Elucidation and genetic intervention of CO2 concentration mechanism in Chlamydomonas reinhardtii for increased plant primary productivity

2020 ◽  
Vol 45 (1) ◽  
Author(s):  
Kokkanti Mallikarjuna ◽  
Kelam Narendra ◽  
Ragireddypalem Ragalatha ◽  
Basuthkar J Rao
Keyword(s):  
Chlamydomonas Reinhardtii ◽  
Primary Productivity ◽  
Co2 Concentration ◽  
Genetic Intervention

scholarly journals Modelling the response of net primary productivity of the Zambezi teak forests to climate change along a rainfall gradient in Zambia

2019 ◽  
Vol 16 (19) ◽  
pp. 3853-3867
Author(s):  
Justine Ngoma ◽  
Maarten C. Braakhekke ◽  
Bart Kruijt ◽  
Eddy Moors ◽  
Iwan Supit ◽  
...  
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Primary Productivity ◽  
General Circulation ◽  
Net Primary Productivity ◽  
Co2 Concentration ◽  
Annual Rainfall ◽  
Rainfall Gradient ◽  
Local Soil ◽  
Parameter Values

Abstract. Understanding climate change effects on forests is important considering the role forests play in mitigating climate change. We studied the effects of changes in temperature, rainfall, atmospheric carbon dioxide (CO2) concentration, solar radiation, and number of wet days (as a measure of rainfall intensity) on net primary productivity (NPP) of the Zambian Zambezi teak forests along a rainfall gradient. Using 1960–1989 as a baseline, we projected changes in NPP for the end of the 21st century (2070–2099). We adapted the parameters of the dynamic vegetation model, LPJ-GUESS, to simulate the growth of Zambian forests at three sites along a moisture gradient receiving annual rainfall of between 700 and more than 1000 mm. The adjusted plant functional type was tested against measured data. We forced the model with contemporary climate data (1960–2005) and with climatic forecasts of an ensemble of five general circulation models (GCMs) following Representative Concentration Pathways (RCPs) RCP4.5 and RCP8.5. We used local soil parameter values to characterize texture and measured local tree parameter values for maximum crown area, wood density, leaf longevity, and allometry. The results simulated with the LPJ-GUESS model improved when we used these newly generated local parameters, indicating that using local parameter values is essential to obtaining reliable simulations at site level. The adapted model setup provided a baseline for assessing the potential effects of climate change on NPP in the studied Zambezi teak forests. Using this adapted model version, NPP was projected to increase by 1.77 % and 0.69 % at the wetter Kabompo and by 0.44 % and 0.10 % at the intermediate Namwala sites under RCP8.5 and RCP4.5 respectively, especially caused by the increased CO2 concentration by the end of the 21st century. However, at the drier Sesheke site, NPP would respectively decrease by 0.01 % and 0.04 % by the end of the 21st century under RCP8.5 and RCP4.5. The projected decreased NPP under RCP8.5 at the Sesheke site results from the reduced rainfall coupled with increasing temperature. We thus demonstrated that differences in the amount of rainfall received in a site per year influence the way in which climate change will affect forest resources. The projected increase in CO2 concentration would thus have more effects on NPP in high rainfall receiving areas, while in arid regions, NPP would be affected more by the changes in rainfall and temperature. CO2 concentrations would therefore be more important in forests that are generally not temperature- or precipitation-limited; however, precipitation will continue to be the limiting factor in the drier sites.

scholarly journals Impacts of land-use change and elevated CO<sub>2</sub> on the interannual variations and seasonal cycles of gross primary productivity in China

2019 ◽  
Author(s):  
Binghao Jia ◽  
Xin Luo ◽  
Ximing Cai ◽  
Atul Jain ◽  
Deborah N. Huntzinger ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Primary Productivity ◽  
Driving Forces ◽  
Southern China ◽  
Co2 Concentration ◽  
Mean Value ◽  
Temporal Changes ◽  
Gross Primary Productivity ◽  
Control Factor

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.

scholarly journals Response of Net Primary Productivity of Zambezi teak forests to climate change along a rainfall gradient in Zambia

2018 ◽  
Author(s):  
Justine Ngoma ◽  
Maarten C. Braakhekke ◽  
Bart Kruijt ◽  
Eddy Moors ◽  
Iwan Supit ◽  
...  
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Co2 Concentration ◽  
Annual Rainfall ◽  
Base Line ◽  
Rainfall Gradient ◽  
Local Soil ◽  
Parameter Values

Abstract. Understanding climate change effects on forests is important considering the role forests play in mitigating climate change. We studied the effects of changes in temperature, rainfall, atmospheric carbon dioxide (CO2) concentration, solar radiation, and number of wet days (as a measure of rainfall intensity) on net primary productivity (NPP) of the Zambian Zambezi teak forests along a rainfall gradient. Using 1960–1989 as base-line, we projected changes in NPP for the end of the 21st century (2070–2099). We adapted the parameters of the dynamic vegetation model, LPJ-GUESS, to simulate the growth of Zambian forests at three sites along a moisture gradient receiving annual rainfall of between 700 mm to more than 1000 mm. The thus adjusted plant functional type was tested against measured data. We forced the model with contemporary climate data (1960–2005) and with climatic forecasts of an ensemble of five General Circulation Models (GCMs) following RCP4.5 and RCP8.5. We used local soil parameter values to characterize texture and measured local tree parameter values for maximum crown area, wood density, leaf longevity, and allometry. While increased CO2 concentration enhances NPP at the wetter Kabompo and the intermediate Namwala sites, NPP decreases at the drier Sesheke site under both scenarios by the end of 21st century. The projected decreased NPP under RCP8.5 at the Sesheke site results from the reduced rainfall. We thus demonstrated that differences in rainfall pattern influence the way in which climate change will affect forests resources. We also showed that using local parameter values is essential to obtaining reasonably reliable simulations.

scholarly journals Impacts of land use change and elevated CO<sub>2</sub> on the interannual variations and seasonal cycles of gross primary productivity in China

2020 ◽  
Vol 11 (1) ◽  
pp. 235-249 ◽  
Author(s):  
Binghao Jia ◽  
Xin Luo ◽  
Ximing Cai ◽  
Atul Jain ◽  
Deborah N. Huntzinger ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Primary Productivity ◽  
Driving Forces ◽  
Southern China ◽  
Co2 Concentration ◽  
Mean Value ◽  
Temporal Changes ◽  
Gross Primary Productivity ◽  
Control Factor

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.

scholarly journals Roles of Climate Change and Increasing CO2 in Driving Changes of Net Primary Productivity in China Simulated Using a Dynamic Global Vegetation Model

2019 ◽  
Vol 11 (15) ◽  
pp. 4176 ◽  
Author(s):  
Qing Huang ◽  
Weimin Ju ◽  
Fangyi Zhang ◽  
Qian Zhang
Keyword(s):  
Climate Change ◽  
Interannual Variability ◽  
Primary Productivity ◽  
Southwest China ◽  
Net Primary Productivity ◽  
Terrestrial Ecosystem ◽  
Co2 Concentration ◽  
Series Data ◽  
Geographical Regions ◽  
The Future

Net primary productivity (NPP) is the key component of the terrestrial carbon cycle, and terrestrial NPP trends under increasing CO2 and climate change in the past and future are of great significance in the study of the global carbon budget. Here, the LPJ-DGVM was employed to simulate the magnitude and pattern of China’s terrestrial NPP using long-term series data to understand the response of terrestrial NPP to increasing CO2 concentration and climate change. The results showed that total NPP of China’s terrestrial ecosystem increased from 2.8 to 3.6 Pg C yr−1 over the period of 1961–2016, with an annual average of 3.1 Pg C yr−1. The average NPP showed a gradient decrease from the southeast to northwest. Southwest China and Northwest China, comprising mostly arid and semi-arid regions, exhibited the largest increase rate in total NPP among the six geographical regions of China. Additionally, large interannual variability around the NPP trends was presented, and NPP anomalies in China’s terrestrial ecosystem are strongly associated with the El Niño-Southern Oscillation (ENSO). Southwest China made the largest contribution to the interannual variability of national total NPP. The total NPP of China’s terrestrial ecosystem continuously increased with the concurrent increase in the CO2 concentration and climate change under different scenarios in the future. During the period from 2091 to 2100, the average total NPP under the A2 and RCP85 scenarios would reach 4.9 and 5.1 Pg C yr−1 respectively, higher than 4.2 and 3.9 Pg C yr−1 under the B1 and RCP45 scenarios. Forests, especially temperate forests, make the largest contribution to the future increase in NPP. The increase in CO2 concentration would play a dominant role in driving further NPP increase in China’s terrestrial ecosystems, and climate change may slightly attenuate the fertilization effect of CO2 on NPP.

scholarly journals Reaching 1.5 °C and 2.0 °C global surface temperature targets using stratospheric aerosol geoengineering

2019 ◽  
Author(s):  
Simone Tilmes ◽  
Douglas E. MacMartin ◽  
Jan T. M. Lenaerts ◽  
Leo van Kampenhout ◽  
Laura Muntjewerf ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Surface Air Temperature ◽  
Co2 Concentration ◽  
Stratospheric Aerosol ◽  
Meridional Overturning Circulation ◽  
Baseline Scenario ◽  
Surface Mass ◽  
Ecosystem Impacts

Abstract. We propose new testbed model experiments for the Geoengineering Model Intercomparison Project (GeoMIP) that are designed to limit global warming to 1.5 °C or 2.0 °C above 1850–1900 conditions using stratospheric aerosol geoengineering (SAG). The new modeling experiments use the overshoot scenario defined in CMIP6 (SSP5-34-OS) as a baseline scenario and are designed to reduce side effects of SAG in reaching three temperature targets: global mean surface temperature, and inter-hemispheric and pole-to-equator surface temperature gradients. We further compare results to another SAG simulation using a high emission scenario (SSP5-85) as a baseline scenario in order to investigate the dependency of impacts using different injection amounts to offset different amounts of warming by SAG. The new testbed simulations are performed with the CESM2(WACCM6). We use a feedback algorithm that identifies the needed amount of sulfur dioxide injections in the stratosphere at four predefined latitudes, 30° N, 15° N, 15° S, and 30° S, to reach the three temperature targets. Here we analyze climate variables and quantities that matter for societal and ecosystem impacts. We find that changes from present day conditions (2015–2025) in some variables depend strongly on the defined temperature target (1.5 °C vs 2.0 °C). These include surface air temperature and related impacts, the Atlantic Meridional Overturning Circulation (AMOC), which impacts ocean net primary productivity, and changes in ice sheet surface mass balance, which impacts sea-level rise. Others, including global precipitation changes and the recovery of the Antarctic ozone hole, depend strongly on the amount of SAG application. Furthermore, land net primary productivity as well as ocean acidification depend mostly on the global atmospheric CO2 concentration and therefore the baseline scenario. Multi-model comparisons of the experiments proposed here would help identify consequences of scenarios that include strong mitigation, carbon dioxide removal with some SAG application, on societal impacts and ecosystems.

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