Climate models serve as indispensable tools to investigate the effect of anthropogenic emissions on current and future climate, including extremes. However, as low-dimensional approximations of the climate system, they will always exhibit biases. Several attempts have been made to correct for biases as they affect extremes prediction, predominantly focused on correcting model-simulated distribution shapes. In this study, the effectiveness of a recently published quantile-based bias correction scheme, as well as a new subset selection method introduced here, are tested out-of-sample using model-as-truth experiments. Results show that biases in the shape of distributions tend to persist through time, and therefore, correcting for shape bias is useful for past and future statements characterizing the probability of extremes. However, for statements characterized by a ratio of the probabilities of extremes between two periods, we find that correcting for shape bias often provides no skill improvement due to the dominating effect of bias in the long-term trend. Using a toy model experiment, we examine the relative importance of the shape of the distribution versus its position in response to long-term changes in radiative forcing. It confirms that the relative position of the two distributions, based on the trend, is at least as important as the shape. We encourage the community to consider all model biases relevant to their metric of interest when using a bias correction procedure and to construct out-of-sample tests that mirror the intended application.

Water availability is the dominant control of global terrestrial primary productivity with concurrent effects on evapotranspiration and ecosystem respiration, especially in water-limited ecosystems. Process-oriented ecosystem models are critical tools for understanding land-atmosphere exchanges and for up-scaling this information to regional and global scales. Thus, it is important to understand how ecosystem models simulate ecosystem fluxes under changing weather conditions. Here, we applied both time-series analysis and meta-analysis techniques to study how five ecosystem process-oriented models-simulated gross primary production (GPP), ecosystem respiration (Reco), and evapotranspiration (ET). Ecosystem fluxes were simulated for 3 years at a daily time step from four evergreen and three deciduous Mediterranean oak woodlands (21 site-year measurements; 105 site-year-simulations). Mediterranean ecosystems are important test-beds for studying the interannual dynamics of soil moisture on ecosystem mass and energy exchange as they experience cool, wet winters with hot, dry summers and are typically subject to drought. Results show data-model disagreements at multiple temporal scales for GPP, Reco, and ET at both plant functional types. Overall there was a systematic underestimation of the temporal variation of Reco at both plant functional types at temporal scales between weeks and months, and an overestimation at the yearly scale. Modeled Reco was systematically overestimated during drought for all sites, but daily GPP was systematically underestimated only for deciduous sites during drought. In contrast, daily estimates of ET showed good data-model agreement even during drought conditions. This meta-analysis brings attention to the importance of drought conditions for modeling purposes in representing forest dynamics in water-limited ecosystems. © 2013 Springer Science+Business Media New York.

Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.