scholarly journals Review of “Atmospheric methane source and sink sensitivity analysis using Gaussian process emulation”

2020 ◽  
Author(s):  
Anonymous
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Process ◽  
Atmospheric Methane ◽  
Methane Source ◽  
Gaussian Process Emulation ◽  
Source And Sink

scholarly journals Atmospheric methane source and sink sensitivity analysis using Gaussian process emulation

2020 ◽  
Author(s):  
Angharad C. Stell ◽  
Luke M. Western ◽  
Matthew Rigby
Keyword(s):  
Gaussian Process ◽  
Mole Fraction ◽  
Transport Model ◽  
Chemical Transport ◽  
Inverse Modelling ◽  
Atmospheric Methane ◽  
Chemical Transport Model ◽  
Gaussian Process Emulation ◽  
Source And Sink ◽  
Inputs And Outputs

Abstract. We present a method to efficiently approximate the response of atmospheric methane mole fraction and δ13C-CH4 to changes in uncertain emission and loss parameters in a three-dimensional global chemical transport model. Our approach, based on Gaussian process emulation, allows relationships between inputs and outputs in the model to be efficiently explored. The presented emulator successfully reproduces the chemical transport model output with a root-mean-square error of 1.2 ppb and 0.06 ‰ for hemispheric methane mole fraction and δ13C-CH4, respectively, for 28 uncertain model inputs. The method is shown to outperform multiple linear regression, because it captures non-linear relationships between inputs and outputs, as well as the interaction between model input parameters. The emulator was used to determine how sensitive methane mole fraction and δ13C-CH4 are to the major source and sink components of the atmospheric budget, given current estimates of their uncertainty. We find that our current knowledge of the methane budget, as inferred through hemispheric mole fraction observations, is limited primarily by uncertainty in the global mean hydroxyl radical concentration and emissions from fresh water. Our work quantitatively determines the added value of measurements of δ13C-CH4, which are sensitive to some uncertain parameters that mole fraction observations on their own are not. However, we demonstrate the critical importance of constraining isotopic initial conditions and isotopic source signatures, small uncertainties in which strongly influence long-term δ13C-CH4 trends, because of the long timescales over which transient perturbations propagate through the atmosphere. Our results also demonstrate that the magnitude and trend of methane mole fraction and δ13C-CH4 can be strongly influenced by the combined uncertainty of more minor components of the atmospheric budget, which are often fixed and assumed to be well-known in inverse modelling studies (e.g. emissions from termites, hydrates, and oceans). Overall, our work provides an overview of the sensitivity of atmospheric observations to budget uncertainties and outlines a method which could be employed to account for these uncertainties in future inverse modelling systems.

scholarly journals Atmospheric-methane source and sink sensitivity analysis using Gaussian process emulation

2021 ◽  
Vol 21 (3) ◽  
pp. 1717-1736
Author(s):  
Angharad C. Stell ◽  
Luke M. Western ◽  
Tomás Sherwen ◽  
Matthew Rigby
Keyword(s):  
Gaussian Process ◽  
Mole Fraction ◽  
Transport Model ◽  
Chemical Transport ◽  
Inverse Modelling ◽  
Atmospheric Methane ◽  
Chemical Transport Model ◽  
Gaussian Process Emulation ◽  
Source And Sink ◽  
Inputs And Outputs

Abstract. We present a method to efficiently approximate the response of atmospheric-methane mole fraction and δ13C–CH4 to changes in uncertain emission and loss parameters in a three-dimensional global chemical transport model. Our approach, based on Gaussian process emulation, allows relationships between inputs and outputs in the model to be efficiently explored. The presented emulator successfully reproduces the chemical transport model output with a root-mean-square error of 1.0 ppb and 0.05 ‰ for hemispheric-methane mole fraction and δ13C–CH4, respectively, for 28 uncertain model inputs. The method is shown to outperform multiple linear regression because it captures non-linear relationships between inputs and outputs as well as the interaction between model input parameters. The emulator was used to determine how sensitive methane mole fraction and δ13C–CH4 are to the major source and sink components of the atmospheric budget given current estimates of their uncertainty. We find that our current knowledge of the methane budget, as inferred through hemispheric mole fraction observations, is limited primarily by uncertainty in the global mean hydroxyl radical concentration and freshwater emissions. Our work quantitatively determines the added value of measurements of δ13C–CH4, which are sensitive to some uncertain parameters to which mole fraction observations on their own are not. However, we demonstrate the critical importance of constraining isotopic initial conditions and isotopic source signatures, small uncertainties in which strongly influence long-term δ13C–CH4 trends because of the long timescales over which transient perturbations propagate through the atmosphere. Our results also demonstrate that the magnitude and trend of methane mole fraction and δ13C–CH4 can be strongly influenced by the combined uncertainty in more minor components of the atmospheric budget, which are often fixed and assumed to be well-known in inverse-modelling studies (e.g. emissions from termites, hydrates, and oceans). Overall, our work provides an overview of the sensitivity of atmospheric observations to budget uncertainties and outlines a method which could be employed to account for these uncertainties in future inverse-modelling systems.

scholarly journals Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters

2011 ◽  
Vol 11 (7) ◽  
pp. 20433-20485 ◽  
Author(s):  
L. A. Lee ◽  
K. S. Carslaw ◽  
K. Pringle ◽  
G. W. Mann ◽  
D. V. Spracklen
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Process ◽  
Interaction Effects ◽  
Model Parameters ◽  
Uncertain Parameters ◽  
Model Output ◽  
Aerosol Model ◽  
Uncertainty Space ◽  
Gaussian Process Emulation ◽  
Gaussian Process Emulator

Abstract. Sensitivity analysis of atmospheric models is necessary to identify the processes that lead to uncertainty in model predictions, to help understand model diversity, and to prioritise research. Assessing the effect of parameter uncertainty in complex models is challenging and often limited by CPU constraints. Here we present a cost-effective application of variance-based sensitivity analysis to quantify the sensitivity of a 3-D global aerosol model to uncertain parameters. A Gaussian process emulator is used to estimate the model output across multi-dimensional parameter space using information from a small number of model runs at points chosen using a Latin hypercube space-filling design. Gaussian process emulation is a Bayesian approach that uses information from the model runs along with some prior assumptions about the model behaviour to predict model output everywhere in the uncertainty space. We use the Gaussian process emulator to calculate the percentage of expected output variance explained by uncertainty in global aerosol model parameters and their interactions. To demonstrate the technique, we show examples of cloud condensation nuclei (CCN) sensitivity to 8 model parameters in polluted and remote marine environments as a function of altitude. In the polluted environment 95 % of the variance of CCN concentration is described by uncertainty in the 8 parameters (excluding their interaction effects) and is dominated by the uncertainty in the sulphur emissions, which explains 80 % of the variance. However, in the remote region parameter interaction effects become important, accounting for up to 40 % of the total variance. Some parameters are shown to have a negligible individual effect but a substantial interaction effect. Such sensitivities would not be detected in the commonly used single parameter perturbation experiments, which would therefore underpredict total uncertainty. Gaussian process emulation is shown to be an efficient and useful technique for quantifying parameter sensitivity in complex global atmospheric model.

scholarly journals Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters

2011 ◽  
Vol 11 (23) ◽  
pp. 12253-12273 ◽  
Author(s):  
L. A. Lee ◽  
K. S. Carslaw ◽  
K. J. Pringle ◽  
G. W. Mann ◽  
D. V. Spracklen
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Process ◽  
Interaction Effects ◽  
Model Parameters ◽  
Uncertain Parameters ◽  
Model Output ◽  
Atmospheric Models ◽  
Aerosol Model ◽  
Gaussian Process Emulation ◽  
Gaussian Process Emulator

Abstract. Sensitivity analysis of atmospheric models is necessary to identify the processes that lead to uncertainty in model predictions, to help understand model diversity through comparison of driving processes, and to prioritise research. Assessing the effect of parameter uncertainty in complex models is challenging and often limited by CPU constraints. Here we present a cost-effective application of variance-based sensitivity analysis to quantify the sensitivity of a 3-D global aerosol model to uncertain parameters. A Gaussian process emulator is used to estimate the model output across multi-dimensional parameter space, using information from a small number of model runs at points chosen using a Latin hypercube space-filling design. Gaussian process emulation is a Bayesian approach that uses information from the model runs along with some prior assumptions about the model behaviour to predict model output everywhere in the uncertainty space. We use the Gaussian process emulator to calculate the percentage of expected output variance explained by uncertainty in global aerosol model parameters and their interactions. To demonstrate the technique, we show examples of cloud condensation nuclei (CCN) sensitivity to 8 model parameters in polluted and remote marine environments as a function of altitude. In the polluted environment 95 % of the variance of CCN concentration is described by uncertainty in the 8 parameters (excluding their interaction effects) and is dominated by the uncertainty in the sulphur emissions, which explains 80 % of the variance. However, in the remote region parameter interaction effects become important, accounting for up to 40 % of the total variance. Some parameters are shown to have a negligible individual effect but a substantial interaction effect. Such sensitivities would not be detected in the commonly used single parameter perturbation experiments, which would therefore underpredict total uncertainty. Gaussian process emulation is shown to be an efficient and useful technique for quantifying parameter sensitivity in complex global atmospheric models.

Gaussian Process emulation of multi-model ice sheet and glacier projections

2021 ◽  
Author(s):  
Tamsin Edwards ◽  
Keyword(s):  
Gaussian Process ◽  
Sea Level Rise ◽  
Sea Level ◽  
Climate Models ◽  
Ice Sheet ◽  
Surface Air Temperature ◽  
Model Parameters ◽  
Gaussian Process Emulation ◽  
Model Ensembles ◽  
Global Mean

<p><strong>The land ice contribution to global mean sea level rise has not yet been predicted with ice sheet and glacier models for the latest set of socio-economic scenarios (SSPs), nor with coordinated exploration of uncertainties arising from the various computer models involved. Two recent international projects (ISMIP6 and GlacierMIP) generated a large suite of projections using multiple models, but mostly used previous generation scenarios and climate models, and could not fully explore known uncertainties. </strong></p><p><strong>Here we estimate probability distributions for these projections for the SSPs using Gaussian Process emulation of the ice sheet and glacier model ensembles. We model the sea level contribution as a function of global mean surface air temperature forcing and (for the ice sheets) model parameters, with the 'nugget' allowing for multi-model structural uncertainty. Approximate independence of ice sheet and glacier models is assumed, because a given model responds very differently under different setups (such as initialisation). </strong></p><p><strong>We find that limiting global warming to 1.5</strong>°<strong>C </strong><strong>would halve the land ice contribution to 21<sup>st</sup> century </strong><strong>sea level rise</strong><strong>, relative to current emissions pledges: t</strong><strong>he median decreases from 25 to 13 cm sea level equivalent (SLE) by 2100. However, the Antarctic contribution does not show a clear response to emissions scenario, due to competing processes of increasing ice loss and snowfall accumulation in a warming climate. </strong></p><p><strong>However, under risk-averse (pessimistic) assumptions for climate and Antarctic ice sheet model selection and ice sheet model parameter values, Antarctic ice loss could be five times higher, increasing the median land ice contribution to 42 cm SLE under current policies and pledges, with the 95<sup>th</sup> percentile exceeding half a metre even under 1.5</strong>°<strong>C warming. </strong></p><p><strong>Gaussian Process emulation can therefore be a powerful tool for estimating probability density functions from multi-model ensembles and testing the sensitivity of the results to assumptions.</strong></p>

scholarly journals The Use of Radiocarbon Measurements in Atmospheric Studies

Radiocarbon ◽  
1990 ◽  
Vol 32 (1) ◽  
pp. 37-58 ◽  
Author(s):  
M R Manning ◽  
D C Lowe ◽  
W H Melhuish ◽  
R J Sparks ◽  
Gavin Wallace ◽  
...  
Keyword(s):  
Nuclear Power ◽  
Circulation Model ◽  
Current Data ◽  
Atmospheric Methane ◽  
Global Circulation Model ◽  
Fossil Carbon ◽  
Global Circulation ◽  
Methane Source ◽  
Clean Air ◽  
Source Current

14C measured in trace gases in clean air helps to determine the sources of such gases, their long-range transport in the atmosphere, and their exchange with other carbon cycle reservoirs. In order to separate sources, transport and exchange, it is necessary to interpret measurements using models of these processes. We present atmospheric 14CO2 measurements made in New Zealand since 1954 and at various Pacific Ocean sites for shorter periods. We analyze these for latitudinal and seasonal variation, the latter being consistent with a seasonally varying exchange rate between the stratosphere and troposphere. The observed seasonal cycle does not agree with that predicted by a zonally averaged global circulation model. We discuss recent accelerator mass spectrometry measurements of atmospheric 14CH4 and the problems involved in determining the fossil fuel methane source. Current data imply a fossil carbon contribution of ca 25%, and the major sources of uncertainty in this number are the uncertainty in the nuclear power source of 14CH4, and in the measured value for δ14C in atmospheric methane.

scholarly journals δ 13 C methane source signatures from tropical wetland and rice field emissions

2021 ◽  
Vol 380 (2215) ◽  
Author(s):  
James L. France ◽  
Rebecca E. Fisher ◽  
David Lowry ◽  
Grant Allen ◽  
Marcos F. Andrade ◽  
...  
Keyword(s):  
Rice Field ◽  
Ambient Air ◽  
Atmospheric Methane ◽  
Isotopic Signatures ◽  
Methane Budget ◽  
Significant Seasonal Variation ◽  
Methane Source ◽  
Wetland Extent ◽  
Term Monitoring

The atmospheric methane (CH 4 ) burden is rising sharply, but the causes are still not well understood. One factor of uncertainty is the importance of tropical CH 4 emissions into the global mix. Isotopic signatures of major sources remain poorly constrained, despite their usefulness in constraining the global methane budget. Here, a collection of new δ 13 C CH 4 signatures is presented for a range of tropical wetlands and rice fields determined from air samples collected during campaigns from 2016 to 2020. Long-term monitoring of δ 13 C CH 4 in ambient air has been conducted at the Chacaltaya observatory, Bolivia and Southern Botswana. Both long-term records are dominated by biogenic CH 4 sources, with isotopic signatures expected from wetland sources. From the longer-term Bolivian record, a seasonal isotopic shift is observed corresponding to wetland extent suggesting that there is input of relatively isotopically light CH 4 to the atmosphere during periods of reduced wetland extent. This new data expands the geographical extent and range of measurements of tropical wetland and rice δ 13 C CH 4 sources and hints at significant seasonal variation in tropical wetland δ 13 C CH 4 signatures which may be important to capture in future global and regional models. This article is part of a discussion meeting issue ‘Rising methane: is warming feeding warming? (part 2)’.

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