University of East Anglia

About: University of East Anglia is a education organization based out in Norwich, Norfolk, United Kingdom. It is known for research contribution in the topics: Population & Climate change. The organization has 13250 authors who have published 37504 publications receiving 1669060 citations. The organization is also known as: UEA.

Linoleic acid, a dietary n-6 polyunsaturated fatty acid, and the aetiology of ulcerative colitis: a nested case-control study within a European prospective cohort study

Abstract: OBJECTIVE: Dietary linoleic acid, an n-6 polyunsaturated fatty acid, is metabolised to arachidonic acid, a component of colonocyte membranes. Metabolites of arachidonic acid have pro-inflammatory p ...

The Navier-Stokes equations : a classification of flows and exact solutions

Abstract: The Navier-Stokes equations were firmly established in the 19th Century as the system of nonlinear partial differential equations which describe the motion of most commonly occurring fluids in air and water, and since that time exact solutions have been sought by scientists. Collectively these solutions allow a clear insight into the behavior of fluids, providing a vehicle for novel mathematical methods and a useful check for computations in fluid dynamics, a field in which theoretical research is now dominated by computational methods. This 2006 book draws together exact solutions from widely differing sources and presents them in a coherent manner, in part by classifying solutions via their temporal and geometric constraints. It will prove to be a valuable resource to all who have an interest in the subject of fluid mechanics, and in particular to those who are learning or teaching the subject at the senior undergraduate and graduate levels.

Structural decline in China’s CO2 emissions through transitions in industry and energy systems

Abstract: As part of the Paris Agreement, China pledged to peak its CO2 emissions by 2030. In retrospect, the commitment may have been fulfilled as it was being made—China’s emissions peaked in 2013 at a level of 9.53 gigatons of CO2, and have declined in each year from 2014 to 2016. However, the prospect of maintaining the continuance of these reductions depends on the relative contributions of different changes in China. Here, we quantitatively evaluate the drivers of the peak and decline of China’s CO2 emissions between 2007 and 2016 using the latest available energy, economic and industry data. We find that slowing economic growth in China has made it easier to reduce emissions. Nevertheless, the decline is largely associated with changes in industrial structure and a decline in the share of coal used for energy. Decreasing energy intensity (energy per unit gross domestic product) and emissions intensity (emissions per unit energy) also contributed to the decline. Based on an econometric (cumulative sum) test, we confirm that there is a clear structural break in China’s emission pattern around 2015. We conclude that the decline of Chinese emissions is structural and is likely to be sustained if the nascent industrial and energy system transitions continue.

Estimating Sampling Errors in Large-Scale Temperature Averages

Abstract: A method is developed for estimating the uncertainty (standard error) of observed regional, hemispheric, and global-mean surface temperature series due to incomplete spatial sampling. Standard errors estimated at the grid-box level [SE2 5 S2(1 2 r)/(1 1 (n 2 1)r)] depend upon three parameters: the number of site records (n) within each box, the average interrecord correlation (r) between these sites, and the temporal variability (S2 )o f each grid-box temperature time series. For boxes without data (n 5 0), estimates are made using values of S2 interpolated from neighboring grid boxes. Due to spatial correlation, large-scale standard errors in a regionalmean time series are not simply the average of the grid-box standard errors, but depend upon the effective number of independent sites (Neff) over the region. A number of assumptions must be made in estimating the various parameters, and these are tested with observational data and complementary results from multicentury control integrations of three coupled general circulation models (GCMs). The globally complete GCMs enable some assumptions to be tested in a situation where there are no missing data; comparison of parameters computed from the observed and model datasets are also useful for assessing the performance of GCMs. As most of the parameters are timescale dependent, the resulting errors are likewise timescale dependent and must be calculated for each timescale of interest. The length of the observed record enables uncertainties to be estimated on the interannual and interdecadal timescales, with the longer GCM runs providing inferences about longer timescales. For mean annual observed data on the interannual timescale, the 95% confidence interval for estimates of the global-mean surface temperature since 1951 is 60.128C. Prior to 1900, the confidence interval widens to 60.188C. Equivalent values on the decadal timescale are smaller: 60.108C (1951‐95) and 60.168C (1851‐1900).