Statistics for climate and environment

Our research within statistics for climate and environment is a mixture of applied, problem-driven projects and fundamental research eligible for publication in scientific journals. Climate research at Norsk Regnesentral has boosted over the past few years. With funding from national and international clients and research grants, the institute has involved itself in a wide range of operations, either as project leaders or as partners in multinational research teams. Our project portfolio includes estimating the climate sensitivity, reconstructing past global climate, verification of climate models and regional downscaling. Within environmental problems, we work with pollution in air and water, with both local and remote sources for the contaminants.

Selected projects

Bayesian estimation of the climate sensitivity

An important climate model parameter, the climate sensitivity due to CO2, is often expressed as the temperature change in °C associated with a doubling of the concentration of carbon dioxide in Earth's atmosphere. We estimate the climate sensitivity by modeling the relationship between (estimates of) radiative forcing and measurements of hemispheric temperatures and global ocean heat content in post-industrial time. Our statistical model combines a deterministic climate model of reduced complexity with a stochastic model to account for proper inference.

Can salt (MgCl2) reduce air pollution from car traffic?

Car traffic is the main source for local air pollution in urban areas. In addition to the direct pollution from the exhaust gas (such as NOx and CO), cars also whirl up dust from the roads into the air. NR has analysed the effect of a dust treatment using a magnesium chloride solution on the road. To be able to detect the effect of the salting treatment, we have to adjust for all other effects that may have an impact on the air quality, such as precipitation and wind conditions.

High-dimensional statistical modelling of changes in wave climate and implications for maritime infrastructure (HDwave)

Future changes in ocean wind wave climate have broad implications for the physical infrastructure and environment in coastal, near- and offshore regions. However, coupled atmosphere-ocean general circulation models used for projecting temperature and precipitation into the future, typically do not include wave parameters as part of their model output. Instead, the HDwave project aims to develop advanced statistical tools that can combine newly developed data sets with current model output to produce wave climate projections.

Mesoscale weather extremes: Theory, spatial modelling and prediction (WEX-MOP)

High-impact extreme weather events, such as heavy rainfall and/or strong winds are very often associated with intensive atmospheric fronts with strong gradients or deep moist convection. In the WEX-MOP project, a team of atmospheric and mathematical scientists will join forces to work on theory, spatial statistical and dynamical modeling, and prediction for mesoscale weather extremes, to advance prognoses of severe weather events. The project will release developments towards a next-generation comprehensive mesoscale prediction system for extreme weather.

Model selection and model verification for point processes (PointProcess)

For many natural systems such as wildfires or disease occurrences, statistical point process models are commonly used to describe their data generating process. However, widespread use of these models by scientists in various fields can be hindered by their complicated nature. In this project, we will develop new methodology for validating and selecting the most appropriate model for a given data set using Bayesian and decision theoretic principles.

MonCO2 - Monitoring Geological CO2 storage

Improved quantitative prediction of the spatial distributions of CO2 in subsurface storage.

Physical and statistical analysis of climate extremes in large data sets (ClimateXL)

Weather and climate extremes such as heavy precipitation events and flooding can cause severe damages to infrastructure. The occurrence, frequency, and intensity of such extremes are determined by a complex interplay of natural and anthropogenic factors. ClimateXL will investigate uncertainties related to simulations of climate extremes in climate models and, subsequently, improve our understanding of present and future changes in climate extremes.

Recovery from acidification

Acid rain and other acid deposition are serious environmental problems that affect large parts of the industrial world. Rivers and lakes that are exposed to acid deposition may become acidified if they are not naturally protected with for instance limestone rock that are able to neutralise acid. In this project we have developed a method for automatic calibration of models used to quantify the effects of reduced deposition in lakes.

SARMA - Statistical Approaches to Regional Climate Models for Adaptation

The field of statistical climatology, placed at the interface between statistics and climate science, deals intimately with various kinds of uncertainty. Understanding of the uncertainties involved is a key element for decision makers and stakeholders in their efforts to provide advice on adaptation to climate change. challengeswith information about the future that includes estimates of uncertainty. SARMA is a Nordic network, funded by Nordforsk, that aims at building capacity for cross-disciplinary training among statisticians and climate scientists through activities such as workshops, student exchange, and collaborative research.

Statistical analysis of climate projections (eSACP)

Long-term planning and decision-making regarding fundamental societal infrastructure such as transportation, energy supply and water and drainage systems must account for a changing climate. The relevant climate projections are typically associated with severe inherent uncertainty and it is critical that the decision-making appropriately accounts for this. In eSACP, climate scientists and mathematical scientists from six partners in three Nordic countries join forces to develop a suite of open-access tools for climate assessment.