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BG3.17

EDI
Novel methods for bridging understanding of carbon and water fluxes from leaf to continents

A robust representation of the terrestrial carbon and water cycles requires fundamental understanding of biosphere-atmosphere interactions, particularly in a changing climate. Multiple processes determine how mass and energy exchanges scale from the leaf, to the whole plant, to the ecosystem, and eventually to the globe. Earth system models continue to evolve and incorporate increasingly complex processes across these scales, without however, reducing the uncertainties. Challenge remains to robustly formulate mechanistic underpinnings of biogeochemical processes across scales. The increasing amount of data at multiple scales, ranging from leaf-level measurements (e.g., gas exchange), tree-level measurements (e.g., sap flow and tree growth, dendroecology), ecosystem-level measurements (eddy covariance towers, UAVs, aircrafts) to Earth observation from space, are now opening new opportunities to tackle these challenges.

This session invites studies that improve our overall understanding of biosphere-atmosphere interactions by combining observations at different temporal and spatial scales as well as their seamless integration into modeling strategies. In addition to empirical multi-scale observations of carbon and water fluxes, we invite research that explore data-driven diagnostics and constraints for model evaluation (e.g., Emergent Constraints), data-driven parameterizations in mechanistic models (e.g., ESMs) and other developments of data-driven/hybrid modelling strategies (i.e., seamless fusion of data-driven approaches and mechanistic models) for an integrated understanding of carbon and water fluxes across scales.

Convener: Mana GharunECSECS | Co-conveners: Alexander J. WinklerECSECS, Rossella Guerrieri, Arthur Geßler, Gregory Duveiller, M. Piles, Pierre Gentine, Markus Reichstein
Presentations
| Fri, 27 May, 15:10–16:32 (CEST)
 
Room 3.16/17

Fri, 27 May, 15:10–16:40

Chairpersons: Mana Gharun, Alexander J. Winkler

15:10–15:20
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EGU22-2150
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solicited
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Virtual presentation

Understanding the coordination of ecosystem functions across global biomes is still a critical challenge in ecology to better predict biosphere response to environmental changes and for developing indicators of ecosystem multifunctionality. Theories such as the leaf economics spectrum and the global spectrum of plant forms and function showed that several plant and organs traits are coordinated in a few key dimensions representing different ecological strategies However, the main axes of variation of ecosystem-scale functions are still largely unknown.

In this contribution, we first derived a set of ecosystem functions from a dataset of surface gas exchange measurements across major terrestrial biomes. Second, we identify the most important axes of variation of ecosystem functions. Third, we identify the variables that explain the axes of variation. Finally, we analyze the extent to which two state-of-the-art land surface models reproduce the key axes of ecosystem functions.

To do so, we used data of carbon, water, and energy exchange for 203 sites (1484 site-years) from global surface flux datasets. Moreover, we compile site information on canopy-scale measurements of foliar chemistry (Nitrogen concentration), vegetation structural variables (maximum leaf area index, aboveground biomass, and vegetation height), and mean climate data at the sites.

We find evidence that three key axes capture the variability of ecosystem functions (71.8%). The first dimension represents maximum ecosystem productivity, which is explained primarily by vegetation structure, followed by mean climate. The second axis represents the water-use strategies driven by vegetation height and climate. The third dimension reflects the ecosystem carbon-use efficiency; it is controlled by vegetation structure but shows a gradient related to aridity.

The first axis of the spectrum is well captured by ecosystem models, while the second and third axes are poorly reproduced. As a result, the ecosystem functional space that the models can simulate tends to be smaller than the observations'. We assumed that the limited variability of the model output points to the uncertain implementation of plant hydraulics in land surface models. This known key limitation explains the differences between observations and models in the water use strategy axis. Concerning the carbon use strategy axis, one limitation is that models lack flexibility in representing the response of respiration rates and carbon-use efficiency to climate, nutrients, disturbances, and substrate availability (including biomass and stand age, which relate to ecosystem management).

The concept of the key axes of ecosystem functions could be used as an indicator of ecosystem health and multifunctionality, and for the development of land surface models, which might help improve the predictability of the terrestrial carbon and water cycle in response to climate and environmental changes.

How to cite: Migliavacca, M. and the major axes of terrestrial ecosystem functions collaborators: The major axes of terrestrial ecosystem functions derived from ecosystem scale flux observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2150, https://doi.org/10.5194/egusphere-egu22-2150, 2022.

15:20–15:26
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EGU22-10633
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On-site presentation
Patrick Fonti et al.

The impacts of climate extremes on forest ecosystems are still poorly understood but important for predicting carbon and water cycle feedbacks to climate. Despite evidences of the different climatic thresholds of tree carbon source and sink activities, implementations of mechanistic growth components into dynamic global vegetation models (DGVM) remain challenged by lacking observational data into the most important terrestrial biological sink, i.e.; into the cambial zone of tree stems.

In this study, we aim to provide a framework for mechanistic understanding of drought impacts on carbon and water dynamics based on accurate analyses of the physiological processes that indirectly regulate these budgets. We quantified the drought impact and resilience of intra-annual carbon sequestration and water use in four mature Norway spruces from a Swiss subalpine site by comparing high-resolution growth (i.e., xylogenesis and wood anatomy) and physiological (i.e., stable carbon isotope ratios) data from an exceptional dry summer (year 2015) with those from a regular growing season (year 2014).

Our observations described the cascade of impacts from leaf physiology to cambial activity during and after a 41-day period of physiological water deficit. During water deficit, all wood formation processes were strongly reduced diminishing carbon sequestration by 67% despite a 11% increased water-use efficiency. However, with the recovery of the positive hydric state in the stem, we observed a fast recovery of the rates of the different cell formation phases at the expenses of the accumulated assimilates produced during the drought event.

Our results clarify how the interaction between source and sink is modulated via external environmental factors and provide evidence that, under specific circumstances, tree growth can be extremely resilient. These novel findings should provide a framework to improve sink model components in DGVMs and consequently help to bridge understanding of carbon and water fluxes between atmosphere and forest ecosystems. 

How to cite: Fonti, P., Treydte, K., Lehmann, M., Rigling, A., and Martínez-Sancho, E.: Drought impacts on forest carbon sequestration and water use – evidence emerging from quantification of tree-ring formation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10633, https://doi.org/10.5194/egusphere-egu22-10633, 2022.

15:26–15:32
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EGU22-7603
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ECS
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On-site presentation
Christoforos Pappas et al.

Sapwood characteristics, such as sapwood area as well as thermal and hydraulic conductivity, are linked to species-specific hydraulic function and resource allocation to water transport tissues (xylem). These characteristics are often unknown and thus a major source of uncertainty in sap flow data processing and transpiration estimates because bulk rather than species-specific values are usually applied. Here, we analyzed the sapwood characteristics of fifteen common tree species in eastern North America from different taxa (i.e., angiosperms and gymnosperms) and xylem porosity groups (i.e., tracheid-bearing, diffuse- or ring-porous species). We quantified their sapwood area changes with stem diameter (allometric scaling) and thermal conductivity. We combined these measurements with species-specific values of wood density and hydraulic conductivity found in literature and assessed the role of wood anatomy in orchestrating their covariation. Angiosperms (ring- and diffuse-porous species), with specialized vessels for water transport, showed steeper relation (scaling) between tree size and sapwood area in comparison to gymnosperms (tracheid-bearing species). Despite the variability in thermal conductivity between species, gymnosperms (angiosperms) were characterized by lower (higher) wood density and higher (lower) sapwood moisture content, resulting in non-significant differences in sapwood thermal conductivity between taxa and xylem porosity groups. Clustering of species sapwood characteristics based on taxa or xylem porosity could facilitate more accurate parameterizations of these attributes. When combined with an increasing number of sap flow observations, these findings can improve tree- and landscape-level transpiration estimates, leading to more robust partitioning of terrestrial water fluxes.

How to cite: Pappas, C., Bélanger, N., Bastien-Beaudet, G., Couture, C., D'Orangeville, L., Duchesne, L., Gennaretti, F., Houle, D., Hurley, A. G., Klesse, S., Lebel Desrosiers, S., Montoro Girona, M., Peters, R. L., Rossi, S., St-Amand, K., and Kneeshaw, D.: Xylem porosity shapes sapwood characteristics and stem water use of temperate and boreal tree species, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7603, https://doi.org/10.5194/egusphere-egu22-7603, 2022.

15:32–15:38
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EGU22-8416
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ECS
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On-site presentation
Weijie Zhang et al.

While eddy covariance is a well established method for measuring energy, carbon and water fluxes, it is still susceptible to known biases and uncertainties. One such issue is the underestimation of latent energy (LE) under high relative humidity (RH) conditions (> 70%) due to the attenuation of fluctuations of water vapor concentration (Mammarella et al., 2009; Massman & Ibrom, 2008), known as 'the high RH error'. This high RH error coincides with the theoretically expected steep increase in water use efficiency (WUE) such that the systematic LE underestimation has unknown and potentially large implications for evapotranspiration (ET,  converted from LE) partitioning and the derivation of WUE parameterisations. We diagnose the high RH error for sites in the FLUXNET2015 dataset, a global eddy flux database, focusing on the difference in error response by different measurement systems (i.e. systems using open, closed, or enclosed gas analysers). Then we propose a method to correct the high RH error and test its implications for partitioning ET into transpiration (T) and evaporation (E) using WUE based methods (Nelson et al., 2018; Zhou et al., 2016) and compare it with T estimated from sap flow measurements (Poyatos et al., 2021).

Overall, we found that closed-path sites experience more severe high RH error than open-path sites, as diagnosed by the residual ratio of LE to available energy and sensible heat. After correcting the high RH error, T estimated from TEA algorithm (Nelson et al., 2018) based WUE (the ratio of GPP to T, where GPP is the gross primary productivity) has an approximately 25% decrease at high RH from closed-path sites, whereas this decrease is only 5% in open-path sites. Correspondingly, T/ET has an approximately 10% and 2% increase from closed-path and open-path sites, respectively. Considering these systematic errors in the FLUXNET2015 dataset is therefore crucial when describing the interactions between water and carbon cycles, especially for closed-path sites during high RH conditions. Furthermore, future studies which implement both open-path and closed-path analysers, as well as sap flow measurements on the same site, will help to better understand the systematic differences caused by gas analysers and to constrain the uncertainty caused by such differences.

 

References:

Mammarella, I., et al. (2009). https://doi.org/10.1175/2009JTECHA1179.1
Massman, W. J., & Ibrom, A. (2008). https://doi.org/10.5194/acp-8-6245-2008
Nelson, J. A., et al. (2018). https://doi.org/10.1029/2018JG004727
Poyatos, R., et al. (2021). https://doi.org/10.5194/essd-13-2607-2021
Zhou, S., et al. (2016). https://doi.org/10.1002/2015WR017766

How to cite: Zhang, W., Jung, M., Migliavacca, M., Poyatos, R., Miralles, D., El-Madanay, T., Carvalhais, N., Reichstein, M., and Nelson, J. A.: Implications of underestimated eddy covariance evapotranspiration at high relative humidity for partitioning into transpiration and evaporation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8416, https://doi.org/10.5194/egusphere-egu22-8416, 2022.

15:38–15:44
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EGU22-4451
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ECS
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On-site presentation
Mathias Hoffmann et al.

Improved agricultural practices increasing the water use efficiency (WUE), reducing greenhouse gas emissions (GHG) and/or improving atmospheric C sequestration rates within the soil are crucial for an adaptation and/or mitigation to the global climate crisis. However, processes driving water (H2O), carbon (C) and GHG fluxes within the soil-plant-atmosphere continuum of agricultural used landscapes are complex and flux dynamics differ substantially in time and space. Hence, to upscale and evaluate the effects/benefits of any new agricultural practice aiming towards improving WUE, soil C sequestration and/or GHG emissions, accurate and precise information on the complex spatio-temporal H2O, C and GHG flux pattern, their drivers and underlying processes are required.

Current approaches to investigate this topic are usually laborious and have to choose between high spatial or temporal resolution due to methodological constraints. On the one hand, often used eddy covariance systems are not suitable to account for small scale spatial heterogeneities and to separate the soil and farming impact, despite growing evidence of their importance. On the other hand, chamber systems either lack temporal resolution (manual chambers) or strongly interfere with the measured system (static automatic chambers). Hence, none of these systems enable a proper upscaling and evaluation of effects/benefits of new farming practices for WUE, C sequestration and GHG emissions at especially heterogeneous agricultural landscapes, such as present within inter-alia the also globally widespread hummocky ground moraine landscape of NE-Germany.

In an effort to overcome this, a novel, fully automated robotic field sensor platform was established and combined with an IoT network and remote sensing approaches. Here, an innovative, continuously operating, automated robotic field sensor platform is presented. The platform was mounted on fixed tracks, stretching over an experimental field (150m x 16m) which covers three different, distinct soil types. It carries multiple sensors to measure GHG and water vapour concentrations as well as water vapour isotope signatures of d18O and dD. Combined with two chambers which can be accurately positioned in three dimensions at the experimental field below, this system facilitates to detect small-scale spatial heterogeneity and short-term temporal variability of H2O, C and GHG flux dynamics as well as crop and soil conditions over a range of possible experimental setups. The automated, continuous estimation of d18O and dD of evapotranspiration further provides the basis to partition water fluxes alongside the flux based partitioning of C and GHG fluxes. This particularly promotes to assess not only ecosystem but component specific WUE. Hence, this platform produces a detailed picture of H2O, C and GHG dynamics across soil and farming practice combinations and crop rotations, with a high-degree of accuracy and reproducibility.

Keywords: innovative sensor platform, greenhouse gases, H2O isotopes, Evapotranspiration, wate ruse efficiency

How to cite: Hoffmann, M., Dubbert, M., Vaidya, S., Dahlmann, A., Schmidt, M., Rakowski, P., Bonk, N., Verch, G., Sommer, M., and Augustin, J.: An innovative sensor platform for in-situ studies of dynamics and underlying processes, driving spatio-temporal water, carbon and greenhouse gas flux pattern in a heterogeneous arable landscape, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4451, https://doi.org/10.5194/egusphere-egu22-4451, 2022.

15:44–15:50
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EGU22-7971
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On-site presentation
Ronald Hutjes et al.

Drained organic soils are an important source of greenhouse gases worldwide. Also in the low lying areas of the Netherlands drainage of its organic soils, with the aim to enable more intensive usage of the land, leads to oxidation of organic material, significant carbon dioxide release and subsequent land subsidence. As part of climate mitigation policies the Dutch government seeks to reduce these emissions by about 25% in 2030. In support of these policies, the National Research programme on Greenhouse gas emissions from Fen meadow areas (in Dutch NOBV: https://www.nobveenweiden.nl/) aims to investigate the effects of various mitigation measures on total greenhouse gas balance of the targeted areas.

One approach, complementing multi-site ground based measurements using various techniques, is to use repeated airborne surveys to measure in-situ turbulent CO2 exchange. The push propellor aircraft is a SkyArrow 650 TCNS  equipped with a BAT probe in combination with a Licor 7500 for eddy covariance fluxes of momentum, sensible and latent heat and CO2, augmented by onboard PAR and net radiation sensors. Survey altitude is 200ft/60m nominally, guaranteeing minimal flux divergence between surface and flight level for well-developed boundary layers. Covariances were spatially integrated over 2 km.

In 2020 and 2021 flights were made twice weekly, weather permitting, to cover three major fen meadow landscapes in the Netherlands: the so-called ‘Groene Hart’ area in the west between the cities of Amsterdam, Utrecht and Rotterdam, predominantly used for intensive dairy farming; the ‘Kop van Overijssel’ between Zwolle, Meppel and Vollenhove with large tracts of nature areas besides dairy farming; and finally the South West of the province of Fiesland between Heerenveen, Drachten and Sneek. Flight patterns were designed such that crosswind, parallel flight tracks, separated ~2km, made typical flux footprints overlapping ensuring full spatial coverage of the respective areas.

We will present first analyses and scaling of airborne flux data for each of the three regions in relation to explanatory variables from vegetation and soil characteristics, land and water management (EO and map based) and weather, using machine learning algorithms. Source partitioning based on high frequency airborne covariance data will be used to separate soil and vegetation fluxes. We aim to ultimately provide a data driven regional greenhouse gas balances for the different fen meadow areas of the Netherlands.

How to cite: Hutjes, R., Kruijt, B., Franssen, W., and van der Poel, L.: Greenhouse gas balance of fen meadow landscapes using airborne flux measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7971, https://doi.org/10.5194/egusphere-egu22-7971, 2022.

15:50–15:56
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EGU22-9890
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ECS
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On-site presentation
Reda ElGhawi et al.

Evapotranspiration (ET) is a central water flux in the global hydrological cycle, closely coupled to the energy balance and carbon cycle. It is primarily governed by non-linear energy processes controlled by meteorological conditions as well as different heterogeneous properties of the ecosystem. Various physical models of ET are widely used, such as the Penman-Monteith (PM) equation, which preserves physical laws and accounts for phenomenological behavior. However, these mechanistic models are often subject to large uncertainties, largely due to the limited understanding of the biological controls, particularly how plants control the land-to-atmosphere water flux by closing and opening their stomata.

Here, we propose a data-adaptive hybrid modeling approach for ET that combines the physics-based PM equation with machine learning (ML) by inferring the biological and aerodynamic regulator of the evaporative water flux from observations. Specifically, the framework comprises setting up a feed-forward neural network and integrating the physically-constraining PM equation in the loss function of the latent heat flux (LE). The stomatal resistance (rs) and aerodynamic resistance (ra) are modeled as intermediate latent variables, based on micro-meteorological observations collected and curated in the FLUXNET database. For baseline comparison, two conceptually different ML models have been set up, where the first model simulates LE directly without imposing any physical constraints and the second model is an alternative pseudo-hybrid model approach [1], where the main distinction lies in the formulation of the loss function.

 Our hybrid model is capable of capturing the diurnal and seasonal variations between the mean values of predicted and observed LE. The obtained data-driven parameterizations of the latent variables rs and ra are evaluated against the micro-meteorological conditions to validate their physical plausibility.  We show that our hybrid modeling approach not only improves the rigid ad-hoc formulations of mechanistic models using observations, but also that hybrid models provide interpretable results that obey the physical laws of energy and mass conservation, in contrast to black-box ML models.

Our presented hybrid modeling approach can be extended to global generalizations of LE flux estimates and serve as observation-based parameterizations of rs and ra in complex land surface and Earth system models.

[1]      W. L. Zhao et al., “Physics‐Constrained Machine Learning of Evapotranspiration,” Geophys. Res. Lett., vol. 46, no. 24, pp. 14496–14507, Dec. 2019, doi: 10.1029/2019GL085291.

How to cite: ElGhawi, R., Winkler, A. J., Kraft, B., Reimers, C., Körner, M., and Reichstein, M.: Hybrid Modelling of Land-Atmosphere Fluxes: Estimating Evapotranspiration using Combined Physics-Based and Data-Driven Machine Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9890, https://doi.org/10.5194/egusphere-egu22-9890, 2022.

15:56–16:02
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EGU22-10625
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ECS
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On-site presentation
Paul Levine et al.

The Amazon River basin contains a substantial amount of carbon stored within terrestrial ecosystems. The unknown fate of this this carbon remains a substantial source of uncertainty in projections of the Earth system. While increasing atmospheric carbon dioxide concentrations could potentially enhance photosynthetic carbon uptake and/or reduce transpiration, increasing vapor pressure deficits have the potential to act with the opposite sign on both of these fluxes. Here, we investigate these competing factors at a process level, using a data assimilation system in which we constrain a parsimonious ecosystem model with observations from river runoff gauging stations, gravimetric water storage anomalies, and solar-induced chlorophyll fluorescence. Our model-data fusion provides us with an observationally consistent reanalysis of 21st-century ecohydrology across 14 Amazon watersheds along side the posterior distribution of key process parameters and emergent ecosystem properties such as water use efficiency (WUE). We find that the response to trends in atmospheric carbon dioxide concentrations and meteorological drivers varies across a hydroclimatic gradient within the Amazon, with implications for how carbon and water cycling could be expected to change subject to future biogeochemical and climatic trends.

How to cite: Levine, P., Bloom, A., Konings, A., Worden, M., Parazoo, N., Braghiere, R., Norton, A., Ma, S., Worden, J., and Reager, J.: Merging multiple observational data streams to constrain carbon uptake and water loss in the Amazon basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10625, https://doi.org/10.5194/egusphere-egu22-10625, 2022.

16:02–16:08
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EGU22-6064
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ECS
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On-site presentation
Na Li et al.

The inter-annual variability (IAV) of the global carbon cycle (C-cycle) is prone to large uncertainty, which in turn, affects uncertainty in future climate projections. Quantifying the imprint of large-scale atmospheric circulation dynamics and associated carbon cycle responses is a key endeavour to improve our understanding of C-cycle dynamics.

C-cycle IAV mainly results from the balance of carbon uptake by gross primary productivity, carbon release from respiration processes, and other disturbance-induced fluxes. These processes are largely controlled by temperature, water availability, and incoming solar radiation, which are modulated by large-scale modes of atmospheric circulation such as the El Niño/Southern Oscillation (ENSO) or the Pacific Decadal Oscillation (PDO). 

Here, we use a data-driven approach [1] to quantify the fraction of IAV in atmospheric CO2 growth rate and the land CO2 sink that are driven by atmospheric circulation variability, by using spatio-temporal sea level pressure-a proxy for large-scale atmospheric circulation-as a predictor in ridge regression models of carbon cycle IAV. We use a regularisation approach [1] to curb the problems of overfitting and multicollinearity due to the limited time interval and large number of spatial predictors (spatial gridded time-series of SLP anomalies). We find that the model based on SLP anomalies can achieve high skill in predictions of the IAV in atmospheric growth rate and global land sink, with Pearson correlations between original and predicted test values of 0.7-0.84. The coefficients of the regression indicate two dominant regions contributing to C-cycle IAV: one in the tropical Pacific corresponding to the well-known influence of ENSO, another one located in the western Pacific. We test how the prediction skill depends on the length of the time-series and show that for short time-series (15-20 years) the correlation of predicted vs. observed test values is strongly dependent on the particular period considered, while it is more stable for periods longer than 30 years. These results indicate that the influence of atmospheric circulation variability on IAV of the C-cycle can limit our ability to draw robust conclusions when using short observational records. 

[1] Sippel, S., Meinshausen, N., Merrifield, A., Lehner, F., Pendergrass, A. G., Fischer, E., and Knutti, R.:  Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning, Journal of Climate, 32(17), 5677–5699, https://doi.org/10.1175/JCLI-D-18-0882.1, 2019.

How to cite: Li, N., Sippel, S., Winkler, A. J., Mahecha, M. D., Reichstein, M., and Bastos, A.: Diagnosing the fraction of inter-annual variability of global carbon cycle driven by atmospheric circulation variability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6064, https://doi.org/10.5194/egusphere-egu22-6064, 2022.

16:08–16:14
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EGU22-6506
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ECS
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On-site presentation
Arpita Verma et al.

The understanding of the terrestrial carbon budget depends on the development of the terrestrial carbon sink, which is influenced by the forest dynamics under climate change and environmental conditions. In this study, we choose a new approach combining the CARAIB-dynamic vegetation model (DVM) and satellite observed data at a high resolution of 3 km over the Austria-Eisenwurzen region for the year 1985-2020. Using machine learning techniques and remote sensing Landsat satellite data, we extracted the land use and land cover (LULC) over the study region. It is giving the precise estimation of spatial and temporal change of forest dynamic over the year 1985-2020. In addition, the geographical distribution of Eisenwurzen 80 % part is the Northern Alps, 11 % of the area belongs to the Northern Alpine Foothills and 9 % belong to the Central Alps. The objective of this study is to understand the model sensitivities and uncertainties in dynamic conditions which are necessary for a reliable and robust estimation of the terrestrial carbon budget. Here, we will conduct our simulation with CARAIB-DVM in different environments – with and without LULC change, no climate change (de-trending), climate change scenario, constant atmospheric CO2. Additionally, we are simulating our model over the course of >100 years for analysis of model sensitivity to climatic parameters. From, this study, we explore how the changes in these parameters affect the estimation of the terrestrial carbon sinks. Given that the parameters we are exploring in this analysis are highly uncertain, especially at the regional level and at high resolution, it is important to see how these adjustments affect the estimation of the carbon budget. Hence, with this study, we understand which input parameters are responsible for the uncertainty in the estimation of carbon sequestration. Further, we will calibrate the dynamic vegetation model to minimize uncertainty in the future projection (until 2070). In conclusion, this study allows us to understand the importance of changing land-use, climate, and environment scenarios and to constrain the model with an improved input dataset that reduces the uncertainty in the model evaluation of the regional carbon budget of terrestrial ecosystems.

 

How to cite: Verma, A., Francois, L., Jacquemin, I., Lanssens, B., Tölle, M., Matej, S., and Egger, C.: Sensitivity analysis of terrestrial carbon budget with changing land use land cover and climate by combining dynamic vegetation model and satellite observed data at high resolution over Austria- Eisenwurzen, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6506, https://doi.org/10.5194/egusphere-egu22-6506, 2022.

16:14–16:20
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EGU22-6626
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ECS
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On-site presentation
Yuan Zhang et al.

Plant transpiration plays a central role in regulating water cycle and land surface energy budget. Correctly representing the controls of such processes in Earth system models is thus critical to accurately project future climate. Transpiration is controlled by physiological processes of stomatal regulation in response to water and temperature stress, and by canopy structure through the aerodynamic transport of water vapor in the boundary layer from leaves to the atmosphere. The strength of the vegetation coupling to the atmosphere can be summarized by a decoupling coefficient Ω. A value of Ω of 0 implies a strong coupling, leaving a dominant role to stomatal conductance in regulating H2O and CO2 fluxes, while Ω of 1 implies a complete decoupling of leaves from the atmosphere, that is, the transfer of H2O and CO2 is limited by aerodynamic transport. In this study, we investigate how well the state-of-the-art land surface model, ORCHIDEE, simulates the decoupling of vegetation to atmosphere using observation-based empirical daily estimates of Ω at 106 FLUXNET sites. We also tested whether calibration of parameters controlling the dependence of the stomatal conductance to the water vapor deficit (VPD), or using observation based canopy height improves the simulated Ω. A set of random forest models were built to further investigate the impacts of different factors on Ω. Our results show that Ω is underestimated by ORCHIDEE (0.20) compared with the observation-based estimates (0.28), and that the calibration of stomatal conductance parameters improved the simulated Ω (0.24). Nevertheless, the bias of simulated Ω remains large in grasslands and croplands after the calibration. We also found that in observation vegetation tends to be more decoupled to atmosphere when there is low wind speed, high temperature, low VPD, large leaf area index (LAI) and in short vegetation. ORCHIDEE generally agrees with this pattern but underestimated the VPD impact when VPD is high, overestimated the contribution of LAI and did not correctly simulate the temperature dependence when temperature is high. Canopy height does not show strong direct impact on Ω. Our results highlight the importance of observational constraints on simulating the vegetation-atmosphere coupling strength, which can help improve the predictive accuracy of water fluxes in Earth system models.

How to cite: Zhang, Y., Narayanappa, D., Philippe, C., Li, W., Goll, D., Vuichard, N., De Kauwe, M. G., and Li, L.: Evaluating the vegetation-atmosphere coupling strength of ORCHIDEE land surface model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6626, https://doi.org/10.5194/egusphere-egu22-6626, 2022.

16:20–16:26
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EGU22-8946
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ECS
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Virtual presentation
Caroline A. Famiglietti et al.

Accurate forecasts of net biosphere exchange (NBE) are vital for understanding the role of terrestrial ecosystems in a changing climate. It is therefore problematic that NBE projections from most state-of-the-art terrestrial biosphere models (TBMs) diverge considerably from one another. Efforts to reduce this divergence have historically focused on improving models’ structural realism, but several lines of evidence have brought the role of poorly determined and/or over-generalized parameters into sharper focus. Here we investigate how different parameterization assumptions propagate into NBE prediction errors across the globe. To do so, we simulate two methods for parameter assignment within a flexible model–data fusion framework (CARDAMOM): (a) the traditional plant functional type (PFT)-based approach, whereby parameters retrieved at a small number of select locations are applied broadly within regions sharing similar land cover characteristics; and (b) a novel top-down “environmental filtering” (EF) approach, whereby a pixel’s parameters are predicted based on relationships with climate, soil, and canopy properties. In an effort to isolate the role of parametric from structural uncertainty, we benchmark the resulting PFT-based and EF-based NBE predictions with estimates from a Bayesian optimization approach (whereby “true” parameters consistent with a suite of data constraints are retrieved on a pixel-by-pixel basis). We find that the EF-based approach outperforms the PFT-based approach at twice as many pixels as the converse and across multiple performance metrics. However, NBE estimates from the EF-based approach may be more susceptible to compensation between errors in component flux predictions. This work provides insight into the relationship between TBM performance and parametric uncertainty, informing efforts to improve model parameterization via nontraditional approaches.

How to cite: Famiglietti, C. A., Worden, M., Quetin, G. R., Smallman, T. L., Dayal, U., Bloom, A. A., Williams, M., and Konings, A. G.: Effects of environmental filtering and PFT-based model parameterization approaches on NBE prediction errors across the globe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8946, https://doi.org/10.5194/egusphere-egu22-8946, 2022.

16:26–16:32
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EGU22-5001
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On-site presentation
Diego G. Miralles et al.

Transpiration (Et) is a key variable in hydrology and climate, yet it remains poorly understood at global scales. In nature, several non-linearly interacting environmental variables, or 'stressors', limit the rates of Et below the demand by the atmosphere. In most process-based formulations of evaporation (E) – e.g., satellite-based algorithms and climate models – only a few of these stressors are considered, and their representation is usually based on limited empirical or experimental studies conducted at local scales. New hybrid approaches offer the opportunity to combine process-based knowledge on Et and machine learning models in a synergistic manner, and to better characterise the influences of this myriad stressors on Et.

Using a hybrid approach, we combine in situ and satellite observations of multiple stress variables using deep learning, aiming to construct a new formulation of transpiration stress (St) – the ratio by which potential transpiration is reduced to Et. The data of St are assembled from 368 flux towers spread across the globe coming from multiple networks, as well as 90 sapflow-instrumented sites from a recently collected global archive. The covariates used as input features include: plant available water to represent water or drought stress, air temperature to represent heat stress, vapor pressure deficit to account for the effect of atmospheric demand on stomatal conductance, microwave vegetation optical depth to consider the phenological state of vegetation, incoming shortwave radiation as an indicator of light stress, and carbon dioxide which directly and indirectly affects ecosystem transpiration.

We show that our ground-up approach without any prior assumptions compares better than traditional formulations of St, both when compared to in situ observations as well as an independent satellite-based stress proxy (SIF/PAR). Embedding the new St function within a process-based model of E (the Global Land Evaporation Amsterdam Model, GLEAM) yields a hybrid model of evaporation (GLEAM-Hybrid) which is evaluated in its performance. In this hybrid model, the St formulation is bidirectionally coupled to the host model at daily timescales. An extensive validation shows that our hybrid approach (GLEAM-Hybrid) has the potential to outperform traditional process-based formulations (GLEAM) and pure machine learning-based estimates of E (FLUXCOM). Overall, the proposed approach provides a suitable framework to improve the estimation of E in satellite-based algorithms and climate models, and consequently increase our understanding of this crucial variable.

How to cite: Miralles, D. G., Koppa, A., Rains, D., Hulsman, P., and Poyatos, R.: A hybrid model of global land evaporation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5001, https://doi.org/10.5194/egusphere-egu22-5001, 2022.