ALBERT

Description: Author Posting. © American Meteorological Society 2006. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 19 (2006): 3337–3353, doi:10.1175/JCLI3800.1.

Description: Eleven coupled climate–carbon cycle models used a common protocol to study the coupling between climate change and the carbon cycle. The models were forced by historical emissions and the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 anthropogenic emissions of CO2 for the 1850–2100 time period. For each model, two simulations were performed in order to isolate the impact of climate change on the land and ocean carbon cycle, and therefore the climate feedback on the atmospheric CO2 concentration growth rate. There was unanimous agreement among the models that future climate change will reduce the efficiency of the earth system to absorb the anthropogenic carbon perturbation. A larger fraction of anthropogenic CO2 will stay airborne if climate change is accounted for. By the end of the twenty-first century, this additional CO2 varied between 20 and 200 ppm for the two extreme models, the majority of the models lying between 50 and 100 ppm. The higher CO2 levels led to an additional climate warming ranging between 0.1° and 1.5°C. All models simulated a negative sensitivity for both the land and the ocean carbon cycle to future climate. However, there was still a large uncertainty on the magnitude of these sensitivities. Eight models attributed most of the changes to the land, while three attributed it to the ocean. Also, a majority of the models located the reduction of land carbon uptake in the Tropics. However, the attribution of the land sensitivity to changes in net primary productivity versus changes in respiration is still subject to debate; no consensus emerged among the models.

Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Biogeosciences 12 (2015): 653-679, doi:10.5194/bg-12-653-2015.

Description: The land and ocean absorb on average just over half of the anthropogenic emissions of carbon dioxide (CO2) every year. These CO2 "sinks" are modulated by climate change and variability. Here we use a suite of nine dynamic global vegetation models (DGVMs) and four ocean biogeochemical general circulation models (OBGCMs) to estimate trends driven by global and regional climate and atmospheric CO2 in land and oceanic CO2 exchanges with the atmosphere over the period 1990–2009, to attribute these trends to underlying processes in the models, and to quantify the uncertainty and level of inter-model agreement. The models were forced with reconstructed climate fields and observed global atmospheric CO2; land use and land cover changes are not included for the DGVMs. Over the period 1990–2009, the DGVMs simulate a mean global land carbon sink of −2.4 ± 0.7 Pg C yr−1 with a small significant trend of −0.06 ± 0.03 Pg C yr−2 (increasing sink). Over the more limited period 1990–2004, the ocean models simulate a mean ocean sink of −2.2 ± 0.2 Pg C yr−1 with a trend in the net C uptake that is indistinguishable from zero (−0.01 ± 0.02 Pg C yr−2). The two ocean models that extended the simulations until 2009 suggest a slightly stronger, but still small, trend of −0.02 ± 0.01 Pg C yr−2. Trends from land and ocean models compare favourably to the land greenness trends from remote sensing, atmospheric inversion results, and the residual land sink required to close the global carbon budget. Trends in the land sink are driven by increasing net primary production (NPP), whose statistically significant trend of 0.22 ± 0.08 Pg C yr−2 exceeds a significant trend in heterotrophic respiration of 0.16 ± 0.05 Pg C yr−2 – primarily as a consequence of widespread CO2 fertilisation of plant production. Most of the land-based trend in simulated net carbon uptake originates from natural ecosystems in the tropics (−0.04 ± 0.01 Pg C yr−2), with almost no trend over the northern land region, where recent warming and reduced rainfall offsets the positive impact of elevated atmospheric CO2 and changes in growing season length on carbon storage. The small uptake trend in the ocean models emerges because climate variability and change, and in particular increasing sea surface temperatures, tend to counter\-act the trend in ocean uptake driven by the increase in atmospheric CO2. Large uncertainty remains in the magnitude and sign of modelled carbon trends in several regions, as well as regarding the influence of land use and land cover changes on regional trends.

Description: S. Sitch acknowledges financial support by RCUK through NERC (grant no. NE/J010154/). N. Gruber and C. Heinze acknowledge financial support by the European Commission through the EU FP7 projects CARBOCHANGE (grant no. 264879) and GEOCARBON (grant no. 283080). N. Gruber was additionally supported through ETH Zurich. S. C. Doney acknowledges support from the US National Science Foundation (NSF AGS-1048827). P. Friedlingstein, A. Arneth, and S. Zaehle acknowledge support by the European Commission through the EU FP7 project EMBRACE (grant no. 282672). A. Arneth and S. Sitch acknowledge the support of the European Commission-funded project LUC4C (grant no. 603542). The research leading to these results received funding from the European Community’s Seventh Framework Programme (FP7 2007–2013) under grant agreement no. 238366. A. Ahlström and B. Smith acknowledge funding through the Mistra Swedish Research Programme on Climate, Impacts and Adaptation (SWECIA). C. Heinze acknowledges support from NOTUR/NorStore projects NN2980K and NS2980K.

Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Earth System Science Data 7 (2015): 349–396, doi:10.5194/essd-7-349-2015.

Description: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates as well as consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO2, and land-cover change (some including nitrogen–carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2005–2014), EFF was 9.0 ± 0.5 GtC yr−1, ELUC was 0.9 ± 0.5 GtC yr−1, GATM was 4.4 ± 0.1 GtC yr−1, SOCEAN was 2.6 ± 0.5 GtC yr−1, and SLAND was 3.0 ± 0.8 GtC yr−1. For the year 2014 alone, EFF grew to 9.8 ± 0.5 GtC yr−1, 0.6 % above 2013, continuing the growth trend in these emissions, albeit at a slower rate compared to the average growth of 2.2 % yr−1 that took place during 2005–2014. Also, for 2014, ELUC was 1.1 ± 0.5 GtC yr−1, GATM was 3.9 ± 0.2 GtC yr−1, SOCEAN was 2.9 ± 0.5 GtC yr−1, and SLAND was 4.1 ± 0.9 GtC yr−1. GATM was lower in 2014 compared to the past decade (2005–2014), reflecting a larger SLAND for that year. The global atmospheric CO2 concentration reached 397.15 ± 0.10 ppm averaged over 2014. For 2015, preliminary data indicate that the growth in EFF will be near or slightly below zero, with a projection of −0.6 [range of −1.6 to +0.5] %, based on national emissions projections for China and the USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the global economy for the rest of the world. From this projection of EFF and assumed constant ELUC for 2015, cumulative emissions of CO2 will reach about 555 ± 55 GtC (2035 ± 205 GtCO2) for 1870–2015, about 75 % from EFF and 25 % from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2015).

Description: NERC provided funding to C. Le Quéré, R. Moriarty, and the GCP through their International Opportunities Fund specifically to support this publication (NE/103002X/1). G. P. Peters and R. M. Andrew were supported by the Norwegian Research Council (236296). J. G. Canadell was supported by the Australian Climate Change Science Programme. S. Sitch was supported by EU FP7 for funding through projects LUC4C (GA603542). R. J. Andres was supported by US Department of Energy, Office of Science, Biological and Environmental Research (BER) programmes under US Department of Energy contract DE-AC05- 00OR22725. T. A. Boden was supported by US Department of Energy, Office of Science, Biological and Environmental Research (BER) programmes under US Department of Energy contract DE-AC05-00OR22725. J. I. House was supported by the Leverhulme foundation and the EU FP7 through project LUC4C (GA603542). P. Friedlingstein was supported by the EU FP7 for funding through projects LUC4C (GA603542) and EMBRACE (GA282672). A. Arneth was supported by the EU FP7 for funding through LUC4C (603542), and the Helmholtz foundation and its ATMO programme. D. C. E. Bakker was supported by the EU FP7 for funding through project CARBOCHANGE (284879), the UK Ocean Acidification Research Programme (NE/H017046/1; funded by the Natural Environment Research Council, the Department for Energy and Climate Change and the Department for Environment, Food and Rural Affairs). L. Barbero was supported by NOAA’s Ocean Acidification Program and acknowledges support for this work from the National Aeronautics and Space Administration (NASA) ROSES Carbon Cycle Science under NASA grant 13-CARBON13_2-0080. P. Ciais acknowledges support from the European Research Council through Synergy grant ERC-2013-SyG-610028 “IMBALANCE-P”. M. Fader was supported by the EU FP7 for funding through project LUC4C (GA603542). J. Hauck was supported by the Helmholtz Postdoc Programme (Initiative and Networking Fund of the Helmholtz Association). R. A. Feely and A. J. Sutton were supported by the Climate Observation Division, Climate Program Office, NOAA, US Department of Commerce. A. K. Jain was supported by the US National Science Foundation (NSF AGS 12-43071) the US Department of Energy, Office of Science and BER programmes (DOE DE-SC0006706) and NASA LCLUC programme (NASA NNX14AD94G). E. Kato was supported by the ERTDF (S-10) from the Ministry of Environment, Japan. K. Klein Goldewijk was supported by the Dutch NWO VENI grant no. 863.14.022. S. K. Lauvset was supported by the project “Monitoring ocean acidification in Norwegian waters” from the Norwegian Ministry of Climate and Environment. V. Kitidis was supported by the EU FP7 for funding through project CARBOCHANGE (264879). C. Koven was supported by the Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy under contract no. DE-AC02-05CH11231 as part of their Regional and Global Climate Modeling Program. P. Landschützer was supported by GEOCarbon. I. T. van der Lann-Luijkx received financial support from OCW/NWO for ICOS-NL and computing time from NWO (SH-060-13). I. D. Lima was supported by the US National Science Foundation (NSF AGS-1048827). N. Metzl was supported by Institut National des Sciences de l’Univers (INSU) and Institut Paul Emile Victor (IPEV) for OISO cruises. D. R. Munro was supported by the US National Science Foundation (NSF PLR-1341647 and NSF AOAS-0944761). J. E. M. S. Nabel was supported by the German Research Foundation’s Emmy Noether Programme (PO1751/1-1) and acknowledges Julia Pongratz and Kim Naudts for their contributions. Y. Nojiri and S. Nakaoka were supported by the Global Environment Research Account for National Institutes (1432) by the Ministry of Environment of Japan. A. Olsen appreciates support from the Norwegian Research Council (SNACS, 229752). F. F. Pérez were supported by BOCATS (CTM2013-41048-P) project co-founded by the Spanish government and the Fondo Europeo de Desarrollo Regional (FEDER). B. Pfeil was supported through the European Union’s Horizon 2020 research and innovation programme AtlantOS under grant agreement no. 633211. D. Pierrot was supported by NOAA through the Climate Observation Division of the Climate Program Office. B. Poulter was supported by the EU FP7 for funding through GEOCarbon. G. Rehder was supported by BMBF (Bundesministerium für Bildung und Forschung) through project ICOS, grant no. 01LK1224D. U. Schuster was supported by NERC UKOARP (NE/H017046/1), NERC RAGANRoCC (NE/K002473/1), the European Space Agency (ESA) OceanFlux Evolution project, and EU FP7 CARBOCHANGE (264879). T. Steinhoff was supported by ICOS-D (BMBF FK 01LK1101C) and EU FP7 for funding through project CARBOCHANGE (264879). J. Schwinger was supported by the Research Council of Norway through project EVA (229771), and acknowledges the Norwegian Metacenter for Computational Science (NOTUR, project nn2980k), and the Norwegian Storage Infrastructure (NorStore, project ns2980k) for supercomputer time and storage resources. T. Takahashi was supported by grants from NOAA and the Comer Education and Science Foundation. B. Tilbrook was supported by the Australian Department of Environment and the Integrated Marine Observing System. B. D. Stocker was supported by the Swiss National Science Foundation and FP7 funding through project EMBRACE (282672). S. van Heuven was supported by the EU FP7 for funding through project CARBOCHANGE (264879). G. R. van der Werf was supported by the European Research Council (280061). A. Wiltshire was supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101) and EU FP7 Funding through project LUC4C (603542). S. Zaehle was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (QUINCY; grant agreement no. 647204). ISAM (PI: Atul K. Jain) simulations were carried out at the National Energy Research Scientific Computing Center (NERSC), which is supported by the US DOE under contract DE-AC02-05CH11231.

Description: © The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Climate of the Past 9 (2013): 1111-1140, doi:10.5194/cp-9-1111-2013.

Description: Both historical and idealized climate model experiments are performed with a variety of Earth system models of intermediate complexity (EMICs) as part of a community contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report. Historical simulations start at 850 CE and continue through to 2005. The standard simulations include changes in forcing from solar luminosity, Earth's orbital configuration, CO2, additional greenhouse gases, land use, and sulphate and volcanic aerosols. In spite of very different modelled pre-industrial global surface air temperatures, overall 20th century trends in surface air temperature and carbon uptake are reasonably well simulated when compared to observed trends. Land carbon fluxes show much more variation between models than ocean carbon fluxes, and recent land fluxes appear to be slightly underestimated. It is possible that recent modelled climate trends or climate–carbon feedbacks are overestimated resulting in too much land carbon loss or that carbon uptake due to CO2 and/or nitrogen fertilization is underestimated. Several one thousand year long, idealized, 2 × and 4 × CO2 experiments are used to quantify standard model characteristics, including transient and equilibrium climate sensitivities, and climate–carbon feedbacks. The values from EMICs generally fall within the range given by general circulation models. Seven additional historical simulations, each including a single specified forcing, are used to assess the contributions of different climate forcings to the overall climate and carbon cycle response. The response of surface air temperature is the linear sum of the individual forcings, while the carbon cycle response shows a non-linear interaction between land-use change and CO2 forcings for some models. Finally, the preindustrial portions of the last millennium simulations are used to assess historical model carbon-climate feedbacks. Given the specified forcing, there is a tendency for the EMICs to underestimate the drop in surface air temperature and CO2 between the Medieval Climate Anomaly and the Little Ice Age estimated from palaeoclimate reconstructions. This in turn could be a result of unforced variability within the climate system, uncertainty in the reconstructions of temperature and CO2, errors in the reconstructions of forcing used to drive the models, or the incomplete representation of certain processes within the models. Given the forcing datasets used in this study, the models calculate significant land-use emissions over the pre-industrial period. This implies that land-use emissions might need to be taken into account, when making estimates of climate–carbon feedbacks from palaeoclimate reconstructions.

Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Earth System Science Data 7 (2015): 47-85, doi:10.5194/essd-7-47-2015.

Description: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuel combustion and cement production (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO2, and land-cover-change (some including nitrogen–carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2004–2013), EFF was 8.9 ± 0.4 GtC yr−1, ELUC 0.9 ± 0.5 GtC yr−1, GATM 4.3 ± 0.1 GtC yr−1, SOCEAN 2.6 ± 0.5 GtC yr−1, and SLAND 2.9 ± 0.8 GtC yr−1. For year 2013 alone, EFF grew to 9.9 ± 0.5 GtC yr−1, 2.3% above 2012, continuing the growth trend in these emissions, ELUC was 0.9 ± 0.5 GtC yr−1, GATM was 5.4 ± 0.2 GtC yr−1, SOCEAN was 2.9 ± 0.5 GtC yr−1, and SLAND was 2.5 ± 0.9 GtC yr−1. GATM was high in 2013, reflecting a steady increase in EFF and smaller and opposite changes between SOCEAN and SLAND compared to the past decade (2004–2013). The global atmospheric CO2 concentration reached 395.31 ± 0.10 ppm averaged over 2013. We estimate that EFF will increase by 2.5% (1.3–3.5%) to 10.1 ± 0.6 GtC in 2014 (37.0 ± 2.2 GtCO2 yr−1), 65% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the global economy. From this projection of EFF and assumed constant ELUC for 2014, cumulative emissions of CO2 will reach about 545 ± 55 GtC (2000 ± 200 GtCO2) for 1870–2014, about 75% from EFF and 25% from ELUC. This paper documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this living data set (Le Quéré et al., 2013, 2014). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2014).

Description: NERC provided funding to C. Le Quéré, R. Moriarty, and the GCP though their International Opportunities Fund specifically to support this publication (NE/103002X/1), and to U. Schuster through UKOARP (NE/H017046/1). G. P. Peters and R. M. Andrews were supported by the Norwegian Research Council (236296). T. A. Boden was supported by US Department of Energy, Office of Science, Biological and Environmental Research (BER) programmes under US Department of Energy contract DEAC05- 00OR22725. Y. Bozec was supported by Region Bretagne, CG29, and INSU (LEFE/MERMEX) for CARBORHONE cruises. J. G. Canadell and M. R. Raupach were supported by the Australian Climate Change Science Programme. M. Hoppema received ICOSD funding through the German Federal Ministry of Education and Research (BMBF) to the AWI (01 LK 1224I). J. I. House was supported by a Leverhulme Early Career Fellowship. A. K. Jain was supported by the US National Science Foundation (NSF AGS 12-43071) the US Department of Energy, Office of Science, and BER programmes (DOE DE-SC0006706) and the NASA LCLUC programme (NASA NNX14AD94G). E. Kato was supported by the Environment Research and Technology Development Fund (S-10) of the Ministry of Environment of Japan. C. Koven was supported by the Director, Office of Science, Office of Biological and Environmental Research, of the US Department of Energy under contract no. DE-AC02-05CH11231 as part of their Regional and Global Climate Modeling Program. I. D. Lima was supported by the U.S. National Science Foundation (NSF AGS-1048827). N. Metzl was supported by Institut National des Sciences de l’Univers (INSU) and Institut Paul Emile Victor (IPEV) for OISO cruises. A. Olsen was supported by the Centre for Climate Dynamics at the Bjerknes Centre for Climate Research. J. E. Salisbury was supported by grants from NOAA/NASA. T. Steinhoff was supported by ICOS-D (BMBF FK 01LK1101C). B. D. Stocker was supported by the Swiss National Science Foundation and FP7 funding through project EMBRACE (282672). A. J. Sutton was supported by NOAA. T. Takahashi was supported by grants from NOAA and the Comer Education and Science Foundation. B. Tilbrook was supported by the Australian Department of the Environment and the Integrated Marine Observing System. A.Wiltshire was supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). P. Ciais,W. Peters, C. Le Quére, P. Regnier, and U. Schuster were supported by the EU FP7 through project GEOCarbon (283080). A. Arneth, P. Ciais, S. Sitch, and A. Wiltshire were supported by COMBINE (226520). V. Kitidis, M. Hoppema, N. Metzl, C. Le Quéré, U. Schuster, J. Schwiger, J. Segschneider, and T. Steinhoff were supported by the EU FP7 through project CARBOCHANGE (264879). A. Arnet, P. Friedlingstein, B. Poulter, and S. Sitch were supported by the EU FP7 through projects LUC4C (GA603542). P. Friedlingstein was also supported by EMBRACE (GA282672). F. Chevallier and G. R. van der Werf were supported by the EU FP7 through project MACC-II (283576).

Description: We designed simulations for the high-temperature event that occurred on 23 July 2003 in East China using a series of forecast lead times, from short-range to medium-range, and four land surface schemes (LSSs) (i.e., SLAB, NOAH, RUC, and PX) in the Weather Research and Forecasting Model (WRF), Version 3. The sensitivities of short- and medium-range simulations to the LSSs systematically varied with the lead times. In general, the model reproduced short-range, high-temperature distributions. The simulated weather was sensitive to the LSSs, and the LSS-induced sensitivity was higher in the medium range than in the short range. Furthermore, the LSS performances were complex, i.e., the PX errors apparently increased in the medium range (longer than 6 days), RUC produced the maximum errors, and SLAB and NOAH had approximately equivalent errors that slightly increased. Additional sensitivity simulations revealed that the WRF modeling system assigns relatively low initial soil moisture for RUC and that soil moisture initialization plays an important role that is comparable to the LSS choice in the simulations. LSS-induced negative feedback between surface air temperature (SAT) and atmospheric circulation in the lower atmosphere was found in the medium range. These sensitivities were mainly caused by the LSS-induced differences in surface sensible heat flux and by errors associated with the lead times. Using the SAT equation, further diagnostic analyses revealed LSS deficiencies in simulating surface fluxes and physical processes that modify the SAT and indicated the main reasons for these deficiencies. These results have implications for model improvement and application. This article is protected by copyright. All rights reserved.

Description: Jatrophodione A ( 1 ), a new diterpene with four rings, together with nine known compounds, caniojane ( 2 ), jatropholone A ( 3 ), jatropholone B ( 4 ), jatrogrossidione ( 5 ), 2-epijatrogrossidione ( 6 ), heudelotinone ( 7 ), gossweilone ( 8 ), (3 α )-3-hydroxy- ent- pimara-8(14),15-dien-12-one ( 9 ), and 12-hydroxy-13-methylpodocarpa-8,11,13-trien-3-one ( 10 ), was isolated from the aerial parts of Jatropha curcas. Compounds 5, 6, 9 , and 10 were found for the first time in this plant. Their structures were established by spectroscopic analysis, including 2D-NMR spectroscopic techniques. Cytotoxicities of compounds 1, 2, 7, 8 , and 9 were tested on the three cancer cell lines A549, Hela, and SMMC-7721. Results showed that 7 exhibited cytotoxicity against SMMC-7721 with an IC 50 value of 21.68 μ M , whereas 7 and 8 were active against A549 with the IC 50 values of 16.04 and 20.47 μ M , and against Hela with the IC 50 values of 10.67 and 22.83 μ M , respectively.

Description: Quantifying the impact of forest management on carbon (C) stock is important for evaluating and enhancing the ability of plantations to mitigate climate change. Near natural forest management (NNFM) through species enrichment planting in single species plantations, structural adjustment, and understory protection is widely used in plantation management. However, its long-term effect on forest ecosystem C stock remains unclear. We therefore selected two typical coniferous plantations in southwest China, Pinus massoniana (Lamb.) and Cunninghamia lanceolate (Lamb.) Hook., to explore the effects of long-term NNFM on ecosystem C storage. The C content and stock of different components in the pure plantations of P. massoniana (PCK) and C. lanceolata (CCK), and their corresponding near natural managed forests (PCN and CCN, respectively), were investigated during eight years of NNFM beginning in 2008. In 2016, there was no change in the vegetation C content, while soil C content in the 0–20 cm and 20–40 cm layers significantly increased, compared to the pure forests. In the P. massoniana and C. lanceolata plantations, NNFM increased the ecosystem C stock by 31.8% and 24.3%, respectively. Overall, the total C stock of soil and arborous layer accounted for 98.2%–99.4% of the whole ecosystem C stock. The increase in the biomass of the retained and underplanted trees led to a greater increase in the arborous C stock in the near natural forests than in the controls. The NNFM exhibited an increasingly positive correlation with the ecosystem C stock over time. Long-term NNFM enhances ecosystem C sequestration by increasing tree growth rate at individual and stand scales, as well as by likely changing the litter decomposition rate resulting from shifts in species composition and stand density. These results indicated that NNFM plays a positive role in achieving multi-objective silviculture and climate change mitigation.

Description: Concerns about environmental safety have led to strict regulations on the discharge of final brewery effluents into water bodies. Brewery wastewater contains huge amounts of organic compounds that can cause environmental pollution. The microalgae wastewater treatment method is an emerging environmentally friendly biotechnological process. Microalgae grow well in nutrient-rich wastewater by absorbing organic nutrients and converting them into useful biomass. The harvested biomass can be used as animal feed, as an alternative energy source for biodiesel production and as biofertilizer. This review discusses conventional and current brewery wastewater treatment methods, and the application and potential of microalgae in brewery wastewater treatment. This study also discusses the benefits as well as challenges associated with microalgae brewery and other industrial wastewater treatments.