Controls of thermal response of temperate lakes to atmospheric warming

Domain: earth_science
Published: 2023-10-16
Editorial outcome: minor_revision

Abstract
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.

Introduction
Climate change has significantly altered lakes worldwide, and is expected to exacerbate current threats to ecosystems and humanity1,2. Lakes are central to hydrological, biogeochemical, and ecological processes, thus knowledge on their responsiveness to climate change is essential to their management and maintenance of ecosystem services3. In particular, recent research has focused on patterns and apparent sensitivity of lakes to atmospheric warming, due to the critical role of lake water temperature (WT) in regulating ecosystem processes, such as organismal growth, biogeochemical cycles, and food-web interactions2. Due to the high specific heat of water, lake temperatures are often buffered against high frequency meteorological variation, and instead integrate longer-term (monthly-to-annual) changes in energy fluxes associated with climatic variability4. As a result, the characteristics of ice cover, stratification, surface temperature, evaporation, and water level have all changed notably in recent decades in response to climate warming2. There is also growing concern that elevated atmospheric temperatures (AT) are enhancing symptoms of eutrophication, such as the frequency, magnitude, and geographic extent of cyanobacterial blooms5–7. Therefore, understanding the response of lake water temperature to climate warming is critical for predicting biotic change and anticipating the repercussions of climatic variability on lakes and associated ecosystems8.

Recent studies have documented multi-decadal trends in lake water temperature, suggesting widespread increases in lake surface WT in response to atmospheric warming8–12. For example, Jane et al.9 indicated that lake surface WT in temperate zone increased 0.39°C per decade from 1980 to 2017, whereas AT increased at 0.30°C per decade over the same period. In addition to differences in rates of air and water temperatures change, individual lakes exhibited a wide range in rate and magnitude of surface WT change, even including whole-lake cooling despite atmospheric warming in some instances13. These findings emphasize the importance of accounting for factors that control heat budgets of basins, rather than assuming that WT responds uniformly to increases in AT.

Heterogeneity in the rate of lake warming may prevent simple statements about lake WT trends13 and underscore the importance of considering possible controls warming, including climate, watershed characteristics, lake geomorphometry, and in situ trophic conditions. Generally, climatic features (e.g., irradiance, humidity, wind speed) are expected to be the predominant factors regulating differences in the rates of lake and atmospheric warming3,14, while parameters controlling the redistribution of heat within the lake have secondary effects on lake warming10. Indeed, variations in lake geomorphology (e.g., depth, water residence time, elevation)15–17, watershed characteristics (e.g., land use)18,19, and trophic status (e.g., water clarity)17,20,21 can modulate climate effects on individual lakes by affecting how energy is distributed with depth. For example, Woolway et al.16 suggests that cold and deep lakes respond more rapidly to variation in AT, while others have found shallow lakes are more sensitive to air warming15,22. Rose et al.21 indicates that lake WT response to AT changes varied among sites in part to differences in water clarity and lake depth. This variation in the responsiveness of WT to atmospheric conditions highlights the heterogeneous and complex responses of lakes to climate and other stressors and makes it difficult to predict the risk of ecosystem damage due to climate change. Further, to date, most mechanistic inferences have been drawn from numerical simulation experiments and still require validation using extensive lake observations. As lake ecosystems are already under serious threat from numerous human-induced stressors (e.g., eutrophication7, deoxygenation9), it is vital to understand where and how global climate change will augment the effects of existing stressors on these important ecosystems22,23, and to implement this knowledge for future management and conservation strategies24.

Here variation in the responsiveness of lake surface and deep WT to atmospheric warming during summer was analyzed by comparing long-term (1979–2017, 24.5±6.7 years) estimates of WT profiles and trophic state from 345 north temperate lakes and reservoirs (Fig. 1) with measures of local climate, lake geomorphometry, and watershed characteristics. We hypothesized that the responsiveness of lake WT to atmospheric warming, and the consequent risk of lentic ecosystems to fundamental changes, is not the same for all lakes, and that clear, cold, and deep basins are more sensitive to AT change. This study aims to improve our understanding of the controls of spatial and temporal variation in lake response to atmospheric warming, help decision-makers prepare for future risks, and develop targeted management strategies.

Results
Long-term variations of air and lake water temperatures
According to observed lake temperature profiles and lake stratification regimes (stratified, unstratified), lake warming was estimated for epilimnetic (surface) and hypolimnetic (deep) waters. In this study, AT, epilimnetic water temperatures (ET), and hypolimnetic water temperature (HT) in summer (hereafter from July 15 to August 31 in the Northern Hemisphere) were 20.6±2.7°C, 22.0±3.0°C, and 9.6±3.0°C, respectively (Fig. 2a). Many northern lakes exhibited warming trends (as Sen’s slope) in both AT (91.0%) and ET (81.7%) during summer, although deep waters changed less consistently, with more than half of sites (58.5%) cooling over the analytical period (Fig. 2b). Epilimnetic temperature generally warmed more rapidly (+0.44±0.57°C per decade) than did summer AT (+0.36±0.33°C per decade), whereas HT often declined (−0.12±0.47°C per decade, Fig. 2b).

For individual lakes, the multi-decadal trends between AT and WT often diverged or even showed opposite trends (Fig. 2c–f). For example, epilimnetic temperature trends (ETT) in 62 lakes (18.0% of sites) and hypolimentic temperature trends (HTT) in 132 lakes (57.6%) were opposite to the air temperature trends (ATT, Fig. 2d, e), while 55.5% of ETT were opposite to HTT (Fig. 2f). Calculated differences in AT and WT trends varied with lake zone, including the trend differences between ETT and ATT (ETT–ATT, +0.08±0.52°C per decade), HTT and ATT (HTT–ATT, −0.47±0.53°C per decade), and ETT and HTT (ETT–HTT, +0.59±0.62°C per decade) (Fig. 2c). In general, lake surface and deep WT responded differently to atmospheric warming, and there was a wide range in rate of WT change in individual lakes (Fig. 2d–f).

Controls of the responsiveness of lake water temperature to air temperature
In this study, the responsiveness of WT to changes in AT during summer was evaluated by calculating the difference between trends in WT and AT for each lake. Analysis with pairwise correlations showed that ETT–ATT was correlated positively with lake volume, forest cover, wetland extent, total summer precipitation (TSP), summer longwave radiation (LR), regional summer latent heat flux (LE), winter AT (WiAT), and humidity (P<0.05), and negatively with the degree of anthropogenic development, agriculture, grassland area, summer wind speed (WS), regional summer sensible heat flux (H), and summer AT (SuAT) (P<0.05, Fig. 3a). Similarly, HTT–ATT was correlated positively with lake area, maximum depth (max depth), ratio of area to depth (area/depth ratio), volume, watershed area (Wshd), forest cover, and WiAT, and negatively with landscape development, SuAT, spring AT (SpAT), and Fall AT (FaAT) (P<0.05, Fig. 3a).

Random forest analysis was used to determine which variables were most important in explaining temperature trend differences between air and water. More than 30% of the variation in the relative rates of air and lake temperature change was correlated to local differences in geomorphic, watershed, climatic, and trophic characteristics (Fig. 3b, c). According to the random forest analysis, the set of predictors used in this study explained 30.3% of ETT–ATT and 31.1% of HTT–ATT, respectively (P<0.001, Fig. 3b, c). Specifically, WS, humidity, SuAT, WiAT, elevation, forest cover, summer shortwave radiation (SR), urban development, wetland, the lake area, and grass were the important factors explaining the differences between ETT to ATT (P<0.05, Fig. 3b), while variations in HTT–ATT were significantly explained by changes in SuAT, forest cover, LR, urban development, shrubland, and water transparency (Secchi depth) (P<0.05, Fig. 3c). Moreover, a variance partitioning analysis showed that variations in ETT–ATT were primarily explained by climate (21.6%), lake geomorphology (2.7%), land use (0.6%), and the combined effects of land use and climate (5.3%) rather than to water transparency (P<0.05, Fig. 3d). In contrast, differences in HTT–ATT were predominantly related to climate (21.7%), land use (5.6%), and water transparency (1.5%) rather than to lake geomorphology (P<0.05, Fig. 3d).

Analysis with generalized additive models (GAMs) revealed that values of ETT–ATT declined significantly with AT warming during summer (Spearman’s r = −0.267, P<0.001, Fig. 4). On average, trend differences between ETT and ATT tended to be negative in warm regions (Fig. 4), indicated that surface WT was less responsive to changes in AT in regions with warmer climates.

Effects of trophic status on lake responsiveness to atmospheric warming
Following Organization for Economic Co-operation and Development (OECD) guidelines25, Secchi depth values were used to categorize lakes according to trophic status. Transparent oligotrophic lakes exhibited greater responsiveness to AT change than did productive turbid sites (Fig. 5). The difference between trends in WT and AT were correlated positively with Secchi transparency (P<0.001, Fig. 5), which was significantly lower in eutrophic and hypereutrophic lakes compared with oligotrophic and mesotrophic sites (P<0.05, Fig. 5). For example, mean ETT–ATT values declined progressively with lake trophic status from oligotrophic (0.20±0.36°C per decade) to mesotrophic (0.19±0.59°C per decade), eutrophic (0.001±0.53°C per decade), and hypereutrophic (−0.17±0.49°C per decade) basins (Fig. 5a), indicating that surface water warmed more slowly in turbid (hypereutrophic) lakes than did the local atmosphere (P<0.05, Fig. 5a). Similarly, HTT–ATT values increased significantly with Secchi depth values (P<0.05, Fig. 5b), and deep water warmed slowly compared with AT, especially in more productive sites (P<0.05, Fig. 5b). Analysis with a subset of lakes using nutrient content (as TP) or phytoplankton abundance (as Chla) confirmed that more productive lakes exhibited lower sensitivity to rising air temperatures than did unproductive systems (Supplementary Fig. 1).

Methods
Dataset
This study uses a large data set incorporating long-term records of water temperature (WT) profiles, local climatic variables, and lake trophic state, as well as a database of lake geomorphic and watershed characteristics collected by academic, government, and not-for-profit sources45,46. Geomorphic characteristics for each lake were sourced from the HydroLAKES project47 and included surface area, maximum depth, area/depth ratio, volume, water residence time, elevation, and watershed area. Water temperature profiles were derived from in situ measurements and had at least one profile sampled annually during the ice-free period40. Meteorological variables derived using the ERA-5 reanalysis from the European Centre for Medium-Range Weather Forecasts48 included various air temperatures (AT) including spring air temperature (SpAT), summer air temperature (SuAT), fall air temperature (FaAT), winter air temperature (WiAT), as well as summer wind speed (WS), humidity, summer short-wave radiation (SR), summer long-wave radiation (LR), total summer precipitation (TSP), regional summer latent heat flux (LE), and regional summer sensible heat flux (H). Climate data were sourced from locations nearest to each lake. The composition of land use within each lake’s watershed was derived from the US national land cover database for most North American sites46, while Landsat images with a spatial resolution of 30 m were used to obtain land use data for basins outside the USA49. The percentage of each land use category for each watershed was characterized as; agriculture, developed, water, forest, wetlands, grass, and shrubland46. In this study, lake trophic state was estimated from measurements of Secchi values from multiple data sources46.

Summer WT is especially important from a lake ecosystem perspective and was the focus of this study. Based on the interval of stable summer stratification, summer period was defined as the period from July 15 to August 31 for lakes situated in the Northern Hemisphere, whereas the few southern hemispheric locations used the interval January 15 to February 28. Selected lakes had at least 15 years of data between 1979 and 2017. For quality control, metadata for each lake was gap-matched for each variable across datasets. Overall, 345 diverse temperate lakes were available in this study (Supplementary Table 3; Supplementary Data file), mostly located in the Northern Hemisphere (Fig. 1).

Lake stratification
Lake stratification was calculated from observed lake temperature profiles. If the vertical range in temperature was <1°C, the water column was considered to be unstratified45. If more than 10% of profiles were considered unstratified, the lake was considered not to have a hypolimnion45. Epilimnion was defined as all depths less than or equal to the uppermost metalimnion depth, and hypolimnion as all depths deeper than the deepest metalimnion depth. In this database, 229 lakes exhibited stratification, whereas 116 lakes exhibited no stratification during summer (Supplementary data). For lake WT, we calculated the mean of all parameters recorded for the epilimnion and hypolimnion.

Lake trophic state
The availability of estimates of trophic state varied with parameter and depth. Water transparency (as Secchi depth, m) was more widely available (334 lakes after removing the sites where the transparency reached the bottom of the lake) and was used to categorize lake into four major trophic status, following the OECD25; oligotrophic (Secchi > 6 m), mesotrophic (3 m < Secchi ≤ 6 m), eutrophic (1.5 m < Secchi ≤ 3 m), and hypereutrophic (Secchi ≤ 1.5 m). Thus defined, there were 79, 111, 77, and 67 lakes classified as oligotrophic, mesotrophic, eutrophic, and hypereutrophic, respectively (Supplementary data).

Long-term trend calculations
To obtain trends for each variable of individual lakes, the annual mean values of climatic variables and all of the epilimnetic and hypolimnetic values were calculated for each lake. Here, Sen’s slope, a commonly-used metric for trend analysis of long-term series, was calculated and used to estimate trends in WT (epilimnetic and hypolimnetic), climatic factors (SpAT, SuAT, FaAT, WiAT, WS, humidity, SR, LR, TSP, H, and LE), and trophic state (Secchi) (Supplementary data). Sen’s slopes and significance (alpha = 0.05) were calculated in R 4.0.4 using the trend package50.

Trend differences between water and air temperatures
In this study, the responsiveness of WT to change in AT during summer was evaluated by calculating the difference between trends in WT and AT for each lake. Specifically, the difference between epilimnetic temperature trend (ETT) and air temperature trend (ATT) in summer (ETT–ATT) were used to indicate the responsiveness of epilimnetic temperature (ET) to AT. Similarly, the responsiveness of hypolimnetic temperature (HT) to AT in summer was calculated as the difference between the hypolimnetic temperature trend (HTT) and ATT, as HTT–ATT. Here, negative values between ETT and ATT indicate that lake water is warming more slowly or cooling faster than the atmosphere.

Regression tree analysis of the trend differences between water and air temperatures
Random forest analysis was used to determine which variables were most important in explaining the responsiveness of WT to changes in AT51. Predictors included geomorphic (lake area, maximum depth, area/depth ratio, volume, water residence time, elevation, watershed area), and watershed characteristics (agriculture, developed, water, forest, wetlands, grass, shrubland), as well as the trends in water transparency (Secchi) and climatic variables (WS, humidity, SR, LR, TSP, H, LE, SpAT, SuAT, FaAT, WiAT). The order of importance was determined by the frequency of variables and their relative position in individual trees across the entire forest. The explanatory power of response variables was estimated as the mean squared error (MSE). Subsequently, preliminary models of interannual variation in WT were used to assess whether the significant variables offered reasonable predictions of WT responsiveness to changes in AT. Random forest analysis was conducted using the randomForest package in R 4.0.452. In addition, the A3 R package was used to assess the significance of the models and cross-validated R2 values with 5000 permutations of the response variables53. For each analysis, only lakes with no missing values for any predictor variables were used.

Variation partitioning analysis
To assess the relative effects of lake geomorphic, watershed, trophic, and climatic variables on the relationship between water temperature trends and ATT, a variation partitioning analysis was performed using ‘varpart’ function of the vegan package in R 4.0.454. Differences in trends of WT and AT were used as the response variable to four sets of explanatory variables: lake geomorphology (area, maximum depth, area/depth ratio, volume, water residence time, elevation, watershed area); watershed characteristics (agriculture, developed, water, forest, wetlands, grass, shrubland); climatic features (WS, humidity, SR, LR, H, LE, TSP, SpAT, SuAT, FaAT, WiAT), and; trophic state (Secchi). Multiple regression using both forward and backward selection was used to reduce collinearity among predictors in each of the explanatory sets55. For ETT–ATT, eight variables (WS, agriculture, SuAT, humidity, elevation, SpAT, SR, area/depth ratio) were selected, while four variables (SuAT, forest, Secchi, WiAT) were retained to explain HTT–ATT. Variation partitioning was performed to evaluate the direct and interactive correlations between the climatic, watershed, geomorphic, and trophic predictors of WT responsiveness to changes in AT. All partitioning fractions of variation were significant in an analysis of variance (ANOVA) permutation test using the vegan package in R 4.0.454.

Statistical analysis
The distributions of ETT–ATT and HTT–ATT with air temperature and water transparency were estimated using generalized additive model (GAM) in the gam package55. Statistical relationships among geomorphic, watershed, climate, and trophic conditions were examined with a one-way analysis of variance (ANOVA) using Tukey’s Honestly Significant Difference test. Correlations between WT trends and lake geomorphic, watershed, climatic, and trophic variables were explored with Spearman’s correlation coefficient using the stats package. All analyses were performed in R 4.0.456. The level of significance used for all tests was P<0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "General comments\nThis is a valuable paper, interesting, novel and generally well written and presented. I have some\nconcerns about terminology and lack of quantification of statements, as well as other points below.\nWith due adjustment relative to the comments below, I will be happy to recommend publication.\nSpecific comments\nAbstract -- many of the points could be better quantified in the abstract, such as the magnitude of\nthe sensitivities (I assume in K/K) being discussed, what a \"significant decrease\" is quantitatively, etc.\nAlso, I find the \"threshold\" language on sensitivity is not really justified. Such language suggests a\nstep change in sensitivity, which is not credible from the data. Even though you have imposed a two-\nfit model on it with an inflection point, use of \"threshold\" to describe that inflection point is\nmisleading, both because there is no step change in sensitivity, and because the step change in slope\nof sensitivity is a feature of your choice of fit rather than a feature of the data.\nLine 43 -- What is long term? Quantify. Lakes integrate over the weather on a range of timescales\nthat is very variable between lakes and can be just days, ie, not really \"long term\".\nLine 44 \"slowly developing\" -- quantify, as above. Ice can form overnight. Is that slow? Suggest\nreword to be clear.\nLine 53 -- O'Reilly is a good reference, but it is pretty old (trends before 14 years ago -- a lot has\nhappened since then). There are annual updates on trends in lake temperature and air temperature\nin the annual BAMS state of the climate reports that could be a source of more contemporary\nstatements, and probably other papers.\nLine 57 - Grammar correction needed for \"apparently cooling\".\nline 58 - There is no mystery that not all lakes follow air temperature, because insolation, windiness,\netc are well known to be important. So a better wording is \"importance of accounting for the other\nfactors\" rather than \"understanding\".\nLine 62 - I agree with these additional factors discussed, but the other meteorological factors are\nomitted. There are trends in insolation, cloudiness (and therefore radiation budget) and wind in\nsome areas, and these modify lake temperature - air temperature relationships too. In fact -- you\npoint this out later, but I think it should be noted together here.\nLine 78 --What is the definition of \"lake temperatures\" here? Is it annual average, summer, ...? What\ndepth(s)? This is in the methods section later, but it needs to be stated here for those reading from\nstart to finish in the order presented.\nLine 82 -- Here you mention irradiance -- I think it should be mentioned a little earlier as per\nprevious comment\nLine 92 -- figure 2 is referenced before figure 1 which is non-standard.\nFigure 2 a and b -- there are 347 lakes, which can't give these smooth distributions. The visual\nimpression can depend on the fit made to give these smooth distributions, and I would prefer to see\nhistograms.\nFigure 2d - I would be more interested in the HTT vs ETT plot than HTT vs ATT, or maybe as well as?\nLine 104 -- again, a sentence framing the HTT changes vs ETT is just as valid to include. The variability\nof HTT vs ETT seems interesting to me.\nLine 107 -- The difference in trend is defined as \"sensitivity\". I wouldn't like this word for this\nquantity to become established as I think it is confusing. A sensitivity is universally a differential\ndx/dy, not dy(dx/dy - 1) which is your definition. You are calculating \"trend differences\", and I don't\nsee a problem calling them trend differences. Always better to call something what it is rather than\nwhat it is not.\nFor an example of why \"sensitivity\" is a bad name, consider a case, which could happen in your\ndataset, of a lake with ETT -0.1 K/dec and ATT +0.1 K/dec. In what sense does \"a sensitivity of -0.2\nK/dec\" make sense: clearly the ETT in such a case is not driven by the ATT and is responding to other\nfactors, rather than responding to ATT with a \"negative sensitivity\".\nTherefore please replace \"sensitivity\" with the more accurate \"trend difference\" throughout.\nLine 109 -- \"Analysis with random forest showed \" is not very clear, please amend.\nLine 116 -- VPA has not been expanded as an acronym.\nFig 3 caption -- need to expand acronym MSE in the caption as this is read before the methods\nsection.\nLine 124 -- seems to me to be more accurately phrased as \"trend differences on average tended to\nbe negative for ATT > X deg C, and close to zero on average below that temperature\". The reason is\nthat your analysis shows many other factors are important and these may be confounding the effect:\nthe warmer lakes warm slightly less fast than air temperature, but potentially because of your other\ninfluences and not because of a lower actual (local) sensitivity (in the dx/dy sense) to AT.\nLine 138 -- this is the first time that it is mentioned that you are looking at \"summer\" air\ntemperature -- needs to be clear before this. Including what \"summer\" means.\nLine 138 -- the numbers are averages and there is variability. I think we need to have the fact these\nare means across 347 locations explicitly stated, and suggest to quote the variability (SD) as well as\nthe mean values (X+/-Y).\nLine 168 -- the argument here seems to be that the land-use effect is mediated by altering the local\nair temperature -- but in that case why isn't all of that wrapped up in the SuAT factor, which is then\ncorrelated?\nLine 176 -- I think this speculation also needs a brief account of why you might think that -- e.g., the\nwarm lakes are in locations of higher water vapour that inhibits the nighttime net radiative cooling\nof the lake.\nLine 189 -- I don't find this very convincing as a causal explanation. You would need to rule out that it\narises from the sampling effect you then go on to discuss, I think, in order for it to be worth making\nthis speculation. Can you rule out that it is all to do with confounding?\nLine 210 -- You need to quantify the statements so that the size of this alleged reduction in\nsensitivity is clear. Looking at your plots, if a lake followed the red lines in figure 4, a lake that\nwarmed from 24 degrees (at which is true sensitivity is on average 1 K/K) to 30 degrees would\nthereafter have a sensitivity of about 0.8 K/K. Another way to look at it is that 3 decades of mean\nsummer T warming of 0.34 K/decade will on average reduce the sensitivity from ~1.0 K/K on average\nto ~0.965 K/K. This modest effect (which I don't even necessarily trust because I think you are seeing\na confounding effect) should be matched by comparably modest wording of the finding. I find the\ncurrent wording without quantification to give an exaggerated impression of importance. Please\nreword with appropriate quantification and appropriate explanation that the \"reducing sensitivity\" is\nat most a small effect compared to the lake warming itself.\nFigure 4 and similar -- I suggest you need to add the zero line across the plot so we can see\ndifferences of the redline from zero more easily.", "reviewer_2": "Key Results\nThis paper presents a correlational analysis of thermal sensitivity of temperate lakes to atmospheric\nwarming using information morphology and multi-decadal thermal profiles of 347 lakes. The paper is\nof interest because of the large data set assembled, which has not been common in the literature.\nAmong the key points noted is that a majority of temperate lakes show increases in summer\nepilimnetic temperature over several decades, but greater than 50% of the studied lakes showed\ncooling trends in the hypolimnion. This is not a new finding and reflects complex interactions\nbetween onset of stratification, stratification strength, and seasonal weather patterns that have\nbeen predicted through modeling (e.g., Stefan et al., 1996), but it is nice to see this finding\nconfirmed across an extensive data set. The paper also states that “sensitivity of surface WT to\natmospheric heating decreased significantly when AT exceeded 20.2°C, suggesting a threshold for\nstrong climate effects on lakes. Specifically, surface WT sensitivity to atmospheric warming may\ndecline in a warmer future…” If true, this would constitute good news as a water temperature\naround 20 °C approximates a threshold for health of many cold water fish species. Unfortunately, I\ndo not think the data fully supports this conclusion.\nValidity\nThe data analysis is based largely on application of random forest classification (followed by\nvariation partitioning analysis and piecewise regression) to the difference between trends in water\ntemperature and air temperature, which the authors refer to as “sensitivity.” Little information is\nprovided on the calculation of trends except to state that they are based on Sen’s slopes. Sen’s non-\nparametric slope estimate is usually based on the median of slope estimates across multiple\nstations; it is apparently used here to represent a slope estimate at a given station over a given time\nperiod. There is no discussion of how these slope estimates may be biased by different start and end\nperiods (as, for instance, might be expected by longer-term SST anomalies) for individual lakes.\nThe validity of the suggested threshold in response at AT of 20.2°C seems questionable. This appears\nto be obtained from the breakpoint in the piecewise regression in Figure 4(a) (line 502) and is\ndiscussed at line 172 of the text. As noted at line 171-177, “effects of atmospheric warming on ET\ndiminished when lakes were in warmer regions (AT > 20.2°C), or were themselves already warm (>\n23.8°C). Because air temperature was correlated to trophic status…it is possible that diminished\nsensitivity to AT reflects arises because eutrophic lakes are often warmer than clear lakes…\nResolution of the underlying mechanism will likely require comprehensive energy budgets, although\nwe speculate that warm lakes may be more likely to lose heat to the atmosphere at night in\ntemperate regions…” Figure 4(a) suggests the evidence for the breakpoint is weak while the\ndiscussion acknowledges that differences among climatic regions may also have a significant impact\non the finding. Thus, I think it is inappropriate to bring the suggested breakpoint forward as a major\nfinding in the Abstract.\nIn general, the validity of inferences presented in the paper is difficult to ascertain as the analysis is\nbased on correlation or classification and not on causal hypotheses.\nSignificance\nI am pleased that the authors have assembled and analyzed a large dataset of temperate lakes.\nWhile the analysis is relatively simple and does not break new ground, surveys across large datasets\nsuch as this are uncommon in the literature. I think, however, that the paper could be greatly\nimproved by comparison to hypotheses of lake response to atmospheric warming obtained from\nsimulation studies.\nData and Methodology\nThis paper is a summary analysis across a large number of lakes, so I do not expect all data to be\nprovided with this paper or its supplemental information. The authors state that all data are\navailable in references [44] and [45]. Can they affirm that this is true? I think it would be advisable to\ninclude summary information (particularly tabular information on Sen’s slopes for each of the\nrelevant variables) in the supplemental information for this paper.\nAnalytical Approach\nThe analytical approach consists of applying random forest analysis to the difference in trends (i.e.,\nrate of change) in air temperature and water temperature, followed by variation partitioning\nanalysis of groups of explanatory variables and piecewise linear regression. The potential\nexplanatory variables include various measures of climate (e.g., summer air temperature, wind),\nlake morphology, and watershed characteristics. The analyses appear to be correctly implemented,\nalthough I would suggest supplying the R code in supplementary information.\nWhile the relationships of trends in epilimnetic and hypolimnetic summer water temperatures to\nvarious explanatory variables is statistically significant, they only explain a small portion of the total\nvariation with an unexplained residual of 80% for epilimnetic water temperature and 74% for\nhypolimnetic water temperature. Climate inputs have the greatest explanatory power, while,\nsomewhat surprisingly, lake morphological characteristics did not emerge as a significant contributor\nto hypolimnetic temperature.\nThe variation analysis is a useful exploratory tool, but can only evaluate information that is supplied\nto it. The low amount of explained variation may reflect the complexity of interactions between\nvarious inputs or the lack of information on key factors. For instance, water clarity may play an\nimportant role in the relative temperature trends in surface and bottom waters, but water clarity is\nnot an explanatory variable in the variation analysis (despite Secchi depth being used in other parts\nof the paper), although it is likely correlated with some of the land use variables. For lake\nmorphology, surface area and depth taken individually are not significant predictors, but it is more\nlikely the ratio of these variables that is a significant influence on mixing regime.\nThe piecewise linear regression is used to propose a breakpoint in the response of water\ntemperature to air temperature, which seems suspect as noted above. Additional statistical analysis\nis needed on this point to determine whether the apparent change in response slope at the\nbreakpoint is actually significant.\nSuggested Improvements\nThe paper suggests relationships between trends in atmospheric temperature and lake water\ntemperature in both the epilimnion and hypolimnion of temperate lakes. However, the analysis is\nprimarily correlational in nature and presents (or perhaps confirms) findings that have already been\nsuggested by others. The paper could be greatly improved by reframing the analyses as an\nevaluation or test of hypotheses that have been proposed in the existing literature.\nMany of the responses evaluated here have been investigated previously through model\nexperiments related to climate change. It would be useful to compare and contrast some of these\ninferences to the results reported here. Some of these findings include the following:\n• Lake surface area and depth (and especially their ratio) affect strength of stratification and can\nresult in a net decrease in whole lake average temperature (Stefan et al., 1996; Tanentzap et al.,\n2008).\n• Increased water clarity can warm deeper waters; conversely reduced clarity can reduce the depth\nof light penetration as well as the mixing depth and reduce hypolimnetic temperatures, although\nresponses in individual lakes are complex (Stefan et al., 1996; Hocking and Straškraba 1999; Rose et\nal., 2019).\n• In addition to effects of lake morphometry, anomalies in the responses of individual lakes to\nincreased atmospheric temperature is modulated by changes in relative humidity and wind speed\n(Schmid et al., 2014; Woolway et al., 2019).\nClarity and Context\nThe writing is generally good; however, organization of the paper could be improved. As currently\nstructured, the paper contains two major section heads (Results, and Methods) with no subheads.\nThe Nature style is to put details of Methods after Results; however, it would be helpful to put\nsomewhat more summary information on methods in the Results section if it is presented first.\nIn addition, the long Results section should be divided with subheads to aid the reader.\nReferences\nThe paper contains a relatively comprehensive survey of relevant literature. The authors should\nconsider also citing the following (see below for complete citations):\n• Hocking and Straškraba, 1999\n• Rose et al., 2016\n• Tanentzap et al., 2008\n• Woolway et al., 2019\nIn addition, Pilla et al. (2020) is mentioned only in passing but likely deserves a bit more discussion.\nReferences Cited in this Review\nHocking GC, Straškraba M. (1999) The effect of light extinction on thermal stratification in reservoirs\nand lakes. Int Rev Hydrobiol 84: 535-556", "reviewer_doi": "10.4319/lo.1996.41.5.1124\nTanentzap AJ, Yan ND, Keller B, Girard R, Heneberry J, Gunn JM, Hamilton DP, Taylor DA. (2008)\nCooling lakes while the world warms: Effects of forest regrowth and increased dissolved organic\nmatter on the thermal regime of a temperate, urban lake. Limnol Oceanogr 53: 404-410,\ndoi:10.4319/lo.2008.53.1.0404\nWoolway, R. I., Merchant, C. J., Van Den Hoek, J., Azorin‐Molina, C., Nõges, P., Laas, A., et al. (2019).\nNorthern Hemisphere atmospheric stilling accelerates lake thermal responses to a warming world.\nGeophysical Research Letters, 46, 11,983-11,992. https://doi.org/10.1029/2019GL082752\nJ. Butcher", "reviewer_author_response": "Author’s response to reviewers' comments\nControls of thermal response of temperate lakes to atmospheric warming\nJian Zhou, Peter R. Leavitt, Kevin C. Rose, Xiwen Wang, Yibo Zhang, Kun Shi, Boqiang Qin\nSubmission ID: NCOMMS-23-05303\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nGeneral comments\nThis is a valuable paper, interesting, novel and generally well written and presented. I have some\nconcerns about terminology and lack of quantification of statements, as well as other points below.\nWith due adjustment relative to the comments below, I will be happy to recommend publication.\nReviewer #2 (Remarks to the Author):\nKey Results\n1. This paper presents a correlational analysis of thermal sensitivity of temperate lakes to atmospheric\nwarming using information morphology and multi-decadal thermal profiles of 347 lakes. The paper\nis of interest because of the large data set assembled, which has not been common in the literature.\nAmong the key points noted is that a majority of temperate lakes show increases in summer\nepilimnetic temperature over several decades, but greater than 50% of the studied lakes showed\ncooling trends in the hypolimnion. This is not a new finding and reflects complex interactions between\nonset of stratification, stratification strength, and seasonal weather patterns that have been predicted\nthrough modeling (e.g., Stefan et al., 1996), but it is nice to see this finding confirmed across an\nextensive data set. The paper also states that “sensitivity of surface WT to atmospheric heating\ndecreased significantly when AT exceeded 20.2°C, suggesting a threshold for strong climate effects\non lakes. Specifically, surface WT sensitivity to atmospheric warming may decline in a warmer\nfuture…” If true, this would constitute good news as a water temperature around 20 °C approximates\na threshold for health of many cold water fish species. Unfortunately, I do not think the data fully\nsupports this conclusion.\nReviewer #1 (Remarks to the Author):\nThe authors have responded to all comments thoughtfully and appropriately, and I am happy to\nrecommend publication.\nReviewer #2 (Remarks to the Author):\nThe authors have responded in detail to my comments as well as to those of Reviewer 1. I had\nalready felt that the work was a useful addition to the literature on lake response to climate change,\nbut pointed out several areas in which greater clarity or details were required in the presentation.\nThe authors have been attentive to those requests and have substantially revised the paper in the\nways recommended by both reviewers. This includes comments relative to results, significance,\nconclusions, data analysis, and methodology. In addition, the authors have clarified and provided\nlinks to the original data used in their analysis. I continue to believe that the research is not\ngroundbreaking but is a useful synopsis of a large data set and has been reframed to test the\nhypotheses proposed by others on less comprehensive data. Given the substantial improvements\nand clarifications in the revised version it is my opinion that the article is now appropriate for\npublication.\nAuthor’s response to reviewers' comments\nControls of thermal response of temperate lakes to atmospheric warming\nJian Zhou, Peter R. Leavitt, Kevin C. Rose, Xiwen Wang, Yibo Zhang, Kun Shi, Boqiang Qin\nSubmission ID: NCOMMS-23-05303A\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have responded to all comments thoughtfully and appropriately, and I am happy to\nrecommend publication.\nreviewer recognizes the care we have taken to address all comments thoroughly. With the reviewer's\napproval, we look forward to the publication process and sharing our work with the scientific\ncommunity. We hope that this research will make a meaningful contribution to the field. Thank you\nagain for your time and the opportunity to improve our manuscript through this peer review process.\nReviewer #2 (Remarks to the Author):\nThe authors have responded in detail to my comments as well as to those of Reviewer 1. I had already\nfelt that the work was a useful addition to the literature on lake response to climate change, but pointed\nout several areas in which greater clarity or details were required in the presentation. The authors\nhave been attentive to those requests and have substantially revised the paper in the ways\nrecommended by both reviewers. This includes comments relative to results, significance,\nconclusions, data analysis, and methodology. In addition, the authors have clarified and provided\nlinks to the original data used in their analysis. I continue to believe that the research is not\ngroundbreaking but is a useful synopsis of a large data set and has been reframed to test the hypotheses\nproposed by others on less comprehensive data. Given the substantial improvements and clarifications\nin the revised version it is my opinion that the article is now appropriate for publication."}, "author_response": ""}]

Decision letter