Measuring the Risk of supply and demand imbalance at the Sea-sonal scale 

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THE FUTURE OF METHANE

Studies show that in recent years, unlike CO2, CH4 concentrations have been rising faster than at any time in the past two decades, with a current atmospheric concentration 150% above pre-industrial levels (in 2016, the global annual mean is 1842.72 +/- 0.51 ppb [Dlugokencky, NOAA/ESRL]). Since 2007, the atmospheric methane growth has increased substantially (from 0.5 +/-3.1ppb per year for 2000-2006, to 6.9 +/- 2.7 ppb per year for 2007-2015 [Dlugokencky, 2016]), however, the relative methane contributors and drivers remain uncertain. Furthermore, annual anthropogenic CH4 emissions are predicted to continue rising substantially; between 400 and 500Tg CH4 in 2030, and between 430 and 680Tg CH4 in 2050. The upper bar chart in Figure 1.1 shows the regional distribution of estimated annual baseline CH4 emissions in 2030, by sector and world region in a continue as usual scenario. The projected increase in emissions are greatest for methane from livestock and oil and gas systems as countries with fast growing economies and populations are expected to increase energy and waste consumption [EPA 430-R-12-006, 2012]. For example, China and Latin & Central America are expected to be the dominating emitters due to extensive coal, and cattle & oil industries respectively. These projected trends are in contradiction to the 2016 Paris Agreement wherein 195 countries adopted the action plan to limit the increase of global average temperatures to 1.5degC in order to reduce the risks and impacts of climate change. Given that anthropogenic methane emissions (responsible for over half of global methane emissions) are dominantly industry related suggests a large potential to reduce emissions. Studies suggest that at present, technically feasible mitigation methods hold the potential to prevent a third [USEPA, 2014] or half of future anthropogenic CH4 emissions by 2030 (reduction potential of about 200 Tg CH4) [Hoglund-Isaksson, 2012]. Of this mitigation potential, more than 60% can be realised in the fossil fuel industry from reduced venting and leakages. Thus, regions with the largest potential for CH4 mitigation are those with extensive fossil fuel extraction industries, in particular China, Latin America and Asia (see Figure 1.1). Other large abatement potentials are the separation and treatment of biodegradable waste to replace landfills. As mitigation calculations from these studies are based strictly on technical abatement options, reductions from agricultural sources is limited due to the requirement of changes in food production and consumption structures which are not deemed feasible on short timescale. To effectively create and implement CH4 mitigation methods, the sources and sinks must be well characterised and understood. Unfortunately, up until now the sizes of fluxes from individual sources still remain highly uncertain.
Figure 1.1 AMAP Assessment 2015: Methane as an Artic climate forcer. Estimates of methane emissions in 2030 by world region from the GAINS model & maximum technically feasible reduction of methane emissions in 2030.
For methane mitigation, it is vital to separate sources to aid planning purposes and green investment. Although methane emissions can now be inferred from inverse modelling as shown by recent studies [Pandey et al., 2016, Alexe et al., 2015, Hein et al., 1997], identifying and attributing contributions from multiple potential sources can be challenging. Hence emission inventories can aid to generate bottom up estimates of sector specific emissions which requires three steps: Identifying the sources of emissions, collecting the activity data and associated emission factors. Currently, existing bottom-up inventories do not well explain top down trends in methane emissions observed in the atmosphere
[Saunois et al., 2016, Hausmann et al, 2016, Kirschke et al. 2013, Nisbet et al 2014.]. Both methods have high uncertainties, as can be seen in the boxplots of Figure 1.3. For bottom-up estimates this may be because although generally sources are very well identified, emissions inventories still have very high uncertainties due to the uncertainties in individual source strength estimates. Brandt et al., (2014) find that inventories and emission factors consistently underestimate actual measured CH4 emissions in both bottom-up and top-down studies, see Figure 1.2. The study is based on 20 years of literature on natural gas emissions in North America. Top down atmospheric studies (i.e. estimating CH4 emissions after atmospheric mixing occurs) are plotted with a common baseline in the inset of Figure 1.2 which shows measured CH4 emissions are systematically higher than predicted by inventories. Results from device and facility scale measurements (generally < 109 g CH4/year) are shown in the main chart of Figure 1.2. While emissions factors were also found to underestimate the bottom up measurements, the results are more scattered than for atmospheric studies. Emission uncertainties are a consequence of the large variations seen in experimental data as emissions from anthropogenic sources can vary across space and time. Often inventories are based on single emissions factors for a given activity and/or from a small number of samples and point sources, e.g. IPCC Tier 1 methods, which do not sufficiently represent the areas and activities which they are applied to. For example, most studies on fugitive methane emissions from oil and gas are based on a limited number of studies specific to certain fields in the USA or Canada, however there are a number of parameters that will be country/site specific or change over time and thus without more systematic measurements their magnitudes will remain largely unknown for most major oil and gas producing countries. It is also possible that substantial sources remain outside of GHG emission inventories, for example abandoned oil & gas wells were found to contribute 5-8% of the estimated annual anthropogenic methane emissions for 2011 in Pennsylvania and are not included in the GHG emission inventory [Kang et al., 2016]. Thus, the requirement to improve current estimates means the partitioning of methane sources by region and processes need to be better constrained. To do this, observations of specific, individual methane sources must be extended.
Figure 1.2 Inventories and emissions factors consistently underestimate actual measured CH4 emissions across scales. Ratios > 1 indicate measured emissions are larger than expected from EFs or inventories. The main graph compares results to the EF or inventory estimate chosen by each study author. The inset compares results to regionally scaled common denominator, scaled to region of study and the sector under examinations. [Brandt et al., 2014].

ANTHROPOGENIC METHANE SOURCE IDENTIFICATION

Besides dedicated emissions measurements, one important way to reduce uncertainties in methane inventories is by correctly distinguishing between emissions from various methane sources, often occurring in the same region. The formation of methane (either by biogenic, thermogenic or pyrogenic formation) dictates the individual characteristics of each methane source, e.g. their isotopic signature or species co-emitted, and as such an understanding of the processes involved in the creation of methane is particularly important.

METHANE FORMATION

Biologically produced methane arises through the decomposition of organic matter by methanogenic bacteria (archaea) under anaerobic conditions. The major anthropogenic sources arise from agriculture and waste. Agriculture, being the category with the largest contribution to anthropogenic CH4 releases (approximately 45% [JRC/PBL, 2012]), has two predominant sources; rice paddies and livestock. Rice paddies, the lesser contributor between the two, mainly emit methane during the flooding period when the anaerobic conditions needed for methane production are present. Livestock emissions are estimated as double that of rice emissions globally, and are a result of the microbiological fermentation that breaks down cellulose and other macro molecules in the rumen [Lassey, K. 2006]. The produced CH4 and CO2 are released from the rumen mainly through the mouth of multi-stomached ruminants (87% of ruminant emissions) [Saunois et al., 2016], generally cattle but can also be other domestic livestock such as sheep, goats, buffalo and camels. Emissions are strongly influenced by the total weight and diet of the animals. In addition, methane emissions arise when the livestock manure is stored or treated in systems that promote anaerobic conditions. The second biogenic category, waste, accounts for approximately 18% of total anthropogenic emissions [Saunois et al., 2016, Bogner et al. 2008] and includes two sub-sources, namely wastewater and landfill. Wastewater emissions occur when anaerobic conditions exist. This can be deliberately induced (specifically for wastewater with high organic content) or happen by coincidence [Andre et al. 2014]. In landfills, methane is produced as a waste gas due to the decomposition of organic material, and accounts for approximately 5-10% of global anthropogenic methane emissions [Bogner et al. 2008].
Thermogenic methane is typically produced during the decomposition of kerogen at depths below 1000m [Floodgate and Judd, 1991] at high temperature and pressures. In such conditions bacteria cannot survive and the process takes place without any microbial activity leading to mature gases with higher CH4 concentrations. Its anthropogenic sources are predominantly fossil fuel methane emissions (hereafter referred to as ffCH4), accounting for approximately 34% of global anthropogenic methane emissions [Saunois et al., 2016]. Fossil fuel CH4 emissions arise from the production and use of Coal, Oil and in particular, natural gas. Coal-related FFCH4, estimated between 8-12% of anthropogenic methane emissions [Chai et al. 2016] is primarily emitted during the mining process when coal seams are fractured, but emissions can also occur during post mining processing such coal waste piles and abandoned mines [Penman et al., 2000 (IPCC)]. Natural gas is composed of >90% CH4, thus it is not surprising that its loss to the atmosphere during extraction, processing and transport can represent a significant component of methane emissions. Natural gas is often co-located with petroleum, therefore, although on a lesser scale (in the US oil operations release one quarter as much CH4 as natural gas systems), trapped methane is also released in large quantities during mining of petroleum (oil itself only contains trace amounts of methane so little is emitted during refinement/transportation [Smith et al., 2010]. It is estimated that emission factors for unconventional gas (gas trapped within shale formations mined via hydraulic fracturing) are larger than conventional oil and gas by 3-17%, due to higher releases in the drilling phase [Caulton, D. et al, 2014, Schneising et al., 2014, Howarth, 2011].
Finally, accounting for approximately 13% of anthropogenic emissions, pyrogenic methane arises from the incomplete combustion of biomass, thus the largest sources can be considered as peat fires, biomass burning and biofuel usage [Saunois et al., 2016]. The fraction of carbon that is released as methane depends on the fuel type and burning conditions, e.g. Burning dry savanna releases relatively small amounts of CH4 compared with forest fires [Encyclopedia of atmospheric sciences, Volume 3].
Estimates of global and European emissions from 5 broad methane source categories is shown in Figure 1.3, taken from the study Saunois et al. [2016]. Agriculture and waste emissions dominate in Europe.
Figure 1.3 Methane global emissions from 5 broad categories (Wetlands, Biomass burning, Fossil fuels, and Agriculture and waste) for the 2003-2012 decade from top down inversion models (left light coloured boxplots) and bottom up models/inventories (right, dark coloured boxplots) in Tg CH4yr-1 taken from Saunois et al. 2016. The inset plots the regional CH4 budget for Europe using the same categories. In Europe, anthropogenic methane (in particular Agriculture and Waste) are dominant over natural methane sources.

VOC EMISSION RATIOS

The formation of methane is often accompanied by a number of other compounds, whose abundances depend strongly on the creation conditions. Thus, it is possible to use correlations of co-emitted compounds with methane to distinguish between individual sources.
The term volatile organic compounds (VOCs) is used to denote the entire set of vapour phase atmospheric organics excluding CO and CO2. Given their short atmospheric lifetimes (fractions of a day to weeks) they have little direct impact on radiative forcing but are central to atmospheric chemistry, participating in atmospheric photochemical reactions and influencing the air quality and climate through their production of ozone and organic aerosols. Within this thesis, non-methane hydrocarbons (NMHC), which are organic chemical compounds consisting of hydrogen and carbon atoms emitted from both natural and anthropogenic sources are used as complementary tracers for CH4. Here, the words NMCHs and VOCs are used interchangeably.
The majority of VOC emissions are related to natural sources which originate from nearly exclusively (approximately 90%) vegetation [Guenther et al., 1995]. Nonetheless, global emissions of anthropogenic VOCs is approximately 186 Tg/year [EDGAR 2005], of which a number of sources are shared with methane. The ratio of methane to light VOCs is very high for biologically produced methane because the biochemical mechanism for methanogensis are very specific, whereas in thermogenic reactions substantial amounts of ethane and propane can also be produced. VOCs can be separated into a number of sub-categories which can be used as trace gasses to identify methane sources, namely: alkanes, alkenes, alkynes, aromatics and oxygenated VOCs (OVOCs). The main sources of alkane emissions, such as ethane and propane, are from exploitation and distribution of natural gas, petrochemical industries and biomass burning. Fossil fuels contain only small amounts of alkenes, thus such VOCs (e.g. ethene and propene) are emitted predominantly from vehicle exhaust (due to incomplete combustion), from biofuel combustion and biomass burning. Aromatics, such as benzene, toluene, xylenes (BTEX) are components in fossil fuels, and are predominantly emitted by vehicle exhaust from fuel evaporation and spillage. Distinction between sources can sometimes be difficult as source characteristics vary spatially and temporally. For example, exhaust contribution to VOC levels were found to vary depending on the time of day and day of the week by Rubin et al. [2006]. Furthermore, the composition of the exhaust was found to be dependent on the type of vehicle and fuel used [Verma and des Tombe, 2002, Schuetzle et al., 1994, Zhao et al., 2011].
The use of emissions ratios is a widely-used method for determining source composition and allows for the separation of sources. In literature, this method has been predominantly used to characterise NMHCs [So et al., 2004, Wang et al., 2010]. Nonetheless there has been a recent surge in publications using VOC emissions ratios to identify and distinguish between thermogenic (in particular oil and gas) methane emissions. [Koss et al, 2015, Warneke et al., 2014, Gilman et al, Petron et al., 2014]. Oil and gas sources can be identified using a number of VOC:CH4 correlations, the predominant being ethane, (C2H6) which is the secondary component in natural gas, as well as other light hydrocarbons C1-C5. An example of how the C2H6:CH4 ratio can be used to identify gas of differing origins can be seen in Figure 1.4 from
Schoell [1983]. The plot indicates that thermogenic gasses formed during or directly after the formation of oil (green regions) are much richer in C2+ hydrocarbons than dry gasses formed later (pink regions). Biogenic methane trace gases can be slightly more complex to distinguish; Yuan et al. 2017 found ammonia and ethanol to be good tracers for animal & waste emissions and feed storage & handling emissions respectively. The major co-emitted VOCs for anthropogenic methane sources can be found in Table 1.1.

Table of contents :

1 Introduction 
1.1 Renewable energy growth
1.2 Variability and its implications
1.2.1 Atmospheric variability
1.2.2 Implication for energy management
1.3 Strategies for forecasting wind energy
1.3.1 State of the art
1.3.2 Toward seasonal prediction
1.4 Objectives of the work
1.5 Outline
1.6 Description of the Data
1.6.1 ERA-Interim Reanalysis
1.6.2 ECMWF seasonal ensemble forecasts
1.6.3 Principal component analysis
2 Modelling the variability of wind energy resource 
2.1 Introduction
2.2 Data & Methods
2.2.1 Data
2.2.2 Methods
2.3 Evaluating the reconstruction methods
2.3.1 Performance of methods for wind speed distribution reconstruction
2.3.2 Performance of the methods for estimating the capacity factor
2.4 Towards monthly and seasonal forecast of the wind speed distribution
2.5 Conclusion
3 Probabilistic forecasts of the wind at the seasonal scale 
3.1 Introduction
3.2 Data & Methods
3.2.1 Data : ECMWF reanalysis and seasonal ensemble forecasts
3.2.2 Methods
3.3 Evaluation and optimization of the model
3.3.1 Diagnostic tools
3.3.2 Optimization of the model
3.4 Probabilistic wind speed forecasting at the monthly and seasonal horizon
3.4.1 Methodology
3.4.2 Results
3.5 Conclusion
4 Measuring the Risk of supply and demand imbalance at the Sea-sonal scale 
4.1 Introduction
4.2 Data & Methodology
4.2.1 Data
4.2.2 Modelling the joint PDF of Consumption and Production
4.2.3 Risk measures
4.3 Estimation of the risk measures
4.3.1 Modelling the risk of deviation from climatological means
4.3.2 Modelling the risk of extreme situations
4.4 Explanatory value of the first PCs
4.5 Discussion and concluding remarks
5 From Numerical Weather Prediction outputs to accurate local sur- face wind speed 
5.1 Introduction
5.2 Data and Methodology
5.2.1 Data
5.2.2 Methodology
5.3 The relationship between analysed and observed winds
5.3.1 10m/100m wind speed variability comparison
5.3.2 Reconstruction of the 10m/100m observed wind speed using NWP outputs
5.4 Summary and concluding remarks
Conclusion

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