scholarly journals Supplementary material to "Towards monitoring CO<sub>2</sub> source-sink distribution over India via inverse modelling: Quantifying the fine-scale spatiotemporal variability of atmospheric CO<sub>2</sub> mole fraction"

2021 ◽  
Author(s):  
Vishnu Thilakan ◽  
Dhanyalekshmi Pillai ◽  
Christoph Gerbig ◽  
Michal Galkowski ◽  
Aparnna Ravi ◽  
...  
Keyword(s):  
Mole Fraction ◽  
Inverse Modelling ◽  
Spatiotemporal Variability ◽  
Fine Scale ◽  
Supplementary Material ◽  
Source Sink

scholarly journals Towards monitoring CO<sub>2</sub> source-sink distribution over India via inverse modelling: Quantifying the fine-scale spatiotemporal variability of atmospheric CO<sub>2</sub> mole fraction

2021 ◽  
Author(s):  
Vishnu Thilakan ◽  
Dhanyalekshmi Pillai ◽  
Christoph Gerbig ◽  
Michal Galkowski ◽  
Aparnna Ravi ◽  
...  
Keyword(s):  
High Resolution ◽  
Atmospheric Co2 ◽  
Inverse Modelling ◽  
Spatiotemporal Variability ◽  
Fine Scale ◽  
Sources And Sinks ◽  
Modelling Framework ◽  
Representation Error ◽  
Coarse Model ◽  
Average Representation

Abstract. The prospect of improving the estimates of CO2 sources and sinks over India through inverse methods calls for a comprehensive atmospheric monitoring system involving atmospheric transport models that make a realistic accounting of atmospheric CO2 variability. In the context of expanding atmospheric CO2 measurement networks over India, this study aims to investigate the importance of a high-resolution modelling framework to utilize these observations and to quantify the uncertainty due to the misrepresentation of fine-scale variability of CO2 in the employed model. The spatial variability of atmospheric CO2 is represented by implementing WRF-Chem at a spatial resolution of 10 km × 10 km. We utilize these high-resolution simulations for sub-grid variability calculation within the coarse model grid at a horizontal resolution of one degree (about 100 km). We show that the unresolved variability in the coarse model reaches up to a value of 10 ppm at the surface, which is considerably larger than the sampling errors, even comparable to the magnitude of mixing ratio enhancements in source regions. We find a significant impact of monsoon circulation in sub-grid variability, causing ~3 ppm average representation error between 12–14 km altitude ranges in response to the tropical easterly jet. The cyclonic storm Ockhi during November 2017 generates completely different characteristics in sub-grid variability than the rest of the period, whose influence increases the average representation error by ~1 ppm at the surface. By employing a first-order inverse modelling scheme using pseudo observations from nine tall tower sites over India and a constellation of satellite instruments, we show that the Net Ecosystem Exchange (NEE) flux uncertainty solely due to unresolved variability is in the range of 6.3 to 16.2 % of the total NEE. We illustrate an example to test the efficiency of a simple parameterization scheme during non-monsoon periods to capture the unresolved variability in the coarse models, which reduces the bias in flux estimates from 9.4 % to 2.2 %. By estimating the fine-scale variability and its impact during different seasons, we emphasise the need for implementing a high-resolution modelling framework over the Indian subcontinent to better understand processes regulating CO2 sources and sinks.

scholarly journals The drivers of West Nile virus human illness: fine scale dynamic effects of weather, mosquito infection, social, and biological conditions

2019 ◽  
Author(s):  
Surendra Karki ◽  
William M. Brown ◽  
John Uelmen ◽  
Marilyn O. Ruiz ◽  
Rebecca Lee Smith
Keyword(s):  
West Nile Virus ◽  
Spatial Scales ◽  
Open Water ◽  
Human Case ◽  
Spatiotemporal Variability ◽  
Complex Data ◽  
Fine Scale ◽  
West Nile ◽  
Human Illness ◽  
Mosquito Infection

AbstractWest Nile virus (WNV) has consistently been reported to be associated with human cases of illness in the region near Chicago, Illinois. However, the number of reported cases of human illness varies across years, with intermittent outbreaks. Several dynamic factors, including temperature, rainfall, and infection status of vector mosquito populations, are responsible for much of these observed variations. However, local landscape structure and human demographic characteristics also play a key role. The geographic and temporal scales used to analyze such complex data affect the observed associations. Here, we used spatial and statistical modeling approaches to investigate the factors that drive the outcome of WNV human illness on fine temporal and spatial scales. Our approach included multi-level modeling of long-term weekly data from 2005 to 2016, with weekly measures of mosquito infection, human illness and weather combined with more stable landscape and demographic factors on the geographical scale of 1000m hexagons. We found that hot weather conditions, warm winters, and higher MIR in earlier weeks increased the probability of an area of having a WNV human case. Higher population and the proportion of urban light intensity in an area also increased the probability of observing a WNV human case. A higher proportion of open water sources, percentage of grass land, deciduous forests, and housing built post 1990 decreased the probability of having a WNV case. Additionally, we found that cumulative positive mosquito pools up to 31 weeks can strongly predict the total annual human WNV cases in the Chicago region. This study helped us to improve our understanding of the fine-scale drivers of spatiotemporal variability of human WNV cases.

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.

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