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Article ; Online: Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal Cycle

Michael F. Howland / Oliver R. A. Dunbar / Tapio Schneider

Journal of Advances in Modeling Earth Systems, Vol 14, Iss 3, Pp n/a-n/a (2022)

2022

Abstract: Abstract Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top‐of‐atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and parameters. ...

Abstract	Abstract Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top‐of‐atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and parameters. Bayesian calibration can estimate climate model parameters and their uncertainty using a larger fraction of the available data and automatically exploring the parameter space more broadly. In Bayesian learning, it is natural to exploit the seasonal cycle, which has large amplitude compared with anthropogenic climate change in many climate statistics. In this study, we develop methods for the calibration and uncertainty quantification (UQ) of model parameters exploiting the seasonal cycle, and we demonstrate a proof‐of‐concept with an idealized general circulation model (GCM). UQ is performed using the calibrate‐emulate‐sample approach, which combines stochastic optimization and machine learning emulation to speed up Bayesian learning. The methods are demonstrated in a perfect‐model setting through the calibration and UQ of a convective parameterization in an idealized GCM with a seasonal cycle. Calibration and UQ based on seasonally averaged climate statistics, compared to annually averaged, reduces the calibration error by up to an order of magnitude and narrows the spread of the non‐Gaussian posterior distributions by factors between two and five, depending on the variables used for UQ. The reduction in the spread of the parameter posterior distribution leads to a reduction in the uncertainty of climate model predictions.
Keywords	uncertainty quantification ; Bayesian learning ; GCM ; seasonal cycle ; Physical geography ; GB3-5030 ; Oceanography ; GC1-1581
Subject code	551
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	American Geophysical Union (AGU)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Quantitative analysis of carbon impurity concentrations in GaN epilayers by cathodoluminescence

Loeto, K. / Kusch, G. / Ghosh, S. / Kappers, M.J. / Oliver, R.A.

Micron. 2023 Sept., v. 172 p.103489-

2023

Abstract: In this work, a technique for quantifying carbon doping concentrations in GaN:C/AlGaN buffer structures using cathodoluminescence (CL) is presented. The method stems from the knowledge that the blue and yellow luminescence intensity in CL spectra of GaN ... ...

Abstract	In this work, a technique for quantifying carbon doping concentrations in GaN:C/AlGaN buffer structures using cathodoluminescence (CL) is presented. The method stems from the knowledge that the blue and yellow luminescence intensity in CL spectra of GaN varies with the carbon doping concentration. By calculating the blue and yellow luminescence peak intensities normalised to the peak GaN near-band-edge intensity for GaN layers of known carbon concentrations, calibration curves that show the change in normalised blue and yellow luminescence intensity with carbon concentration in the 10¹⁶ − 10¹⁹ cm⁻³ range were derived at both room temperature and 10 K. The utility of such calibration curves was then examined by testing against an unknown sample containing multiple carbon-doped GaN layers. The results obtained from CL using the normalised blue luminescence calibration curves are in close agreement with those from secondary-ion mass spectroscopy (SIMS). However,the method fails when applying calibration curves obtained from the normalised yellow luminescence likely due to the influence of native VGₐ defects acting in this luminescence region. Although this work shows that indeed CL can be used as a quantitative tool to measure carbon doping concentrations in GaN:C, it is noted that the intrinsic broadening effects innate to CL can make it difficult to differentiate between the intensity variations in thin (< 500 nm) multilayered GaN:C structures such as the ones studied in this work.
Keywords	ambient temperature ; calibration ; carbon ; light intensity ; luminescence ; mass spectrometry ; quantitative analysis ; Cathodoluminescence ; Carbon doping level ; Gallium nitride ; High-electron-mobility-transistor
Language	English
Dates of publication	2023-09
Publishing place	Elsevier Ltd
Document type	Article ; Online
ZDB-ID	207808-9
ISSN	1878-4291 ; 0047-7206 ; 0968-4328
ISSN (online)	1878-4291
ISSN	0047-7206 ; 0968-4328
DOI	10.1016/j.micron.2023.103489
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Article ; Online: Dismantling barriers faced by women in STEM.

Jebsen, J M / Nicoll Baines, K / Oliver, R A / Jayasinghe, I

Nature chemistry

2022 Volume 14, Issue 11, Page(s) 1203–1206

MeSH term(s)	Female ; Humans ; Chemistry
Language	English
Publishing date	2022-10-27
Publishing country	England
Document type	Journal Article
ZDB-ID	2464596-5
ISSN	1755-4349 ; 1755-4330
ISSN (online)	1755-4349
ISSN	1755-4330
DOI	10.1038/s41557-022-01072-2
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Article ; Online: Quantitative analysis of carbon impurity concentrations in GaN epilayers by cathodoluminescence.

Loeto, K / Kusch, G / Ghosh, S / Kappers, M J / Oliver, R A

Micron (Oxford, England : 1993)

2023 Volume 172, Page(s) 103489

Abstract	In this work, a technique for quantifying carbon doping concentrations in GaN:C/AlGaN buffer structures using cathodoluminescence (CL) is presented. The method stems from the knowledge that the blue and yellow luminescence intensity in CL spectra of GaN varies with the carbon doping concentration. By calculating the blue and yellow luminescence peak intensities normalised to the peak GaN near-band-edge intensity for GaN layers of known carbon concentrations, calibration curves that show the change in normalised blue and yellow luminescence intensity with carbon concentration in the 10
Language	English
Publishing date	2023-06-22
Publishing country	England
Document type	Journal Article
ZDB-ID	207808-9
ISSN	1878-4291 ; 0047-7206 ; 0968-4328
ISSN (online)	1878-4291
ISSN	0047-7206 ; 0968-4328
DOI	10.1016/j.micron.2023.103489
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Book ; Online: Models for information propagation on graphs

Dunbar, Oliver R. A. / Elliott, Charles M. / Kreusser, Lisa Maria

2022

Abstract: We propose and unify classes of different models for information propagation over graphs. In a first class, propagation is modelled as a wave which emanates from a set of known nodes at an initial time, to all other unknown nodes at later times with an ... ...

Abstract	We propose and unify classes of different models for information propagation over graphs. In a first class, propagation is modelled as a wave which emanates from a set of known nodes at an initial time, to all other unknown nodes at later times with an ordering determined by the arrival time of the information wave front. A second class of models is based on the notion of a travel time along paths between nodes. The time of information propagation from an initial known set of nodes to a node is defined as the minimum of a generalised travel time over subsets of all admissible paths. A final class is given by imposing a local equation of an eikonal form at each unknown node, with boundary conditions at the known nodes. The solution value of the local equation at a node is coupled to those of neighbouring nodes with lower values. We provide precise formulations of the model classes and prove equivalences between them. Motivated by the connection between first arrival time model and the eikonal equation in the continuum setting, we derive formal limits for graphs based on uniform grids in Euclidean space under grid refinement. For a specific parameter setting, we demonstrate that the solution on the grid approximates the Euclidean distance, and illustrate the use of front propagation on graphs to trust networks and semi-supervised learning.
Keywords	Mathematics - Numerical Analysis ; Computer Science - Machine Learning ; Computer Science - Social and Information Networks ; Mathematics - Analysis of PDEs
Subject code	000
Publishing date	2022-01-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM

Oliver R. A. Dunbar / Alfredo Garbuno‐Inigo / Tapio Schneider / Andrew M. Stuart

Journal of Advances in Modeling Earth Systems, Vol 13, Iss 9, Pp n/a-n/a (2021)

2021

Abstract: Abstract Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification of uncertainties. Traditional Bayesian calibration methods that allow ... ...

Abstract	Abstract Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification of uncertainties. Traditional Bayesian calibration methods that allow uncertainty quantification are too expensive for climate models; they are also not robust in the presence of internal climate variability. For example, Markov chain Monte Carlo (MCMC) methods typically require O(105) model runs and are sensitive to internal variability noise, rendering them infeasible for climate models. Here we demonstrate an approach to model calibration and uncertainty quantification that requires only O(102) model runs and can accommodate internal climate variability. The approach consists of three stages: (a) a calibration stage uses variants of ensemble Kalman inversion to calibrate a model by minimizing mismatches between model and data statistics; (b) an emulation stage emulates the parameter‐to‐data map with Gaussian processes (GP), using the model runs in the calibration stage for training; (c) a sampling stage approximates the Bayesian posterior distributions by sampling the GP emulator with MCMC. We demonstrate the feasibility and computational efficiency of this calibrate‐emulate‐sample (CES) approach in a perfect‐model setting. Using an idealized general circulation model, we estimate parameters in a simple convection scheme from synthetic data generated with the model. The CES approach generates probability distributions of the parameters that are good approximations of the Bayesian posteriors, at a fraction of the computational cost usually required to obtain them. Sampling from this approximate posterior allows the generation of climate predictions with quantified parametric uncertainties.
Keywords	uncertainty quantification ; model calibration ; machine learning ; general circulation model ; parametric uncertainty ; inverse problem ; Physical geography ; GB3-5030 ; Oceanography ; GC1-1581
Subject code	310
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	American Geophysical Union (AGU)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Parameter uncertainty quantification in an idealized GCM with a seasonal cycle

Howland, Michael F. / Dunbar, Oliver R. A. / Schneider, Tapio

2021

Abstract: Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top-of-atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and parameters. ... ...

Abstract	Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top-of-atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and parameters. Bayesian calibration can estimate climate model parameters and their uncertainty using a larger fraction of the available data and automatically exploring the parameter space more broadly. In Bayesian learning, it is natural to exploit the seasonal cycle, which has large amplitude, compared with anthropogenic climate change, in many climate statistics. In this study, we develop methods for the calibration and uncertainty quantification (UQ) of model parameters exploiting the seasonal cycle, and we demonstrate a proof-of-concept with an idealized general circulation model (GCM). Uncertainty quantification is performed using the calibrate-emulate-sample approach, which combines stochastic optimization and machine learning emulation to speed up Bayesian learning. The methods are demonstrated in a perfect-model setting through the calibration and UQ of a convective parameterization in an idealized GCM with a seasonal cycle. Calibration and UQ based on seasonally averaged climate statistics, compared to annually averaged, reduces the calibration error by up to an order of magnitude and narrows the spread of posterior distributions by factors between two and five, depending on the variables used for UQ. The reduction in the size of the parameter posterior distributions leads to a reduction in the uncertainty of climate model predictions. Comment: 21 pages, 11 figures, 2 tables
Keywords	Physics - Atmospheric and Oceanic Physics ; Physics - Data Analysis ; Statistics and Probability
Subject code	310 ; 551
Publishing date	2021-07-22
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Directly correlated microscopy of trench defects in InGaN quantum wells.

O'Hanlon, T J / Massabuau, F C-P / Bao, A / Kappers, M J / Oliver, R A

Ultramicroscopy

2021 Volume 231, Page(s) 113255

Abstract: Directly correlated measurements of the surface morphology, light emission and subsurface structure and composition were carried out on the exact same nanoscale trench defects in InGaN quantum well (QW) structures. Multiple scanning probe, scanning ... ...

Abstract	Directly correlated measurements of the surface morphology, light emission and subsurface structure and composition were carried out on the exact same nanoscale trench defects in InGaN quantum well (QW) structures. Multiple scanning probe, scanning electron and transmission electron microscopy techniques were used to explain the origin of their unusual emission behaviour and the relationship between surface morphology and cathodoluminescence (CL) redshift. Trench defects comprise of an open trench partially or fully enclosing material in InGaN QWs with different CL emission properties to their surroundings. The CL redshift was shown to typically vary with the width of the trench and the prominence of the material enclosed by the trench above its surroundings. Three defects, encompassing typical and atypical features, were prepared into lamellae for transmission electron microscopy (TEM). A cross marker technique was used in the focused ion beam-scanning electron microscope (FIB-SEM) to centre the previously characterised defects in each lamella for further analysis. The defects with wider trenches and strong redshifts in CL emission had their initiating basal-plane stacking fault (BSF) towards the bottom of the QW stack, while the BSF formed near the top of the QW stack for a defect with a narrow trench and minimal redshift. The raised-centre, prominent defect showed a slight increase in QW thickness moving up the QW stack while QW widths in the level-centred defect remained broadly constant. The indium content of the enclosed QWs increased above the BSF positions up to a maximum, with an increase of approximately 4% relative to the surroundings seen for one defect examined. Gross fluctuations in QW width (GWWFs) were present in the surrounding material in this sample but were not seen in QWs enclosed by the defect volumes. These GWWFs have been linked with indium loss from surface step edges two or more monolayers high, and many surface step edges appear pinned by the open trenches, suggesting another reason for the higher indium content seen in QWs enclosed by trench defects.
Language	English
Publishing date	2021-03-11
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1479043-9
ISSN	1879-2723 ; 0304-3991
ISSN (online)	1879-2723
ISSN	0304-3991
DOI	10.1016/j.ultramic.2021.113255
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Article ; Online: Dislocations at coalescence boundaries in heteroepitaxial GaN/sapphire studied after the epitaxial layer has completely coalesced.

O'Hanlon, T J / Zhu, T / Massabuau, F C-P / Oliver, R A

Ultramicroscopy

2021 Volume 231, Page(s) 113258

Abstract: We have performed cross-sectional scanning capacitance microscopy (SCM), cathodoluminescence (CL) microscopy in the scanning electron microscope (SEM) and transmission electron microscopy (TEM) all on the same few-micron region of a GaN/sapphire sample. ... ...

Abstract	We have performed cross-sectional scanning capacitance microscopy (SCM), cathodoluminescence (CL) microscopy in the scanning electron microscope (SEM) and transmission electron microscopy (TEM) all on the same few-micron region of a GaN/sapphire sample. To achieve this, it was necessary to develop a process flow which allowed the same features viewed in a cleaved cross-section to be traced from one microscope to the next and to adapt the focused ion beam preparation of the TEM lamella to allow preparation of a site-specific sample on a pre-cleaved cross-section. Growth of our GaN/sapphire samples involved coalescence of three-dimensional islands to form a continuous film. Highly doped marker layers were included in the sample so that coalescence boundaries formed late in the film growth process could be identified in SCM and CL. Using TEM, we then identified one or more dislocations associated with each of several such late-coalescing boundaries. In contrast, previous studies have addressed coalescence boundaries formed earlier in the growth process and have shown that early-stage island coalescence does not lead to dislocation formation.
Language	English
Publishing date	2021-03-11
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1479043-9
ISSN	1879-2723 ; 0304-3991
ISSN (online)	1879-2723
ISSN	0304-3991
DOI	10.1016/j.ultramic.2021.113258
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Article ; Online: Application of highly silicon-doped marker layers in the investigation of unintentional doping in GaN on sapphire.

Oliver, R A

Ultramicroscopy

2010 Volume 111, Issue 1, Page(s) 73–78

Abstract: To provide a route to the assessment of the impact of inclined facets on unintentional n-type doping during the growth of c-plane GaN on sapphire, thin (100 nm), highly Si-doped (at 10¹⁹ cm⁻³) marker layers have been incorporated into a GaN epitaxial ... ...

Abstract	To provide a route to the assessment of the impact of inclined facets on unintentional n-type doping during the growth of c-plane GaN on sapphire, thin (100 nm), highly Si-doped (at 10¹⁹ cm⁻³) marker layers have been incorporated into a GaN epitaxial layer grown by a method involving a transition from initial three-dimensional island growth to later, two-dimensional, planar growth. Imaging of the completed epitaxial layer in cross-section by scanning capacitance microscopy reveals the shapes of the islands, which were present during the early stages of growth and the relationship between the facets present and the incorporation of unintentional dopants. The results show that unintentional dopants are mostly incorporated on facets inclined to the [0001] direction, and suggest that gaseous impurities present in the MOVPE reactor are one source of dopant species.
Language	English
Publishing date	2010-12
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1479043-9
ISSN	1879-2723 ; 0304-3991
ISSN (online)	1879-2723
ISSN	0304-3991
DOI	10.1016/j.ultramic.2010.10.008
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