@misc{12855,
  abstract     = {{Global warming is shifting the thermal dynamics of lakes, with resulting climatic variability heavily affecting their mixing dynamics. We present a dual ensemble workflow coupling climate models with lake models. We used a large set of simulations across multiple domains, multi‐scenario, and multi GCM‐ RCM combinations from CORDEX data. We forced a set of multiple hydrodynamic lake models by these multiple climate simulations to explore climate change impacts on lakes. We also quantified the contributions from the different models to the overall uncertainty. We employed this workflow to investigate the effects of climate change on Lake Sevan (Armenia). We predicted for the end of the 21st century, under RCP 8.5, a sharp increase in surface temperature  and substantial bottom warming , longer stratification periods (+55 days) and disappearance of ice cover leading to a shift in mixing regime. Increased insufficient cooling during warmer winters points to the vulnerability of Lake Sevan to climate change. Our workflow leverages the strengths of multiple models at several levels of the model chain to provide a more robust projection and at the same time a better uncertainty estimate that accounts for the contributions of the different model levels to overall uncertainty. Although for specific variables, for example, summer bottom temperature, single lake models may perform better, the full ensemble provides a robust estimate of thermal dynamics that has a high transferability so that our workflow can be a blueprint for climate impact studies in other systems.}},
  author       = {{Shikhani, Muhammed and Feldbauer, Johannes and Ladwig, Robert and Mercado‐Bettín, Daniel and Moore, Tadhg N. and Gevorgyan, Artur and Misakyan, Amalya and Mi, Chenxi and Schultze, Martin and Boehrer, Bertram and Shatwell, Tom and Barfus, Klemens and Rinke, Karsten}},
  booktitle    = {{Water resources research : an AGU journal}},
  issn         = {{1944-7973}},
  keywords     = {{multi model ensemble (MME), CORDEX, LakeEnsemblR, lake modeling, climate change impacts, variance decomposition}},
  number       = {{11}},
  publisher    = {{American Geophysical Union (AGU)}},
  title        = {{{Combining a Multi‐Lake Model Ensemble and a Multi‐Domain CORDEX Climate Data Ensemble for Assessing Climate Change Impacts on Lake Sevan}}},
  doi          = {{10.1029/2023wr036511}},
  volume       = {{60}},
  year         = {{2024}},
}

@misc{12231,
  abstract     = {{In temperate lakes, it is generally assumed that light rather than temperature constrains phytoplankton growth in winter. Rapid winter warming and increasing observations of winter blooms warrant more investigation of these controls. We investigated the mechanisms regulating a massive winter diatom bloom in a temperate lake. High frequency data and process-based lake modeling demonstrated that phytoplankton growth in winter was dually controlled by light and temperature, rather than by light alone. Water temperature played a further indirect role in initiating the bloom through ice-thaw, which increased light exposure. The bloom was ultimately terminated by silicon limitation and sedimentation. These mechanisms differ from those typically responsible for spring diatom blooms and contributed to the high peak biomass. Our findings show that phytoplankton growth in winter is more sensitive to temperature, and consequently to climate change, than previously assumed. This has implications for nutrient cycling and seasonal succession of lake phytoplankton communities. The present study exemplifies the strength in integrating data analysis with different temporal resolutions and lake modeling. The new lake ecological model serves as an effective tool in analyzing and predicting winter phytoplankton dynamics for temperate lakes.}},
  author       = {{Kong, Xiangzhen and Seewald, Michael and Dadi, Tallent and Friese, Kurt and Mi, Chenxi and Boehrer, Bertram and Schultze, Martin and Rinke, Karsten and Shatwell, Tom}},
  booktitle    = {{Water research : a journal of the International Water Association}},
  issn         = {{1879-2448}},
  keywords     = {{Winter diatom bloom, High frequency monitoring, Lake modeling, Light limitation, Temperature}},
  publisher    = {{Elsevier BV}},
  title        = {{{Unravelling winter diatom blooms in temperate lakes using high frequency data and ecological modeling}}},
  doi          = {{10.1016/j.watres.2020.116681}},
  volume       = {{190}},
  year         = {{2020}},
}

@misc{12242,
  abstract     = {{Hutchinson and Löffler's (1956) classification of lakes based on the seasonal thermal mixing regime has become a cornerstone of any analysis of lakes as elements of the earth surface. Until now however the lake classification has lacked a physically sound quantitative criterion distinguishing between two fundamental lake types: thermally stratified during a large portion of the year (mono- and dimictic) and predominantly mixed to the bottom (polymictic). Using the mechanistic balance between potential and kinetic energy we review the different formulations of the Richardson number to derive a generalized scaling for seasonal stratification in a closed lake basin. The scaling parameter is the critical mean basin depth, Hcrit, that delineates lakes that mix regularly from those that stratify seasonally based on lake water transparency, lake length, and an annual mean estimate for the Monin-Obukhov length. We validate the scaling on available data of lakes worldwide using logistic regression. The scaling criterion consistently described the mixing regime significantly better than either the conventional unbounded basin scaling or a simple depth threshold. Thus, the generalized scaling is universal for freshwater lakes and allows the seasonal mixing regime to be estimated without numerically solving the heat transport equations.}},
  author       = {{Kirillin, G. and Shatwell, Tom}},
  booktitle    = {{Earth-Science Reviews}},
  issn         = {{0012-8252}},
  keywords     = {{Richardson number, Lake classification, Seasonal stratification, Dimixis, Polymixis, Water transparency, Lake databases, Lake modeling, Secchi depth}},
  pages        = {{179--190}},
  publisher    = {{Elsevier BV}},
  title        = {{{Generalized scaling of seasonal thermal stratification in lakes}}},
  doi          = {{10.1016/j.earscirev.2016.08.008}},
  volume       = {{161}},
  year         = {{2016}},
}