@article{fdi:010087361,
  title = {{T}oward consistent observational constraints in climate predictions and projections},
  author = {{H}egerl, {G}.{C}. and {B}allinger, {A}.{P}. and {B}ooth, {B}.{B}.{B}. and {B}orchert, {L}.{F}. and {B}runner, {L}. and {D}onat, {M}.{G}. and {D}oblas-{R}eyes, {F}.{J}. and {H}arris, {G}.{R}. and {L}owe, {J}. and {M}ahmood, {R}. and {M}ignot, {J}uliette and {M}urphy, {J}.{M}. and {S}wingedouw, {D}. and {W}eisheimer, {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{O}bservations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. {C}onstraints for uninitialized projections are generally based on model performance in simulating climatology and 





climate change. {F}or 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. {P}redictions and projections combined can, 





in principle, provide seamless decadal to multi-decadal climate information. {F}or 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. 





{T}his paper discusses the challenges in doing so, illustrated by examples of state-of-the-art methods for predicting and projecting changes in {E}uropean climate. {I}t 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. {W}e also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. {T}o illustrate constraints on projections, we discuss their use in the {UK}'s climate prediction system {UKCP}18, the case of model performance weights obtained from the {C}limate model {W}eighting by {I}ndependence and {P}erformance ({C}lim{WIP}) method, and the estimated magnitude of the forced signal in observations from detection and attribution. {F}or 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. {S}kill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. {T}his complicates the systematic use of observational constraints. {F}urthermore, 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. {F}inally, the methods described in this paper potentially add value to projections and predictions for users, but must be used with caution},
  keywords = {{EUROPE}},
  booktitle = {},
  journal = {{F}rontiers in {C}limate},
  volume = {3},
  numero = {},
  pages = {678109 [22 ]},
  ISSN = {2624-9553},
  year = {2021},
  DOI = {10.3389/fclim.2021.678109},
  URL = {https://www.documentation.ird.fr/hor/fdi:010087361},
}