The Role of Internal Variability in Seasonal Hindcast Trend Errors

Abstract Initialized hindcasts inherit knowledge of the observed climate state, so studies of multidecadal trends in seasonal and decadal hindcast models have focused on the ensemble mean when benchmarking against observed trends. However, this neglects the role of short-time-scale variability in contributing to long-term trends, and hence trend errors. Using a single-model coupled hindcast ensemble, we generate a distribution of 10 000 hindcast trends over 1981–2022 by randomly sampling a single ensemble member in each year. We find that the hindcast model supports a wide range of trends in various features of the large-scale climate, even when sampled at leads of just 1–3 months following initialization. The spread in hindcast global surface temperature trends is equivalent to approximately a sixth of the total observed warming over the same period, driven by large seasonal variability of temperatures over land. The hindcasts also lend support for observed poleward jet shifts, but the magnitude of the shifts varies widely across the ensemble. Our results show that a fair comparison of hindcast trends to observations should consider the full range of model trends, not only the ensemble mean. More broadly, we argue that the hindcast trend distribution offers a largely untapped tool for studying multidecadal climate trends in a very large ensemble, through exploiting existing hindcast data. Significance Statement We show that seasonal forecast models support a wide range of long-term trends in various climate features, from global surface temperature to shifts of the jet streams. This is important because trends in these models are often compared to observed trends to test the model’s performance. However, comparisons have typically used the model ensemble mean, neglecting the contribution of short-time-scale variability to long-term trends. We argue that accounting for the full range of model trends is necessary to avoid misdiagnosing trend errors in the models, particularly for features that are sensitive to atmospheric circulation variability, such as regional trends in the extratropics.