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0000-0002-3671-8626
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Publisher: Springer Science and Business Media LLC
Date: 07-06-2014
Publisher: Copernicus GmbH
Date: 08-01-2020
Publisher: Copernicus GmbH
Date: 29-04-2015
Abstract: Abstract. Irrigation agriculture plays an increasingly important role in food supply. Many evapotranspiration models are used today to estimate the water demand for irrigation. They consider different stages of crop growth by empirical crop coefficients to adapt evapotranspiration throughout the vegetation period. We investigate the importance of the model structural versus model parametric uncertainty for irrigation simulations by considering six evapotranspiration models and five crop coefficient sets to estimate irrigation water requirements for growing wheat in the Murray–Darling Basin, Australia. The study is carried out using the spatial decision support system SPARE:WATER. We find that structural model uncertainty among reference ET is far more important than model parametric uncertainty introduced by crop coefficients. These crop coefficients are used to estimate irrigation water requirement following the single crop coefficient approach. Using the reliability ensemble averaging (REA) technique, we are able to reduce the overall predictive model uncertainty by more than 10%. The exceedance probability curve of irrigation water requirements shows that a certain threshold, e.g. an irrigation water limit due to water right of 400 mm, would be less frequently exceeded in case of the REA ensemble average (45%) in comparison to the equally weighted ensemble average (66%). We conclude that multi-model ensemble predictions and sophisticated model averaging techniques are helpful in predicting irrigation demand and provide relevant information for decision making.
Publisher: Wiley
Date: 28-02-2018
DOI: 10.1111/GCB.14072
Abstract: Tree-grass savannas are a widespread biome and are highly valued for their ecosystem services. There is a need to understand the long-term dynamics and meteorological drivers of both tree and grass productivity separately in order to successfully manage savannas in the future. This study investigated the interannual variability (IAV) of tree and grass gross primary productivity (GPP) by combining a long-term (15 year) eddy covariance flux record and model estimates of tree and grass GPP inferred from satellite remote sensing. On a seasonal basis, the primary drivers of tree and grass GPP were solar radiation in the wet season and soil moisture in the dry season. On an interannual basis, soil water availability had a positive effect on tree GPP and a negative effect on grass GPP. No linear trend in the tree-grass GPP ratio was observed over the 15-year study period. However, the tree-grass GPP ratio was correlated with the modes of climate variability, namely the Southern Oscillation Index. This study has provided insight into the long-term contributions of trees and grasses to savanna productivity, along with their respective meteorological determinants of IAV.
Publisher: Copernicus GmbH
Date: 13-05-2016
DOI: 10.5194/BG-2016-192
Abstract: Abstract. Forest ecosystems play a crucial role in the global carbon cycle by sequestering a considerable fraction of anthropogenic CO2 thereby contributing to climate change mitigation. However, there is a gap in our understanding about the carbon dynamics of eucalypt (broadleaf evergreen) forests in temperate climates, which might differ from temperate coniferous or deciduous forests given their fundamental differences in physiology, phenology and growth dynamics. To address this gap we undertook a three year study (2010–2012) using eddy covariance measurements in a dry temperate eucalypt forest in south-eastern Australia. We determined the annual net ecosystem carbon exchange (NEE) and investigated the temporal (seasonal and inter-annual) variability and environmental controls of NEE, gross primary productivity (GPP) and ecosystem respiration (ER). The forest was a large and constant carbon sink throughout the study period, even in winter, with an overall mean NEE of −1062 ± 53 g C m−2 yr−1. Gross CO2 ecosystem fluxes showed no significant inter-annual variability and mean annual estimate of GPP was 2521 ± 35 g C m−2 yr−1 and ER was 1458 ± 31 g C m−2 yr−1. GPP and ER had a pronounced seasonality with GPP being greatest during spring and summer and ER during summer whereas peaks of NEE occurred in early spring and again in summer. High NEE in spring was caused by a delayed increase in ER due to low temperatures. A random forest analysis showed that variability in GPP was mostly explained by incoming solar radiation whilst air temperature was the main environmental driver of ER on seasonal and inter-annual time scales. The forest experienced unusual above average annual rainfall during the first two years of this three year period so that soil moisture content remained relatively high and the forest was not water limited. Our results show the potential of temperate eucalypt forests to sequester large amounts of carbon when not water limited. Our observations can provide data on an underrepresented biome to test and parameterise ecosystem models. However, longer monitoring is needed to assess the inter-annual variability of the carbon sink strength particularly during years with drought conditions.
Publisher: Copernicus GmbH
Date: 24-04-2019
Abstract: Abstract. There is a significant knowledge gap in the current state of the terrestrial carbon (C) budget. Recent studies have highlighted a poor understanding particularly of C pool transit times and of whether productivity or biomass dominate these biases. The Arctic, accounting for approximately 50 % of the global soil organic C stocks, has an important role in the global C cycle. Here, we use the CARbon DAta MOdel (CARDAMOM) data-assimilation system to produce pan-Arctic terrestrial C cycle analyses for 2000–2015. This approach avoids using traditional plant functional type or steady-state assumptions. We integrate a range of data (soil organic C, leaf area index, biomass, and climate) to determine the most likely state of the high-latitude C cycle at a 1∘ × 1∘ resolution and also to provide general guidance about the controlling biases in transit times. On average, CARDAMOM estimates regional mean rates of photosynthesis of 565 g C m−2 yr−1 (90 % confidence interval between the 5th and 95th percentiles: 428, 741), autotrophic respiration of 270 g C m−2 yr−1 (182, 397) and heterotrophic respiration of 219 g C m−2 yr−1 (31, 1458), suggesting a pan-Arctic sink of −67 (−287, 1160) g Cm−2 yr−1, weaker in tundra and stronger in taiga. However, our confidence intervals remain large (and so the region could be a source of C), reflecting uncertainty assigned to the regional data products. We show a clear spatial and temporal agreement between CARDAMOM analyses and different sources of assimilated and independent data at both pan-Arctic and local scales but also identify consistent biases between CARDAMOM and validation data. The assimilation process requires clearer error quantification for leaf area index (LAI) and biomass products to resolve these biases. Mapping of vegetation C stocks and change over time and soil C ages linked to soil C stocks is required for better analytical constraint. Comparing CARDAMOM analyses to global vegetation models (GVMs) for the same period, we conclude that transit times of vegetation C are inconsistently simulated in GVMs due to a combination of uncertainties from productivity and biomass calculations. Our findings highlight that GVMs need to focus on constraining both current vegetation C stocks and net primary production to improve a process-based understanding of C cycle dynamics in the Arctic.
Publisher: Copernicus GmbH
Date: 04-06-2014
DOI: 10.5194/HESS-18-2065-2014
Abstract: Abstract. In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers – using the model of their choice – for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to in idual modelling experience and costs of added information. In this qualitative analysis of a statistically small number of predictions we learned (i) that soft information such as the modeller's system understanding is as important as the model itself (hard information), (ii) that the sequence of modelling steps matters (field visit, interactions between differently experienced experts, choice of model, selection of available data, and methods for parameter guessing), and (iii) that added process understanding can be as efficient as adding data for improving parameters needed to satisfy model requirements.
Publisher: Copernicus GmbH
Date: 09-12-2016
DOI: 10.5194/BG-2016-506
Abstract: Abstract. An improvement in our process-based understanding of carbon (C) exchange in the Arctic, and its climate sensitivity, is critically needed for understanding the response of tundra ecosystems to a changing climate. In this context, we analyzed the net ecosystem exchange (NEE) of CO2 in West Greenland tundra (64° N) across eight snow-free periods in eight consecutive years, and characterized the key processes of net ecosystem exchange, and its two main modulating components: gross primary production (GPP) and ecosystem respiration (Reco). Overall, the ecosystem acted as a consistent sink of CO2, accumulating −30 g C m−2 on average (range −17 to −41 g C m−2) during the years 2008–2015, except 2011 that was associated with a major pest outbreak. The results do not reveal a marked meteorological effect on the net CO2 uptake despite the high inter-annual variability in the timing of snowmelt, start and duration of the growing season. The ranges in annual GPP (−182 to −316 g C m−2) and Reco (144 to 279 g C m−2) were 5 fold larger and they were also more variable (Coefficients of variation are 3.6 and 4.1 % respectively) than for NEE (0.7 %). GPP and Reco were sensitive to insolation and temperatures and there was a tendency towards larger GPP and Reco during warmer and wetter years. The relative lack of sensitivity of NEE to climate was a result of the correlated meteorological response of GPP and Reco. During the 2011 anomalous year, the studied ecosystem released 41 g C m−2 as biological disturbance reduced GPP more strongly than Reco. With continued warming temperatures and longer growing seasons, tundra systems will increase rates of C cycling although shifts in sink strength will likely be triggered by factors such as biological disturbances, events that will challenge the forecast of upcoming C states.
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: 29-01-2013
Abstract: Abstract. Hydro-biogeochemical models are used to foresee the impact of mitigation measures on water quality. Usually, scenario-based studies rely on single model applications. This is done in spite of the widely acknowledged advantage of ensemble approaches to cope with structural model uncertainty issues. As an attempt to demonstrate the reliability of such multi-model efforts in the hydro-biogeochemical context, this methodological contribution proposes an adaptation of the reliability ensemble averaging (REA) philosophy to nitrogen losses predictions. A total of 4 models are used to predict the total nitrogen (TN) losses from the well-monitored Ellen Brook catchment in Western Australia. Simulations include re-predictions of current conditions and a set of straightforward management changes targeting fertilisation scenarios. Results show that, in spite of good calibration metrics, one of the models provides a very different response to management changes. This behaviour leads the simple average of the ensemble members to also predict reductions in TN export that are not in agreement with the other models. However, considering the convergence of model predictions in the more sophisticated REA approach assigns more weight to previously less well-calibrated models that are more in agreement with each other. This method also avoids having to disqualify any of the ensemble members.
Publisher: Elsevier BV
Date: 07-2012
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Copernicus GmbH
Date: 19-09-2017
Publisher: Springer Science and Business Media LLC
Date: 06-02-2019
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: 28-02-2011
Abstract: Abstract. Post-processing the output of different rainfall-runoff models allows one to pool strengths of each model to produce more reliable predictions. As a new approach in the frame of the "Prediction in Ungauged Basins" initiative, this study investigates the geographical transferability of different parameter sets and data-fusion methods which were applied to 5 different rainfall-runoff models for a low-land catchment in Central Sweden. After usual calibration, we adopted a proxy-basin validation approach between two similar but non-nested sub-catchments in order to simulate ungauged conditions. Many model combinations outperformed the best single model predictions with improvements of efficiencies from 0.70 for the best single model predictions to 0.77 for the best ensemble predictions. However no "best" data-fusion method could be determined as similar performances were obtained with different merging schemes. In general, poorer model performance, i.e. lower efficiency, was less likely to occur for ensembles which included more in idual models.
Publisher: Copernicus GmbH
Date: 11-12-2014
Abstract: Abstract. Recent studies have identified the first-order representation of microbial decomposition as a major source of uncertainty in simulations and projections of the terrestrial carbon balance. Here, we use a reduced complexity model representative of current state-of-the-art models of soil organic carbon decomposition. We undertake a systematic sensitivity analysis to disentangle the effect of the time-invariant baseline residence time (k) and the sensitivity of microbial decomposition to temperature (Q10) on soil carbon dynamics at regional and global scales. Our simulations produce a range in total soil carbon at equilibrium of ~ 592 to 2745 Pg C, which is similar to the ~ 561 to 2938 Pg C range in pre-industrial soil carbon in models used in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). This range depends primarily on the value of k, although the impact of Q10 is not trivial at regional scales. As climate changes through the historical period, and into the future, k is primarily responsible for the magnitude of the response in soil carbon, whereas Q10 determines whether the soil remains a sink, or becomes a source in the future mostly by its effect on mid-latitude carbon balance. If we restrict our simulations to those simulating total soil carbon stocks consistent with observations of current stocks, the projected range in total soil carbon change is reduced by 42% for the historical simulations and 45% for the future projections. However, while this observation-based selection dismisses outliers, it does not increase confidence in the future sign of the soil carbon feedback. We conclude that despite this result, future estimates of soil carbon and how soil carbon responds to climate change should be more constrained by available data sets of carbon stocks.
Publisher: Copernicus GmbH
Date: 21-09-2016
DOI: 10.5194/GMD-2016-214
Abstract: Abstract. This study evaluates the ability of the JULES Land Surface Model (LSM) to simulate Gross Primary Productivity (GPP) at regional and global scales for 2001–2010. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, JULES was found to simulate interannual variability (IAV) at global scales. When JULES was driven with the WFDEI-GPCC dataset (at 0.5º × 0.5º spatial resolution), it was found that the annual average global GPP simulated by JULES for 2001–2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 % and 8 %, respectively, and CARDAMOM estimates by 23 %. Secondly, GPP fluxes simulated by JULES for various biomes (forests, grasslands and shrubs) at global and regional scales were compared. It was found that differences between JULES, FLUXNET-MTE, MODIS and CARDAMOM at global scales were mostly due to differences in the tropics with CARDAMOM performing better than JULES in this region. Thirdly, it was shown that spatial resolution (0.5º × 0.5º, 1º × 1º and 2º × 2º) had no impact on simulated GPP on these large scales. Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 4 PgC year−1. At regional scales, differences between two were observed with the WFDEI-GPCC driven model simulations estimating higher GPP in the tropics (at 5º N–5º S) and the PRINCETON driven model simulations estimating higher GPP in the extratropics (at 30º N–60º N).
Publisher: Proceedings of the National Academy of Sciences
Date: 19-01-2016
Abstract: Quantitative knowledge of terrestrial carbon pathways and processes is fundamental for understanding the biosphere’s response to a changing climate. Carbon allocation, stocks, and residence times together define the dynamic state of the terrestrial carbon cycle. These quantities are difficult to measure and remain poorly quantified on a global scale. Here, we retrieve global 1° × 1° carbon state and process variables by combining a carbon balance model with satellite observations of biomass and leaf area (where and when available) and global soil carbon data. Our results reveal emergent continental-scale patterns and relationships between carbon states and processes. We find that conventional land cover types cannot capture continental-scale variations of retrieved carbon variables this mismatch has strong implications for terrestrial carbon cycle predictions.
Publisher: Copernicus GmbH
Date: 22-05-2018
DOI: 10.5194/ESD-2018-19
Abstract: Abstract. There is a significant knowledge gap in the current state of the terrestrial carbon (C) budget. The Arctic accounts for approximately 50 % of the global soil organic C stock, emphasizing the important role of Arctic regions in the global C cycle. Recent studies have pointed to the poor understanding of C pools turnover, although remain unclear as to whether productivity or biomass dominate the biases. Here, we use an improved version of the CARDAMOM data-assimilation system, to produce pan-Arctic terrestrial C-related variables without using traditional plant functional type or steady-state assumptions. Our approach integrates a range of data (soil organic C, leaf area index, biomass, and climate) to determine the most likely state of the high latitude C cycle at a 1° × 1° resolution for the first 15 years of the 21st century, but also to provide general guidance about the controlling biases in the turnover dynamics. As average, CARDAMOM estimates 513 (456, 579), 245 (208, 290) and 204 (109, 427) g C m−2 yr−1 (90 % confidence interval) from photosynthesis, autotrophic and heterotrophic respiration respectively, suggesting that the pan-Arctic region acted as a likely sink −55 (−152, 157) g C m−2 yr−1, weaker in tundra and stronger in taiga, but our confidence intervals remain large (and so the region could be a source of C). In general, we find a good agreement between CARDAMOM and different sources of assimilated and independent data at both pan-Arctic and local scale. Using CARDAMOM as a benchmarking tool for global vegetation models (GVM), we also conclude that turnover time of vegetation C is weakly simulated in vegetation models and is a major component of error in their forecasts. Our findings highlight that GVM modellers need to focus on the vegetation C stocks dynamics, but also their respiratory losses, to improve our process-based understanding of internal C cycle dynamics in the Arctic.
Publisher: Copernicus GmbH
Date: 08-11-2013
Abstract: Abstract. Reliable projections of future climate require land–atmosphere carbon (C) fluxes to be represented realistically in Earth system models (ESMs). There are several sources of uncertainty in how carbon is parameterised in these models. First, while interactions between the C, nitrogen (N) and phosphorus (P) cycles have been implemented in some models, these lead to erse changes in land–atmosphere fluxes. Second, while the first-order parameterisation of soil organic matter decomposition is similar between models, formulations of the control of the soil physical state on microbial activity vary widely. For the first time, we address these sources of uncertainty simultaneously by implementing three soil moisture and three soil temperature respiration functions in an ESM that can be run with three degrees of biogeochemical nutrient limitation (C-only, C and N, and C and N and P). All 27 possible combinations of response functions and biogeochemical mode are equilibrated before transient historical (1850–2005) simulations are performed. As expected, implementing N and P limitation reduces the land carbon sink, transforming some regional sinks into net sources over the historical period. Meanwhile, regardless of which nutrient mode is used, various combinations of response functions imply a two-fold difference in the net ecosystem accumulation and a four-fold difference in equilibrated total soil C. We further show that regions with initially larger pools are more likely to become carbon sources, especially when nutrient availability limits the response of primary production to increasing atmospheric CO2. Simulating changes in soil C content therefore critically depends on both nutrient limitation and the choice of respiration functions.
Publisher: Copernicus GmbH
Date: 13-11-2014
Abstract: Abstract. Soil carbon storage simulated by the Coupled Model Intercomparison Project (CMIP5) models varies 6-fold for the present day. Here, we confirm earlier work showing that this range already exists at the beginning of the CMIP5 historical simulations. We additionally show that this range is largely determined by the response of microbial decomposition during each model's spin-up procedure from initialization to equilibration. The 6-fold range in soil carbon, once established prior to the beginning of the historical period (and prior to the beginning of a CMIP5 simulation), is then maintained through the present and to 2100 almost unchanged even under a strong business-as-usual emissions scenario. We therefore highlight that a commonly ignored part of CMIP5 analyses – the land surface state achieved through the spin-up procedure – can be important for determining future carbon storage and land surface fluxes. We identify the need to better constrain the outcome of the spin-up procedure as an important step in reducing uncertainty in both projected soil carbon and land surface fluxes in CMIP5 transient simulations.
Publisher: Copernicus GmbH
Date: 12-2010
DOI: 10.5194/HESS-14-2383-2010
Abstract: Abstract. Model predictions of biogeochemical fluxes at the landscape scale are highly uncertain, both with respect to stochastic (parameter) and structural uncertainty. In this study 5 different models (LASCAM, LASCAM-S, a self-developed tool, SWAT and HBV-N-D) designed to simulate hydrological fluxes as well as mobilisation and transport of one or several nitrogen species were applied to the mesoscale River Fyris catchment in mid-eastern Sweden. Hydrological calibration against 5 years of recorded daily discharge at two stations gave highly variable results with Nash-Sutcliffe Efficiency (NSE) ranging between 0.48 and 0.83. Using the calibrated hydrological parameter sets, the parameter uncertainty linked to the nitrogen parameters was explored in order to cover the range of possible predictions of exported loads for 3 nitrogen species: nitrate (NO3), ammonium (NH4) and total nitrogen (Tot-N). For each model and each nitrogen species, predictions were ranked in two different ways according to the performance indicated by two different goodness-of-fit measures: the coefficient of determination R2 and the root mean square error RMSE. A total of 2160 deterministic Single Model Ensembles (SME) was generated using an increasing number of members (from the 2 best to the 10 best single predictions). Finally the best SME for each model, nitrogen species and discharge station were selected and merged into 330 different Multi-Model Ensembles (MME). The evolution of changes in R2 and RMSE was used as a performance descriptor of the ensemble procedure. In each studied case, numerous ensemble merging schemes were identified which outperformed any of their members. Improvement rates were generally higher when worse members were introduced. The highest improvements were achieved for the nitrogen SMEs compiled with multiple linear regression models with R2 selected members, which resulted in the RMSE decreasing by up to 90%.
Publisher: Copernicus GmbH
Date: 09-12-2016
Publisher: American Geophysical Union (AGU)
Date: 06-2011
DOI: 10.1029/2011GL047522
Publisher: Copernicus GmbH
Date: 11-10-2017
Abstract: Abstract. An improvement in our process-based understanding of carbon (C) exchange in the Arctic and its climate sensitivity is critically needed for understanding the response of tundra ecosystems to a changing climate. In this context, we analysed the net ecosystem exchange (NEE) of CO2 in West Greenland tundra (64° N) across eight snow-free periods in 8 consecutive years, and characterized the key processes of net ecosystem exchange and its two main modulating components: gross primary production (GPP) and ecosystem respiration (Reco). Overall, the ecosystem acted as a consistent sink of CO2, accumulating −30 g C m−2 on average (range of −17 to −41 g C m−2) during the years 2008–2015, except 2011 (source of 41 g C m−2), which was associated with a major pest outbreak. The results do not reveal a marked meteorological effect on the net CO2 uptake despite the high interannual variability in the timing of snowmelt and the start and duration of the growing season. The ranges in annual GPP (−182 to −316 g C m−2) and Reco (144 to 279 g C m−2) were 5 fold larger than the range in NEE. Gross fluxes were also more variable (coefficients of variation are 3.6 and 4.1 % respectively) than for NEE (0.7 %). GPP and Reco were sensitive to insolation and temperature, and there was a tendency towards larger GPP and Reco during warmer and wetter years. The relative lack of sensitivity of NEE to meteorology was a result of the correlated response of GPP and Reco. During the snow-free season of the anomalous year of 2011, a biological disturbance related to a larvae outbreak reduced GPP more strongly than Reco. With continued warming temperatures and longer growing seasons, tundra systems will increase rates of C cycling. However, shifts in sink strength will likely be triggered by factors such as biological disturbances, events that will challenge our forecasting of C states.
Publisher: Copernicus GmbH
Date: 22-05-2018
Publisher: Copernicus GmbH
Date: 23-08-2017
Abstract: Abstract. Forest ecosystems play a crucial role in the global carbon cycle by sequestering a considerable fraction of anthropogenic CO2, thereby contributing to climate change mitigation. However, there is a gap in our understanding about the carbon dynamics of eucalypt (broadleaf evergreen) forests in temperate climates, which might differ from temperate evergreen coniferous or deciduous broadleaved forests given their fundamental differences in physiology, phenology and growth dynamics. To address this gap we undertook a 3-year study (2010–2012) of eddy covariance measurements in a dry temperate eucalypt forest in southeastern Australia. We determined the annual net carbon balance and investigated the temporal (seasonal and inter-annual) variability in and environmental controls of net ecosystem carbon exchange (NEE), gross primary productivity (GPP) and ecosystem respiration (ER). The forest was a large and constant carbon sink throughout the study period, even in winter, with an overall mean NEE of −1234 ± 109 (SE) g C m−2 yr−1. Estimated annual ER was similar for 2010 and 2011 but decreased in 2012 ranging from 1603 to 1346 g C m−2 yr−1, whereas GPP showed no significant inter-annual variability, with a mean annual estimate of 2728 ± 39 g C m−2 yr−1. All ecosystem carbon fluxes had a pronounced seasonality, with GPP being greatest during spring and summer and ER being highest during summer, whereas peaks in NEE occurred in early spring and again in summer. High NEE in spring was likely caused by a delayed increase in ER due to low temperatures. A strong seasonal pattern in environmental controls of daytime and night-time NEE was revealed. Daytime NEE was equally explained by incoming solar radiation and air temperature, whereas air temperature was the main environmental driver of night-time NEE. The forest experienced unusual above-average annual rainfall during the first 2 years of this 3-year period so that soil water content remained relatively high and the forest was not water limited. Our results show the potential of temperate eucalypt forests to sequester large amounts of carbon when not water limited. However, further studies using bottom-up approaches are needed to validate measurements from the eddy covariance flux tower and to account for a possible underestimation in ER due to advection fluxes.
Publisher: American Geophysical Union (AGU)
Date: 25-02-2013
DOI: 10.1029/2012JD018122
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: 11-07-2017
Abstract: Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate gross primary productivity (GPP) on regional and global scales for 2001–2010. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, when JULES was driven with the WFDEI-GPCC dataset (at 0. 5° × 0. 5° spatial resolution), the annual average global GPP simulated by JULES for 2001–2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 and 8 %, respectively, and CARDAMOM estimates by 23 %. JULES was able to simulate the standard deviation of monthly GPP fluxes compared to CARDAMOM and the observation-based estimates on global scales. Secondly, GPP simulated by JULES for various biomes (forests, grasslands and shrubs) on global and regional scales were compared. Differences among JULES, MODIS, FLUXNET-MTE and CARDAMOM on global scales were due to differences in simulated GPP in the tropics. Thirdly, it was shown that spatial resolution (0. 5° × 0. 5°, 1° × 1° and 2° × 2°) had little impact on simulated GPP on these large scales, with global GPP ranging from 140 to 142 PgC year−1. Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 3 PgC year−1. On regional scales, differences between the two were observed, with the WFDEI-GPCC-driven model simulations estimating higher GPP in the tropics (5° N–5° S) and the PRINCETON-driven model simulations estimating higher GPP in the extratropics (30–60° N).
Publisher: Springer Science and Business Media LLC
Date: 15-11-2017
DOI: 10.1038/S41598-017-15788-6
Abstract: Since the 1960s, large-scale deforestation in the Amazon Basin has contributed to rising global CO 2 concentrations and to climate change. Recent advances in satellite observations enable estimates of gross losses of above-ground biomass (AGB) stocks due to deforestation. However, because of simultaneous regrowth, the net contribution of deforestation emissions to rising atmospheric CO 2 concentrations is poorly quantified. Climate change may also reduce the potential for forest regeneration in previously disturbed regions. Here, we address these points of uncertainty with a machine-learning approach that combines satellite observations of AGB with climate data across the Amazon Basin to reconstruct annual maps of potential AGB during 1993–2012, the above-ground C storage potential of the undisturbed landscape. We derive a 2.2 Pg C loss of AGB over the study period, and, for the regions where these losses occur, we estimate a 0.7 Pg C reduction in potential AGB. Thus, climate change has led to a decline of ~1/3 in the capacity of these disturbed forests to recover and recapture the C lost in disturbances during 1993–2012. Our approach further shows that annual variations in land use change mask the natural relationship between the El Niño/Southern Oscillation and AGB stocks in disturbed regions.
Publisher: Copernicus GmbH
Date: 15-11-2012
Abstract: Abstract. The surrounding landscape of a stream has crucial impacts on the aquatic environment. This study pictures the hydro-biogeochemical situation of the Tyrebækken creek catchment in central Jutland, Denmark. The intensively managed agricultural landscape is dominated by rotational croplands. The small catchment mainly consist of sandy soil types besides organic soils along the streams. The aim of the study was to characterise the relative influence of soil type and land use on stream water quality. Nine snapshot s ling c aigns were undertaken during the growing season of 2009. Total dissolved nitrogen (TDN), nitrate (NO3−), ammonium nitrogen and dissolved organic carbon (DOC) concentrations were measured, and dissolved organic nitrogen (DON) was calculated for each grabbed s le. Electrical conductivity, pH and flow velocity were measured during s ling. Statistical analyses showed significant differences between the northern, southern and converged stream parts, especially for NO3− concentrations with average values between 1.4 mg N l−1 and 9.6 mg N l−1. Furthermore, throughout the s ling period DON concentrations increased to 2.8 mg N l−1 in the northern stream contributing up to 81% to TDN. Multiple-linear regression analyses performed between chemical data and landscape characteristics showed a significant negative influence of organic soils on instream N concentrations and corresponding losses in spite of their overall minor share of the agricultural land (12.9%). On the other hand, organic soil frequency was positively correlated to the corresponding DOC concentrations. Croplands also had a significant influence but with weaker correlations. For our case study we conclude that the fractions of coarse textured and organic soils have a major influence on N and DOC export in this intensively used landscape. Meanwhile, the contribution of DON to the total N losses was substantial.
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
Publisher: American Geophysical Union (AGU)
Date: 19-04-2015
DOI: 10.1002/2015GL063497
Location: France
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 Jean-François Exbrayat.