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Abstract Climate change is contributing to rapid changes in lake ice cover across the Northern Hemisphere, thereby impacting local communities and ecosystems. Using lake ice cover time‐series spanning over 87 yr for 43 lakes across the Northern Hemisphere, we found that the interannual variability in ice duration, measured as standard deviation, significantly increased in only half of our studied lakes. We observed that the interannual variability in ice duration peaked when lakes were, on average, covered by ice for about 1 month, while both longer and shorter long‐term mean ice cover duration resulted in lower interannual variability in ice duration. These results demonstrate that the ice cover duration can become so short that the interannual variability rapidly declines. The interannual variability in ice duration showed a strong dependency on global temperature anomalies and teleconnections, such as the North Atlantic Oscillation and El Niño–Southern Oscillation. We conclude that many lakes across the Northern Hemisphere will experience a decline in interannual ice cover variability and shift to open water during the winter under a continued global warming trend which will affect lake biological, cultural, and economic processes. 

Severe deterioration of water quality in lakes, characterized by overabundance of algae and declining dissolved oxygen in the deep lake (DO B ), was one of the ecological crises of the 20th century. Even with large reductions in phosphorus loading, termed “reoligotrophication,” DO B and chlorophyll (CHL) have often not returned to their expected pre–20th-century levels. Concurrently, management of lake health has been confounded by possible consequences of climate change, particularly since the effects of climate are not neatly separable from the effects of eutrophication. Here, using Lake Geneva as an iconic example, we demonstrate a complementary alternative to parametric models for understanding and managing lake systems. This involves establishing an empirically-driven baseline that uses supervised machine learning to capture the changing interdependencies among biogeochemical variables and then combining the empirical model with a more conventional equation-based model of lake physics to predict DO B over decadal time-scales. The hybrid model not only leads to substantially better forecasts, but also to a more actionable description of the emergent rates and processes (biogeochemical, ecological, etc.) that drive water quality. Notably, the hybrid model suggests that the impact of a moderate 3°C air temperature increase on water quality would be on the same order as the eutrophication of the previous century. The study provides a template and a practical path forward to cope with shifts in ecology to manage environmental systems for non-analogue futures. 

Abstract In recent decades, lakes have experienced unprecedented ice loss with widespread ramifications for winter ecological processes. The rapid loss of ice, resurgence of winter biology, and proliferation of remote sensing technologies, presents a unique opportunity to integrate disciplines to further understand the broad spatial and temporal patterns in ice loss and its consequences. Here, we summarize ice phenology records for 78 lakes in 12 countries across North America, Europe, and Asia to permit the inclusion and harmonization of in situ ice phenology observations in future interdisciplinary studies. These ice records represent some of the longest climate observations directly collected by people. We highlight the importance of applying the same definition of ice-on and ice-off within a lake across the time-series, regardless of how the ice is observed, to broaden our understanding of ice loss across vast spatial and temporal scales. 

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making. 

Abstract. Empirical evidence demonstrates that lakes and reservoirs are warming acrossthe globe. Consequently, there is an increased need to project futurechanges in lake thermal structure and resulting changes in lakebiogeochemistry in order to plan for the likely impacts. Previous studies ofthe impacts of climate change on lakes have often relied on a single modelforced with limited scenario-driven projections of future climate for arelatively small number of lakes. As a result, our understanding of theeffects of climate change on lakes is fragmentary, based on scatteredstudies using different data sources and modelling protocols, and mainlyfocused on individual lakes or lake regions. This has precludedidentification of the main impacts of climate change on lakes at global andregional scales and has likely contributed to the lack of lake water qualityconsiderations in policy-relevant documents, such as the Assessment Reportsof the Intergovernmental Panel on Climate Change (IPCC). Here, we describe asimulation protocol developed by the Lake Sector of the Inter-SectoralImpact Model Intercomparison Project (ISIMIP) for simulating climate changeimpacts on lakes using an ensemble of lake models and climate changescenarios for ISIMIP phases 2 and 3. The protocol prescribes lakesimulations driven by climate forcing from gridded observations anddifferent Earth system models under various representative greenhouse gasconcentration pathways (RCPs), all consistently bias-corrected on a0.5∘ × 0.5∘ global grid. In ISIMIP phase 2, 11 lakemodels were forced with these data to project the thermal structure of 62well-studied lakes where data were available for calibration underhistorical conditions, and using uncalibrated models for 17 500 lakesdefined for all global grid cells containing lakes. In ISIMIP phase 3, thisapproach was expanded to consider more lakes, more models, and moreprocesses. The ISIMIP Lake Sector is the largest international effort toproject future water temperature, thermal structure, and ice phenology oflakes at local and global scales and paves the way for future simulations ofthe impacts of climate change on water quality and biogeochemistry in lakes.