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Abstract The Kuril Islands stretch southwest from Kamchatka, Russia, to Hokkaido, Japan and separate the Sea of Okhotsk from the northern Pacific Ocean. A series of transgressions and regressions linked to variations in climatically affected global ice volume are among the most important drivers of Holocene environmental changes in the region. Despite a long research history, reconstructions of the Holocene palaeoenvironment are sparse with inconsistent interpretations, arising from insufficient dating control, different temporal resolutions, and specific local geographical features, such as high tectonic activity and the isolated nature of the area. We have investigated a 550 cm lake sediment section from Iturup Island, the largest among the Kuril Islands. The 6600 year old sediment section was studied using sedimentological, geochemical, chironomid, diatom, and pollen analyses to reconstruct environmental and climatic changes and sea level fluctuations (transgression – regression stages). During the warm late phase of the Middle Holocene (6600–4400 cal BP) an open bay or lagoon with shallow overgrown littorals existed at the sampling site. The cooling between 5600 and 4400 cal BP can be correlated with Neoglacial cooling. The cool period between 4200 and 3200 cal BP was a transition towards the formation of a freshwater lagoon and can be related to a decline of the Japan Late Jomon transgression (Sakaguchi, 1983). Between 3200 and 2800 cal BP the lagoon separated from the marine environment in response to a further sea level decrease during the Japan Latest Jomon cold stage and regression. The following increase in the share of broad-leaved pollen indicated a slight warming (Yayoi transition stage) that was interrupted by a short-term cooling spell between 1500 and 1400 cal BP (cold Japan Kofun stage). The period between ca 1100 and 800 cal BP can be related to the European Medieval Climate Anomaly (MCA) or relatively dry Japan Nara-Heian-Kamakura warm stage. The Little Ice Age cooling and Edo regression were evident after ca 800 cal BP. Modern warming however is not well seen in the investigated core.

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

Energetic particle precipitation leads to ionization in the Earth's atmosphere, initiating the formation of active chemical species which destroy ozone and have the potential to impact atmospheric composition and dynamics down to the troposphere. We report on one exceptionally strong high-energy electron precipitation event detected by balloon measurements in geomagnetic midlatitudes on 14 December 2009, with ionization rates locally comparable to strong solar proton events. This electron precipitation was possibly caused by wave–particle interactions in the slot region between the inner and outer radiation belts, connected with still poorly understood natural phenomena in the magnetosphere. Satellite observations of odd nitrogen and nitric acid are consistent with widespread electron precipitation into magnetic midlatitudes. Simulations with a 3D chemistry–climate model indicate the almost complete destruction of ozone in the upper mesosphere over the region where high-energy electron precipitation occurred. Such an extraordinary type of energetic particle precipitation can have major implications for the atmosphere, and their frequency and strength should be carefully studied. © Author(s) 2022. This work has been done in the frame of the German–Russian cooperation project, H-EPIC. Work at the Jet Propulsion Laboratory, California Institute of Technology, was carried out under a contract with the National Aeronautics and Space Administration. Extreme EEP event selection, ionization rates calculation and numerical experiments with HAMMONIA model was done in the frame of the Russian Science Foundation grant. Analysis of MLS data and numerical experiment results were done in the SPBU Ozone Layer and Upper Atmosphere Research Laboratory, supported by the Ministry of Science and Higher Education of the Russian Federation. This research has been supported by the Russian Foundation for Basic Research (grant no. 20-55-12020), the Deutsche Forschungsgemeinschaft (grant no. SI 1088/7-1), the Russian Science Foundation (grant no. 20-67-46016) and the Ministry of Science and Higher Education of the Russian Federation (grant no. 075-15-2021-583). The article processing charges for this open-access publication were covered by the Karlsruhe Institute of Technology (KIT). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709. Peer reviewed

Arctic river deltas and deltaic near-shore zones represent important land–ocean transition zones influencing sediment dynamics and nutrient fluxes from permafrost-affected terrestrial ecosystems into the coastal Arctic Ocean. To accurately model fluvial carbon and freshwater export from rapidly changing river catchments as well as assess impacts of future change on the Arctic shelf and coastal ecosystems, we need to understand the sea floor characteristics and topographic variety of the coastal zones. To date, digital bathymetrical data from the poorly accessible, shallow, and large areas of the eastern Siberian Arctic shelves are sparse. We have digitized bathymetrical information for nearly 75 000 locations from large-scale (1:25 000–1:500 000) current and historical nautical maps of the Lena Delta and the Kolyma Gulf region in northeastern Siberia. We present the first detailed and seamless digital models of coastal zone bathymetry for both delta and gulf regions in 50 and 200 m spatial resolution. We validated the resulting bathymetry layers using a combination of our own water depth measurements and a collection of available depth measurements, which showed a strong correlation (r>0.9). Our bathymetrical models will serve as an input for a high-resolution coupled hydrodynamic–ecosystem model to better quantify fluvial and coastal carbon fluxes to the Arctic Ocean, but they may be useful for a range of other studies related to Arctic delta and near-shore dynamics such as modeling of submarine permafrost, near-shore sea ice, or shelf sediment transport. The new digital high-resolution bathymetry products are available on the PANGAEA data set repository for the Lena Delta (https://doi.org/10.1594/PANGAEA.934045; Fuchs et al., 2021a) and Kolyma Gulf region (https://doi.org/10.1594/PANGAEA.934049; Fuchs et al., 2021b), respectively. Likewise, the depth validation data are available on PANGAEA as well (https://doi.org/10.1594/PANGAEA.933187; Fuchs et al., 2021c).

During the winter of 2019/2020, as the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project started its work, the Arctic Oscillation (AO) experienced some of its largest shifts, ranging from a highly negative index in November 2019 to an extremely positive index during January–February–March (JFM) 2020. The permanent positive AO phase for the 3 months of JFM 2020 was accompanied by a prevailing positive phase of the Arctic Dipole (AD) pattern. Here we analyze the sea ice thickness (SIT) distribution based on CryoSat-2/SMOS satellite-derived data augmented with results from the hindcast simulation by the fully coupled Regional Arctic System Model (RASM) from November 2019 through March 2020. A notable result of the positive AO phase during JFM 2020 was large SIT anomalies of up to 1.3 m that emerged in the Barents Sea (BS), along the northeastern Canadian coast and in parts of the central Arctic Ocean. These anomalies appear to be driven by nonlinear interactions between thermodynamic and dynamic processes. In particular, in the Barents and Kara seas (BKS), they are a result of enhanced ice growth connected with low-temperature anomalies and the consequence of intensified atmospherically driven sea ice transport and deformations (i.e., ice divergence and shear) in this area. The Davies Strait, the east coast of Greenland and the BS regions are characterized by convergence and divergence changes connected with thinner sea ice at the ice borders along with an enhanced impact of atmospheric wind forcing. Low-pressure anomalies that developed over the eastern Arctic during JFM 2020 increased northerly winds from the cold Arctic Ocean to the BS and accelerated the southward drift of the MOSAiC ice floe. The satellite-derived and simulated sea ice velocity anomalies, which compared well during JFM 2020, indicate a strong acceleration of the Transpolar Drift relative to the mean for the past decade, with intensified speeds of up to 6 km d−1. As a consequence, sea ice transport and deformations driven by atmospheric surface wind forcing accounted for the bulk of the SIT anomalies, especially in January 2020 and February 2020. RASM intra-annual ensemble forecast simulations with 30 ensemble members forced with different atmospheric boundary conditions from 1 November 2019 through 30 April 2020 show a pronounced internal variability in the sea ice volume, driven by thermodynamic ice-growth and ice-melt processes and the impact of dynamic surface winds on sea ice formation and deformation. A comparison of the respective SIT distributions and turbulent heat fluxes during the positive AO phase in JFM 2020 and the negative AO phase in JFM 2010 corroborates the conclusion that winter sea ice conditions in the Arctic Ocean can be significantly altered by AO variability.

The Sun sporadically produces eruptive events leading to intense fluxes of solar energetic particles (SEPs) that dramatically disrupt the near-Earth radiation environment. Such events have been directly studied for the last decades but little is known about the occurrence and magnitude of rare, extreme SEP events. Presently, a few events that produced measurable signals in cosmogenic radionuclides such as 14C, 10Be and 36Cl have been found. Analyzing annual 14C concentrations in tree-rings from Switzerland, Germany, Ireland, Russia, and the USA we discovered two spikes in atmospheric 14C occurring in 7176 and 5259 BCE. The ~2% increases of atmospheric 14C recorded for both events exceed all previously known 14C peaks but after correction for the geomagnetic field, they are comparable to the largest event of this type discovered so far at 775 CE. These strong events serve as accurate time markers for the synchronization with floating tree-ring and ice core records and provide critical information on the previous occurrence of extreme solar events which may threaten modern infrastructure. Nature Communications, 13 ISSN:2041-1723

Abstract. In the Baltic Sea, salinity and its large variability, both horizontal and vertical, are key physical factors in determining the overall stratification conditions. In addition to that, salinity and its changes also have large effects on various ecosystem processes. Several factors determine the observed two-layer vertical structure of salinity. Due to the excess of river runoff to the sea, there is a continuous outflow of water masses in the surface layer with a compensating inflow to the Baltic in the lower layer. Also, the net precipitation plays a role in the water balance and consequently in the salinity dynamics. The salinity conditions in the sea are also coupled with changes in the meteorological conditions. The ecosystem is adapted to the current salinity level: a change in the salinity balance would lead to ecological stress for flora and fauna, as well as related negative effects on possibilities to carry on sustainable development of the ecosystem. The Baltic Sea salinity regime has been studied for more than 100 years. In spite of that, there are still gaps in our knowledge of the changes in salinity in space and time. An important part of our understanding of salinity is its long-term changes. However, the available scenarios for the future development of salinity are still uncertain. We still need more studies on various factors related to the salinity dynamics. Among others, more knowledge is needed, e.g., from meteorological patterns at various space scales and timescales as well as mesoscale variability in precipitation. Also, updated information on river runoff and inflows of saline water is needed to close the water budget. We still do not understand the water mass exchange accurately enough between North Sea and Baltic Sea and within its sub-basins. Scientific investigations of the complicated vertical mixing processes are additionally required. This paper is a continuation and update of the BACC (Baltic Assessment of Climate Change for the Baltic Sea Region) II book, which was published in 2015, including information from articles issued until 2012. After that, there have been many new publications on the salinity dynamics, not least because of the major Baltic inflow (MBI) which took place in December 2014. Several key topics have been investigated, including the coupling of long-term variations of climate with the observed salinity changes. Here the focus is on observing and indicating the role of climate change for salinity dynamics. New results on MBI dynamics and related water mass interchange between the Baltic Sea and the North Sea have been published. Those studies also included results from the MBI-related meteorological conditions, variability in salinity, and exchange of water masses between various scales. All these processes are in turn coupled with changes in the Baltic Sea circulation dynamics.

The Tyrrhenian Sea plays an important role in the winter deep water formation in the North Western Mediterranean through the water that enters the Ligurian Sea via the Corsica Channel. Therefore, the study of the impact of the changes in the future climate on the Tyrrhenian circulation and its consequences represents an important issue. Furthermore, the seasonally-dependent, rich in dynamical mesoscale structures, Tyrrhenian circulation is dominated by the interplay of local climate and the basin-wide Mediterranean circulation via the water transport across its major straits and an adequate representation of its features represents an important modeling challenge. In this work we examine with a regionally-coupled atmosphere-ocean model the changes in the Tyrrhenian circulation by the end of the 21st century under the RCP8.5 emission scenario, their driving mechanisms, as well as their possible impact on winter convection in the NW Mediterranean. Our model successfully reproduces the main features of the Mediterranean Sea and Tyrrhenian basin present-day circulation. We find that toward the end of the century the winter cyclonic, along-slope stream around the Tyrrhenian basin becomes weaker. This weakening increases the wind work transferred to the mesoscale structures, which become more intense than at present in winter and summer. We also find that, in the future, the northward water transport across the Corsica Channel towards the Liguro-Provençal basin becomes smaller than today. Also, water that flows through this channel presents a stronger stratification because of a generalized warming with a saltening of intermediate waters. Both factors may contribute to the interruption of deep water formation in the Gulf of Lions by the end of the century.

This work presents unprecedented, high-resolution palaeomagnetic data from the sedimentary record of Lake Levinson-Lessing, the deepest lake in northern central Siberia. Palaeomagnetic analyses were carried out on 730 discrete samples from the upper 38 m of the 46 m long core Co1401, which was recovered from the central part of the lake. Alternating field demagnetization experiments were carried out to obtain the characteristic remanent demagnetization. The relative palaeointensity is determined using the magnetic susceptibility, the anhysteretic remanent magnetization, and the isothermal remanent magnetization for normalization of the partial natural remanent magnetization. The chronology of Co1401 derives from correlation of the relative palaeointensity of 642 discrete samples with the GLOPIS-75 reference curve, accelerated mass spectrometer radiocarbon ages, and optically stimulated luminescence dating. This study focuses on the part > 10 ka but also presents preliminary results for the younger part of the core. The record includes the geomagnetic excursions Laschamps and Mono Lake and resolves sufficient geomagnetic features to establish a chronology that continuously covers the last ∼ 62 kyr. The results reveal continuous sedimentation at high rates between 45 and 95 cm kyr−1. The low variability of the magnetic record compared to datasets of reference records with lower sedimentation rates may be due to a smoothing effect associated with the lock-in depths. Because Co1401 was cored without core segment overlap the horizontal component of the characteristic remanent magnetization can only be used with caution. Nevertheless, the magnetic record of Co1401 is exceptional as it is the only high-resolution record of relative palaeointensity and palaeosecular variations from the Arctic tangent cylinder going back to ∼ 62 ka.

Abstract The past three decades have seen a sustained and coordinated effort to refine the seismic stratigraphic framework of the Antarctic margin that has underpinned the development of numerous geological drilling expeditions from the continental shelf and beyond. Integration of these offshore drilling datasets covering the Cenozoic era with Antarctic inland datasets, provides important constraints that allow us to understand the role of Antarctic tectonics, the Southern Ocean biosphere, and Cenozoic ice sheet dynamics and ice sheet–ocean interactions on global climate as a whole. These constraints are critical for improving the accuracy and precision of future projections of Antarctic ice sheet behaviour and changes in Southern Ocean circulation. Many of the recent advances in this field can be attributed to the community-driven approach of the Scientific Committee on Antarctic Research (SCAR) Past Antarctic Ice Sheet Dynamics (PAIS) research programme and its two key subcommittees: Paleoclimate Records from the Antarctic Margin and Southern Ocean (PRAMSO) and Palaeotopographic-Palaeobathymetric Reconstructions. Since 2012, these two PAIS subcommittees provided the forum to initiate, promote, coordinate and study scientific research drilling around the Antarctic margin and the Southern Ocean. Here we review the seismic stratigraphic margin architecture, climatic and glacial history of the Antarctic continent following the break-up of Gondwanaland in the Cretaceous, with a focus on records obtained since the implementation of PRAMSO. We also provide a forward-looking approach for future drilling proposals in frontier locations critically relevant for assessing future Antarctic ice sheet, climatic and oceanic change.