%A Myrvoll-Nilsen,Eirik
%A Fredriksen,Hege-Beate
%A Sørbye,Sigrunn H.
%A Rypdal,Martin
%D 2019
%J Frontiers in Earth Science
%C
%F
%G English
%K Trend detection,Climate Change,Long-range dependence,fractional Gaussian noise,bayesian methods
%Q
%R 10.3389/feart.2019.00214
%W
%L
%N 214
%M
%P
%7
%8 2019-August-21
%9 Original Research
%#
%! Trends and long-range dependent climate variability
%*
%<
%T Warming Trends and Long-Range Dependent Climate Variability Since Year 1900: A Bayesian Approach
%U https://www.frontiersin.org/article/10.3389/feart.2019.00214
%V 7
%0 JOURNAL ARTICLE
%@ 2296-6463
%X Temporal persistence in unforced climate variability makes detection of trends in surface temperature difficult. Part of the challenge is methodological since standard techniques assume a separation of time scales between trend and noise. In this work we present a novel Bayesian approach to trend detection under the assumption of long-range dependent natural variability, and we use estimates of historical forcing to test if the method correctly discriminates trends from low-frequency natural variability. As an application we analyze 2° × 2° gridded data from the GISS Surface Temperature Analysis. In the time period from 1900 to 2015 we find positive trends for 99% of the grid points. For 84% of the grid points we are confident that the trend is positive, meaning that the 95% credibility interval for the temperature trend contained only positive values. This number increased to 89% when we used estimates of historical forcing to specify the noise model. For the time period from 1900 to 1985 the corresponding ratios were 42 and 52%. Our findings demonstrate that positive trends since 1900 are now detectable locally over most of Earth's surface.