Importance of boundary layer minimum height in NAME . 174

A caveat of the above analysis lies in the default value assigned to the minimum boundary layer height in NAME. This is set to 100 m to be consistent with the dened `surface inuence' altitude range (0-100 m agl). 100 m was chosen to

6.6 Summary

de-weight local inuences which occur on a sub-grid spatial scale within the UM meteorology (i.e. small-scale eddies). Below 100 m, local inuences dominate on this scale. Relatively large errors in dispersion could propagate through to the trajectory and InTEM analysis as these inuences are not fully represented in the UM meteorology. Areas of at and low-lying topography can experience very low boundary layer heights (Stull, 1988). This is particularly prevalent at night when the boundary layer is already lower than during the day.

To assess the sensitivity of this minimum boundary layer height NAME analysis was rerun using a new minimum limit of 10 m. This recorded nocturnal boundary layer heights below 100 m were recorded 9 % of the time at Tacolneston during 2014. This accounted for 4 % of all observations at Tacolneston during this time period. With this NAME setup, datasets at 75 m and 100 m would experience more free tropospheric air, which could impact the InTEM results. If NAME underestimates the amount of free tropospheric air experienced at Tacolneston this could underestimate emissions inferred from this site's data. It is dicult to ascertain the impact this could have on results without running a full repeat of the Tacolneston analysis. This would have less of an impact at the other measurement sites as they rarely sample from above the boundary layer. Multiple site analysis has been proven to reduce individual site biases in Section 6.3, and thus any potential bias in Tacolneston's contribution to the four site estimates are hopefully minimised. To ensure this, it would be wise to rerun the NAME air history maps with a lower default minimum boundary layer height and the equivalent surface inuence altitude range, however due to time constraints this was not possible for this thesis.

6.6 Summary

This chapter discussed the major results of this thesis. InTEM was run using the nal setup described in Section 5.11 to estimate methane emissions for the East of England over various periods of time. Emission estimates were rst produced using all four observation sites for a one year period between June 2013 and May 2014. These were compared to the 2012 NAEI which had been regridded onto the InTEM solution grid resolution. The ne resolution maps showed a similar distribution of methane emissions in both inventories but the individual magnitudes could vary, especially for point sources of methane, i.e. landlls. Particular dierences between the InTEM and NAEI estimates

Chapter 6 East of England methane emission estimates

included the absence of emissions west of Haddenham in InTEM. This suggests the decommissioned landlls, which are estimated to still be high emitters of methane may not be as strong a source as the NAEI suggests. Emissions to the north east of Haddenham, which are present in the InTEM inventory but not the NAEI could also correspond to fenland emissions. These are not currently included in the NAEI. One nal dierence between the two inventories was a large source east of Tacolneston observed in InTEM but not the NAEI. This could correspond to biogenic emissions from the Norfolk Broads.

This section then regridded the ne spatial resolution emissions to the county-based regional estimates. This was to reduce uncertainties associated with the ne spatial resolution. The county regions showed that the two inventories complemented each other well in regions close to the observation sites. Regions further away from the observation sites produced estimates that were dierent to each other in InTEM and the NAEI. The Norfolk, Suolk and Cambridge countries are estimated to produce 80.4 ±3.3 kt yr^-1 of methane for the period between June 2013 - May 2014 (NAEI equivalent of 89.6 kt yr^-1).

The following section (Section 6.2) assessed seasonal methane estimates, particularly focusing on the regions within EA (Norfolk, Suolk and Cambridgeshire) and the London region. These regions showed opposing seasonal cycles which were thought to be due to the dominating source sectors within each region. The London region saw the most obvious seasonal cycle, with a maxima in winter and spring and a minima in summer.

Section 6.3 then focused on the sensitivity of the methane emission estimates when varying the number of sites included in the inversion setup. This section also focused its results mainly on the EA regions, which have been determined to be more robust and consistent throughout this analysis. Single site analysis showed each site had a `footprint' which roughly corresponded to the county they were situated in. Relatively robust regional estimates of EA could be simulated using only two sites within InTEM but the individual county estimates could vary quite signicantly. It also appeared that individual site biases would be reected in the larger regional estimates. Care should be taken when choosing sites in future multiple site projects. Biases should be identied early so they can be taken into account in the analysis. The optimum number of sites was found to be dependent on the size of the region of interest.

Section 6.4 presented methane emission estimates for dierent periods of time.

Estimates for the preceding year (June 2012 to May 2013) showed higher emissions

6.6 Summary

for EA. Estimates for a two year inversion from June 2012 to May 2014 showed estimates to be more similar to the 2012-2013 than the 2013-2014 emissions.

Reasons for these dierences were not easily identiable. A longer dataset would possibly allow for a more thorough analysis of the interannual trends in methane emissions.

The nal section (Section 6.5) of this chapter assessed the sensitivity of the Tacolneston sampling height in the InTEM setup. The Tacolneston only inversion produced much lower emission estimates than the other single site runs. A doubling of the sampling height in the NAME air history model runs produced emission estimates which were 39 % larger in the NSC region for the Tacolneston only InTEM estimates. This had a lower eect on the four site inversions which suggests that individual site biases are minimised when incorporated into a multiple site analysis. If the NAME vertical transport scheme can be trusted then this suggests that measurements taken at higher altitudes have a signicantly lower footprint than sites at lower altitudes. The analysis within this section has the caveat that all NAME runs were conducted with a default minimum boundary layer height of 100 m. In this region of the UK the boundary layer height can reach lower levels, especially during the winter nocturnal hours. It was suggested that this analysis be rerun with a lower minimum boundary layer height for completeness, although little changes in the emission estimates from the four site InTEM run are expected.

7 Concluding discussion and further work

This chapter summarises the major results from this thesis. An overview of each chapter is discussed highlighting the main scientic ndings. To conclude, this chapter then discusses the potential further work which could be conducted to develop the scientic ndings from this thesis.

7.1 Overview

This thesis explains the development of a novel technique to estimate methane emissions at high spatial resolution. This inversion technique, named InTEM, incorporates in situ atmospheric methane measurements with computer dispersion modelling into a statistical method to infer methane emission estimates, via cost function analysis.

The rst chapter gives an introduction to the atmosphere and the layers within it.

Methane's major atmospheric chemical reactions are summarised and a detailed overview of its global sources and sinks is explained. The major techniques used to estimate regional and global methane budgets are divided into two distinct categories: bottom-up and top-down. Strengths and weaknesses exist for both techniques, which are described in Section 1.6. Chapter 1 concludes with a description of the UK National Atmospheric Emissions Inventory (NAEI) for methane.

Chapter 2 introduces the project upon which this thesis is based. Motivations for choosing East Anglia as a pilot region to develop this novel technique are explained, and the instrumental site locations are described. A detailed introduction to the methane sources within this region is then discussed. East Anglia is dominated by three main source sectors: waste, agriculture and oshore.

The four sites are inuenced by these sectors dierently. Haddenham and Tilney

Chapter 7 Concluding discussion and further work

experience much large waste contributions whereas Tacolneston and Weybourne have a larger agricultural contribution.

Chapter 3 discusses in detail the instrumental setup and the modelling methods used throughout this project. Four measurement sites are used in the analysis of this thesis, which record atmospheric methane. All sites have GC-FID instruments installed with the exception of one, which uses a Picarro CRDS.

A modelled representation of the physical atmospheric processes occurring at these sites is calculated using the UK Met Oce's NAME model. So-called `air history maps' are calculated for each instrument site for every hour observation throughout the measurement time period (July 2012 - June 2014). Both modelling data and measured observations are fed into the InTEM technique to produce the methane emission elds for the East of England. Strengths and weaknesses of InTEM's chosen cost function were also discussed in this chapter.

Chapter 4 is the rst of the three results chapters. It gives a detailed analysis of the measured methane concentrations. Methane varies over dierent time frames and is dependent on numerous meteorological variables, particularly boundary layer height and wind speed. The NAEI emission can be converted to pseudo-observations using the NAME output. This analysis shows the NAEI has a reasonable distribution of methane emissions but the magnitudes do not directly compare to measured methane concentrations. The chapter concludes with a case study on the Haddenham site, which shows a comparison of modelled and measured meteorology using a SNAQ node. In addition, this section shows Haddenham is largely inuenced by its local landll sources. This is conrmed using correlation analysis with carbon dioxide, and isotopic analysis.

Chapter 5 focuses on the variables within the InTEM setup that inuence the emission results. Sensitivity studies are conducted to ascertain the impact of these variables to establish a nal setup. This nal setup is formulated to ensure InTEM will produce robust and consistent emission estimates. Analysis shows that increasing the grid resolution improves the cost score but increases computer time. Chapter 5 highlights that care should be taken when considering an accurate estimation of uncertainty and how it varies temporally. Analysis shows that InTEM is sensitive to the starting regions dened before the solution grid is calculated. These sensitivities can be addressed, for example, ensuring regions of interest are central to the inversion domain and several `buer' regions exist to combat baseline issues.

The concluding results chapter applies a direct comparison of the nal InTEM