The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.

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We describe the method of history matching, a method currently used to help quantify parametric uncertainty in climate models, and argue for its use in identifying and removing structural biases in climate models at the model development stage. We illustrate the method using an investigation of the potential to improve upon known ocean circulation biases in a coupled non-flux-adjusted climate model (the third Hadley Centre Climate Model; HadCM3). In particular, we use history matching to investigate whether or not the behaviour of the Antarctic Circumpolar Current (ACC), which is known to be too strong in HadCM3, represents a structural bias that could be corrected using the model parameters. We find that it is possible to improve the ACC strength using the parameters and observe that doing this leads to more realistic representations of the sub-polar and sub-tropical gyres, sea surface salinities (both globally and in the North Atlantic), sea surface temperatures in the sinking regions in the North Atlantic and in the Southern Ocean, North Atlantic Deep Water flows, global precipitation, wind fields and sea level pressure. We then use history matching to locate a region of parameter space predicted not to contain structural biases for ACC and SSTs that is around 1% of the original parameter space. We explore qualitative features of this space and show that certain key ocean and atmosphere parameters must be tuned carefully together in order to locate climates that satisfy our chosen metrics. Our study shows that attempts to tune climate model parameters that vary only a handful of parameters relevant to a given process at a time will not be as successful or as efficient as history matching.

Abstract. In this paper we discuss climate model tuning and present an iterative automatic tuning method from the statistical science literature. The method, which we refer to here as iterative refocussing (though also known as history matching), avoids many of the common pitfalls of automatic tuning procedures that are based on optimisation of a cost function, principally the over-tuning of a climate model due to using only partial observations. This avoidance comes by seeking to rule out parameter choices that we are confident could not reproduce the observations, rather than seeking the model that is closest to them (a procedure that risks over-tuning). We comment on the state of climate model tuning and illustrate our approach through three waves of iterative refocussing of the NEMO (Nucleus for European Modelling of the Ocean) ORCA2 global ocean model run at 2° resolution. We show how at certain depths the anomalies of global mean temperature and salinity in a standard configuration of the model exceeds 10 standard deviations away from observations and show the extent to which this can be alleviated by iterative refocussing without compromising model performance spatially. We show how model improvements can be achieved by simultaneously perturbing multiple parameters, and illustrate the potential of using low-resolution ensembles to tune NEMO ORCA configurations at higher resolutions.

The calibration of complex computer codes using uncertainty quantification (UQ) methods is a rich area of statistical methodological development. When applying these techniques to simulators with spatial output, it is now standard to use principal component decomposition to reduce the dimensions of the outputs in order to allow Gaussian process emulators to predict the output for calibration. We introduce the 'terminal case', in which the model cannot reproduce observations to within model discrepancy, and for which standard calibration methods in UQ fail to give sensible results. We show that even when there is no such issue with the model, the standard decomposition on the outputs can and usually does lead to a terminal case analysis. We present a simple test to allow a practitioner to establish whether their experiment will result in a terminal case analysis, and a methodology for defining calibration-optimal bases that avoid this whenever it is not inevitable. We present the optimal rotation algorithm for doing this, and demonstrate its efficacy for an idealised example for which the usual principal component methods fail. We apply these ideas to the CanAM4 model to demonstrate the terminal case issue arising for climate models. We discuss climate model tuning and the estimation of model discrepancy within this context, and show how the optimal rotation algorithm can be used in developing practical climate model tuning tools. * The authors gratefully acknowledge support from EPSRC fellowship No. EP/K019112/1 and support from the NSERC funded Canadian Network for Regional Climate and Weather Processes (CNRCWP). We would also like to thank Yanjun Jiao for managing our ensembles of CanAM4.

The rapid emergence of SARS-CoV-2 mutants with new phenotypic properties is a critical challenge to the control of the ongoing pandemic. B.1.1.7 was monitored in the UK through routine testing and S-gene target failures (SGTF), comprising over 90% of cases by March 2021. Now, the reverse is occurring: SGTF cases are being replaced by an S-gene positive variant, which we associate with B.1.617.2. Evidence from the characteristics of S-gene positive cases demonstrates that, following importation, B.1.617.2 is transmitted locally, growing at a rate higher than B.1.1.7 and a doubling time between 5-14 days. S-gene positive cases should be prioritised for sequencing and aggressive control in any countries in which this variant is newly detected.One-Sentence SummaryThe B.1.617.2 variant of SARS-CoV-2 is replacing B.1.1.7 and emerging as the dominant variant in England, evidenced by sustained local transmission.

The Notch-signaling pathway is normally activated by Notch-ligand interactions. A recent structural analysis suggested that a novel -linked hexose modification on serine 435 of the mammalian NOTCH1 core ligand-binding domain lies at the interface with its ligands. This serine occurs between conserved cysteines 3 and 4 of Epidermal Growth Factor-like (EGF) repeat 11 of NOTCH1, a site distinct from those modified by protein-glucosyltransferase 1 (POGLUT1), suggesting that a different enzyme is responsible. Here, we identify two novel protein -glucosyltransferases, POGLUT2 and POGLUT3 (formerly KDELC1 and KDELC2, respectively), which transfer-glucose (-Glc) from UDP-Glc to serine 435. Mass spectrometric analysis of NOTCH1 produced in HEK293T cells lacking ,, or both genes showed that either POGLUT2 or POGLUT3 can add this novel -Glc modification. EGF11 of NOTCH2 does not have a serine residue in the same location for this-glucosylation, but EGF10 of NOTCH3 (homologous to EGF11 in NOTCH1 and -2) is also modified at the same position. Comparison of the sites suggests a consensus sequence for modification. In vitro assays with POGLUT2 and POGLUT3 showed that both enzymes modified only properly folded EGF repeats and displayed distinct acceptor specificities toward NOTCH1 EGF11 and NOTCH3 EGF10. Mutation of the -Glc modification site on EGF11 (serine 435) in combination with sensitizing-fucose mutations in EGF8 or EGF12 affected cell-surface presentation of NOTCH1 or reduced activation of NOTCH1 by Delta-like1, respectively. This study identifies a previously undescribed mechanism for fine-tuning the Notch-signaling pathway in mammals.

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