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emep:emep-experts:tfmmeurodeltacarb [2020-02-28 11:52:16] augustin.colette@ineris.fr created |
emep:emep-experts:tfmmeurodeltacarb [2022-05-31 09:29:32] (current) |
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===== Participants: | ===== Participants: | ||
+ | Coordination | ||
+ | * Augustin Colette (INERIS, EMEP/TFMM Chair, CAMS_50 Scientific Advisor) | ||
+ | Modellers: | ||
+ | * EMEP/MSC-W: Hilde Fagerli, David Simpson, Svetlana Tsyro (Met Norway MSC-W, NO) | ||
+ | * EMEP/MSC-E: Alexey Gusev (MSC-East, RU) | ||
+ | * CHIMERE: Augustin Colette, | ||
+ | * DEHM: Camilla Geels, Lise Frohn (Aarhus, DK) | ||
+ | * GEM-AQ: Joanna Strużewska (IEP, PL) | ||
+ | * Lotos-Euros: | ||
+ | * MATCH: Camilla Andersson, Ana Carvalho, Lennart Robertson (SMHI, SE) | ||
+ | * MINNI: Mihaela Mircea, Mario Adani (ENEA, IT) | ||
+ | * MOCAGE: Joaquim Arteta (Meteo-France, | ||
+ | * MONARCH: Oriol Jorbal (BCS, ES) | ||
+ | * SILAM: Rostislav Kouznetsov (FMI, FI) | ||
+ | * WRF-CHEM: Aura Lupascu (IASS, DE) | ||
- | * Augustin Colette, Bertrand Bessagnet, Elsa Real, Florian Couvidat (INERIS/ | + | Observations |
- | * Marta Garcia Vivanco | + | * Maria Cruz Minguillon |
- | * Mihaela Mircea, Gino Briganti, Andrea Cappelletti, | + | * Wenche Aas, Stephen Platt, (NILU/CCC, NO) |
- | * Sebnem Aksoyoglu (PSI/ | + | |
- | * Kees Cuvelier (exJRC/ | + | Emissions |
- | * Yelva Roustan & Valentin Raffort (CEREA/ | + | * Jeroen Kuenen |
- | * Hilde Fagerli & Svetlana Tsyro (met.no/ | + | |
- | * Camilla Andersson (SMHI/MATCH). | + | |
- | * Maria-Teresa Pay (BSC/ | + | |
- | * Noelia Otero Felipe, Kathleen Mar (IASS/ | + | |
- | * Martijn Schaap | + | |
===== Background ===== | ===== Background ===== | ||
+ | Main motivation: improve the modelling of Black Carbon (Tagged Elemental Carbon), SOA (Condensables), | ||
+ | Take stock of (i) Developments in emission inventories (LRTAP BC, condensables) and (ii) Winter 2017/2018 field campaign (EMEP/ | ||
- | In 2014, the Task Force on Measurement and Modelling initiated a dedicated exercise to assess the efficiency of air pollution mitigation strategies over the past 20 years. More specifically, | ||
- | |||
- | Several observation-based trend analyses will be undertaken, either by the EMEP Centres or by voluntary national experts. In addition, the Eurodelta community proposed to coordinate a dedicated modelling exercise that aims (i) to simulate at least three key years (1990, 2000, 2010) and (ii) to extend the analysis to produce an ensemble of air quality hindcasts over the past 20 years. These hindcasts will be complemented by selected sensitivity analyses in order to assess the role of meteorological variability, | ||
- | |||
- | In contrast with other model intercomparison initiatives, | ||
- | |||
- | **NOTE: An updated description of the present modelling plan is now available as a GMDD paper: | ||
- | |||
- | http:// | ||
- | ** | ||
===== Modelling experiment ===== | ===== Modelling experiment ===== | ||
+ | //Assess the impact of a more consistent emission inventory for wood burning// | ||
- | In order to assess the efficiency of the Gothenburg protocol in reducing exposure to air pollution in Europe, models can usefully complement observation-based analyses by allowing to disentangle the role of inter-annual meteorological variability, | + | A: CAMS-REG-AP_v2.2.1_2015_REF1: EMEP National Totals + TNO Spatialisation |
- | Acknowledging the computational demand, the model experiment plan is split in three tiers (explicated below). | + | |
- | + | ||
- | The choice of the modelled years is a trade-off between the policy relevance of the present initiative (that should support the assessment of the efficiency of the Gothenburg protocol) and technical feasibility. Despite the agreement of the protocol in 1999, we will use 2000 as an intermediate year because of the better quality of emission data for this year (as well as more complete observation coverage). | + | |
- | + | ||
- | {{: | + | |
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- | Table 1: Summary of model experiments and corresponding key scientific questions. For each tier, the number of simulated years is expressed in addition to the previous tier. | + | |
- | + | ||
- | + | ||
- | __Tier 1: 5 annual simulations__ | + | |
- | + | ||
- | Participants: | + | |
- | + | ||
- | __Tier 2: 7 additional annual simulations__ | + | |
- | + | ||
- | Participants: | + | |
- | + | ||
- | __Tier 3: 38 additional annual simulations__ | + | |
- | + | ||
- | Participants: | + | |
- | + | ||
- | + | ||
- | {{ : | + | |
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- | ===== Geographical Domain ===== | + | |
- | + | ||
- | The modelling domain is displayed in the Figure below. The model used for the analysis of the Campaigns (green lines below)does not fit in the Euro-Cordex domain (grey dots, see section on meteorology), | + | |
- | + | ||
- | The coordinates of the centre of grid cells are [[https:// | + | |
- | {{ : | + | |
- | + | ||
- | ===== Meteorology ===== | + | |
- | + | ||
- | The ECMWF/IFS model was used for recent Campaign analyses since its horizontal resolution is about 25km for recent years. For longer time periods, the horizontal resolution of the ERA-Interim (about 80km) and ERA-Clim (125km) are considered too coarse. Therefore, we propose to use a dynamical downscaling of the ERA-Interim global reanalysis with the Weather Research and Forecast Model at 0.44 degrees of resolution. | + | |
- | + | ||
- | Building upon the expertise developed in the EuroCordex climate downscaling programme (Jacob et al., 2013) a regional climate model evaluation with perfect boundary conditions (reanalyses, | + | |
- | + | ||
- | Changing meteorological driver might have an impact on natural emissions as well as other processes. Modelling groups are welcome to share the outcome of any sensitivity simulation performed to assess the impact of using WRF simulations. | + | |
- | + | ||
- | Whereas this model setup has been thoroughly validated in the past, a specific meteorological evaluation for the need of air quality modelling will be performed as part of this Eurodelta3-II exercise. | + | |
- | + | ||
- | The volume of data for the meteorological forcing is at least 50G/yr (estimate for the list of variables required for Chimere ). The data is available at: | + | |
- | + | ||
- | [[https:// | + | |
- | + | ||
- | ===== Biogenic and natural Emissions ===== | + | |
- | + | ||
- | There is no constrain for biogenic emissions (NO & VOCs) and for natural and road resuspension of dust emissions, although the corresponding emission used in each model and tracking of corresponding concentrations (where relevant) will be reported. | + | |
- | + | ||
- | Forest fires should be ignored. | + | |
- | ===== Anthropogenic Emissions ===== | + | |
- | + | ||
- | + | ||
- | Following up on the Eurodelta3 phase I methodology, | + | |
- | + | ||
- | ==== Annual totals ==== | + | |
- | + | ||
- | GAINS emissions, provided as country totals under SNAP sectors, will be used for the years 1990, 1995, 2000, 2005, 2010. Intermediate years will be linearly interpolated by country and by sectors. The version of these emissions is ECLIPSE-V5. | + | |
- | + | ||
- | + | ||
- | ==== Emission Spatialisation & Disaggregation ==== | + | |
- | + | ||
- | + | ||
- | The temporal (monthly and hourly) profiles will be those of TNO as provided for the AQMEII3 exercise. | + | |
- | + | ||
- | A NO2/NOx ratio of 20% will be used. Given the resolution of the model (about 25km), this ratio is not expected to bear upon the results substantially. | + | |
- | + | ||
- | The emissions are provided on the ED-Trend domain using the spatial regridding methodology of INERIS. It consists in: | + | |
- | * Europe-wide road and shipping proxies for SNAP7 and 8 (constant for 20yrs) | + | |
- | * Population density proxy for residential emissions (trained on French bottom-up 1km inventory) (constant for 20yrs) | + | |
- | * EPRTR for industrial sources location and magnitude (time varying over the past 20yrs), although the quality of this data is not guaranteed, localised differences should have a limited impact at 25km resolution, and the broad patterns should be respected. | + | |
- | * Use of bottom-up emission inventories as spatialisation proxies for France & United Kingdom. | + | |
- | * TNO-MACC inventory for NH3 emissions | + | |
- | + | ||
- | In the proposed spatialisation method, only the location and fluxes of large point sources change in time over the past decades. While emissions at a 50km resolution are available for selected countries, pollutants, and sectors, it is considered that the spatialisation of these emissions is not necessarily better than the more recent techniques involving consistent and high-resolution proxies over Europe. This should in particular be the case for road and residential sectors. For the industrial sector, there is however the potential to improve the quality of inventories in the 1990s. This potential added value will be investigated when preparing inventories for 1990 and 2000 (by 20150228). | + | |
- | + | ||
- | The INERIS-EDT 2010 emission data and TNO profiles are available at: [[https:// | + | |
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- | TNO will make a sensitivity simulation to compare the result with the above spatialisation strategy with the TNO-MACC3 emissions for 2010. Other modelling groups are welcome to share the outcome of any corresponding sensitivity simulations. | + | |
- | + | ||
- | ===== Boundary Conditions ===== | + | |
- | + | ||
- | There are two options for boundary conditions (i) climatology of observational data, (ii) global model results. Both have pros and cons. Global model carry biases, and observations are fitted at measurement points (in the present case the temporal trend of Mace Head is applied throughout the domain). It has been decided to use the first option for most simulations, | + | |
- | + | ||
- | Both sources are provided as monthly means, participating groups are free to make a temporal interpolation, | + | |
- | + | ||
- | ==== Climatology of observational data ==== | + | |
- | + | ||
- | For ozone, a 3D climatology based on observational vertical profiles is consolidated and updated by J. Logan et al. Proposal is to use this 3D climatology in conjunction with a temporal (monthly) variation over the past 20 years. Long lived species (CH4) are held fixed in the simulation. A few other species such as sulphate, ammonium, nitrate and sea salt are also provided. Dust derived from a global model are also given. Particulate species are split between the fine (0-2.5µm) and coarse fraction (2.5-10.0µm) with the following median diameter: 0.33e-6 m for all fine, 3.0e-6 m for coarse nitrate, 4.8e-6 m for coarse sea salt, 5.0e-6 m for coarse dust. The molar mass to be used for the conversion of dust to µg/m3 is 200g/ | + | |
- | + | ||
- | The data is available as Obs_EMEP_1990-2010.tar under : https:// | + | |
- | + | ||
- | + | ||
- | The brief documentation is provided here {{: | + | |
- | + | ||
- | ==== Global model results ==== | + | |
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- | The Climate-Chemistry Model Initiative is currently undertaking global atmospheric chemistry reanalyses over the 1960-2010 time period (Eyring, 2014) based on the MACC City emissions (Granier et al., 2011). We propose to use the CamChem member of the ensemble (Lamarque et al., 2012) produced by Simone Tilmes at NCAR provided by Katerina Sindelarova (katerina.sindelarova@latmos.ipsl.fr) and Claire Granier (cgranier@latmos.ipsl.fr) at LATMOS/ | + | |
- | The data is available as CAM4CHEM_CCMI.tar: | + | |
- | https:// | + | |
- | + | ||
- | + | ||
- | Validation of this global reanalysis is ongoing, but the preliminary results are encouraging as illustrated in the Figure below showing the ozone trend at the Mace Head station. | + | |
- | {{ :emep:emep-experts: | + | B: CAMS-REG-AP_v2.2.1_2015_REF2: |
- | Monthly variation of ozone at the Mace Head station | + | |
+ | //Compare with the EMEP reference inventory (main difference compared to A regards spatialisation proxies)// | ||
+ | C: EMEP 0.1 official country inventories | ||
- | The Eurodelta participants accept not to use these results for publication in the peer reviewed litterature before the end of 2015 without asking the explicit authorisation to Claire Granier | + | // |
- | ===== Output Format ===== | + | |
- | A common format for the results of modelling | + | D: Same as C + BC modelling |
- | {{: | + | |
+ | //Test the LRTAP BaP inventory// | ||
- | Participating groups can provide either model output at the first model level, or downscalled vertically | + | E: Same as C + BaP |
- | For the case of VOC output, | + | Note: C/D/E can be part of the same model run |
- | {{: | + | |
- | The simulation status for each modelling group is summarised in the table below. " | ||
- | {{ : | ||
+ | ===== Technical Specifications ===== | ||
+ | all input/ | ||
+ | ===== Model Setup ===== | ||
+ | * Geographical domain: 0.1 deg resolution, 25°W-45°E, | ||
+ | * Time period: 20171201-20180228 | ||
+ | * Meteorology: | ||
+ | * Boundary Conditions: C-IFS ftpmoca.ineris.fr:/ | ||
+ | * Emissions: ftpmoca.ineris.fr:/ | ||
+ | * Unit in emission files (CAMS-REG) kg/grid, NH3 is kg NH3, SO2 is kg SO2, NOx is kg NO2 | ||
+ | * A: CAMS-REG-AP_v2.2.1_2015_REF1.csv | ||
+ | * B: CAMS-REG-AP_v2.2.1_2015_REF2.csv | ||
+ | * C-E: pending | ||
+ | * PM Splits (EC, OC, SO4, Na, OthMin in PM10 and PM2.5, note the different versions for REF1& | ||
+ | * Wood Burning / Fossil Fuel Share in PM10 and PM2.5 emissions (note the different versions for REF1& | ||
+ | * EC_wb is defined as the wood burning share of EC emissions in the PM2.5 fraction. It is computed from the national/ | ||
+ | * EC_ff is defined as the fossil fuel share of EC emissions in the PM2.5 fraction. It includes all the remaining EC_fine | ||
+ | * Splits only available for coarse/fine (above/ | ||
+ | * Temporal variations: not prescribed (degree days or monthly climatologies) | ||
- | ===== Bibliography | + | ===== Model Outputs |
- | * Banzhaf, S., M. Schaap, R. Kranenburg, A.M.M. Manders, A.J. Segers, A.H.J. Visschedijk, | + | Surface only |
- | * Eyring, V., Report on the IGAC/SPARC Chemistry-Climate Model Initiative | + | * CAMS grib (for CAMS models) or netcdf: 1 file per species |
- | * Kotlarski, S., K. Keuler, O.B. Christensen, | + | * Units: kg/m3 for the grib files, µg/m3 for the netcdf files (gas/ |
- | * Stegehuis, A.I., R. Vautard, P. Ciais, A.J. Teuling, D.G. Miralles and M. Wild, An observation-constrained multi-physics RCM ensemble for simulating European mega-heatwaves, | + | * Hourly |
- | * Jacob, D., J. Petersen, B. Eggert, A. Alias, O. Christensen, | + | |
- | * Lamarque, J.F., L.K. Emmons, P.G. Hess, D.E. Kinnison, S. Tilmes, F. Vitt, C.L. Heald, E.A. Holland, P.H. Lauritzen, J. Neu, J.J. Orlando, P.J. Rasch and G.K. Tyndall, CAM-chem: description and evaluation of interactive atmospheric chemistry in the Community Earth System Model, Geosci. Model Dev. 5(2012), pp. 369-411. | + | |
- | * Granier, C., B. Bessagnet, T. Bond, A. D' | + | |
- | * van Vuuren, D., J. Edmonds, M. Kainuma, K. Riahi, A. Thomson, K. Hibbard, G. Hurtt, T. Kram, V. Krey, J.-F. Lamarque, T. Masui, M. Meinshausen, | + | |
+ | Species: (grib codes in brackets for models using grib format, when available) | ||
+ | * Please deliver as many species as possible, the priority is on PM10, PM2.5, ECff_2.5, ECwb_2.5. We note that PM1 is not available in emissions, and therefore in many models | ||
+ | * PM10 (40008), PM2.5 (40009), PM1 (62078), NO2 (5), O3 (0), | ||
+ | * PM Species in PM2.5 fraction: ECff_25 (62097), ECwb_25 (62098), POA_25 (62011), SOA_25 (62012), NO3_25 (13), NH4_25 (10), SO4_25 (22), OPPM_25 (any other primary PM, 62094) | ||
+ | * PM Species in PM1 fraction: ECff_1 (62089), ECwb_1 (62091), POA_1 (62087), SOA_1 (62085), NO3_1 (62079), NH4_1 (62081), SO4_1 (62083), OPPM_1 (62093) | ||
+ | * PM Species in PM10 fraction: ECff_10 (62090), ECwb_10 (62092), POA_10 (62088), SOA_10 (62086), NO3_10 (62080), NH4_10 (62082), SO4_10 (62084), OPPM_10 (62095) | ||
+ | ===== Time line ===== | ||
+ | * 31.1.20 Draft Specifications & Input data circulated by Ineris | ||
+ | * 14.2.20 Specifications finalised | ||
+ | * 13.3.20 First series of model results delivered on ftp for experiments A&B to be presented to Condensable workshop & EMEP Extended Bureaux | ||
+ | * 04.5.20 Final model results (A-E) to be presented to TFMM | ||
+ | * June onwards: in depth analysis of models vs. observations | ||