Temperatures in transient climates: Improved methods for simulations with evolving temporal covariances

Abstract

Future climate change impacts depend on temperatures not only through changes in their means but also through changes in their variability. General circulation models (GCMs) predict changes in both means and variability; however, GCM output should not be used directly as simulations for impacts assessments because GCMs do not fully reproduce present-day temperature distributions. This paper addresses an ensuing need for simulations of future temperatures that combine both the observational record and GCM projections of changes in means and temporal covariances. Our perspective is that such simulations should be based on transforming observations to account for GCM projected changes, in contrast to methods that transform GCM output to account for discrepancies with observations. Our methodology is designed for simulating transient (non-stationary) climates, which are evolving in response to changes in CO 2 concentrations (as is the Earth at present). This work builds on previously described methods for simulating equilibrium (stationary) climates. Since the proposed simulation relies on GCM projected changes in covariance, we describe a statistical model for the evolution of temporal covariances in a GCM under future forcing scenarios, and apply this model to an ensemble of runs from one GCM, CCSM3. We find that, at least in CCSM3, changes in the local covariance structure can be explained as a function of the regional mean change in temperature and the rate of change of warming. This feature means that the statistical model can be used to emulate the evolving covariance structure of GCM temperatures under scenarios for which the GCM has not been run. When combined with an emulator for mean temperature, our methodology can simulate evolving temperatures under such scenarios, in a way that accounts for projections of changes while still retaining fidelity with the observational record. The emulator for variability changes is also of interest on its own as a summary of GCM projections of variability changes.

Insights from Earth system model initial-condition large ensembles and future prospects

“…Statistical emulation of internal variability may also be advantageous in the context of ESMs when the cost of conducting a sufficiently large LE is prohibitive; for example, in the case of models with increased spatial resolution and/or complexity (discussed further below in the sub-section titled 'Designing future initial-condition LEs'). These statistical emulation methods will need to take into account any projected changes in internal variability 60 .…”

The Value of Initial Condition Large Ensembles to Robust Adaptation Decision‐Making

“…Tools under active development like the Observational Large Ensemble (McKinnon et al, 2017; McKinnon & Deser, 2018), enable scientists to assess a large ensemble's internal variability vis‐à‐vis a comparable estimate of observed variability. While an observational large ensemble does not position an evaluation of a large ensemble's future variability, it could, in combination with estimates of nonstationary variability from a large ensemble itself (e.g., Poppick et al, 2016). Increasingly, climate scientists are combining analyses of large and multimodel ensembles in an effort to better attribute uncertainty in impacts analyses (see, e.g., https://usclivar.org/working-groups/large-ensemble-working-group).…”

A periodic mixed linear state‐space model to monthly long‐term temperature data

“…Several studies based on different types of periodicity of data have also been considered in the literature. For instance, the following works were based on daily temperature data: Poppick, McInerney, Moyer, and Stein () studied changes in the distribution of daily temperatures in an ensemble of general circulation model runs predicting changes in both means and variability, and Trewin () developed a new homogenized daily maximum and minimum temperature data set for Australia, among others (Fischer, ; Kleiber, Katz, & Rajagopalan, ; Ngo & Horton, ; Trigo & Palutikof, ; Wang et al, ).…”

“…Several studies based on different types of periodicity of data have also been considered in the literature. For instance, the following works were based on daily temperature data: Poppick, McInerney, Moyer, and Stein (2016)…”