Archive: Videos and Abstracts
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Winter Term 2024/2025
07.11.2024 | Sibel Eker, Radboud University
Improving the representation of social systems in climate policy models
Feasible climate change mitigation pathways require considering the feedbacks within and between human and earth systems. Currently, the models that support climate polices, namely the integrated assessment models (IAMs) of climate, economy and environment have a very weak representation of the human systems and the feedbacks between human and earth systems. In this talk, I will introduce the FeliX model, a feedback-rich system dynamics model of climate, economy, environment and society interactions. I will focus on how the FeliX model is used to explore long-term dietary changes, sustainable development, poverty and human wellbeing resulting from the feedbacks between society, climate, economy, energy and land use dynamics. Based on these examples, I will discuss how system dynamics models can help developing feasible demand-side mitigation scenarios and analyzing the potential of social tipping points.
Summer Term 2024
11. Juli 2024 | Stefanie Arndt, Universität Hamburg und Alfred Wegener Institut
It’s all about snow: What can we learn from local snow properties for large-scale Antarctic ice pack volume?
Snow on sea ice is a crucial climate variable, affecting energy and momentum exchanges across the atmosphere-ice-ocean interfaces and contributing to the sea ice mass budget. The year-round snow cover on Antarctic sea ice prevents summer surface melt and promotes ice growth through snow-to-ice conversion. However, limited knowledge of seasonal stratigraphy and large-scale snow depth causes significant uncertainties in satellite data and climate models.
The Young Investigator Group SNOWflAke, a joint research group between the University of Hamburg and the Alfred Wegener Institute for Polar and Marine Research (AWI), aims to test the hypothesis that seasonal variations in Antarctic snowpack properties are indicators of atmospheric changes and could trigger snow-albedo feedbacks, accelerating sea ice melt and retreat. This research will develop techniques to create comprehensive snow and sea ice datasets, improve snow parameterization for satellite data, and enhance snow models to reduce uncertainties in sea ice predictions. These efforts will provide insights into Antarctic sea ice changes and snow's role as a climate change indicator.
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04. Juli 2024 | Stephen Sitch, University of Exeter
The role of the terrestrial biosphere in the Earth System and for achieving Paris climate targets
In this presentation I will first give a broad overview of the role of the terrestrial ecosystems in the contemporary global carbon cycle, and ecosystems mitigate climate change already today. A key question relates to the efficiency of land ecosystems to sequester CO2 and how it will change in the near-term future, e.g. can it help us achieve the Paris agreement to avoid dangerous climate change. To begin to answer this question we first need to understand processes and regional attribution of the contemporary land carbon sink, e.g. the relative contribution of changing atmospheric composition, climate and land-use change to the land sink dynamics. I will draw upon research conducted using the TRENDY ensemble of land models which supports the annual Global Carbon Budget. I will highlight recent work in boreal and tropical forests and dryland ecosystems, and the role of cascading effects of deforestation on the land sink. Finally, I present work on how land-use emissions play a critical role in land-based mitigation for Paris climate targets.
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30. Mai 2024 | Christopher Kadow, DKRZ
The technology deleting photobombs can do climate research? The chat bot writing poems can do climate analysis?
Climate change research today relies on climate information from the past. Historical climate records of temperature observations form global gridded datasets that are examined, for example, in IPCC reports. However, the datasets combining measurement records are sparse in the past. Even today, they contain missing values. We found that recently successful image inpainting technologies, such as those found on smartphones to get rid of unwanted objects or people in photos, are useful here. The derived AI networks are able to reconstruct artificially cropped versions in the grid space for any given month using the missing values observation mask. So herewith we have found with AI a technique that gives us data from the past that we never measured with instruments. Other important datasets used in the Assessment Report 6 of the IPCC to study climate change, as well as advanced applications such as downscaling in atmosphere and ocean, a hybrid (AI&ESM) data assimilation approach within ICON, or precipitation in broken radar fields are shown in this presentation.
Climate research, including the study mentioned in the previous paragraph, often requires substantial technical expertise. This involves managing data standards, various file formats, software engineering, and high-performance computing. Translating scientific questions into code that can answer them demands significant effort. The question is, why? Data analysis platforms like Freva (Kadow et al. 2021, e.g., gems.dkrz.de) aim to enhance user convenience, yet programming expertise is still required. In this context, we introduce a large language model setup and chat bot interface based on GPT-4/ChatGPT, which enables climate analysis without technical obstacles, including language barriers. This approach is tailored to the needs of the broader climate community, which deals with massive data sets from kilometer-scale modeling and requires a processing environment utilizing modern technologies, but addressing society after all - such as those in the Earth Virtualization Engines (EVE eve4climate.org).
Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nat. Geosci. 13, 408-413 (2020). https://doi.org/10.1038/s41561-020-0582-5
Kadow, C., Illing, S., Lucio-Eceiza, E. E., Bergemann, M., Ramadoss, M., Sommer, P. S., Kunst, O., Schartner, T., Pankatz, K., Grieger, J., Schuster, M., Richling, A., Thiemann, H., Kirchner, I., Rust, H. W., Ludwig, T., Cubasch, U., and Ulbrich, U.: Introduction to Freva – A Free Evaluation System Framework for Earth System Modeling, Journal of Open Research Software, 9, p. 13, https://doi.org/10.5334/jors.253, 2021
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23. Mai 2024 | Louis Kotzé, THE NEW INSTITUTE
Thinking Through Planetary Commons Governance
The Anthropocene signifies the start of a no-analogue trajectory of the Earth system that is fundamentally different from the Holocene. This new trajectory is characterized by rising risks of triggering irreversible and unmanageable shifts in Earth system functioning. We urgently need a new global approach to safeguard critical Earth system regulating functions more effectively and comprehensively. The global commons framework is the closest example of an existing approach with the aim of governing biophysical systems on Earth upon which the world collectively depends. Derived during stable Holocene conditions, the global commons framework must now evolve in the light of new Anthropocene dynamics. This requires a fundamental shift from a focus only on governing shared resources beyond national jurisdiction, to one that secures critical functions of the Earth system irrespective of national boundaries. We propose a new framework—the planetary commons—which differs from the global commons framework by including not only globally shared geographic regions but also critical biophysical systems that regulate the resilience and state, and therefore livability, on Earth. The new planetary commons should articulate and create comprehensive stewardship obligations through Earth system governance aimed at restoring and strengthening planetary resilience and justice.
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11. April 2024 | Da Yang, University of Chicago
The Incredible Lightness of Water Vapor
Conventional wisdom suggests that warm air rises while cold air sinks. However, recent satellite observations show that, on average, rising air is colder than sinking air in the tropical free troposphere. This is due to the buoyancy effect of water vapor: the molar mass of water vapor is less than that of dry air, making humid air lighter than dry air at the same temperature and pressure. Unfortunately, this vapor buoyancy effect has been considered negligibly small and thereby overlooked in large-scale climate dynamics. Here we use theory, reanalysis data, and a hierarchy of climate models to show that vapor buoyancy has a similar magnitude to thermal buoyancy in the tropical free troposphere. As a result, cold air rises in the tropical free troposphere. We further show that vapor buoyancy enhances thermal radiation, increases subtropical stratiform low clouds, favors convective aggregation, and stabilizes Earth’s climate. However, some state-of-the-art climate models fail to represent vapor buoyancy properly. This flaw leads to inaccurate simulations of cloud distributions—the largest uncertainty in predicting climate change. Implications of our results on paleoclimate and planetary habitability will also be discussed.
Winter Term 2023/2024
01. Februar 2024 | Galen McKinley, Columbia University and Lamont-Doherty Earth Observatory
All hands on deck! Improved ocean carbon sink estimates by combining models and data
Since the preindustrial era, the ocean has removed about 40% of fossil CO2 from the atmosphere, and it will eventually absorb at least 80% of human CO2 emissions. There is no doubt that the ocean is a critical player in the global carbon cycle, but many questions remain. Critically, these uncertainties reduce confidence in projections of the future global carbon cycle and climate. In this talk, I demonstrate how multiple approaches can be used together to reduce these uncertainties. Specifically, I introduce a machine-learning approach that merges observations and models to improve skill against independent data. With this approach, air-sea CO2 fluxes for 1959-2022 can be estimated and, at the same time, large-scale model biases are revealed. These biases propagate directly into future projections under both high and low-emission scenarios. The clearest paths to improving quantification of the ocean carbon sink are (1) targeted observations to fill identified gaps and (2) reduced mean-state biases in modeled circulation and biogeochemistry.
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Donnerstag 7. Dezember 2023 | Nima Shokri, Institute of Geo-Hydroinformatics, Hamburg University of Technology (TUHH)
Climate Informed Engineering: An Essential Pillar of Industry 4.0 Transformation
Breakthroughs in computing have led to development of new generations of Earth Systems Models providing detailed information on how our planet may locally respond to the ongoing global warming. This presents an unprecedented opportunity for engineers to make tangible contributions to climate adaptation through integration of climate information in their products and designs. This is precisely the key focus of Climate Informed Engineering (CIE) Research Initiative founded at TUHH in collaboration with MPI-M and United Nations University. The concept behind CIE is to enable engineers to build infrastructure, devices, sensors or develop new materials and processes that are informed by climate information, thus contributing to concepts like resilience and climate change adaptation. We believe CIE will be an increasingly important dimension of Engineering Science resonating with engineers and scientists with different backgrounds (1) with the details discussed in this talk.
(1) Shokri, N., Stevens, B., Madani, K., Grabe, J., Schlüter, M., Smirnova, I. (2023), Climate Informed Engineering: An essential pillar of Industry 4.0 transformation, ACS Eng. Au, 3, 1, 3–6.
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23. November 2023 | Tiffany A. Shaw, University of Chicago and currently at MPI-M as a Friedrich Wilhelm Bessel Research Award holder by the Alexander von Humboldt Foundation
Fast upper-level jet stream winds get faster under climate change
Earth's upper-level jet streams influence the speed and direction of travel of weather systems and commercial aircraft and are linked to severe-weather occurrence. Climate change is projected to accelerate the average upper-level jet stream winds. However, little is known about how fast (> 99th percentile) upper-level jet stream winds will change. Here we show fast upper-level jet stream winds get faster under climate change using daily data from climate model projections across a hierarchy of physical complexity. Fast winds also increase ~2.5 times more than the average wind response. We show the multiplicative increase underlying the fast-get-faster response follows from the non-linear Clausius-Clapeyron relation (moist-get-moister response). The signal is projected to emerge across both hemispheres by 2050 when considering scenario uncertainty. The results can be used to explain projected changes in commercial flight times, record-breaking winds, clear-air turbulence, and a potential increase in severe-weather occurrence under climate change.
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Donnerstag 16. November 2023 | Ayako Abe-Ouchi, University of Tokyo
Millennial scale climate variability and ice age cycle
Glacial periods were punctuated by abrupt millennial scale climate changes, such as Dansgaard-Oeschger events, Boeling-Allerod and Younger Dryas. Although glacial abrupt climate changes were shown to have a strong link to the Atlantic Meridional overturning circulation (AMOC) changes and the glacial background climate, simulating the millennial scale climate change and understanding its condition with fully coupled ocean-atmosphere GCM have been challenging. Here we present several cases of millennial scale climate variability simulated with our Japanese Atmosphere Ocean coupled GCM, MIROC4m. A series of long transient experiments (> 10,000 years) were performed systematically with different steady glacial conditions (CO2 level, obliquity, precession, ice sheet size and meltwater amount) in order to study the dependence of millennial scale variability on the background climate and summarize the results as phase diagrams. We found that a sweet spot of millennial scale oscillation exists under a certain condition, while the AMOC is in a stable strong (weak) mode of about 18 (10) Sv (Sverdrup) without the oscillation. In the sweet spot, self-sustained oscillation with bipolar seesaw pattern appears and shifts between interstadials with strong AMOC and stadials with weak AMOC occur. The interval between abrupt events ranges from 1000 years to more than 5000 years, while an abrupt shift from stadial to interstadial mode occurs in about 100 years, just like geological evidence, ice core analysis and the deep-sea cores. The mechanism of the millennial scale climate variability as well as its threshold of occurrence, which is likely related to the thermal condition of global climate, can be now discussed.
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Summer Term 2023
29. Juni 2023 | Isla Simpson, NCAR
Humidity trends in dry regions are inconsistent with climate models
Arid and semi-arid regions of the world are particularly vulnerable to greenhouse gas driven hydroclimate change. The American Southwest is a particularly clear example where recent drought has led to unprecedented water shortages in the Colorado River, and some of the most extreme wildfire seasons in recent history, and this has almost certainly been exacerbated by the substantial warming and aridification that has resulted from rising greenhouse gases. Climate models are our primary tool for projecting the future hydroclimate that society in these regions must adapt to, but here a concerning discrepancy between observed and model-based historical hydroclimate trends will be discussed. While observations of many of the processes of relevance to the hydroclimate, such as soil moisture and evapotranspiration, are limited, we do have a reasonably complete network of station-based near surface atmospheric humidity measurements as well as reanalysis-based estimates of atmospheric water vapor. Here, we will use these datasets along with CMIP6 models to demonstrate a rather drastic difference in the nature of near surface humidity trends over the period 1980 to 2020. Where and when this discrepancy occurs is closely tied to climatological aridity, with it being most apparent in arid/semi-arid regions of the world, but also visible in the most arid seasons of more humid regions. It will be shown that models tend to exhibit increases in atmospheric water vapor that are close to those expected from Clausius Clapeyron scaling, while atmospheric humidity in reality has stayed roughly constant over arid and semi-arid regions of the world. This suggests that the availability of moisture to satisfy the increased atmospheric demand is lower in reality than in models in arid and semi-arid regions and it indicates a major gap in our understanding and modeling capabilities, which could have severe implications for hydroclimate projections, including fire hazard, moving forward.
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22. Juni 2023, 15:15 Uhr | Wilco Hazeleger, Dean of the Faculty of Geosciences and professor of Climate System Science at Utrecht University
Perspectives on Digital Twin Earth
Digital twins are a hype in many scientific domains. A digital twin of the Earth is envisaged in leading programs, such as Europe’s Destination Earth. While some see this as a continuous development towards km-scale global non-hydrostatic modelling enabled by increasing computational capabilities, others regard this as paradigmatic leading to fundamental new workflows in weather and climate research and services. I will present a perspective on the latter and will discuss digital twins in the weather and climate domain related to emerging digital technologies and data sciences up to considerations from social sciences and humanities.
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08. Juni 2023 | Edwin Gerber, Professor at the Courant Institute NYU
Revealing the statistics of extreme events hidden in short weather forecast data: A case study of Sudden Stratospheric Warmings
Climate change will be felt primarily through changes in extreme weather: intense storms, precipitation events, and temperature anomalies. Extreme events in the stratosphere, namely Sudden Stratospheric Warmings (SSWs), are known to impact surface weather extremes, driving an equatorward shift of the storm tracks and associated jet streams. Efforts to quantify potential changes in SSWs in response to anthropogenic forcing, both their frequency and their surface impact, however, have been hampered by the large uncertainty in the observational record. The problem becomes more acute for the most extreme SSWs, which are known to have a stronger surface impact. A once-in-a-century event takes, on average, 100 years of observations or simulation time to appear just once. This is far beyond the typical integration length of our most accurate weather models, which provide the best representation of stratosphere-troposphere coupling, so the task is often left to cheaper, but less accurate, low-resolution or statistical models. One reduces the sampling error (aleatoric uncertainty) at the expense of increased model error (epistemic uncertainty).
In this work, we propose methods to extract climatological information from subseasonal forecast ensembles. Despite being short in duration, weather forecast ensembles are produced multiple times a week, collectively, adding up to thousands of years of data. Using ensemble hindcasts produced by the European Center for Medium-range Weather Forecasting (ECMWF) archived in the subseasonal-to-seasonal (S2S) database, we compute multi-centennial return times of extreme SSW events. Consistent results are found between alternative methods, including basic counting strategies and Markov state modeling. By combining different trajectories together in a statistically rigorous way, we obtain estimates of SSW frequencies and their seasonal distributions that are consistent with reanalysis-derived estimates for moderately rare events, but can be extended to events of unprecedented severity that have not yet been observed historically. The same methods hold potential for assessing extreme events throughout the climate system, beyond the example of stratospheric extremes presented here, and could be adopted in the context of climate change integrations to quantify the impact of anthropogenic forcing on extreme weather.
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25. Mai 2023 | Kerry A. Emanuel, Professor Emeritus of Atmospheric Science, MIT
What Sets the Climatology of High CAPE?
Severe convective storms are a significant source of weather-related losses and injury, worldwide. Yet very little is known about what sets their climatology in the current climate, and why climate models generally indicate increased severe storm activity as the climate warms. In this talk, I will focus on one of the main ingredients in severe convective storms: Convective Available Potential Energy (CAPE). The global climatology of CAPE differs significantly from that of deep convection in general; for example, high CAPE values are quite rare over the ocean. Using both an observational analysis and a 1-D model coupled to a model of soil and vegetation, I will argue that high CAPE results when air masses that have been significantly modified by passage over dry, lightly vegetated soils are advected over moist soils with moderate to extensive vegetation. This suggests that widespread agricultural practices may significantly modify the climatology of severe convection and points to how climate change might affect the prevalence and intensity of severe convective storms.
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Winter Term 2022/2023
19. January 2023 | Leonie Wenz, Potsdam Institute for Climate Impact Research
The economic cost of climate change
Changing climate conditions and weather extremes can affect various fundamental elements of our economies such as labor productivity or agricultural yields – thereby posing a threat to economic prosperity and societal welfare. Estimates of the macroeconomic costs of climate change hence play an important role in climate policy debates and decisions. However, current estimates differ strongly – partly because it is unclear how resilient regions, sectors and communities are and how persistently weather extremes can thus affect them. In this talk, I will give an overview of some recent findings in this research area. Specifically, I will present insights gained from a novel data set comprising subnational income data from the past 40 years and more than 1500 regions worldwide. Based on these granular data, we have empirically estimated historic temperature and precipitation impacts at different time scales, from daily fluctuations and extremes to changes in the long-term mean. Our findings show that economic productivity is strongly affected by rainfall and temperature changes but that these effects display large spatial heterogeneity. Whereas low-income, low-latitude regions are most vulnerable to rising and erratic temperatures, increases in the number of rainy days and extreme rainfall events are most harmful in wealthy, industrialized countries. In our economically interconnected world, these local impacts can have repercussions in other parts of the work as well. I will conclude by presenting preliminary results on such spill-over effects in firm networks.
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12. January 2023 | Laure Zanna, Professor of Mathematics & Atmosphere/Ocean Science [she/her] Courant Institute, NYU
Transforming Climate Modeling for Machine Learning: Hype or Reality?
Climate simulations remain one of the best tools to understand and predict global and regional climate change. Uncertainties in climate predictions originate partly from the poor or lacking representation of processes, such as ocean turbulence and clouds, that are not resolved in global climate models but impact the large-scale temperature, rainfall, sea level, etc. The representation of these unresolved processes has been a bottleneck in improving climate simulations and projections. The explosion of climate data and the power of machine learning (ML) algorithms are suddenly offering new opportunities: can we deepen our understanding of these unresolved processes and simultaneously improve their representation in climate models to reduce climate projections uncertainty? In this talk, I will discuss the advantages and challenges of using machine learning for climate projections. I will focus on our recent work in which we leverage ML tools to learn representations of unresolved ocean processes - in particular, learning symbolic expression. Some of our work suggests that machine learning could open the door to discovering new physics from data and enhance existing climate modeling. Yet, many questions remain unanswered, making the next decade exciting and challenging for ML + climate modeling for robust and actionable climate projections. The work presented is part of M²LInES – an international effort to improve climate models with scientific machine learning.
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15. Dezember 2022 | Michael Byrne, Reader in Earth & Environmental Sciences – University of St Andrews Marie Skłodowska-Curie Research Fellow – University of Oxford
Extreme tropical temperatures in a changing climate: theory and simulations
Understanding the future of extreme temperatures is a critical goal for science and society. Simulations with coupled climate models suggest that hot days over tropical land will warm substantially more than the average day. For example, averaged across models, warming of the hottest 5% of days is projected to be approximately 20% larger than the annual-mean warming. Amplified warming of extreme temperatures implies severe impacts on humans and ecosystems, yet the physical mechanisms underpinning this emergent behaviour of numerical models remain unclear. Here, I interpret the response of extreme temperatures over tropical land to climate change using a theory based on convective coupling and the weak temperature gradient approximation. According to the theory, warming is amplified for hot land days because those days are dry: this is termed the ‘drier get hotter’ mechanism and can be predicted given properties of the current climate. Changes in near-surface relative humidity further increase tropical land warming, with decreases in land relative humidity particularly important. The theory advances understanding of extreme weather in the tropics, and highlights land-surface dryness as a key factor determining how hot days will respond to climate change.
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08. Dezember 2022 | Timo Goeschl, Heidelberg University, Department of Economics and Research Centre for Environmental Economics
Attribution to anthropogenic causes helps prevent adverse events
The science of extreme event attribution (EEA) has rapidly established itself as a popular tool for quantifying - ex post - the anthropogenic contribution to recent adverse events in the climate system. Yet, whether backward-looking causal attribution can lead to future behavioral change is conceptually unclear, and evidence that it can reduce anthropogenic stress on these systems is lacking. Our online experiment with 3,031 participants in three treatment conditions provides a proof of principle. There, adverse events can arise either as a result of excess stress on the system by participants’ pursuit of individual material benefits (anthropogenic cause) or as a result of chance (natural cause). We examine both the impact on future anthropogenic stress of making past adverse events causally attributable and the demand for attributability. We find that whether an adverse event can be causally attributed is behaviorally relevant: Attribution to an anthropogenic cause reduces future anthropogenic stress and leads to fewer adverse events compared to no attributability and compared to attribution to a natural cause. Joint causation has no effect. There is demand for ex-post event attribution in the population of participants, even when costly. The conjecture that attribution science can be behaviorally impactful, socially valuable, and in demand therefore rests on promising experimental foundations.
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03. November 2022 | Steven Yearley, University of Edinburgh, School of Social and Political Science
Has the climate crisis put an end to green ambivalence about science?
The idea at the heart of this colloquium presentation is that there is an elective affinity between environmental campaign organisations/the environmental movement and scientific claims that is, to a large degree, distinctive among social movements. This gives environmentalists and green campaigning bodies an urgent interest in science communication issues and has turned many of them into significant science-communication actors. However, these ties to science have – as is well known – not been problem-free. Demands for scientific “proof” of environmental problems have often been used by governments or regulators as grounds for inaction. Green campaigners have themselves been sceptical about scientific arguments in relation to GMOs and – before that – nuclear safety. At the same time, environmentalists’ opponents have learned to deploy doubt and uncertainty as ways to counter science-based claims. Scientific claims have also typically stemmed from the “Global North”, giving rise to concerns about their applicability or sensitivity to issues in the Global South. Finally, scientific claims are typically presented as empirical assertions and thus non-normative; this may be beneficial when identifying problems but tells one less about solutions and the ways we should live with environmental change. The urgency of the climate crisis intensifies these questions about the environmental movement’s attachment to science: has the climate crisis put an end to green ambivalence about science?