MSc and PhD projects at DMI
Sensitivity of winter climatic conditions in Europe to various surface forcing in the EC-Earth model
Mads Lundsgaard Andersen
The goal of this thesis is to investigate systematically varied surface forcings(sea surface temperatures & Arctic sea ice extent) in order to shed some light on their influence on the year to year variability of winter climate in Europe. This is done using the atmospheric circulation part(IFS) of the EC-Earth model.
Dates: 1.2.2023 to 30.5.2023
DMI supervisor: Shuting Yang, DMI
Uni. supervisor: Jens Hesselbjerg, NBI, KU
Scientific Machine Learning for Flood Forecasting Systems
Floods are costly at a socio-economic level but can be reduced if forecasts are accurate and in-time. The aim of the project is to develop a new generation of models for runoff processes and hydraulics in river systems based on scientific machine learning. These models integrates machine learning and physical undestanding which makes the models more scale-able than existing models, easier to integrate with new data sources, better at simulating river processes impacted by vegetation and sea levels, and faster at simulating the hydraulic processes.
Dates: 1.03.2023 to 1.03.2026
DMI supervisor: Michael Brian Butts and Jonas Wied Pedersen, DMI
Uni. supervisor: Roland Löwe and Peter Bauer-Gottwein, DTU
Earth observation for Surface mass balance
This project aims to estimate the GrIS mass balance and investigate methods to separate its components, e.g. ice flow, surface melt, and snowfall processes. The project will develop new methods to quantify ice sheet SMB from EO data that are independent of climate models. To achieve this, the project will take advantage of combining different EO data sets e.g. ice sheet velocity and changes in ice sheet surface topography. The project's outcome will be an independent method to better constrain the ice sheet processes and a component to validate and improve mass balance modeling necessary for future sea level rise projections.
Dates: 1.01.2023 to 1.01.2026
DMI supervisor: Ruth Mottram, DMI
Uni. supervisor: Sebastian B. Simonsen and Louise S. Sørensen, DTU Space, Anne M. Solgaard (GEUS)
Windstorms: Methods for climate adaptation in the built environment
Johanne Kristine Haandbæk Øelund
Existing and planned infrastructure and buildings in Denmark are designed against the background of contemporary climatic conditions, while the climate is expected to change during the expected long life of the building. The buildings may be vulnerable to extreme weather events, such as storms, cloudbursts, floods, but statistical analyses of meteorological data only provide limited insight, as extreme weather events are by definition very rare. The aim of this project is to analyse the significance of possible changes in the occurrence of extreme wind speeds in connection with the passage of storms over Denmark, as a background for technically related failures in construction. The project has three main focuses: Storms hitting Denmark - past and future, identification of geographical differences in storm damage and address how current construction rules may be insufficient to withstand the storms of the future.
Dates: 15.12.2022 to 14.12.2025
DMI supervisor: Henrik Vedel, DMI
Uni. supervisor: Jens Hesselbjerg, NBI, Kirsten Halsnæs and Holger Koss, DTU
Extreme Climate Events and their Consequences
Ane Carina Reiter
Decomposing drivers of extreme weather and climate events constitutes a fundamental challenge. Traditional univariate approaches, focusing on single drivers, do not capture the complex interactions of drivers and may ultimately underestimate the associated risks. Adopting a multivariate statistical framework, however, brings its own challenges. Extreme events are by definition rare, and observations of extreme values of multiple climate variables jointly are less sampled than their univariate counterparts. A lack of data thus forces such methods to extrapolate heavily on the tails, introducing a high level of uncertainty. In this PhD project, a novel multivariate approach from dynamical systems theory is adopted to study the interactions of multiple climate drivers. Exploring the chaotic nature of the atmosphere, the method computes instantaneous dynamical properties of the underlying chaotic attractor. The aim of the PhD is to investigate how well this method can identify drivers of extreme weather and climate patterns now and in the future.
Dates: 1.12.2022 to 31.08.2026
DMI supervisor: Mark Payne and Morten Andreas Dahl Larsen, DMI
Uni. supervisor: Martin Drews, DTU
Climate extremes and their consequences.
Matthew Lee Newell
Anthropogenic climate change is projected to result in alterations to the frequency, severity, and spatiotemporal patterns of weather and surface conditions across the globe. To assess the full risk from climate events, analyses need to be concentrated on multivariate dependencies that can result in co-occurring hazards and a potential for the amplification of impacts in the physical environment. In Denmark, recent occurrences of sequential precipitation events have resulted in several winter seasons with periods of above-average precipitation, which has caused local flooding due to saturated soil conditions. Additionally, recent cases of summertime drought in Denmark—which have historically been less of a concern—reduced water availability and put a strain on water resources in periods of peak demand. Both independently and compounded, the emergence of wintertime flooding and summertime drought will continue to have major implications across society. The aim of this PhD project is to be able to detect, characterize, and better understand the dynamics of compound wet and dry events in Denmark and Europe across different time scales, and to identify (localize) areas specifically at risk to these extremes under the current and future climate.
Dates: 1.12.2022 to 1.12.2025
DMI supervisor: Mark Payne and Morten Andreas Dahl, DMI
Uni. supervisor: Martin Drews, DTU
Mapping of Greenland surface temperatures
The aim of the project is to design, develop, build and run a sustainable Artificial Intelligence/Machine Learning system to create a consistent near surface air temperature data set with verified uncertainty information covering all of Greenland from 1873 to present. This new surface temperature product linking long temperature records from DMIs land station network, Earth Observation surface skin temperature observations, PROMICE automatic weather station observations and ice core bore hole temperature records can be used directly to assess climate change. Furthermore it can be used in climate and Earth Systems models
Dates: 15.12.2021 to 31.01.2025
DMI supervisor: Jacob L. Høyer and Eigil Kaas, DMI
Uni. supervisor: Bo M. Vinther, NBI
High resolution modeling of the Greenland ice sheet surface mass balance
Modeling ice sheet surface processes, in particular melt and accumulation, using the HARMONIE/HCLIM regional model system and the SMB model run at DMI. The aim is to produce a Greenland-wide very high resolution climate dataset. This product will also produce surface mass budget and runoff estimates from the ice sheet. The unprecedented spatial resolution of the CARRA and HCLIM simulations over Greenland will provide more spatial detail in the modeling of the ice sheet surface processes, which has the potential for improving estimates of future sea level contributions.
Dates: 1.12.2021 to 1.12.2024
DMI supervisor: Ruth Mottram, DMI
Uni. supervisor: Peter Langen, Aarhus University
Decadal climate variability and predictability
Understanding climate variability in the Arctic – North Atlantic region is pivotal to mitigate the impacts of negative future environmental and climatic changes. Herein lies a thorough understanding of the oceans role in characterizing Earths climate. The focus in the project is to get an understanding of how the variability of the physical dynamics in the ocean affects the biogeochemical processes such as the global carbon pump (the oceans natural carbon sink), primary production, ocean acidification and nutrient availability. This will be done on the basis of data from EC-EARTH-Earth3 and CMIP6 data.
Dates: 1.12.2021 to 1.12.2024
DMI supervisor: Steffen M. Olsen
Uni. supervisor: Carolin Löscher, SDU
Statistical mode of extreme rainfall with very high spatio-temporal resolution
Almost all of the world will experience an increase in the frequency and severity of extreme precipitation due to increased levels of greenhouse gas in the atmosphere, subsequently leading to more urban pluvial flooding events. Merging and analysing data across different observational and modeling platforms will yield an improved understand of the spatio-temporal properties of extreme rainfall at a given location and also provide a platform for improved description of the regional variation of the precipitation extremes and identify atmospheric drivers.
Dates: 1.12.2021 to 30.11.2024
DMI supervisor: Torben Schmith, DMI
Uni. supervisor: Karsten Arnberg-Nielsen, DTU
Extension of sea ice climate time series with historical satellite data
Wiebke Margitta Kolbe
Arctic sea ice is an important climate indicator, because the effects of global climate change are amplified in the arctic. Current sea ice climate data records (CDRs) beginning in the late 1970’s are based on satellite data. However there are also satellite missions from the early and mid 1970’s which can be used for mapping sea ice and for extending the current CDRs. While older satellite instruments have their limitations compared to modern multi-channel sensors, they still provide useful data for mapping sea ice extent and the distribution of sea ice type. This PhD project will build and run a method to process historical satellite data in order to extend existing sea ice climate data records of sea ice extent in the past. The project is a part of the Danish National Center for Climate Research (NCKF) at DMI and the research will provide insight into historical sea ice development and serve as an important sea ice extent reference from the 1970s, which can be used for input to climate models and reanalysis
Dates: 1.12.2021 to 30.11.2024
DMI supervisor: Gorm Dybkjær and Eigil Kaas, DMI
Uni. supervisor: Rasmus Tage Tonboe, DTU Space
Prediction of atmospheric dispersion on all scales for emergency preparedness
Long-range atmospheric dispersion modeling is used at DMI to predict concentration and deposition fields of various kinds of hazardous matter such as radioactive gasses and particles, toxic chemicals and smoke, aerosols containing infectious germs, volcanic ash particles etc. as a service to the responsible authorities. Thereby early warnings are enabled facilitating implementation of optimum countermeasures. However there is a need to extend this capability seamlessly to shorter range (up to about 50 km from source), which requires development of novel methods and techniques. Additionally recent events have demonstrated that there is a need to be able to carry out inverse modeling at all scales as an operational service to the responsible authorities enabling localization of an unknown source as well as characterization of the temporal development of the release of emitted gasses and particles. The PhD project will provide DMI with fundamentally new expertise and capabilities within short-range atmospheric dispersion modeling as well as inverse modeling.
Dates: 1.2.2021 to 31.1.2024
DMI supervisor: Jens Havskov Sørensen, Henrik Feddersen, DMI
Uni. supervisor: Eigil Kaas, KU NBI
Arctic sea ice climate data records and the consistency between SST and sea ice satellite products
Accurate global and gap-free sea surface temperature (SST) observations are important for climate monitoring, understanding of air-sea interactions and numerical weather prediction. STT has been retrieved from infrared satellite observations since 1981, but there are limited by clouds and biased from aerosols. Passive microwave observations (PMW) are not prevented by non-precipitating clouds and the bias from aerosols is small, and therefore holds a large potential for monitoring the Arctic. The aim of the PhD is to improve the algorithms to retrieve SST from PMW satellite observations, including an assessment of the impact of using different channel selections in retrieving in the retrieval algorithms and to assess how the SST observations can be integrated with sea ice parameters in a multi-sensor gab free SST and sea ice product for the Arctic.
Dates: 1.11.2018 to 4.7.2023
DMI supervisor: Jacob L. Høyer, DMI
Uni. supervisor: Rasmus Tonboe and Leif Toudal Pedersen, DTU Space
Combining Cryosat data with ocean-sea-ice models to improve the understanding of Arctic sea ice thickness
Arctic sea ice extend is rapidly changing with major impact on Arctic Ocean circulation and the global climate. However the amount of Arctic sea ice can only be estimated. The aim of the project is to develop strategies for sea ice data assimilation to improve this estimate. For this data for the polar orbiting satellite CryoSat2 is used. Most existing techniques assimilating CryoSat2 data assimilate sea ice thickness which is derived from sea ice free-board, which is the variable directly measured by the satellite. Furthermore the project aim to develop a technique to directly assimilate CryoSat2 free-board.
Dates: 15.12.2019 to 1.07.2023
DMI supervisor: Till S. Rasmussen, DMI
Uni. supervisor: Lars Stenseng, DTU Orbit
Completed MSc projects
Assessing albedo parameterizations in the Cissembel model using genetic algoritm
In this project the albedo parameterization of the Copenhagen Ice-Snow Surface Energy and Mass Balance Model (CISSEMBEL) is assessed using generic algorithm. The idea is to compare the temperature output from the CISSEMBEL model with some measurements made at the Greenlandic ice sheet (PROMICE weather data). This comparison can then be analysed such that the most likely albedo parameterization in the model given the model output and observed measurements
Dates: 2.9.2022 to 2.1.2023
DMI supervisor: Ruth Mottram and Christian Rodehacke
Uni. supervisor: Johan Rønby Pedersen, RUC
Road stretch forecasting
Anne Helene Koch Borrits
One of the most dangerous weather phenomenon is slippery roads and that is why it is important to research the thermal mapping data to improve the forecast.
The focus in this project is to make quality control for thermal mapping data, identify thermal finger-prints of roads for each winter month and look into the physio-geographical and local conditions for improvement of the road stretch forecast for the Danish road network.
This project will improve road stretch forecasting for the Danish road network by: 1)Elaborating PP and QC for thermal mapping data. 2) Identify thermal finger-prints for roads.
Dates: 1.9.2016 to 28.2.2018
DMI-supervisor: Claus Petersen
Uni. supervisor: Eigil Kaas (KU), Alexander Mahura (Uni Helsinki)
Analysis of data from Ice Mass Balance Buoys and Satellites
Mathilde Thorn Ljungdahl
In the project, a dataset will be constructed using buoy data GPS locations and timestamps as reference for co-locating satellite data and NWP data from relevant sources.
By combining these different near-simultaneous data we obtain a unique dataset that can be used to analyze the variability in the physical parameters of the snow and ice and the resulting satellite observations. By use of linear regression or optimal estimation to invert the problem it is then the aim to use the satellite data to estimate the associated snow and ice parameters.
Dates: March 2014 – June 2016
DMI-supervisor: Leif Toudal Pedersen
Uni. supervisor: Eigil Kaas, NBI-KU
Green Infrastructure development as a strategy to mitigate Urban Heat Island effect: Case study of Copenhagen Metropolitan Area
Aleksander Andrzej Stysiak
Urban areas are concentrations of climate vulnerability, and under future urbanization and climate change impacts projections, the well-being and comfort of the urban population will become progressively compromised. Green Infrastructure is an important tool in the process of adapting cities to climate change. Thesis aims at testing impact of various greening scenarios on meteorological parameters (with special focus on temperatures, humidity and wind operation) at the scale of Copenhagen Metropolitan Area. Thesis employs meteorology-chemistry Enviro-HIRLAM model.
Dates: 1.3.2015 to 31.8.2015
DMI supervisor: Alexander Mahura
Uni. supervisor: Marina Bergen Jensen, University of Copenhagen – IGN
Impact of albedo parameterizations on surface mass balance and runoff on the Greenland Ice Sheet
The main motivation is to create a better and more physically based albedo parameterization to be implemented and tested in an offline stand-alone version of the surface mass balance scheme in HIRHAM5. Comparisons are made with MODIS satellite derived albedo data and in-situ observations of surface conditions by PROMICE stations. Further, other choices of albedo formulations are implemented in order to test the influence on e.g. melt and surface mass balance.
Dates: 1.9.2014 to 31.8.2015
DMI supervisors: Peter Langen and Ruth Mottram
Uni. Supervisor: Christine Hvidberg, University of Copenhagen, Niels Bohr Institute, Centre for Ice and Climate
Completed PhD projects
Statistical down-scaling of precipitation to very high spatio-temporal resolutions
Emma D. Thomassen
Climate models are used to project climate change impact on extreme precipitation events and can help to understand how to adapt to these events. However, climate models are highly dependent on the grid scale on which these simulations are run. The PhD study analyses state-of-the-art, high resolution convective-permitting climate model simulations to understand how well such models represent heavy precipitation events. Several metrics analysing spatial and temporal aspects of precipitation are used. These metrics are used to compare climate models and observations to evaluate the performance of the climate models. DMI has produced new climate model simulations on a sub-kilometer scale to analyse the added benefit of moving to an extremely high resolution.
Dates: 1.12.2018 to 14.10.2022
DMI supervisor: Ole B. Christiansen
Uni. supervisor: Karsten Arnberg-Nielsen and Hjalte J. D. Sørup, DTU Sustain
Reconciling fundamental climate variables for determining the Antarctic Mass balance
The Antarctic ice sheet is the largest ice sheet on Earth. It has the potential to raise the global mean sea level by 58 meters if completely melted. It is therefore important to know the mass balance. The thesis focuses on reconciling/determining the altimetry and mass budget and calculated the mass balance from 1979 to 2021, and used the altimetry method to derive the mass balance of Antarctica from 2018 to 2021. A firn model has been further developed to model the Antarctic firn pack, which also calculates the surface mass balance to be used in the mass balance method.
Dates: 1.9.2019 to 31.8.2022
DMI supervisor: Senior Climate Scientist Ruth Mottram, DMI
Uni. supervisor: Senior Researcher Sebastian Bjerregaard Simonsen and Professor Rene Forsberg, DTU Space
Hyper-local forecasting system for agricultural applications
This PhD thesis aims at investigating the use of a dense network of private weather stations (PWSs) for agricultural applications with a focus on producing site-specific forecasts. A framework for tuning spatial quality control methods for a dense network of meteorological stations from a numerical weather prediction (NWP) model are developed and site-specific forecasts are produced. First, linear and adaptive methods requiring only a short training period are investigated, where after the transformer model, a non-linear machine learning model, is developed. The transformer model is hereafter extended to post-processing of 2 m relative humidity forecast. In addition, PWS 2 m wind speed observations are used to estimate the roughness length and are evaluated for extrapolation to 10 m. These results are further applied as the transformer model is extended to post-processing of near-surface wind speed forecasts in a preliminary study. Overall, this study has illustarted the potential af a dense network of PWSs and post-processing to obtain site-specific forecasts for agricultural applications.
Dates: 1.3.2019 to 31.5.2022
DMI-supervisor: Xiaohua Yang, DMI
Uni. supervisor: Eigil Kaas, KU NBI, Andreas Troelsen and John Smedegaard, FieldSense
Improving radiation schemes in weather and climate models by using machine learning and code optimization
Simulating how solar and terrestrial radiation interact with Earth’s atmosphere, surface and clouds is a crucial component of weather and climate models, but also computationally demanding. This PhD project explored the use of machine learning and code optimization techniques to improve the trade-off between speed and accuracy for these important computations. The focus was on maintaining accuracy and reliability, which resulted in combining machine learning with traditional physical modeling. This meant using neutral networks for predicting optical properties but not to replace the entire radiation scheme. Code optimization alone was also found to be highly useful, improving the efficiency of the ECMWF radiation scheme by a factor of 2-3, and enabling affordable computations of cloud 3D radiative effects. Finally an important contribution of the growing research field on using machine learning to model physical processes was made by developing a new physically inspired method based on recurrent neutral networks to more accurately emulate radiative transfer.
Dates: 21.12.2018 to 31.02.2022
DMI-supervisor: Kristian Pagh Nielsen, DMI
Uni. supervisor: Eigil Kaas KU, NBI
Advancing actionable knowledge on sealevel extremes through an ocean modelling framework
Extreme sea levels are widely recognized as one of the most dangerous natural hazards. Assessing events that have a very low probability of occurring is crucial for disaster risk management due to their potentially disastrous impacts. This project aims to improve our understanding of extreme sea-level events by probing the boundaries of what is physically plausible with the help of an ocean model.
Dates: 1.9.2018 to 8.10.2021
DMI-supervisor: Kristine Skovgaard Madsen, DMI
Uni. supervisor: Martin Drews, DTU management
On the Usage of Crowdsourced Data in Numerical Weather Prediction
Kasper Stener Hintz
The usage of crowdsourced data within the atmospheric sciences is still relatively unexplored but it is believed to have a great potential. Crowdsourced data can provide a great source of high temporal and spatial resolution, real-time data. Currently, there exist no general methods to validate crowdsourced data and so there exist no answers of to which extent crowdsourced data can be used in numerical weather prediction. This PhD project performs fundamental research within the field of using crowdsourced data in numerical weather prediction.
Dates: 1.6.2016 to 31.05.2019
DMI-supervisor: Henrik Vedel and Niels Woetmann Nielsen
Uni. supervisor: Eigil Kaas (KU), Juan Munos-Gomez (Vaavud ApS)
How does the Arctic sea ice impact the Greenland Ice Sheet and climate in general?
Ida Margrethe Ringgaard
Observations from ice cores show large variations in both Arctic sea ice and the Greenland Ice Sheet in the past. Additionally, the Arctic sea ice cover has been observed to decrease over the last decades. As part of the Ice2ice project, this PhD project focuses on how these changes in the Arctic sea ice interacts with the Greenland Ice Sheet and how it impacts climate in general. Using the coupled global climate model, EC-Earth, scenarios with varying Arctic sea ice covers are simulated and analyzed with focus on the interaction between Arctic sea ice changes, the Greenland Ice Sheet and climate.These simulations are performed for both past and present conditions.
Dates: 15.12.2015 to 15.12.2018
DMI supervisor: Shuting Yang
Uni. supervisors: Eigil Kaas and Jens Hesselbjerg Christensen, Climate and Geophysics, Nils Bohr Institute, University of Copenhagen
Modelling of the Greenland Ice Sheet
Roughly 5 lines about the project: Couple the ice sheet model PISM to the ocean by implementation of a 3D physical based calving scheme in PISM. A full two-way coupling between the Greenland Ice Sheet (GIS) and the general climate system will be done by letting PISM interact with the global climate model EC-Earth. Surface mass balance simulations of the GIS will be done to access the quality of the coupled PISM - EC-Earth system in comparison to observations and regional climate model outputs.
Dates: 1.3.2015 to 28.2.2018
DMI supervisor: Jens Hesselbjerg Christensen
Uni. supervisor: Christine S. Hvidberg, Centre for Ice and Climate
High resolution regional climate modelling in the Arctic
The focus is on performance of the regional climate model HIRHAM5 over Greenland and with a special emphasis of representing the local climate at the east cost regarding local surface mass balance over the ice sheet and the Renland glacier in particular. The work will contribute to the development of a dynamic high resolution system model of the Greenland ice sheet, the coastal regions and interactions with the surrounding ocean, and to study changes to the ice sheet as a consequence of abrupt changes in Arctic climate and sea ice.
Dates: 1.3.2015 to 28.2.2018
DMI supervisors: Jens Hesselbjerg Christensen and Peter Langen
Uni. supervisor: Eigil Kaas, University of Copenhagen
Marine climate effects on primary production around the Faroe Islands
Based on a broad suite of observations that includes in situ time series approaching 20 years or more, large-scale data sets and output from a high resolution model, this project will try to explain the large variation in primary production in the waters surrounding the Faroe Islands. This includes: · To explore the hydrographical settings and especially the dynamics of the mixed layer (ML) around and on the Faroe shelf. Study the depth and timing of the ML and the relative role of tidal mixing, air-sea heat exchanges and horizontal advection. · To explore the effect of the physical dynamics on the primary production in well mixed and stratified areas.
Dates: 1.11.2013 to 1.11.2016
DMI supervisors: Till A. Rasmussen and Steffen M. Olsen
Uni. supervisors: Dr. Karin Margretha Húsgarð Larsen, Physical Oceanographer, Faroe Marine Research Institute Prof. Bogi Hansen, Physical Oceanographer, Faroe Marine Research Institute Dr. Hjálmar Hátún, Physical Oceanographer, Faroe Marine Research Institute Dr. Høgni Debes, Biological Oceanographer, Faroe Marine Research Institute
Modelling Interglacial Climate
Rasmus Anker Pedersen
The last interglacial Eemian climate is investigated through experiments with the EC-Earth climate model. The scientific focus is on the dynamics of climate change in a warming climate with special focus on the Arctic. Key objectives are to compare the simulated Eemian warming to the climatic reconstructions from Greenland ice cores, and analyze how well the Eemian serves as an analogue to future, CO2-driven warming. Sensitivity experiments will further investigate the importance of changes in different climatic properties (e.g. ice sheet, sea ice, and vegetation configurations), and will help clarifying the mechanisms of climate change.
Dates: 15.5.2013 to 14.5.2016
DMI supervisors: Peter L. Langen and Jens Hesselbjerg Christensen
Uni. supervisor: Bo M. Vinther, Centre for Ice and Climate, Niels Bohr Institute, University of Copenhagen
Impact of black carbon on air quality and climate in Northern Europe and Arctic (under project NordForsk CarboNord)
The research is focused on simulations of black carbon aerosol concentrations using Enviro-HIRLAM model (Environment - High Resolution Limited Area Model) and aiming at providing new information on distribution and effects of black carbon in Northern Europe and Arctic regions, and evaluation of reliability of the model predictions with focus on Nordic conditions.
Dates: 01.12.2010 to 31.12.2015
DMI supervisor: Drs. Alexander Mahura and Bent H. Sass
Uni. supervisor: Dr. Roman Nuterman and prof. Eigil Kaas, Niels Bohr Institute of University of Copenhagen
The impact of bacteria on ice nucleation in mixed phase clouds- A model study
Bacteria are present in the atmosphere and they are abundant. Specific types of bacteria have the capability of nucleating the ice in mixed phase clouds which may help in precipitation formation process. Thus, bacteria may affect the radiation budget and influence the weather and climate. What makes bacteria distinct from other types of ice nuclei is that they are efficient in nucleating ice heterogeneously at temperatures up to -2°C. Bacteria have the unique capacity of synthesizing ice-nucleation-active (INA) proteins and exposing them at their outer membrane surface. Our main focus was to investigate the impact of bacterial IN on ice nucleation and try to introduce it into a forecasting model.
Dates: 31.12.2010 to 1.11.2015
DMI supervisors: Ulrik Smith Korsholm; Niels Woetmann Nielsen; Jens Havskov Sørensen
Uni. supervisors: Prof. Kai Finster, Department of Bioscience - Microbiology, Aarhus University Dr. Urich Bay Gosewinkel, Head of section, Department of Environmental Science - Environmental microbiology & biotechnology, Aarhus university
Understanding a high resolution regional climate model’s ability in simulating tropical East Africa climate variability and change (under project CLIVET)
CLIVET is a 5 year capacity building project that aims to increase the capacity in Tanzania to project climate changes and impacts on water resources relevant for the agricultural sector. The overall objective of CLIVET is to contribute to the capabilities of Tanzania to encounter the impacts of climate change and develop best strategies to adapt to these changes, particularly as they relate to water resources and the use of water within the agricultural sector by (a) supporting individual and institutional capacity building to do climate change and adaptation research, (b) informing national and development assistance policy dialogue on appropriate strategies in water management and (c) building climate change research alliances between South-South and North-South partners.
Dates: Sep 2009 to Dec 2014
DMI supervisor: Martin Stendel
Uni. supervisor: Prof. Bruce Hewitson, University of Cape Town, South Africa