Constraining estimates of terrestrial carbon uptake: new opportunities using long-term satellite observations and data assimilation

Land carbon sink function variation across bedrock types in Southwest China

Terrestrial ecosystem carbon sinks are a natural deposit that absorbs carbon from the atmosphere. A stable land carbon sink facilitates more reliable predictions of carbon sequestration under changing climate conditions. In contrast, a highly variable land carbon sink will introduce significant uncertainty into model predictions. Karst regions have attracted increasing attention due to their significant contribution to global land carbon sequestration capacity. However, understanding the stability of land carbon sinks and its driving factors in karst areas remains limited. This study focused on the world's largest karst zone, located in Southwest China (SWC), to assess the stability of land carbon sinks. By analyzing inter-annual variation (IAV) in net ecosystem productivity (NEP), we aimed to elucidate the spatial distribution of the stability of land carbon sinks and the dominant climatic drivers. We compared the stability of land carbon sinks across bedrocks, which were classified by carbonate content: non-karst, Discontinuous Carbonate Rocks (DCR), and Continuous Carbonate Rocks (CCR). Our findings showed that while land carbon sinks in karst bedrocks exhibited higher increased NEP rates than those in non-karst areas. Notably, we observed an inverse relationship between the rate and stability-regions with rapid land carbon sink enhancement were often characterized by instability, particularly in karst areas. Moreover, the drivers of the stability of land carbon sinks varied significantly between bedrock types. In non-karst regions, water availability was the primary factor influencing stability, whereas temperature was more dominant in karst regions. DCR regions showed lower stability due to the high sensitivity of land carbon sinks to temperature, while CCR regions experienced reduced stability linked to greater temperature variability. Our results highlight the need to consider the combined effects of bedrock type and climate factors on stability, offering valuable insights for managing and enhancing carbon sequestration capacity in a changing environment.

Large global-scale vegetation sensitivity to daily rainfall variability

Rainfall events are globally becoming less frequent but more intense under a changing climate, thereby shifting climatic conditions for terrestrial vegetation independent of annual rainfall totals1-3. However, it remains uncertain how changes in daily rainfall variability are affecting global vegetation photosynthesis and growth3-17. Here we use several satellite-based vegetation indices and field observations indicative of photosynthesis and growth, and find that global annual-scale vegetation indices are sensitive to the daily frequency and intensity of rainfall, independent of the total amount of rainfall per year. Specifically, we find that satellite-based vegetation indices are sensitive to daily rainfall variability across 42 per cent of the vegetated land surfaces. On average, the sensitivity of vegetation to daily rainfall variability is almost as large (95 per cent) as the sensitivity of vegetation to annual rainfall totals. Moreover, we find that wet-day frequency and intensity are projected to change with similar magnitudes and spatial extents as annual rainfall changes. Overall, our findings suggest that daily rainfall variability and its trends are affecting global vegetation photosynthesis, with potential implications for the carbon cycle and food security.

Emergent constraints on historical and future global gross primary productivity

Terrestrial gross primary productivity (GPP) is the largest carbon flux in the global carbon cycle and plays a crucial role in terrestrial carbon sequestration. However, historical and future global GPP estimates still vary markedly. In this study, we reduced uncertainties in global GPP estimates by employing an innovative emergent constraint method on remote sensing-based GPP datasets (RS-GPP), using ground-based estimates of GPP from flux towers as the observational constraint. Using this approach, the global GPP in 2001-2014 was estimated to be 126.8 ± 6.4 PgC year-1, compared to the original RS-GPP ensemble mean of 120.9 ± 10.6 PgC year-1, which reduced the uncertainty range by 39.6%. Independent space- and time-based (different latitudinal zones, different vegetation types, and individual year) constraints further confirmed the robustness of the global GPP estimate. Building on these insights, we extended our constraints to project global GPP estimates in 2081-2100 under various Shared Socioeconomic Pathway (SSP) scenarios: SSP126 (140.6 ± 9.3 PgC year-1), SSP245 (153.5 ± 13.4 PgC year-1), SSP370 (170.7 ± 16.9 PgC year-1), and SSP585 (194.1 ± 23.2 PgC year-1). These findings have important implications for understanding and projecting climate change, helping to develop more effective climate policies and carbon reduction strategies.

Innovations in plant science from integrative remote sensing research: an introduction to a Virtual Issue

This article is an Editorial to the Virtual issue on ‘Remote sensing’ that includes the following papers Chavana‐Bryant et al. (2017), Coupel‐Ledru et al. (2022), Cushman & Machado (2020), Disney (2019), D'Odorico et al. (2020), Dong et al. (2022), Fischer et al. (2019), Gamon et al. (2023), Gu et al. (2019), Guillemot et al. (2020), Jucker (2021), Koh et al. (2022), Konings et al. (2019), Kothari et al. (2023), Martini et al. (2022), Richardson (2019), Santini et al. (2021), Schimel et al. (2019), Serbin et al. (2019), Smith et al. (2019, 2020), Still et al. (2021), Stovall et al. (2021), Wang et al. (2020), Wong et al. (2020), Wu et al. (2021), Wu et al. (2017), Wu et al. (2018), Wu et al. (2019), Xu et al. (2021), Yan et al. (2021). Access the Virtual Issue at www.newphytologist.com/virtualissues.

Leveraging research infrastructure co-location to evaluate constraints on terrestrial carbon cycling in northern European forests

Integrated long-term, in-situ observations are needed to document ongoing environmental change, to "ground-truth" remote sensing and model outputs and to predict future Earth system behaviour. The scientific and societal value of in-situ observations increases with site representativeness, temporal duration, number of parameters measured and comparability within and across sites. Research Infrastructures (RIs) can support harmonised, cross-site data collection, curation and publication. Integrating RI networks through site co-location and standardised observation methods can help answers three questions about the terrestrial carbon sink: (i) What are present and future carbon sequestration rates in northern European forests? (ii) How are these rates controlled? (iii) Why do the observed patterns exist? Here, we present a conceptual model for RI co-location and highlight potential insights into the terrestrial carbon sink achievable when long-term in-situ Earth observation sites participate in multiple RI networks (e.g., ICOS and eLTER). Finally, we offer recommendations to promote RI co-location.

Widespread and complex drought effects on vegetation physiology inferred from space

The response of vegetation physiology to drought at large spatial scales is poorly understood due to a lack of direct observations. Here, we study vegetation drought responses related to photosynthesis, evaporation, and vegetation water content using remotely sensed data, and we isolate physiological responses using a machine learning technique. We find that vegetation functional decreases are largely driven by the downregulation of vegetation physiology such as stomatal conductance and light use efficiency, with the strongest downregulation in water-limited regions. Vegetation physiological decreases in wet regions also result in a discrepancy between functional and structural changes under severe drought. We find similar patterns of physiological drought response using simulations from a soil-plant-atmosphere continuum model coupled with a radiative transfer model. Observation-derived vegetation physiological responses to drought across space are mainly controlled by aridity and additionally modulated by abnormal hydro-meteorological conditions and vegetation types. Hence, isolating and quantifying vegetation physiological responses to drought enables a better understanding of ecosystem biogeochemical and biophysical feedback in modulating climate change.

CO2 fertilization contributed more than half of the observed forest biomass increase in northern extra-tropical land

The existence of a large-biomass carbon (C) sink in Northern Hemisphere extra-tropical ecosystems (NHee) is well-established, but the relative contribution of different potential drivers remains highly uncertain. Here we isolated the historical role of carbon dioxide (CO2 ) fertilization by integrating estimates from 24 CO2 -enrichment experiments, an ensemble of 10 dynamic global vegetation models (DGVMs) and two observation-based biomass datasets. Application of the emergent constraint technique revealed that DGVMs underestimated the historical response of plant biomass to increasing [CO2 ] in forests (β Forest Mod ) but overestimated the response in grasslands (β Grass Mod ) since the 1850s. Combining the constrainedβ Forest Mod (0.86 ± 0.28 kg C m-2  [100 ppm]-1 ) with observed forest biomass changes derived from inventories and satellites, we identified that CO2 fertilization alone accounted for more than half (54 ± 18% and 64 ± 21%, respectively) of the increase in biomass C storage since the 1990s. Our results indicate that CO2 fertilization dominated the forest biomass C sink over the past decades, and provide an essential step toward better understanding the key role of forests in land-based policies for mitigating climate change.

Global net biome CO2 exchange predicted comparably well using parameter-environment relationships and plant functional types

Accurate estimation and forecasts of net biome CO₂ exchange (NBE) are vital for understanding the role of terrestrial ecosystems in a changing climate. Prior efforts to improve NBE predictions have predominantly focused on increasing models' structural realism (and thus complexity), but parametric error and uncertainty are also key determinants of model skill. Here, we investigate how different parameterization assumptions propagate into NBE prediction errors across the globe, pitting the traditional plant functional type (PFT)-based approach against a novel top-down, machine learning-based "environmental filtering" (EF) approach. To do so, we simulate these contrasting methods for parameter assignment within a flexible model-data fusion framework of the terrestrial carbon cycle (CARDAMOM) at a global scale. In the PFT-based approach, model parameters from a small number of select locations are applied uniformly within regions sharing similar land cover characteristics. In the EF-based approach, a pixel's parameters are predicted based on underlying relationships with climate, soil, and canopy properties. To isolate the role of parametric from structural uncertainty in our analysis, we benchmark the resulting PFT-based and EF-based NBE predictions with estimates from CARDAMOM's Bayesian optimization approach (whereby "true" parameters consistent with a suite of data constraints are retrieved on a pixel-by-pixel basis). When considering the mean absolute error of NBE predictions across time, we find that the EF-based approach matches or outperforms the PFT-based approach at 55% of pixels-a narrow majority. However, NBE estimates from the EF-based approach are susceptible to compensation between errors in component flux predictions and predicted parameters can align poorly with the assumed "true" values. Overall, though, the EF-based approach is comparable to conventional approaches and merits further investigation to better understand and resolve these limitations. This work provides insight into the relationship between terrestrial biosphere model performance and parametric uncertainty, informing efforts to improve model parameterization via PFT-free and trait-based approaches.

Field-based tree mortality constraint reduces estimates of model-projected forest carbon sinks

Considerable uncertainty and debate exist in projecting the future capacity of forests to sequester atmospheric CO₂. Here we estimate spatially explicit patterns of biomass loss by tree mortality (LOSS) from largely unmanaged forest plots to constrain projected (2015–2099) net primary productivity (NPP), heterotrophic respiration (HR) and net carbon sink in six dynamic global vegetation models (DGVMs) across continents. This approach relies on a strong relationship among LOSS, NPP, and HR at continental or biome scales. The DGVMs overestimated historical LOSS, particularly in tropical regions and eastern North America by as much as 5 Mg ha⁻¹ y⁻¹. The modeled spread of DGVM-projected NPP and HR uncertainties was substantially reduced in tropical regions after incorporating the field-based mortality constraint. The observation-constrained models show a decrease in the tropical forest carbon sink by the end of the century, particularly across South America (from 2 to 1.4 PgC y⁻¹), and an increase in the sink in North America (from 0.8 to 1.1 PgC y⁻¹). These results highlight the feasibility of using forest demographic data to empirically constrain forest carbon sink projections and the potential overestimation of projected tropical forest carbon sinks.

Here the authors use broad-scale tree mortality data to estimate biomass loss, constraining uncertainty of projected forest net primary productivity in 6 models, finding weaker tropical forest carbon sinks with climate change.

Ecological forecasting of tree growth: Regional fusion of tree-ring and forest inventory data to quantify drivers and characterize uncertainty

Robust ecological forecasting of tree growth under future climate conditions is critical to anticipate future forest carbon storage and flux. Here, we apply three ingredients of ecological forecasting that are key to improving forecast skill: data fusion, confronting model predictions with new data, and partitioning forecast uncertainty. Specifically, we present the first fusion of tree-ring and forest inventory data within a Bayesian state-space model at a multi-site, regional scale, focusing on Pinus ponderosa var. brachyptera in the southwestern US. Leveraging the complementarity of these two data sources, we parsed the ecological complexity of tree growth into the effects of climate, tree size, stand density, site quality, and their interactions, and quantified uncertainties associated with these effects. New measurements of trees, an ongoing process in forest inventories, were used to confront forecasts of tree diameter with observations, and evaluate alternative tree growth models. We forecasted tree diameter and increment in response to an ensemble of climate change projections, and separated forecast uncertainty into four different causes: initial conditions, parameters, climate drivers, and process error. We found a strong negative effect of fall-spring maximum temperature, and a positive effect of water-year precipitation on tree growth. Furthermore, tree vulnerability to climate stress increases with greater competition, with tree size, and at poor sites. Under future climate scenarios, we forecast increment declines of 22%-117%, while the combined effect of climate and size-related trends results in a 56%-91% decline. Partitioning of forecast uncertainty showed that diameter forecast uncertainty is primarily caused by parameter and initial conditions uncertainty, but increment forecast uncertainty is mostly caused by process error and climate driver uncertainty. This fusion of tree-ring and forest inventory data lays the foundation for robust ecological forecasting of aboveground biomass and carbon accounting at tree, plot, and regional scales, including iterative improvement of model skill.

Reducing model uncertainty of climate change impacts on high latitude carbon assimilation

The Arctic-Boreal Region (ABR) has a large impact on global vegetation-atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO₂ . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.

Response to Comments on "Recent global decline of CO2 fertilization effects on vegetation photosynthesis"

[Figure: see text].

Improving CLM5.0 Biomass and Carbon Exchange Across the Western United States Using a Data Assimilation System

The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is an opportunity for data assimilation systems that combine land surface models and satellite-derived observations to provide improved carbon monitoring. Here, we use the Data Assimilation Research Testbed to adjust the Community Land Model (CLM5.0) with remotely sensed observations of leaf area and above-ground biomass. The adjusted simulation significantly reduced the above-ground biomass and leaf area, leading to a reduction in both photosynthesis and respiration fluxes. The reduction in the carbon fluxes mostly offset, thus both the adjusted and free simulation projected a weak carbon sink to the land. This result differed from a separate observation-constrained model (FLUXCOM) that projected strong carbon uptake to the land. Simulation diagnostics suggested water limitation had an important influence upon the magnitude and spatial pattern of carbon uptake through photosynthesis. We recommend that additional observations important for water cycling (e.g., snow water equivalent, land surface temperature) be included to improve the veracity of the spatial pattern in carbon uptake. Furthermore, the assimilation system should be enhanced to maximize the number of the simulated state variables that are adjusted, especially those related to the recommended observed quantities including water cycling and soil carbon.

Greening drylands despite warming consistent with carbon dioxide fertilization effect

The rising atmospheric CO₂ concentration leads to a CO₂ fertilization effect on plants-that is, increased photosynthetic uptake of CO₂ by leaves and enhanced water-use efficiency (WUE). Yet, the resulting net impact of CO₂ fertilization on plant growth and soil moisture (SM) savings at large scale is poorly understood. Drylands provide a natural experimental setting to detect the CO₂ fertilization effect on plant growth since foliage amount, plant water-use and photosynthesis are all tightly coupled in water-limited ecosystems. A long-term change in the response of leaf area index (LAI, a measure of foliage amount) to changes in SM is likely to stem from changing water demand of primary productivity in water-limited ecosystems and is a proxy for changes in WUE. Using 34-year satellite observations of LAI and SM over tropical and subtropical drylands, we identify that a 1% increment in SM leads to 0.15% (±0.008, 95% confidence interval) and 0.51% (±0.01, 95% confidence interval) increments in LAI during 1982-1998 and 1999-2015, respectively. The increasing response of LAI to SM has contributed 7.2% (±3.0%, 95% confidence interval) to total dryland greening during 1999-2015 compared to 1982-1998. The increasing response of LAI to SM is consistent with the CO₂ fertilization effect on WUE in water-limited ecosystems, indicating that a given amount of SM has sustained greater amounts of photosynthetic foliage over time. The LAI responses to changes in SM from seven dynamic global vegetation models are not always consistent with observations, highlighting the need for improved process knowledge of terrestrial ecosystem responses to rising atmospheric CO₂ concentration.

Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2

Atmospheric carbon dioxide concentration ([CO₂ ]) is increasing, which increases leaf-scale photosynthesis and intrinsic water-use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO₂ ] increase and thus climate change. However, ecosystem CO₂ responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO₂ ]-driven terrestrial carbon sink can appear contradictory. Here we synthesize theory and broad, multidisciplinary evidence for the effects of increasing [CO₂ ] (iCO₂ ) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre-industrial times. Established theory, supported by experiments, indicates that iCO₂ is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO₂ responses are high in comparison to experiments and predictions from theory. Plant mortality and soil carbon iCO₂ responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO₂ , albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change.

Climate-Driven Variability and Trends in Plant Productivity Over Recent Decades Based on Three Global Products

Variability in climate exerts a strong influence on vegetation productivity (gross primary productivity; GPP), and therefore has a large impact on the land carbon sink. However, no direct observations of global GPP exist, and estimates rely on models that are constrained by observations at various spatial and temporal scales. Here, we assess the consistency in GPP from global products which extend for more than three decades; two observation-based approaches, the upscaling of FLUXNET site observations (FLUXCOM) and a remote sensing derived light use efficiency model (RS-LUE), and from a suite of terrestrial biosphere models (TRENDYv6). At local scales, we find high correlations in annual GPP among the products, with exceptions in tropical and high northern latitudes. On longer time scales, the products agree on the direction of trends over 58% of the land, with large increases across northern latitudes driven by warming trends. Further, tropical regions exhibit the largest interannual variability in GPP, with both rainforests and savannas contributing substantially. Variability in savanna GPP is likely predominantly driven by water availability, although temperature could play a role via soil moisture-atmosphere feedbacks. There is, however, no consensus on the magnitude and driver of variability of tropical forests, which suggest uncertainties in process representations and underlying observations remain. These results emphasize the need for more direct long-term observations of GPP along with an extension of in situ networks in underrepresented regions (e.g., tropical forests). Such capabilities would support efforts to better validate relevant processes in models, to more accurately estimate GPP.

Using Landsat satellites to assess the impact of check dams built across erosive gullies on vegetation rehabilitation

Gully erosion, a process of soil removal due to water accumulation and runoff, is a worldwide problem affecting agricultural lands. Building check dams perpendicular to the flow direction is one of the suggested control practices to stabilize this process. Though there are many studies on the effect of erosive controls on land stabilization, few examine its effect on the rehabilitation of vegetation. Here we use information from the satellites Landsat-7 (1999-2018) and Landsat-8 (2013-2018) to assess the effect of soil check dams built during 2012 across three gullies with distinct structures in a dryland area on vegetative cover and water status. We use a time series analysis technique to decompose Landsat-derived soil adjusted vegetation index (SAVI) into woody (SAVI_W) and herbaceous (iSAVI_H) contributions. The integral over the seasonal signal of the normalized difference water index (iNDWI) was used to assess changes in water status in the gully. We used herbaceous biomass collected in the field in 2014-2017 to validate iSAVI_H as a proxy of herbaceous biomass. Our results show that following the construction of the check dams, the change in woody vegetation cover is best described by a sigmoid model with an increase of ~57% (95% CI: 39%-76%; p < 0.0001), while the herbaceous vegetation increases linearly at a rate of ~71% per year (95% CI: 48%-93% y^-1; p < 0.0001). The correlation between iSAVI_H and herbaceous biomass (R² = 0.56; n = 16; p < 0.001) corroborates this increase. We found higher herbaceous productivity in the deeper gully compared to the shallower gullies but not statistically different increase rates. An increase in iNDWI of ~68% (95% CI: 43%-95%; p < 0.0001) likely implies an improved water infiltration rate that favored the vegetation expansion. Our satellite-based approach can be used to assess the impact of erosive control practices on vegetation rehabilitation in heterogeneous gullies.

Phenological Characteristics of Global Ecosystems Based on Optical, Fluorescence, and Microwave Remote Sensing

Growing seasons of vegetation generally start earlier and last longer due to anthropogenic warming. To facilitate the detection and monitoring of these phenological changes, we developed a discrete, hierarchical set of global “phenoregions” using self-organizing maps and three satellite-based vegetation indices representing multiple aspects of vegetation structure and function, including the normalized difference vegetation index (NDVI), solar-induced chlorophyll fluorescence (SIF), and vegetation optical depth (VOD). Here, we describe the distribution and phenological characteristics of these phenoregions, including their mean temperature and precipitation, differences among the three satellite indices, the number of annual growth cycles within each phenoregion and index, and recent changes in the land area of each phenoregion. We found that the phenoregions “self-organized” along two primary dimensions: degree of seasonality and peak productivity. The three satellite-based indices each appeared to provide unique information on land surface phenology, with SIF and VOD improving the ability to detect distinct annual and subannual growth cycles in some regions. Over the nine-year study period (limited in length by the short satellite SIF record), there was generally a decrease in the spatial extent of the highest productivity phenoregions, though whether due to climate or land use change remains unclear.