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Results from the fully and biogeochemically coupled simulations in which CO2 increases at a rate of 1%yr-1 (1pctCO2) from its preindustrial value are analyzed to quantify the magnitude of carbon-concentration and carbon-climate feedback parameters which measure the response of ocean and terrestrial carbon pools to changes in atmospheric CO2 concentration and the resulting change in global climate, respectively. The results are based on 11 comprehensive Earth system models from the most recent uncertain over land than over ocean as has been seen in existing studies. These values and their spread from 11 CMIP6 models have not changed significantly compared to CMIP5 models. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The transient climate response to cumulative emissions (TCRE) from the 11 CMIP6 models considered here is 1.77±0.37 ° C EgC-1 and is similar to that found in CMIP5 models (1.63±0.48 °C EgC-1) but with somewhat reduced model spread. The expressions for feedback parameters based on the fully and biogeochemically coupled configurations of the 1pctCO2 simulation are simplified when the small temperature change in the biogeochemically coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters is used to gain insight into the reasons for differing responses among ocean and land carbon cycle models.

The ability to predict NO2 concentrations ([NO2]) within urban street networks is important for the evaluation of strategies to reduce exposure to NO2. However, models aiming to make such predictions involve the coupling of several complex processes: traffic emissions under different levels of congestion; dispersion via turbulent mixing; chemical processes of relevance at the street-scale. Parameterisations of these processes are challenging to quantify with precision. Predictions are therefore subject to uncertainties which should be taken into account when using models within decision making. This paper presents an analysis of mean [NO2] predictions from such a complex modelling system applied to a street canyon within the city of York, UK including the treatment of model uncertainties and their causes. The model system consists of a micro-scale traffic simulation and emissions model, and a Reynolds averaged turbulent flow model coupled to a reactive Lagrangian particle dispersion model. The analysis focuses on the sensitivity of predicted in-street increments of [NO2] at different locations in the street to uncertainties in the model inputs. These include physical characteristics such as background wind direction, temperature and background ozone concentrations; traffic parameters such as overall demand and primary NO2 fraction; as well as model parameterisations such as roughness lengths, turbulent time- and length-scales and chemical reaction rate coefficients. Predicted [NO2] is shown to be relatively robust with respect to model parameterisations, although there are significant sensitivities to the activation energy for the reaction NO + O3 as well as the canyon wall roughness length. Under off-peak traffic conditions, demand is the key traffic parameter. Under peak conditions where the network saturates, road-side [NO2] is relatively insensitive to changes in demand and more sensitive to the primary NO2 fraction. The most important physical parameter was found to be the background wind direction. The study highlights the key parameters required for reliable [NO2] estimations suggesting that accurate reference measurements for wind direction should be a critical part of air quality assessments for in-street locations. It also highlights the importance of street scale chemical processes in forming road-side [NO2], particularly for regions of high NOx emissions such as close to traffic queues. [ABSTRACT FROM AUTHOR]

Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of groundbased observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimise the uncertainty on the flux estimates for methane (CO4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to five new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CO4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that, while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size. [ABSTRACT FROM AUTHOR]

Earth System Models (ESMs) that incorporate carbon-climate feedbacks represent the present state of the art in climate modelling. Here, we describe the Australian Community Climate and Earth System Simulator (ACCESS)-ESM1 that combines existing ocean and land carbon models into the physical climate model to simulate exchanges of carbon between the land, atmosphere and ocean. The land carbon model can optionally include both nitrogen and phosphorous limitation on the land carbon uptake. The ocean carbon model simulates the evolution of nitrate, oxygen, dissolved inorganic carbon, alkalinity and iron with one class of phytoplankton and zooplankton. From two multi-centennial simulations of the pre-industrial period with different land carbon model configurations, we evaluate the equilibration of the carbon cycle and present the spatial and temporal variability in key carbon exchanges. For the land carbon cycle, leaf area index is simulated reasonably, and seasonal carbon exchange is well represented. Interannual variations of land carbon exchange are relatively large, driven by variability in precipitation and temperature. We find that the response of the ocean carbon cycle shows reasonable agreement with observations and very good agreement with existing Coupled Model Intercomparison Project (CMIP5) models. While our model over estimates surface nitrate values, the primary productivity agrees well with observations. Our analysis highlights some deficiencies inherent in the carbon models and where the carbon simulation is negatively impacted by known biases in the underlying physical model. We conclude the study with a brief discussion of key developments required to further improve the realism of our model simulation. [ABSTRACT FROM AUTHOR]

tmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground based observing stations that supply these measurements can be determined by network design. 5 Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimize the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined net10 work using multiple objectives. Optimal networks are generated by adding up to 5 new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically 15 productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. 20 Our results suggest that while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size. [ABSTRACT FROM AUTHOR]

This is the second part of a two-part paper considering a measurement network design based on a stochastic Lagrangian particle dispersion model (LPDM) developed by Marek Uliasz, in this case for South Africa. A sensitivity analysis was performed for different specifications of the network design parameters which were applied to this South African test case. The LPDM, which can be used to derive the sensitivity matrix used in an atmospheric inversion, was run for each candidate station for the months of July (representative of the Southern Hemisphere winter) and January (summer). The network optimisation procedure was carried out under a standard set of conditions, similar to those applied to the Australian test case in Part 1, for both months and for the combined 2 months, using the incremental optimisation (IO) routine. The optimal network design setup was subtly changed, one parameter at a time, and the optimisation routine was re-run under each set of modified conditions and compared to the original optimal network design. The assessment of the similarity between network solutions showed that changing the height of the surface grid cells, including an uncertainty estimate for the ocean fluxes, or increasing the night-time observation error uncertainty did not result in any significant changes in the positioning of the stations relative to the standard design. However, changing the prior flux error covariance matrix, or increasing the spatial resolution, did. Large aggregation errors were calculated for a number of candidate measurement sites using the resolution of the standard network design. Spatial resolution of the prior fluxes should be kept as close to the resolution of the transport model as the computing system can manage, to mitigate the exclusion of sites which could potentially be beneficial to the network. Including a generic correlation structure in the prior flux error covariance matrix led to pronounced changes in the network solution. The genetic algorithm (GA) was able to find a marginally better solution than the IO procedure, increasing uncertainty reduction by 0.3 %, but still included the most influential stations from the standard network design. In addition, the computational cost of the GA compared to IO was much higher. Overall the results suggest that a good improvement in knowledge of South African fluxes is available from a feasible atmospheric network, and that the general features of this network are invariable under several reasonable choices in a network design study. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystem models are employed to calculate the sources and sinks of carbon dioxide between land and atmosphere. These models may be heavily parameterised. Where reliable estimates are unavailable for a parameter, it remains highly uncertain; uncertainty of parameters can substantially contribute to overall model output uncertainty. This paper builds on the work of the terrestrial Carbon Cycle Data Assimilation System (CCDAS), which, here, assimilates atmospheric CO2 concentrations to optimise 19 parameters of the underlying terrestrial ecosystem model (Biosphere Energy Transfer and Hydrology scheme, BETHY). Previous experiments have shown that the identified minimum may contain non-physical parameter values. One way to combat this problem is to use constrained optimisation and so avoid the optimiser searching non-physical regions. Another technique is to use penalty terms in the cost function, which are added when the optimisation searches outside of a specified region. The use of parameter transformations is a further method of avoiding this problem, where the optimisation is carried out in a transformed parameter space, thus ensuring that the optimal parameters at the minimum are in the physical domain. We compare these different methods of achieving meaningful parameter values, finding that the parameter transformation method shows consistent results and that the other two provide no useful results. [ABSTRACT FROM AUTHOR]

This paper describes the generation of optimal atmospheric measurement networks for determining carbon dioxide fluxes over Australia using inverse methods. A Lagrangian particle dispersion model is used in reverse mode together with a Bayesian inverse modelling framework to calculate the relationship between weekly surface fluxes, comprising contributions from the biosphere and fossil fuel combustion, and hourly concentration observations for the Australian continent. Meteorological driving fields are provided by the regional version of the Australian Community Climate and Earth System Simulator (ACCESS) at 12 km resolution at an hourly timescale. Prior uncertainties are derived on a weekly timescale for biosphere fluxes and fossil fuel emissions from high-resolution model runs using the Community Atmosphere Biosphere Land Exchange (CABLE) model and the Fossil Fuel Data Assimilation System (FFDAS) respectively. The influence from outside the modelled domain is investigated, but proves to be negligible for the network design. Existing ground-based measurement stations in Australia are assessed in terms of their ability to constrain local flux estimates from the land. We find that the six stations that are currently operational are already able to reduce the uncertainties on surface flux estimates by about 30 %. A candidate list of 59 stations is generated based on logistic constraints and an incremental optimisation scheme is used to extend the network of existing stations. In order to achieve an uncertainty reduction of about 50 %, we need to double the number of measurement stations in Australia. Assuming equal data uncertainties for all sites, new stations would be mainly located in the northern and eastern part of the continent. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystem models (TEMs) contain the coupling of many biogeochemical processes with a large number of parameters involved. In many cases those parameters are highly uncertain. In order to reduce those uncertainties, parameter estimation methods can be applied, which allow the model to be constrained against observations. We compare the performance and results of two such parameter estimation techniques - the Metropolis algorithm (MA) which is a Markov Chain Monte Carlo (MCMC) method and the adjoint approach as it is used in the Carbon Cycle Data Assimilation System (CCDAS). Both techniques are applied here to derive the posterior probability density function (PDF) for 19 parameters of the Biosphere Energy Transfer and Hydrology (BETHY) scheme. We also use the MA to sample the posterior parameter distribution from the adjoint inversion. This allows us to assess if the commonly made assumption in variational data assimilation, that everything is normally distributed, holds. The comparison of the posterior parameter PDF derived by both metfiods shows that in most cases an approximation of the PDF by a normal distribution as used by the adjoint approach is a valid assumption. The results also indicate that the global minimum has been identified by both methods for the given set up. However, the adjoint approach outperforms the MA by several orders of magnitude in terms of computational time. Both methods show good agreement in the PDF of estimated net carbon fluxes for the decades of the 1980s and 1990s [ABSTRACT FROM AUTHOR]

The article discusses the carbon cycle modelling using the Carbon Cycle Data Assimilation System (CCDAS) that evaluates the Biosphere Energy Transfer and Hydrology scheme (BETHY). It discusses the impact of terrestrial biosphere on global carbon cycle and the accumulation of carbon dioxide (CO2). It reports that the method serves as a parameter for carbon cycle-climate and for future climate change.

Better estimates of the net exchange of CO2 between the atmosphere and the terrestrial biosphere are urgently needed to improve predictions of future CO2 levels in the atmosphere. The carbon cycle data assimilation system (CCDAS) offers the capability of inversion, while it is at the same time based on a process model that can be used independent of observational data. CCDAS allows the assimilation of atmospheric CO2 concentrations into the terrestrial biosphere model BETHY, constraining its process parameters via an adjoint approach. Here, we investigate the effect of spatial differentiation of a universal carbon balance parameter of BETHY on posterior net CO2 fluxes and their uncertainties. The parameter, β, determines the characteristics of the slowly decomposing soil carbon pool and represents processes that are difficult to model explicitly. Two cases are studied with an assimilation period of 1979 to 2003. In the base case, there is a separate β for each plant functional type (PFT). In the regionalization case, β is differentiated not only by PFT, but also according to each of 11 large continental regions as used by the TransCom project. We find that the choice of spatial differentiation has a profound impact not only on the posterior (optimized) fluxes and their uncertainties, but even more so on the spatial covariance of the uncertainties. Differences are most pronounced in tropical regions, where observations are sparse. While regionalization leads to an improved fit to the observations by about 20% compared to the base case, we notice large spatial variations in the posterior net CO2 flux on a grid cell level. The results illustrate the need for universal process formulations in global-scale atmospheric CO2 inversion studies, at least as long as the observational network is too sparse to resolve spatial fluctuations at the regional scale. [ABSTRACT FROM AUTHOR]

This paper presents a global sensitivity analysis of simulations of low-temperature isothermal cyclohexane oxidation under fuel-rich conditions using the method of high-dimensional model representation (HDMR). The analysis is used to investigate the important features of the oxidation process, as well as possible factors underlying qualitative discrepancies between simulations and experiments. The particular feature of interest is the characteristic of quadratic autocatalysis, which is observed experimentally and leads to the maximum rate of reaction occurring at 50% consumption of the deficient reactant (oxygen), with the fuel consumption exerting only a weak dependence. The kinetic mechanisms tested do not exhibit this characteristic when simulating the experimental conditions. The models also exhibit shorter induction times than those observed in the experiment. The HDMR study demonstrates a sensitivity of these features to the A-factors of key reactions of the cyclohexylperoxy radical (C6H11OO). At the low temperatures studied here, these are peroxy-peroxy radical reactions rather than the isomerisation routes that have been the subject of other investigations at higher temperatures. The low temperature product channels for reactions of the cyclohexylperoxy radical are therefore an important area for future kinetic studies. The effects of wall reactions of peroxy and peroxide species were not found to outweigh the impact of the main A-factors, but including wall losses led to significant higher order interactions between input parameters. This constitutes an interesting and important area for further research. [ABSTRACT FROM AUTHOR]

The use of complex mechanisms is increasing within models describing a range of important chemical processes, including combustion. Parameters describing chemical reaction rates and thermodynamics can often be very uncertain. Highlighting the main parameters contributing to predictive uncertainty is an important part of model development. However, due to the computational cost of the models and their nonlinearity, traditional methods for sensitivity analysis are often not suitable. The high-dimensional model representation (HDMR) method was developed to express the input–output relationship of a complex model with a high-dimensional input space. A fully functional surrogate model can be constructed with low computational effort. First- and second-order sensitivity indices can then be calculated in an automatic way, over large input parameter ranges. These provide an importance ranking for the input parameters. An extension to the existing set of HDMR tools is developed in this work, where the number of HMDR component functions can be reduced to explore large dimensional input spaces very efficiently. The HDMR tools are demonstrated for a case study of a one-dimensional low-pressure premixed methane flame model doped with trace sulfur and nitrogen species. Uncertainties in rate constants and thermodynamic data are considered, leading to a study of 176 input parameters. Using the new HDMR tools, the use of screening methods such as the Morris method, which aim to identify unimportant parameters beforehand, can generally be avoided. However, in certain cases, a combination of a screening method and HDMR is computationally more efficient than using HDMR alone. The final ranking of important parameters is shown to be critically dependent on the uncertainty ranges chosen due to the nonlinearity of the model. The study demonstrates that the proposed HDMR method provides a powerful tool for general application to global sensitivity analysis of complex chemical mechanisms. © 2008 Wiley Periodicals, Inc. Int J Chem Kinet 40: 742–753, 2008 [ABSTRACT FROM AUTHOR]

Traditional methods for global uncertainty and sensitivity analysis (SA) such as Monte Carlo methods based on full model simulations are often not suitable for application in environmental modelling due to the nonlinearity and computational complexity of the models. The high dimensional model representation (HDMR) method was developed to express the input–output relationships of a complex model with a high dimensional input space. HDMR provides a model replacement that can be easily employed within global SA. In this work an optimisation method is developed as an extension to the existing set of HDMR tools to improve the accuracy of the mapping process. An application from the field of urban flow and dispersion is chosen to demonstrate the effectiveness of the approach. Model replacements of a k– model simulating the flow field in an urban street canyon are constructed. The necessary computational effort for their construction is considerably lower than required by traditional global SA methods. First- and second-order sensitivity indices can then be calculated using the replacements without the need for additional full model runs. Comparison with a large sample of full model runs demonstrates that the output statistics of the full model are well represented by the model replacements. The proposed HDMR method therefore provides a powerful tool for general application to global SA of environmental models. [Copyright &y& Elsevier]

Carbon cycle feedbacks were often quantified through the carbon-concentration and carbon-climate feedbacks with the assumption of no significant interaction between the two feedbacks in most previous studies. Here we calculated the strength of the interactions between the two responses using simulations of models participated in the phase 6 of the Coupled Model Intercomparison Project (CMIP6). We found that the nonlinear interaction contributed 11% of the land-atmosphere carbon exchange on average with large intermodel variation (from -20% to +162%). This nonlinear interaction is largely driven by the pattern of net primary production (NPP), with shifts in heterotrophic respiration that dampen the overall positive interactions from NPP. Photosynthetic rate per unit leaf area alone cannot adequately explain a wide variation of interactions in global NPP simulated by CMIP6 models. Plant respiration and processes that regulate leaf area are also important contributors to the interactions. Dominant factors that induce carbon-concentration and carbon-climate interactions are highly variable among models. One of those dominant factors is nutrient limitation. Using additional simulations of ACCESS-ESM1.5 that include both nitrogen and phosphorus limitation, we found that the estimated interactions by ACCESS-ESM1.5 with or without nutrient limitations covered the large intermodel variations among the CMIP6 models. It remains largely unknown how nutrient limitation complicates ecosystem's responses to simultaneously CO 2 fertilization and warming at the global scale. Our modeling results point to a potential important role of nutrients, especially phosphorus on the nonlinear interactions. Yet, more studies are needed on ecosystem responses to concurrent changes in nutrient availability, atmospheric CO 2 concentration, and warming., (© 2021 Commonwealth of Australia. Global Change Biology © 2021 John Wiley & Sons Ltd.)

Many nations responded to the corona virus disease-2019 (COVID-19) pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO 2 , other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. 12 models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate., (© 2021. Crown Copyright. © 2021. Her Majesty the Queen in Right of Canada. This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland. Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

Robust estimates of CO 2 budget, CO 2 exchanged between the atmosphere and terrestrial biosphere, are necessary to better understand the role of the terrestrial biosphere in mitigating anthropogenic CO 2 emissions. Over the past decade, this field of research has advanced through understanding of the differences and similarities of two fundamentally different approaches: "top-down" atmospheric inversions and "bottom-up" biosphere models. Since the first studies were undertaken, these approaches have shown an increasing level of agreement, but disagreements in some regions still persist, in part because they do not estimate the same quantity of atmosphere-biosphere CO 2 exchange. Here, we conducted a thorough comparison of CO 2 budgets at multiple scales and from multiple methods to assess the current state of the science in estimating CO 2 budgets. Our set of atmospheric inversions and biosphere models, which were adjusted for a consistent flux definition, showed a high level of agreement for global and hemispheric CO 2 budgets in the 2000s. Regionally, improved agreement in CO 2 budgets was notable for North America and Southeast Asia. However, large gaps between the two methods remained in East Asia and South America. In other regions, Europe, boreal Asia, Africa, South Asia, and Oceania, it was difficult to determine whether those regions act as a net sink or source because of the large spread in estimates from atmospheric inversions. These results highlight two research directions to improve the robustness of CO 2 budgets: (a) to increase representation of processes in biosphere models that could contribute to fill the budget gaps, such as forest regrowth and forest degradation; and (b) to reduce sink-source compensation between regions (dipoles) in atmospheric inversion so that their estimates become more comparable. Advancements on both research areas will increase the level of agreement between the top-down and bottom-up approaches and yield more robust knowledge of regional CO 2 budgets., (© 2019 John Wiley & Sons Ltd.)