Warming Trends and Long-Range Dependent Climate Variability Since Year 1900: A Bayesian Approach

Abstract

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.

The Structure of Climate Variability Across Scales

“…Although such an assumption is hardly realistic and is theoretically limiting, it is nevertheless of practical relevance since the overwhelming majority of trends in geophysical records are reported as the slope from a linear regression model. Most studies are based on the up-front assumption that a time series can be described by a nonstationary linear trend with a stochastic long-range dependence component (e.g., Bunde et al, 2014;Capparelli et al, 2013;Franzke, 2010Franzke, , 2012Lennartz & Bunde, 2009;Ludescher et al, 2015;Myrvoll-Nilsen et al, 2019;Rybski & Bunde, 2009) and focus on the assessment of the corresponding uncertainty (e.g., Cohn & Lins, 2005;Koutsoyiannis, 2006;and Koutsoyiannis & Montanari, 2007).…”

“…Recent developments in temperature trend significance testing with long-range dependent noise show how we can also incorporate information about forced global temperature changes in the trend estimate (Myrvoll-Nilsen et al, 2019). In that way, one avoids attributing forced changes deviating from, for example, a linear trend as part of the stochastic variability (Gil-Alana, 2005;Fatichi et al, 2009;Franzke, 2012Franzke, , 2014.…”

Functional thermal limits are determined by rate of warming during simulated marine heatwaves

“…Historical temperature records have now detected positive temperature trends for the majority of the Earth's surface (Myrvoll-Nilsen et al 2019), with the oceans being key to the regulation and capture of much of the excess heat present in the atmosphere (Marshall et al 2015). As a result, marine environments are changing both physically and biochemically (Bopp et al 2013).…”

Bayesian analysis of early warning signals using a time-dependent model

“…H ∈ (0.5, 1) is the memory coefficient known as the Hurst exponent. Fractional Gaussian noise have been shown to be more realistic for describing components in the Earth system where the power spectrum does not follow an exponential decay, such as monthly to centennial global and local mean surface temperature data (Lovejoy and Schertzer, 2013;Huybers and Curry, 2006;Rybski et al, 2006;Rypdal and Rypdal, 2016;Franzke et al, 2015;Fredriksen and Rypdal, 2016;Løvsletten and Rypdal, 2016;Myrvoll-Nilsen et al, 2019). Rypdal (2016) was able to detect an increase of variance of the high-frequency fluctuations for the ensemble average of the 17 DO events at a 5% significance level, and individually for five separate events.…”