Fant 10273 publikasjoner. Viser side 132 av 411:
2017
2015
2015
Evaluating the Combined Effect of Land and Marine CDR
With the global annual mean temperature in 2024 exceeding 1.5°C above preindustrial levels, the world faces increasing risks from climate impacts. Achieving the long-term temperature goals of the Paris Agreement will require not only deep emission reductions but likely also large-scale deployment of carbon dioxide removal (CDR). However, major uncertainties remain regarding the Earth system’s response to CDR, its efficacy under overshoot conditions, and the potential of CDR to reverse warming beyond net-zero emissions.
Here, we use emission-driven simulations with activity-driven implementation of CDR in the Norwegian Earth System Model (NorESM2-LM) to assess the carbon sequestration efficacy and climate response of two CDR methods, Bioenergy with Carbon Capture and Storage (BECCS) and Ocean Alkalinity Enhancement (OAE), deployed individually and in combination. Our scenarios follow a high-overshoot trajectory (SSP5-3.4-OS) combined with ramped-up deployment of CDR. Additional CDR amounted to 5.2 million km² of bioenergy feedstock for BECCS in addition to the BECCS already present in the SSP5-3.4-OS and a CaO deployment rate of 2.7 Gt/year for OAE, derived from life cycle analysis. OAE is applied across the exclusive economic zones of Europe, the United States, and China. BECCS alone accounts for a 16 ppm reduction using 5.2 million km² of bioenergy crops, while OAE contributes 7 ppm reduction with a cumulative addition of 82.3 Gt of CaO, yielding a CDR effectiveness of 0.08 ppm per Gt of CaO. During the overshoot phase (2050–2060), the combined simulation shows a gross atmospheric CO₂ reduction of 2-4 ppm, increasing to a reduction of 23 ppm by 2100, indicating nearly additive contributions from the two methods.
Despite the substantial CO₂ drawdown and a net reduction of anthropogenic emissions by 5.4 GtCO₂/year by 2100 through additional CDR, the global temperature response remains modest and indistinguishable from internal variability. This highlights the importance of designing robust, scalable CDR portfolios along with ambitious emission cuts. Our results also call for better integration of CDR pathways into IAMs scenarios so that we can have them in ESMs to fully capture biogeophysical feedback and Earth system constraints in overshoot scenarios.
2025
Evaluating the effectiveness of a stove exchange programme on PM 2.5 emission reduction
Residential wood combustion (RWC) is one of the most important sources of particulate matter () in urban areas. As a consequence, different types of regulatory instruments are being implemented to reduce emissions. In this study, we evaluate both the potential and actual effect of a subsidy programme for stove exchange, which has been in place for over 20 years in Oslo (Norway). The subsidy programme provides economic support to the inhabitants for substituting old stoves for RWC with new and cleaner stoves as a measure to reduce emissions. Different approaches were selected to assess the potential effect of the Oslo subsidy programme. First, we evaluate the potential for reductions in emissions and pollution levels through the use of emission and dispersion modelling under different scenarios. We then assess the actual reductions associated with the stoves already replaced with the subsidy. We conclude the study by evaluating the time variation (2005 to 2018) in emissions, wood consumption and emission factors in Oslo in comparison with other municipalities with and without subsidy programmes in place. Results from emission and dispersion modelling show that the replacement of old wood stoves for new ones could have a significant effect on the reduction of emissions (up to 46%) and levels (up to 21%). Despite that, with near 8% of the total existing stoves in Oslo being exchanged with subsidy, the potential for reduction based on improved emission factors was estimated to be smaller by an order of magnitude. We find no evidence that municipalities with subsidy reduce emissions faster than those without subsidy. We therefore conclude that there is no evidence from our modelling results, supported by available observation data, that indicate that the emissions or concentrations in Oslo have been reduced as a result of the subsidy programme.
2020
2006
2013
2013
Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring
We evaluate the added value of integrating validated Low-Cost Sensor (LCS) data into a Machine Learning (ML) framework for providing surface PM2.5 estimates over Central Europe at 1 km spatial resolution. The synergistic ML-based S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) approach is extended, to incorporate LCS data through two strategies: using validated LCS data as a target variable (LCST) and as an input feature via an inverse distance weighted spatial convolution layer (LCSI). Both strategies are implemented within a stacked XGBoost model that ingests satellite-derived aerosol optical depth, meteorological variables, and CAMS (Copernicus Atmospheric Monitoring Service) regional forecasts. Model performance for 2021–2022 is evaluated against a baseline trained on air quality monitoring stations without any form of LCS integration. Our results indicate that the LCSI approach consistently outperforms both the baseline and LCST models, particularly in urban areas, with RMSE reductions of up to 15–20 %. It also exhibits higher accuracy than the CAMS regional interim reanalysis with a lower annual mean absolute error (MAE) of 2.68 μg/m3 compared to 3.32 μg/m3. SHapley Additive exPlanations based analysis indicates that LCSI information improves both spatial and temporal representativeness, with the LCSI strategy better capturing localized pollution dynamics. However, the LCSI's dependency on the spatial LCS layer limits its ability to capture inter-urban pollution transport in regions with sparse or no LCS data. These findings highlight the value of large-scale sensor networks in addressing spatial coverage gaps in official air quality monitoring stations and advancing high-resolution air quality modeling.
2026
2011
Evaluation and Global-Scale Observation of Nitrous Oxide from IASI on Metop-A
Nitrous oxide (N2O) is a greenhouse gas difficult to estimate by satellite because of its weak spectral signature in the infra-red band and its low variability in the troposphere. Nevertheless, this study presents the evaluation of new tropospheric N2O observations from the Infrared Atmospheric Sounder Interferometer (IASI) on Metop-A using the Toulouse N2O Retrieval Version 2.0 tool. This tool is based on the Radiative Transfer for Tiros Operational Vertical sounder (RTTOV) model version 12.3 coupled to the Levenberg-Marquardt optimal estimation method enabling the simultaneous retrieval of methane, water vapour, temperature profiles together with surface temperature and emissivity within the 1240–1350 cm−1 window. In this study, we focused on the upper troposphere (300 hPa) where the sensitivity of IASI is significant. The IASI N2O data has been evaluated using aircraft N2O observations from the High-performance Instrumented Airborne Platform for Environmental Research Pole-to-Pole Observations (HIPPO) campaigns in 2009, 2010, and 2011 and from the National Oceanic and Atmospheric Administration’s (NOAA) Global Greenhouse Gas Reference Network (GGGRN) in 2011. In addition, we evaluated the IASI N2O using ground-based N2O measurements from 9 stations belonging to the Network for the Detection of Atmospheric Composition Change (NDACC). We found a total random error of ∼2 ppbv (0.6%) for one single retrieval at 300 hPa. Under favorable conditions, this error is also found in the vertical level pressure range 300–500 hPa. It decreases rapidly to ∼0.4 ppbv (0.1%) when we average on a 1° × 1° box. In addition, independent observations allows the estimation of bias with the IASI TN2OR v2.0 N2O. The bias between IASI and aircraft N2O data at 300 hPa is ∼1.0 ppbv (∼0.3%). We found an estimated random error of ∼2.3 ppbv (∼0.75%). This study also shows relatively high correlations between IASI data and aircraft in situ profiles but more varying correlations over the year 2011 depending on the location between IASI and NDACC remote sensing data. Finally, we present daily, monthly, and seasonal IASI N2O horizontal distributions in the upper troposphere as well as cross sections for different seasons that exhibit maxima in the Tropical band especially over Africa and South America.
2022
2007
2007
2007
2007
2010
2008
Evaluation and optimization of ICOS atmosphere station data as part of the labeling process
The Integrated Carbon Observation System (ICOS) is a pan-European research infrastructure which provides harmonized and high-precision scientific data on the carbon cycle and the greenhouse gas budget. All stations have to undergo a rigorous assessment before being labeled, i.e., receiving approval to join the network. In this paper, we present the labeling process for the ICOS atmosphere network through the 23 stations that were labeled between November 2017 and November 2019. We describe the labeling steps, as well as the quality controls, used to verify that the ICOS data (CO2, CH4, CO and meteorological measurements) attain the expected quality level defined within ICOS. To ensure the quality of the greenhouse gas data, three to four calibration gases and two target gases are measured: one target two to three times a day, the other gases twice a month. The data are verified on a weekly basis, and tests on the station sampling lines are performed twice a year. From these high-quality data, we conclude that regular calibrations of the CO2, CH4 and CO analyzers used here (twice a month) are important in particular for carbon monoxide (CO) due to the analyzer's variability and that reducing the number of calibration injections (from four to three) in a calibration sequence is possible, saving gas and extending the calibration gas lifespan. We also show that currently, the on-site water vapor correction test does not deliver quantitative results possibly due to environmental factors. Thus the use of a drying system is strongly recommended. Finally, the mandatory regular intake line tests are shown to be useful in detecting artifacts and leaks, as shown here via three different examples at the stations.
2021
2007
2006
2015