Found 9985 publications. Showing page 88 of 400:
2019
Individual variability in contaminants and physiological status in a resident Arctic seabird species
2019
Interactions between the atmosphere, cryosphere, and ecosystems at northern high latitudes
The Nordic Centre of Excellence CRAICC (Cryosphere–Atmosphere Interactions in a Changing Arctic Climate), funded by NordForsk in the years 2011–2016, is the largest joint Nordic research and innovation initiative to date, aiming to strengthen research and innovation regarding climate change issues in the Nordic region. CRAICC gathered more than 100 scientists from all Nordic countries in a virtual centre with the objectives of identifying and quantifying the major processes controlling Arctic warming and related feedback mechanisms, outlining strategies to mitigate Arctic warming, and developing Nordic Earth system modelling with a focus on short-lived climate forcers (SLCFs), including natural and anthropogenic aerosols.
The outcome of CRAICC is reflected in more than 150 peer-reviewed scientific publications, most of which are in the CRAICC special issue of the journal Atmospheric Chemistry and Physics. This paper presents an overview of the main scientific topics investigated in the centre and provides the reader with a state-of-the-art comprehensive summary of what has been achieved in CRAICC with links to the particular publications for further detail. Faced with a vast amount of scientific discovery, we do not claim to completely summarize the results from CRAICC within this paper, but rather concentrate here on the main results which are related to feedback loops in climate change–cryosphere interactions that affect Arctic amplification.
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.
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Instrumenter som skal brukes til måling av lokal luftkvalitet i henhold til forurensningsforskriften skal være godkjente for dette formålet. Norge har per i dag ingen godkjenningsordning. Inntil videre godkjennes derfor de instrumenter som det svenske referanselaboratoriet for luft har godkjent.
Denne rapporten beskriver hvordan en godkjenningsordning kan etableres i Norge, basert på rutinen brukt i Sverige, gjennom å belyse den lovmessige forankringen og prosedyren for typegodkjenning. Oppgavene og ansvarsfordelingen mellom den ansvarlige forvaltningsmyndigheten (Miljødirektoratet) og Referanselaboratoriet er forklart.
Miljødirektoratet rapport, M-1327/2019.
NILU
2019
Trends in measured NO2 and PM. Discounting the effect of meteorology.
This report documents a study on long-term trends in observed atmospheric levels of NO2, PM10 and PM2.5 based on data from the European Environmental Agency (EEA) Airbase v8 (EEA, 2018). The main aim is to evaluate to what extent the observed time series could be simulated as a function of various local meteorological data plus a time-trend by a Generalized Additive Model (GAM). The GAM could be regarded an advanced multiple regression model. If successful, such a model could be used for several purposes; to estimate the long-term trends in NO2 and PM when the effect of the inter-annual variations in meteorology is removed, and secondly, to “explain” the concentration levels in one specific year in terms of meteorological anomalies and long-term trends. The GAM method was based on a methodology developed during a similar project in 2017 looking at the links between surface ozone and meteorology.
The input to the study consisted of gridded model meteorological data provided through the EURODELTA Trends project (Colette et al., 2017) for the 1990-2010 period as well as measured data on NO2, PM10 and PM2.5 extracted from Airbase v8. The measurement data was given for urban, suburban and rural stations, respectively. The analysis was split into two time periods, 1990-2000 and 2000-2010 since the number of stations differ substantially for these periods and since there is reason to believe that the trends differ considerably between these two periods.
The study was focused on the 4-months winter period (Nov-Feb) since it was important to assure a period of the year with consistent and homogeneous relationships between the input explanatory data (local meteorology) and the levels of NO2 and PM. For NO2, this period will likely cover the season with the highest concentration levels whereas for PM high levels could be expected outside this period due to processes such as secondary formation, transport of Saharan dust and sea spray.
When measured by the R2 statistic, the GAM method performed best for NO2 in Belgium, the Netherlands, NW Germany and the UK. Significantly poorer performance was found for Austria and areas in the south. For PM10 there were less clear geographical patterns in the GAM performance.
Based on a comparison between the meteorologically adjusted trends and plain linear regression, our results indicate that for the 1990-2000 period meteorology caused an increase in NO2 concentrations that counteracted the effect of reduced emissions. For the period 2000-2010 we find that meteorology lead to reduced NO2 levels in the northwest and a slight increase in the south.
The amount of observational data is much less for PM than for NO2. For the 1990-2000 period the number of sites with sufficient length of time series is too small to apply the GAM method on a European scale. For the 2000-2010 period, we find that the general performance of the GAM method is poorer for PM10 than for NO2. With respect to the link between PM10 and temperature, the results indicate a marked geographical pattern with a negative relationship in central Europe and a positive relationship in Spain, southern France and northern Italy.
For PM10 during 2000-2010, the vast majority of the estimated trends are found to be negative. The difference between the GAM trend and the plain linear regression, indicates that meteorology lead to increased PM10 levels in the southern and central parts and decreased levels in the north.
For PM2.5 it turned out that the amount of data in the entire period 1990-2010 was too small to use the GAM method in a meaningful way on a European scale. Only a few sites had sufficient time series and thus more recent data are required.
ETC/ACM
2019
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We present here emissions estimated from a newly developed emission model for residential wood combustion (RWC) at high spatial and temporal resolution, which we name the MetVed model. The model estimates hourly emissions resolved on a 250 m grid resolution for several compounds, including particulate matter (PM), black carbon (BC) and polycyclic aromatic hydrocarbons (PAHs) in Norway for a 12-year period. The model uses novel input data and calculation methods that combine databases built with an unprecedented high level of detail and near-national coverage. The model establishes wood burning potential at the grid based on the dependencies between variables that influence emissions: i.e. outdoor temperature, number of and type and size of dwellings, type of available heating technologies, distribution of wood-based heating installations and their associated emission factors. RWC activity with a 1 h temporal profile was produced by combining heating degree day and hourly and weekday activity profiles reported by wood consumers in official statistics. This approach results in an improved characterisation of the spatio-temporal distribution of wood use, and subsequently of emissions, required for urban air quality assessments. Whereas most variables are calculated based on bottom-up approaches on a 250 m spatial grid, the MetVed model is set up to use official wood consumption at the county level and then distributes consumption to individual grids proportional to the physical traits of the residences within it. MetVed combines consumption with official emission factors that makes the emissions also upward scalable from the 250 m grid to the national level.
The MetVed spatial distribution obtained was compared at the urban scale to other existing emissions at the same scale. The annual urban emissions, developed according to different spatial proxies, were found to have differences up to an order of magnitude. The MetVed total annual PM2.5 emissions in the urban domains compare well to emissions adjusted based on concentration measurements. In addition, hourly PM2.5 concentrations estimated by an Eulerian dispersion model using MetVed emissions were compared to measurements at air quality stations. Both hourly daily profiles and the seasonality of PM2.5 show a slight overestimation of PM2.5 levels. However, a comparison with black carbon from biomass burning and benzo(a)pyrene measurements indicates higher emissions during winter than that obtained by MetVed. The accuracy of urban emissions from RWC relies on the accuracy of the wood consumption (activity data), emission factors and the spatio-temporal distribution. While there are still knowledge gaps regarding emissions, MetVed represents a vast improvement in the spatial and temporal distribution of RWC.
2019
Highly unusual open fires burned in western Greenland between 31 July and 21 August 2017, after a period of warm, dry and sunny weather. The fires burned on peatlands that became vulnerable to fires by permafrost thawing. We used several satellite data sets to estimate that the total area burned was about 2345 ha. Based on assumptions of typical burn depths and emission factors for peat fires, we estimate that the fires consumed a fuel amount of about 117 kt C and emitted about 23.5 t of black carbon (BC) and 731 t of organic carbon (OC), including 141 t of brown carbon (BrC). We used a Lagrangian particle dispersion model to simulate the atmospheric transport and deposition of these species. We find that the smoke plumes were often pushed towards the Greenland ice sheet by westerly winds, and thus a large fraction of the emissions (30 %) was deposited on snow- or ice-covered surfaces. The calculated deposition was small compared to the deposition from global sources, but not entirely negligible. Analysis of aerosol optical depth data from three sites in western Greenland in August 2017 showed strong influence of forest fire plumes from Canada, but little impact of the Greenland fires. Nevertheless, CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar data showed that our model captured the presence and structure of the plume from the Greenland fires. The albedo changes and instantaneous surface radiative forcing in Greenland due to the fire emissions were estimated with the SNICAR model and the uvspec model from the libRadtran radiative transfer software package. We estimate that the maximum albedo change due to the BC and BrC deposition was about 0.007, too small to be measured. The average instantaneous surface radiative forcing over Greenland at noon on 31 August was 0.03–0.04 W m−2, with locally occurring maxima of 0.63–0.77 W m−2 (depending on the studied scenario). The average value is up to an order of magnitude smaller than the radiative forcing from other sources. Overall, the fires burning in Greenland in the summer of 2017 had little impact on the Greenland ice sheet, causing a small extra radiative forcing. This was due to the – in a global context – still rather small size of the fires. However, the very large fraction of the emissions deposited on the Greenland ice sheet from these fires could contribute to accelerated melting of the Greenland ice sheet if these fires become several orders of magnitude larger under future climate.
2019
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Snow initialization has been previously investigated as a potential source of predictability atthe subseasonal‐to‐seasonal (S2S) timescale in winter and spring, through its local radiative,thermodynamical, and hydrological feedbacks. However, previous studies were conducted with low‐topmodels over short periods only. Furthermore, the potential role of the land surface‐stratosphere connectionupon the S2S predictability had remained unclear. To this end, we have carried out twin 30‐memberensembles of 2‐month (November and December) retrospective forecasts over the period 1985–2016, witheither realistic or degraded snow initialization. A high‐top version of the Norwegian Climate PredictionModel is used, based on the Whole Atmosphere Community Climate Model, to insure improved couplingwith the stratosphere. In a composite difference of high versus low initial Eurasian snow, the surfacetemperature is strongly impacted by the presence of snow, and wave activityfluxes into the stratosphere areenhanced at a 1‐month lag, leading to a weakened polar vortex. Focusing further on 7 years characterized bya strongly negative phase of the Arctic Oscillation, wefind a weak snow feedback contributing to themaintenance of the negative Arctic Oscillation. By comparing the twin forecasts, we extracted the predictiveskill increment due to realistic snow initialization. The prediction of snow itself is greatly improved, andthere is increased skill in surface temperature over snow‐covered land in thefirst 10 days, and localized skillincrements in the mid‐latitude transition regions on the southernflanks of the snow‐covered land areas, atlead times longer than 30 days.
2019