Abstract
Abstract. Given uncertainties in physical theory and numerical climate simulations, the historical temperature record is often used as a source of empirical information about climate change. Many historical trend analyses appear to de-emphasize physical and statistical assumptions: examples include regression models that treat time rather than radiative forcing as the relevant covariate, and time series methods that account for internal variability in nonparametric rather than parametric ways. However, given a limited data record and the presence of internal variability, estimating radiatively forced temperature trends in the historical record necessarily requires some assumptions. Ostensibly empirical methods can also involve an inherent conflict in assumptions: they require data records that are short enough for naive trend models to be applicable, but long enough for long-timescale internal variability to be accounted for. In the context of global mean temperatures, empirical methods that appear to de-emphasize assumptions can therefore produce misleading inferences, because the trend over the twentieth century is complex and the scale of temporal correlation is long relative to the length of the data record. We illustrate here how a simple but physically motivated trend model can provide better-fitting and more broadly applicable trend estimates and can allow for a wider array of questions to be addressed. In particular, the model allows one to distinguish, within a single statistical framework, between uncertainties in the shorter-term vs. longer-term response to radiative forcing, with implications not only on historical trends but also on uncertainties in future projections. We also investigate the consequence on inferred uncertainties of the choice of a statistical description of internal variability. While nonparametric methods may seem to avoid making explicit assumptions, we demonstrate how even misspecified parametric statistical methods, if attuned to the important characteristics of internal variability, can result in more accurate uncertainty statements about trends.
Highlights
The physical basis of climate change is understood through a combination of theory, numerical simulations and analyses of historical data
Climate change is driven by radiative forcing, a change in net radiation resulting from an imposed perturbation of a climate in equilibrium, for example by increasing the atmospheric concentration of a greenhouse gas
Given the physical uncertainties inherent in all climate simulations, the observed temperature record since the late nineteenth century is often used as a source of empirical information about the Earth’s systematic response to forcing. (Figure 1 shows one estimate of annually averaged global mean surface temperatures from the past 136 years, along with estimates of radiative forcings from various constituents during that period, with the data sources described in Sect. 2.) Analysis of the observed temperature record is complicated, by the short available record of direct measurements, by uncertainties in the historical radiative forcings themselves, and by the internal temperature variability that exists even in the absence of forcing
Summary
The physical basis of climate change is understood through a combination of theory, numerical simulations and analyses of historical data. Given the physical uncertainties inherent in all climate simulations, the observed temperature record since the late nineteenth century is often used as a source of empirical information about the Earth’s systematic response to forcing. Poppick et al.: Estimating trends in the global mean temperature record cal record about the response to forcing: given the data, what do we know about how global temperatures have warmed in response to forcing, how much warming can we expect in plausible future forcing scenarios, and how do we expect uncertainties to change as we continue to observe the Earth’s temperatures? This analysis requires estimates of historical global mean temperatures and radiative forcings. The index combines land and sea surface temperature measurements to estimate annual average global mean surface temperature anomalies (relative to a base period from 1951 to 1980), extending from the year 1880 to the present (comprising 136 years in total). NASA GISS has made some attempt to provide pointwise uncertainty estimates for their data (e.g., Fig. 9a of Hansen et al, 2010), but it is important to realize www.adv-stat-clim-meteorol-oceanogr.net/3/33/2017/
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