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About Topic 1


Topic 1 addresses three research areas of societal relevance for the Atmosphere in Global Change: Air Quality, Climate Feedbacks, and Future Weather and Extremes. Knowledge in these areas provides guidance to assess adaptation and mitigation measures as we approach the 1.5°C global warming limit. We will develop observational and modeling solutions for improved atmospheric predictions and regional climate projections at unprecedented resolution, in a ‘whole atmosphere’ approach, to address entire process- and event chains from the drivers of global change or extreme events to their ramifications for air quality, water resources, greenhouse gas budgets, land use change, ecosystem viability, and the risk of more extreme weather in a warmer world. 

Our focus:

The common goal of all work in Topic is to establish the scientific foundation for global change mitigation and adaptation measures that are regionally explicit, effective, and applicable. This requires the reliable modeling of coupling processes and interactions between all Earth system compartments relevant for climate change (atmosphere, ocean, cryosphere, land surface and biosphere, socio-economics), particularly on decadal temporal and regional spatial scales. To reach this long-term goal, we will further develop modeling components of compartment-crossing processes, in conjunction with integrated observation programs, and synthesize them into coupled regional modeling systems.

In detail we focus on the following objectives:

Assessment of future air quality and implications for human and environmental health in conjunction with climate change. To achieve this objective, we will determine consequences of global change for sources, sinks and transformation processes that affect air quality and atmospheric composition (incl. GHG), as well as their impacts on health, economy, and quality of life.

Reliable projections and predictions on the interconnected future developments of climate, atmosphere, and land use at global to regional scales, to assess future living conditions and options for the sustainable use of resources. For this objective, we will explore and quantify the feedback-web between physical, chemical, biological, and socio-economic processes and mechanisms in the Earth system, through the development of scale-crossing observation and modeling methodologies.

Improved predictive skill of natural hazards, like floods and droughts, with a strong application focus on adaptation to extreme weather and climate change, as well as disaster management. Towards this goal, we will assess extreme weather events in terms of their predictability and trends in frequency and magnitude with climate change, through in-depth knowledge of atmospheric processes gained in observational programs (e.g., MOSES), new observational techniques and complex numerical modeling at unprecedented resolution.

Integration and enhancement of multi-purpose and modular observational infrastructure toward a concerted ‘system of systems’ for atmospheric and climate research (ATMOsense, planned). Implementation of novel techniques for data assimilation will facilitate future approaches in model-data fusion.

To build up both IT infrastructure and expertise in the emerging field of environmental data science to an internationally leading level in atmospheric and climate research, following a coordinated strategy across the Program.

To assume an internationally leading role in regional Earth system modeling, and a formative role in the emerging national strategy on Earth system modeling, together with partners within the Program and the German research community at large. The planned new ATMOsense infrastructure will provide data for model evaluation across scales.

Establishment of an internationally leading structured qualification and talent management program in atmospheric and climate sciences, including all levels from students, graduate students to junior group leaders. Building blocks of such a program (specific to T1 needs) already exist in the participating centers.

Recent Highlights

New utilization of GNSS data

A novel neural network model of Earth’s topside ionosphere

The Earth’s ionosphere affects the propagation of signals from the Global Navigation Satellite Systems (GNSS). The future appearance of the autonomous vehicles requires now more accurate GNSS navigation tools. The development of accurate models of the ionosphere has been a long-standing challenge. A new study presents a Neural network-based model of Electron density in the topside ionosphere (NET), which is constructed using 19 years of GNSS data.


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Subtopics in detail

Participating Centers