The primary objective of the European Space Agency's 7th Earth Explorer mission, BIOMASS, is to determine the worldwide distribution of forest above-ground biomass (AGB) in order to reduce the major ...uncertainties in calculations of carbon stocks and fluxes associated with the terrestrial biosphere, including carbon fluxes associated with Land Use Change, forest degradation and forest regrowth. To meet this objective it will carry, for the first time in space, a fully polarimetric P-band synthetic aperture radar (SAR). Three main products will be provided: global maps of both AGB and forest height, with a spatial resolution of 200 m, and maps of severe forest disturbance at 50 m resolution (where “global” is to be understood as subject to Space Object tracking radar restrictions). After launch in 2022, there will be a 3-month commissioning phase, followed by a 14-month phase during which there will be global coverage by SAR tomography. In the succeeding interferometric phase, global polarimetric interferometry Pol-InSAR coverage will be achieved every 7 months up to the end of the 5-year mission. Both Pol-InSAR and TomoSAR will be used to eliminate scattering from the ground (both direct and double bounce backscatter) in forests. In dense tropical forests AGB can then be estimated from the remaining volume scattering using non-linear inversion of a backscattering model. Airborne campaigns in the tropics also indicate that AGB is highly correlated with the backscatter from around 30 m above the ground, as measured by tomography. In contrast, double bounce scattering appears to carry important information about the AGB of boreal forests, so ground cancellation may not be appropriate and the best approach for such forests remains to be finalized. Several methods to exploit these new data in carbon cycle calculations have already been demonstrated. In addition, major mutual gains will be made by combining BIOMASS data with data from other missions that will measure forest biomass, structure, height and change, including the NASA Global Ecosystem Dynamics Investigation lidar deployed on the International Space Station after its launch in December 2018, and the NASA-ISRO NISAR L- and S-band SAR, due for launch in 2022. More generally, space-based measurements of biomass are a core component of a carbon cycle observation and modelling strategy developed by the Group on Earth Observations. Secondary objectives of the mission include imaging of sub-surface geological structures in arid environments, generation of a true Digital Terrain Model without biases caused by forest cover, and measurement of glacier and icesheet velocities. In addition, the operations needed for ionospheric correction of the data will allow very sensitive estimates of ionospheric Total Electron Content and its changes along the dawn-dusk orbit of the mission.
•BIOMASS will be the first spaceborne P-band mission.•Global estimates of forest biomass and height, subject to US DoD restrictions•The first systematic use of Pol-InSAR to measure forest height from space•The first systematic use of spaceborne SAR tomography•Sub-surface imaging, icesheet motion estimation and a bias-free DTM
It is critical to accurately estimate carbon (C) turnover time as it dominates the uncertainty in ecosystem C sinks and their response to future climate change. In the absence of direct observations ...of ecosystem C losses, C turnover times are commonly estimated under the steady state assumption (SSA), which has been applied across a large range of temporal and spatial scales including many at which the validity of the assumption is likely to be violated. However, the errors associated with improperly applying SSA to estimate C turnover time and its covariance with climate as well as ecosystem C sequestrations have yet to be fully quantified. Here, we developed a novel model‐data fusion framework and systematically analyzed the SSA‐induced biases using time‐series data collected from 10 permanent forest plots in the eastern China monsoon region. The results showed that (a) the SSA significantly underestimated mean turnover times (MTTs) by 29%, thereby leading to a 4.83‐fold underestimation of the net ecosystem productivity (NEP) in these forest ecosystems, a major C sink globally; (b) the SSA‐induced bias in MTT and NEP correlates negatively with forest age, which provides a significant caveat for applying the SSA to young‐aged ecosystems; and (c) the sensitivity of MTT to temperature and precipitation was 22% and 42% lower, respectively, under the SSA. Thus, under the expected climate change, spatiotemporal changes in MTT are likely to be underestimated, thereby resulting in large errors in the variability of predicted global NEP. With the development of observation technology and the accumulation of spatiotemporal data, we suggest estimating MTTs at the disequilibrium state via long‐term data assimilation, thereby effectively reducing the uncertainty in ecosystem C sequestration estimations and providing a better understanding of regional or global C cycle dynamics and C‐climate feedback.
Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption (SSA) written summary: Considerable biases may arise when improperly invoking the SSA to estimate carbon turnover time at realistic dynamic disequilibrium state. This issue has not yet been carefully examined. Our finding provides a better understanding of the SSA‐induced uncertainty and the global carbon cycle dynamics and carbon‐climate feedback for future research. The SSA significantly underestimates the carbon turnover time by 29% and its sensitivity to temperature and precipitation by 22% and 42%, respectively, thereby leading to a 4.83‐fold underestimation of NEP in China's monsoonal forests, a principal C sink globally. These biases are negatively associated with forest age.
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.
Despite their importance for understanding the role of terrestrial ecosystems in a changing climate, forecasts of net biome CO2 exchange are hindered by uncertainty in model parameters. Here, we compare the traditional plant functional type (PFT)‐based parameterization approach to a novel top‐down, machine learning‐based “environmental filtering” (EF) approach. We find that the EF‐based approach matches or outperforms the PFT‐based approach at a narrow majority of vegetated pixels across the globe.
Many terrestrial landscapes are heterogeneous. Mixed land cover and land use generate a complex mosaic of fragmented ecosystems at fine spatial resolutions with contrasting ecosystem stocks, traits, ...and processes, each differently sensitive to environmental and human factors. Representing spatial complexity within terrestrial ecosystem models is a key challenge for understanding regional carbon dynamics, their sensitivity to environmental gradients, and their resilience in the face of climate change. Heterogeneity underpins this challenge due to the trade-off between the fidelity of ecosystem representation within modelling frameworks and the computational capacity required for fine-scale model calibration and simulation. We directly address this challenge by quantifying the sensitivity of simulated carbon fluxes in a mixed-use landscape in the UK to the spatial resolution of the model analysis. We test two different approaches for combining Earth observation (EO) data into the CARDAMOM model–data fusion (MDF) framework, assimilating time series of satellite-based EO-derived estimates of ecosystem leaf area and biomass stocks to constrain estimates of model parameters and their uncertainty for an intermediate complexity model of the terrestrial C cycle. In the first approach, ecosystems are calibrated and simulated at pixel level, representing a “community average” of the encompassed land cover and management. This represents our baseline approach. In the second, we stratify each pixel based on land cover (e.g. coniferous forest, arable/pasture) and calibrate the model independently using EO data specific to each stratum. We test the scale dependence of these approaches for grid resolutions spanning 1 to 0.05∘ over a mixed-land-use region of the UK. Our analyses indicate that spatial resolution matters for MDF. Under the community average baseline approach biological C fluxes (gross primary productivity, Reco) simulated by CARDAMOM are relatively insensitive to resolution. However, disturbance fluxes exhibit scale variance that increases with greater landscape fragmentation and for coarser model domains. In contrast, stratification of assimilated data based on fine-resolution land use distributions resolved the resolution dependence, leading to disturbance fluxes that were 40 %–100 % higher than the baseline experiments. The differences in the simulated disturbance fluxes result in estimates of the terrestrial carbon balance in the stratified experiment that suggest a weaker C sink compared to the baseline experiment. We also find that stratifying the model domain based on land use leads to differences in the retrieved parameters that reflect variations in ecosystem function between neighbouring areas of contrasting land use. The emergent differences in model parameters between land use strata give rise to divergent responses to future climate change. Accounting for fine-scale structure in heterogeneous landscapes (e.g. stratification) is therefore vital for ensuring the ecological fidelity of large-scale MDF frameworks. The need for stratification arises because land use places strong controls on the spatial distribution of carbon stocks and plant functional traits and on the ecological processes controlling the fluxes of C through landscapes, particularly those related to management and disturbance. Given the importance of disturbance to global terrestrial C fluxes, together with the widespread increase in fragmentation of forest landscapes, these results carry broader significance for the application of MDF frameworks to constrain the terrestrial C balance at regional and national scales.
Inter-annual variations in the tropical land carbon (C) balance are a
dominant component of the global atmospheric CO2 growth rate.
Currently, the lack of quantitative knowledge on processes ...controlling net
tropical ecosystem C balance on inter-annual timescales inhibits accurate understanding and projections of land–atmosphere C exchanges. In particular, uncertainty on the relative contribution of ecosystem C fluxes attributable
to concurrent forcing anomalies (concurrent effects) and those attributable
to the continuing influence of past phenomena (lagged effects) stifles
efforts to explicitly understand the integrated sensitivity of a tropical ecosystem to climatic variability. Here we present a conceptual
framework – applicable in principle to any land biosphere model – to
explicitly quantify net biospheric exchange (NBE) as the sum of anomaly-induced
concurrent changes and climatology-induced lagged changes to terrestrial
ecosystem C states (NBE = NBECON+NBELAG). We apply this framework to an
observation-constrained analysis of the 2001–2015 tropical C balance: we use
a data–model integration approach (CARbon DAta-MOdel fraMework – CARDAMOM) to merge satellite-retrieved land-surface C observations (leaf area, biomass, solar-induced fluorescence), soil C inventory data and satellite-based atmospheric
inversion estimates of CO2 and CO fluxes to produce a data-constrained
analysis of the 2001–2015 tropical C cycle. We find that the inter-annual
variability of both concurrent and lagged effects substantially contributes to the 2001–2015 NBE inter-annual variability throughout 2001–2015 across
the tropics (NBECON IAV = 80 % of total NBE IAV, r = 0.76;
NBELAG IAV = 64 % of NBE IAV, r = 0.61), and the prominence of NBELAG IAV persists across both wet and dry tropical ecosystems. The
magnitude of lagged effect variations on NBE across the tropics is largely
attributable to lagged effects on net primary productivity (NPP; NPPLAG IAV
113 % of NBELAG IAV, r = −0.93, p value < 0.05), which emerge due to the dependence of NPP on inter-annual variations in foliar C and
plant-available H2O states. We conclude that concurrent and lagged
effects need to be explicitly and jointly resolved to retrieve an accurate
understanding of the processes regulating the present-day and future trajectory of the terrestrial land C sink.
Accurate estimation and forecasts of net biome CO
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.
Terrestrial carbon cycle models are routinely used to determine the response of the land carbon sink under expected future climate change, yet these predictions remain highly uncertain. Increasing ...the realism of processes in these models may help with predictive skill, but any such addition should be confronted with observations and evaluated in the context of the aggregate behavior of the carbon cycle. Here, two formulations for leaf area index (LAI) phenology are coupled to the same terrestrial biosphere model: one is climate agnostic, and the other incorporates direct environmental controls on both timing and growth. Each model is calibrated simultaneously to observations of LAI, net ecosystem exchange (NEE), and biomass using the CARbon DAta-MOdel fraMework (CARDAMOM) and validated against withheld data, including eddy covariance estimates of gross primary productivity (GPP) and ecosystem respiration (Re) across six ecosystems from the tropics to high latitudes. Both model formulations show similar predictive skill for LAI and NEE. However, with the addition of direct environmental controls on LAI, the integrated model explains 22 % more variability in GPP and Re and reduces biases in these fluxes by 58 % and 77 %, respectively, while also predicting more realistic annual litterfall rates due to changes in carbon allocation and turnover. We extend this analysis to evaluate the inferred climate sensitivity of LAI and NEE with the new model and show that the added complexity shifts the sign, magnitude, and seasonality of NEE sensitivity to precipitation and temperature. This highlights the benefit of process complexity when inferring underlying processes from Earth observations and representing the climate response of the terrestrial carbon cycle.
Tropical forests such as the Amazonian rainforests play an important role for climate, are large carbon stores and are a treasure of biodiversity. Amazonian forests have been exposed to large-scale ...deforestation and degradation for many decades. Deforestation declined between 2005 and 2012 but more recently has again increased with similar rates as in 2007–2008. The resulting forest fragments are exposed to substantially elevated temperatures in an already warming world. These temperature and land cover changes are expected to affect the forests, and an important diagnostic of their health and sensitivity to climate variation is their carbon balance. In a recent study based on CO2 atmospheric vertical profile observations between 2010 and 2018, and an air column budgeting technique used to estimate fluxes, we reported the Amazon region as a carbon source to the atmosphere, mainly due to fire emissions. Instead of an air column budgeting technique, we use an inverse of the global atmospheric transport model, TOMCAT, to assimilate CO2 observations from Amazon vertical profiles and global flask measurements. We thus estimate inter- and intra-annual variability in the carbon fluxes, trends over time and controls for the period of 2010–2018. This is the longest period covered by a Bayesian inversion of these atmospheric CO2 profile observations to date. Our analyses indicate that the Amazon is a small net source of carbon to the atmosphere (mean 2010–2018 = 0.13 ± 0.17 Pg C yr-1, where 0.17 is the 1σ uncertainty), with the majority of the emissions coming from the eastern region (77 % of total Amazon emissions). Fire is the primary driver of the Amazonian source (0.26 ± 0.13 Pg C yr-1), while forest carbon uptake removes around half of the fire emissions to the atmosphere (-0.13 ± 0.20 Pg C yr-1). The largest net carbon sink was observed in the western-central Amazon region (72 % of the fire emissions). We find larger carbon emissions during the extreme drought years (such as 2010, 2015 and 2016), correlated with increases in temperature, cumulative water deficit and burned area. Despite the increase in total carbon emissions during drought years, we do not observe a significant trend over time in our carbon total, fire and net biome exchange estimates between 2010 and 2018. Our analysis thus cannot provide clear evidence for a weakening of the carbon uptake by Amazonian tropical forests.
We present a method to derive atmospheric-observation-based estimates of carbon dioxide (CO2) fluxes
at the national scale, demonstrated using data from a network of surface
tall-tower sites across ...the UK and Ireland over the period 2013–2014. The
inversion is carried out using simulations from a Lagrangian chemical
transport model and an innovative hierarchical Bayesian Markov chain Monte
Carlo (MCMC) framework, which addresses some of the traditional problems
faced by inverse modelling studies, such as subjectivity in the
specification of model and prior uncertainties. Biospheric fluxes related to
gross primary productivity and terrestrial ecosystem respiration are solved
separately in the inversion and then combined a posteriori to determine net
ecosystem exchange of CO2. Two different models, Data
Assimilation Linked Ecosystem Carbon (DALEC) and Joint UK Land Environment Simulator (JULES),
provide prior estimates for these fluxes. We carry out separate inversions
to assess the impact of these different priors on the posterior flux
estimates and evaluate the differences between the prior and posterior
estimates in terms of missing model components. The Numerical Atmospheric
dispersion Modelling Environment (NAME) is used to relate fluxes to the
measurements taken across the regional network. Posterior CO2 estimates
from the two inversions agree within estimated uncertainties, despite large
differences in the prior fluxes from the different models. With our method,
averaging results from 2013 and 2014, we find a total annual net biospheric
flux for the UK of 8±79 Tg CO2 yr−1 (DALEC prior) and
64±85 Tg CO2 yr−1 (JULES prior), where negative values represent an
uptake of CO2. These biospheric CO2 estimates show that annual UK
biospheric sources and sinks are roughly in balance. These annual mean
estimates consistently indicate a greater net release of CO2 than the
prior estimates, which show much more pronounced uptake in summer months.
Pollen dispersal and deposition (PDD) modelling has been instrumental in reconstructing historical vegetation in temperate regions, but its application has been limited in the tropics where there is ...greatest uncertainty in past land cover change. Here, we apply PDD modelling to Amazonian savanna and forested ecosystems. Empirical pollen data from lakes situated in southwestern Amazonia were used to calibrate the PDD model for a two-component landscape of forest and non-forest. The PDD model was then used to simulate pollen assemblages for different combinations of landscape arrangements (the multiple scenario approach) that reflect possible anthropogenic and climate-driven forest cover change in the late-Holocene. We show that pollen records from large Amazonian lakes vary greatly in their sensitivity to forest loss depending on the baseline forest cover. Lakes in landscapes containing >80% forest will detect small reductions (5% of total cover), but this sensitivity degrades rapidly with forest cover loss. There are a wide range of uncertainties in pollen reconstructions from mosaic and ecotonal landscapes. In forest-savanna mosaics, large reductions of forest cover could be undetectable through the pollen record. In ecotonal landscapes, the relationship between forest cover and its representation in the pollen record rapidly weakens with increasing distance from the forest boundary. Further application of PDD modelling in combination with the multiple scenario approach can address the uncertainties in pollen-based reconstructions of past land cover in the tropics, but require further investment and development.
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