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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.

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).

The social confinement resulting from the COVID-19 crisis temporarily reduced greenhouse gas emissions. Although experts contend that the decrease in pollution rates was not drastic, some surveys detect growth in social concern about the climate. In this new climate-conscious environment, municipalities and local governments are promoting a new way of living and caring for cities, even before they can regain national and international freedom of movement. This work analyzes the connections between new climate awareness arising from the COVID-19 crisis, proposals of sustainable citizenship around the world, and its communication on Twitter to educate the new eco-conscious audience. The methodology mixes quantitative and qualitative analysis, using the Twitonomy Premium tool and the Twitter research tool with data extracted at the end of December 2020. Among the top ten most influential and active accounts, the results show educational institutions, local institutions, companies, neighborhoods, associations, and influencers. The impossibility of living in the city has not prevented citizen education and commitment to make real change for when that city and its citizens return to normality. However, this new normality must be different: more ecological, more responsible, more sustainable, and practiced from early childhood.

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.

We have developed a method that enables better taking into account of need of space of actions of moving objects in a building when Building Information Modeling (BIM) is used. The 3D spatial objects for real space requirements of actions created with our method can be used with various design tools as such in dimensioning or to generate video game colliders defining the need of space. Together with the gamified model of the structure from BIM they enable dimensioning in designs and simulations. We can dimension spatial objects for example related to safety. There are a lot of needs and applications for our methods. It is possible to design buildings and other structures that fit their purpose well.

The European Union has set ambitious targets of raising the share of EU energy consumption produced from renewable resources from 20% by 2020 to 27% by 2030. The aim of this paper is to assess the role of woody biomass in renewable energy as gross final energy consumption in the European Union (the EU-28). The paper identifies leading and lagging countries in biomass development by focusing on their current biomass use and forecasts future perspectives. The research compares and evaluates the role of biomass in renewable energy in the EU-28 focusing on countries' potential resources and policy support. The study shows that all countries are making efforts to reach the 20% target in 2020 and exhibit a trend of increasing renewable energy as gross final energy consumption towards the new target of 2030. Solid biomass plays an important role in reaching the EU's renewable energy targets. The majority of the EU-28 countries are close to reaching their national renewable energy targets and show a very attractive biomass development. Unless energy consumption decreases however, some member states will face serious problems in reaching their renewable energy target in 2020. Following our analysis, the largest problems occur in those MS having a relative high-energy consumption pattern: France, Germany and the United Kingdom. It is unlikely that they can comply with expected renewable energy demand, unless they mobilize more woody biomass from their available domestic potential (France, Germany) or considerably increase their woody biomass imports (mostly wood pellets) from elsewhere (United Kingdom).

According to the United Nations Framework Convention on Climate Change (UNFCCC) and Kyoto Protocol under it, industrial countries have to estimate their greenhouse gas emissions annually, and assess the uncertainties in these estimates. In Finland, agricultural methane (CH4) and nitrous oxide (N2O) emissions represent 7% of anthropogenic greenhouse gas emissions, and globally the share is much higher. Agriculture is one of the most uncertain emission categories (representing over 20% of greenhouse gas inventory uncertainty in Finland), due to both high natural variability of the emission sources and poor knowledge of the emission-generating processes. In this paper, we present an uncertainty estimate of agricultural CH4 and N2O emissions from Finland in 2002. Uncertainties were estimated based on measurement data, literature and expert judgement, and total uncertainty in agriculture was calculated using Monte Carlo simulation. According to the calculations, agricultural CH4 and N2O emissions from Finland were 3.7 to 7.8 Tg carbon dioxide (CO2) equivalents, 5.4 Tg being the mean value.Estimates of CH4 emissions are more reliable than those of N2O. N2O from agricultural soils was the most uncertain emission category, and the uncertainty was not reduced by using available national measurement data of N2O fluxes. Sensitivity study revealed that the uncertainty in total agricultural inventory could be 7% points lower, if more accurate emission estimation methods were used, including 1) improved data collection in area estimates of organic soils, 2) climate-specific methods for N2O from agricultural soils as already presented in literature, and 3) more detailed CH4 estimation methods for enteric fermentation which can be achieved by investigating national circumstances and digestible systems of animals in more detail.

In security and foreign policy discourse, environmental issues have been discussed increasingly as security threats that require immediate action. Yet, as the traditional security sector does not provide straightforward means to deal with climate change and other environmental issues, this has prompted concerns over undue securitisation and ill-placed extreme measures. We argue that an effective policy to address foreseeable environmental security threats can only be developed and maintained by ensuring that it remains resolutely within the domain of civil society. In this article, we consider the case of Finland, where the policy concept of comprehensive security has been presented as the official guideline for security and preparedness activities in different sectors. Comprehensive security aims to safeguard the vital functions of society through cooperation between authorities, business operators, organisations, and citizens. We analyse the opportunities and challenges of Finland’s comprehensive security policy in addressing environmental changes through a three-level framework of local, geopolitical and structural security impacts. Our empirical evidence is based on a set of expert interviews (n = 40) that represent a wide range of fields relevant to unconventional security issues. We find that the Finnish comprehensive security model provides an example of a wide and inclusive perspective to security which would allow for taking into account environmental security concerns. However, due to major challenges in the implementation of the model, it does not fully incorporate the long-term, cross-sectoral, and cascading aspects of environmental threats. This weakens Finland’s preparedness against climate change which currently poses some of the most urgent environmental security problems.

Half the population of China live in coastal zones where 70% of large cities are also located. Intensive human activities pose significant environmental and ecological hazards to these cities that are already vulnerable to natural hazards and climate change. The sustainable development of coastal cities is thus both a national and international issue. Rongcheng is a typical coastal city in east China. It is a national marine ranch demonstration area that is subjected to multi-stressors from human activities and climate change. The dominant economic sectors include aquaculture and fisheries, agriculture, shipping and tourism. A multitude of resulting pressures come mainly from intensified human activities, such as intensive aquaculture, overfishing, industrial pollutants, agricultural runoff, land reclamation and port expansion. In addition, Rongcheng is also facing exogenic pressures from extreme climate events such as intensified storms, storm surges, droughts and sea ice. A growing awareness of these problems brought together a trans-disciplinary group from local government, research institutions, local practitioners and coastal representatives to jointly explore and co-design adaptive coastal management options. In this transdisciplinary study, a social-ecological analysis based on a combination of the Systems Approach Framework and the Drivers-Pressures-States-Impacts-Responses framework was used to analyze and formulate an adaptive management plan for the sustainability of Rongcheng. More than 40 stakeholders including government, companies, civil society and institutions participated in the study through questionnaires and on-site meetings. A statistical analysis of the results identified urgent issues impeding the sustainable development of Rongcheng. The issues identified were poorly regulated aquaculture, loss of shoreline, and the decline of seagrass and cultural heritage. The study identified management options and measures, some of which were adopted by the local government in a co-designed management plan. The measures included upgrading of aquaculture industry, habitat conservation and restoration, and the development of cultural tourism. Another outcome was the increased knowledge exchange between stakeholders to inform management, policy, and decision making, as well as raised awareness of vulnerability to natural hazards and climate change. The success of this case study provides a reference for the adaptive management of other coastal cities and their sustainable development in a changing climate.

It has been claimed that technological advances will make it possible to make anything anywhere and to do so sustainably. In particular, making anything anywhere would increase the diversity of locations and participants involved in production, with positive effects for sustainability. For example, increasing the diversity of locations can reduce the long-distance transportation of materials and goods, which can improve the ecological sustainability of production. At the same time, increasing the diversity of people included in manufacturing can contribute to the spread of manufacturing communities, which can improve the social sustainability of production. However, physical production continues to be dominated by the same countries that have dominated global manufacturing in recent decades. Meanwhile, trade imbalances between rich and poor countries are similar to those of the past. In this paper, limitations and opportunities are explained for moveable production systems to increase the diversity of locations and participants in global production and trade. In addition, potential geopolitical barriers to the deployment of moveable production systems are explained.