Long-range persistence in global surface temperatures explained by linear multibox energy balance models

Source at: http://doi.org/10.1175/JCLI-D-16-0877.1 The temporal fluctuations in global mean surface temperature are an example of a geophysical quantity that can be described using the notions of long-range persistence and scale invariance/scaling, but this description has suffered from lack of a generally accepted physical explanation. Processes with these statistical signatures can arise from nonlinear effects, for instance, through cascade-like energy transfer in turbulent fluids, but they can also be produced by linear models with scale-invariant impulse–response functions. This paper demonstrates that, on time scales from months to centuries, the scale-invariant impulse–response function of global surface temperature can be explained by simple linear multibox energy balance models. This explanation describes both the scale invariance of the internal variability and the lack of a characteristic time scale of the response to external forcings. With parameters estimated from observational data, the climate response is approximately scaling in these models, even if the response function is not chosen to be scaling a priori. It is also demonstrated that the differences in scaling exponents for temperatures over land and for sea surface temperatures can be reproduced by a version of the multibox energy balance model with two distinct surface boxes

Warming trends and long-range dependent climate variability since year 1900: A Bayesian approach

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

Estimating Remaining Carbon Budgets Using Temperature Responses Informed by CMIP6

A remaining carbon budget (RCB) estimates how much CO2 we can emit and still reach a specific temperature target. The RCB concept is attractive since it easily communicates to the public and policymakers, but RCBs are also subject to uncertainties. The expected warming levels for a given carbon budget has a wide uncertainty range, which increases with less ambitious targets, i.e., with higher CO2 emissions and temperatures. Leading causes of RCB uncertainty are the future non-CO2 emissions, Earth system feedbacks, and the spread in the climate sensitivity among climate models. The latter is investigated in this paper, using a simple carbon cycle model and emulators of the temperature responses of the Earth System Models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble. Driving 41 CMIP6 emulators with 127 different emission scenarios for the 21st century, we find almost perfect linear relationship between maximum global surface air temperature and cumulative carbon emissions, allowing unambiguous estimates of RCB for each CMIP6 model. The range of these estimates over the model ensemble is a measure of the uncertainty in the RCB arising from the range in climate sensitivity over this ensemble, and it is suggested that observational constraints imposed on the transient climate response in the model ensemble can reduce uncertainty in RCB estimates

The Structure of Climate Variability Across Scales

One of the most intriguing facets of the climate system is that it exhibits variability across all temporal and spatial scales; pronounced examples are temperature and precipitation. The structure of this variability, however, is not arbitrary. Over certain spatial and temporal ranges it can be described by scaling relationships in the form of power‐laws in probability density distributions and autocorrelation functions. These scaling relationships can be quantified by scaling exponents which measure how the variability changes across scales and how the intensity changes with frequency of occurrence. Scaling determines the relative magnitudes and persistence of natural climate fluctuations. Here, we review various scaling mechanisms and their relevance for the climate system. We show observational evidence of scaling and discuss the application of scaling properties and methods in trend detection, climate sensitivity analyses, and climate predictio

Long-range memory in Earth surface temperatures: spatial scale dependence and land-sea differences

The use of long-range memory models as a description of the noise in Earth surface temperatures has increased the recent years, and as a measure of the persistence for such time series we have the Hurst exponent. It is known that sea surface temperatures are more persistent than land temperatures, and that global temperatures are more persistent than local temperatures. We also know that the persistence is higher for lower latitudes than for higher latitudes. My results confirm these observations, and in addition they reveal what the Hurst exponents are for spatial scales between local and global. This is done by performing spatial averaging over gridded temperature data to obtain new time series in more coarse-grained grid boxes. To find an explanation for the increase in Hurst exponent that is seen when increasing the spatial scale, I have studied how the autocovariance function for a large grid box depends on the spatial cross-covariances within the box. If these are strong compared to the autocovariances in that area they will have an impact on the Hurst exponent. Scale free long-range memory models are found to give a good description for global temperature and many of the local temperatures on time scales from a few months to ten years. The largest deviations are observed in the eastern equatorial Pacific where ENSO is a very dominating process

Origins and impacts of spatial and temporal long-range dependence in the climate system

The internal variability of most Earth surface temperatures has a power spectral density well described by a power law, S(f) ~ f-β, and we typically observe 0 < β < 1. This characterizes variability exhibiting long-range dependence (LRD), which has no characteristic time scale. However, there is no consensus about the physical mechanisms behind this property, and the topic of this thesis is to explore where it comes from. In Paper I, the spectral characteristics of detrended instrumental temperature records and temperatures simulated in climate models are studied. The persistence, as measured by β, is found to be stronger for sea than for land, and increasing with the degree of spatial averaging. An interpretation of the increase with spatial averaging is that high-frequency variability is averaged out to a larger degree than low-frequency variability. Paper II presents a spatiotemporal model with this property, and more specifically, it predicts that global β is twice the value of the local β on a uniform sphere. For temperatures in observation data and climate models, there are some regional differences in the spectral characteristics, but on average they are consistent with the model. Paper III demonstrates how LRD in global temperature can be explained by a linear multibox energy balance model (EBM). In linear models, temperatures can be described as a response function convolved with the forcing. For the multibox EBM, the response function consists of a sum of exponential responses, which with the parameters estimated, is well approximated by a power-law response. Only three boxes, implying three response times, are needed to approximate the power-law response on scales from months to centuries. When driven by white noise forcing, a power-law response gives a process exhibiting LRD, which is well approximated by the sum of Ornstein-Uhlenbeck processes obtained for the exponential responses. This thesis also puts these concepts into the context of simple climate models in general. The findings of multiple and long response times means that we can expect continued responses to past forcing for a long time into the future – longer than predicted from simple models that do not include interaction with the deep ocean

En investering i tvil

Innlegg i avisa Nordlys, torsdag 30. juli 2015.Ifølge Ole Henrik Ellestads innlegg i Nordlys 20. juli er alle modeller som ikke stemmer overens med observasjoner gale. Det kan man godt si, men det betyr at alle modeller er gale. En modell er en forenklet versjon av virkeligheten, og vil alltid avvike noe fra observasjonene. Man lærer likevel mye av å bruke modeller.Klimamodellene er gode på å forutsi forventet temperaturrespons på eksterne pådriv, som drivhusgasser, solvariasjoner, og vulkansk aktivitet. Men i tillegg til dette kommer naturlige variasjoner i klimasystemet, som kan være så komplekse at de blir så godt som umulige å forutsi langt fram i tid, på samme måte som at man ikke kan forutsi været her noe lenger enn cirka 10 dager fram i tid. Selv den mest perfekte modell kan ikke modellere disse komplekse systemene over lengre tid.Disse usikkerhetene i modellene blir misbrukt av de så-kalte klimaskeptikerne

Long-range memory in Earth surface temperatures: spatial scale dependence and land-sea differences

The use of long-range memory models as a description of the noise in Earth surface temperatures has increased the recent years, and as a measure of the persistence for such time series we have the Hurst exponent. It is known that sea surface temperatures are more persistent than land temperatures, and that global temperatures are more persistent than local temperatures. We also know that the persistence is higher for lower latitudes than for higher latitudes. My results confirm these observations, and in addition they reveal what the Hurst exponents are for spatial scales between local and global. This is done by performing spatial averaging over gridded temperature data to obtain new time series in more coarse-grained grid boxes. To find an explanation for the increase in Hurst exponent that is seen when increasing the spatial scale, I have studied how the autocovariance function for a large grid box depends on the spatial cross-covariances within the box. If these are strong compared to the autocovariances in that area they will have an impact on the Hurst exponent. Scale free long-range memory models are found to give a good description for global temperature and many of the local temperatures on time scales from a few months to ten years. The largest deviations are observed in the eastern equatorial Pacific where ENSO is a very dominating process

Eilertsen-bløffen

Kristian Eilertsen i Troms FRP prøver i Nordlys 24. juni febrilsk å justere fakta etter sine politiske oppfatninger – og ikke motsatt – når han oppdager at seriøse argumenter mot menneskeskapte klimaendringer forsvinner som dugg for solen. Han er heldigvis på kollisjonskurs med FRPs egen olje- og energiminister, som sammen med direktøreni Statoil har erklært at det skjer en menneskeskapt global oppvarming, og at Norges oljeproduksjon «er en del av problemet.» Beskyldningene har haglet fra nesten alle kanter om hvor forferdelig både statsbudsjettet og regjeringens politikk er for klimakampen. Og med godgrunn, for det burde vært adskillig flere tiltak for å redusere utslipp av CO2. Jeg har ikke tenkt å diskutere de mange tiltak som lanseres uke ut og uke inn. Mer interessant er det å gripe fatt i den vaklende forståelsen av det vitenskapelige og faglige grunnlaget som brukes for å underbygge påstandene mot menneskeskapte klimaendringer