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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).

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.

Aim: Animal migration strategies balance trade-offs between mortality and reproduction in seasonal environments. Knowledge of broad-scale biogeographical patterns of animal migration is important for understanding ecological drivers of migratory behaviours. Here we present a flyway-scale assessment of the spatial structure and seasonal dynamics of the Afro-Palearctic bird migration system and explore how phenology of the environment guides long-distance migration. Location: Europe and Africa. Time period: 2009–2017. Major taxa studied: Birds. Methods: We compiled an individual-based dataset comprising 23 passerine and near-passerine species of 55 European breeding populations where a total of 564 individuals were tracked migrating between Europe and sub-Saharan Africa. In addition, we used remote sensed observations on primary productivity (NDVI) to estimate the timing of vegetation green-up in spring and senescence in autumn across Europe. First, we described how individual breeding and non-breeding sites and the migratory flyways link geographically. Second, we examined how migration timing along the two major Afro-Palearctic flyways is tuned with vegetation phenology en route and at the breeding sites. Results: While we found the longitudes of individual breeding and non-breeding sites to be strongly positively related, the latitudes of breeding and non-breeding sites were negatively related. In autumn, timing of migration was similar along the Western and the Eastern flyways and happened ahead of the autumnal senescence of vegetation. In spring, migration timing was approximately two weeks later along the Eastern flyway than on the Western flyway which coincided with the later spring green-up in Eastern Europe. Main Conclusions: Migration of the Afro-Palearctic landbirds follows a longitudinally parallel leap-frog migration pattern where migrants track vegetation green-up in spring and depart before vegetation senescence in autumn. However, the ongoing global change have the potential to disrupt this spatiotemporal synchronization between migration timing and spring green-up with variable effects on different migrant populations.

Ambitious international targets are being developed to protect and restore biodiversity under the Convention on Biological Diversity's post-2020 Global Biodiversity Framework and the European Union's Green Deal. Yet, the land system consequences of meeting such targets are unclear, as multiple pathways may be able to deliver on the set targets. This paper introduces a novel scenario approach assessing the plural implementations of these targets. The Nature Futures Framework (NFF) developed by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services aims to illustrate the different, positive ways in which society can value nature. It therefore offers a lens through which the spatial implementation of sustainability targets may be envisioned. We used CLUMondo, a spatially explicit model, to simulate plural land system scenarios for Europe for 2050. The model builds on current land system representations of Europe and explores how and where sustainability targets can be implemented under projected population trends and commodity demands. We created three different scenarios in which the sustainability targets are met, each representing an alternative, normative view on nature as represented by the NFF, favoring land systems providing strong climate regulation (Nature for Society), species conservation (Nature for Nature), or agricultural heritage features (Nature as Culture). Our results show that, irrespective of the NFF view, meeting sustainability targets will require European land systems to drastically change, as natural grasslands and forests are forecast to expand while productive areas are projected to undergo a dual intensification and diversification trajectory. Despite each NFF perspective showcasing a similar direction of change, 20% of Europe's land area will differ based on the adopted NFF perspective, with hotspots of disagreement identified in eastern and western Europe. These simulations go beyond existing scenario approaches by not only depicting broad societal developments for Europe, but also by quantifying the land system synergies and trade-offs associated with alternative, archetypal, interpretations and values of how nature may be managed for sustainability. This quantification exemplifies a means towards constructive dialogue, on the one hand by acknowledging areas of contention, and bringing such issues to the fore, and on the other by highlighting points of convergence in a vision for a sustainable Europe.

Understanding the genetic bases underlying climate adaptation is a key element to predict the potential of species to face climate warming. Although substantial climate variation is observed at a micro-geographic scale, most genomic maps of climate adaptation have been established at broader geographical scales. Here, by using a Pool-Seq approach combined with a Bayesian hierarchical model that control for confounding by population structure, we performed a genome-environment association (GEA) analysis to investigate the genetic basis of adaptation to six climate variables in 168 natural populations of Arabidopsis thaliana distributed in south-west of France. Climate variation among the 168 populations represented up to 24% of climate variation among 521 European locations where A. thaliana inhabits. We identified neat and strong peaks of association, with most of the associated SNPs being significantly enriched in likely functional variants and/or in the extreme tail of genetic differentiation among populations. Furthermore, genes involved in transcriptional mechanisms appear predominant in plant functions associated with local climate adaptation. Globally, our results suggest that climate adaptation is an important driver of genomic variation in A. thaliana at a small spatial scale and mainly involves genome-wide changes in fundamental mechanisms of gene regulation. The identification of climate-adaptive genetic loci at a micro-geographic scale also highlights the importance to include within-species genetic diversity in ecological niche models for projecting potential species distributional shifts over short geographic distances.

Chronic, low intensity herbivory by invertebrates, termed background herbivory, has been understudied in tundra, yet its impacts are likely to increase in a warmer Arctic. The magnitude of these changes is however hard to predict as we know little about the drivers of current levels of invertebrate herbivory in tundra. We assessed the intensity of invertebrate herbivory on a common tundra plant, the dwarf birch (Betula glandulosa-nana complex), and investigated its relationship to latitude and climate across the tundra biome. Leaf damage by defoliating, mining and gall-forming invertebrates was measured in samples collected from 192 sites at 56 locations. Our results indicate that invertebrate herbivory is nearly ubiquitous across the tundra biome but occurs at low intensity. On average, invertebrates damaged 11.2% of the leaves and removed 1.4% of total leaf area. The damage was mainly caused by external leaf feeders, and most damaged leaves were only slightly affected (12% leaf area lost). Foliar damage was consistently positively correlated with mid-summer (July) temperature and, to a lesser extent, precipitation in the year of data collection, irrespective of latitude. Our models predict that, on average, foliar losses to invertebrates on dwarf birch are likely to increase by 6--7% over the current levels with a 1 textdegreeC increase in summer temperatures. Our results show that invertebrate herbivory on dwarf birch is small in magnitude but given its prevalence and dependence on climatic variables, background invertebrate herbivory should be included in predictions of climate change impacts on tundra ecosystems.