Atmospheric stilling leads to prolonged thermal stratification in a large shallow polymictic lake
To quantify the effects of recent and potential future decreases in surface wind speeds on lake thermal stratification, we apply the one-dimensional process-based model MyLake to a large, shallow, polymictic lake, Võrtsjärv. The model is validated for a 3-year period and run separately for 28 years using long-term daily atmospheric forcing data from a nearby meteorological station. Model simulations show exceptionally good agreement with observed surface and bottom water temperatures during the 3-year period. Similarly, simulated surface water temperatures for 28 years show remarkably good agreement with long-term in situ water temperatures. Sensitivity analysis demonstrates that decreasing wind speeds has resulted in substantial changes in stratification dynamics since 1982, while increasing air temperatures during the same period had a negligible effect. Atmospheric stilling is a phenomenon observed globally, and in addition to recent increases in surface air temperature, needs to be considered when evaluating the influence of climate change on lake ecosystems.
Thermal stratification is a natural phenomenon that occurs in lakes as a result of the thermal expansion properties of water. It is determined by the balance between turbulence, which acts to enhance mixing, and buoyancy forces, which act to suppress turbulence and result in a vertical layering (Boehrer and Schultze 2008). The epilimnion is defined as that part of the water column immediately below the water surface, directly influenced by atmospheric forcing. The hypolimnion, the coolest and densest layer, lies in contact with the bottom of the lake and is separated from the epilimnion above by a temperature-driven density gradient known as the thermocline. The vertical layering that exists during stratification has several implications for the ecosystem, as it not only inhibits the downward penetration of direct vertical mixing and thus influences temperatures at depth (Livingstone 2003) but it also separates processes of production and nutrient depletion in the epilimnion from processes of decomposition and nutrient regeneration in the hypolimnion and sediment.
Stratification can be transient or persistent, and its duration can vary from hours to months or even be permanent in some lakes. For lakes of sufficient depth, the water column typically evolves seasonally from being isothermal in the early spring, developing stratification as the weather warms and then overturning sometime in the autumn. Some shallow lakes, however, do not develop a full seasonal stratification but rather experience alternate periods of mixing and stratification driven strongly by meteorological conditions. These lakes are often termed polymictic and, according to Hutchinson’s (1957) mixing classification, are among the most abundant at mid-latitudes. Polymictic lakes can be categorised further into continuous and discontinuous polymictic, depending on whether stratification occurs at most on a daily basis or for periods of several days to weeks, but with irregular interruption by mixing events (Lewis 1983). The mixing class a lake belongs to is mostly determined by lake morphometry, although the strength and extent of stratification are also influenced by extrinsic features of a lake, such as altitude (Woolway et al. 2015a), inflows (Rimmer et al. 2011), and meteorological conditions (Churchill and Kerfoot 2007), and intrinsic factors such as water clarity (Heiskanen et al. 2015).
Mixing regime shifts, the term used to describe a change in the mixing classification of lakes, have been reported (Shatwell et al. 2016), and polymictic lakes have been described as particularly susceptible to mixing regime shifts due to climate change (Kirillin 2010). Despite the ubiquitous recognition that climate change affects lake stratification (Verburg and Hecky 2009), most studies typically only consider the response of a lake to increasing air temperature (Elo et al. 1998). Climate, however, is much more than temperature and studies have found climate-induced changes in a number of other meteorological variables known to influence lake stratification, including cloud cover (Eastman and Warren 2013) and solar radiation (Wild 2012). In addition, several studies have shown that, in recent years, wind speeds from around the world have been decreasing, in a phenomenon termed atmospheric stilling, where observations indicate that the annual land 10-m wind speed has decreased during the past few decades (Vautard et al. 2010), attributed to, among other things, increased surface roughness (Vautard et al. 2010; Bichet et al. 2012), and changes in urbanisation (Xu et al. 2006).
Despite the recognition that wind stress is one of the most important factors driving mixing in lakes, the response of lake stratification to atmospheric stilling has not yet been investigated. In this contribution, we aim to address this research gap by investigating the response of a large (average surface area 270 km2) and shallow (mean depth of 2.8 m; maximum depth = 6.1 m) lake, Võrtsjärv, to decreasing surface wind speeds. Traditionally, Võrtsjärv has been classified as continuous polymictic (Jaani 1973; Nõges & Nõges 2012; Laas et al. 2012), but recent high frequency in situ temperature measurements demonstrate that the lake now stratifies occasionally, thus suggesting a shift to a discontinuous polymictic state. The main aim of this study is therefore to investigate if stratification in Võrtsjärv has changed in response to the large-scale decline in surface wind speed or to some other factor, and if the magnitude of observed atmospheric stilling is sufficient to influence stratification dynamics. Due to the lack of available long-term depth-resolved temperature data for Võrtsjärv, we restore a multi-decadal stratification history of the lake with the use of a numerical model and evaluate, in terms of a sensitivity analysis, changes in stratification during the past ∼30 years, in response to atmospheric stilling.
2 Study site
3.1 Lake water temperature observations
Lake water temperatures used in this investigation were as follows: (i) high-resolution lake surface (0.5 m from surface) and bottom (0.5 m above bed) temperatures recorded with a PME MiniDOT logger at 10 min intervals from 2013 to 2015; and (ii) long-term observations of surface temperatures measured from 1982 to 2009 at approximately monthly intervals.
3.2 Lake temperature model
In this investigation, we use the one-dimensional process-based lake model, MyLake (v1.2; Saloranta and Andersen 2007). MyLake has been designed to simulate accurately the daily vertical profiles of lake water temperature and thus thermal and density stratification, and has been used successfully in numerous lakes from around the world (Dibike et al. 2012; Pätynen et al. 2014; Couture et al. 2015). The modelling principles of MyLake are similar to other one-dimensional lake model codes, such as DYRESM-CAEDYM (Hamilton and Schladow 1997). The model code is written in MATLAB and has been designed to be computationally efficient and easily adaptable to different lakes. In addition to the model code, three different parameter and input data files are required to run MyLake. These include (i) time series of daily atmospheric data, (ii) lake morphometry and initial vertical water temperature profiles, and (iii) model parameter values. The model also requires information on water clarity, included in terms of the light attenuation coefficient (Kd, m−1). In this study, we followed the methods of Woolway et al. (2015b) and estimated Kd as a function of secchi depth (zsecchi) as: Kd = 1.75/zsecchi. An average zsecchi was estimated from monthly averaged observations from Võrtsjärv. Initial profiles of water temperature were set equivalent to the surface air temperature at the start of the investigation. Thus, the water column was assumed vertically mixed. Model parameters were kept similar to those suggested in the user manual. MyLake also contains five switches, which can be used to disable some particular model processes if desired. These include (i) snow compaction, (ii) river inflow, (iii) sediment heat flux, (iv) self-shading, and (v) tracer simulation. Each of these features was disabled in this investigation. Simulated temperatures were validated for a 3-year period using the high-resolution lake surface and bottom temperatures described above.
3.3 Atmospheric forcing data
Atmospheric forcing data used in this investigation were available from two different sources: (i) a monitoring buoy situated on Võrtsjärv, equipped with a Vaisala multi-weather station (WXT520) and Li-Cor pyranometer at a height of 2 m above the lake surface; and (ii) a meteorological station situated in Tõravere (58.26 °N, 26.47 °E), 30 km from the lake, data for which were provided by the Estonian Environmental Agency. These two datasets were used in the modelling study. In addition to the observations available from Tõravere, we also analysed wind speed observations from six other meteorological stations situated throughout Estonia. Wind speed measurements from Pärnu-Sauga (58.37 °N, 24.50 °E; 1973–2014) and Tallinn-Harku (59.47 °N, 24.82 °E; 1973–2016) were available from HadISD (Dunn et al. 2012), which is a quality-controlled synoptic meteorological dataset used for climate applications at sub-daily resolution. Wind speed measurements from Jõgeva (58.75 °N, 26.39 °E), Türi (58.80 °N, 25.42 °E), Valga (57.78 °N, 26.05 °E), and Viljandi (58.37 °N, 25.59 °E) meteorological stations were available from the Estonian Environmental Agency.
3.3.1 Scaling of atmospheric data
which was then used to scale the long-term wind measurements to be representative of over-lake conditions.
3.4 MyLake experiments
To evaluate the relative contribution of changes in air temperature and changes in wind speed, the two variables that varied significantly in recent years (see Section 4), on stratification dynamics in Võrtsjärv, we performed a series of MyLake simulations. Firstly, lake temperatures were simulated using the observed atmospheric forcing data over the 28-year period. These simulations were then repeated, except using the wind speed observations for 1982, the first year of available meteorological data, for every year, thereby removing any effect of wind speed change from the results. A similar set of simulations were then performed but with the air temperatures, rather than the wind speeds, held at the 1982 levels in order to remove the impact of systematic changes in air temperature. The first year of observations (i.e., 1982) was chosen to represent conditions that have not varied since the start of the investigation, and thus to investigate how would stratification in the lake have evolved had air temperatures or wind speed not changed since 1982. To ensure that using the data from 1982 to determine a constant annual cycle in both wind speed and air temperature did not bias our modelling results, we repeated all simulations but replacing the 1982 constant annual cycle with the climatological average daily meteorological forcing for both these variables. This represents the mean atmospheric conditions for each day throughout the 28 years. These model results were then compared to those produced from the first sensitivity analysis (i.e., using the 1982 data as a constant annual cycle) for validation.
Though vertical temperature differences are often used to define stratification (Stefan et al. 1996; Woolway et al. 2014), stratification is the result of the associated density differences and these vary non-linearly with temperatures. Thus, at 5 °C, a 1 °C temperature difference is equivalent to less than a 0.025 kg m−3 density difference, while at 19 °C a 1 °C temperature difference represents more than a 0.2 kg m−3 density difference. To capture all stratification periods here, we therefore used a threshold of a 0.025 kg m−3 density difference between the top and the bottom of the lake to define stratification. For all model experiments, the effects of wind speed and air temperature on stratification were determined by firstly calculating the number of stratified days per year (May–August, see Section 4). A linear regression model was then used to evaluate the rate of change in the number of stratified days during the 28-year period. To evaluate if the regression slopes were different among model runs, and thus determine if the influence of air temperature and/or wind speed on stratification were statistically distinguishable, we followed an analysis of covariance (ANCOVA) approach. Specifically, ANCOVA was used to compare the different regression slopes by testing the effect of a categorical factor (e.g., different model) on the dependent variable (e.g., the number of stratified days; y-variable) while controlling for the effect of the continuous co-variable (e.g., time; x-variable). A statistically significant interaction between the categorical factors demonstrates that the regressions have statistically different slopes. In contrast, if the interaction is not statistically significant, the covariate has the same effect for all levels of the categorical factor, meaning that a single regression line can be used to represent the different relationships. All calculations in this study were performed in R (R Development Core Team 2014).
4.1 Long-term atmospheric forcing in Võrtsjärv
4.2 Lake temperature modelling
Using 28 years of atmospheric forcing data from Tartu-Tõravere, MyLake generally captured the temporal dynamics of lake surface temperatures with great success (Fig. 3c). The model performed less well in winter, occasionally resulting in large temperature differences between observed and modelled values (Fig. 3d), in particular during periods of ice cover, which is an important factor for lakes in the northern hemisphere, having decreased in recent decades (Magnuson et al. 2000; Kheyrollah Pour et al. 2012; Duguay et al. 2013). During the May–August (MJJA) months, modelled temperatures closely matched those observed (MAD = 0.97 °C; RMSE = 1.37 °C; R2 = 0.88; Fig. 3e). These months were used to assess changes in stratification from the model.
4.2.1 Modelled changes in thermal stratification
4.2.2 Impacts of wind change versus air temperature change
Based on long-term meteorological data available from in situ meteorological stations in Estonia, we found a large-scale decrease in surface wind speeds during the past ∼30 years, consistent with other locations globally (Vautard et al. 2010). Previous investigations attribute this regional (i.e., in Estonia) decrease to changes in atmospheric circulation (Jaagus and Kull 2011) with others suggesting that changes in surface roughness surrounding the meteorological stations (Suursaar and Kullas 2006), and inconsistencies introduced by instrumental change (Jaagus and Kull 2011), may bias the observed trends. Analysis of wind speed data from seven meteorological stations, some of which were separated by up to 200 km, provide evidence against the latter and suggest that the observed decrease in surface wind speed is not confined to a particular location or meteorological station in Estonia. In this investigation, we examined the influence of this large-scale decrease in surface wind speed on thermal stratification in Võrtsjärv, a large and shallow lake situated in Central Estonia.
High-resolution in situ temperature measurements from Võrtsjärv illustrate that, in recent years, the lake stratifies for several days during spring/summer. This is unexpected, as previous investigations have described Võrtsjärv as traditionally having no thermal stratification (Frisk et al. 1999). In this study, we found a strong influence of atmospheric stilling on stratification dynamics and suggest that a decrease in surface wind speed has resulted in an increase in the number of stratified days in recent years. In particular, our model results illustrate that the number of stratified days in Võrtsjärv has increased at a rate of 2 days per decade. Few studies have examined impacts of systematic wind speed changes on lake stratification, but those that do have tended to focus on the influence of local changes such as deforestation and forest regrowth (France 1997; Tanentzap et al. 2007). To our knowledge, this is the first study to relate regional atmospheric stilling to changes in lake stratification.
In contrast, many studies have discussed the likely effects of rising air temperatures on lake stratification and the focus on this one parameter has drawn attention away from the possible influences of other aspects of directional climate changes, with only a very few studies having investigated the response of lake thermal dynamics to changing surface wind speeds (Tanentzap et al. 2008; Desai et al. 2009; Kerimoglu and Rinke 2013). Despite air temperatures increasing in Estonia (0.52 °C decade−1), at a rate twice that of the global average (0.25 °C decade−1) (Hartmann et al. 2013) over a similar time period (1979–2012), our modelling suggests this increase has not been sufficient to nudge the large, shallow, typically mixed, Võrtsjärv to a different mixing regime. The contemporaneous changes in wind speed, though, have been sufficient to do this. This suggests that ongoing changes in wind speed resulting from atmospheric stilling may be a more potent driver of stratification change in some lakes than the changes in air temperature, despite the overwhelming preference for studying the latter. Other factors are also known to influence stratification in some lakes. For example, water level has previously been shown to be an important factor influencing thermal stratification in Lake Kinneret, Israel (Rimmer et al. 2011). Moreover, lake water level has been described as one of the most influential factors affecting the physics (Nõges & Järvet 1995), chemistry (Nõges & Nõges 1999), and biology (Nõges et al. 2003; Järvalt et al. 2005) of Võrtsjärv. However, in this study we found no systematic change in water level during the past 28 years (e.g., Fig. 2e).
Our study highlights the importance of taking high frequency measurements in mixed and polymictic systems, without which transient periods of stratification and long-term changes to stratification may be missed. Such measurements are proliferating globally (e.g., Woolway et al. 2016) enabling more detailed studies of stratification to now take place than would have been possible just a decade ago. High frequency, in situ measurements are being further complemented by advancements in Earth Observation.
Projected changes in lake stratification under future climate are likely to induce a variety of impacts on the ecology and chemistry of lakes, which in turn will have implications for water quality and for adaptation strategies and management of lakes. For example, thermal stratification has been shown to restrict the supply of oxygen to deep waters (Foley et al. 2012; North et al. 2014), leading to deoxygenation in productive lakes and potential increases in the rate of recycling of phosphorus from the sediment to the water column (Søndergaard et al. 2003), with consequences for lake productivity (O’Reilly et al. 2003; Verburg et al. 2003). Also, ecosystem properties such as metabolism (Staehr et al. 2010) and gas flux at the air-water interface (Coloso et al. 2011) are highly influenced by lake thermal dynamics, thus being important for the global carbon cycle. Changing stratification can affect the likelihood of cyanobacterial bloom formation (Paerl and Huisman 2008; Jöhnk et al. 2008) and, through the impact on both temperature and oxygen, can impact the available habitat for fish species (Jones et al. 2008; Kangur et al. 2016). Our results illustrate that if wind speeds continue to decrease, Võrtsjärv may stratify for longer, having large consequences for the ecosystem. With lakes worldwide being subjected to increased air temperature against the potential backdrop of decreasing wind speed, future investigations should aim to understand the relative importance of these factors and how they interact to influence thermal stratification and potentially impair water quality.
RIW was funded by EUSTACE (EU Surface Temperature for All Corners of Earth) which received funding from the European Union’s Horizon 2020 Programme for Research and Innovation, under Grant Agreement no 640171. Data collection in this manuscript was collected under the Estonian Ministry of Education and Research grants (IUT 21–02 and PUT 777). Initial discussions for this work took place at a NETLAKE (Networking Lake Observatories in Europe) working group meeting in Riga, Latvia. The authors are grateful for the support of NETLAKE and its chair, Eleanor Jennings. We thank three anonymous reviewers for suggestions that improved an earlier version of this manuscript.
PN, AL, and PM developed the concept of the study; RIW, IDJ, and AL designed the model experiments; RIW and IDJ analysed the results; RIW and PM led the paper writing; all authors contributed to discussions, revisions and the production of the final manuscript.
- Bichet A, Wild M, Folini S, Schär C (2012) Causes for decadal variations of wind speed over land: sensitivity studies with a global climate model. Geophys Res Lett 39(11). doi:10.1029/2012GL051685
- Boehrer B, Schultze M (2008) Stratification of lakes. Rev Geophys 46(2). doi:10.1029/2006RG000210
- Duguay C, Brown L, Kang K-K, Kheyrollah Pour H (2013) Arctic—lake ice, in “State of the Climate in 2012”. Bull Am Meteorol Soc 94:S124–S126Google Scholar
- Elo A-R, Huttula T, Peltonen A, Virta J (1998) The effects of climate change on the temperature conditions of lakes. Boreal Environ Res 3:137–150Google Scholar
- Hartmann DL (2013) IPCC fifth assessment report, climate change 2013. In: Stocker TF (ed) The physical science basis. Cambridge Univ. Press, CambridgeGoogle Scholar
- Hutchinson E (1957) A treatise on limnology vol 1. Wiley, New York, 1015pGoogle Scholar
- Jaani A (1973) Hydrobiology. In: Timm, T. (Ed.), L. Võrtsjärv (in Estonian) Valgus, Tallinn, pp. 37–60Google Scholar
- Järvalt A, Laas A, Nõges P, Pihu E (2005) The influence of water level fluctuations and associated hypoxia on the fishery of Lake Vortsjarv, Estonia. Ecohydrol Hydrobiol 4:487–497Google Scholar
- Jones ID, Winfield IJ, Carse F (2008) Assessment of long-term changes in habitat availability for Arctic charr (Salvelinus alpinus) in a temperate lake using oxygen profiles and hydroacoustic surveys. Freshwater Biol 53:393–402Google Scholar
- Kirillin G (2010) Modeling the impact of global warming on water temperature and seasonal mixing regimes in small temperate lakes. Boreal Environ Res 15:279–293Google Scholar
- Nõges P, Järvet A (1995) Water level control over light conditions in shallow lakes. Report Series in Geophysics. University of Helsinki 32:81–92Google Scholar
- Nõges P, Nõges T (2012) Lake Võrtsjärv. In: Bengtsson L, Herschy R, Fairbridge R (eds) Encyclopedia of lakes and reservoirs. Springer, Dordrecht, pp 850–863Google Scholar
- Pätynen A, Elliott JA, Kiuru P, Sarvala J, Ventelä A-M, Jones RI (2014) Modelling the impact of higher temperature on the phytoplankton of a boreal lake. Boreal Environ Res 19(1):66–78Google Scholar
- Shatwell T, Adrian R, Kirillin G (2016) Plantonik events may cause polymictic-dimictic regime shifts in temperate lakes. Sci Reports 6(24361). doi:10.1038/srep24361
- Suursaar U, Kullas T (2006) Influence of wind climate on the mean sea level and current regime in the coastal waters of west Estonia, Baltic Sea. Oceanologia 48:361–383Google Scholar
- Tanentzap AJ et al (2007) Cooling lakes while the world warms: effects of forest regrowth and increased dissolved organic matter on the thermal regime of a temperate, urban lake. Limnol Oceanogr 53:404–410Google Scholar
- Tuvikene L, Kisand A, Tõnno I, Nõges P (2004) In: Pihu E, Raukas A, Haberman J (eds) Chemistry of lake water and bottom sediments. Estonian Encyclopaedia Publishers, Tallinn, pp 89–101Google Scholar
- Woolway RI, Maberly SC, Jones ID, Feuchtmayr H (2014) A novel method for estimating the onset of thermal stratification in lakes from surface water measurements. Wat Resour Res 50. doi:10.1002/2013WR014975
- Zeng X, Zhao M, Dickinson RE (1998) Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using TOGA COARE and TAO data. J Clim 11:2628e2644Google Scholar
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