ALBERT

Description: Transportation and mobility patterns contribute to greenhouse gas emissions. Understanding the drivers of these emissions, particularly for high emitters, is key to designing appropriate climate and mobility policies. In this article, we study the distribution of emissions from mobility in Germany and their drivers. We use a 2017 nation-wide mobility survey to calculate the carbon footprint of individuals associated with day-to-day and long-distance travels. We use quantile regression to investigate both socio-economic and attitudinal drivers of emissions across different categories of emitters, and for different mobility types. We discuss our results with respect to previous findings in the literature. Overall, we find that the top 10% of emitters are responsible for 51% of total emissions, and for 80% of emissions from long-distance travel. The statistical analysis reveals strong differences regarding the contribution of socio-economic drivers such as income or location at different levels of emissions. Attitudes towards different transportation modes also strongly correlate with differences in mobility behaviors.

Description: Climate change heavily threatens forest ecosystems worldwide and there is urgent need to understand what controls tree survival and forests stability. There is evidence that biodiversity can enhance ecosystem stability (Loreau and de Mazancourt in Ecol Lett 16:106–115, 2013; McCann in Nature 405:228–233, 2000), however it remains largely unclear whether this also holds for climate change and what aspects of biodiversity might be most important. Here we apply machine learning to outputs of a flexible-trait Dynamic Global Vegetation Model to unravel the effects of enhanced functional tree trait diversity and its sub-components on climate-change resistance of temperate forests (http://www.pik-potsdam.de/~billing/video/Forest_Resistance_LPJmLFIT.mp4). We find that functional tree trait diversity enhances forest resistance. We explain this with 1. stronger complementarity effects (~ 25% importance) especially improving the survival of trees in the understorey of up to + 16.8% (± 1.6%) and 2. environmental and competitive filtering of trees better adapted to future climate (40–87% importance). We conclude that forests containing functionally diverse trees better resist and adapt to future conditions. In this context, we especially highlight the role of functionally diverse understorey trees as they provide the fundament for better survival of young trees and filtering of resistant tree individuals in the future.

Description: Background: Inherited blood disorders affect 7% of the population worldwide, with higher prevalences in countries in the “thalassemia belt,” which includes Bangladesh. Clinical management options for severely affected individuals are expensive; thus, targeted government policies are needed to support prevention and treatment programs. In Bangladesh, there is a lack of data, in particular community-based estimates, to determine population prevalence. This study aims to estimate the prevalence of a wide range of hemoglobinopathies and their associations with anemia in a community-based sample of women and young children in rural Sylhet, Bangladesh. - Methods: Capillary blood samples from 900 reproductive-aged women and 395 children (aged 6–37 months) participating in the Food and Agricultural Approaches to Reducing Malnutrition (FAARM) trial in two sub-districts of Habiganj, Sylhet Division, Bangladesh were analyzed for alpha thalassemia, beta thalassemia, and other hemoglobinopathies. We examined the association of each inherited blood disorder with hemoglobin concentration and anemia using linear and logistic regression. - Results: We identified at least one inherited blood disorder in 11% of women and 10% of children. Alpha thalassemia was most prevalent, identified in 7% of women and 5% of children, followed by beta thalassemia and hemoglobin E in 2–3%. We also identified cases of hemoglobin S and hemoglobin D in this population. Having any of the identified inherited blood disorders was associated with lower hemoglobin values among non-pregnant women, largely driven by alpha and beta thalassemia. Pregnant women with beta thalassemia were also more likely to have lower hemoglobin concentrations. Among children, we found weak evidence for a relationship between hemoglobinopathy and lower hemoglobin concentrations. - Conclusions: We found a high prevalence of alpha thalassemia among both women and children in rural Sylhet, Bangladesh–higher than all other identified hemoglobinopathies combined. Community-based estimates of alpha thalassemia prevalence in Bangladesh are scarce, yet our findings suggest that alpha thalassemia may comprise the majority of inherited blood disorders in some regions of the country. We recommend that future research on inherited blood disorders in Bangladesh include estimates of alpha thalassemia in their reporting for public health awareness and to facilitate couples counseling.

Description: Oil palm cultivation has become one of the world's most important drivers of land use change in the tropics causing biodiversity loss and greenhouse gas emissions. The impact of climate change and rising carbon dioxide (CO2) concentrations in the atmosphere on oil palm productivity is not well understood. If environmental change leads to declining palm oil yields in existing cultivation areas, cultivation areas may expand or shift to other regions. Here we assess climate change impacts on palm oil production using an extended version of the dynamic global vegetation model with managed land, LPJmL4, and a range of climate scenarios from the inter-sectoral impact model intercomparison project. We find increasing average yields under all future climate scenarios. This contradicts earlier studies, which did not consider the potential positive effect of CO2 fertilization. If we do not account for CO2 fertilization, future yields also decrease in our simulations. Our results indicate the potentially large role of rising CO2 levels on oil palm cultivation. This highlights the importance of further applied plant science to better understand the impact of climate change and elevated CO2 levels on oil palm growth and productivity.

Description: Many physical, biological, and social systems exhibit emergent properties arising from their components’ interactions (cells). In this study, we systematically treat every-pair interactions (a) that exhibit power-law dependence on the Euclidean distance and (b) act in structures that can be characterized using fractal geometry. It can represent the two-body interaction potential, the heat flux between two parts of a structure, friendship strength between two people, etc.. We analytically derive the average intensity of influence that one cell has on the others or, conversely, receives from them. This quantity is referred to as the mean interaction field of the cells, and we find that (i) in a long-range interaction regime, the mean interaction field increases following a power-law with the size of the system, (ii) in a short-range interaction regime, the field saturates, and (iii) in the intermediate range it follows a logarithmic behavior. To validate our analytical solution, we perform numerical simulations. For long-range interactions, the theoretical calculations align closely with the numerical results. However, for short-range interactions, we observe that discreteness significantly impacts the continuum approximation used in the derivation, leading to incorrect asymptotic behavior in this regime. To address this issue, we propose an expansion that substantially improves the accuracy of the analytical expression. We discuss applications of the every-pair interactions system proposed, and one of them is to explore a framework for estimating the fractal dimension of unknown structures. This approach offers an alternative to established methods such as box-counting or sandbox methods. Overall, we believe that our analytical work will have broad applicability in systems where every-pair interactions play a role.

Description: Adaptive management of crop growing periods by adjusting sowing dates and cultivars is one of the central aspects of crop production systems, tightly connected to local climate. However, it is so far underrepresented in crop-model based assessments of yields under climate change. In this study, we integrate models of farmers’ decision making with biophysical crop modeling at the global scale to simulate crop calendars adaptation and its effect on crop yields of maize, rice, sorghum, soybean and wheat. We simulate crop growing periods and yields (1986-2099) under counterfactual management scenarios assuming no adaptation, timely adaptation or delayed adaptation of sowing dates and cultivars. We then compare the counterfactual growing periods and corresponding yields at the end of the century (2080-2099). We find that (i) with adaptation, temperature-driven sowing dates (typical at latitudes 〉30°N-S) will have larger shifts than precipitation-driven sowing dates (at latitudes 〈30°N-S); (ii) later-maturing cultivars will be needed, particularly at higher latitudes; (iii) timely adaptation of growing periods would increase actual crop yields by ~12%, reducing climate change negative impacts and enhancing the positive CO2 fertilization effect. Despite remaining uncertainties, crop growing periods adaptation require consideration in climate change impact assessments.

Description: Mountain glaciers are sensitive recorders of natural and human-induced climate change. Therefore, it is imperative to obtain a comprehensive understanding of the interplay between climate and glacier response on both short and long timescales. Here we present an analysis of oxygen and carbon isotope data from speleothems formed mainly below a glacier-covered catchment in the Alps 300,000 to 200,000 years ago. Isotope-enabled climate model simulations reveal that δ18O of precipitation in the Alps was higher by approximately 1 ‰ during interstadials compared to stadials. This agrees with interstadial-stadial amplitudes of our new speleothem-based estimate after correcting for cave-internal effects. We propose that the variability of these cave-internal effects offers a unique tool for reconstructing long-term dynamics of warm-based Alpine palaeoglaciers. Our data thereby suggests a close link between North Atlantic interstadial-stadial variability and the meltwater dynamics of Alpine glaciers during Marine Isotope Stage 8 and 7d.

Description: The instability with respect to global glaciation is a fundamental property of the climate system caused by the positive ice-albedo feedback. The atmospheric concentration of carbon dioxide (CO2) at which this Snowball bifurcation occurs changes through Earth's history, most notably because of the slowly increasing solar luminosity. Quantifying this critical CO2 concentration is not only interesting from a climate dynamics perspective but also constitutes an important prerequisite for understanding past Snowball Earth episodes, as well as the conditions for habitability on Earth and other planets. Earlier studies are limited to investigations with very simple climate models for Earth's entire history or studies of individual time slices carried out with a variety of more complex models and for different boundary conditions, making comparisons and the identification of secular changes difficult. Here, we use a coupled climate model of intermediate complexity to trace the Snowball bifurcation of an aquaplanet through Earth's history in one consistent model framework. We find that the critical CO2 concentration decreased more or less logarithmically with increasing solar luminosity until about 1 billion years ago but dropped faster in more recent times. Furthermore, there was a fundamental shift in the dynamics of the critical state about 1.2 billion years ago (unrelated to the downturn in critical CO2 values), driven by the interplay of wind-driven sea-ice dynamics and the surface energy balance: for critical states at low solar luminosities, the ice line lies in the Ferrel cell, stabilised by the poleward winds despite moderate meridional temperature gradients under strong greenhouse warming. For critical states at high solar luminosities, on the other hand, the ice line rests at the Hadley cell boundary, stabilised against the equatorward winds by steep meridional temperature gradients resulting from the increased solar energy input at lower latitudes and stronger Ekman transport in the ocean.

Description: To represent the impact of grazing livestock on carbon (C) and nitrogen (N) dynamics in grasslands, we implement a livestock module into LPJmL5.0-tillage, a global vegetation and crop model with explicit representation of managed grasslands and pastures, forming LPJmL5.0-grazing. The livestock module uses lactating dairy cows as a generic representation of grazing livestock. The new module explicitly accounts for forage quality in terms of dry-matter intake and digestibility using relationships derived from compositional analyses for different forages. Partitioning of N into milk, feces, and urine as simulated by the new livestock module shows very good agreement with observation-based relationships reported in the literature. Modelled C and N dynamics depend on forage quality (C:N ratios in grazed biomass), forage quantity, livestock densities, manure or fertilizer inputs, soil, atmospheric CO2 concentrations, and climate conditions. Due to the many interacting relationships, C sequestration, GHG emissions, N losses, and livestock productivity show substantial variation in space and across livestock densities. The improved LPJmL5.0-grazing model can now assess the effects of livestock grazing on C and N stocks and fluxes in grasslands. It can also provide insights about the spatio-temporal variability of grassland productivity and about the trade-offs between livestock production and environmental impacts.

Description: Temperature targets of the Paris Agreement limit global net cumulative emissions to very tight carbon budgets. The possibility to overshoot the budget and offset near-term excess emissions by net-negative emissions is considered economically attractive as it eases near-term mitigation pressure. While potential side effects of carbon removal deployment are discussed extensively, the additional climate risks and the impacts and damages have attracted less attention. We link six models for an integrative analysis of the climatic, environmental and socio-economic consequences of temporarily overshooting a carbon budget consistent with the 1.5 °C temperature target along the cause-effect chain from emissions and carbon removals to climate risks and impact. Global climatic indicators such as CO2-concentration and mean temperature closely follow the carbon budget overshoot with mid-century peaks of 50 ppmv and 0.35 °C, respectively. Our findings highlight that investigating overshoot scenarios requires temporally and spatially differentiated analysis of climate, environmental and socioeconomic systems. We find persistent and spatially heterogeneous differences in the distribution of carbon across various pools, ocean heat content, sea-level rise as well as economic damages. Moreover, we find that key impacts, including degradation of marine ecosystem, heat wave exposure and economic damages, are more severe in equatorial areas than in higher latitudes, although absolute temperature changes being stronger in higher latitudes. The detrimental effects of a 1.5 °C warming and the additional effects due to overshoots are strongest in non-OECD countries (Organization for Economic Cooperation and Development). Constraining the overshoot inflates CO2 prices, thus shifting carbon removal towards early afforestation while reducing the total cumulative deployment only slightly, while mitigation costs increase sharply in developing countries. Thus, scenarios with carbon budget overshoots can reverse global mean temperature increase but imply more persistent and geographically heterogeneous impacts. Overall, the decision about overshooting implies more severe trade-offs between mitigation and impacts in developing countries.

Description: Over the past decade, Greenland has experienced several extreme melt events, the most pronounced ones in the years 2010, 2012 and 2019. With progressing climate change, such extreme melt events can be expected to occur more frequently and potentially become more severe and persistent. So far, however, projections of ice loss and sea level change from Greenland typically rely on scenarios which only take gradual changes in the climate into account. Using the Parallel Ice Sheet Model (PISM), we investigate the effect of extreme melt events on the overall mass balance of the Greenland Ice Sheet and the changes in ice flow, invoked by the altered surface topography. As a first constraint, this study estimates the overall effect of extreme melt events on the cumulative mass loss of the Greenland Ice Sheet. We find that the sea level contribution from Greenland might increase by 2 to 45 cm (0.2 % to 14 %) by the year 2300 if extreme events occur more frequently in the future under a Representative Concentration Pathway 8.5 (RCP8.5) scenario, and the ice sheet area might be reduced by an additional 6000 to 26 000 km2 by 2300 in comparison to future warming scenarios without extremes. In conclusion, projecting the future sea level contribution from the Greenland Ice Sheet requires consideration of the changes in both the frequency and intensity of extreme events. It is crucial to individually address these extremes at a monthly resolution as temperature forcing with the same excess temperature but evenly distributed over longer timescales (e.g., seasonal) leads to less sea level rise than for the simulations of the resolved extremes. Extremes lead to additional mass loss and thinning. This, in turn, reduces the driving stress and surface velocities, ultimately dampening the ice loss attributed to ice flow and discharge. Overall, we find that the surface elevation feedback largely amplifies melting for scenarios with and without extremes, with additional mass loss attributed to this feedback having the greatest impact on projected sea level.

Description: The timescales of the flow and retreat of Greenland’s and Antarctica’s outlet glaciers and their potential instabilities are arguably the largest uncertainty in future sea-level projections. Here we derive a scaling relation that allows the comparison of the timescales of observed complex ice flow fields with geometric similarity. The scaling relation is derived under the assumption of fast, laterally confined, geometrically similar outlet-glacier flow over a slippery bed, i.e., with negligible basal friction. According to the relation, the time scaling of the outlet flow is determined by the product of the inverse of 1) the fourth power of the width-to-length ratio of its confinement, 2) the third power of the confinement depth and 3) the temperature- dependent ice softness. For the outflow at the grounding line of streams with negligible basal friction this means that the volume flux is proportional to the ice softness and the bed depth, but goes with the fourth power of the gradient of the bed and with the fifth power of the width of the stream. We show that the theoretically derived scaling relation is supported by the observed velocity scaling of outlet glaciers across Greenland as well as by idealized numerical simulations of marine ice-sheet instabilities (MISIs) as found in Antarctica. Assuming that changes in the ice-flow velocity due to ice-dynamic imbalance are proportional to the equilibrium velocity, we combine the scaling relation with a statistical analysis of the topography of 13 MISI-prone Antarctic outlets. Under these assumptions the timescales in response to a potential destabilization are fastest for Thwaites Glacier in West Antarctica and Mellor, Ninnis and Cook Glaciers in East Antarctica; between 16 and 67 times faster than for Pine Island Glacier. While the applicability of our results is limited by several strong assumptions, the utilization and potential further development of the presented scaling approach may help to constrain time-scale estimates of outlet glacier- flow, augmenting the commonly exploited and comparatively computationally expensive approach of numerical modeling.

Description: We present a new open source dataset FLODIS that links estimates of flood-induced human displacements, fatalities, and economic damages to flooded areas observed through remote sensing. The dataset connects displacement data from the Internal Displacement Monitoring Centre (IDMC), as well as data on fatalities and damages from the Emergency Events Database (EM-DAT), with the Global Flood Database (GFD), a satellite-based inventory of historic flood footprints. It thereby provides a spatially explicit estimate of the flood hazard underlying each individual disaster event. FLODIS contains two datasets with event-specific information for 335 human displacement events and 695 mortality/damage events that occurred around the world between 2000 and 2018. Additionally, we provide estimates of affected population, GDP, and critical infrastructure, as well as socio-economic indicators; and we provide geocoding for displacement events ascribed to other types of disasters, such as tropical cyclones, so that they may be linked to corresponding hazard estimates in future work. FLODIS facilitates integrated flood risk analysis, allowing, for example, for detailed assessments of local flood-damage and displacement vulnerability.

Description: Many phenomena of high relevance for economic development such as human capital, geography and climate vary considerably within countries as well as between them. Yet, global data sets of economic output are typically available at the national level only, thereby limiting the accuracy and precision of insights gained through empirical analyses. Recent work has used interpolation and downscaling to yield estimates of sub-national economic output at a global scale, but respective data sets based on official, reported values only are lacking. We here present DOSE — the MCC-PIK Database Of Sub-national Economic Output. DOSE contains harmonised data on reported economic output from 1,661 sub-national regions across 83 countries from 1960 to 2020. To avoid interpolation, values are assembled from numerous statistical agencies, yearbooks and the literature and harmonised for both aggregate and sectoral output. Moreover, we provide temporally- and spatially-consistent data for regional boundaries, enabling matching with geo-spatial data such as climate observations. DOSE provides the opportunity for detailed analyses of economic development at the subnational level, consistent with reported values.

Description: The summer of 2018 was an extraordinary season in climatological terms for northern and central Europe, bringing simultaneous, widespread, and concurrent heat and drought extremes in large parts of the continent with extensive impacts on agriculture, forests, water supply, and socio-economic sector. Here, we present a comprehensive, multi-faceted analysis of the 2018 extreme summer in terms of heat and drought in central and northern Europe, with a particular focus on Germany. The heatwave first affected Scandinavia by mid-July, shifted towards central Europe in late July, while Iberia was primarily affected in early August. The atmospheric circulation was characterized by strongly positive blocking anomalies over Europe, in combination with a positive summer North Atlantic Oscillation and a double jet stream configuration before the initiation of the heatwave. In terms of possible precursors common to previous European heatwaves, the Eurasian double jet structure and a tripolar sea-surface temperature anomaly over the North Atlantic were identified already in spring. While in the early stages over Scandinavia the air masses at mid- and upper-levels were often of remote, maritime origin, at later stages over Iberia the air masses had primarily a local-to-regional origin. The drought affected Germany the most, starting with warmer than average conditions in spring, associated with enhanced latent heat release that initiated a severe depletion of soil moisture. During summer, a continued precipitation deficit exacerbated the problem, leading to hydrological and agricultural drought. A probabilistic attribution assessment of the heatwave in Germany showed that such events of prolonged heat have become more likely due to anthropogenic global warming. Regarding future projections, an extreme summer such as this of 2018 is expected to occur every two out of three years in Europe under a 1.5 °C warmer world and virtually every single year under 2 °C of global warming. With such large-scale and impactful extreme events becoming more frequent and intense under anthropogenic climate change, comprehensive and multi-faceted studies like the one presented here quantify the multitude of effects and provide valuable information as basis for adaptation and mitigation strategies.

Description: Background: Heat exposure, which can negatively affect human health and wellbeing, is heterogeneous within US cities. However, little is known about who can avoid heat stress by adjusting their everyday behaviour. We aimed to analyse the effect of ambient temperature on mobility, specifically subway (ie, the underground railway system) use, in New York City, NY, USA, during 2014–19. - Methods: For this empirical study, subway use across New York City was measured with turnstile data from the New York City Metropolitan Transportation Authority between Jan 1, 2014, and Dec 31, 2019. Passenger numbers were then aggregated to the zip code tabulation area (ZCTA) level. Daily observational climate data were obtained from the US National Weather Service between Jan 1, 2014, and Dec 31, 2019. Socioeconomic data at the ZCTA level originated from the American Community Survey 2019. We extracted data on population age, ethnicity, commuting, employment, median household income, rent, and health-insurance coverage. We used a fixed-effects panel-regression model to assess the influence of temperature on subway use in New York City, which was the main outcome of our study. - Findings: We obtained data for 438 subway stations across New York City. After data cleaning and preprocessing, the final aggregated data sample consisted of 238 508 instances of subway use in 1955 days across 6 years for 122 ZCTAs, with 168 days missing in the raw data and 67 days removed as outliers. The results of the fixed-effects panel-regression analysis showed a strong, non-linear effect of daily maximum temperature on subway use. Subway use was highest at 11·5°C and substantially decreased for temperatures that were colder and warmer than that, with reductions reaching 6·5% (95% CI 2·5–10·5) for the coldest temperature (ie, –6·5°C) and 10·5% (6·0–14·0) for the hottest temperature (ie, 34·5°C). Reductions differed between weekdays and weekends, when residents generally had more freedom to adjust their behaviour. Neighbourhoods that were at a socioeconomic disadvantage experienced smaller or no reductions in mobility in heat; mobility increased in neighbourhoods with beach access. - Interpretation: Our study showed that temperature had a strong, non-linear effect on subway use, but the magnitude of the effect on subway use was heterogeneous across areas of the city on warm days. Weaker avoidance of heat stress correlated with less privilege, indicating compounding health risks. Everyday behavioural adaptation to heat is therefore an effect pathway that contributes to unequal heat effects and should be explored in future research.

Description: Vine phenology modelling is increasingly important for winegrowers and viticulturists. Model calibration is often required before practical applications. However, when multiple models and optimization methods are applied for different varieties, it is rarely known which factor tends to mostly affect the calibration results. We mainly aim to investigate the main source of the variability in the modelling errors for the flowering timings of two important varieties of vine in the Douro Demarcated Region (DDR) of Portugal; this is based on five phenology model simulations that use optimal parameters and that are estimated by three optimization algorithms (MLE, SA and SCE-UA). Our results indicate that the main source of the variability in calibration can be affected by the initially assumed parameter boundary. Restricting the initial parameter distribution to a narrow range impedes the algorithm from exploring the full parameter space and searching for optimal parameters. This can lead to the largest variation in different models. At an identified appropriate boundary, the difference between the two varieties represents the largest source of uncertainty, while the choice of algorithm for calibration contributes least to the overall uncertainty. The smaller variability among different models or algorithms (tools for analysis) compared to between different varieties could indicate the overall reliability of the calibration. All optimization algorithms show similar results in terms of the obtained goodness-of-fit: the RMSE (MAE) is 5–6 (4–5) days with a negligible mean bias and moderately good R2 (0.5–0.6) for the ensemble median predictor. Nevertheless, a similar predictive performance can result from differently estimated parameter values, due to the equifinality or multi-modal issue in which different parameter combinations give similar results. This mainly occurs for models with a non-linear structure compared to those with a near-linear one. Yet, the former models are found to outperform the latter ones in predicting the flowering timing of the two varieties in the DDR. Overall, our findings highlight the importance of carefully defining the initial parameter boundary and decomposing the total variance of prediction errors. This study is expected to bring new insights that will help to better inform users about the importance of choice when these factors are involved in calibration. Nonetheless, the importance of each factor can change depending on the specific situation. Details of how the optimization methods are applied and of the continuous model improvement are important.

Description: Data on women’s living conditions and socio-economic development are important for understanding and addressing the pronounced challenges and inequalities faced by women worldwide. While such information is increasingly available at the national level, comparable data at the sub-national level are missing. We here present the LivWell global longitudinal dataset, which includes a set of key indicators on women’s socio-economic status, health and well-being, access to basic services and demographic outcomes. It covers 447 regions in 52 countries and includes a total of 265 different indicators. The majority of these are based on 199 Demographic and Health Surveys (DHS) for the period 1990–2019 and are complemented by extensive information on socio-economic and climatic conditions in the respective regions. The resulting dataset offers various opportunities for policy-relevant research on gender inequality, inclusive development and demographic trends at the sub-national level.

Description: Classic Maya populations living in peri-urban states were highly dependent on seasonally distributed rainfall for reliable surplus crop yields. Despite intense study of the potential impact of decadal to centennial-scale climatic changes on the demise of Classic Maya sociopolitical institutions (750-950 CE), its direct importance remains debated. We provide a detailed analysis of a precisely dated speleothem record from Yok Balum cave, Belize, that reflects local hydroclimatic changes at seasonal scale over the past 1600 years. We find that the initial disintegration of Maya sociopolitical institutions and population decline occurred in the context of a pronounced decrease in the predictability of seasonal rainfall and severe drought between 700 and 800 CE. The failure of Classic Maya societies to successfully adapt to volatile seasonal rainfall dynamics likely contributed to gradual but widespread processes of sociopolitical disintegration. We propose that the complex abandonment of Classic Maya population centres was not solely driven by protracted drought but also aggravated by year-to-year decreases in rainfall predictability, potentially caused by a regional reduction in coherent Intertropical Convergence Zone-driven rainfall.

Description: We present the Land Input Generator (LandInG) version 1.0, a new toolbox for generating input datasets for terrestrial ecosystem models (TEMs) from diverse and partially conflicting data sources. While LandInG 1.0 is applicable to process data for any TEM, it is developed specifically for the open-source dynamic global vegetation, hydrology, and crop growth model LPJmL (Lund–Potsdam–Jena with managed Land). The toolbox documents the sources and processing of data to model inputs and allows for easy changes to the spatial resolution. It is designed to make inconsistencies between different sources of data transparent so that users can make their own decisions on how to resolve these should they not be content with the default assumptions made here. As an example, we use the toolbox to create input datasets at 5 and 30 arcmin spatial resolution covering land, country, and region masks, soil, river networks, freshwater reservoirs, irrigation water distribution networks, crop-specific annual land use, fertilizer, and manure application. We focus on the toolbox describing the data processing rather than only publishing the datasets as users may want to make different choices for reconciling inconsistencies, aggregation, spatial extent, or similar. Also, new data sources or new versions of existing data become available continuously, and the toolbox approach allows for incorporating new data to stay up to date.

Description: This dataset describes a series of aerosol and meteorological measurements collected in the Great Barrier Reef (GBR) Marine Park collected during the "GBR as a significant source of climatically relevant aerosol particles" campaign, known as "Reef to Rainforest" (R2R). The data covers a broad area of the GBR marine park (-27.3091 to -16.95169 latitudinally, 145.9705 to 154.1146 longitudinally) over approximately one month, from 28th September, 2016 to 24th October, 2016. The data was collected at two sites – one aboard the Australian Government Research Vessel Investigator (RVI), and an onshore site at Garner's Beach, QLD, Australia (-17.8222S, 146.1023E). Parameters measured include particle size distribution, number concentration, composition cloud condensation nuclei (CCN) properties, concentrations of gases and markers including black carbon, radon, carbon monoxide, carbon dioxide, volatile organic compounds (VOCs), dimethylsulfide (DMS), upper air particle concentration/composition, cloud or low lying fog presence, cloud top height, depolarisation ratio, aerosol scattering and optical thickness. The observations were collected in the hope of improving our understanding of the local climate and aerosol properties, which in turn will improve local models and better inform regulatory bodies protecting the GBR.

Description: Along with the radiation measurements, ROV positions were obtained from acoustic LBL (Long BaseLine)-positioning and all parameters of vehicle depth, distance to the ice and attitude recorded. All times are given in UTC.

Description: Along with the radiation measurements, ROV positions were obtained from acoustic LBL (Long BaseLine)-positioning and all parameters of vehicle depth, distance to the ice and attitude recorded. All times are given in UTC.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The dataset comprises elemental data that was obtained on sediment samples from sub-Antarctic South Georgia. The sediments were collected from a range of different sites, including lakes, peat deposits, a marine inlet and a fjord during RV Polarstern expedition PS81 in 2013. Prior to the geochemical analyses reported here, the sediments were freeze-dried and ground. More details on the study sites, sampling and data evaluation can be found in Berg et al. (submitted manuscript). Total organic carbon (TOC) was analyzed with a DIMATOC 200 (DIMATEC Corp., Canada) analyser. The TOC concentration is derived from the difference between total carbon (TC) and total inorganic carbon (TIC). Total carbon (C), total nitrogen (N) and sulfur (S) were analyzed using a Vario Micro Cube combustion elemental analyser (Elementar, Germany).

Description: The dataset comprises N-alkane composition data that was obtained on sediment samples from sub-Antarctic South Georgia. The sediments were collected from a range of different sites, including lakes, peat deposits, a marine inlet and a fjord during RV Polarstern expedition PS81 in 2013. Prior to the geochemical analyses reported here, the sediments were freeze-dried and ground. More details on the study sites, sampling and data evaluation can be found in Berg et al. (submitted manuscript). Alkyl lipid biomarkers were extracted by accelerated solvent extraction (ASE 300, Thermo, USA) with dichloromethane and methanol (DCM, MeOH; 9:1, v/v at 120°C, 75 bar) (cores Co1305, PS81/283, and Co1308) or via consecutive ultrasonication in DCM:Hexan (1:1), MeOH:DCM (1:1) and MeOH (core Co1306 and the peat and soil samples). The total lipid extract (TLE) was saponified with 0.5 M KOH in MeOH and water (9:1, v/v) at 80°C for 2 h. Neutral lipids (NL) were extracted from the TLE with dichloromethane by liquid-liquid phase separation. Alkanes were purified from NL by silica gel column chromatography (SiO2, deactivated, mesh-size 60) by elution with hexane. Analysis was conducted by gas chromatography (GC, Agilent 7890B, Agilent Technologies, USA) equipped with a flame ionization detector (FID). Alkanes were identified and quantified against authentic external standards. Concentrations are given as fractional abundances for the compound class.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: This dataset contains 3 minute resolution data with measurements from the SMPS (SMPS – TSI 3080 Classifier) and Condensation Particle Counters (CPC3787, CPC3782 and CPC3772). The SMPS gives the size distribution and the CPC gives the total particle number concentration. The number following the X is the midpoint diameter of the SMPS size bin, in nm. For instance, X11.3 denote the size bin with a diameter midpoint of 11.3 nm. As not all the CPCs were available to be used at all times, a linear model was used to model the total particle number concentration based off measurements from the three CPCs.

Description: 15 minute resolution data file with measurements from two Scanning Mobility Particle Sizers (SMPS) and one Aerodynamic Particle Sizer (APS). These instruments all measure the concentration of particles at a given size, increasing size incrementally to build a size distribution. The three size distributions are stitched together in a single distribution.

Description: Number concentrations for the 19 size classes measured by the LOAC. And Speciation index for the size classes. Species are classified into carbon, minerals, salts and water droplets. The upper and lower limit for each species varies between each size class. A speciation index chart is provided on a separate sheet (see "Additional metadata"). Numbers in the column heading represent the size bin range, as with the number concentration columns.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the ARTofMELT2023 expedition in May and June 2023. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the ARTofMELT2023 expedition in May and June 2023. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the ARTofMELT2023 expedition in May and June 2023. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the ARTofMELT2023 expedition in May and June 2023. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: This dataset contains calculated fluxes of CH4 and CO2 from surface waters in September 2021 in Lake 3 near the Mackenzie Delta, Northwest Territories, Canada. We measured surface water gas concentrations from a small boat using a closed loop chamber and a Microportable Greenhouse Gas Analyzer (Los Gatos Research). The measurements were part of the "Mackenzie Delta Permafrost Field Campaign" (mCan2021) within the "Modular Observation solutions for Earth Systems" (MOSES) program.

Description: The dataset comprises alkanol composition data that was obtained on sediment samples from sub-Antarctic South Georgia. The sediments were collected from a range of different sites, including lakes, peat deposits, a marine inlet and a fjord during RV Polarstern expedition PS81 in 2013. Prior to the geochemical analyses reported here, the sediments were freeze-dried and ground. More details on the study sites, sampling and data evaluation can be found in Berg et al. (submitted manuscript). Alkyl lipid biomarkers were extracted by accelerated solvent extraction (ASE 300, Thermo, USA) with dichloromethane and methanol (DCM, MeOH; 9:1, v/v at 120°C, 75 bar) (cores Co1305, PS81/283, and Co1308) or via consecutive ultrasonication in DCM:Hexan (1:1), MeOH:DCM (1:1) and MeOH (core Co1306 and the peat and soil samples). The total lipid extract (TLE) was saponified with 0.5 M KOH in MeOH and water (9:1, v/v) at 80°C for 2 h. Neutral lipids (NL) were extracted from the TLE with dichloromethane by liquid-liquid phase separation. Alkanes were purified from NL by silica gel column chromatography (SiO2, deactivated, mesh-size 60) by elution with hexane and alkanols by elution with chloroform. Prior to analysis by gas chromatography (GC, Agilent 7890B, Agilent Technologies, USA) equipped with a flame ionization detector (FID), alkanols were acetylated. Alkanols were identified and quantified against authentic external standards. Concentrations are given as fractional abundances for the compound class.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: 20 minute resolution data file with measurements from the GC Pulsed Flame Photometric Detector (PFPD). This instrument measures atmospheric DMS, in ppb and in nmol/m3

Description: 10 minute resolution data file with measurements from the Time-of-Flight Aerosol Mass Spectrometer (ToF-AMS), the Multi-Angle Absorption Photometer (MAAP) and the Radon detector. This data file contains measurements of markers such as black carbon and radon, as well as atmospheric composition data such as the concentration of sulfates, nitrates, organics, etc.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the ARTofMELT2023 expedition in May and June 2023. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the ARTofMELT2023 expedition in May and June 2023. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the ARTofMELT2023 expedition in May and June 2023. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the ARTofMELT2023 expedition in May and June 2023. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the ARTofMELT2023 expedition in May and June 2023. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.

Description: This dataset contains measured concentrations of CH4 and CO2 from surface waters in September 2021 in Swiss Cheese Lake in the Mackenzie Delta, Northwest Territories, Canada. We measured surface water gas concentrations from a small boat using a closed loop chamber and a Microportable Greenhouse Gas Analyzer (Los Gatos Research). The measurements were part of the "Mackenzie Delta Permafrost Field Campaign" (mCan2021) within the "Modular Observation solutions for Earth Systems" (MOSES) program.