dataesr/scientific-paragraphs-categorization huggingface dataset:
Such agro-hydrological models can be used to estimate at the basin scale the risk of pesticide transfer including the contribution of pond, while improving predictions in a context of global change  [102]. As an example and to illustrate a model adaptation to integrate the role of pond at the catchment scale, the PESTIPOND project is developing a new module. This lumped 0D module integrates at a daily time step the processes associated with pesticide dissipation at the SWI and the water column in each pond using simplified multi-parameters and multi-compartments formalisms (see Section 3. 1. , Figure  1C  and Table  2 ). Model calibration and validation is a critical step that strongly depends on the data series obtained at the pond and at the catchment scales under various hydrological conditions (Figure  1A ).
6 [75]; 7 following the approach developed by [79] ;8 A detailed mass balance can be performed from the existing databases to identify the optimal formalisms and provide a diagnosis with existing data from upstream catchment and ponds. Datasets are obtained from the well-referred experimental sites of the PESTIPOND project (FigureS1and TableS1of the Supplementary Materials). The PESTIPOND experimental sites belong to the RI-OZCAR (Research Infrastructure-Critical Zone Observatories: Research and Application [80] ), the RECOTOX network (Research initiative in toxicology/ecotoxicology [81] ), the RI-RZA (LTSER Zones Ateliers network [82] ), and/or the European RI-eLTER (European Long-Term Ecosystem Research). 9 Monitored river-basin such as the Save (Site of the LTSER Zone Atelier Pyrénées-Garonne, Gascony, 1100 km 2 ) [77, 83, 84]  can support the development and the validation of the last upscaling step of impact of ponds from the headwater catchments to the large river basins.
Introduction The Copernicus Atmosphere Monitoring Service (CAMS; http://atmosphere. copernicus. eu) is a component of the European Union's Earth Observation Programme Copernicus. This service is designed to meet the needs of policy makers and stakeholders for data and information concerning environmental issues such as climate change, air pollution, and other atmospheric challenges like volcanic eruptions. The CAMS core services include, among others, the daily production of near-real-time (NRT) analyses and forecasts of global atmospheric composition (AC), European air quality products with an ensemble system of regional models, and solar and ultraviolet (UV) radiation products. CAMS is also producing global reanalysis data sets of reactive trace gases, greenhouse gases, and aerosol concentrations.
, 2015). After the release of a test reanalysis for reactive gases and aerosols (CAMS Interim Reanalysis;  Flemming et al. , 2017) , the CAMS reanalysis was produced, which covers the years from 2003 onward  (Inness et al. , 2019). The MACC reanalysis suffered from known inconsistencies in the assimilated data, which led to drifts in carbon monoxide (CO) and ozone (O 3 ) model fields and limited its use for reliable trend studies. An important part of the CAMS service is the provision of independent quality assurance information to the CAMS users. A dedicated validation team produces updated evaluations of the CAMS forecast products every 3 months, based on a multitude of independent observational data sets. A description of these validation activities and results are presented in  Eskes et al.
In order to thoroughly assess the impact of data assimilation and the influence of emissions, the results from a separate model run, for which data assimilation has been switched off (further referred to as "control run"), are included and analyzed. Improvements and shortcomings of the CAMS reanalysis compared to the previous MACC reanalysis are likewise quantified and discussed. The article is structured in the following way: Section 2 provides an overview of the reanalysis model system, the validation data, and methods. Section 3 discusses the validation results, and Section 4 handles the conclusions. 2. Description of the CAMS reanalysis system, validation data, and methods.
2  (Kaiser et al. , 2012). Table  S1  lists the major differences between the MACC and CAMS reanalysis data sets. In order to assess the impact of data assimilation, our evaluations also include the CAMS control run. The control run applies the same settings as the CAMS reanalysis, except that data assimilation is switched off. It consists of 24-h cycling forecasts and uses the meteorological fields from the CAMS reanalysis. A more detailed documentation of the CAMS reanalysis model setup can be found on the CAMS Confluence webpage. The CAMS 3D reanalysis products are stored as 3-hourly fields. Data are publicly available from the CAMS Atmosphere Data Store.
winter (see Figure  S2 ). The seasonal biases with respect to MLS are much smaller than for MIPAS. This indicates that the CAMS reanalysis is much more constrained by MLS than by MIPAS and that the seasonal patterns mostly reflect differences between MIPAS and MLS. Seasonal biases between MIPAS and MLS have already been published for the midlatitudes  (Errera et al. , 2019)  and are shown here in the tropical and polar regions. While such interinstrument biases deserve more investigation, they are beyond the scope of this article. In the middle stratosphere (10-30 hPa, middle row), after 2004, the bias is generally within +5% for all limb-scanning satellite data sets. This is in very good agreement with a recent comparison of ACE-FTS with MLS and MIPAS observations, which also reports biases within 5% in the middle stratosphere  (Sheese et al.
, 2017). In the lower stratosphere (30-70 hPa, bottom row), the spread of the biases against the different instruments is larger. The bias against MLS and ACE-FTS is positive (3%-7%) in the Tropics. In the polar regions, the bias against MLS is low (<1. 5%), except during the O 3 hole events (September-October in the 90 -60 S latitude band) when O 3 abundances are approximately 3% larger than in the MLS data set, that is, O 3 depletion is slightly underestimated. The biases against MI-PAS and ACE-FTS are negative but remain always lower than 10% and 5%, respectively. where the limited availability of such data has a negative impact on the validation results. The bias against ACE-FTS is stable over time, except for a slight drift after 2013 in the lower tropical stratosphere (but <1% over 5 years).
g. ,  Cariolle and Teyssedre, 2007). In practice, this leads to considerable biases in the stratospheric profile observed in the control run when comparing with   S2 ) and NDACC data (Figure  S8 , Table  S3 ) in the stratosphere, reflecting uncertainties in the Cariolle O 3 parametrization. Large positive biases of up to 40% appear over Antarctica and the Arctic and modified normalized mean biases (MNMBs) up to 20% over the Northern midlatitudes. For NDACC stations in the Southern midlatitudes and over the Tropics, the control run shows negative biases up to -15% (Figure  S8 ). The assimilation of O 3 total columns and stratospheric profiles in the CAMS reanalysis successfully compensates for this lack in explicit stratospheric chemistry and thus proves to be very effective in constraining stratospheric O 3.
Data assimilation effectively increases tropospheric O 3 , which improves MNMBs in most regions for the CAMS reanalysis compared to the control run. Likewise, the seasonality in the biases is largely eliminated by the data assimilation. In the Tropics, data assimilation is less effective in improving the biases for the CAMS reanalysis compared to the control run. Two periods with larger MNMBs in the time series are noticeable for the CAMS reanalysis, which affect the longterm consistency and relate to data assimilation issues: Tropics, we find larger differences between the control run and the CAMS reanalysis after these changes in 2013 (Figure  S11 ). The evaluation with IAGOS data shows MNMBs of -5% and 25% over North America, -10% to 20% over Europe, between -10% and 40% over East Asia, and +35% over India (Figure  5 ).
Surface ozone Modeled surface O 3 is compared to Global Atmosphere Watch (GAW) data (Figure  7 ), International Arctic Systems for Observing the Atmosphere (IASOA) network Figure  7  shows the time series of MNMBs for surface O 3 calculated from model and GAW observational data, as regional average over stations in four different latitude zones. Biases for surface O 3 are generally larger than for O 3 in the free troposphere but remain within +30%. Largest biases for both CAMS reanalysis and control run appear during Arctic spring (MNMBs up to 80%). A closer look at Arctic stations from the IASOA network (Figure  S17 , Table  S6 ) reveals that the High Arctic coastal stations (Alert, Barrow, and Villum Research Station) are influenced by O 3 depletion events during arctic spring (MAM: March to May).
There is a drift in the interannual time series of seasonal MNMBs toward larger positive biases for the Northern midlatitudes (Figure  10 ). Similar drifts can be observed in the evaluation with EMEP data for Northern Europe (Figure  S18 ) as well as in the evaluation with IAGOS surface data over Europe and East Asia (Figures  S19  and S21 ). Park et al. (2020)  accordingly note inadequateness of the CAMS reanalysis to capture O 3 trends for East Asia. In the Tropics, a drift is visible for JJA (Figure  S22 ). Although further investigations and sensitivity analysis will be needed to prove this, it is likely that unreasonable trends in the emissions are responsible for the drifts (see Section 3. 4). In a recent study,  Gaubert et al. (2020)  show that running CTMs with biased CO and volatile organic compound (VOC) emissions can lead to poorly modeled O 3.
The CAMS reanalysis that assimilates only MOPITT data is more consistent here and thus shows significant improvement. For tropical regions in the Northern Hemisphere (Izana, Mauna Loa, and Altzomoni), the CAMS reanalysis has an increased bias, which is not observed in the MACC run, however. The validation with GAW surface data shows that the MACC reanalysis has a negative offset for surface CO mixing ratios in Europe and North America (see Figure  15 , GAW; Figure  S26 , IAGOS), which is likely related to the unrealistically low wintertime road traffic emissions that were still unscaled in the MACC reanalysis  (Inness et al. , 2013 (Inness et al. , , 2019)).
Fire emissions used by the CAMS reanalysis seem to be largely overestimated for this El Nino year, resulting in an overestimation of up to a factor of 1. 8 compared to the observations. A similar overestimation was also reported for the CAMS NRT product, for which it was shown that this is not due to cloud flagging applied to the satellite and model data  (Huijnen et al. , 2016a)   Comparison with the MACC reanalysis shows that the CAMS reanalysis has lower MNMBs for Indonesia and East Asia during the summer period (Figure  S31 ), likely related to the differences in the fire emissions used (GFED/GFAS v0 in MACC and GFAS v1. 2 in CAMS). For other regions and periods, the CAMS reanalysis results in larger MNMBs.
Conclusions CAMS provides its users with a variety of different products in the field of air quality and AC. Beside the NRT forecasts, there is also a large and growing interest in longterm retrospective analysis (reanalysis) data sets. After the release of the MACC reanalysis in 2013, and an interim test product in 2015, CAMS has now produced a new reanalysis data set (CAMS reanalysis), which is freely available to the public. We have comprehensively validated the reactive gas species (O 3 , CO, NO 2 , and HCHO) of this new product in the period 2003-2018 with multiple independent observations. For reanalysis data sets, a temporal stability of the model results over time is crucial, for example, for trend studies on chemical species. Special focus was thus set on the long-term consistency shown in the time series of biases and on the assessment of seasonal and interseasonal changes in biases.
In order to thoroughly evaluate the impact of data assimilation on the long-term quality of results, a comparison with a control run without assimilated data is conducted. Finally, improvements and shortcomings of the CAMS reanalysis compared to the previous MACC reanalysis are quantified and discussed. Our evaluations show that the CAMS reanalysis reproduces O 3 with MNMBs mostly within +10% in the stratosphere and troposphere of the Northern midlatitudes compared to sonde observations and satellite instruments. Larger biases (up to +38%) appear over the high latitudes, the Tropics, and generally for surface O 3. Total column CO over Europe, the United States, East Asia, and North Africa is reproduced with MNMBs mostly within +10%. Larger MNMBs appear over East Asia and for surface CO, reaching up to +40%.
Furthermore, the control run shows large overestimation for CO in the SH, likely related to the overestimation of fire and biogenic emissions, together with shortcomings in the simulation of the global loss, production, and large-scale transport of CO. Overestimations of HCHO concentrations likewise suggest that fire emissions are overestimated over boreal regions, Indonesia, Africa, and East Asia, especially during years with high fire activity like during the strong El Nin ˜o event 2015/2016. Finally, we also discovered positive drifts in the interannual time series of biases for various species (CO, O 3 , and NO 2 ) in the control run, likely triggered by unrealistic emission trends, especially after 2010. Data assimilation is able to successfully constrain stratospheric and tropospheric O 3 and CO and thus ensures the long-term consistency and stability of the CAMS reanalysis.
However, this works less effectively near the surface and for short-lived species like NO 2. Our evaluations concerning the long-term stability of the CAMS reanalysis show that the consistency in the quality of model results is also essentially affected by limitations in the availability of high-quality data for assimilation and by changes in the assimilated satellite data sets. Especially during the first years, degraded quality data and the lack of O 3 profile data deteriorated the validation results for O 3 and NO 2. Modifications in the assimilation system in 2012/2013 cause jumps in the interannual seasonal time series of biases for tropospheric and surface O 3 , especially over high-latitude regions. For trend analysis, these effects related to changes in the data assimilation need to be considered and removed.
Compared to the MACC reanalysis, the CAMS reanalysis has systematically lower biases, better correlation, and a weaker seasonal pattern for O 3 especially in the free troposphere and at the surface. Aside from the improved data assimilation, especially the change in the chemistry module, that is, the online-coupled IFS model combined with the different chemistry treatment in the CB05 model leads to a reduction of biases for tropospheric and surface O 3. Largest improvement in the magnitude of biases is thus found over the Arctic and Antarctic regions. For CO, the comparison with the MACC reanalysis shows that the scaling of the winter road traffic emissions and a more consistent data assimilation improve the results. For NO 2 , the CAMS reanalysis shows a better reproduction of wintertime NO 2 over East Asia.
For South Africa, however, the CAMS reanalysis has larger underestimations of observed NO 2 during SH summer and autumn. For HCHO, improvements compared to the MACC reanalysis can be seen over Indonesia, but for North Africa and the Eastern United States, the MACC reanalysis shows smaller biases. For next-generation CAMS reanalyses, challenges in the data assimilation will include the integration of more species (e. g. , HCHO) and additional sensors (O 3 profile data turned out to have a crucial impact) while assuring good long-term stability of results. New sensors such as sentinel 5P will provide promising perspectives. Our results concerning the control run suggest that deficiencies in the model's chemistry and transport scheme need to be investigated and improved further, especially in combination with the emission data sets, to remove large zonal errors such as the overestimation of CO in the SH as well as drifts and seasonal patterns in the biases of the control run.
Given the large impact of anthropogenic, fire, and biogenic emissions on CO, more care should be taken to investigate and consolidate emission rates and trends. Simple scaling approaches as conducted for the wintertime traffic emissions could be replaced by more sophisticated approximations, available from recent bottom-up and top-down inventories. A more comprehensive stratospheric chemistry scheme could improve the model results in the stratosphere. Furthermore, the integration of small-scale local processes, such as halogen chemistry, could help to reduce more local sources of errors in the model system, such as the large biases for surface O 3 over the Arctic in springtime. The CAMS reanalysis data set that was not produced in the frame of our study but as a product of CAMS can be found at Atmosphere Data Store.
Acknowledgments The Copernicus Atmosphere Monitoring Service is operated by the  European Centre for Medium-Range Weather Forecasts (ECMWF)  on behalf of the  European Commission  as part of the  Copernicus program  (http:// copernicus. eu). The authors would like to acknowledge the provision of Global Atmosphere Watch and European Monitoring and Evaluation Programme hourly station data (CO and O 3 ) and aerosol data from the World Data Centre of Wagner et al: Evaluation of the Copernicus Atmosphere Monitoring Service reanalysis Art. 9(1) page 25 of 31 Downloaded from http://online. ucpress. edu/elementa/article-pdf /9/1/00171/463928/elementa. 2020. 00171. pdfby guest on 17 April 2022 Greenhouse Gases (WDCGG), from the EBAS database, and from the  Norwegian Institute for Air Research (NILU) database.
To introduce a control variable corresponding to the application of an entomopathogenic fungus, we then extend the model and include the fungus dynamics. We formulate an optimal problem which consists in maximising the coffee yield, while minimising the control cost, as well as the CBB population for the next cropping season. The existence of the optimal control and the necessary optimality condition are established using Pontryagin's Maximum Principle. The optimal control problem is solved numerically using the BOCOP software and simulations are provided, showing that the use of entomopathogenic fungus effectively controls CBB.
In this work, we propose a more realistic optimal control problem, based on the release of the entomopathogenic fungus B. bassiana and a linear objective function, which represents the cost of fungus application and hence the profit better than a quadratic function. This paper is organised as follows. The mathematical model is formulated in Section 2, based on the CBB life cycle; the positivity and asymptotic behaviour of the model are determined in Section 3. Section 4 presents the optimal control problem, which aims at maximising the profit while minimising the CBB population for the next cropping season. The problem is solved analytically using Pontryagin's maximum principle (PMP)  21  in Section 4 and numerically using a direct method implemented in the BOCOP software in Section 5. Finally, conclusions are drawn in Section 6.
Optimal control To solve our optimal control problem numerically, we use the BOCOP software,  42, 43  an open source toolbox in which the optimal control problem is approximated by a finite dimensional optimisation problem. In BOCOP we choose the Lobatto discretisation method (implicit, 4 stages, order 6), 2000 time steps, and a 10 -5 tolerance. We use the following initial conditions. At the beginning of a cropping season, there are no berries, so s(0) = 0. Moreover, we assume that there are only colonising females, as infesting females were eliminated from the plantation when berries were manually removed during the preceding harvest, so z(0) = 0. We also assume that the entomopathogenic fungus is absent in the plantation, so the initial load v(0) = 0. We consider several initial conditions for the colonising females: (0) = 10 2 in Figure  2 , a fairly low number of females corresponding to a new infestation of the plantation; (0) = 10 6 in Figure  4 , which is close to the coexistence equilibrium value without control and hence corresponds to a worst case scenario, where all females have survived since the end of the preceding cropping season; and (0) = 10 4 in Figure  3 , which is an intermediate value.
ACKNOWLEDGEMENTS This work was supported by  EPITAG , an  Inria  associated team part of the LIRIMA (https://team. inria. fr/epitag/),as well as the  Collège doctoral régional de l'Afrique Centrale et des Grands Lacs "Mathématiques, Informatique, Biosciences et Géosciences de l'environnement" (AUF, French Embassy in Cameroon, IRD ). We would like to thank  Dr. Pierre Martinon  for helpful discussions about the BOCOP software.
Informed Consent Statement: The process adhered entirely to the ESOMAR guidelines on ethical online research. This includes assurances that respondents gave informed explicit consent to take part in the survey and had their personal data protected. Indeed, after being informed of the objectives of the survey and how the given information will be used, all respondents gave their informed consent for inclusion of their answers before and after they participated in the study. Respondent details have been collected in an anonymous way with no personally identifiable information and with an option not to answer.
, 2018a, for LULUCF; Peters et al. , 2009 , for fossil sectors) but not in a systematic and comprehensive way including both fossil and land-based CO 2 fluxes. The work presented here represents many distinct datasets and use of models in addition to the individual country submissions to the UNFCCC for all European countries, which while following the general guidance laid out in  IPCC (2006)  still differ in specific approaches, models and parameters, in addition to differences in underlying activity datasets. A A. M. R. Petrescu et al. :  European synthesis of CO 2 emissions and removals for the EU27 and  UK: 1990 UK: -2018  comprehensive investigation of detailed differences between all datasets is beyond the scope of this paper, though attempts have been previously made for specific subsectors (Petrescu et al.
, 2020b, for AFOLU 3 ; Federici et al. , 2015, for  FAOSTAT versus NGHGIs). As this is the most comprehensive comparison of NGHGIs and research datasets (including both bottom-up (BU) and top-down (TD) approaches) for Europe to date, we focus here on a set of questions that such a comparison raises. How can one fairly compare the detailed sectoral NGHGIs to observation-based estimates? What new information do the observation-based estimates provide, for instance on the mean fluxes, spatial disaggregation, trends and inter-annual variation? What can one expect from such complex studies, where are the key knowledge gaps, what is the added value to policy makers and what are the next steps to take? We compare official anthropogenic NGHGI emissions with research datasets correcting wherever needed research data on total emissions/sinks to separate out anthropogenic emissions.
We analyze differences and inconsistencies between emissions and sinks and make recommendations towards future actions to evaluate NGHGI data. While NGHGIs include uncertainty estimates, special disaggregated research datasets of emissions often lack quantification of uncertainty. While this is also a call to those developers to associate more detailed uncertainty estimates with their products, here we use the median and minimum/maximum (min/max) range of different research products of the same type to get a first estimate of overall uncertainty. Table  A2  in Appendix A presents the methodological differences of current study with respect to  Petrescu et al. (2020b).