“…2.2) includes the "shape factor" coefficient C θ , which determines the vertical temperature profile below the mixed layer. Evolution of this parameter is controlled by a relaxation timescale t rc (Mironov, 2008). This timescale includes the dimensionless relaxation constant C rc with a default value of 0.003 in the model.…”
Section: Methodsmentioning
confidence: 99%
“…Within the first class there are the recently developed models ALBM and MTCR-1, while k − ε models include LAKE and LAKEoneD. The FLake model stands aside from the other 1-D models due to the two-layer bulk structure which employs the concept of self-similarity to estimate the temperature profile in the mixed layer and thermocline, respectively (Mironov, 2008). In the mixed layer the temperature is assumed to be constant, whereas below it is parameterized as a function of non-dimensional depth.…”
Section: Lake Modelsmentioning
confidence: 99%
“…LakeMIP simulations have been performed using seven 1-D lake models: (1) CLM4-LISSS (Hostetler and Bartlein, 1990;Subin et al, 2012), (2) FLake (Mironov, 2003),…”
mentioning
confidence: 99%
“…Simulations were run for Harp Lake, a lake in Ontario, Canada, with long-term high-frequency monitoring data for meteorology, water temperature, CO 2 and O 2 . Five lake models were used in this study to simulate thermal dynamics and turbulent diffusivity in the lake: (1) Arctic Lake Biogeochemistry Model, ALBM (Tan et al, 2015(Tan et al, , 2017(Tan et al, , 2018Tan and Zhuang, 2015), (2) FLake (Mironov, 2003), (3) LAKE (Stepanenko et al, 2011(Stepanenko et al, , 2016, ( 4) LAKEoneD (Jöhnk and Umlauf, 2001;Jöhnk et al, 2008), and (5) Modelagem do Transporte de Calor no Reservatório, MTCR-1 Bleninger, 2015, 2018). The ALBM and LAKE models include comprehensive biogeochemical modules for calculation of dissolved gas concentrations, which were then tested in their ability to reproduce CO 2 and oxygen O 2 dynamics.…”
Abstract. In recent decades, several lake models of varying complexity have been developed and incorporated into numerical weather prediction systems and climate models. To foster enhanced forecasting ability and verification, improvement of these lake models remains essential. This especially applies to the limited simulation capabilities of biogeochemical processes in lakes and greenhouse gas exchanges with the atmosphere. Here we present multi-model simulations of physical variables and dissolved gas dynamics in a temperate lake (Harp Lake, Canada). The five models (ALBM, FLake, LAKE, LAKEoneD, MTCR-1) considered within this most recent round of the Lake Model Intercomparison Project (LakeMIP) all captured the seasonal temperature variability well. In contrast, none of the models is able to reproduce the exact dates of ice cover and ice off, leading to considerable errors in the simulation of eddy diffusivity around those dates. We then conducted an additional modeling experiment with a diffusing passive tracer to isolate the effect of the eddy diffusivity on gas concentration. Remarkably, sophisticated k−ε models do not demonstrate a significant difference in the vertical diffusion of a passive tracer compared to models with much simpler turbulence closures. All the models simulate less intensive spring overturn compared to autumn. Reduced mixing in the models consequently leads to the accumulation of the passive tracer distribution in the water column. The lake models with a comprehensive biogeochemical module, such as the ALBM and LAKE, predict dissolved oxygen dynamics adequate to the observed data. However, for the surface carbon dioxide concentration the correlation between modeled (ALBM, LAKE) and observed data is weak (∼0.3). Overall our results indicate the need to improve the representation of physical and biogeochemical processes in lake models, thereby contributing to enhanced weather prediction and climate projection capabilities.
“…2.2) includes the "shape factor" coefficient C θ , which determines the vertical temperature profile below the mixed layer. Evolution of this parameter is controlled by a relaxation timescale t rc (Mironov, 2008). This timescale includes the dimensionless relaxation constant C rc with a default value of 0.003 in the model.…”
Section: Methodsmentioning
confidence: 99%
“…Within the first class there are the recently developed models ALBM and MTCR-1, while k − ε models include LAKE and LAKEoneD. The FLake model stands aside from the other 1-D models due to the two-layer bulk structure which employs the concept of self-similarity to estimate the temperature profile in the mixed layer and thermocline, respectively (Mironov, 2008). In the mixed layer the temperature is assumed to be constant, whereas below it is parameterized as a function of non-dimensional depth.…”
Section: Lake Modelsmentioning
confidence: 99%
“…LakeMIP simulations have been performed using seven 1-D lake models: (1) CLM4-LISSS (Hostetler and Bartlein, 1990;Subin et al, 2012), (2) FLake (Mironov, 2003),…”
mentioning
confidence: 99%
“…Simulations were run for Harp Lake, a lake in Ontario, Canada, with long-term high-frequency monitoring data for meteorology, water temperature, CO 2 and O 2 . Five lake models were used in this study to simulate thermal dynamics and turbulent diffusivity in the lake: (1) Arctic Lake Biogeochemistry Model, ALBM (Tan et al, 2015(Tan et al, , 2017(Tan et al, , 2018Tan and Zhuang, 2015), (2) FLake (Mironov, 2003), (3) LAKE (Stepanenko et al, 2011(Stepanenko et al, , 2016, ( 4) LAKEoneD (Jöhnk and Umlauf, 2001;Jöhnk et al, 2008), and (5) Modelagem do Transporte de Calor no Reservatório, MTCR-1 Bleninger, 2015, 2018). The ALBM and LAKE models include comprehensive biogeochemical modules for calculation of dissolved gas concentrations, which were then tested in their ability to reproduce CO 2 and oxygen O 2 dynamics.…”
Abstract. In recent decades, several lake models of varying complexity have been developed and incorporated into numerical weather prediction systems and climate models. To foster enhanced forecasting ability and verification, improvement of these lake models remains essential. This especially applies to the limited simulation capabilities of biogeochemical processes in lakes and greenhouse gas exchanges with the atmosphere. Here we present multi-model simulations of physical variables and dissolved gas dynamics in a temperate lake (Harp Lake, Canada). The five models (ALBM, FLake, LAKE, LAKEoneD, MTCR-1) considered within this most recent round of the Lake Model Intercomparison Project (LakeMIP) all captured the seasonal temperature variability well. In contrast, none of the models is able to reproduce the exact dates of ice cover and ice off, leading to considerable errors in the simulation of eddy diffusivity around those dates. We then conducted an additional modeling experiment with a diffusing passive tracer to isolate the effect of the eddy diffusivity on gas concentration. Remarkably, sophisticated k−ε models do not demonstrate a significant difference in the vertical diffusion of a passive tracer compared to models with much simpler turbulence closures. All the models simulate less intensive spring overturn compared to autumn. Reduced mixing in the models consequently leads to the accumulation of the passive tracer distribution in the water column. The lake models with a comprehensive biogeochemical module, such as the ALBM and LAKE, predict dissolved oxygen dynamics adequate to the observed data. However, for the surface carbon dioxide concentration the correlation between modeled (ALBM, LAKE) and observed data is weak (∼0.3). Overall our results indicate the need to improve the representation of physical and biogeochemical processes in lake models, thereby contributing to enhanced weather prediction and climate projection capabilities.
“…Hence, the primary drawback of the models used in respect to DIC simulation is likely to be not explicitly simulating transport of carbon species from catchment to a water body. Thus, modelling approaches coupling the catchment and a lake presented recently (Futter et al, 2008;Duffy et al, 2018;McCullough et al, 2018) should be elaborated and wider used.…”
1.(1) Concerns arise however about the way one of the models -FLake -was treated in the study. The authors correctly state in the description of the model experiments that FLake "stands aside from the other 1-D models due to the . . . bulk-structure which employs the concept of self-similarity. . . ". This high level of parameterization ensures computational efficiency of the model, which was primarily designed for prediction of surface temperatures in global/regional climate models and numerical weather prediction (NWP). On the other hand, the model parameterizations put some constraints on the model application to real lakes. One crucially important feature of FLake is that the model equations are derived in the assumption of preserving the heat capacity or volume of the lake. In this regard, the "baseline" configuration C1
Hydrological events transport large proportions of annual or seasonal dissolved organic carbon (DOC) loads from catchments to streams. The timing, magnitude and intensity of these events are very sensitive to changes in temperature and precipitation patterns, particularly across the boreal region where snowpacks are declining and summer droughts are increasing. It is important to understand how landscape characteristics modulate event-scale DOC dynamics in order to scale up predictions from sites across regions, and to understand how climatic changes will influence DOC dynamics across the boreal forest. The goal of this study was to assess variability in DOC concentrations in boreal headwater streams across catchments with varying physiographic characteristics (e.g., size, proportion of wetland) during a range of
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