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Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

Earth’s rapidly changing climate is particularly evident in the Arctic. Outside of the Arctic, the emergence of large-sample catchment databases has transformed science from an emphasis on local case-studies towards more systematic insights into drivers of watershed functioning. Here we present an integrated pan-ARctic CAtchments summary DatabasE (ARCADE) of >40,000 catchments, including small and medium-sized watersheds, draining into the Arctic Ocean. These watersheds, delineated at a high-resolution (90 m), are provided with 103 geospatial, environmental, climatic, and physiographic catchment properties. ARCADE is the first aggregated database of pan-Arctic river catchments that includes small watersheds at a high resolution. These small catchments are experiencing the greatest climatic warming while also storing large quantities of soil carbon in landscapes that are especially prone to degradation of permafrost (i.e., ice wedge polygon terrain) and associated hydrological regime shifts. The publication of this database is a necessary step toward more integrated monitoring of the pan-Arctic watershed.

Abstract. Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model specific uncertainties when reported. In comparing NGHGI with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions and geological sources together account for the gap between NGHGI and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).

This paper deals the study of the lead-acid behavior and its modelling based on the Kinetic Battery Model. Results of experimental tests are used to realize the analysis and to estimate the model parameters. These last can serve for prediction and estimation of the battery lifetime according to the actual operating conditions.

Abstract. Regional land carbon budgets provide insights on the spatial distribution of the land uptake of atmospheric carbon dioxide, and can be used to evaluate carbon cycle models and to define baselines for land-based additional mitigation efforts. The scientific community has been involved in providing observation-based estimates of regional carbon budgets either by downscaling atmospheric CO2 observations into surface fluxes with atmospheric inversions, by using inventories of carbon stock changes in terrestrial ecosystems, by upscaling local field observations such as flux towers with gridded climate and remote sensing fields or by integrating data-driven or process-oriented terrestrial carbon cycle models. The first coordinated attempt to collect regional carbon budgets for nine regions covering the entire globe in the RECCAP-1 project has delivered estimates for the decade 2000–2009, but these budgets were not comparable between regions, due to different definitions and component fluxes reported or omitted. The recent recognition of lateral fluxes of carbon by human activities and rivers, that connect CO2 uptake in one area with its release in another also requires better definition and protocols to reach harmonized regional budgets that can be summed up to the globe and compared with the atmospheric CO2 growth rate and inversion results. In this study, for the international initiative RECCAP-2 coordinated by the Global Carbon Project, which aims as an update of regional carbon budgets over the last two decades based on observations, for 10 regions covering the globe, with a better harmonization that the precursor project, we provide recommendations for using atmospheric inversions results to match bottom-up carbon accounting and models, and we define the different component fluxes of the net land atmosphere carbon exchange that should be reported by each research group in charge of each region. Special attention is given to lateral fluxes, inland water fluxes and land use fluxes.

We designed a controlled laboratory experiment to investigate the responses of a subarctic liverwort-based (Anthelia juratzkana) BSC from the south of Iceland to different levels of temperature, moisture and light. We studied how these environmental factors affect the capacity of subarctic BSC to fix N, and whether these responses were linked to changes in the abundance of N fixers and/or to structural changes in the BSC microbial communities. 1. Sample collection In September 2018 we collected BSC from a site adjacent to the Climate Research Unit at Subarctic Temperatures (CRUST) experiment (Salazar et al., in progress), near Landmannahellir, Iceland (64°02' N, 19°13' W; 590 m.a.s.l.). Mean annual temperature and precipitation at the site are ca. 5 °C and 1500 mm, respectively. Surface cover in this area is primarily liverwort-based BSC (ca. 50%), followed by mosses (ca. 30%) and Salix herbacea dwarf willow (ca. 20%), on an andosol/vitrisol substratum. We randomly collected eight BSC blocks (i.e. replicates) of 13x16 cm2 and ca. 5 cm deep (Figure S1a in article). Blocks were separated by at least 10 meters. Since the focus of this study is on BSC, patches of moss or vascular plants were avoided. We transported (approx. 5 h) the BSC blocks in coolers with ice packs and stored them in a dark room at 5 °C for 2 to 5 weeks while we performed the analyses described below. We kept wet paper towels inside the coolers to prevent desiccation. We subsampled BSC disks of 5 cm diameter and 1.5 cm depth out of the 13x16x5 cm3 BSC blocks (Figure S1c) for N fixation analyses (section 3). Then, we subsampled BSC disks of 1.5 cm diameter, 1.5 cm depth from each 5 cm diameter BSC disk, for Chl a (section 4) and cyanobacteria and liverwort cover (section 5) analyses and for DNA extractions (section 6). 2. Experimental design and environmental treatments We studied the effects of temperature, moisture and light on N fixation and the microbial community structure. For this, we conducted a factorial experiment (4 x 2 x 2) with four levels of temperature: 10, 15, 20 and 25 °C; two levels of moisture: ca. 75% (close to moisture at the moment of sampling) and 100% (saturated); and two levels of light ca. 2 μmol m-2 s-1 (low intensity) and ca. 90 μmol m-2 s-1 (high intensity; Figure S2 in article). Light was available all the time (i.e. we did not set day/night cycles), to simulate conditions similar to those in the sampling site during the summer. Temperature and light treatments were set in a growth chamber (Termaks series 8000, Bergen, Norway), and monitored hourly with temperature/light loggers (HOBO Pendant® MX Temperature/Light Data Logger, MX2202, Onset, Bourne, MA, USA). Levels of these environmental variables were selected within ranges commonly experienced by BSC at the sampling site (between ca. >0 and 25°C; 0 and >100 μmol m-2 s-1; and between dryness for short periods of time during the summer, and saturation e.g. after the winter snow is melted; unpublished observations) and comparable ecosystems (e.g. a mesic-dry heath in Greenland; Rousk et al., 2018). We compared ambient vs. saturation moisture levels because mean annual precipitation in subarctic and arctic regions is projected to increase in the coming decades (IPCC, 2021). The maximum temperatures in our experimental design were selected based on peaks of warming (measured at the soil surface) recorded during previous growing seasons (unpublished data). In this sense, our high temperature treatment should simulate BSC responses to heat waves at the study site, under different moisture and light conditions. Average temperature and light intensity inside the jars were 11.1 ± 0.7, 16.5 ± 0.7, 21.5 ± 0.7 and 26.6 ± 0.9 °C (2 loggers x 2 light levels; n = 4) and 2.3 ± 0.04 and 88.0 ± 1.6 μmol m-2 s-1 (2 loggers x 4 temperature levels; n = 8) respectively (Figure S2a and b in article). Temperature levels inside the jars were slightly higher than temperatures set in the growing chamber due to a greenhouse effect. To create a saturation level in the moisture treatment, we wetted each sample with an excess of deionized water and waited for approximately one minute until it stopped dripping. Moisture was maintained between analyses by placing wet towels in the coolers stored in the cold, dark room. After environmental treatments and N fixation measurements (see following section), we oven dried (60 °C, 24 h) BSC disks to estimate the dried weight of the samples, and to prepare them for chlorophyll a analysis and DNA extraction. Average moisture content was 75.5 ± 2.4 and 107.2 ± 2.3 % (Figure S3c in article). 3. N fixation under controlled temperature, moisture and light conditions We estimated N fixation rates with the Acetylene Reduction Assay (ARA; Hardy et al., 1968). We used eight 5-cm subsampled disks (i.e. replicates) per combination of temperature and moisture treatments. Thus, each temperature-specific ARA analysis was composed of a total of 16 samples with two levels of moisture, eight saturated and eight unsaturated, plus controls with acetylene, ethylene and air. The BSC disks were weighed (for further water content analysis) and placed in 350 mL glass jars with rubber septa in the lids (Figure S1c in article). These jars were then placed in an environmental chamber (Termaks series 8000, Bergen, Norway) at fixed temperature and light conditions. We acclimated the samples to each combination of temperature and light for 24 h. We then manually aerated the jars for a few seconds, closed the jars tightly and replaced 10% of the headspace with acetylene (except in jars used as ethylene and air controls). We incubated the jars at the set temperature and light conditions for 24 h. Then, we collected 22 mL of gas from each jar and analyzed it using a Clarus 400 gas chromatograph (PerkinElmer Ltd., Beaconsfield, UK) equipped with an automatic split/splitless injector and a flame ionization detector (FID), and an Elite-Alumina column (30 m, 0.53 mm; PerkinElmer Ltd., Beaconsfield, UK). At the end of each 48 h acclimation-incubation period, we manually aerated the samples and started a new acclimation-incubation at a different light (but same temperature) condition. To control for a possible effect of the storing time in the cold room, we randomized the order of the temperatures for the incubations. We incubated first samples (8 replicates at ca. 75% and 8 at 100% moisture content) at 20°C, then at 10, 25 and 15 °C. Also, to control for a possible cumulative effect between light levels, we switched the order of the light levels for each temperature treatment. For example, for samples incubated at 20°C we measured ethylene production first at low light (48 h) and then at high light (48 h). For the next quarter of the samples, incubated at 10°C, we measured ethylene production first at high light (48 h) and then at low light (48 h), and so on, for the other two temperature treatments. Since ARA is a non-destructive method, we were able to estimate N fixation rates on the same sample at different light treatments. For the rest of the analysis, based on destructive methods (see details below), we measured BSC responses to moisture and temperature. 4. Cyanobacteria and liverwort cover on BSC We estimated the cover of cyanobacteria and liverwort (Anthelia juratzkana; Figure S1b in article) on the BSC surface by epifluorescence microscopy (Figure S3 in article; similar to Lan et al., 2019). After ARA measurements, BSC samples were stored in a dark room at 5 °C for 1 to 4 days. Plant and cyanobacterial growth was assumed to be minimal under these conditions. From each 5 cm diameter BSC disk (Figure S1c), we subsampled a 1.5 cm diameter BSC disk and imaged the plant (liverwort) chlorophyll using a Leica DM6000B fluorescent microscope (Leica, Heerbrugg Switzerland) equipped with an I3 filter cube (Ex 450/90, Di 510, Em 515), and the cyanobacterial phycocyanin with a TX2 filter cube (Ex 560/40, Di 595, Em 630/30). Multiple fields of view were measured using both filter cubes and stitched together to form an image of 1x1 cm of BCS surface (Figure S3) using the Leica software. Images were analyzed in ImageJ/Fiji (Collins, 2007; Schindelin et al., 2012), and estimates of cyanobacterial and plant covers calculated as percentage of BSC surface cover. We did not subsample BSC disks between light levels, but rather used samples that were exposed to low light for 48 h (24 h acclimation plus 24 h ARA) and then to high light for another 48 h, or vice versa. Therefore, the treatments in this part of our analysis include temperature and moisture, but not light. 5. Chlorophyll a We estimated Chl a content as an indicator of net photosynthetic rate in BSC (Yan-Gui et al., 2011). Similar to our BSC cover analysis, we subsampled a 1.5 cm diameter, 1.5 cm depth BSC disk from each 5 cm diameter BSC disk (Figure S1c in article) used for ARA analysis. We dried subsamples at 60 °C for 24 h, extracted Chl a using DMSO (65 °C, 90 min) and then estimated Chl a content by spectrophotometry (665 and 750 nm; Genesys 20, Thermo Scientific, Waltham, MA), as in Caesar et al. (2018): Chl a µg = (11.9035 × (A665 − A750)) × S (1) Chl a [mg × m−2] = Chl a [µg] / (AR × 1000) (2) Where S is volume of solvent, AR is area (in m-2) and A665 and A750 are absorbances at 665 and 750 nm, respectively. As for BSC cover, treatments in this part of our analysis included temperature and moisture, but not light. 6. DNA extraction and analysis Immediately after the fluorescence microscopy measurements (section 4), we dried (60 °C, 24 h) and ground (1 min, Mini bead beater 16; Biospec products) the 1.5 cm diameter, 1.5 cm depth BSC disks used for the cyanobacteria/liverwort cover analysis and stored them at -80 °C for up to four months for DNA extraction. We pooled together replicates in pairs, combining them in equal weight parts (125 mg each for a total of 250 mg). We used the PowerSoil® DNA extraction kit (MOBIO/Qiagen), and shotgun sequencing approaches and analyses via the alignment-free fast taxonomic annotation tool Kraken2 (Wood and Langmead, 2019) with the Kraken2 Refseq Standard plus protozoa and fungi database and the web-based pipeline Kaiju (Menzel et al., 2016). We estimated relative abundance of microbial groups using Kraken2 and fungal:bacteria ratios based on Kaiju taxonomic assignments (see sections below). After quality filtering the raw reads using Trim Galore microbial metagenome functional profiling was performed using HUMAnN 3 (Beghiji et al., 2021). For the functional annotation, UniRef50 (Suzek et al., 2015), KEGG (Kanehisa and Goto, 2000), and BioCyc databases (Karp et al., 2019) were used. As for BSC cover and Chl a, treatments for this part of our analysis included temperature and moisture but not light. We characterized microbial communities only at two temperature levels: 10 and 20 °C, which showed significant differences in N fixation and cyanobacterial cover (see Results in article) 7. Fungal:bacterial ratios Fungi and bacteria decompose organic matter at different rates, which affects the N and C biogeochemistry of substrates like BSC. To study potential effects of the environment on the biogeochemistry of BSC via differential effects on fungi and bacteria, we estimated fungal:bacterial ratios. We calculated fungal:bacterial ratios based on numbers of gene copies assigned to each group by Kaiju. 8. Microbial community and statistical analyses Microbial community analyses were performed using the microeco package in R (version 3.5.0). We first investigated the most important Orders for classifying samples into different treatments using a random forest approach. We then conducted an ANOVA test followed by a Tukey’s HSD test, α<0.05, as well as Pearson correlations and PERMANOVA analyses between the Bray–Curtis dissimilarity score and moisture content. Finally, we conducted a Distance-based redundancy analysis (dbRDA) to assess the effects of the abiotic treatments on the top most abundant bacterial orders. To identify distinctive molecular pathways between treatments, we performed a linear discriminant analysis (LDA) effect size (LEfSe) analysis as implemented in the microeco package, then we selected the functions with a LDA score ≥ 3.5. We used a mixed model (lmer function in R, version 3.6.1) to analyze the fixed effects of environmental manipulations on N fixation, while accounting for the random effect of measurements on the same sample at two light levels. For the other response variables, which varied in response to temperature and moisture but not light, we used fixed models (lm function in R, version 3.6.1). We compared models based on the Bayesian Information Criterion (BIC; Figure S4 in article). Together, Biological Soil Crust (BSC) and other cryptogamic groundcovers can contribute up to half of the global nitrogen (N) fixation. BSC also stabilizes the soil (reducing erosion and dust emissions), fixes carbon (C), retains moisture, and acts as a hotspot of microbial diversity and activity. Much of the knowledge about how climate change is affecting the composition and functioning of BSC comes from hot arid and semiarid regions. The comparatively smaller body of research on BSC from cold and mesic environments has been primarily observational, for example along chronosequences after a glacier retreat. Few studies have experimentally investigated the effects of the environment on BSC from high latitudes. Such experiments allow unraveling of relationships at a resolution that can only be achieved by controlling for confounding factors. We measured short-term (2-4 days) responses of a liverwort-based (Anthelia juratzkana) BSC from the south of Iceland to a range of temperature, moisture and light conditions. Warming increased N fixation rates, especially when moisture was at a saturation level, and only when light was not limiting. A correlation analysis suggests that increases in N fixation rates were linked to cyanobacterial abundance on the BSC surface and to the rates of their metabolic activity. Warming and moisture changes also induced compositional and structural modification of the bacterial community, with consequences at the functional level. In contrast to many observations on BSC from hot drylands, the BSC from our cold and mesic study site is more limited by low temperature and light than by moisture. Our findings show possible ways in which BSC from cold and mesic ecosystems can respond to short-term manifestations of climate change, such as increasingly frequent heat waves. We used phyloseq and metaphlan2 to open tsv files.Other options include: QIIME MG-RAST PICRUSt Mothur phyloseq MEGAN VAMPS metagenomeSeq Phinch RDP Classifier USEARCH PhyloToAST EBI Metagenomics GCModeller MetaPhlAn 2 More about this in https://biom-format.org/

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

This reference data set representing the status quo of the German electricity, heat, and natural gas sectors was compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector). While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany. The data set is documented in DIW Data Documentation 92 (see references). The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A-D. {"references": ["Kunz, Friedrich et. al. (2017). Electricity, Heat and Gas Sector Data for Modeling the German System. DIW Data Documentation 92."]}