Environmental Assessment Agency (the Netherlands) and Utrecht University (the Netherlands). More information can be found on our website: www.reinvent-project.eu.

This data publication provides two datasets of turbulent kinetic energy dissipation rates sampled during the MomarSat 2022 cruise. One dataset was gathered with a deep autonomous Vertical Microstructure Profiler (VMP-6000). The second dataset was gathered with the MicroRiYo mooring as described in the reference paper (Ferron et al. 2024). The two datasets, one for each instrument, are available as tar files. Each tar file contains fourteen NetCDF files. Each NetCDF file contains the dissipation rate profile, the time (UTC) of the profile start, the geographical position (deployment of the VMP or mooring position), and the mean pressure for each dissipation rate estimate (two estimates at each pressure level from the two shear sensors). Each dissipation rate comes with a quality control matrix QC (14 x 4) that characterizes how the associated mean shear spectrum fitted the expected theoretical Nasmyth spectrum: QC( 1:10, 1 ) : Value of the 10 criteria used (see reference paper) for the dissipation rates of shear 1. QC( 1:10, 2 ): Criteria met (=1) or not met (=0) for shear 1 dissipation rates. QC(11,1): Same criteria as QC(10,1) expressed in terms of mean absolute deviation (MAD) instead of variance (see Lueck et al. 2022) (shear 1). QC(11,2): state whether criteria QC(11,1) is met (=1) or not met (=0) (shear 1). QC(12,1): Number of shear spectra averaged to compute one dissipation rate estimate (shear 1). QC(12,2): Number of accelerometer used to remove vibrations (Goodman et al. 2006; Lueck et al. 2022; Ferron et al. 2023) (shear 1) QC(13,1): MAD (shear 1) QC(13,2): unused QC(14,1): index of first used spectral component to compute the shear variance used in the dissipation rate estimate (shear 1). QC(14,2): index of last used spectral component to compute the shear variance used in the dissipation rate estimate (shear 1). QC(:,3): same as QC(:,1) for shear 2. QC(:,4): same as QC(:,2) for shear 2. Shear data were processed following the processing flow chart of the Atomix SCOR Working Group 160 (https://wiki.app.uib.no/atomix/index.php?title=Flow_chart_for_shear_probes). References: Ferron, B., S. Leizour, M. Hamon, O. Peden, 2024: MicroRiYo : An observing system for deep repeated profiles of kinetic energy dissipation rates from shear-microstructure turbulence along a mooring line, submitted to J. Atmos. Ocean. Tech. Lueck, R. G., 2022: The Statistics of Oceanic Turbulence Measurements. Part II: Shear Spectra and a New Spectral Model. J. Atmos. Oceanic Technol., 39, 1273–1282, https://doi.org/10.1175/JTECH-D-21-0050.1.