Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050.

Based on an extensive model intercomparison, we assessed trends in biodiversity and ecosystem services from historical reconstructions and future scenarios of land-use and climate change. During the 20th century, biodiversity declined globally by 2 to 11%, as estimated by a range of indicators. Provisioning ecosystem services increased several fold, and regulating services decreased moderately. Going forward, policies toward sustainability have the potential to slow biodiversity loss resulting from land-use change and the demand for provisioning services while reducing or reversing declines in regulating services. However, negative impacts on biodiversity due to climate change appear poised to increase, particularly in the higher-emissions scenarios. Our assessment identifies remaining modeling uncertainties but also robustly shows that renewed policy efforts are needed to meet the goals of the Convention on Biological Diversity.

Remotely sensing potential climate change tipping points across scales.

Potential climate tipping points pose a growing risk for societies, and policy is calling for improved anticipation of them. Satellite remote sensing can play a unique role in identifying and anticipating tipping phenomena across scales. Where satellite records are too short for temporal early warning of tipping points, complementary spatial indicators can leverage the exceptional spatial-temporal coverage of remotely sensed data to detect changing resilience of vulnerable systems. Combining Earth observation with Earth system models can improve process-based understanding of tipping points, their interactions, and potential tipping cascades. Such fine-resolution sensing can support climate tipping point risk management across scales.

Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems.

Climate warming is expected to increase global methane (CH4 ) emissions from wetland ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can potentially detect CH4 flux changes, most EC systems have only a few years of data collected, so temporal trends in CH4 remain uncertain. Here, we use established drivers to hindcast changes in CH4 fluxes (FCH4 ) since the early 1980s. We trained a machine learning (ML) model on CH4 flux measurements from 22 [methane-producing sites] in wetland, upland, and lake sites of the FLUXNET-CH4 database with at least two full years of measurements across temperate and boreal biomes. The gradient boosting decision tree ML model then hindcasted daily FCH4 over 1981-2018 using meteorological reanalysis data. We found that, mainly driven by rising temperature, half of the sites (n = 11) showed significant increases in annual, seasonal, and extreme FCH4 , with increases in FCH4 of ca. 10% or higher found in the fall from 1981-1989 to 2010-2018. The annual trends were driven by increases during summer and fall, particularly at high-CH4 -emitting fen sites dominated by aerenchymatous plants. We also found that the distribution of days of extremely high FCH4 (defined according to the 95th percentile of the daily FCH4 values over a reference period) have become more frequent during the last four decades and currently account for 10-40% of the total seasonal fluxes. The share of extreme FCH4 days in the total seasonal fluxes was greatest in winter for boreal/taiga sites and in spring for temperate sites, which highlights the increasing importance of the non-growing seasons in annual budgets. Our results shed light on the effects of climate warming on wetlands, which appears to be extending the CH4 emission seasons and boosting extreme emissions.

Integrated Quantum-Classical Protocol for the Realistic Description of Solvated Multinuclear Mixed-Valence Transition-Metal Complexes and Their Solvatochromic Properties.

Linear cyanide-bridged polymetallic complexes, which undergo photoinduced metal-to-metal charge transfer, represent prototypical systems for studying long-range electron-transfer reactions and understanding the role played by specific solute-solvent interactions in modulating the excited-state dynamics. To tackle this problem, while achieving a statistically meaningful description of the solvent and of its relaxation, one needs a computational approach capable of handling large polynuclear transition-metal complexes, both in their ground and excited states, as well as the ability to follow their dynamics in several environments up to nanosecond time scales. Here, we present a mixed quantum classical approach, which combines large-scale molecular dynamics (MD) simulations based on an accurate quantum mechanically derived force field (QMD-FF) and self-consistent QMD polarized point charges, with IR and UV-vis spectral calculations to model the solvation dynamics and optical properties of a cyano-bridged trinuclear mixed-valence compound (trans-[(NC)5FeIII(µ-CN)RuII(pyridine)4(µ-NC)FeIII(CN)5]4-). We demonstrate the reliability of the QMD-FF/MD approach in sampling the solute conformational space and capturing the local solute-solvent interactions by comparing the results with higher-level quantum mechanics/molecular mechanics (QM/MM) MD reference data. The IR spectra calculated along the classical MD trajectories in different solvents correctly predict the red shift of the CN stretching band in the aprotic medium (acetonitrile) and the subtle differences measured in water and methanol, respectively. By explicitly including the solvent molecules around the cyanide ligands and calculating the thermal averaged absorption spectra using time-dependent density functional theory calculations within the Tamm-Dancoff approximation, the experimental solvatochromic shift is quantitatively reproduced going from water to methanol, while it is overestimated for acetonitrile. This discrepancy can likely be traced back to the lack of important dispersion interactions between the solvent cyano groups and the pyridine substituents in our micro solvation model. The proposed protocol is applied to the ground state in water, methanol, and acetonitrile and can be flexibly generalized to study excited-state nonequilibrium solvation dynamics.

Drivers of mangrove vulnerability and resilience to tropical cyclones in the North Atlantic Basin.

The North Atlantic Basin (NAB) has seen an increase in the frequency and intensity of tropical cyclones since the 1980s, with record-breaking seasons in 2017 and 2020. However, little is known about how coastal ecosystems, particularly mangroves in the Gulf of Mexico and the Caribbean, respond to these new "climate normals" at regional and subregional scales. Wind speed, rainfall, pre-cyclone forest height, and hydro-geomorphology are known to influence mangrove damage and recovery following cyclones in the NAB. However, previous studies have focused on local-scale responses and individual cyclonic events. Here, we analyze 25 years (1996-2020) of mangrove vulnerability (damage after a cyclone) and 24 years (1996-2019) of short-term resilience (recovery after damage) for the NAB and subregions, using multi-annual, remote sensing-derived databases. We used machine learning to characterize the influence of 22 potential variables on mangrove responses, including human development and long-term climate trends. Our results document variability in the rates and drivers of mangrove vulnerability and resilience, highlighting hotspots of cyclone impacts, mangrove damage, and loss of resilience. Cyclone characteristics mainly drove vulnerability at the regional level. In contrast, resilience was driven by site-specific conditions, including long-term climate trends, pre-cyclone forest structure, soil organic carbon stock, and coastal development (i.e., proximity to human infrastructure). Coastal development is associated with both vulnerability and resilience at the subregional level. Further, we highlight that loss of resilience occurs mostly in areas experiencing long-term drought across the NAB. The impacts of increasing cyclone activity on mangroves and their coastal protection service must be framed in the context of compound climate change effects and continued coastal development. Our work offers descriptive and spatial information to support the restoration and adaptive management of NAB mangroves, which need adequate health, structure, and density to protect coasts and serve as Nature-based Solutions against climate change and extreme weather events.

Revealing core-valence interactions in solution with femtosecond X-ray pump X-ray probe spectroscopy.

Femtosecond pump-probe spectroscopy using ultrafast optical and infrared pulses has become an essential tool to discover and understand complex electronic and structural dynamics in solvated molecular, biological, and material systems. Here we report the experimental realization of an ultrafast two-color X-ray pump X-ray probe transient absorption experiment performed in solution. A 10 fs X-ray pump pulse creates a localized excitation by removing a 1s electron from an Fe atom in solvated ferro- and ferricyanide complexes. Following the ensuing Auger-Meitner cascade, the second X-ray pulse probes the Fe 1s → 3p transitions in resultant novel core-excited electronic states. Careful comparison of the experimental spectra with theory, extracts +2 eV shifts in transition energies per valence hole, providing insight into correlated interactions of valence 3d with 3p and deeper-lying electrons. Such information is essential for accurate modeling and predictive synthesis of transition metal complexes relevant for applications ranging from catalysis to information storage technology. This study demonstrates the experimental realization of the scientific opportunities possible with the continued development of multicolor multi-pulse X-ray spectroscopy to study electronic correlations in complex condensed phase systems.

Uncovering the 3d and 4d Electronic Interactions in Solvated Ru Complexes with 2p3d Resonant Inelastic X-ray Scattering.

The electronic structure and dynamics of ruthenium complexes are widely studied given their use in catalytic and light-harvesting materials. Here we investigate three model Ru complexes, [RuIII(NH3)6]3+, [RuII(bpy)3]2+, and [RuII(CN)6]4-, with L3-edge 2p3d resonant inelastic X-ray scattering (RIXS) to probe unoccupied 4d valence orbitals and occupied 3d orbitals and to gain insight into the interactions between these levels. The 2p3d RIXS maps contain a higher level of spectral information than the L3 X-ray absorption near edge structure (XANES). This study provides a direct measure of the 3d spin-orbit splittings of 4.3, 4.0, and 4.1 eV between the 3d5/2 and 3d3/2 orbitals of the [RuIII(NH3)6]3+, [RuII(bpy)3]2+, and [RuII(CN)6]4- complexes, respectively.

Designing an Observing System to Study the Surface Biology and Geology (SBG) of the Earth in the 2020s.

Observations of planet Earth from space are a critical resource for science and society. Satellite measurements represent very large investments and United States (US) agencies organize their effort to maximize the return on that investment. The US National Research Council conducts a survey of Earth science and applications to prioritize observations for the coming decade. The most recent survey prioritized a visible to shortwave infrared imaging spectrometer and a multispectral thermal infrared imager to meet a range of needs for studying Surface Biology and Geology (SBG). SBG will be the premier integrated observatory for observing the emerging impacts of climate change by characterizing the diversity of plant life and resolving chemical and physiological signatures. It will address wildfire risk, behavior, and recovery as well as responses to hazards such as oil spills, toxic minerals in minelands, harmful algal blooms, landslides, and other geological hazards. The SBG team analyzed needed instrument characteristics (spatial, temporal, and spectral resolutions, measurement uncertainty) and assessed the cost, mass, power, volume, and risk of different architectures. We present an overview of the Research and Applications trade-study analysis of algorithms, calibration and validation needs, and societal applications with specifics of substudies detailed in other articles in this special collection. We provide a value framework to converge from hundreds down to three candidate architectures recommended for development. The analysis identified valuable opportunities for international collaboration to increase the revisit frequency, adding value for all partners, leading to a clear measurement strategy for an observing system architecture.

Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates.

The recent rise in atmospheric methane (CH4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year-1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

A multi-satellite framework to rapidly evaluate extreme biosphere cascades: The Western US 2021 drought and heatwave.

The increasing frequency and intensity of climate extremes and complex ecosystem responses motivate the need for integrated observational studies at low latency to determine biosphere responses and carbon-climate feedbacks. Here, we develop a satellite-based rapid attribution workflow and demonstrate its use at a 1-2-month latency to attribute drivers of the carbon cycle feedbacks during the 2020-2021 Western US drought and heatwave. In the first half of 2021, concurrent negative photosynthesis anomalies and large positive column CO2 anomalies were detected with satellites. Using a simple atmospheric mass balance approach, we estimate a surface carbon efflux anomaly of 132 TgC in June 2021, a magnitude corroborated independently with a dynamic global vegetation model. Integrated satellite observations of hydrologic processes, representing the soil-plant-atmosphere continuum (SPAC), show that these surface carbon flux anomalies are largely due to substantial reductions in photosynthesis because of a spatially widespread moisture-deficit propagation through the SPAC between 2020 and 2021. A causal model indicates deep soil moisture stores partially drove photosynthesis, maintaining its values in 2020 and driving its declines throughout 2021. The causal model also suggests legacy effects may have amplified photosynthesis deficits in 2021 beyond the direct effects of environmental forcing. The integrated, observation framework presented here provides a valuable first assessment of a biosphere extreme response and an independent testbed for improving drought propagation and mechanisms in models. The rapid identification of extreme carbon anomalies and hotspots can also aid mitigation and adaptation decisions.

Toward a roadmap for space-based observations of the land sector for the UNFCCC global stocktake.

Space-based remote sensing can make an important contribution toward monitoring greenhouse gas emissions and removals from the agriculture, forestry, and other land use (AFOLU) sector, and to understanding and addressing human-caused climate change through the UNFCCC Paris Agreement. Space agencies have begun to coordinate their efforts to identify needs, collect and harmonize available data and efforts, and plan and maintain a long-term roadmap for observations. International cooperation is crucial in developing and realizing the roadmap, and the Committee on Earth Observation Satellites (CEOS) is a key coordinating driver of this effort. Here, we first identify the data and information that will be useful to support the global stocktake (GST) of the Paris Agreement. Then, the paper explains how existing and planned space-based capabilities and products can be used and combined, particularly in the land use sector, and provides a workflow for their harmonization and contribution to greenhouse gas inventories and assessments at the national and global level.

Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling.

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.

Sub-continental-scale carbon stocks of individual trees in African drylands.

The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales1-14. This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems15-18. We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0-1,000 mm year-1 rainfall zone by coupling field data19, machine learning20-22, satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha-1 and 63 kg C tree-1 in the arid zone to 3.7 Mg C ha-1 and 98 kg tree-1 in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies23 for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation24-29. We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops.

Soil respiration-driven CO2 pulses dominate Australia's flux variability.

The Australian continent contributes substantially to the year-to-year variability of the global terrestrial carbon dioxide (CO2) sink. However, the scarcity of in situ observations in remote areas prevents the deciphering of processes that force the CO2 flux variability. In this study, by examining atmospheric CO2 measurements from satellites in the period 2009-2018, we find recurrent end-of-dry-season CO2 pulses over the Australian continent. These pulses largely control the year-to-year variability of Australia's CO2 balance. They cause two to three times larger seasonal variations compared with previous top-down inversions and bottom-up estimates. The pulses occur shortly after the onset of rainfall and are driven by enhanced soil respiration preceding photosynthetic uptake in Australia's semiarid regions. The suggested continental-scale relevance of soil-rewetting processes has substantial implications for our understanding and modeling of global climate-carbon cycle feedbacks.

Extensive global wetland loss over the past three centuries.

Wetlands have long been drained for human use, thereby strongly affecting greenhouse gas fluxes, flood control, nutrient cycling and biodiversity1,2. Nevertheless, the global extent of natural wetland loss remains remarkably uncertain3. Here, we reconstruct the spatial distribution and timing of wetland loss through conversion to seven human land uses between 1700 and 2020, by combining national and subnational records of drainage and conversion with land-use maps and simulated wetland extents. We estimate that 3.4 million km2 (confidence interval 2.9-3.8) of inland wetlands have been lost since 1700, primarily for conversion to croplands. This net loss of 21% (confidence interval 16-23%) of global wetland area is lower than that suggested previously by extrapolations of data disproportionately from high-loss regions. Wetland loss has been concentrated in Europe, the United States and China, and rapidly expanded during the mid-twentieth century. Our reconstruction elucidates the timing and land-use drivers of global wetland losses, providing an improved historical baseline to guide assessment of wetland loss impact on Earth system processes, conservation planning to protect remaining wetlands and prioritization of sites for wetland restoration4.

Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions.

Wetlands are the largest natural source of methane (CH4 ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH4 , but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model-iPEACE-reasonably reproduced CH4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH4 production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH4 emissions. Based on a sensitivity analysis, CH4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH4 production and CH4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH4 production, plant-mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH4 emissions across biomes. These processes and associated parameters for CH4 emissions among and within the wetlands provide useful insights for interpreting observed net CH4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH4 fluxes.

Wetland emission and atmospheric sink changes explain methane growth in 2020.

Atmospheric methane growth reached an exceptionally high rate of 15.1 ± 0.4 parts per billion per year in 2020 despite a probable decrease in anthropogenic methane emissions during COVID-19 lockdowns1. Here we quantify changes in methane sources and in its atmospheric sink in 2020 compared with 2019. We find that, globally, total anthropogenic emissions decreased by 1.2 ± 0.1 teragrams of methane per year (Tg CH4 yr-1), fire emissions decreased by 6.5 ± 0.1 Tg CH4 yr-1 and wetland emissions increased by 6.0 ± 2.3 Tg CH4 yr-1. Tropospheric OH concentration decreased by 1.6 ± 0.2 per cent relative to 2019, mainly as a result of lower anthropogenic nitrogen oxide (NOx) emissions and associated lower free tropospheric ozone during pandemic lockdowns2. From atmospheric inversions, we also infer that global net emissions increased by 6.9 ± 2.1 Tg CH4 yr-1 in 2020 relative to 2019, and global methane removal from reaction with OH decreased by 7.5 ± 0.8 Tg CH4 yr-1. Therefore, we attribute the methane growth rate anomaly in 2020 relative to 2019 to lower OH sink (53 ± 10 per cent) and higher natural emissions (47 ± 16 per cent), mostly from wetlands. In line with previous findings3,4, our results imply that wetland methane emissions are sensitive to a warmer and wetter climate and could act as a positive feedback mechanism in the future. Our study also suggests that nitrogen oxide emission trends need to be taken into account when implementing the global anthropogenic methane emissions reduction pledge5.

Process-oriented analysis of dominant sources of uncertainty in the land carbon sink.

The observed global net land carbon sink is captured by current land models. All models agree that atmospheric CO2 and nitrogen deposition driven gains in carbon stocks are partially offset by climate and land-use and land-cover change (LULCC) losses. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years. This uncertainty is driven by plant productivity, allocation, and turnover response to atmospheric CO2 (and to a smaller extent to LULCC), and the response of soil to LULCC (and to a lesser extent climate). Overall, differences in turnover explain ~70% of model spread in both vegetation and soil carbon changes. Further analysis of internal plant and soil (individual pools) cycling is needed to reduce uncertainty in the controlling processes behind the global land carbon sink.

Anthropogenic emission is the main contributor to the rise of atmospheric methane during 1993-2017.

Atmospheric methane (CH4) concentrations have shown a puzzling resumption in growth since 2007 following a period of stabilization from 2000 to 2006. Multiple hypotheses have been proposed to explain the temporal variations in CH4 growth, and attribute the rise of atmospheric CH4 either to increases in emissions from fossil fuel activities, agriculture and natural wetlands, or to a decrease in the atmospheric chemical sink. Here, we use a comprehensive ensemble of CH4 source estimates and isotopic δ13C-CH4 source signature data to show that the resumption of CH4 growth is most likely due to increased anthropogenic emissions. Our emission scenarios that have the fewest biases with respect to isotopic composition suggest that the agriculture, landfill and waste sectors were responsible for 53 ± 13% of the renewed growth over the period 2007-2017 compared to 2000-2006; industrial fossil fuel sources explained an additional 34 ± 24%, and wetland sources contributed the least at 13 ± 9%. The hypothesis that a large increase in emissions from natural wetlands drove the decrease in atmospheric δ13C-CH4 values cannot be reconciled with current process-based wetland CH4 models. This finding suggests the need for increased wetland measurements to better understand the contemporary and future role of wetlands in the rise of atmospheric methane and climate feedback. Our findings highlight the predominant role of anthropogenic activities in driving the growth of atmospheric CH4 concentrations.