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 Comparing atmospheric transport models for future regional inversions over Europe – Part 1: mapping the atmospheric CO<sub>2</sub> signals

2007 ◽  
Vol 7 (13) ◽  
pp. 3461-3479 ◽  
Author(s):  
C. Geels ◽  
M. Gloor ◽  
P. Ciais ◽  
P. Bousquet ◽  
P. Peylin ◽  
...  
Keyword(s):  
High Resolution ◽  
High Altitude ◽  
Large Scale ◽  
Fossil Fuel ◽  
Atmospheric Transport ◽  
Inverse Methods ◽  
Spatiotemporal Variability ◽  
Transport Models ◽  
Sources And Sinks ◽  
Low Altitude

Abstract. The CO2 source and sink distribution across Europe can be estimated in principle through inverse methods by combining CO2 observations and atmospheric transport models. Uncertainties of such estimates are mainly due to insufficient spatiotemporal coverage of CO2 observations and biases of the models. In order to assess the biases related to the use of different models the CO2 concentration field over Europe has been simulated with five different Eulerian atmospheric transport models as part of the EU-funded AEROCARB project, which has the main goal to estimate the carbon balance of Europe. In contrast to previous comparisons, here both global coarse-resolution and regional higher-resolution models are included. Continuous CO2 observations from continental, coastal and mountain sites as well as flasks sampled on aircrafts are used to evaluate the models' ability to capture the spatiotemporal variability and distribution of lower troposphere CO2 across Europe. 14CO2 is used in addition to evaluate separately fossil fuel signal predictions. The simulated concentrations show a large range of variation, with up to ~10 ppm higher surface concentrations over Western and Central Europe in the regional models with highest (mesoscale) spatial resolution. The simulation – data comparison reveals that generally high-resolution models are more successful than coarse models in capturing the amplitude and phasing of the observed short-term variability. At high-altitude stations the magnitude of the differences between observations and models and in between models is less pronounced, but the timing of the diurnal cycle is not well captured by the models. The data comparisons show also that the timing of the observed variability on hourly to daily time scales at low-altitude stations is generally well captured by all models. However, the amplitude of the variability tends to be underestimated. While daytime values are quite well predicted, nighttime values are generally underpredicted. This is a reflection of the different mixing regimes during day and night combined with different vertical resolution between models. In line with this finding, the agreement among models is increased when sampling in the afternoon hours only and when sampling the mixed portion of the PBL, which amounts to sampling at a few hundred meters above ground. The main recommendations resulting from the study for constraining land carbon sources and sinks using high-resolution concentration data and state-of-the art transport models through inverse methods are given in the following: 1) Low altitude stations are presently preferable in inverse studies. If high altitude stations are used then the model level that represents the specific sites should be applied, 2) at low altitude sites only the afternoon values of concentrations can be represented sufficiently well by current models and therefore afternoon values are more appropriate for constraining large-scale sources and sinks in combination with transport models, 3) even when using only afternoon values it is clear that data sampled several hundred meters above ground can be represented substantially more robustly in models than surface station records, which emphasize the use of tower data in inverse studies and finally 4) traditional large scale transport models seem not sufficient to resolve fine-scale features associated with fossil fuel emissions, as well as larger-scale features like the concentration distribution above the south-western Europe. It is therefore recommended to use higher resolution models for interpretation of continental data in future studies.

scholarly journals Comparing atmospheric transport models for future regional inversions over Europe. Part 1: Mapping the CO<sub>2</sub> atmospheric signals

2006 ◽  
Vol 6 (3) ◽  
pp. 3709-3756 ◽  
Author(s):  
C. Geels ◽  
M. Gloor ◽  
P. Ciais ◽  
P. Bousquet ◽  
P. Peylin ◽  
...  
Keyword(s):  
High Resolution ◽  
High Altitude ◽  
Large Scale ◽  
State Of The Art ◽  
Atmospheric Transport ◽  
Spatiotemporal Variability ◽  
Concentration Data ◽  
Transport Models ◽  
Sources And Sinks ◽  
Low Altitude

Abstract. The CO2 source and sink distribution across Europe can be estimated in principle through inverse methods by combining CO2 observations and atmospheric transport models. Uncertainties of such estimates are mainly due to insufficient spatiotemporal coverage of CO2 observations and biases of the models. In order to assess the biases related to the use of different models the CO2 concentration field over Europe has been simulated with five different Eulerian atmospheric transport models as part of the EU-funded AEROCARB project, which has the main goal to estimate the carbon balance of Europe. In contrast to previous comparisons, here both global coarse-resolution and regional higher-resolution models are included. Continuous CO2 observations from continental, coastal and mountain in-situ atmospheric stations as well as flask samples sampled on aircrafts are used to evaluate the models' ability to capture the spatiotemporal variability and distribution of lower troposphere CO2 across Europe. 14CO2 is used in addition to evaluate separately fossil fuel signal predictions. The simulated concentrations show a large range of variation, with up to ~10 ppm higher surface concentrations over Western and Central Europe in the regional models with highest (mesoscale) spatial resolution. The simulation – data comparison reveals that generally high-resolution models are more successful than coarse models in capturing the amplitude and phasing of the observed short-term variability. At high-altitude stations the magnitude of the differences between observations and models and in between models is less pronounced, but the timing of the diurnal cycle is not well captured by the models. The data comparisons show also that the timing of the observed variability on hourly to daily time scales at low-altitude stations is generally well captured by all models. However, the amplitude of the variability tends to be underestimated. While daytime values are quite well predicted, nighttime values are generally underpredicted. This is a reflection of the different mixing regimes during day and night combined with different vertical resolution between models. In line with this finding, the agreement among models is increased when sampling in the afternoon hours only and when sampling the mixed portion of the PBL, which amounts to sampling at a few hundred meters above ground. Main recommendations resulting from the study for constraining land carbon sources and sinks using high-resolution concentration data and state-of-the art transport models are therefore: 1) low altitude stations are preferable over high altitude stations as these locations are difficult to represent in state-of-the art models, 2) at low altitude stations only afternoon values can be represented sufficiently well to be used to constrain large-scale sources and sinks in combination with transport models, 3) even when using only afternoon values it is clear that data sampled several hundred meters above ground can be represented substantially more robust in models than surface station records, and finally 4) traditional large scale transport models seem not sufficient to resolve CO2 distributions over regions of the size of for example Spain and thus seem too coarse for interpretation of continental data.

Satellite-derived Indian methane emission sources with TROPOMI retrievals and a high-resolution modelling framework: Initial comparison with WRF-GHG model results

2020 ◽  
Author(s):  
Dhanyalekshmi Pillai ◽  
Monish Deshpande ◽  
Julia Marshall ◽  
Christoph Gerbig ◽  
Oliver Schneising ◽  
...  
Keyword(s):  
High Resolution ◽  
Greenhouse Gas ◽  
Methane Emission ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
International Standards ◽  
Inverse Modelling ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Modelling Framework

&lt;p&gt;In accordance with the Global stocktake under Article 14 of Paris Agreement, each county estimates its own greenhouse gas (GHG) emissions based on standardised bottom-up management methods. However, the accuracy of these methods along with the standards differs from country to country, resulting in large uncertainties that make it difficult to implement effective climate change mitigation strategies. India plays an important role in global methane emission scenario, necessitating the accurate quantification of its sources at the regional and the local levels. However, the country lacks sufficient long term, continuous and accurate observations of the atmospheric methane which are required to quantify its source, to understand changes in the carbon cycle and the climate system. Recent technological advancements in the use of satellite remote-sensing dedicated to the greenhouse gases enforce international standards for the observation methods; hence enabling those high-resolution-high-density observations to be utilised for this quantification purpose. This study focuses on exploring the use of such dedicated observations of the column-averaged dry-air mixing ratio of methane (XCH&lt;sub&gt;4&lt;/sub&gt;) retrieved from TROPOMI onboard Sentinel-5 Precursor to quantify the major CH&lt;sub&gt;4&lt;/sub&gt; anthropogenic and natural emission fluxes over India.&lt;/p&gt;&lt;p&gt;Our inverse modelling approach at the mesoscale includes a high-resolution atmospheric modelling framework consisting of the Weather Research and Forecasting model with greenhouse gas module (WRF-GHG) and a set of prior emission inventory model data. We use TROPOMI retrievals derived using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) retrieval algorithm. WRF-GHG simulations are performed in hourly time intervals at a horizontal resolution of&amp;#160;10 km &amp;#215;10 km for a month. In order to compare our CH&lt;sub&gt;4 &lt;/sub&gt;simulations with the satellite column data, we have also taken into account the different vertical sensitivities of the instrument by applying the averaging kernel to the model simulations. To demonstrate the model performance, our simulations are also compared with the CAMS reanalysis product based on ECMWF (European Centre for Medium-Range Weather Forecasts) numerical weather prediction reanalysis data available at a horizontal resolution of&amp;#160;0.25&lt;sup&gt;o&lt;/sup&gt; &amp;#215; 0.25&lt;sup&gt;o&lt;/sup&gt;. Our comparison of these modelling results against unique satellite dataset indicates high potential of using TROPOMI retrievals in distinguishing the major CH&lt;sub&gt;4&lt;/sub&gt; anthropogenic and natural sources over India via inverse modelling. The results will help to objectively investigate the claims of emission reductions and the efficiency of reduction countermeasures, as well as the establishment of standards and advancement of technology. The details about our approach and preliminary results based on our analysis using above satellite measurements and WRF-GHG simulations over India will be presented.&amp;#160;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Uncertainty analysis of a European high-resolution emission inventory of CO<sub>2</sub> and CO to support inverse modelling and network design

2020 ◽  
Vol 20 (3) ◽  
pp. 1795-1816 ◽  
Author(s):  
Ingrid Super ◽  
Stijn N. C. Dellaert ◽  
Antoon J. H. Visschedijk ◽  
Hugo A. C. Denier van der Gon
Keyword(s):  
High Resolution ◽  
Network Design ◽  
Emission Inventory ◽  
Atmospheric Transport ◽  
Temporal Distribution ◽  
Inverse Modelling ◽  
Climate Mitigation ◽  
Emission Inventories ◽  
Mixing Ratios ◽  
Modelling Framework

Abstract. Quantification of greenhouse gas emissions is receiving a lot of attention because of its relevance for climate mitigation. Complementary to official reported bottom-up emission inventories, quantification can be done with an inverse modelling framework, combining atmospheric transport models, prior gridded emission inventories and a network of atmospheric observations to optimize the emission inventories. An important aspect of such a method is a correct quantification of the uncertainties in all aspects of the modelling framework. The uncertainties in gridded emission inventories are, however, not systematically analysed. In this work, a statistically coherent method is used to quantify the uncertainties in a high-resolution gridded emission inventory of CO2 and CO for Europe. We perform a range of Monte Carlo simulations to determine the effect of uncertainties in different inventory components, including the spatial and temporal distribution, on the uncertainty in total emissions and the resulting atmospheric mixing ratios. We find that the uncertainties in the total emissions for the selected domain are 1 % for CO2 and 6 % for CO. Introducing spatial disaggregation causes a significant increase in the uncertainty of up to 40 % for CO2 and 70 % for CO for specific grid cells. Using gridded uncertainties, specific regions can be defined that have the largest uncertainty in emissions and are thus an interesting target for inverse modellers. However, the largest sectors are usually the best-constrained ones (low relative uncertainty), so the absolute uncertainty is the best indicator for this. With this knowledge, areas can be identified that are most sensitive to the largest emission uncertainties, which supports network design.

scholarly journals Uncertainty analysis of a European high-resolution emission inventory of CO<sub>2</sub> and CO to support inverse modelling and network design

2019 ◽  
Author(s):  
Ingrid Super ◽  
Stijn N. C. Dellaert ◽  
Antoon J. H. Visschedijk ◽  
Hugo A. C. Denier van der Gon
Keyword(s):  
High Resolution ◽  
Network Design ◽  
Emission Inventory ◽  
Atmospheric Transport ◽  
Temporal Distribution ◽  
Inverse Modelling ◽  
Climate Mitigation ◽  
Emission Inventories ◽  
Mixing Ratios ◽  
Modelling Framework

Abstract. Quantification of greenhouse gas emissions is receiving a lot of attention, because of its relevance for climate mitigation. Quantification is often done with an inverse modelling framework, combining atmospheric transport models, prior gridded emission inventories and a network of atmospheric observations to optimize the emission inventories. An important aspect of such method is a correct quantification of the uncertainties in all aspects of the modelling framework. The uncertainties in gridded emission inventories are, however, not systematically analysed. In this work, a statistically coherent method is used to quantify the uncertainties in a high-resolution gridded emission inventory of CO2 and CO for Europe. We perform a range of Monte Carlo simulations to determine the effect of uncertainties in different inventory components, including the spatial and temporal distribution, on the uncertainty in total emissions and the resulting atmospheric mixing ratios. We find that the uncertainty in the total emissions for the selected domain are 1 % for CO2 and 6 % for CO. Introducing spatial disaggregation causes a significant increase in the uncertainty of up to 40 % for CO2 and 70 % for CO for specific grid cells. Using gridded uncertainties specific regions can be defined that have the largest uncertainty in emissions and are thus an interesting target for inverse modelers. However, the largest sectors are usually the best-constrained ones (low relative uncertainty), so the absolute uncertainty is the best indicator for this. With this knowledge areas can be identified that are most sensitive to the largest emission uncertainties, which supports network design.

High Resolution Stereography of Complementary Freeze-Fracture Replicas Reveals the Presence of Depressions Opposite Membrane Associated Particles of Split Membranes

1971 ◽  
Vol 29 ◽  
pp. 442-443
Author(s):  
Russell L. Steere
Keyword(s):  
High Resolution ◽  
Cardiac Muscle ◽  
Electron Micrographs ◽  
Chromic Acid ◽  
Freeze Fracture ◽  
Distilled Water ◽  
Fine Scale ◽  
Acid Cleaning ◽  
Cleaning Solution

Complementary replicas have revealed the fact that the two common faces observed in electron micrographs of freeze-fracture and freeze-etch specimens are complementary to each other and are thus the new faces of a split membrane rather than the original inner and outer surfaces (1, 2 and personal observations). The big question raised by published electron micrographs is why do we not see depressions in the complementary face opposite membrane-associated particles? Reports have appeared indicating that some depressions do appear but complementarity on such a fine scale has yet to be shown.Dog cardiac muscle was perfused with glutaraldehyde, washed in distilled water, then transferred to 30% glycerol (material furnished by Dr. Joaquim Sommer, Duke Univ., and VA Hospital, Durham, N.C.). Small strips were freeze-fractured in a Denton Vacuum DFE-2 Freeze-Etch Unit with complementary replica tooling. Replicas were cleaned in chromic acid cleaning solution, then washed in 4 changes of distilled water and mounted on opposite sides of the center wire of a Formvar-coated grid.

Very high resolution environmental predictors in species distribution models

2013 ◽  
Vol 38 (1) ◽  
pp. 79-96 ◽  
Author(s):  
Jean-Nicolas Pradervand ◽  
Anne Dubuis ◽  
Loïc Pellissier ◽  
Antoine Guisan ◽  
Christophe Randin
Keyword(s):  
High Resolution ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Environmental Data ◽  
Swiss Alps ◽  
Fine Scale ◽  
Distribution Models ◽  
Model Quality ◽  
Vegetation Height ◽  
Very High

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species’ micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions – and therefore local management – compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.

Sign in / Sign up

Export Citation Format

Share Document