scholarly journals Land use cover change has non-trivial and potentially dominant impact on global gross primary productivity (2000-2100)

Author(s):  
Haiyan Hou ◽  
Bing-Bing Zhou ◽  
Fengsong Pei ◽  
Guohua Hu ◽  
Zhongbo Su ◽  
...  

Abstract Anthropogenic land use/cover (LULC) change alters terrestrial gross primary productivity (GPP), which is a major atmospheric carbon sink. Identifying the impacts of future LULC changes on terrestrial GPP has been challenging due to the complexity of incorporating future LULC into ecosystem models. Here, we present eight-scenario-based projections of global spatially explicit LULC at 1km resolution over the period 2015-2100. We further conducted Fourteen experiments to quantify the contribution of LULC change to GPP dynamics relative to that of climate change under different scenarios. We find that global GPP change would be underestimated by 10.92%‒16.16% during 2000-2050 and 1.41%‒14.57% during 2050-2100 when modeled without LULC dynamics—as in most existing GPP modeling efforts. Particularly, LULC-change-dominated areas would account globally for 1.65‒2.20 the size of the Amazon rainforest. Our findings underline the necessity of incorporating future LULC dynamics into process-based models and highlight the non-trivial role of LULC in transitioning toward sustainability.

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2371 ◽  
Author(s):  
Dennis Baldocchi ◽  
Youngryel Ryu ◽  
Trevor Keenan

A growing literature is reporting on how the terrestrial carbon cycle is experiencing year-to-year variability because of climate anomalies and trends caused by global change. As CO2 concentration records in the atmosphere exceed 50 years and as satellite records reach over 30 years in length, we are becoming better able to address carbon cycle variability and trends. Here we review how variable the carbon cycle is, how large the trends in its gross and net fluxes are, and how well the signal can be separated from noise. We explore mechanisms that explain year-to-year variability and trends by deconstructing the global carbon budget. The CO2 concentration record is detecting a significant increase in the seasonal amplitude between 1958 and now. Inferential methods provide a variety of explanations for this result, but a conclusive attribution remains elusive. Scientists have reported that this trend is a consequence of the greening of the biosphere, stronger northern latitude photosynthesis, more photosynthesis by semi-arid ecosystems, agriculture and the green revolution, tropical temperature anomalies, or increased winter respiration. At the global scale, variability in the terrestrial carbon cycle can be due to changes in constituent fluxes, gross primary productivity, plant respiration and heterotrophic (microbial) respiration, and losses due to fire, land use change, soil erosion, or harvesting. It remains controversial whether or not there is a significant trend in global primary productivity (due to rising CO2, temperature, nitrogen deposition, changing land use, and preponderance of wet and dry regions). The degree to which year-to-year variability in temperature and precipitation anomalies affect global primary productivity also remains uncertain. For perspective, interannual variability in global gross primary productivity is relatively small (on the order of 2 Pg-C y-1) with respect to a large and uncertain background (123 +/- 4 Pg-C y-1), and detected trends in global primary productivity are even smaller (33 Tg-C y-2). Yet residual carbon balance methods infer that the terrestrial biosphere is experiencing a significant and growing carbon sink. Possible explanations for this large and growing net land sink include roles of land use change and greening of the land, regional enhancement of photosynthesis, and down regulation of plant and soil respiration with warming temperatures. Longer time series of variables needed to provide top-down and bottom-up assessments of the carbon cycle are needed to resolve these pressing and unresolved issues regarding how, why, and at what rates gross and net carbon fluxes are changing.


2019 ◽  
Author(s):  
Binghao Jia ◽  
Xin Luo ◽  
Ximing Cai ◽  
Atul Jain ◽  
Deborah N. Huntzinger ◽  
...  

Abstract. Climate change, rising CO2 concentration, and land use and land cover change (LULCC) are primary driving forces for terrestrial gross primary productivity (GPP), but their impacts on the temporal changes in GPP are confounded. In this study, the effects of the three main factors on the interannual variation (IAV) and seasonal cycle amplitude (SCA) of GPP in China were investigated using 12 terrestrial biosphere models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project. The simulated ensemble mean value of China's GPP, driven by common climate forcing, LULCC, and CO2 data, was found to be 7.4 ± 1.8 Pg C yr−1, which was in close agreement with the independent upscaling GPP estimate (7.1 Pg C yr−1). In general, climate was the dominant control factor of the annual trends, IAV, and seasonality of China's GPP. The overall rising CO2 led to enhanced plant photosynthesis, thus increasing annual mean and IAV of China's total GPP, especially in northeastern and southern China where vegetation is dense. LULCC decreased the IAV of China's total GPP by ~ 7 %, whereas rising CO2 induced an increase of 8 %. Compared to climate change and elevated CO2, LULCC showed less contributions to GPP's temporal variation and its impact acted locally, mainly in southwestern China. Furthermore, this study also examined subregional contributions to the temporal changes in China's total GPP. Southern and southeastern China showed higher contributions to China's annual GPP, whereas southwestern and central parts of China explained larger fractions of the IAV in China's GPP.


2021 ◽  
Author(s):  
Katerina Georgiou ◽  
Avni Malhotra ◽  
William R. Wieder ◽  
Jacqueline H. Ennis ◽  
Melannie D. Hartman ◽  
...  

AbstractThe storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships a priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions.


2021 ◽  
Vol 97 (02) ◽  
pp. 179-190
Author(s):  
Georgina K. Magnus ◽  
Elizabeth Celanowicz ◽  
Mihai Voicu ◽  
Mark Hafer ◽  
Juha M. Metsaranta ◽  
...  

The United Nations Framework Convention on Climate Change (UNFCCC) requires its signatories, including Canada, to estimate and report their annual greenhouse gas (GHG) emissions and removals. Forests are an important natural resource as they slow the accumulation of atmospheric carbon through the process of carbon sequestration. Due to the role of forests as carbon sinks, governments consider afforestation projects as feasible climate change mitigation strategies. This article outlines a spatially-explicit approach to validating afforestation data in Ontario, Canada. Validation is a user-supervised process that uses satellite imagery, remote sensing tools, and other auxiliary data to confirm the presence of seedlings planted through Forests Ontario’s 50 Million Tree program. Of the 12 466 hectares assessed, 83% is identified as afforested, 6% is not afforested and 10% is not determined. The area classified as successful afforestation is used as input for the Generic Carbon Budget Model (GCBM), to simulate afforestation effects on carbon stocks. Our findings show the afforestation activities will create a small carbon sink by 2060. From this project, it is evident that spatial validation of afforestation data is feasible, although the collection of additional standardized auxiliary data is recommended for future afforestation projects, if carbon benefits are to be reported.


2020 ◽  
Vol 11 (1) ◽  
pp. 235-249 ◽  
Author(s):  
Binghao Jia ◽  
Xin Luo ◽  
Ximing Cai ◽  
Atul Jain ◽  
Deborah N. Huntzinger ◽  
...  

Abstract. Climate change, rising CO2 concentration, and land use and land cover change (LULCC) are primary driving forces for terrestrial gross primary productivity (GPP), but their impacts on the temporal changes in GPP are uncertain. In this study, the effects of the three main factors on the interannual variation (IAV) and seasonal cycle amplitude (SCA) of GPP in China were investigated using 12 terrestrial biosphere models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project. The simulated ensemble mean value of China's GPP between 1981 and 2010, driven by common climate forcing, LULCC and CO2 data, was found to be 7.4±1.8 Pg C yr−1. In general, climate was the dominant control factor of the annual trends, IAV and seasonality of China's GPP. The overall rising CO2 led to enhanced plant photosynthesis, thus increasing annual mean and IAV of China's total GPP, especially in northeastern and southern China, where vegetation is dense. LULCC decreased the IAV of China's total GPP by ∼7 %, whereas rising CO2 induced an increase of 8 %. Compared to climate change and elevated CO2, LULCC showed less contributions to GPP's temporal variation, and its impact acted locally, mainly in southwestern China. Furthermore, this study also examined subregional contributions to the temporal changes in China's total GPP. Southern and southeastern China showed higher contributions to China's annual GPP, whereas southwestern and central parts of China explained larger fractions of the IAV in China's GPP.


2017 ◽  
Vol 14 (15) ◽  
pp. 3685-3703 ◽  
Author(s):  
Jakob Zscheischler ◽  
Miguel D. Mahecha ◽  
Valerio Avitabile ◽  
Leonardo Calle ◽  
Nuno Carvalhais ◽  
...  

Abstract. Understanding the global carbon (C) cycle is of crucial importance to map current and future climate dynamics relative to global environmental change. A full characterization of C cycling requires detailed information on spatiotemporal patterns of surface–atmosphere fluxes. However, relevant C cycle observations are highly variable in their coverage and reporting standards. Especially problematic is the lack of integration of the carbon dioxide (CO2) exchange of the ocean, inland freshwaters and the land surface with the atmosphere. Here we adopt a data-driven approach to synthesize a wide range of observation-based spatially explicit surface–atmosphere CO2 fluxes from 2001 to 2010, to identify the state of today's observational opportunities and data limitations. The considered fluxes include net exchange of open oceans, continental shelves, estuaries, rivers, and lakes, as well as CO2 fluxes related to net ecosystem productivity, fire emissions, loss of tropical aboveground C, harvested wood and crops, as well as fossil fuel and cement emissions. Spatially explicit CO2 fluxes are obtained through geostatistical and/or remote-sensing-based upscaling, thereby minimizing biophysical or biogeochemical assumptions encoded in process-based models. We estimate a bottom-up net C exchange (NCE) between the surface (land, ocean, and coastal areas) and the atmosphere. Though we provide also global estimates, the primary goal of this study is to identify key uncertainties and observational shortcomings that need to be prioritized in the expansion of in situ observatories. Uncertainties for NCE and its components are derived using resampling. In many regions, our NCE estimates agree well with independent estimates from other sources such as process-based models and atmospheric inversions. This holds for Europe (mean ± 1 SD: 0.8 ± 0.1 PgC yr−1, positive numbers are sources to the atmosphere), Russia (0.1 ± 0.4 PgC yr−1), East Asia (1.6 ± 0.3 PgC yr−1), South Asia (0.3 ± 0.1 PgC yr−1), Australia (0.2 ± 0.3 PgC yr−1), and most of the Ocean regions. Our NCE estimates give a likely too large CO2 sink in tropical areas such as the Amazon, Congo, and Indonesia. Overall, and because of the overestimated CO2 uptake in tropical lands, our global bottom-up NCE amounts to a net sink of −5.4 ± 2.0 PgC yr−1. By contrast, the accurately measured mean atmospheric growth rate of CO2 over 2001–2010 indicates that the true value of NCE is a net CO2 source of 4.3 ± 0.1 PgC yr−1. This mismatch of nearly 10 PgC yr−1 highlights observational gaps and limitations of data-driven models in tropical lands, but also in North America. Our uncertainty assessment provides the basis for setting priority regions where to increase carbon observations in the future. High on the priority list are tropical land regions, which suffer from a lack of in situ observations. Second, extensive pCO2 data are missing in the Southern Ocean. Third, we lack observations that could enable seasonal estimates of shelf, estuary, and inland water–atmosphere C exchange. Our consistent derivation of data uncertainties could serve as prior knowledge in multicriteria optimization such as the Carbon Cycle Data Assimilation System (CCDAS) and atmospheric inversions, without over- or under-stating bottom-up data credibility. In the future, NCE estimates of carbon sinks could be aggregated at national scale to compare with the official national inventories of CO2 fluxes in the land use, land use change, and forestry sector, upon which future emission reductions are proposed.


2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Sha Zhou ◽  
Yao Zhang ◽  
Philippe Ciais ◽  
Xiangming Xiao ◽  
Yiqi Luo ◽  
...  

2015 ◽  
Vol 15 (18) ◽  
pp. 25627-25645 ◽  
Author(s):  
L. Xia ◽  
A. Robock ◽  
S. Tilmes ◽  
R. R. Neely

Abstract. Stratospheric sulfate geoengineering could impact the terrestrial carbon cycle by enhancing the carbon sink. With an 8 Tg yr−1 injection of SO2 to balance a Representative Concentration Pathway 6.0 (RCP6.0) scenario, we conducted climate model simulations with the Community Earth System Model, with the Community Atmospheric Model 4 fully coupled to tropospheric and stratospheric chemistry (CAM4-chem). During the geoengineering period, as compared to RCP6.0, land-averaged downward visible diffuse radiation increased 3.2 W m−2 (11 %). The enhanced diffuse radiation combined with the cooling increased plant photosynthesis by 2.4 %, which could contribute to an additional 3.8 ± 1.1 Gt C yr−1 global gross primary productivity without nutrient limitation. This increase could potentially increase the land carbon sink. Suppressed plant and soil respiration due to the cooling would reduce natural land carbon emission and therefore further enhance the terrestrial carbon sink during the geoengineering period. This beneficial impact of stratospheric sulfate geoengineering would need to be balanced by a large number of potential risks in any future decisions about implementation of geoengineering.


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