Reconciling disparate views on decadal climate variability from proxies and models

Abstract

Introduction Researchers studying decadal variability over the instrumental period are often confronted with two major obstacles. First, the observational record is short compared to the timescales of interest, sampling at best only a few realizations of decadal-scale phenomena (Meehl et al., 2009). Second, most climate variables include long-term trends driven by human activity (e.g., land use change, aerosol pollution, and of course the impact of greenhouse gas emissions), which sometimes mask decadal variability from natural causes. The climate research community therefore often turns to both paleoclimate archives of past changes, as well as multi-century integrations of general circulation models (GCMs). Both types of data can provide insights into the amplitudes, patterns, and plausible mechanisms of internal decadal variability, which could ultimately help inform and evaluate predictions of near-term climate evolution. In principle, proxy and GCM data should yield a consistent view of the climate system on these timescales. In practice, current paleoclimate data-model comparisons of decadal variability must contend with at least one of the challenges delineated below. To address these concerns, I submit several heuristic recommendations to help to identify fundamental similarities-and critical differences-between paleoclimate and climate model perspectives on decadal variability of the last millennium. (i) Paleoclimate archives filter climate variability in ways that are difficult to quantify. Most paleoclimate archives "redden" climate information by storing information from one time period to the next (e.g., Matalas, 1962; Evans et al., 2013; Ault 2013; Dee et al., 2015). This reddening, in turn, has the effect of amplifying decadal fluctuations in proxy records relative to their climatic drivers. Consequently, the mere presence of high amplitude decadal variability in a given paleoclimate time series cannot be taken as evidence of correspondingly energetic climatic variability (the details of this effect are considered extensively in Ault et al., 2013 and also Dee et al., in revision). In addition to reddening the spectrum of underlying climate variables, many paleoclimate archives preferentially record information from certain seasons. For example, St. George et al. (2010) showed that tree-ring reconstructions of North American PDSI (Cook et al., 2004) exhibit variable seasonal sensitivity to temperature and precipitation depending on the region. In the US Southwest, for example, the PDSI is highly sensitive to winter moisture, while in the Pacific Northwest, it depends more strongly on summer temperature. These seasonal dependencies reflect, in part, the dependence of tree growth on different environmental factors during the seasonal cycle (St George and Ault, 2014), a finding consistent with basic dendroclimatological theory (Fritts, 1976). On interannual timescales, diagnosing the filtering effects of tree growth on climate input is relatively straightforward because data are annually resolved and overlap with the instrumental period. However, this problem has not been widely studied on decadal time horizons, and it remains a possibility that trees grow in response to different climate factors across timescales (e.g., Franke et al., 2013). (ii) Forward models of paleoclimate archives might be biased by spatial and temporal patterns in GCMs. Given the tendency for proxies to redden and filter climate information, one might be tempted to simply run GCM output through "forward models" of various proxy systems and compare the resulting output with actual archives. Caution would be recommended for such an approach because models themselves exhibit systematic geographic and frequency biases. Consider a case in which a forward model of tree-ring growth is run to predict annual ring-width anomalies as a function of monthly temperature and precipitation (e.g. the "Vaganov-Shashkin-Lite" model of Tolwinski-Ward et al., 2011; VS-Lite). If this model were to be run with raw output from a GCM with a wet bias (as is common for the American Southwest), VS-Lite would produce simulations where tree growth is never limited by the availability of soil moisture, even during the "driest" year. Similar considerations apply to other types of proxy systems, and although standard bias-correction techniques are available for removing systematic model errors (e.g. Maurer et al., 2007), these tools have not been widely adopted for paleoclimate model-data comparisons. (iii) Climate teleconnections are not necessarily stable through time. There are inherent biases in the structure of GCM teleconnections linking remote climate variations (e.g., in the Pacific basin) to the locations