Found 10489 publications. Showing page 47 of 420:
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Assesment of wind, snow and seasalt. Hammerfest 2009-2010. NILU OR
NILU has made an assesment of windconditions, amount of snow and seasalt impact in Hammerfest. The assesment has been made on behalf of Statnett connected to a projected construction of power supply network.
2011
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2026
Climate change is expected to intensify the frequency and severity of droughts across Europe,
posing significant risks to agricultural production and the food systems that depend on it. As drought
severity can range widely within one country, estimates of supply chains with higher spatial
resolution are needed to characterize drought risk exposure. This study develops an inter-regional
(NUTS-2) version of the Food and Agriculture Biomass Input Output (FABIO)[1] model to quantify
the exposure of European food trade to drought risk under current and future climate conditions.
Drought exposure is quantified by linking regional agricultural trade flows to Copernicus
satellite-based soil moisture anomaly data.
Input output (IO) analysis has been widely used to study supply-chain dependencies, resource
use, and environmental pressures embedded in production and trade. The development of
multi-regional input output (MRIO) databases, which combine national IO tables into a single
balanced global framework, has enabled such analyses at the global scale. Prominent examples
include EXIOBASE[2], Eora[3], and FIGARO[4], which have been applied to assess environmental
impacts embodied in trade. However, while these databases provide global coverage, their
national-level resolution limits their ability to capture spatially heterogeneous climate risks such as
droughts, which often vary substantially within countries. Subnational or interregional IO tables are
rarely published due to data and resource constraints, creating a critical gap for climate risk
assessments that require finer spatial detail.
This study extends FABIO[1], an established physical MRIO framework for food, feed, and biomass
flows, into an interregional European model. The interregional FABIO disaggregates national food
trade into NUTS2-level trade using regional crop and livestock production, regional demand, and
reported trade flows. The FABIO database is regionalized following two main steps, similarly to
Ouyang et al[5]. First, international trade is estimated using reported subnational trade data where
available, subject to the constraints that regional exports and imports do not exceed supply and
demand, and that national totals remain consistent with the original FABIO data. Second, domestic
interregional flows are estimated using the CHARM approach in combination with a gravity model[6].
The model distinguishes 67 sectors and 332 NUTS 2 regions.
Using this framework, drought exposure is assessed by linking regional agricultural trade with
satellite-based drought intensity metrics, considering both historical events and projected future
conditions. The results show that accounting for trade significantly increases estimated drought
exposure of food consumption in Northern Europe compared with production-based assessments.
Exposure varies strongly across food categories; non-essential commodities are generally more
exposed than staple foods. Trade can both amplify and buffer drought impacts, depending on the
geographic extent of the drought and the availability of alternative sourcing regions.
Overall, this study demonstrates the value of combining interregional input output modeling with
satellite-based climate indicators to assess the vulnerability of European food trade to droughts. The
results reveal the exposure to drought risk in regions and support the design of diversification and
resilience strategies in agricultural supply chains. The annual resolution of the model, consistent
with input output tables and available trade data, cannot capture dynamics such as monthly stock
variations that can be critical for drought impacts, and merits further research. Beyond drought risk
assessment, the interregional FABIO framework can be applied to other spatially explicit
environmental questions, including consumption-based analyses of nitrogen emissions, land use,
and other local pressures.
2026
Assessing Earth system responses arising from carbon dioxide removal (CDR) requires developing and simulating pairs of scenarios – a mitigation scenario with deployment of CDR and a corresponding no-CDR baseline. The latter describes a world where no CDR is deployed, such that net carbon emissions are higher and a given temperature target may be missed. While over the past years a rich literature on mitigation scenarios with CDR has been emerging, no-CDR baselines have mostly been explored in stylized Earth system model (ESM) experiments. In such simulations, a no-CDR baseline simply assumes that CDR is “switched off”, while socio-economic constraints are not considered. However, the deployment of CDR in mitigation scenarios, created by integrated assessment models (IAMs), is embedded in a consistent socio-economic description of plausible futures, and disallowing CDR may affect climate drivers due to changes in the energy system and in land-use dynamics. Particularly, when moving towards an activity-driven representation of CDR in emission-driven ESMs, where the activity that draws down CO2 from the atmosphere is explicitly modelled, the creation of no-CDR baselines comes with challenges and trade-offs. Here, we conceptualize a framework for emission-driven ESM simulations of IAM scenarios that allows us to determine carbon-cycle feedbacks and biogeophysical effects of CDR deployment using no-CDR baselines. We show that different options exist for the creation of no-CDR baselines, which offer different insights and have their specific advantages and limitations. We also demonstrate that internal variability of the climate system inherently limits our ability to detect the small signals related to CDR deployment and its feedbacks. Hence, unless a sufficiently large initial conditions ensemble is employed, stylized modelling approaches may remain preferable for some applications, e.g., the quantification of regional biogeophysical effects of CDR deployment. Both, the efficiency of CDR (defined as CO2 removed per unit of resources employed) as well as related carbon-cycle feedbacks and biogeophysical effects are expected to be scenario- and model-dependent. Our simulation design of concentration- and emission-driven no-CDR baselines, together with an improved representation of CDR in the IAM – ESM modelling chain, opens an avenue towards estimating CDR efficiencies and their uncertainties under various future scenarios in upcoming model intercomparison activities.
2026
2011
BACKGROUND: In order to use in situ measurements to constrain urban anthropogenic emissions of carbon dioxide (CO2), we use a Lagrangian methodology based on diffusive backward trajectory tracer reconstructions and Bayesian inversion. The observations of atmospheric CO2 were collected within the Tokyo Bay Area during the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) flights, from the Tsukuba tall tower of the Meteorological Research Institute (MRI) of the Japan Meteorological Agency and at two surface sites (Dodaira and Kisai) from the World Data Center for Greenhouse Gases (WDCGG).
RESULTS: We produce gridded estimates of the CO2 emissions and calculate the averages for different areas within the Kanto plain where Tokyo is located. Using these inversions as reference we investigate the impact of perturbing different elements in the inversion system. We modified the observations amount and location (surface only sparse vs. including aircraft CO2 observations), the background representation, the wind data used to drive the transport model, the prior emissions magnitude and time resolution and error parameters of the inverse model.
CONCLUSIONS: Optimized fluxes were consistent with other estimates for the unperturbed simulations. Inclusion of CONTRAIL measurements resulted in significant differences in the magnitude of the retrieved fluxes, 13% on average for the whole domain and of up to 21% for the spatiotemporal cells with the highest fluxes. Changes in the background yielded differences in the retrieved fluxes of up to 50% and more. Simulated biases in the modelled transport cause differences in the retrieved fluxes of up to 30% similar to those obtained using different meteorological winds to advect the Lagrangian trajectories. Perturbations to the prior inventory can impact the fluxes by ~ 10% or more depending on the assumptions on the error covariances. All of these factors can cause significant differences in the estimated flux, and highlight the challenges in estimating regional CO2 fluxes from atmospheric observations.
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