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Atmospheric warming heats lakes, but the causes of variation among basins are poorly understood. Here, multi-decadal profiles of water temperatures, trophic state, and local climate from 345 temperate lakes are combined with data on lake geomorphology and watershed characteristics to identify controls of the relative rates of temperature change in water (WT) and air (AT) during summer. We show that differences in local climate (AT, wind speed, humidity, irradiance), land cover (forest, urban, agriculture), geomorphology (elevation, area/depth ratio), and water transparency explain >30% of the difference in rate of lake heating compared to that of the atmosphere. Importantly, the rate of lake heating slows as air warms (P < 0.001). Clear, cold, and deep lakes, especially at high elevation and in undisturbed catchments, are particularly responsive to changes in atmospheric temperature. We suggest that rates of surface water warming may decline relative to the atmosphere in a warmer future, particularly in sites already experiencing terrestrial development or eutrophication.

 

Climate warming in combination with nutrient enrichment can greatly promote phytoplankton proliferation and blooms in eutrophic waters. Lake Taihu, China, is a large, shallow and eutrophic system. Since 2007, this lake has experienced extensive nutrient input reductions aimed at controlling cyanobacterial blooms. However, intense cyanobacterial blooms have persisted through 2017 with a record‐setting bloom occurring in May 2017. Causal analysis suggested that this bloom was sygenerically driven by high external loading from flooding in 2016 in the Taihu catchment and a notable warmer winter during 2016/2017. High precipitation during 2016 was associated with a strong 2015/2016 El Niño in combination with the joint effects of Atlantic Multi‐decadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), while persistent warmth during 2016/2017 was strongly related to warm phases of AMO and PDO. The 2017 blooms elevated water column pH and led to dissolved oxygen depletion near the sediment, both of which mobilized phosphorus from the sediment to overlying water, further promoting cyanobacterial blooms. Our finding indicates that regional climate anomalies exacerbated eutrophication via a positive feedback mechanism, by intensifying internal nutrient cycling and aggravating cyanobacterial blooms. In light of global expansion of eutrophication and blooms, especially in large, shallow and eutrophic lakes, these regional effects of climate anomalies are nested within larger scale global warming predicted to continue in the foreseeable future.

 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.