Consistent Dataset Research Articles

A global ensemble of ocean wave climate statistics from contemporary wave reanalysis and hindcasts

There are numerous global ocean wave reanalysis and hindcast products currently being distributed and used across different scientific fields. However, there is not a consistent dataset that can sample across all existing products based on a standardized framework. Here, we present and describe the first coordinated multi-product ensemble of present-day global wave fields available to date. This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, includes general and extreme statistics of significant wave height (Hs), mean wave period (Tm) and mean wave direction (θm) computed across 1980–2014, at different frequency resolutions (monthly, seasonally, and annually). This coordinated global ensemble has been derived from fourteen state-of-the-science global wave products obtained from different atmospheric reanalysis forcing and downscaling methods. This data set has been processed, under a specific framework for consistency and quality, following standard Data Reference Syntax, Directory Structures and Metadata specifications. This new comprehensive dataset provides support to future broad-scale analysis of historical wave climatology and variability as well as coastal risk and vulnerability assessments across offshore and coastal engineering applications.

Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS

Adequate and up-to-date knowledge of the spatiotemporal dynamics of electricity power consumption (EPC) is important for the sustainable use of global electricity power resources. However, global EPC patterns were not clear after Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) in 2013 due to the significant differences between Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) and DMSP-OLS. In this paper, global EPC patterns in the recent decade are investigated and assessed for the first time by the proposed locally adaptive method with integrating two nighttime light (NTL) images to global pixel-level EPC from 1992 to 2019. The geospatial dataset of built-up area density (BUAD) is adopted with a higher spatial resolution and more direct relation to human activities. A two-step regression method is designed to simulate DMSP-like images after 2013, based on the inter-annual relationships of provincial-level VIIRS. With this consistent nighttime light dataset, pixel-level EPC over the 28 years are estimated for the first time, and then the spatiotemporal dynamics of EPC are investigated from global, continental, to national scales. The obtained EPC estimates are of satisfactory accuracy in 92.6% of the countries with a MARE (Mean of the Absolute Relative Error) of less than 20%. Over these 28 years, Japan, South Korea, and China experienced high proportion of EPC high-growth. These results provide reliable scientific basis for exploring the spatial pattern and temporal variations of global EPC, especially for the latest years.

Abstract 3080: A quality-controlled patient-derived tumor organoid biobank facilitates applications such as an integrated database of chemotherapeutic drug response using deep learning-based imaging methods

Abstract Patient-derived tumor organoids (PDTOs) have recently been used as an innovative preclinical model to predict patient-specific drug responses. However, it is critical to establish high quality, reproducible, and well-annotated PDTOs that closely recapitulate the original tumors from which they were derived. Here, we established a biobank of over 20 colorectal cancer (CRC) PDTOs representing a racially and ethnically diverse patient population that has been subjected to rigorous quality control (QC) measures. We demonstrate the utility of a well-characterized biobank across several applications including the development of an integrated chemotherapeutic drug response database and image-based phenotypic analyses using deep-learning neural networks (NNs). To ensure consistency across our biobank, PDTOs were subjected to a meticulous processing pipeline which includes the extensive collection of pertinent culturing metadata (i.e., multi-timepoint brightfield images pre- and post-passaging for consistent size and health of the PDTOs, short tandem repeat sequencing to establish a unique patient barcode, and routine mycoplasma testing results to minimize contamination) and DNA/RNA sequencing to verify the PDTOs reflect the genetic profile of the original tumor. A dashboard is used to manage culturing and characterization tasks across the Cell Line Team and samples are registered in a Lab Information Management System (LIMS) for tracking of data associated with the samples, including detailed patient information. Our high-quality biobank supports a diverse project portfolio including the creation of a database of PDTO drug responses to clinically-approved chemotherapy treatments 5-Fluorouracil and Irinotecan. This database compares PDTO-specific dose-response curves between ATP-based viability assays and image-based NN analysis and we found the IC50 values from the two analytical methods to be comparable. The NNs were designed to segment and classify organoids as live or dead from brightfield images. The label-free, non-destructive nature of this method enables dynamic imaging and analysis over multiple timepoints. Notably, our biobank and NN analysis work in concert where the biobank provides valuable, consistent datasets that are used to train and validate NNs and the NN is subsequently used to understand phenotypic drug responses of PDTOs. From these results we can show inter-patient heterogeneity in drug responses. In conclusion, we generated a reliable biobank to support numerous applications including the creation of a PDTO drug response database with deep learning validation. Such a biobank is only feasible through meticulous QC and can serve as a great resource to understand treatment outcomes and identify better therapeutic options for patients in the future. Citation Format: Scott Valena, Pratiksha Kshetri, Brandon Choi, Ah Young Yoon, Shohei Imamura, Seungil Kim, Michael E. Doche, Shannon M. Mumenthaler. A quality-controlled patient-derived tumor organoid biobank facilitates applications such as an integrated database of chemotherapeutic drug response using deep learning-based imaging methods [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3080.

The global potential for increased storage of carbon on land

Constraining the climate crisis requires urgent action to reduce anthropogenic emissions while simultaneously removing carbon dioxide from the atmosphere. Improved information about the maximum magnitude and spatial distribution of opportunities for additional land-based removals of CO2 is needed to guide on-the-ground decision-making about where to implement climate change mitigation strategies. Here, we present a globally consistent spatial dataset (approximately 500-m resolution) of current, potential, and unrealized potential carbon storage in woody plant biomass and soil organic matter. We also provide a framework for prioritizing actions related to the restoration, management, and maintenance of woody carbon stocks and associated soils. By comparing current to potential carbon storage, while excluding areas critical to food production and human habitation, we find 287 petagrams (PgC) of unrealized potential storage opportunity, of which 78% (224 PgC) is in biomass and 22% (63 PgC) is in soil. Improved management of existing forests may offer nearly three-fourths (206 PgC) of the total unrealized potential, with the majority (71%) concentrated in tropical ecosystems. However, climate change is a source of considerable uncertainty. While additional research is needed to understand the impact of natural disturbances and biophysical feedbacks, we project that the potential for additional carbon storage in woody biomass will increase (+17%) by 2050 despite projected decreases (−12%) in the tropics. Our results establish an absolute reference point and conceptual framework for national and jurisdictional prioritization of locations and actions to increase land-based carbon storage.

Objective Bayesian Nets for Integrating Consistent Datasets

This paper addresses a data integration problem: given several mutually consistent datasets each of which measures a subset of the variables of interest, how can one construct a probabilistic model that fits the data and gives reasonable answers to questions which are under-determined by the data? Here we show how to obtain a Bayesian network model which represents the unique probability function that agrees with the probability distributions measured by the datasets and otherwise has maximum entropy. We provide a general algorithm, OBN-cDS, which offers substantial efficiency savings over the standard brute-force approach to determining the maximum entropy probability function. Furthermore, we develop modifications to the general algorithm which enable further efficiency savings but which are only applicable in particular situations. We show that there are circumstances in which one can obtain the model (i) directly from the data; (ii) by solving algebraic problems; and (iii) by solving relatively simple independent optimisation problems.

Thirty-five years of tiger shark Galeocerdo cuvier relative abundance near Bimini, The Bahamas, and the Southeastern United States with a comparison across jurisdictional bounds.

Abundances of large sharks are reported to have declined worldwide, and in response various levels of fisheries management and conservation efforts have been established. For example, marine-protected areas have been suggested as a means to protect large expanses of ocean from fishing and other industrial activities (e.g., habitat destruction), and in 2011 The Commonwealth of The Bahamas established The Bahamas Shark Sanctuary. Nonetheless, assessing the effectiveness of conservation efforts is challenging because consistent long-term data sets of shark abundances are often lacking, especially throughout the Caribbean and The Bahamas. In this study, the authors investigated the catch rates and demographics of tiger sharks Galeocerdo cuvier caught in a fishery-independent survey near Bimini, The Bahamas, from 1984 to 2019 to assess relative abundance trends following the banning of longline fishing in 1993 and the subsequent establishment of the shark sanctuary. To contextualize the relative abundance trends near Bimini, the authors compared this to the relative abundance of tiger sharks in a fishery-dependent survey from the Southeastern USA (SE USA), conducted from 1994 to 2019. The data of this study suggest that local abundance of tiger sharks has been stable near Bimini since the 1980s, including after the ban of longline fishing and the implementation of the shark sanctuary. In comparison, the abundance near the SE USA has slowly increased in the past decade, following potential declines in the decade preceding the USA Shark Management Plan. The results of this study provide some optimism that current conservation efforts in The Bahamas have been effective to maintain local tiger shark abundance within the protected area. In addition, current fisheries management in the SE USA is allowing this species to recover within those waters.

Assessing representation of remote sensing derived forest structure and land cover across a network of protected areas.

Protected areas (PA) are an effective means of conserving biodiversity and protecting suites of valuable ecosystem services. Currently, many nations and international governments use proportional area protected as a critical metric for assessing progress towards biodiversity conservation. However, the areal and other common metrics do not assess the effectiveness of PA networks, nor do they assess how representative PA are of the ecosystems they aim to protect. Topography, stand structure, and land cover are all key drivers of biodiversity within forest environments, and are well‐suited as indicators to assess the representation of PA. Here, we examine the PA network in British Columbia, Canada, through drivers derived from freely‐available data and remote sensing products across the provincial biogeoclimatic ecosystem classification system. We examine biases in the PA network by elevation, forest disturbances, and forest structural attributes, including height, cover, and biomass by comparing a random sample of protected and unprotected pixels. Results indicate that PA are commonly biased towards high‐elevation and alpine land covers, and that forest structural attributes of the park network are often significantly different in protected versus unprotected areas (426 out of 496 forest structural attributes found to be different; p < 0.01). Analysis of forest structural attributes suggests that establishing additional PA could ensure representation of various forest structure regimes across British Columbia's ecosystems. We conclude that these approaches using free and open remote sensing data are highly transferable and can be accomplished using consistent datasets to assess PA representations globally.

An immunological glimpse of human virus peptides: Distance from self, MHC class I binding, proteasome cleveage, TAP transport and sequence composition entropy

Adaptive immune response is triggered when specific pathogen peptides called epitopes are recognised as exogenous according to the paradigm of self/non-self. To be recognized by immune cells, epitopes have to be exposed (presented) on the surface of the cell. Predicting if a peptide is exposed is important to shed light on the rules that govern immune response and, thus, identify potential targets and design vaccine and drugs. We focused on peptides exposed on cell surface and made accessible to immune system through the MHC Class I complex. Before this can happen, three successive selection steps have to take place: a) Proteasome cleveage, b) TAP Transport, and c) binding to MHC-class I. Starting from a set of 211 host human reference viruses, we computed the set of unique peptides occurring in the correspondent proteomes. Then, we obtained the probability values of Proteasome Cleveage, TAP Transport and Binding to MHC Class I associated to those peptides through established prediction software tools. Such values were analysed in conjunction with two other features that could play a major role: the distance from self, strictly linked to the concept of nullomers, and the sequence entropy, measuring the complexity of the peptide amino acid composition. The analysis confirmed and extended previous results on a larger, more significant and consistent data set; we showed that the higher the distances from self, the higher the score of TAP Transport and binding to MHC class I; no significant association was instead found between distance from self and Proteasome Cleveage. Additionally, amino acid peptide composition entropy was significantly associated with the other features. In particular, higher entropies were linked with higher scores of Proteasome Cleveage, TAP Transport, Binding to MHC Class I, and higher distance from self. The relationship among the three selection steps provided evidence of a tight inter-correlation, clearly suggesting it could be the product of a co-evolutive process. We believe that these results give new insights on the complex processes that regulate peptide presentation through MHC class I, and unveil the mechanisms the allow the immune system to distinguish self and viral non-self peptides.

Projecting future nitrogen inputs: are we making the right assumptions?

Global use of reactive nitrogen (N) has increased over the past century to meet growing food and biofuel demand, while contributing to substantial environmental impacts. Addressing continued N management challenges requires anticipating pathways of future N use. Several studies in the scientific literature have projected future N inputs for crop production under a business-as-usual scenario. However, it remains unclear how using yield response functions to characterize a given level of technology and management practices (TMP) will alter the projections when using a consistent dataset. In this study, to project N inputs to 2050, we developed and tested three approaches, namely ‘Same nitrogen use efficiency (NUE)’, ‘Same TMP’, and ‘Improving TMP’. We found the approach that considers diminishing returns in yield response functions (‘Same TMP’) resulted in 268 Tg N yr−1 of N inputs, which was 61 and 48 Tg N yr−1 higher than when keeping NUE at the current level with and without considering changes in crop mix, respectively. If TMP continue to evolve at the pace of past five decades, projected N inputs reduce to 204 Tg N yr−1, a value that is still 59 Tg N yr−1 higher than the inputs in the baseline year 2006. Overall, our results suggest that assuming a constant NUE may be too optimistic in projecting N inputs, and the full range of projection assumptions need to be carefully explored when investigating future N budgets.

Soil moisture estimation in South Asia via assimilation of SMAP retrievals

Abstract. A soil moisture retrieval assimilation framework is implemented across South Asia in an attempt to improve regional soil moisture estimation as well as to provide a consistent regional soil moisture dataset. This study aims to improve the spatiotemporal variability of soil moisture estimates by assimilating Soil Moisture Active Passive (SMAP) near-surface soil moisture retrievals into a land surface model. The Noah-MP (v4.0.1) land surface model is run within the NASA Land Information System software framework to model regional land surface processes. NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA2) and Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals (IMERG) provide the meteorological boundary conditions to the land surface model. Assimilation is carried out using both cumulative distribution function (CDF)-corrected (DA-CDF) and uncorrected SMAP retrievals (DA-NoCDF). CDF matching is applied to correct the statistical moments of the SMAP soil moisture retrieval relative to the land surface model. Comparison of assimilated and model-only soil moisture estimates with publicly available in situ measurements highlights the relative improvement in soil moisture estimates by assimilating SMAP retrievals. Across the Tibetan Plateau, DA-NoCDF reduced the mean bias and RMSE by 8.4 % and 9.4 %, even though assimilation only occurred during less than 10 % of the study period due to frozen (or partially frozen) soil conditions. The best goodness-of-fit statistics were achieved for the IMERG DA-NoCDF soil moisture experiment. The general lack of publicly available in situ measurements across irrigated areas limited a domain-wide direct model validation. However, comparison with regional irrigation patterns suggested correction of biases associated with an unmodeled hydrologic phenomenon (i.e., anthropogenic influence via irrigation) as a result of SMAP soil moisture retrieval assimilation. The greatest sensitivity to assimilation was observed in cropland areas. Improvements in soil moisture potentially translate into improved spatiotemporal patterns of modeled evapotranspiration, although limited influence from soil moisture assimilation was observed on modeled processes within the carbon cycle such as gross primary production. Improvement in fine-scale modeled estimates by assimilating coarse-scale retrievals highlights the potential of this approach for soil moisture estimation over data-scarce regions.

A global record of annual terrestrial Human Footprint dataset from 2000 to 2018

Human Footprint, the pressure imposed on the eco-environment by changing ecological processes and natural landscapes, is raising worldwide concerns on biodiversity and ecological conservation. Due to the lack of spatiotemporally consistent datasets of Human Footprint over a long temporal span, many relevant studies on this topic have been limited. Here, we mapped the annual dynamics of the global Human Footprint from 2000 to 2018 using eight variables that reflect different aspects of human pressures. The accuracy assessment revealed a good agreement between our mapped results and the previously developed datasets in different years. We found more than two million km2 of wilderness (i.e., regions with Human Footprint values below one) were lost over the past two decades. The biome dominated by mangroves experienced the most significant loss (i.e., above 5%) of wilderness, likely attributed to intensified human activities in coastal areas. The derived annual and spatiotemporally consistent global Human Footprint can be a fundamental dataset for many relevant studies about human activities and natural resources.

Satellite detection of snow avalanches using Sentinel-1 in a transitional snow climate

Snow avalanches endanger lives and infrastructure in mountainous regions worldwide. Consistent and accurate datasets of avalanche events are critical for improving hazard forecasting and understanding the spatial and temporal patterns of avalanche activity. Remote sensing-based identification of avalanche debris allow for the acquisition of continuous and spatially consistent avalanches datasets. This study utilizes expert manual interpretations of Sentinel-1 synthetic aperture radar (SAR) satellite backscatter images to identify avalanche debris and compares those detections against historical field records of observed avalanches in the transitional snow climates of Wyoming and Utah, USA. We explore and quantify the ability of an expert using Sentinel-1 (a SAR satellite) images to detect avalanche debris on a dataset comprised exclusively of dry slab avalanches. This research utilized four avalanche cycles with 258 field reported avalanches. Due to individual avalanches appearing in multiple overlapping Sentinel-1 images this resulted in 506 potential detections of avalanches in our SAR images, representing the possibility of multiple detections of a single avalanche event in different images. The overall probability of detection (POD) for avalanches large enough to destroy trees or bury a car (i.e., ≥D3 on the destructive size scale) was 65%. There was a significant variance in the POD among the 13 individual SAR image pairs considered (15–86%). Additionally, this study investigated the connection between successful avalanche detections and SAR-specific, topographic, and avalanche type variables. The most correlated variables with higher detection rates were avalanche path lengths, destructive size of the avalanche, incidence angles for the incoming microwaves, average path slope angle, and elapsed time between the avalanche and a Sentinel-1 satellite image acquisition. This study provides a quantification of the controlling variables in the likelihood of detecting avalanches using Sentinel-1 backscatter temporal change detection techniques, as specifically applied to a transitional snow climate.

A spatially-explicit harmonized global dataset of critical infrastructure

Critical infrastructure (CI) is fundamental for the functioning of a society and forms the backbone for socio-economic development. Natural and human-made threats, however, pose a major risk to CI. Therefore, geospatial data on the location of CI are fundamental for in-depth risk analyses, which are required to inform policy decisions aiming to reduce risk. We present a first-of-its-kind globally harmonized spatial dataset for the representation of CI. In this study, we: (1) collect and harmonize detailed geospatial data of the world’s main CI systems into a single geospatial database; and (2) develop the Critical Infrastructure Spatial Index (CISI) to express the global spatial intensity of CI. The CISI aggregates high-resolution geospatial OpenStreetMap (OSM) data of 39 CI types that are categorized under seven overarching CI systems. The detailed geospatial data are rasterized into a harmonized and consistent dataset with a resolution of 0.10 × 0.10 and 0.25 × 0.25 degrees. The dataset can be applied to explore the current landscape of CI, identify CI hotspots, and as exposure input for large-scale risk assessments.

Climate refugia for Pinus spp. in topographic and bioclimatic environments of the Madrean sky islands of México and the United States

Climate refugia, or places where habitats are expected to remain relatively buffered from regional climate extremes, provide an important focus for science and conservation planning. Within high-priority, multi-jurisdictional landscapes like the Madrean sky islands of the United States and México, efforts to identify and manage climate refugia are hindered by the lack of high-quality and consistent transboundary datasets. To fill these data gaps, we assembled a bi-national field dataset (n = 1416) for five pine species (Pinus spp.) and used generalized boosted regression to model pine habitats in relation to topographic variability as a basis for identifying potential microrefugia at local scales in the context of current species’ distribution patterns. We developed additional models to quantify climatic refugial attributes using coarse scale bioclimatic variables and finer scale seasonal remote sensing indices. Terrain metrics including ruggedness, slope position, and aspect defined microrefugia for pines within elevation ranges preferred by each species. Response to bioclimatic variables indicated that small shifts in climate were important to some species (e.g., P. chihuahuana, P. strobiformis), but others exhibited a broader tolerance (e.g., P. arizonica). Response to seasonal climate was particularly important in modeling microrefugia for species with open canopy structure and where regular fires occur (e.g., P. engelmannii and P. chihuahuana). Hotspots of microrefugia differed among species and were either limited to northern islands or occurred across central or southern latitudes. Mapping and validation of refugia and their ecological functions are necessary steps in developing regional conservation strategies that cross jurisdictional boundaries. A salient application will be incorporation of climate refugia in management of fire to restore and maintain pine ecology. Una versión en español de este artículo está disponible como descarga.

RooHammerModel: interfacing the HAMMER software tool with HistFactory and RooFit

Recent B-physics results have sparkled great interest in the search for beyond-the-Standard-Model (BSM) physics in b ⟶ cℓν̅ transitions. The need to analyse in a consistent manner big datasets for these searches, using high-statistics Monte-Carlo (MC) samples, led to the development of HAMMER, a software tool which enables to perform a fast morphing of MC-derived templates to include BSM effects and/or alternative parameterisations of long-distance effects, avoiding the need to re-generate simulated samples. This paper describes the development of RooHammerModel, an interface between this tool and the commonly-used data-fitting frameworks HistFactory and RooFit. In this document, the structure and functionality of the user interface are explained. Information of a public repository where it can be accessed is provided, as well as validation and performance studies of the interface. The methods developed in the construction of RooHammerModel can provide useful information for alternative future attempts to interface HAMMER with other data-fitting frameworks.

Thermolab: A Thermodynamics Laboratory for Nonlinear Transport Processes in Open Systems

AbstractWe developed a numerical thermodynamics laboratory called “Thermolab” to study the effects of the thermodynamic behavior of nonideal solution models on reactive transport processes in open systems. The equations of the state of internally consistent thermodynamic data sets are implemented in MATLAB functions and form the basis for calculating Gibbs energy. A linear algebraic approach is used in Thermolab to compute Gibbs energy of mixing for multicomponent phases to study the impact of the nonideality of solution models on transport processes. The Gibbs energies are benchmarked with experimental data, phase diagrams, and other thermodynamic software. Constrained Gibbs minimization is exemplified with MATLAB codes and iterative refinement of composition of mixtures may be used to increase precision and accuracy. All needed transport variables such as densities, phase compositions, and chemical potentials are obtained from Gibbs energy of the stable phases after the minimization in Thermolab. We demonstrate the use of precomputed local equilibrium data obtained with Thermolab in reactive transport models. In reactive fluid flow the shape and the velocity of the reaction front vary depending on the nonlinearity of the partitioning of a component in fluid and solid. We argue that nonideality of solution models has to be taken into account and further explored in reactive transport models. Thermolab Gibbs energies can be used in Cahn‐Hilliard models for nonlinear diffusion and phase growth. This presents a transient process toward equilibrium and avoids computational problems arising during precomputing of equilibrium data.

Dale W. Jorgenson: An Intellectual Biography

Dale W. Jorgenson has been a central contributor to a wide range of economic and policy issues over a long and productive career. His research is characterized by a tight integration of economic theory, appropriate data that matches the theory, and sound econometrics. His groundbreaking work on the theory and empirics of investment established the research path for the economics profession. He is a founder of modern growth accounting: Official statistics in many countries, including the United States, implement Jorgenson’s methods. Relatedly, without Jorgenson’s unflagging efforts, consistent industry KLEMS datasets for many countries—which have been widely used in recent decades for growth accounting, econometrics, and other applications—would not exist. Jorgenson is also a pioneer in econometric modeling of producer and consumer behavior and of econometrically estimated, intertemporal general equilibrium modeling for policy analysis.

Accurate multiconfiguration calculations of energy levels, transition rates, and lifetimes for Kr XXIII

Extensive Multiconfiguration Dirac-Hartree-Fock calculations are performed for the lowest 97 levels belonging to the 3s23p2, 3s3p3, 3s23p3d, 3s3p23d, 3s23d2, and 3p4 configurations in silicon-like Kr XXIII. Complete and consistent data sets of energy levels, transition rates and lifetimes for electric-dipole (E1), electric-quadrupole (E2), magnetic-dipole (M1), and magnetic-quadrupole (M2) transitions among all these levels are given. High-accuracy calculations have been performed with accurate treatment of relativity, electron correlation, and quantum electrodynamic effects, as well as inter-electron correlation. The calculated energy levels are in excellent agreement with available theoretical and experimental data. The present results are reported as benchmarks for future calculations and measurements.

New land-use-change emissions indicate a declining CO2 airborne fraction.

About half of the anthropogenic CO2 emissions remain in the atmosphere and half are taken up by the land and ocean1. If the carbon uptake by land and ocean sinks becomes less efficient, for example, owing to warming oceans2 or thawing permafrost3, a larger fraction of anthropogenic emissions will remain in the atmosphere, accelerating climate change. Changes in the efficiency of the carbon sinks can be estimated indirectly by analysing trends in the airborne fraction, that is, the ratio between the atmospheric growth rate and anthropogenic emissions of CO2 (refs. 4-10). However, current studies yield conflicting results about trends in the airborne fraction, with emissions related to land use and land cover change (LULCC) contributing the largest source of uncertainty7,11,12. Here we construct a LULCC emissions dataset using visibility data in key deforestation zones. These visibility observations are a proxy for fire emissions13,14, which are - in turn - related to LULCC15,16. Although indirect, this provides a long-term consistent dataset of LULCC emissions, showing that tropical deforestation emissions increased substantially (0.16 Pg C decade-1) since the start of CO2 concentration measurements in 1958. So far, these emissions were thought to be relatively stable, leading to an increasing airborne fraction4,5. Our results, however, indicate that the CO2 airborne fraction has decreased by 0.014 ± 0.010 decade-1 since 1959. This suggests that the combined land-ocean sink has been able to grow at least as fast as anthropogenic emissions.

The EXPRES Stellar Signals Project II. State of the Field in Disentangling Photospheric Velocities

Abstract Measured spectral shifts due to intrinsic stellar variability (e.g., pulsations, granulation) and activity (e.g., spots, plages) are the largest source of error for extreme-precision radial-velocity (EPRV) exoplanet detection. Several methods are designed to disentangle stellar signals from true center-of-mass shifts due to planets. The Extreme-precision Spectrograph (EXPRES) Stellar Signals Project (ESSP) presents a self-consistent comparison of 22 different methods tested on the same extreme-precision spectroscopic data from EXPRES. Methods derived new activity indicators, constructed models for mapping an indicator to the needed radial-velocity (RV) correction, or separated out shape- and shift-driven RV components. Since no ground truth is known when using real data, relative method performance is assessed using the total and nightly scatter of returned RVs and agreement between the results of different methods. Nearly all submitted methods return a lower RV rms than classic linear decorrelation, but no method is yet consistently reducing the RV rms to sub-meter-per-second levels. There is a concerning lack of agreement between the RVs returned by different methods. These results suggest that continued progress in this field necessitates increased interpretability of methods, high-cadence data to capture stellar signals at all timescales, and continued tests like the ESSP using consistent data sets with more advanced metrics for method performance. Future comparisons should make use of various well-characterized data sets—such as solar data or data with known injected planetary and/or stellar signals—to better understand method performance and whether planetary signals are preserved.