Update and clean-up of references

source/bibs/main.bib

@@ -789,14 +789,15 @@ @article{rogner_assessment_1997
 }
 
 
-@article{riahi_shared_2016,
+@article{riahi_shared_2017,
 	title = {The {Shared} {Socioeconomic} {Pathways} and their {Energy}, {Land} {Use}, and {Greenhouse} {Gas} {Emissions} {Implications}},
-	volume = {in press},
+	volume = {42},
+	pages = {153-168},
 	doi = {10.1016/j.gloenvcha.2016.05.009},
 	journal = {Global Environmental Change},
 	author = {Riahi, Keywan and Vuuren, Detlef P. van and Kriegler, Elmar and Edmonds, Jae and O’Neill, Brian and Fujimori, Shinichiro and Bauer, Nico and Calvin, Katherine and Dellink, Rob and Fricko, Oliver and Lutz, Wolfgang and Popp, Alexander and Cuaresma, Jesus Crespo and KC, Samir and Leimbach, Marian and Jiang, Leiwen and Kram, Tom and Rao, Shilpa and Emmerling, Johannes and Ebi, Kristie and Hasegawa, Tomoko and Havlik, Petr and Humpenoder, Florian and Silva, Lara Aleluia Da and Smith, Steve and Stehfest, Elke and Bosetti, Valentina and Eom, Jiyong and Gernaat, David and Masui, Toshihiko and Rogelj, Joeri and Strefler, Jessica and Drouet, Laurent and Krey, Volker and Luderer, Gunnar and Harmsen, Mathijs and Takahashi, Kiyoshi and Baumstark, Lavinia and Doelman, Jonathan and Kainuma, Mikiko and Klimont, Zbigniew and Marangoni, Giacomo and Lotze-Campen, Hermann and Obersteiner, Michael and Tabeau, Andrzej and Tavoni, Massimo},
 	url = {http://pure.iiasa.ac.at/13280/},
-	year = {2016}
+	year = {2017}
 }
 
 
@@ -849,21 +850,23 @@ @article{pietzcker_solar_2014
 }
 
 
-@article{eurek_wind_2016,
+@article{eurek_wind_2017,
 	title = {An improved global wind resource estimate for integrated assessment models},
 	author = {Eurek, K. and Sullivan, P. and Gleason, M. and Hettinger, D. and Heimiller, D.M. and Lopez, A.},
 	journal = {Energy Economics},
-	volume = {In Review},
-	year = {2016}
+	volume = {64},
+	pages = {552-567},
+	year = {2017}
 }
 
 
-@article{fricko_marker_2016,
+@article{fricko_marker_2017,
 	title = {The marker quantification of the shared socioeconomic pathway 2: a middle-of-the-road scenario for the 21st century},
-	volume = {In press},
+	volume = {42},
+	pages = {251-267},
 	journal = {Global Environmental Change},
 	author = {Fricko, Oliver and Havlik, Petr and Rogelj, Joeri and Klimont, Zbigniew and Gusti, Mykola and Johnson, Nils and Kolp, Peter and Strubegger, Manfred and Valin, Hugo and Amann, Markus and Ermolieva, Tatiana and Forsell, Nicklas and Herrero, Mario and Heyes, Chris and Kindermann, Georg and Krey, Volker and McCollum, David L. and Obersteiner, Michael and Pachauri, Shonali and Rao, Shilpa and Schmid, Erwin and Schoepp, Wolfgang and Riahi, Keywan},
-	year = {2016}
+	year = {2017}
 }
 
 
@@ -1544,7 +1547,6 @@ @article{loew_2016
 	publisher = {IOP Publishing}
 }
 
-
 @article{Raptis_2016_powerplant_data,
 	author = {Raptis, Catherine E. and Pfister, Stephan},
 	title = {{Global freshwater thermal emissions from steam-electric power plants with once-through cooling systems}},
@@ -1555,12 +1557,13 @@ @article{Raptis_2016_powerplant_data
 	year = {2016}
 }
 
-@article{huppmann_2019_MESSAGEix,
-   author = {Huppmann, Daniel and Gidden, Matthew and Fricko, Oliver and Kolp, Peter and Orthofer, Clara and Pimmer, Michael and Kushin, Nikolay and Vinca, Adriano and Mastrucci, Alessio and Riahi, Keywan and Krey, Volker},
-   title = {{The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development}},
-   journal = {Environmental Modelling \& Software},
-   volume = {112},
-   pages = {143-156},
-   doi = {https://doi.org/10.1016/j.envsoft.2018.11.012},
-   year = {2019}
-}
+@article{huppmann_message_2019,
+	author = {Huppmann, Daniel and Gidden, Matthew and Fricko, Oliver and Kolp, Peter and Orthofer, Clara and Pimmer, Michael and Kushin, Nikolay and Vinca, Adriano and Mastrucci, Alessio and Riahi, Keywan and Krey, Volker},
+	title = {The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development},
+	journal = {Environmental Modelling & Software},
+	volume = {112},
+	pages = {143–156},
+	year = {2019},
+	url = {https://www.sciencedirect.com/science/article/pii/S1364815218302330 },
+	type = {Journal Article}
+}

source/climate/index.rst

@@ -9,6 +9,6 @@ like air pollutants, together with consistent projections of radiative forcing,
 MAGICC is most commonly used in a deterministic setup (Meinshausen et al., 2011b :cite:`meinshausen_rcp_2011`), but also a probabilistic setup (Meinshausen et al., 2009 
 :cite:`meinshausen_greenhouse-gas_2009`) is available which allows to estimate the probabilities of limiting warming to below specific temperature levels given a specified emissions 
 path (Rogelj et al., 2013a :cite:`rogelj_2020_2013`; Rogelj et al., 2013b :cite:`rogelj_probabilistic_2013`; Rogelj et al., 2015 :cite:`rogelj_mitigation_2015`). Climate feedbacks on 
-the global carbon cycle are accounted for through the interactive coupling of the climate model and a range of gas-cycle models. (Fricko et al., 2016 :cite:`fricko_marker_2016`)
+the global carbon cycle are accounted for through the interactive coupling of the climate model and a range of gas-cycle models. (Fricko et al., 2017 :cite:`fricko_marker_2017`)
 
 For more information about the model, see `www.magicc.org <http://www.magicc.org/>`_.

source/emissions/globiom/index.rst

@@ -5,21 +5,42 @@ Emissions from land (GLOBIOM)
 
 Crop sector emissions
 ~~~~
-Crop emissions sources accounted in GLOBIOM are N2O fertilization emissions, from synthetic fertilizer and from organic fertilizers, as well as CH4 methane emissions from rice cultivation. Synthetic fertilizers are calculated on a Tier 1 approach, using the information provided by EPIC on the fertilizer use for each management system at the Simulation Unit level and applying the emission factor from IPCC AFOLU guidelines. Synthetic fertilizer use is therefore built in a bottom up approach, but upscaled to the International Fertilizer Association statics on total fertilizer use per crop at the national level for the case where calculated fertilizers are found too low at the aggregated level. This correction ensures a full consistency with observed fertilizer purchases. In the case of rice, only a Tier 1 approach was applied, with a simple formula where emissions are proportional to the area of rice cultivated. Emission factor is taken from EPA (EPA 2012 :cite:`environmental_protection_agency_epa_US_2012`).
+Crop emissions sources accounted in GLOBIOM are N2O fertilization emissions, from synthetic fertilizer and from organic fertilizers, as well as CH4 methane emissions from rice cultivation. 
+Synthetic fertilizers are calculated on a Tier 1 approach, using the information provided by EPIC on the fertilizer use for each management system at the Simulation Unit level and applying 
+the emission factor from IPCC AFOLU guidelines. Synthetic fertilizer use is therefore built in a bottom up approach, but upscaled to the International Fertilizer Association statics on total 
+fertilizer use per crop at the national level for the case where calculated fertilizers are found too low at the aggregated level. This correction ensures a full consistency with observed fertilizer purchases. 
+In the case of rice, only a Tier 1 approach was applied, with a simple formula where emissions are proportional to the area of rice cultivated. Emission factor is taken from EPA 
+(2012) :cite:`environmental_protection_agency_epa_US_2012`.
 
 Livestock emissions
 ~~~~
-In GLOBIOM, the following emission accounts were assigned to livestock directly: CH4 from enteric fermentation, CH4 and N2O from manure management, and N2O from excreta on pasture (N2O from manure applied on cropland is reported in a separate account linked to crop production). In brief, CH4 from enteric fermentation is a simultaneous output of the feed-yield calculations done with the RUMINANT model, as well as nitrogen content of excreta and the amount of volatile solids. The assumptions about proportions of different manure management systems, manure uses, and emission coefficients are based on detailed literature review. A detailed description of how these coefficients have been determined including the literature review is provided in (Herrero, Havlik et al. 2013 :cite:`herrero_global_2013`).
+In GLOBIOM, the following emission accounts were assigned to livestock directly: CH4 from enteric fermentation, CH4 and N2O from manure management, and N2O from excreta on pasture 
+(N2O from manure applied on cropland is reported in a separate account linked to crop production). In brief, CH4 from enteric fermentation is a simultaneous output of the feed-yield 
+calculations done with the RUMINANT model, as well as nitrogen content of excreta and the amount of volatile solids. The assumptions about proportions of different manure management systems, 
+manure uses, and emission coefficients are based on detailed literature review. A detailed description of how these coefficients have been determined including the literature review is provided 
+in (Herrero et al., 2013 :cite:`herrero_global_2013`).
 
 Land use change emissions
 ~~~~
-Land use change emissions are computed based on the difference between initial and final land cover equilibrium carbon stock. For forest, above and below-ground living biomass carbon data are sourced from (Kindermann, Obersteiner et al. 2008    :cite:`kindermann_global_2008`), where geographically explicit allocation of the carbon stocks is provided. The carbon stocks are consistent with the 2010 Forest Assessment Report (FAO 2010 :cite:`food_and_agricultural_organization_fao_global_2010`). Therefore, the emission factors for deforestation are in line with those of FAO. Additionally, carbon stock from grasslands and other natural vegetation is also taken into account using the above and below ground carbon from the biomass map from (Ruesch and Gibbs 2008 :cite:`ruesch_new_ipcc_2008`). When forest or natural vegetation is converted into agricultural use, it is considered in this approach that all below and above ground biomass is released in the atmosphere. However, the following are not accounted for: litter, dead wood and soil organic carbon.
+Land use change emissions are computed based on the difference between initial and final land cover equilibrium carbon stock. For forest, above and below-ground living biomass carbon data are sourced from 
+Kindermann et al. (2008) :cite:`kindermann_global_2008`, where geographically explicit allocation of the carbon stocks is provided. The carbon stocks are consistent with the 2010 Forest Assessment Report 
+(FAO, 2010 :cite:`food_and_agricultural_organization_fao_global_2010`). Therefore, the emission factors for deforestation are in line with those of FAO. Additionally, carbon stock from grasslands and other 
+natural vegetation is also taken into account using the above and below ground carbon from the biomass map from (Ruesch and Gibbs, 2008 :cite:`ruesch_new_ipcc_2008`). 
+When forest or natural vegetation is converted into agricultural use, it is considered in this approach that all below and above ground biomass is released in the atmosphere. 
+However, the following are not accounted for: litter, dead wood and soil organic carbon.
 
 Comparison with other literature
 ~~~~
-In order to put the numbers in perspective with other sources they were compared with FAO (Tubiello, Salvatore et al. 2013 :cite:`tubiello_faostat_2013`) where a simple but transparent approach is used, largely relying on FAOSTAT activity numbers and IPCC Tier 1 emission coefficients (see :numref:`tab-globff`).
+In order to put the numbers in perspective with other sources they were compared with FAO (Tubiello et al., 2013 :cite:`tubiello_faostat_2013`) where a simple but transparent approach is used, largely relying on FAOSTAT 
+activity numbers and IPCC Tier 1 emission coefficients (see :numref:`tab-globff`).
 
-The 2000 data for crops are overall about 11% higher than Tubiello et al., mainly because of rice where the data are closer to EPA (EPA 2012 :cite:`environmental_protection_agency_epa_US_2012`) which is higher than Tubiello et al. For livestock, it is by some 18% lower than Tubiello et al. So in total there is about 10% GHG emissions less in 2000 than the values reported. The year 2010 is already the result of simulations and hence may be interesting to compare with the data. In order to facilitate the comparison, the columns e), f) and g) in Table 1 are3 included. Columns e) and f) compare GLOBIOM data for 2000 and projections for 2010 respectively, with numbers reported by Tubiello et al. Column g) compares the relative change in emissions between 2000 and 2010 from these two sources (1.00 would indicate the same relative change in GLOBIOM and in Tubiello et al.). It is apparent that the relative change in total agricultural emissions in GLOBIOM is the same as the development reported by Tubiello et al. – an increase by 11%. The behavior of GLOBIOM is over this period very close to the reported trends also at the level of individual accounts. The only exception is emissions from manure management where the relative change projected in GLOBIOM is by 13% higher than the relative change observed in Tubiello's numbers. 
+The 2000 data for crops are overall about 11% higher than Tubiello et al., mainly because of rice where the data are closer to EPA (EPA 2012 :cite:`environmental_protection_agency_epa_US_2012`) which is higher than 
+Tubiello et al. For livestock, it is by some 18% lower than Tubiello et al. So in total there is about 10% GHG emissions less in 2000 than the values reported. The year 2010 is already the result of simulations 
+and hence may be interesting to compare with the data. In order to facilitate the comparison, the columns e), f) and g) in Table 1 are3 included. Columns e) and f) compare GLOBIOM data for 2000 and projections for 
+2010 respectively, with numbers reported by Tubiello et al. Column g) compares the relative change in emissions between 2000 and 2010 from these two sources (1.00 would indicate the same relative change in GLOBIOM 
+and in Tubiello et al.). It is apparent that the relative change in total agricultural emissions in GLOBIOM is the same as the development reported by Tubiello et al. – an increase by 11%. The behavior of GLOBIOM 
+is over this period very close to the reported trends also at the level of individual accounts. The only exception is emissions from manure management where the relative change projected in GLOBIOM is by 13% higher 
+than the relative change observed in Tubiello's numbers. 
 
 .. _tab-globff:
 .. list-table:: Comparison of agricultural GHG emissions from GLOBIOM and from FAO for the years 2000 and 2010

source/emissions/message/ghgs.rst

@@ -2,8 +2,14 @@ GHGs
 ===========
 Carbon-dioxide (CO2)
 ---------------------
-The MESSAGE model includes a detailed representation of energy-related and land-use CO2 emissions (Riahi and Roehrl, 2000 :cite:`riahi_greenhouse_2000`; Riahi, Rubin et al., 2004 :cite:`riahi_prospects_2004`; Rao and Riahi, 2006 :cite:`rao_role_2006`; Riahi et al., 2011 :cite:`riahi_rcp_2011`). Energy related CO2 mitigation options include technology and fuel shifts; efficiency improvements; and carbon capture. A number of specific mitigation technologies are modeled bottom-up in MESSAGE with a dynamic representation of costs and efficiencies. MESSAGE also includes a detailed representation of carbon capture and sequestration from both fossil fuel and biomass combustion. Land-use CO2 was previously represented using methodology documented in Riahi et al. (2007) :cite:`riahi_scenarios_2007` but is currently updated based on information from the GLOBIOM model.
+The MESSAGE model includes a detailed representation of energy-related and land-use CO2 emissions (Riahi and Roehrl, 2000 :cite:`riahi_greenhouse_2000`; Riahi, Rubin et al., 2004 :cite:`riahi_prospects_2004`; 
+Rao and Riahi, 2006 :cite:`rao_role_2006`; Riahi et al., 2011 :cite:`riahi_rcp_2011`). Energy related CO2 mitigation options include technology and fuel shifts; efficiency improvements; and carbon capture. 
+A number of specific mitigation technologies are modeled bottom-up in MESSAGE with a dynamic representation of costs and efficiencies. MESSAGE also includes a detailed representation of carbon capture 
+and sequestration from both fossil fuel and biomass combustion. Land-use CO2 was previously represented using methodology documented in Riahi et al. (2007) :cite:`riahi_scenarios_2007` but is currently 
+updated based on information from the GLOBIOM model.
 
 Non-CO2 GHGs
 -------------------
-MESSAGE includes a representation of non-CO2 GHGs (CH4, N2O, HFCs, SF6, PFCs) mandated by the Kyoto Protocol (Rao and Riahi, 2006 :cite:`rao_role_2006`). Included is a representation of emissions and mitigation options from both energy related processes as well as non-energy sources like livestock, municipal solid waste disposal, manure management, fertilizer use, rice cultivation, wastewater, and crop residue burning.
+MESSAGE includes a representation of non-CO2 GHGs (CH4, N2O, HFCs, SF6, PFCs) mandated by the Kyoto Protocol (Rao and Riahi, 2006 :cite:`rao_role_2006`). 
+Included is a representation of emissions and mitigation options from both energy related processes as well as non-energy sources like livestock, municipal solid waste disposal, 
+manure management, fertilizer use, rice cultivation, wastewater, and crop residue burning.