Kuniko Yamazaki's research while affiliated with The University of Edinburgh and other places

Publications (8)

Article
A very large ensemble is used to identify subgrid-scale parameter settings for the HadCM3 model that are capable of best simulating the ocean state over the recent past (1980-2010). A simple particle filtering technique based upon the agreement of basin mean sea surface temperature (SST) and upper 700-m ocean heat content with EN3 observations is a...
Article
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This study investigates the hypothesis that a significant portion of spread in climate model projections of sea ice is due to poorly-constrained model parameters. New automated methods for optimization are applied to historical sea ice in a global coupled climate model (HadCM3) in order to calculate the combination of parameters required to reduce...
Article
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Optimisation methods were successfully used to calibrate parameters in an atmospheric component of a climate model using two variants of the Gauss–Newton line-search algorithm: (1) a standard Gauss–Newton algorithm in which, in each iteration, all parameters were perturbed and (2) a randomised block-coordinate variant in which, in each iteration, a...
Article
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Optimisation methods were successfully used to calibrate parameters in an atmospheric component of a climate model using two variants of the Gauss-Newton line-search algorithm. 1) A standard Gauss-Newton algorithm in which, in each iteration, all parameters were perturbed. 2) A randomized block-coordinate variant in which, in each iteration, a rand...
Article
We apply an established statistical methodology called history matching to constrain the parameter space of a coupled non-flux-adjusted climate model (the third Hadley Centre Climate Model; HadCM3) by using a 10,000-member perturbed physics ensemble and observational metrics. History matching uses emulators (fast statistical representations of clim...
Article
A number of studies have set out to obtain a range of atmosphere and ocean model behavior by perturbing parameters in a single climate model (perturbed physics ensemble: PPE). Early studies used shallow layer slab ocean or flux-adjusted coupled ocean-atmosphere models to obtain a broad range of behavior as characterized by climate sensitivity. A re...
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El Niño-Southern Oscillation (ENSO) amplitudes in an ensemble of a coupled global climate model (GCM) in the Climate Model Intercomparison Project Phase 5 (CMIP5) 20th-century experiment were found to have a systematic relationship with the timing at which each ensemble member had been branched off from the pre-industrial control experiment. This r...
Article
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Incomplete understanding of three aspects of the climate system-equilibrium climate sensitivity, rate of ocean heat uptake and historical aerosol forcing-and the physical processes underlying them lead to uncertainties in our assessment of the global-mean temperature evolution in the twenty-first century 1,2. Explorations of these uncertainties hav...

Citations

... The first is climate sensitivity analysis where plausible ranges of climate sensitivity are mapped through generating large perturbed parameter ensembles (e.g. (Millar et al., 2015;Rowlands et al., 2012;Sparrow et al., 2018b;Stainforth et al., 2005;Yamazaki et al., 2013). The second is simulation bias reduction methods through perturbed 55 parameter studies (e.g. ...
... In its simplest form, when S k is a scaled sampling matrix, J S can be thought of as a random subselection of columns of the full Jacobian J, which leads to variants of our framework that are Block-Coordinate Gauss-Newton (BC-GN) methods. In this case, for example, if the Jacobian were being calculated by finite-differences of the residual r, only a small number of evaluations of r along coordinate directions would be needed; such a BC-GN variant has already been used for parameter estimation in climate modelling [96]. Note that theoretically, the convergence of BC-GN method requires an upper bound on ∇f (x k ) ∞ ∇f (x k ) 2 for all k ∈ N (for more details, see the discussion of sampling matrices on page 90) and Theorem 4.5.6. ...
... To mitigate the drawbacks and computational cost of the above approaches, a number of more objective parameter calibration methods have been proposed for systematically choosing or fine-tuning the final set of optimized parameters. These methods include the use of Latin hypercube sampling techniques (Posselt et al., 2015), the downhill simplex method (Zhang et al., 2015), the multiple very fast simulated annealing (Zou et al., 2014), and Gauss-Newton line search algorithms (Tett et al., 2013(Tett et al., , 2017Roach et al., 2018). The above estimation methods require a large suite of experiments, which are computationally expensive. ...
... It is, however, at least conceptually possible to automate this process and find optimal sets of parameters with respect to certain targets. ' could be employed to find the optimal set of critical parameters in weather and climate models, as has been done with rigorous statistical inference to determine model coefficients in turbulence modelling [70] and using inverse methods and other statistical parameter estimation methods in weather and climate modelling [71][72][73]. However, uncertainties and limitations from the choice of physics-based model structure cannot be improved by ML [74]. ...
... Such problems have huge importance in engineering, e.g. in designing jet engines [2] and materials [35] where the objective can be minimizing drag or maximizing durability, and inverse parameter inference (i.e. history matching) [36,37,38] where the objective can be maximum a posteriori estimation. To solve such problem, classical methods include adjoint method [39,40], shooting method [41], collocation method [42], etc. ...
... Our use of the PPE approach is rooted in its use in many previous studies to investigate process uncertainties in GCM simulations in a controlled manner, by perturbing uncertain parameters in physical parameterisation schemes within expertly elicited ranges (Murphy et al. 2004;Stainforth et al. 2005;Collins et al. 2007;Sanderson 2011;Rowlands et al. 2012;Shiogama et al. 2012;Yamazaki et al. 2013;Irvine et al. 2013;Sexton et al. 2019). For example, the method has been widely used in attempts to constrain equilibrium climate sensitivity (ECS) based on emergent constraints. ...
... These processes have the potential to modulate zonal wind and SST anomalies through the wind-evaporation-SST feedback mechanism, which propagate extratropical influence into the tropics and further act as the precursor indicators to the subsequent ENSO events (Chiang and Vimont 2004;Chen et al. 2014;Vimont et al. 2001Vimont et al. , 2003. Recently, there has been increasing evidence to show that extratropical Pacific climate variability in the Southern Hemisphere (SH) makes an indispensable contribution to the development of ENSO, including via the extratropical SST anomalies (Ding et al. 2015b;Yamazaki and Watanabe 2015;Zhang et al. 2014), the transverse cell in southern Australia (Hong and Jin 2014), and the SH annular mode (SAM; Zheng et al. 2017). The mechanism involved is driven mainly by the extratropical seasonal SST footprints (Zhang et al. 2014;Ding et al. 2015b), like its NH counterpart, or by the response of the meridional circulation to extratropical thermal forcing (Yamazaki and Watanabe 2015;Zheng et al. 2017), both of which have the potential to propagate the extratropical influence into the tropical Pacific and regulate zonal wind and SST anomalies in the same region. ...
... high population growth, reactive environmental protection, and vulnerabilities to climate change vary regionally). For each scenario, we used 17 General Circulation Models (GCMs; Table S6) in climate change projections, and then calculated the median value of 14 bioclimatic variables (Table S5) across 17 GCMs to account for the large uncertainties between different models (Rowlands et al., 2012). ...