Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NOx, and SO2

We present a modelling framework for fossil fuel CO2 emissions in an urban environment, which allows constraints from emission inventories to be combined with atmospheric observations of CO2 and its co-emitted species CO, NOx , and SO2. Rather than a static assignment of average emission rates to each unit area of the urban domain, the fossil fuel emissions we use are dynamic: they vary in time and space in relation to data that describe or approximate the activity within a sector, such as traffic density, power demand, 2m temperature (as proxy for heating demand), and sunlight and wind speed (as proxies for renewable energy supply). Through inverse modelling, we optimize the relationships between these activity data and the resulting emissions of all species within the dynamic fossil fuel emission model, based on atmospheric mole fraction observations. The advantage of this novel approach is that the optimized parameters (emission factors and emission ratios, N D 44) in this dynamic emission model (a) vary much less over space and time, (b) allow for a physical interpretation of mean and uncertainty, and (c) have better defined uncertainties and covariance structure. This makes them more suited to extrapolate, optimize, and interpret than the gridded emissions themselves. The merits of this approach are investigated using a pseudo-observation-based ensemble Kalman filter inversion set-up for the Dutch Rijnmond area at 1km-1km resolution. We find that the fossil fuel emission model approximates the gridded emissions well (annual mean differences < 2 %, hourly temporal r2 D 0:21-0.95), while reported errors in the underlying parameters allow a full covariance structure to be created readily. Propagating this error structure into atmospheric mole fractions shows a strong dominance of a few large sectors and a few dominant uncertainties, most notably the emission ratios of the various gases considered. If the prior emission ratios are either sufficiently well-known or well constrained from a dense observation network, we find that including observations of co-emitted species improves our ability to estimate emissions per sector relative to using CO2 mole fractions only. Nevertheless, the total CO2 emissions can be well constrained with CO2 as the only tracer in the inversion. Because some sectors are sampled only sparsely over a day, we find that propagating solutions from day-to-day leads to largest uncertainty reduction and smallest CO2 residuals over the 14 consecutive days considered. Although we can technically estimate the temporal distribution of some emission categories like shipping separate from their total magnitude, the controlling parameters are difficult to distinguish. Overall, we conclude that our new system looks promising for application in verification studies, provided that reliable urban atmospheric transport fields and reasonable a priori emission ratios for CO2 and its co-emitted species can be produced

The combined effect of elevation and meteorology on potato crop dynamics : a 10-year study in the Gamo Highlands, Ethiopia

Potato (Solanum tuberosum L.) is an important crop in the Gamo Highlands in Ethiopia. The region is characterised by a complex topography with large inter-annual weather variations, where potatoes grow in a range of altitudes between 1,600 and 3,200 m above sea level (a.s.l.). Traditional large-scale crop modelling studies only crudely represent the effect of complex topography, misrepresenting spatial variability in meteorology and potato growth in the region. Here, we investigate how weather influenced by topography affects crop growth. We used the Weather Research and Forecasting (WRF) model to simulate weather in relation to topography in coarse (54 km × 54 km) and fine (2 km × 2 km) resolution domains. The first has a resolution similar to those used by large-scale crop modelling studies that only crudely resolve the horizontal and vertical spatial effects of topography. The second realistically represents the most important topographical variations. The weather variables modelled in both the coarse and fine resolution domains are given as input to the GECROS model (Genotype-by-Environment interaction on CROp growth Simulator) to simulate the potato growth. We modelled potato growth from 2001 to 2010 and studied its inter-annual variability. This enabled us to determine for the first time in Ethiopia how variations in weather are linked to crop dynamics as a function of elevation at a fine resolution. We found that due to its finer representation of topography, weather and crop growth spatio-temporal variations were better represented in the fine than in the coarse resolution domain. The magnitude of crop growth variables such as Leaf Area Index (LAI) and Length of the Growing Season (LGS) obtained with weather from the coarse resolution domain were unrealistically low, hence unacceptable. Nevertheless, the resulting potato yields in the coarse resolution domain were comparable with the yields from the fine resolution domain. We explain this paradoxical finding in terms of a compensating effect, as the opposite effects of temperature and precipitation on yield compensated for each other along the major potato growing transect in the Gamo Highlands. These offsetting effects were also dependent on the correct estimations of the LGS, LAI. We conclude that a well-resolved representation of complex topography is crucial to realistically model meteorology and crop physiology in tropical mountainous areas

The CarbonTracker Data Assimilation System for CO2 and delta C-13 (CTDAS-C13 v1.0):retrieving information on land-atmosphere exchange processes

To improve our understanding of the global carbon balance and its representation in terrestrial biosphere models, we present here a first dual-species application of the CarbonTracker Data Assimilation System (CTDAS). The system's modular design allows for assimilating multiple atmospheric trace gases simultaneously to infer exchange fluxes at the Earth surface. In the prototype discussed here, we interpret signals recorded in observed carbon dioxide (CO2) along with observed ratios of its stable isotopologues (CO2)-C-13/(CO2)-C-12 (delta C-13). The latter is in particular a valuable tracer to untangle CO2 exchange from land and oceans. Potentially, it can also be used as a proxy for continent-wide drought stress in plants, largely because the ratio of (CO2)-C-13 and (CO2)-C-12 molecules removed from the atmosphere by plants is dependent on moisture conditions. The dual-species CTDAS system varies the net exchange fluxes of both (CO2)-C-13 and CO2 in ocean and terrestrial biosphere models to create an ensemble of (CO2)-C-13 and CO2 fluxes that propagates through an atmospheric transport model. Based on differences between observed and simulated (CO2)-C-13 and CO2 mole fractions (and thus delta C-13) our Bayesian minimization approach solves for weekly adjustments to both net fluxes and isotopic terrestrial discrimination that minimizes the difference between observed and estimated mole fractions. With this system, we are able to estimate changes in terrestrial delta C-13 exchange on seasonal and continental scales in the Northern Hemisphere where the observational network is most dense. Our results indicate a decrease in stomatal conductance on a continent-wide scale during a severe drought. These changes could only be detected after applying combined atmospheric CO2 and delta C-13 constraints as done in this work. The additional constraints on surface CO2 exchange from delta C-13 observations neither affected the estimated carbon fluxes nor compromised our ability to match observed CO2 variations. The prototype presented here can be of great benefit not only to study the global carbon balance but also to potentially function as a data-driven diagnostic to assess multiple leaf-level exchange parameterizations in carbon-climate models that influence the CO2, water, isotope, and energy balance

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

A multi-model approach to monitor emissions of CO₂ and CO from an urban–industrial complex

Monitoring urban–industrial emissions is often challenging because observations are scarce and regional atmospheric transport models are too coarse to represent the high spatiotemporal variability in the resulting concentrations. In this paper we apply a new combination of an Eulerian model (Weather Research and Forecast, WRF, with chemistry) and a Gaussian plume model (Operational Priority Substances – OPS). The modelled mixing ratios are compared to observed CO2 and CO mole fractions at four sites along a transect from an urban–industrial complex (Rotterdam, the Netherlands) towards rural conditions for October–December 2014. Urban plumes are well-mixed at our semi-urban location, making this location suited for an integrated emission estimate over the whole study area. The signals at our urban measurement site (with average enhancements of 11 ppm CO2 and 40 ppb CO over the baseline) are highly variable due to the presence of distinct source areas dominated by road traffic/residential heating emissions or industrial activities. This causes different emission signatures that are translated into a large variability in observed ΔCO : ΔCO2 ratios, which can be used to identify dominant source types. We find that WRF-Chem is able to represent synoptic variability in CO2 and CO (e.g. the median CO2 mixing ratio is 9.7 ppm, observed, against 8.8 ppm, modelled), but it fails to reproduce the hourly variability of daytime urban plumes at the urban site (R2 up to 0.05). For the urban site, adding a plume model to the model framework is beneficial to adequately represent plume transport especially from stack emissions. The explained variance in hourly, daytime CO2 enhancements from point source emissions increases from 30 % with WRF-Chem to 52 % with WRF-Chem in combination with the most detailed OPS simulation. The simulated variability in ΔCO :  ΔCO2 ratios decreases drastically from 1.5 to 0.6 ppb ppm−1, which agrees better with the observed standard deviation of 0.4 ppb ppm−1. This is partly due to improved wind fields (increase in R2 of 0.10) but also due to improved point source representation (increase in R2 of 0.05) and dilution (increase in R2 of 0.07). Based on our analysis we conclude that a plume model with detailed and accurate dispersion parameters adds substantially to top–down monitoring of greenhouse gas emissions in urban environments with large point source contributions within a  ∼  10 km radius from the observation sites

Responses of Canopy Growth and Yield of Potato Cultivars to Weather Dynamics in a Complex Topography: <i>Belg</i> Farming Seasons in the Gamo Highlands, Ethiopia

Potato is an increasingly important crop in Ethiopia. The Gamo Highlands are one of the large potential potato producing regions in Ethiopia. The growing conditions are different from those in the temperate regions, where most of the agronomical expertise on potato has been developed. The influence of environmental conditions on the crop in the Gamo Highlands is poorly understood. We conducted field trials with eight potato cultivars in six locations and during two seasons. The canopy cover (CC) and plant height (PH) were measured with high temporal resolution and tuber yields were assessed as well. The experiments were conducted near our newly installed weather stations at different elevations. CC and PH were strongly correlated with temperature sum (Tsum). Tuber yields differed among elevations and cultivars. Nevertheless, these differences were poorly explained by environmental variables. We also found that no single cultivar performed best at all elevations. The number of branches was a predictor of yield, suggesting that radiation interception was limiting tuber growth. Tuber yield was optimal when the number of days to crop maturity was around 100&#8211;110 days. We conclude that Tsum is a predictor of crop growth, but environmental variables poorly explain yield variations, which calls for further investigation

Correction: Responses of canopy growth and yield of potato cultivars to weather dynamics in a complex topography: Belg farming seasons in the gamo highlands, Ethiopia

In Minda et al. [1], an error was introduced. We propose the following amendment: Figure 9, in Section 3.2.4 (Days to Maturity and Yield), should be replaced by the following updated figure. (Figure Presented) The authors apologize for any inconvenience caused to the readers by these changes. The manuscript will be updated and the original will remain online on the article webpage, with a reference to this correction.</p

Responses of Canopy Growth and Yield of Potato Cultivars to Weather Dynamics in a Complex Topography: Belg Farming Seasons in the Gamo Highlands, Ethiopia

Potato is an increasingly important crop in Ethiopia. The Gamo Highlands are one of the large potential potato producing regions in Ethiopia. The growing conditions are different from those in the temperate regions, where most of the agronomical expertise on potato has been developed. The influence of environmental conditions on the crop in the Gamo Highlands is poorly understood. We conducted field trials with eight potato cultivars in six locations and during two seasons. The canopy cover (CC) and plant height (PH) were measured with high temporal resolution and tuber yields were assessed as well. The experiments were conducted near our newly installed weather stations at different elevations. CC and PH were strongly correlated with temperature sum (Tsum). Tuber yields differed among elevations and cultivars. Nevertheless, these differences were poorly explained by environmental variables. We also found that no single cultivar performed best at all elevations. The number of branches was a predictor of yield, suggesting that radiation interception was limiting tuber growth. Tuber yield was optimal when the number of days to crop maturity was around 100–110 days. We conclude that Tsum is a predictor of crop growth, but environmental variables poorly explain yield variations, which calls for further investigatio

The CarbonTracker Data Assimilation System for CO&lt;sub&gt;2&lt;/sub&gt; and &lt;i&gt;δ&lt;/i&gt;&lt;sup&gt;13&lt;/sup&gt;C (CTDAS-C13 v1.0): retrieving information on land–atmosphere exchange processes

To improve our understanding of the global carbon balance and its representation in terrestrial biosphere models, we present here a first dual-species application of the CarbonTracker Data Assimilation System (CTDAS). The system's modular design allows for assimilating multiple atmospheric trace gases simultaneously to infer exchange fluxes at the Earth surface. In the prototype discussed here, we interpret signals recorded in observed carbon dioxide (CO2) along with observed ratios of its stable isotopologues (CO2)-C-13/(CO2)-C-12 (delta C-13). The latter is in particular a valuable tracer to untangle CO2 exchange from land and oceans. Potentially, it can also be used as a proxy for continent-wide drought stress in plants, largely because the ratio of (CO2)-C-13 and (CO2)-C-12 molecules removed from the atmosphere by plants is dependent on moisture conditions. The dual-species CTDAS system varies the net exchange fluxes of both (CO2)-C-13 and CO2 in ocean and terrestrial biosphere models to create an ensemble of (CO2)-C-13 and CO2 fluxes that propagates through an atmospheric transport model. Based on differences between observed and simulated (CO2)-C-13 and CO2 mole fractions (and thus delta C-13) our Bayesian minimization approach solves for weekly adjustments to both net fluxes and isotopic terrestrial discrimination that minimizes the difference between observed and estimated mole fractions. With this system, we are able to estimate changes in terrestrial delta C-13 exchange on seasonal and continental scales in the Northern Hemisphere where the observational network is most dense. Our results indicate a decrease in stomatal conductance on a continent-wide scale during a severe drought. These changes could only be detected after applying combined atmospheric CO2 and delta C-13 constraints as done in this work. The additional constraints on surface CO2 exchange from delta C-13 observations neither affected the estimated carbon fluxes nor compromised our ability to match observed CO2 variations. The prototype presented here can be of great benefit not only to study the global carbon balance but also to potentially function as a data-driven diagnostic to assess multiple leaf-level exchange parameterizations in carbon-climate models that influence the CO2, water, isotope, and energy balance