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 ◽  
...  

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.

2007 ◽  
Vol 7 (13) ◽  
pp. 3461-3479 ◽  
Author(s):  
C. Geels ◽  
M. Gloor ◽  
P. Ciais ◽  
P. Bousquet ◽  
P. Peylin ◽  
...  

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.


2018 ◽  
Author(s):  
Rishi Rajalingham ◽  
Elias B. Issa ◽  
Pouya Bashivan ◽  
Kohitij Kar ◽  
Kailyn Schmidt ◽  
...  

ABSTRACTPrimates—including humans—can typically recognize objects in visual images at a glance even in the face of naturally occurring identity-preserving image transformations (e.g. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNNIC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC models were significantly non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks—such as those obtained here—could serve as direct guides for discovering such models.SIGNIFICANCE STATEMENTRecently, specific feed-forward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys, at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models significantly diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system.


2015 ◽  
Vol 15 (6) ◽  
pp. 9767-9813 ◽  
Author(s):  
R. Paugam ◽  
M. Wooster ◽  
S. R. Freitas ◽  
M. Val Martin

Abstract. Landscape fires produce smoke containing a very wide variety of chemical species, both gases and aerosols. For larger, more intense fires that produce the greatest amounts of emissions per unit time, the smoke tends initially to be transported vertically or semi-vertically close by the source region, driven by the intense heat and convective energy released by the burning vegetation. The column of hot smoke rapidly entrains cooler ambient air, forming a rising plume within which the fire emissions are transported. This characteristics of this plume, and in particular the height to which it rises before releasing the majority of the smoke burden into the wider atmosphere, are important in terms of how the fire emissions are ultimately transported, since for example winds at different altitudes maybe quite different. This difference in atmospheric transport then may also affect the longevity, chemical conversion and fate of the plumes chemical consituents, with for example very high plume injection heights being associated with extreme long-range atmospheric transport. Here we review how such landscape-scale fire smoke plume injection heights are represented in larger scale atmospheric transport models aiming to represent the impacts of wildfire emissions on component of the Earth system. The use of satellite Earth observation (EO) data is commonly used for this, and detail the EO datasets capable of being used to remotely assess wildfire plume height distributions and the driving characteristics of the causal fires. We also discus both the physical mechanisms and dynamics taking place in fire plumes, and investigate the efficiency and limitations of currently available injection height parameterizations. Finally, we conclude by suggestion some future parameterization developments and ideas on EO data selection that maybe relevant to the instigation of enhanced methodologies aimed at injection height representation.


Nature ◽  
2002 ◽  
Vol 415 (6872) ◽  
pp. 626-630 ◽  
Author(s):  
Kevin Robert Gurney ◽  
Rachel M. Law ◽  
A. Scott Denning ◽  
Peter J. Rayner ◽  
David Baker ◽  
...  

2010 ◽  
Vol 10 (20) ◽  
pp. 9981-9992 ◽  
Author(s):  
S. Houweling ◽  
I. Aben ◽  
F.-M. Breon ◽  
F. Chevallier ◽  
N. Deutscher ◽  
...  

Abstract. This study presents a synthetic model intercomparison to investigate the importance of transport model errors for estimating the sources and sinks of CO2 using satellite measurements. The experiments were designed for testing the potential performance of the proposed CO2 lidar A-SCOPE, but also apply to other space borne missions that monitor total column CO2. The participating transport models IFS, LMDZ, TM3, and TM5 were run in forward and inverse mode using common a priori CO2 fluxes and initial concentrations. Forward simulations of column averaged CO2 (xCO2) mixing ratios vary between the models by σ=0.5 ppm over the continents and σ=0.27 ppm over the oceans. Despite the fact that the models agree on average on the sub-ppm level, these modest differences nevertheless lead to significant discrepancies in the inverted fluxes of 0.1 PgC/yr per 106 km2 over land and 0.03 PgC/yr per 106 km2 over the ocean. These transport model induced flux uncertainties exceed the target requirement that was formulated for the A-SCOPE mission of 0.02 PgC/yr per 106 km2, and could also limit the overall performance of other CO2 missions such as GOSAT. A variable, but overall encouraging agreement is found in comparison with FTS measurements at Park Falls, Darwin, Spitsbergen, and Bremen, although systematic differences are found exceeding the 0.5 ppm level. Because of this, our estimate of the impact of transport model uncerainty is likely to be conservative. It is concluded that to make use of the remote sensing technique for quantifying the sources and sinks of CO2 not only requires highly accurate satellite instruments, but also puts stringent requirements on the performance of atmospheric transport models. Improving the accuracy of these models should receive high priority, which calls for a closer collaboration between experts in atmospheric dynamics and tracer transport.


2019 ◽  
Vol 491 (2) ◽  
pp. 1600-1621
Author(s):  
Yi Mao ◽  
Jun Koda ◽  
Paul R Shapiro ◽  
Ilian T Iliev ◽  
Garrelt Mellema ◽  
...  

ABSTRACT Cosmic reionization was driven by the imbalance between early sources and sinks of ionizing radiation, both of which were dominated by small-scale structure and are thus usually treated in cosmological reionization simulations by subgrid modelling. The recombination rate of intergalactic hydrogen is customarily boosted by a subgrid clumping factor, 〈n2〉/〈n〉2, which corrects for unresolved fluctuations in gas density n on scales below the grid-spacing of coarse-grained simulations. We investigate in detail the impact of this inhomogeneous subgrid clumping on reionization and its observables, as follows: (1) Previous attempts generally underestimated the clumping factor because of insufficient mass resolution. We perform a high-resolution N-body simulation that resolves haloes down to the pre-reionization Jeans mass to derive the time-dependent, spatially varying local clumping factor and a fitting formula for its correlation with local overdensity. (2) We then perform a large-scale N-body and radiative transfer simulation that accounts for this inhomogeneous subgrid clumping by applying this clumping factor-overdensity correlation. Boosting recombination significantly slows the expansion of ionized regions, which delays completion of reionization and suppresses 21 cm power spectra on large scales in the later stages of reionization. (3) We also consider a simplified prescription in which the globally averaged, time-evolving clumping factor from the same high-resolution N-body simulation is applied uniformly to all cells in the reionization simulation, instead. Observables computed with this model agree fairly well with those from the inhomogeneous clumping model, e.g. predicting 21 cm power spectra to within 20 per cent error, suggesting it may be a useful approximation.


2021 ◽  
Author(s):  
Florian Haenel ◽  
Wolfgang Woiwode ◽  
Jennifer Buchmüller ◽  
Felix Friedl-Vallon ◽  
Michael Höpfner ◽  
...  

Abstract. Water vapour and ozone are important for the thermal and radiative balance of the upper troposphere (UT) and lowermost stratosphere (LMS). Both species are modulated by transport processes. Chemical and microphysical processes affect them differently. Thus, representing the different processes and their interactions is a challenging task for dynamical cores, chemical modules and microphysical parameterisations of state-of-the-art atmospheric model components. To test and improve the models, high resolution measurements of the UT/LMS are required. Here, we use measurements taken in a challenging case study by the GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) instrument on HALO. The German research aircraft HALO (High Altitude and LOng range research aircraft) performed a research flight on 26 February 2016, which covered deeply subsided air masses of the aged 2015/16 Arctic vortex, high-latitude LMS air masses, a highly textured troposphere-to-stratosphere exchange mixing region, and high-altitude cirrus clouds. Therefore, it provides a multifaceted case study for comparing GLORIA observations with state-of-the-art atmospheric model simulations in a complex UT/LMS region at a late stage of the Arctic winter 2015/16. Using GLORIA observations in this manifold scenario, we test the ability of the numerical weather prediction (NWP)-model ICON (ICOsahedral Nonhydrostatic) with the extension ART (Aerosols and Reactive Trace gases) and the chemistry-climate model (CCM) EMAC (ECHAM5/MESSy Atmospheric Chemistry) to model the UT/LMS composition of water vapour (H2O), ozone (O3), nitric acid (HNO3) and clouds. Within the scales resolved by the respective model, we find good overall agreement of both models with GLORIA. The applied high-resolution ICON-ART setup involving a R2B7 nest (local grid refinement with a horizontal resolution of about 20 km), covering the HALO flight region, reproduces mesoscale dynamical structures well. An observed troposphere-to-stratosphere exchange connected to an occluded Icelandic low is clearly reproduced by the model. Given the lower resolution (T106) of the nudged simulation of the EMAC model, we find that this model also reproduces these features well. Overall, trace gas mixing ratios simulated by both models are in a realistic range, and major cloud systems observed by GLORIA are mostly reproduced. However, we find both models to be affected by a well-known systematic moist-bias in the LMS. Further biases are diagnosed in the ICON-ART O3, EMAC H2O and EMAC HNO3 distributions. Finally, we use sensitivity simulations to investigate (i) short-term cirrus cloud impacts on the H2O distribution (ICON-ART), (ii) the overall impact of polar winter chemistry and microphysical processing on O3 and HNO3 (ICON-ART/EMAC), (iii) the impact of the model resolution on simulated parameters (EMAC), and (iv) consequences of scavenging processes by cloud particles (EMAC). We find that changing of the horizontal model resolution results in notable systematic changes for all species in the LMS, while scavenging processes play only a role in case of HNO3. We need to understand the representativeness of our results. However, this is a unique opportunity to characterise model biases that potentially affect forecasts and projection (adversely), and to discover deficits and define paths for further model improvements.


2020 ◽  
Author(s):  
Dominik Brunner ◽  
Jean-Matthieu Haussaire ◽  
Julia Marshall ◽  
Arjo Segers ◽  
Hugo Denier van der Gon ◽  
...  

&lt;p&gt;Emissions of carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) will have to be drastically reduced in the coming decades to reach the goal of the Paris Agreement to limit the global temperature increase to no more than 2&amp;#176;C. To support this process, Europe is planning to establish a CO&lt;sub&gt;2&lt;/sub&gt; anthropogenic emission monitoring system, which will assist countries, cities and facility operators in monitoring their emissions and evaluating the progress towards their reduction targets. The system will combine measurements from ground-based networks with observations from a new constellation of CO&lt;sub&gt;2&lt;/sub&gt; satellites, which will provide high-resolution images of total column CO&lt;sub&gt;2&lt;/sub&gt; allowing tracking the plumes of large emission sources. A suite of atmospheric transport modelling systems will assimilate these observations and inversely estimate emissions from the continental to the country scale and down to the scale of individual cities and power plants.&lt;/p&gt;&lt;p&gt;In the European project &quot;CO&lt;sub&gt;2&lt;/sub&gt; Human Emissions&quot; (CHE), the components of such a modelling framework are explored, which includes the generation of a library of realistic atmospheric CO&lt;sub&gt;2&lt;/sub&gt; simulations. These &quot;nature runs&quot; are obtained by running global and regional atmospheric transport models at the highest possible resolution affordable today and using state-of-the-art inputs of anthropogenic emissions and natural CO&lt;sub&gt;2&lt;/sub&gt; fluxes. The library includes global simulations at 9 km x 9 km resolution with the CAMS-IFS model, European simulations at 5 km x 5 km resolution with WRF-GHG, COSMO-GHG and LOTOS-EUROS, and high-resolution simulations at 1 km x 1 km over the city of Berlin and several power plants with COSMO-GHG and LOTOS-EUROS.&lt;/p&gt;&lt;p&gt;Here we analyse and compare the model simulations to address the following questions: How realistically are atmospheric gradients in CO&lt;sub&gt;2&lt;/sub&gt; caused by spatial and temporal variations in biospheric and anthropogenic fluxes and by atmospheric dynamics represented at the different model resolutions? What resolution is required to resolve the plumes of individual cities and power plants? How large are the differences in near surface and total column CO&lt;sub&gt;2&lt;/sub&gt; due to uncertainties in atmospheric transport including uncertainties in vertical mixing? Information on transport uncertainties is derived from an ensemble of CAMS-IFS simulations and from the spread between the individual models.&lt;/p&gt;&lt;p&gt;Answering these questions is critical for the design of a future operational capacity to monitor anthropogenic CO&lt;sub&gt;2&lt;/sub&gt; emissions, which should optimally support decision makers at facility, city, and country scale as well as the global stocktake process of the Paris Agreement.&lt;/p&gt;


Tellus B ◽  
1996 ◽  
Vol 48 (4) ◽  
pp. 568-582 ◽  
Author(s):  
P. Bousquet ◽  
P. Ciais ◽  
P. Monfray ◽  
Y. Balkanski ◽  
M. Ramonet ◽  
...  

2016 ◽  
Vol 16 (2) ◽  
pp. 907-925 ◽  
Author(s):  
R. Paugam ◽  
M. Wooster ◽  
S. Freitas ◽  
M. Val Martin

Abstract. Landscape fires produce smoke containing a very wide variety of chemical species, both gases and aerosols. For larger, more intense fires that produce the greatest amounts of emissions per unit time, the smoke tends initially to be transported vertically or semi-vertically close by the source region, driven by the intense heat and convective energy released by the burning vegetation. The column of hot smoke rapidly entrains cooler ambient air, forming a rising plume within which the fire emissions are transported. The characteristics of this plume, and in particular the height to which it rises before releasing the majority of the smoke burden into the wider atmosphere, are important in terms of how the fire emissions are ultimately transported, since for example winds at different altitudes may be quite different. This difference in atmospheric transport then may also affect the longevity, chemical conversion, and fate of the plumes chemical constituents, with for example very high plume injection heights being associated with extreme long-range atmospheric transport. Here we review how such landscape-scale fire smoke plume injection heights are represented in larger-scale atmospheric transport models aiming to represent the impacts of wildfire emissions on component of the Earth system. In particular we detail (i) satellite Earth observation data sets capable of being used to remotely assess wildfire plume height distributions and (ii) the driving characteristics of the causal fires. We also discuss both the physical mechanisms and dynamics taking place in fire plumes and investigate the efficiency and limitations of currently available injection height parameterizations. Finally, we conclude by suggesting some future parameterization developments and ideas on Earth observation data selection that may be relevant to the instigation of enhanced methodologies aimed at injection height representation.


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