Abstract
Observations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. Constraints for uninitialized projections are generally based on model performance in simulating climatology and climate change. For initialized predictions, skill scores over the hindcast period provide insight into the relative performance of models, and the value of initialization as compared to projections. Predictions and projections combined can, in principle, provide seamless decadal to multi-decadal climate information. For that, though, the role of observations in skill estimates and constraints needs to be understood in order to use both consistently across the prediction and projection time horizons. This paper discusses the challenges in doing so, illustrated by examples of state-of-the-art methods for predicting and projecting changes in European climate. It discusses constraints across prediction and projection methods, their interpretation, and the metrics that drive them such as process accuracy, accurate trends or high signal-to-noise ratio. We also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. To illustrate constraints on projections, we discuss their use in the UK's climate prediction system UKCP18, the case of model performance weights obtained from the Climate model Weighting by Independence and Performance (ClimWIP) method, and the estimated magnitude of the forced signal in observations from detection and attribution. For initialized predictions, skill scores are used to evaluate which models perform well, what might contribute to this performance, and how skill may vary over time. Skill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. This complicates the systematic use of observational constraints. Furthermore, we illustrate that sub-selecting simulations from large ensembles based on reproduction of the observed evolution of climate variations is a good testbed for combining projections and predictions. Finally, the methods described in this paper potentially add value to projections and predictions for users, but must be used with caution.
Highlights
Information about future climate relies on climate model simulations
Observational constraints for projections may both originate from weighting schemes that weight according to performance (Knutti et al, 2017; Sanderson et al, 2017; Lorenz et al, 2018; Brunner et al, 2020b), as well as from a binary decision which models are within an observational constraint and which outside
Constraining projections based on the agreement with the observed climate state can phase in modes of climate variability and add skill, similar to initialization in decadal predictions
Summary
Information about future climate relies on climate model simulations. Given the uncertainty in the future climate’s response to external forcings and climate models’ persistent biases, there is a need for coordinated multi-model experiments. It would be useful to consider forecast evaluation terminology used in predictions and to assess reliability (i.e., if model simulations that are synthetically predicted are within the uncertainty range of the prediction, given the statistical expectation; Schurer et al, 2018; Gillett et al, 2021), and if they show improved sharpness, i.e., their RMS error is smaller in order to avoid penalizing more confident methods unnecessarily) Another avenue is to draw perfect models from a different generation as explored, e.g., by Brunner et al (2020b) where the skill of weighting CMIP6 was explored based on models from CMIP5 in order to provide an out-of-sample test to the extent that CMIP6 can be considered independent of CMIP5. The limited overlap between models providing individually forced simulations necessary for ASK, and being used in initialized predictions makes it difficult to pursue this further
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