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Ice-wedge polygons are widespread periglacial features and influence landscape hydrology and carbon storage. The influence of climate and topography on polygon development is not entirely clear, however, giving high uncertainties to projections of permafrost development. We studied the mid- to late Holocene development of modern ice-wedge polygon sites to explore drivers of change and reasons for long-term stability. We analyzed organic carbon, total nitrogen, stable carbon isotopes, grain size composition and plant macrofossils in six cores from three polygons. We found that ail sites developed from aquatic to wetland conditions. In the mid-Holocene, shallow lakes and partly submerged ice-wedge polygons existed at the studied sites. An erosional hiatus of ca 5000 years followed, and ice-wedge polygons re-initiated within the last millennium. Ice-wedge melt and surface drying during the last century were linked to climatic warming. The influence of climate on ice-wedge polygon development was outweighed by geomorphology during most of the late Holocene. Recent warming, however, caused ice-wedge degradation at all sites. Our study showed that where waterlogged ground was maintained, low-centered polygons persisted for millennia. Ice-wedge melt and increased drainage through geomorphic disturbance, however, triggered conversion into high-centered polygons and may lead to self-enhancing degradation under continued warming.
This study, based on new and high quality in situ observations, quantifies for the first time, the individual contributions of light-absorbing aerosols (black carbon (BC), brown carbon (BrC) and dust) to aerosol absorption over the Indo-Gangetic Plain (IGP) and the Himalayan foothill region, a relatively poorly studied region with several sensitive ecosystems of global importance, as well as highly vulnerable populations. The annual and seasonal average single scattering albedo (SSA) over Kathmandu is the lowest of all the locations. The SSA over Kathmandu is < 0.89 during all seasons, which confirms the dominance of light-absorbing carbonaceous aerosols from local and regional sources over Kathmandu. It is observed here that the SSA decreases with increasing elevation, confirming the dominance of light absorbing carbonaceous aerosols at higher elevations. In contrast, the SSA over the IGP does not exhibit a pronounced spatial variation. BC dominates (>= 75%) the aerosol absorption over the IGP and the Himalayan foothills throughout the year. Higher BC concentration at elevated locations in the Himalayas leads to lower SSA at elevated locations in the Himalayas. The contribution of dust to aerosol absorption is higher throughout the year over the IGP than over the Himalayan foothills. The aerosol absorption over South Asia is very high, exceeding available observations over East Asia, and also exceeds previous model estimates. This quantification will be valuable as observational constraints to help improve regional simulations of climate change, impacts on the glaciers and the hydrological cycle, and will help to direct the focus towards BC as the main contributor to aerosol-induced warming in the region.
Ecosystems play a pivotal role in addressing climate change but are also highly susceptible to drastic environmental changes. Investigating their historical dynamics can enhance our understanding of how they might respond to unprecedented future environmental shifts. With Arctic lakes currently under substantial pressure from climate change, lessons from the past can guide our understanding of potential disruptions to these lakes. However, individual lake systems are multifaceted and complex. Traditional isolated lake studies often fail to provide a global perspective because localized nuances—like individual lake parameters, catchment areas, and lake histories—can overshadow broader conclusions. In light of these complexities, a more nuanced approach is essential to analyze lake systems in a global context.
A key to addressing this challenge lies in the data-driven analysis of sedimentological records from various northern lake systems. This dissertation emphasizes lake systems in the northern Eurasian region, particularly in Russia (n=59). For this doctoral thesis, we collected sedimentological data from various sources, which required a standardized framework for further analysis. Therefore, we designed a conceptual model for integrating and standardizing heterogeneous multi-proxy data into a relational database management system (PostgreSQL). Creating a database from the collected data enabled comparative numerical analyses between spatially separated lakes as well as between different proxies.
When analyzing numerous lakes, establishing a common frame of reference was crucial. We achieved this by converting proxy values from depth dependency to age dependency. This required consistent age calculations across all lakes and proxies using one age-depth modeling software. Recognizing the broader implications and potential pitfalls of this, we developed the LANDO approach ("Linked Age and Depth Modelling"). LANDO is an innovative integration of multiple age-depth modeling software into a singular, cohesive platform (Jupyter Notebook). Beyond its ability to aggregate data from five renowned age-depth modeling software, LANDO uniquely empowers users to filter out implausible model outcomes using robust geoscientific data. Our method is not only novel but also significantly enhances the accuracy and reliability of lake analyses.
Considering the preceding steps, this doctoral thesis further examines the relationship between carbon in sediments and temperature over the last 21,000 years. Initially, we hypothesized a positive correlation between carbon accumulation in lakes and modelled paleotemperature. Our homogenized dataset from heterogeneous lakes confirmed this association, even if the highest temperatures throughout our observation period do not correlate with the highest carbon values. We assume that rapid warming events contribute more to high accumulation, while sustained warming leads to carbon outgassing. Considering the current high concentration of carbon in the atmosphere and rising temperatures, ongoing climate change could cause northern lake systems to contribute to a further increase in atmospheric carbon (positive feedback loop). While our findings underscore the reliability of both our standardized data and the LANDO method, expanding our dataset might offer even greater assurance in our conclusions.
Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making.
Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop-and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.