ORCID Profile
0000-0002-0835-1003
Current Organisation
The University of Edinburgh
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Publisher: Copernicus GmbH
Date: 08-01-2020
Publisher: Copernicus GmbH
Date: 04-04-2019
Abstract: Abstract. 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.
Publisher: American Geophysical Union (AGU)
Date: 09-2018
DOI: 10.1029/2018JG004386
Publisher: Copernicus GmbH
Date: 02-03-2022
Abstract: Abstract. Land–atmosphere carbon and water exchanges have large uncertainty in terrestrial biosphere models (TBMs). Using observations to reduce TBM structural and parametric errors and uncertainty is a critical priority for both understanding and accurately predicting carbon and water fluxes. Recent implementations of the Bayesian CARbon DAta–MOdel fraMework (CARDAMOM) have yielded key insights into ecosystem carbon and water cycling. CARDAMOM estimates parameters for an associated TBM of intermediate complexity (Data Assimilation Linked Ecosystem Carbon – DALEC). These CARDAMOM analyses – informed by co-located C and H2O flux observations – have exhibited considerable skill in both representing the variability of assimilated observations and predicting withheld observations. CARDAMOM and DALEC have been continuously developed to accommodate new scientific challenges and an expanding variety of observational constraints. However, so far there has been no concerted effort to globally and systematically validate CARDAMOM performance across in idual model–data fusion configurations. Here we use the FLUXNET 2015 dataset – an ensemble of 200+ eddy covariance flux tower sites – to formulate a concerted benchmarking framework for CARDAMOM carbon (photosynthesis and net C exchange) and water (evapotranspiration) flux estimates (CARDAMOM-FluxVal version 1.0). We present a concise set of skill metrics to evaluate CARDAMOM performance against both assimilated and withheld FLUXNET 2015 photosynthesis, net CO2 exchange, and evapotranspiration estimates. We further demonstrate the potential for tailored CARDAMOM evaluations by categorizing performance in terms of (i) in idual land-cover types, (ii) monthly, annual, and mean fluxes, and (iii) length of assimilation data. The CARDAMOM benchmarking system – along with the CARDAMOM driver files provided – can be readily repeated to support both the intercomparison between existing CARDAMOM model configurations and the formulation, development, and testing of new CARDAMOM model structures.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-10918
Abstract: Predicting the fate of the terrestrial ecosystems and their role in the Earth system requires a quantitative and mechanistic understanding of carbon, water, and energy exchanges between the land surface and the atmosphere. While the current generation of land surface models show skill in representing many ecosystem processes, they largely disagree in the integrated response of the terrestrial biosphere to climatic change. These disagreements may be reconciled by confronting models with the erse and expanding suite of Earth system observations in order to better constrain the underlying processes. In light of this goal, we have implemented substantial developments to the CARbon DAta-MOdel FraMework (CARDAMOM)& #8212 a data assimilation system that optimally estimates parameters of a parsimonious ecosystem model& #8212 which expand its original scope as a diagnostic tool for estimating carbon states and fluxes into a system that can infer and predict the response of carbon, water and energy cycles to climate and CO2 concentrations at seasonal-to-decadal timescales. CARDAMOM 3.0 retains all functionality and model structures of previous versions, but now features a flagship model which includes coupled carbon, water, and energy cycles, along with semi-mechanistic representations of photosynthetic assimilation, allocation, phenology, autotrophic and heterotrophic respiration, snow and cold-weather processes, and soil hydrology. Additionally, the underlying framework was substantially updated in order to facilitate community use of CARDAMOM by simplifying the interface and increasing the ease with which users can integrate new observations and develop new model structures. With these new developments, CARDAMOM 3.0 provides a versatile tool for applying information from a broad array of Earth observation data to investigate carbon, water, and energy cycles and their responses to climate and atmospheric CO2 across the full range of terrestrial ecosystems, from leaf level to continental scales.
Publisher: Authorea, Inc.
Date: 11-09-2023
Publisher: Copernicus GmbH
Date: 19-09-2017
DOI: 10.5194/ESD-2017-83
Abstract: Abstract. Multi-model averaging techniques provide opportunities to extract additional information from large ensembles of simulations. In particular, present-day model skill can be used to evaluate their potential performance in future climate simulations. Multi-model averaging methods have been used extensively in climate and hydrological sciences, but they have not been used to constrain projected plant productivity responses to climate change, which is a major uncertainty in earth system modelling. Here, we use three global observation-orientated estimates of current net primary productivity (NPP) to perform a reliability ensemble averaging (REA) using 30 global simulations of the 21st century change in NPP based on the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) business as usual emissions scenario. We find that the three REAs support an increase in global NPP by the end of the 21st century (2090s) compared to the 2000s, which is 4–6 % stronger than the ensemble ISIMIP mean value of 23.7 Pg C y−1. Using REA also leads to a 43–67 % reduction in the global uncertainty of 21st century NPP projection, which strengthens confidence in the resilience of the CO2-fertilization effect to climate change. This reduction in uncertainty is especially clear for boreal ecosystems. Conversely, the large uncertainty that remains on the sign of the response of NPP in semi-arid regions points to the need for better observations and model development in these regions.
Publisher: Copernicus GmbH
Date: 28-06-2023
Abstract: Abstract. 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.
Publisher: Copernicus GmbH
Date: 11-10-2023
Publisher: Copernicus GmbH
Date: 09-07-2021
Publisher: Copernicus GmbH
Date: 08-01-2020
DOI: 10.5194/BG-2019-459
Abstract: Abstract. 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 ecosystems 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 meteorological 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 tropical ecosystem to climatic variability. Here we present a conceptual framework – applicable in principle to any meteorology-forced 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 2010–2015 tropical C balance: we use a data-model integration approach (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 2010–2015 tropical C cycle. We find that the inter-annual variability of lagged effects explain the majority of NBE inter-annual variability (IAV) throughout 2010–2015 across the tropics (NBELAG IAV = 112 % of NBE IAV, r = 0.87) relative to concurrent effects (NBECON IAV = 54 % of total NBE IAV, r = 0.03) and the dominance 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 88 % of NBELAG IAV, r = −0.99, p-value
Publisher: American Geophysical Union (AGU)
Date: 03-2017
DOI: 10.1002/2016JG003520
Publisher: Copernicus GmbH
Date: 19-09-2017
Publisher: Copernicus GmbH
Date: 17-12-2020
Abstract: Abstract. 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.
Publisher: Copernicus GmbH
Date: 09-12-2022
DOI: 10.5194/EGUSPHERE-2022-1265
Abstract: Abstract. 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 in representing the climate response of the terrestrial carbon cycle.
Publisher: Copernicus GmbH
Date: 09-07-2021
DOI: 10.5194/GMD-2021-190
Abstract: Abstract. Land-atmosphere carbon and water exchanges have large uncertainty in land surface and biosphere models. Using observations to reduce land biosphere model structural and parametric errors is a key priority for both understanding and accurately predicting carbon and water fluxes. Recent implementations of the Bayesian CARDAMOM model-data fusion framework have yielded key insights into ecosystem carbon and water cycling. CARDAMOM analyses—informed by co-located C and H2O flux observations—have exhibited considerable skill in both representing the variability of assimilated observations and predicting withheld observations. While CARDAMOM model configurations (namely CARDAMOM-compatible biogeochemical model structures) have been continuously developed to accommodate new scientific challenges and an expanding variety of observational constraints, there has so far been no concerted effort to globally and systematically validate CARDAMOM performance across in idual model-data fusion configurations. Here we use the FLUXNET-2015 dataset—an ensemble of 200+ eddy covariance flux tower sites—to formulate a concerted benchmarking framework for CARDAMOM carbon (GPP, NEE) and water (ET) flux estimates (CARDAMOM-FLUXVal version 1.0). We present a concise set of skill metrics to evaluate CARDAMOM performance against both assimilated and withheld FLUXNET-2015 GPP, NEE and ET data. We further demonstrate the potential for tailored CARDAMOM evaluations by categorizing performance in terms of (i) in idual land cover types, (ii) monthly, annual and mean fluxes, and (iii) length of assimilation data. The CARDAMOM benchmarking system—along with CARDAMOM driver files provided—can be readily repeated to support both the intercomparison between existing CARDAMOM model configurations and the formulation, development and testing of new CARDAMOM model structures.
Publisher: Copernicus GmbH
Date: 11-10-2023
Publisher: Copernicus GmbH
Date: 21-02-2018
Abstract: Abstract. Multi-model averaging techniques provide opportunities to extract additional information from large ensembles of simulations. In particular, present-day model skill can be used to evaluate their potential performance in future climate simulations. Multi-model averaging methods have been used extensively in climate and hydrological sciences, but they have not been used to constrain projected plant productivity responses to climate change, which is a major uncertainty in Earth system modelling. Here, we use three global observationally orientated estimates of current net primary productivity (NPP) to perform a reliability ensemble averaging (REA) method using 30 global simulations of the 21st century change in NPP based on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) ”business as usual” emissions scenario. We find that the three REA methods support an increase in global NPP by the end of the 21st century (2095–2099) compared to 2001–2005, which is 2–3 % stronger than the ensemble ISIMIP mean value of 24.2 Pg C y−1. Using REA also leads to a 45–68 % reduction in the global uncertainty of 21st century NPP projection, which strengthens confidence in the resilience of the CO2 fertilization effect to climate change. This reduction in uncertainty is especially clear for boreal ecosystems although it may be an artefact due to the lack of representation of nutrient limitations on NPP in most models. Conversely, the large uncertainty that remains on the sign of the response of NPP in semi-arid regions points to the need for better observations and model development in these regions.
Publisher: American Geophysical Union (AGU)
Date: 12-2018
DOI: 10.1029/2018GB005925
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Thomas Smallman.