Methane Emissions in a Chemistry-Climate Model: Feedbacks and Climate Response.

Understanding the past, present, and future evolution of methane remains a grand challenge. Here we have used a hierarchy of models, ranging from simple box models to a chemistry-climate model (CCM), UM-UKCA, to assess the contemporary and possible future atmospheric methane burden. We assess two emission data sets for the year 2000 deployed in UM-UKCA against key observational constraints. We explore the impact of the treatment of model boundary conditions for methane and show that, depending on other factors, such as CO emissions, satisfactory agreement may be obtained with either of the CH4 emission data sets, highlighting the difficulty in unambiguous choice of model emissions in a coupled chemistry model with strong feedbacks. The feedbacks in the CH4-CO-OH system, and their uncertainties, play a critical role in the projection of possible futures. In a future driven by large increases in greenhouse gas forcing, increases in tropospheric temperature drive, an increase in water vapor, and, hence, [OH]. In the absence of methane emission changes this leads to a significant decrease in methane compared to the year 2000. However, adding a projected increase in methane emissions from the RCP8.5 scenario leads to a large increase in methane abundance. This is modified by changes to CO and NOx emissions. Clearly, future levels of methane are uncertain and depend critically on climate change and on the future emission pathways of methane and ozone precursors. We highlight that further work is needed to understand the coupled CH4-CO-OH system in order to understand better future methane evolution.

An estimation of noise levels in HMPAO RCBF SPECT images using simulation and phantom data; comparison with results obtained from repeated normal controls.

The purpose of this work was to determine noise levels in HMPAO RCBF SPECT images. Eight simulated images of a uniform sphere of activity were made at each of three different count levels. Three images of the Amersham brain phantom were obtained at each of three count levels, roughly corresponding to the simulation levels. Image reconstruction involved a modified Shepp-Logan filter with and without attenuation correction. The scaling constant in the Budinger equation was shown to vary little over the count range used with a mean value of 23 for uncorrected phantom data and 27 for corrected phantom data, corresponding to RMS noise levels of 7%-15%. The variance due to noise was calculated as a percentage of the variance obtained for 53 normal control studies following image registration and normalization. Values of 54% for uncorrected images and 67% for corrected images were obtained. For 10 normal controls a repeated study was performed. The ratio of within-subject to (single sample) between-subject variance was determined as 73% for uncorrected images and 78% for corrected images.