Central IT Services
IT services are provided at MPI-M by the Central IT Services (CIS) group.
The most important services of the Central IT Services are:
- Procurement, setup and management of IT hardware and software for both users (laptops, PCs) and infrastructure (servers, networks, etc.)
- Central user administration
- Provision of an efficient network (LAN, WLAN)
- Central IT help desk as a contact point for all IT-related issues
- Provision of services to support daily work (e.g. version management, project management, websites, etc.)
- Ensuring secure IT operations (failover, backup, IT security)
Detailed documentation on the IT Group’s offerings can be found in the Wiki of the institute.
An account (username and password) is required to use most IT services. Usually, an account will be created for you as soon as you have a contract with MPI-M. If you are a guest at MPI-M and need an account, your group leader at MPI can request an account for you. Further details are described in the institutes Wiki.
If you have any questions or problems using the IT systems at MPI-M, please contact the IT help desk.
Please note that questions regarding the DKRZ systems (e.g. Levante or data archive) will be answered by the DKRZ user support.
Contact
Rainer Weigle
Group leader
Tel.: +49 (0)40 41173-373
rainer.weigle@mpimet.mpg.de
Helpdesk
Tel.: +49 (0)40 41173-361
help-it@mpimet.mpg.de
More Content
How can Thermokarst Lakes be Incorporated into Climate Models?
Global warming is particularly evident — and visible — in the Arctic. Since the region is warming four times faster than the global average, the permafrost — soil that has been frozen for many years — is thawing across the region. This creates landscapes that are sometimes rugged and fissured, which researchers refer to as “thermokarst”: When the soil contains a large amount of ice and this ice melts, the ground subsides, creating the characteristic unevenness. Lakes can form in these depressions, thereby influencing hydrology, the energy exchange between land and atmosphere, and the carbon cycle. Among other things, they serve as a source of the greenhouse gas methane, which has a warming effect about 28 times stronger than carbon dioxide over a 100-year period.
The challenge of modeling thermokarst lakes
Although thermokarst lakes are important for the Arctic hydrology and climate, it has not yet been possible to represent them in Earth system models. While the evolution of individual lakes can be calculated when the ice content in the subsurface and environmental conditions are precisely known, the processes are far too small-scale, dynamic, and heterogeneous to be calculated deterministically for all lakes when considering the entire Arctic or its individual regions. A new study led by the Max Planck Institute for Meteorology (MPI-M) shows that stochastic modeling could be a viable approach for representing thermokarst lakes in Earth system models.
Stochastic modeling allows for a certain degree of randomness. More specifically, probability distributions are used to capture the heterogeneity of characteristics. The research team used this approach to conduct simulations spanning 1,000 years, illustrating how the total area of lakes can change through the formation of new lakes, their expansion or drainage, and their merging.
Depending on the parameter settings, the model can simulate one of three long-term dynamic regimes: 1) Either all lakes dry up; 2) the landscape fluctuates between different states; or 3) the surface area of the lakes stabilizes at a certain value. MPI-M researcher Constanze Reinken, the paper’s lead author, considers all three situations plausible. “All three regimes are fundamentally possible and could each be relevant in different geographical regions.”
Additional measurement data is urgently needed
To determine which scenarios are realistic in which regions, the model’s parameters must be calibrated using measurement data. In the current study, the researchers used Landsat satellite observations for this purpose. However, they note that this data is not yet sufficient in terms of quality, longevity, and geographical coverage and that further research is needed. Such research is on the agenda of many international teams and projects, including the ERC project “Q-Arctic,” in which the MPI-M conducts land surface and climate modelling, a task that includes the present study.
“There is a lot of potential in stochastically modeling thermokarst lakes,” Reinken concludes. “In the future, the stochastic approach could be integrated within Earth system models to improve climate projections.”
Original publication
Reinken, C., Brovkin, V., de Vrese, P., Nitze, I., Bergstedt, H., and Grosse, G. (2026) Stochastic Modelling of Thermokarst Lakes: Size Distributions and Dynamic Regimes, The Cryosphere, 20, 1967–1995, DOI: 10.5194/tc-20-1967-2026
Contact
Constanze Reinken
Max Planck Institute for Meteorology
constanze.reinken@mpimet.mpg.de
How can Thermokarst Lakes be Incorporated into Climate Models?
Global warming is particularly evident — and visible — in the Arctic. Since the region is warming four times faster than the global average, the permafrost — soil that has been frozen for many years — is thawing across the region. This creates landscapes that are sometimes rugged and fissured, which researchers refer to as “thermokarst”: When the soil contains a large amount of ice and this ice melts, the ground subsides, creating the characteristic unevenness. Lakes can form in these depressions, thereby influencing hydrology, the energy exchange between land and atmosphere, and the carbon cycle. Among other things, they serve as a source of the greenhouse gas methane, which has a warming effect about 28 times stronger than carbon dioxide over a 100-year period.
The challenge of modeling thermokarst lakes
Although thermokarst lakes are important for the Arctic hydrology and climate, it has not yet been possible to represent them in Earth system models. While the evolution of individual lakes can be calculated when the ice content in the subsurface and environmental conditions are precisely known, the processes are far too small-scale, dynamic, and heterogeneous to be calculated deterministically for all lakes when considering the entire Arctic or its individual regions. A new study led by the Max Planck Institute for Meteorology (MPI-M) shows that stochastic modeling could be a viable approach for representing thermokarst lakes in Earth system models.
Stochastic modeling allows for a certain degree of randomness. More specifically, probability distributions are used to capture the heterogeneity of characteristics. The research team used this approach to conduct simulations spanning 1,000 years, illustrating how the total area of lakes can change through the formation of new lakes, their expansion or drainage, and their merging.
Depending on the parameter settings, the model can simulate one of three long-term dynamic regimes: 1) Either all lakes dry up; 2) the landscape fluctuates between different states; or 3) the surface area of the lakes stabilizes at a certain value. MPI-M researcher Constanze Reinken, the paper’s lead author, considers all three situations plausible. “All three regimes are fundamentally possible and could each be relevant in different geographical regions.”
Additional measurement data is urgently needed
To determine which scenarios are realistic in which regions, the model’s parameters must be calibrated using measurement data. In the current study, the researchers used Landsat satellite observations for this purpose. However, they note that this data is not yet sufficient in terms of quality, longevity, and geographical coverage and that further research is needed. Such research is on the agenda of many international teams and projects, including the ERC project “Q-Arctic,” in which the MPI-M conducts land surface and climate modelling, a task that includes the present study.
“There is a lot of potential in stochastically modeling thermokarst lakes,” Reinken concludes. “In the future, the stochastic approach could be integrated within Earth system models to improve climate projections.”
Original publication
Reinken, C., Brovkin, V., de Vrese, P., Nitze, I., Bergstedt, H., and Grosse, G. (2026) Stochastic Modelling of Thermokarst Lakes: Size Distributions and Dynamic Regimes, The Cryosphere, 20, 1967–1995, DOI: 10.5194/tc-20-1967-2026
Contact
Constanze Reinken
Max Planck Institute for Meteorology
constanze.reinken@mpimet.mpg.de
How can Thermokarst Lakes be Incorporated into Climate Models?
Global warming is particularly evident — and visible — in the Arctic. Since the region is warming four times faster than the global average, the permafrost — soil that has been frozen for many years — is thawing across the region. This creates landscapes that are sometimes rugged and fissured, which researchers refer to as “thermokarst”: When the soil contains a large amount of ice and this ice melts, the ground subsides, creating the characteristic unevenness. Lakes can form in these depressions, thereby influencing hydrology, the energy exchange between land and atmosphere, and the carbon cycle. Among other things, they serve as a source of the greenhouse gas methane, which has a warming effect about 28 times stronger than carbon dioxide over a 100-year period.
The challenge of modeling thermokarst lakes
Although thermokarst lakes are important for the Arctic hydrology and climate, it has not yet been possible to represent them in Earth system models. While the evolution of individual lakes can be calculated when the ice content in the subsurface and environmental conditions are precisely known, the processes are far too small-scale, dynamic, and heterogeneous to be calculated deterministically for all lakes when considering the entire Arctic or its individual regions. A new study led by the Max Planck Institute for Meteorology (MPI-M) shows that stochastic modeling could be a viable approach for representing thermokarst lakes in Earth system models.
Stochastic modeling allows for a certain degree of randomness. More specifically, probability distributions are used to capture the heterogeneity of characteristics. The research team used this approach to conduct simulations spanning 1,000 years, illustrating how the total area of lakes can change through the formation of new lakes, their expansion or drainage, and their merging.
Depending on the parameter settings, the model can simulate one of three long-term dynamic regimes: 1) Either all lakes dry up; 2) the landscape fluctuates between different states; or 3) the surface area of the lakes stabilizes at a certain value. MPI-M researcher Constanze Reinken, the paper’s lead author, considers all three situations plausible. “All three regimes are fundamentally possible and could each be relevant in different geographical regions.”
Additional measurement data is urgently needed
To determine which scenarios are realistic in which regions, the model’s parameters must be calibrated using measurement data. In the current study, the researchers used Landsat satellite observations for this purpose. However, they note that this data is not yet sufficient in terms of quality, longevity, and geographical coverage and that further research is needed. Such research is on the agenda of many international teams and projects, including the ERC project “Q-Arctic,” in which the MPI-M conducts land surface and climate modelling, a task that includes the present study.
“There is a lot of potential in stochastically modeling thermokarst lakes,” Reinken concludes. “In the future, the stochastic approach could be integrated within Earth system models to improve climate projections.”
Original publication
Reinken, C., Brovkin, V., de Vrese, P., Nitze, I., Bergstedt, H., and Grosse, G. (2026) Stochastic Modelling of Thermokarst Lakes: Size Distributions and Dynamic Regimes, The Cryosphere, 20, 1967–1995, DOI: 10.5194/tc-20-1967-2026
Contact
Constanze Reinken
Max Planck Institute for Meteorology
constanze.reinken@mpimet.mpg.de