Biosphere Model Research Articles

Observing System Choice Can Minimize Interference of the Biosphere in Studies of Urban CO2 Emissions

AbstractCities around the world have introduced initiatives to reduce CO2 emissions. Atmospheric observations can provide evaluation and assessment of these initiatives by quantifying emissions, considering local sources and sinks. The relative importance of the urban biosphere, which can act as both a source (respiration) and sink (photosynthesis) of CO2, has previously been suggested to strongly impact urban CO2 measurements, confounding the ability to use observations to study fossil emissions. However, if using an observing framework that measures a local urban background and the direct urban core outflow, for example, along a downwind airborne transect, the biosphere’s role may be minimized. Here, we combine real, airborne observations of CO2 downwind of select cities in the Northeast US with high‐resolution, back‐trajectory modeling and spatially and temporally resolved surface biosphere and fossil fuel fluxes to characterize the relative biosphere importance to urban CO2 profiles. We show the biosphere influence using this urban observing system to be small, averaging only 15% of the local CO2 enhancement annually, <10% outside of summer, and with a maximum influence of 29% in summer when the biosphere drawdown is most pronounced. Furthermore, when considering two biosphere models that differ by >80%, the impact on observed urban CO2 signals is reduced to only 12% on average. Urban observing frameworks that utilize this local background approach—including those via aircraft or satellite observations—can minimize the biosphere's influence and thus help facilitate robust assessments of urban fossil fuel CO2 emissions.

A Robust Estimate of Continental‐Scale Terrestrial Carbon Sinks Using GOSAT XCO2 Retrievals

AbstractSatellite XCO2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknown on what scales these estimates are robust. Here, we use the time‐dependent Bayesian synthesis top‐down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO2 retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) decrease significantly at decreasing spatial scales. On the continental‐scale, the mean URR is about 57%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO2 concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO2 can only promise a robust continental‐scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage.

Performance of Seven Land Surface Schemes in the WRFv4.3 Model for Simulating Precipitation in the Record-Breaking Meiyu Season Over the Yangtze-Huaihe River Valley in China.

In 2020, the Yangtze-Huai river valley (YHRV) experienced the highest record-breaking Meiyu season since 1961, which was mainly characterized by the longest duration of precipitation lasting from early-June to mid-July, with frequent heavy rainstorms that caused severe flooding and deaths in China. Many studies have investigated the causes of this Meiyu season and its evolution, but the accuracy of precipitation simulations has received little attention. It is important to provide more accurate precipitation forecasts to help prevent and reduce flood disasters, thereby facilitating the maintenance of a healthy and sustainable earth ecosystem. In this study, we determined the optimal scheme among seven land surface model (LSMs) schemes in the Weather Research and Forecasting model for simulating the precipitation in the Meiyu season during 2020 over the YHRV region. We also investigated the mechanisms in the different LSMs that might affect precipitation simulations in terms of water and energy cycling. The results showed that the simulated amounts of precipitation were higher under all LSMs than the observations. The main differences occurred in rainstorm areas (>12mm/day), and the differences in low rainfall areas were not significant (<8mm/day). Among all of the LSMs, the Simplified Simple Biosphere (SSiB) model obtained the best performance, with the lowest root mean square error and the highest correlation. The SSiB model even outperformed the Bayesian model averaging result. Finally, some factors responsible for the differences modeling results were investigated to understand the related physical mechanism.

Improved Estimation of the Gross Primary Production of Europe by Considering the Spatial and Temporal Changes in Photosynthetic Capacity from 2001 to 2016

The value of leaf photosynthetic capacity (Vcmax) varies with time and space, but state-of-the-art terrestrial biosphere models rarely include such Vcmax variability, hindering the accuracy of carbon cycle estimations on a large scale. In particular, while the European terrestrial ecosystem is particularly sensitive to climate change, current estimates of gross primary production (GPP) in Europe are subject to significant uncertainties (2.5 to 8.7 Pg C yr−1). This study applied a process-based Farquhar GPP model (FGM) to improve GPP estimation by introducing a spatially and temporally explicit Vcmax derived from the satellite-based leaf chlorophyll content (LCC) on two scales: across multiple eddy covariance tower sites and on the regional scale. Across the 19 EuroFLUX sites selected for independent model validation based on 9 plant functional types (PFTs), relative to the biome-specific Vcmax, the inclusion of the LCC-derived Vcmax improved the model estimates of GPP, with the coefficient of determination (R2) increased by 23% and the root mean square error (RMSE) decreased by 25%. Vcmax values are typically parameterized with PFT-specific Vcmax calibrated from flux tower observations or empirical Vcmax based on the TRY database (which includes 723 data points derived from Vcmax field measurements). On the regional scale, compared with GPP, using the LCC-derived Vcmax, the conventional method of fixing Vcmax using the calibrated Vcmax or TRY-based Vcmax overestimated the annual GPP of Europe by 0.5 to 2.9 Pg C yr−1 or 5 to 31% and overestimated the interannually increasing GPP trend by 0.007 to 0.01 Pg C yr−2 or 14 to 20%, respectively. The spatial pattern and interannual change trend of the European GPP estimated by the improved FGM showed general consistency with the existing studies, while our estimates indicated that the European terrestrial ecosystem (including part of Russia) had higher carbon assimilation potential (9.4 Pg C yr−1). Our study highlighted the urgent need to develop spatially and temporally consistent Vcmax products with a high accuracy so as to reduce uncertainties in global carbon modeling and improve our understanding of how terrestrial ecosystems respond to climate change.

A Simple 3D Cell Culture Method for Studying the Interactions between Human Mesenchymal Stromal/Stem Cells and Patients Derived Glioblastoma.

We have developed a 3D biosphere model using patient-derived cells (PDCs) from glioblastoma (GBM), the major form of primary brain tumors in adult, plus cancer-activated fibroblasts (CAFs), obtained by culturing mesenchymal stem cells with GBM conditioned media. The effect of MSC/CAFs on the proliferation, cell-cell interactions, and response to treatment of PDCs was evaluated. Proliferation in the presence of CAFs was statistically lower but the spheroids formed within the 3D-biosphere were larger. A treatment for 5 days with Temozolomide (TMZ) and irradiation, the standard therapy for GBM, had a marked effect on cell number in monocultures compared to co-cultures and influenced cancer stem cells composition, similar to that observed in GBM patients. Mathematical analyses of spheroids growth and morphology confirm the similarity with GBM patients. We, thus, provide a simple and reproducible method to obtain 3D cultures from patient-derived biopsies and co-cultures with MSC with a near 100% success. This method provides the basis for relevant in vitro functional models for a better comprehension of the role of tumor microenvironment and, for precision and/or personalized medicine, potentially to predict the response to treatments for each GBM patient.

Machine learning for accelerating process-based computation of land biogeochemical cycles.

Global change ecology nowadays embraces ever-growing large observational datasets (big-data) and complex mathematical models that track hundreds of ecological processes (big-model). The rapid advancement of the big-data-big-model has reached its bottleneck: high computational requirements prevent further development of models that need to be integrated over long time-scales to simulate the distribution of ecosystems carbon and nutrient pools and fluxes. Here, we introduce a machine-learning acceleration (MLA) tool to tackle this grand challenge. We focus on the most resource-consuming step in terrestrial biosphere models (TBMs): the equilibration of biogeochemical cycles (spin-up), a prerequisite that can take up to 98% of the computational time. Through three members of the ORCHIDEE TBM family part of the IPSL Earth System Model, including versions that describe the complex interactions between nitrogen, phosphorus and carbon that do not have any analytical solution for the spin-up, we show that an unoptimized MLA reduced the computation demand by 77%-80% for global studies via interpolating the equilibrated state of biogeochemical variables for a subset of model pixels. Despite small biases in the MLA-derived equilibrium, the resulting impact on the predicted regional carbon balance over recent decades is minor. We expect a one-order of magnitude lower computation demand by optimizing the choices of machine learning algorithms, their settings, and balancing the trade-off between quality of MLA predictions and need for TBM simulations for training data generation and bias reduction. Our tool is agnostic to gridded models (beyond TBMs), compatible with existing spin-up acceleration procedures, and opens the door to a wide variety of future applications, with complex non-linear models benefit most from the computational efficiency.

Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling

Abstract. We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by &gt;2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).

Toward mechanistic modeling and rational engineering of plant respiration.

Plant respiration not only provides energy to support all cellular processes, including biomass production, but also plays a major role in the global carbon cycle. Therefore, modulation of plant respiration can be used to both increase the plant yield and mitigate the effects of global climate change. Mechanistic modeling of plant respiration at sufficient biochemical detail can provide key insights for rational engineering of this process. Yet, despite its importance, plant respiration has attracted considerably less modeling effort in comparison to photosynthesis. In this update review, we highlight the advances made in modeling of plant respiration, emphasizing the gradual but important change from phenomenological to models based on first principles. We also provide a detailed account of the existing resources that can contribute to resolving the challenges in modeling plant respiration. These resources point at tangible improvements in the representation of cellular processes that contribute to CO2 evolution and consideration of kinetic properties of underlying enzymes to facilitate mechanistic modeling. The update review emphasizes the need to couple biochemical models of respiration with models of acclimation and adaptation of respiration for their effective usage in guiding breeding efforts and improving terrestrial biosphere models tailored to future climate scenarios.

Using Orbiting Carbon Observatory-2 (OCO-2) column CO2 retrievals to rapidly detect and estimate biospheric surface carbon flux anomalies

Abstract. The global carbon cycle is experiencing continued perturbations via increases in atmospheric carbon concentrations, which are partly reduced by terrestrial biosphere and ocean carbon uptake. Greenhouse gas satellites have been shown to be useful in retrieving atmospheric carbon concentrations and observing surface and atmospheric CO2 seasonal-to-interannual variations. However, limited attention has been placed on using satellite column CO2 retrievals to evaluate surface CO2 fluxes from the terrestrial biosphere without advanced inversion models at low latency. Such applications could be useful to monitor, in near real time, biosphere carbon fluxes during climatic anomalies like drought, heatwaves, and floods, before more complex terrestrial biosphere model outputs and/or advanced inversion modelling estimates become available. Here, we explore the ability of Orbiting Carbon Observatory-2 (OCO-2) column-averaged dry air CO2 (XCO2) retrievals to directly detect and estimate terrestrial biosphere CO2 flux anomalies using a simple mass-balance approach. An initial global analysis of surface–atmospheric CO2 coupling and transport conditions reveals that the western US, among a handful of other regions, is a feasible candidate for using XCO2 for detecting terrestrial biosphere CO2 flux anomalies. Using the CarbonTracker model reanalysis as a test bed, we first demonstrate that a well-established mass-balance approach can estimate monthly surface CO2 flux anomalies from XCO2 enhancements in the western United States. The method is optimal when the study domain is spatially extensive enough to account for atmospheric mixing and has favorable advection conditions with contributions primarily from one background region. We find that errors in individual soundings reduce the ability of OCO-2 XCO2 to estimate more frequent, smaller surface CO2 flux anomalies. However, we find that OCO-2 XCO2 can often detect and estimate large surface flux anomalies that leave an imprint on the atmospheric CO2 concentration anomalies beyond the retrieval error/uncertainty associated with the observations. OCO-2 can thus be useful for low-latency monitoring of the monthly timing and magnitude of extreme regional terrestrial biosphere carbon anomalies.

Soil Moisture Assimilation Improves Terrestrial Biosphere Model GPP Responses to Sub-Annual Drought at Continental Scale

Due to the substantial gross exchange fluxes with the atmosphere, the terrestrial carbon cycle plays a significant role in the global carbon budget. Drought commonly affects terrestrial carbon absorption negatively. Terrestrial biosphere models exhibit significant uncertainties in capturing the carbon flux response to drought, which have an impact on estimates of the global carbon budget. Through plant physiological processes, soil moisture tightly regulates the carbon cycle in the environment. Therefore, accurate observations of soil moisture may enhance the modeling of carbon fluxes in a model–data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36-year satellite-derived surface soil moisture observations in combination with flask samples of atmospheric CO2 concentrations. We find that, compared to the default model, the performance of optimized net ecosystem productivity (NEP) and gross primary productivity (GPP) has increased with the RMSEs reduced by 1.62 gC/m2/month and 10.84 gC/m2/month, which indicates the added value of the ESA-CCI soil moisture observations as a constraint on the terrestrial carbon cycle. Additionally, the combination of soil moisture and CO2 concentration in this study improves the representation of inter-annual variability of terrestrial carbon fluxes as well as the atmospheric CO2 growth rate. We thereby investigate the ability of the optimized GPP in responding to drought by comparing continentally aggregated GPP with the drought index. The assimilation of surface soil moisture has been shown to efficiently capture the influences of the sub-annual (≤9 months drought durations) and large-scale (e.g., regional to continental scales) droughts on GPP. This study highlights the significant potential of satellite soil moisture for constraining inter-annual models of the terrestrial biosphere’s carbon cycle and for illustrating how GPP responds to drought at a continental scale.

Evaluation of the Community Land Model-Simulated Specific Leaf Area with Observations over China: Impacts on Modeled Gross Primary Productivity

Specific leaf area (SLA) is a key leaf functional trait associated with the ability to acquire light. Substantial variations in SLA have not been well described in the community land model (CLM) and similar terrestrial biosphere models. How these SLA variations influence the simulation of gross primary productivity (GPP) remains unclear. Here, we evaluated the mismatch in SLA between the CLM4.5 and observed data collected from China and quantified the impacts of SLA variation calculated from both observations and the default values across seven terrestrial biosphere models on modeled GPP using CLM4.5. The results showed that CLM4.5 tended to overestimate SLA values at the top and gradient of the canopy. The higher default SLA values could cause an underestimation of the modeled GPP by 5–161 g C m−2 yr−1 (1%–7%) for temperate needleleaf evergreen tree (NET), temperate broadleaf deciduous tree (BDT), and C3 grass and an overestimation by 50 g C m−2 yr−1 (2%) for temperate broadleaf evergreen tree (BET). Moreover, the observed SLA variation among species ranged from 21% to 59% for 14 plant functional types (PFTs), which was similar to the variation in default SLA values across models (9%–60%). These SLA variations would lead to greater changes in modeled GPP by 7%–19% for temperate NET and temperate BET than temperate BDT and C3 grass (2%–9%). Our study suggested that the interspecific variation in SLA and its responses to environmental factors should be involved in terrestrial biosphere models; otherwise, it would cause substantial bias in the prediction of ecosystem productivity.

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

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

Improved representation of phosphorus exchange on soil mineral surfaces reduces estimates of phosphorus limitation in temperate forest ecosystems

Abstract. Phosphorus (P) availability affects the response of terrestrial ecosystems to environmental and climate change (e.g., elevated CO2), yet the magnitude of this effect remains uncertain. This uncertainty arises mainly from a lack of quantitative understanding of the soil biological and geochemical P cycling processes, particularly the P exchange with soil mineral surfaces, which is often described by a Langmuir sorption isotherm. We first conducted a literature review on P sorption experiments and terrestrial biosphere models (TBMs) using a Langmuir isotherm. We then developed a new algorithm to describe the inorganic P exchange between soil solution and soil matrix based on the double-surface Langmuir isotherm and extracted empirical equations to calculate the sorption capacity and Langmuir coefficient. We finally tested the conventional and new models of P sorption at five beech forest sites in Germany along a soil P stock gradient using the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) TBM. We found that the conventional (single-surface) Langmuir isotherm approach in most TBMs largely differed from P sorption experiments regarding the sorption capacities and Langmuir coefficients, and it simulated an overly low soil P-buffering capacity. Conversely, the double-surface Langmuir isotherm approach adequately reproduced the observed patterns of soil inorganic P pools. The better representation of inorganic P cycling using the double-surface Langmuir approach also improved simulated foliar N and P concentrations as well as the patterns of gross primary production and vegetation carbon across the soil P gradient. The novel model generally reduces the estimates of P limitation compared with the conventional model, particularly at the low-P site, as the model constraint of slow inorganic P exchange on plant productivity is reduced.

Drylands contribute disproportionately to observed global productivity increases

Drylands cover about 40% of the terrestrial surface and are sensitive to climate change, but their relative contributions to global vegetation greening and productivity increase in recent decades are still poorly known. Here, by integrating satellite data and biosphere modeling, we showed that drylands contributed more to global gross primary productivity (GPP) increase (65% ± 16%) than to Earth greening (33% ± 15%) observed during 1982–2015. The enhanced productivity per unit leaf area, i.e., light-use efficiency (LUE), was the mechanism behind this pattern. We also found that LUE was more sensitive to soil moisture than to atmospheric vapor pressure deficit (VPD) in drylands, while the opposite was observed (i.e., LUE was more sensitive to VPD) in humid areas. Our findings suggest the importance of using different moisture stress metrics in projecting the vegetation productivity changes of dry versus humid regions and highlight the prominent role of drylands as key controllers of the global carbon cycle.

2020-2050年CO2施肥效应促进全球陆地生态系统碳吸收

PDF HTML阅读 XML下载 导出引用 引用提醒 2020-2050年CO2施肥效应促进全球陆地生态系统碳吸收 DOI: 10.5846/stxb202107051782 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 福建省自然科学基金面上项目(2021J01502);福建省农业科学院自由探索科技创新项目(ZYTS202233);福建省农业高质量发展协同创新工程(XTCXGC2021015);福建省智慧农业科技创新团队 (CXTD2021013-1) CO2 fertilization promoting the carbon uptake of global terrestrial ecosystems in 2020-2050 Author: Affiliation: Fund Project: the Natural Science Foundation of Fujian Province (2021J01502)，Fujian Academy of Agricultural Sciences Free Exploration Science and Technology Innovation Project (ZYTS202233); Collaborative Innovation Project of High-Quality Development of Agriculture in Fujian Province (XTCXGC2021015), and Fujian Intelligent Agricultural Science and Technology Innovation Team (CXTD2021013-1) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:CO2施肥效应是全球变绿的主要原因,随着大气中CO2浓度的持续增加,预估未来气候变化条件下,CO2施肥效应对陆地生态系统的影响对减缓全球气候变化具有重大意义。基于未来气候情景数据和Farquhar模型,并结合生态过程模型BEPS(Boreal Ecosystem Productivity Simulator),定量化研究2020-2050年CO2施肥效应对全球叶面积指数(LAI)和总初级生产力(GPP)的影响。研究结果显示2020-2050年,在RCP2.6、RCP4.5和RCP8.5气候情景下,CO2施肥效应导致的LAI年际变化趋势分别为0.002、0.003和0.005 m-2m-2a-1;三个气候情景下CO2施肥效应对LAI的影响为CO2每增加0.1%,LAI平均增加约8.1%-9.2%,由此导致GPP对应增加7.9%-14.6%;由CO2施肥效应导致的全球LAI的增加对未来GPP年际变化趋势的贡献分别为66.7%、48.7%和57.1%。表明在未来气候变化情景下,LAI的增加仍然主要受CO2施肥效应的影响,CO2施肥效应导致的LAI的增加将显著促进全球陆地生态系统碳吸收。 Abstract:CO2 fertilization is the main reason for global greening. With the persistent increase in CO2 concentration, it is critically significant to evaluate the effect of CO2 fertilization on the global terrestrial ecosystem. Gross primary productivity (GPP) quantifies the photosynthetic uptake of carbon by terrestrial ecosystems, and it is the basis of the global carbon cycle. Leaf area index (LAI) is a vegetation structural parameter that modulates the interaction between the land surface and the atmosphere and therefore is used in many terrestrial biosphere models. LAI is a critical parameter for leaf-to-canopy upscaling in the terrestrial biosphere models at the regional and global scales. Therefore, a reliable estimation of CO2 fertilization on LAI is crucial for understanding and predicting the terrestrial carbon cycle under future climate change. Due to the complexity and spatiotemporal difference in the global terrestrial ecosystems, there are still large uncertainties in simulating global LAI based on phenology and carbon dynamic allocation in Earth system models. Thus, in this study, we used the future scenario climate data, combined with the Farquhar and Boreal Ecosystem Productivity Simulator (BEPS) model to investigate the effect of CO2 fertilization on the global LAI and GPP during 2020-2050. The results showed that for RCP2.6, RCP4.5, and RCP8.5 scenarios, the CO2 fertilization led to the global LAI interannual trends were 0.002, 0.003, and 0.005 m-2m-2a-1, respectively. The LAI increased 8.1%-9.2%, resulting in a corresponding increase in GPP of 7.9%-14.6% per 0.1% CO2 concentration. The contributions of LAI to the global terrestrial ecosystem GPP were 66.7%, 48.7%, and 57.1%, respectively. It shows that CO2 fertilization is still the primary reason for increasing LAI under future climate scenarios. The increase of LAI caused by CO2 fertilization will significantly promote the carbon uptake of global terrestrial ecosystems. 参考文献 相似文献 引证文献

Why Biomass Fuels Are Principally Not Carbon Neutral

In order to realistically fulfil global and national climate protection targets, all potential measures have to be made use of to a maximum extent. Because it is readily available, biomass energy has been playing a key practical role for decades, supported by the traditional assumption of its carbon neutrality: under sustainable conditions, carbon dioxide emitted during combustion is held to be equal to its absorption during plant growth. In order to clarify conditions of carbon (C) neutrality, it is therefore necessary to model the annual natural C cycle on the entire planet and to include changes caused by a variety of growth strategies for biomass fuels. The “Combined Energy and Biosphere Model” CEBM calculates the cycle of plant growth, decay, biomass fuel production and its combustion on 2433 grid elements worldwide. CEBM results suggest that over many decades, the C pools of litter and especially soil organic carbon (i.e., humus layer) deplete considerably as a consequence of the interrupted natural carbon cycle. Overall, based on this finding, the earlier assumption of “carbon-neutral biomass fuels” is disapproved of in a long-term evaluation and—as a coarse rule of thumb—might be reduced to “half as carbon neutral as previously assumed” (when compared to a current fuel mix). On top of this principal effect, it is well known that life-cycle emissions, indirect or secondary emissions such as energy input related to production, transport and conversion into fuels will still add to this already principally highly incomplete carbon neutrality of biomass.

Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture

Abstract. Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We designed an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The integrated model, LPJ-PM (LPJ-PT-JPLSM Model) as the underlying model, was coupled from the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ-DGVM version 3.01) and a hydrology module (i.e., the updated Priestley–Taylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the Global LAnd and Surface Satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 ( coefficient of determination) of 0.91 and 0.81 and an ubRMSD (unbiased root mean square deviation) reduced by 40.3 % and 29.9 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The GPP and ET resulting from the assimilation demonstrated a better performance in the arid and semi-arid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m−2 d−1; ET: R2 = 0.73, ubRMSD = 0.61 mm d−1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m−2 d−1; ET: R2 = 0.66; ubRMSD = 0.67 mm d−1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS data had a slight difference, and the SMAP soil moisture data performed better than SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, and GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (μ): 3.4 mm per month; estimated standard deviation of μ: 1.91 mm per month). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.

Correction to a Simple Biosphere Model 2 (SiB2) Simulation of Energy and Carbon Dioxide Fluxes over a Wheat Cropland in East China Using the Random Forest Model

Modeling the heat and carbon dioxide (CO2) exchanges in agroecosystems is critical for better understanding water and carbon cycling, improving crop production, and even mitigating climate change, in agricultural regions. While previous studies mainly focused on simulations of the energy and CO2 fluxes in agroecosystems on the North China Plain, their corrections, simulations and driving forces in East China are less understood. In this study, the dynamic variations of heat and CO2 fluxes were simulated by a standalone version of the Simple Biosphere 2 (SiB2) model and subsequently corrected using a Random Forest (RF) machine learning model, based on measurements from 1 January to 31 May 2015–2017 in eastern China. Through validation with direct measurements, it was found that the SiB2 model overestimated the sensible heat flux (H) and latent heat flux (LE), but underestimated soil heat flux (G0) and CO2 flux (Fc). Thus, the RF model was used to correct the results modeled by SiB2. The RF model showed that disturbances in temperature, net radiation, the G0 output of SiB2, and the Fc output of SiB2 were the key driving factors modulating the H, LE, G0, and Fc. The RF model performed well and significantly reduced the biases for H, LE, G0, and Fc simulated by SiB2, with higher R2 values of 0.99, 0.87, 0.75, and 0.71, respectively. The SiB2 and RF models combine physical mechanisms and mathematical correction to enable simulations with both physical meaning and accuracy.

Plant biomass responses to elevated CO2 are mediated by phosphorus uptake

Elevated atmospheric CO2 concentrations [CO2] potentially alter carbon (C) and phosphorus (P) cycles in terrestrial ecosystems. Although numerous field experiments and a few meta-analyses have been conducted, it is still largely unclear how the P cycle affects plant biomass responses under elevated [CO2] globally. Here, we conducted a global synthesis by analyzing 111 studies on the responses of above- and belowground P cycling to elevated [CO2], to examine how changes in the P cycle affect the plant biomass response to elevated [CO2]. Our results show that elevated [CO2] significantly increased plant aboveground biomass (+13 %), stem biomass (+4 %), leaf biomass (+11 %), belowground biomass (+12 %), and the root: shoot ratio (+7 %). Effects of elevated [CO2] on aboveground biomass, belowground biomass, and root: shoot ratio were best explained by plant P uptake. In addition, elevated [CO2]-induced changes in the aboveground P pool, leaf P pool, and leaf P concentration were modulated by ecological drivers, such as ΔCO2, experimental duration, and aridity index. Our findings highlight the importance of plant P uptake for both above- and belowground plant biomass responses under elevated [CO2], which should be considered in future biosphere models to improve predictions of terrestrial carbon-climate feedbacks.

High resolution modeling of vegetation reveals large summertime biogenic CO2 fluxes in New York City

Abstract Cities are beginning to monitor atmospheric carbon dioxide (CO2) to assess the efficacy of their climate policies. However, changes in anthropogenic CO2 emissions must be separated from biospheric CO2 fluxes which have a large seasonal cycle. Urban vegetation (e.g. lawns, trees along street and in parks, etc) in developed land covers is often omitted in regional biogenic CO2 flux models. We set up a biosphere model to estimate the regional biogenic CO2 fluxes in New York City (NYC) and assess the importance of vegetation within developed land covers. The model incorporates a high-resolution (30 m) land cover map which identifies the mixture of impervious surfaces and vegetation that is ubiquitous across developed land covers. We designed three model scenarios to evaluate the role of developed land covers in regional biogenic CO2 fluxes by assuming (a) there is no vegetation versus scenarios where all remotely sensed vegetation in developed land covers is either (b) grassland or (c) deciduous forest. Despite relatively low tree canopy cover in NYC, the regional biogenic CO2 fluxes are surprisingly large when vegetation within the developed land covers is included. Furthermore, the types of vegetation within the developed land covers are crucially important for estimating regional biogenic CO2 fluxes, demonstrated by a doubling in estimates of total biogenic CO2 flux when this vegetation is assumed to be grassland compared to forest. Using a Lagrangian atmospheric transport model, we find that the regional biogenic CO2 uptake offsets up to 40% of atmospheric CO2 enhancements attributed to anthropogenic emissions in summer afternoons and completely balances on-road traffic in one of the most congested cities in the United States. Accurate characterization of the vegetation and biogenic carbon fluxes in cities are essential to the development of effective atmospheric monitoring tools. Future measurements should focus on constraining CO2 fluxes in urban grasslands (i.e. lawns).