scholarly journals Confirmation of Elevated Methane Emissions in Utah's Uintah Basin With Ground‐Based Observations and a High‐Resolution Transport Model

2017 ◽  
Vol 122 (23) ◽  
Author(s):  
C. S. Foster ◽  
E. T. Crosman ◽  
L. Holland ◽  
D. V. Mallia ◽  
B. Fasoli ◽  
...  
Keyword(s):  
High Resolution ◽  
Transport Model ◽  
Methane Emissions ◽  
Uintah Basin

High resolution mapping of methane emissions from marine and terrestrial sources using a Cluster-Tuned Matched Filter technique and imaging spectrometry

2013 ◽  
Vol 134 ◽  
pp. 305-318 ◽  
Author(s):  
Andrew K. Thorpe ◽  
Dar A. Roberts ◽  
Eliza S. Bradley ◽  
Christopher C. Funk ◽  
Philip E. Dennison ◽  
...  
Keyword(s):  
High Resolution ◽  
Matched Filter ◽  
Methane Emissions ◽  
Imaging Spectrometry ◽  
Filter Technique ◽  
High Resolution Mapping ◽  
Resolution Mapping

scholarly journals CTDAS-Lagrange v1.0: A high-resolution data assimilation system for regional carbon dioxide observations

2017 ◽  
Author(s):  
Wei He ◽  
Ivar R. van der Velde ◽  
Arlyn E. Andrews ◽  
Colm Sweeney ◽  
John Miller ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Transport Model ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions ◽  
Sensitivity Tests ◽  
Lagrange System ◽  
Data Assimilation System ◽  
Flux Adjustment ◽  
Assimilation System

Abstract. We have implemented a regional carbon dioxide data assimilation system based on the CarbonTracker Data Assimilation Shell (CTDAS) and a high-resolution Lagrangian transport model, the Stochastic Time-Inverted Lagrangian Transport model driven by the Weather Forecast and Research meteorological fields (WRF-STILT). With this system, named as CTDAS‑Lagrange, we simultaneously optimize terrestrial biosphere fluxes and four parameters that adjust the lateral boundary conditions (BCs) against CO2 observations from the NOAA ESRL North America tall tower and aircraft Programmable Flask Packages (PFPs) sampling program. Least-squares optimization is performed with a time-stepping ensemble Kalman smoother, over a time window of 10 days and assimilating sequentially a time series of observations. Because the WRF-STILT footprints are pre-computed, it is computationally efficient to run the CTDAS-Lagrange system. To estimate the uncertainties of the optimized fluxes from the system, we performed sensitivity tests with various a priori biosphere fluxes (SiBCASA, SiB3, CT2013B) and BCs (optimized mole fraction fields from CT2013B and CTE2014, and an empirical data set derived from aircraft observations), as well as with a variety of choices on the ways that fluxes are adjusted (additive or multiplicative), covariance length scales, biosphere flux covariances, BC parameter uncertainties, and model-data mismatches. In pseudo-data experiments, we show that in our implementation the additive flux adjustment method is more flexible in optimizing NEE than the multiplicative flux adjustment method, and that the CTDAS-Lagrange system has the ability to correct for the potential biases in the lateral boundary conditions and to resolve large biases in the prior biosphere fluxes. Using real observations, we have derived a range of estimates for the optimized carbon fluxes from a series of sensitivity tests, which places the North American carbon sink for the year 2010 in a range from −0.92 to −1.26 PgC/yr. This is comparable to the TM5-based estimates of CarbonTracker (version CT2016, −0.91 ± 1.10 PgC/yr) and CarbonTracker Europe (version CTE2016, −0.91 ± 0.31 PgC/yr). We conclude that CTDAS-Lagrange can offer a versatile and computationally attractive alternative to these global systems for regional estimates of carbon fluxes, which can take advantage of high-resolution Lagrangian footprints that are increasingly easy to obtain.

scholarly journals Sensitivity of the recent methane budget to LMDz sub-grid scale physical parameterizations

2015 ◽  
Vol 15 (8) ◽  
pp. 11853-11888
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
M. Saunois ◽  
F. Chevallier ◽  
C. Cressot
Keyword(s):  
Satellite Data ◽  
Transport Model ◽  
Deep Convection ◽  
Chemical Transport ◽  
Global Scale ◽  
Methane Emissions ◽  
Inverse Modelling ◽  
Chemical Transport Model ◽  
Methane Budget ◽  
Physical Parameterizations

Abstract. With the densification of surface observing networks and the development of remote sensing of greenhouse gases from space, estimations of methane (CH4) sources and sinks by inverse modelling face new challenges. Indeed, the chemical transport model used to link the flux space with the mixing ratio space must be able to represent these different types of constraints for providing consistent flux estimations. Here we quantify the impact of sub-grid scale physical parameterization errors on the global methane budget inferred by inverse modelling using the same inversion set-up but different physical parameterizations within one chemical-transport model. Two different schemes for vertical diffusion, two others for deep convection, and one additional for thermals in the planetary boundary layer are tested. Different atmospheric methane datasets are used as constraints (surface observations or satellite retrievals). At the global scale, methane emissions differ, on average, from 4.1 Tg CH4 per year due to the use of different sub-grid scale parameterizations. Inversions using satellite total-column retrieved by GOSAT satellite are less impacted, at the global scale, by errors in physical parameterizations. Focusing on large-scale atmospheric transport, we show that inversions using the deep convection scheme of Emanuel (1991) derive smaller interhemispheric gradient in methane emissions. At regional scale, the use of different sub-grid scale parameterizations induces uncertainties ranging from 1.2 (2.7%) to 9.4% (14.2%) of methane emissions in Africa and Eurasia Boreal respectively when using only surface measurements from the background (extended) surface network. When using only satellite data, we show that the small biases found in inversions using GOSAT-CH4 data and a coarser version of the transport model were actually masking a poor representation of the stratosphere–troposphere gradient in the model. Improving the stratosphere–troposphere gradient reveals a larger bias in GOSAT-CH4 satellite data, which largely amplifies inconsistencies between surface and satellite inversions. A simple bias correction is proposed. The results of this work provide the level of confidence one can have for recent methane inversions relatively to physical parameterizations included in chemical-transport models.

Quantifying the contribution of regional methane emissions to the global methane budget between 2008 and 2018 using the TOMCAT chemical transport model

2021 ◽  
Author(s):  
Emily Dowd ◽  
Christopher Wilson ◽  
Martyn Chipperfield ◽  
Manuel Gloor
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
Transport Model ◽  
Chemical Transport ◽  
Methane Emissions ◽  
Anthropogenic Sources ◽  
Chemical Transport Model ◽  
Methane Budget

<p>Methane (CH<sub>4</sub>) is the second most important atmospheric greenhouse gas after carbon dioxide. Global concentrations of CH<sub>4</sub> have been rising in the last decade and our understanding of what is driving the increase remains incomplete. Natural sources, such as wetlands, contribute to the uncertainty of the methane budget. However, anthropogenic sources, such as fossil fuels, present an opportunity to mitigate the human contribution to climate change on a relatively short timescale, since CH<sub>4</sub> has a much shorter lifetime than carbon dioxide. Therefore, it is important to know the relative contributions of these sources in different regions.</p><p>We have investigated the inter-annual variation (IAV) and rising trend of CH<sub>4</sub> concentrations using a global 3-D chemical transport model, TOMCAT. We independently tagged several regional natural and anthropogenic CH<sub>4</sub> tracers in TOMCAT to identify their contribution to the atmospheric CH<sub>4</sub> concentrations over the period 2009 – 2018. The tagged regions were selected based on the land surface types and the predominant flux sector within each region and include subcontinental regions, such as tropical South America, boreal regions and anthropogenic regions such as Europe. We used surface CH<sub>4</sub> fluxes derived from a previous TOMCAT-based atmospheric inversion study (Wilson et al., 2020). These atmospheric inversions were constrained by satellite and surface flask observations of CH<sub>4</sub>, giving optimised monthly estimates for fossil fuel and non-fossil fuel emissions on a 5.6° horizontal grid. During the study period, the total optimised CH<sub>4</sub> flux grew from 552 Tg/yr to 593 Tg/yr. This increase in emissions, particularly in the tropics, contributed to the increase in atmospheric CH<sub>4 </sub>concentrations and added to the imbalance in the CH<sub>4</sub> budget. We will use the results of the regional tagged tracers to quantify the contribution of regional methane emissions at surface observation sites, and to quantify the contributions of the natural and anthropogenic emissions from the tagged regions to the IAV and the rising methane concentrations.</p><p>Wilson, C., Chipperfield, M. P., Gloor, M., Parker, R. J., Boesch, H., McNorton, J., Gatti, L. V., Miller, J. B., Basso, L. S., and Monks, S. A.: Large and increasing methane emissions from Eastern Amazonia derived from satellite data, 2010–2018, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1136, in review, 2020.</p>

scholarly journals Spatiotemporal patterns of the fossil-fuel CO<sub>2</sub> signal in central Europe: Results from a high-resolution atmospheric transport model

2017 ◽  
Author(s):  
Yu Liu ◽  
Nicolas Gruber ◽  
Dominik Brunner
Keyword(s):  
High Resolution ◽  
Central Europe ◽  
Western Europe ◽  
Fossil Fuel ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Temporal Variations ◽  
Atmospheric Co2 ◽  
Fuel Component ◽  
Atmospheric Transport Model

Abstract. The emission of CO2 from the burning of fossil fuel is a prime determinant of variations in atmospheric CO2. Here, we simulate this fossil fuel signal together with the natural and background components with a regional high-resolution atmospheric transport model for central and southern Europe considering separately the emissions from different sectors and countries on the basis of emission inventories and hourly emission time functions. The simulated variations in atmospheric CO2 agree very well with observation-based estimates, although the observed variance is slightly underestimated, particularly for the fossil fuel component. Despite relatively rapid atmospheric mixing, the simulated fossil fuel signal reveals distinct annual mean structures deep into the troposphere reflecting the spatially dense aggregation of most emissions. The fossil fuel signal accounts for more than half of the total (fossil fuel + biospheric + background) temporal variations in atmospheric CO2 in most areas of northern and western central Europe, with the largest variations occurring on diurnal timescales owing to the combination of diurnal variations in emissions and atmospheric mixing/transport out of the surface layer. Their co-variance leads to a fossil-fuel diurnal rectifier effect with a magnitude as large as 9 ppm compared to a case with time-constant emissions. The spatial pattern of CO2 from the different sectors largely reflects the distribution and relative magnitude of the corresponding emissions, with power plant emissions leaving the most distinguished mark. An exception is southern and western Europe, where the emissions from the transportation sector dominate the fossil fuel signal. Most of the fossil fuel CO2 remains within the country responsible for the emission, although in smaller countries, up to 80 % of the fossil fuel signal can come from abroad. A fossil fuel emission reduction of 30 % is clearly detectable for a surface-based observing system for atmospheric CO2, while it is beyond the edge of detectability for the current generation of satellites with the exception of a few hotspot sites. Changes in variability in atmospheric CO2 might open an additional door for the monitoring and verification of changes in fossil fuel emissions, primarily for surface based systems.

The influence of transport model resolution on the inverse modelling of synthetic greenhouse gas emissions in Switzerland

2020 ◽  
Author(s):  
Ioannis Katharopoulos ◽  
Dominique Rust ◽  
Martin Vollmer ◽  
Dominik Brunner ◽  
Stefan Reimann ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Transport Model ◽  
Stratospheric Ozone ◽  
Inverse Modelling ◽  
Model Resolution ◽  
Gas Emissions ◽  
Stratospheric Ozone Layer

&lt;p&gt;Climate change is one of the biggest challenges of the modern era. Halocarbons contribute already about 14% to current anthropogenic radiative forcing, and their future impact may become significantly larger due to their long atmospheric lifetimes and continued and increasing usage. In addition to their influence on climate change, chlorine and bromine-containing halocarbons are the main drivers of the destruction of the stratospheric ozone layer. Therefore, observing their atmospheric abundance and quantifying their sources is critical for predicting the related future impact on climate change and on the recovery of the stratospheric ozone layer.&lt;/p&gt;&lt;p&gt;Regional scale atmospheric inverse modelling can provide observation-based estimates of greenhouse gas emissions at a country scale and, hence, makes valuable information available to policy makers when reviewing emission mitigation strategies and confirming the countries' pledges for emission reduction. Considering that inverse modelling relies on accurate atmospheric transport modelling any advances to the latter are of key importance. The main objective of this work is to characterize and improve the Lagrangian particle dispersion model (LPDM) FLEXPART-COSMO at kilometer-scale resolution and to provide estimates of Swiss halocarbon emissions by integrating newly available halocarbon observations from the Swiss Plateau at the Berom&amp;#252;nster tall tower. The transport model is offline coupled with the regional numerical weather prediction model (NWP) COSMO. Previous inverse modelling results for Swiss greenhouse gases are based on a model resolution of 7 km x 7 km. Here, we utilize higher resolution (1 km x 1 km) operational COSMO analysis fields to drive FLEXPART and compare these to the previous results.&lt;/p&gt;&lt;p&gt;The higher resolution simulations exhibit increased three-dimensional dispersion, leading to a general underestimation of observed tracer concentration at the receptor location and when compared to the coarse model results. The concentration discrepancies due to dispersion between the two model versions cannot be explained by the parameters utilized in FLEPXART&amp;#8217;s turbulence parameterization, (Obhukov length, surface momentum and heat fluxes, atmospheric boundary layer heights, and horizontal and vertical wind speeds), since a direct comparison of these parameters between the different model versions showed no significant differences. The latter suggests that the dispersion differences may originate from a duplication of turbulent transport, on the one hand, covered by the high resolution grid of the Eulerian model and, on the other hand, diagnosed by FLEXPART's turbulence scheme. In an attempt to reconcile FLEXPART-COSMO&amp;#8217;s turbulence scheme at high resolution, we introduced additional scaling parameters based on analysis of simulated mole fraction deviations depending on stability regime. In addition, we used FLEXPART-COSMO source sensitivities in a Bayesian inversion to obtain optimized emission estimates. Inversions for both the high and low resolution models were carried out in order&amp;#160;to quantify the impact of model resolution on posterior emissions and estimate about the uncertainties of these emissions.&amp;#160;&amp;#160;&lt;/p&gt;

Do high resolution GCMs overestimate precipitation over land?

2020 ◽  
Author(s):  
Omar Müller ◽  
Pier Luigi Vidale ◽  
Patrick McGuire ◽  
Benoît Vannière ◽  
Reinhard Schiemann ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
River Discharge ◽  
Hydrological Cycle ◽  
Transport Model ◽  
Meteorological Data ◽  
Grid Point ◽  
Climate Simulation ◽  
Surface Model ◽  
Orographic Precipitation

&lt;p&gt;Previous studies showed that high resolution GCMs overestimate land precipitation when compared against gridded observations or reanalysis (Demory et al. 2014, Vanni&amp;#232;re et al. 2019). In particular, grid point models (eg. HadGEM3) show a significant increase of precipitation on regions dominated by complex orography, where the scarcity of gauge stations increase the uncertainty of gridded observations. The goal of this work is to assess the effect of such differences in precipitation on river discharge, considering it as an integrator of the water balance at catchment scale. A set of JULES and CLM simulations have been conducted turning rivers on with Total Runoff Integrating Pathways (TRIP) and the River Transport Model (RTM) respectively. The simulations form three ensembles for each land surface model (LSM) which main difference is given by the forcing dataset. The forcings are WFDEI (reanalysis), LR (~1&amp;#176; resolution in meteorological data from GCMs) and HR (~0.25&amp;#176; resolution in meteorological data from GCMs). These ensembles are evaluated in a set of 280 catchments distributed around the world.&lt;/p&gt;&lt;p&gt;In terms of correlation between simulated and observed river discharge observations, the results show that LSMs forced by reanalysis have higher performance than LSMs forced by GCMs as expected. In terms of biases, the river discharge is underestimated in eight out of eleven major basins when LSMs are forced by reanalysis. On those basins, the extra precipitation estimated by GCMs help to simulate an amount of river discharge closer to observations (Eg. Yenisey and Lena). Moreover, 37 small basins with a strong component of orographic precipitation over the Andes, the Rocky Mountains, the Alps and in the Maritime Continent were evaluated. In most cases HR offers notably better results than LR and WFDEI, suggesting that high resolution models produce orographic precipitation in the correct place and time.&lt;/p&gt;&lt;p&gt;In future works offline TRIP simulations will be carried out directly forced by runoff and subsurface runoff from GCMs. It will allow to discard errors in evapotranspiration produced by JULES or CLM when they are used to simulate river discharge. This work is part of the European Process-based climate sIMulation: AdVances in high resolution modelling and European climate Risk Assessment (PRIMAVERA) Project. PRIMAVERA is a collaboration between 19 funded by the European Union&amp;#8217;s Horizon 2020 Research &amp; Innovation Programme.&lt;/p&gt;&lt;p&gt;Demory, M. E., Vidale, P. L., Roberts, M. J., Berrisford, P., Strachan, J., Schiemann, R., &amp; Mizielinski, M. S. (2014). The role of horizontal resolution in simulating drivers of the global hydrological cycle.&amp;#160;CLIM DYNAM,&amp;#160;42(7-8), 2201-2225.&lt;/p&gt;&lt;p&gt;Vanni&amp;#232;re, B., Demory, M. E., Vidale, P. L., Schiemann, R., Roberts, M. J., Roberts, C. D., ... &amp; Senan, R. (2018). Multi-model evaluation of the sensitivity of the global energy budget and hydrological cycle to resolution.&amp;#160;CLIM DYNAM, 1-30.&lt;/p&gt;

scholarly journals Trends and inter-annual variability of methane emissions derived from 1979-1993 global CTM simulations

2003 ◽  
Vol 3 (1) ◽  
pp. 73-88 ◽  
Author(s):  
F. Dentener ◽  
M. van Weele ◽  
M. Krol ◽  
S. Houweling ◽  
P. van Velthoven
Keyword(s):  
Transport Model ◽  
Meteorological Data ◽  
Stratospheric Ozone ◽  
Methane Emissions ◽  
Anthropogenic Emission ◽  
Top Down ◽  
Bottom Up ◽  
Annual Variability ◽  
The Difference ◽  
Ozone Precursor

Abstract. The trend and interannual variability of methane sources are derived from multi-annual simulations of tropospheric photochemistry using a 3-D global chemistry-transport model. Our semi-inverse analysis uses the fifteen years (1979--1993) re-analysis of ECMWF meteorological data and annually varying emissions including photo-chemistry, in conjunction with observed CH4 concentration distributions and trends derived from the NOAA-CMDL surface stations. Dividing the world in four zonal regions (45--90 N, 0--45 N, 0--45 S, 45--90 S) we find good agreement in each region between (top-down) calculated emission trends from model simulations and (bottom-up) estimated anthropogenic emission trends based on the EDGAR global anthropogenic emission database, which amounts for the period 1979--1993 2.7 Tg CH4 yr-1. Also the top-down determined total global methane emission compares well with the total of the bottom-up estimates. We use the difference between the bottom-up and top-down determined emission trends to calculate residual emissions. These residual emissions represent the inter-annual variability of the methane emissions. Simulations have been performed in which the year-to-year meteorology, the emissions of ozone precursor gases, and the stratospheric ozone column distribution are either varied, or kept constant. In studies of methane trends it is most important to include the trends and variability of the oxidant fields. The analyses reveals that the variability of the emissions is of the order of 8Tg CH4 yr-1, and likely related to wetland emissions and/or biomass burning.

Sign in / Sign up

Export Citation Format

Share Document