Found 9884 publications. Showing page 310 of 396:
Quality assurance and quality control procedure for national and Union GHG projections 2019
The quality assurance and quality control (QA/QC) procedure is an element of the QA/QC programme of the Union system for policies and measures and projections to be established in 2019 according to Article 12 of the MMR. The European Environment Agency (EEA) is responsible for the annual implementation of the QA/QC procedures and is assisted by the European Topic Centre on Climate change mitigation and energy (ETC/CME). The QA/QC procedure document describes QA/QC checks carried out at EU level on the national reported projections from Member States and on the compiled Union GHG projections. QA/QC procedures are performed at several different stages during the preparation of the national and Union GHG projections in order to aim to ensure the timeliness, transparency, accuracy, consistency, comparability and completeness of the reported information. The results of the 2019 QA/QC procedure are presented in the related paper ETC/CME Eionet Report 2019/6.
ETC/CME
2019
Monitoring of greenhouse gases and aerosols at Svalbard and Birkenes in 2018. Annual report.
The report summaries the activities and results of the greenhouse gas monitoring at the Zeppelin Observatory situated on Svalbard in Arctic Norway during the period 2001-2018, and the greenhouse gas monitoring and aerosol observations from Birkenes for 2009-2018.
NILU
2019
Although aerosols in the Arctic have multiple and complex impacts on the regional climate, their removal due to deposition is still not well quantified. We combined meteorological, aerosol, precipitation, and snowpack observations with simulations to derive information about the deposition of sea salt components and black carbon (BC) from November 2011 to April 2012 to the Arctic snowpack at two locations close to Ny-Ålesund, Svalbard. The dominating role of sea salt and the contribution of dust for the composition of atmospheric aerosols were reflected in the seasonal composition of the snowpack. The strong alignment of the concentrations of the major sea salt components in the aerosols, the precipitation, and the snowpack is linked to the importance of wet deposition for transfer from the atmosphere to the snowpack. This agreement was less strong for monthly snow budgets and deposition, indicating important relocation of the impurities inside the snowpack after deposition. Wet deposition was less important for the transfer of nitrate, non-sea-salt sulfate, and BC to the snow during the winter period. The average BC concentration in the snowpack remains small, with a limited impact on snow albedo and melting. Nevertheless, the observations also indicate an important redistribution of BC in the snowpack, leading to layers with enhanced concentrations. The complex behavior of bromide due to modifications during sea salt aerosol formation and remobilization in the atmosphere and in the snow were not resolved because of the lack of bromide measurements in aerosols and precipitation.
2019
This paper describes the CityChem extension of the Eulerian urban dispersion model EPISODE. The development of the CityChem extension was driven by the need to apply the model in largely populated urban areas with highly complex pollution sources of particulate matter and various gaseous pollutants. The CityChem extension offers a more advanced treatment of the photochemistry in urban areas and entails specific developments within the sub-grid components for a more accurate representation of dispersion in proximity to urban emission sources. Photochemistry on the Eulerian grid is computed using a numerical chemistry solver. Photochemistry in the sub-grid components is solved with a compact reaction scheme, replacing the photo-stationary-state assumption. The simplified street canyon model (SSCM) is used in the line source sub-grid model to calculate pollutant dispersion in street canyons. The WMPP (WORM Meteorological Pre-Processor) is used in the point source sub-grid model to calculate the wind speed at plume height. The EPISODE–CityChem model integrates the CityChem extension in EPISODE, with the capability of simulating the photochemistry and dispersion of multiple reactive pollutants within urban areas. The main focus of the model is the simulation of the complex atmospheric chemistry involved in the photochemical production of ozone in urban areas. The ability of EPISODE–CityChem to reproduce the temporal variation of major regulated pollutants at air quality monitoring stations in Hamburg, Germany, was compared to that of the standard EPISODE model and the TAPM (The Air Pollution Model) air quality model using identical meteorological fields and emissions. EPISODE–CityChem performs better than EPISODE and TAPM for the prediction of hourly NO2 concentrations at the traffic stations, which is attributable to the street canyon model. Observed levels of annual mean ozone at the five urban background stations in Hamburg are captured by the model within ±15 %. A performance analysis with the FAIRMODE DELTA tool for air quality in Hamburg showed that EPISODE–CityChem fulfils the model performance objectives for NO2 (hourly), O3 (daily max. of the 8 h running mean) and PM10 (daily mean) set forth in the Air Quality Directive, qualifying the model for use in policy applications. Envisaged applications of the EPISODE–CityChem model are urban air quality studies, emission control scenarios in relation to traffic restrictions and the source attribution of sector-specific emissions to observed levels of air pollutants at urban monitoring stations.
2019
2019
2019
2019
2019
2019
2019
2019
2019
2019
A number of studies have shown that assimilation of satellite derived soil moisture using the ensemble Kalman Filter (EnKF) can improve soil moisture estimates, particularly for the surface zone. However, the EnKF is computationally expensive since an ensemble of model integrations have to be propagated forward in time. Here, assimilating satellite soil moisture data from the Soil Moisture Active Passive (SMAP) mission, we compare the EnKF with the computationally cheaper ensemble Optimal Interpolation (EnOI) method over the contiguous United States (CONUS). The background error–covariance in the EnOI is sampled in two ways: (i) by using the stochastic spread from an ensemble open-loop run, and (ii) sampling from the model spinup climatology. Our results indicate that the EnKF is only marginally superior to one version of the EnOI. Furthermore, the assimilation of SMAP data using the EnKF and EnOI is found to improve the surface zone correlation with in situ observations at a 95% significance level. The EnKF assimilation of SMAP data is also found to improve root-zone correlation with independent in situ data at the same significance level; however this improvement is dependent on which in situ network we are validating against. We evaluate how the quality of the atmospheric forcing affects the analysis results by prescribing the land surface data assimilation system with either observation corrected or model derived precipitation. Surface zone correlation skill increases for the analysis using both the corrected and model derived precipitation, but only the latter shows an improvement at the 95% significance level. The study also suggests that assimilation of satellite derived surface soil moisture using the EnOI can correct random errors in the atmospheric forcing and give an analysed surface soil moisture close to that of an open-loop run using observation derived precipitation. Importantly, this shows that estimates of soil moisture could be improved using a combination of assimilating SMAP using the computationally cheap EnOI while using model derived precipitation as forcing. Finally, we assimilate three different Level-2 satellite derived soil moisture products from the European Space Agency Climate Change Initiative (ESA CCI), SMAP and SMOS (Soil Moisture and Ocean Salinity) using the EnOI, and then compare the relative performance of the three resulting analyses against in situ soil moisture observations. In this comparison, we find that all three analyses offer improvements over an open-loop run when comparing to in situ observations. The assimilation of SMAP data is found to perform marginally better than the assimilation of SMOS data, while assimilation of the ESA CCI data shows the smallest improvement of the three analysis products.
MDPI
2019