Multimodel simulation of vertical gas transfer in a temperate lake

Abstract

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.

Validation and Sensitivity Analysis of a 1‐D Lake Model Across Global Lakes

“…The average parameter uncertainty was 2.10°C and 1.86°C for epilimnion and full-profile temperature simulations, respectively ( Figure S3). The model generally performed better in predicting the full-profile temperature than the epilimnion temperature, consistent with our previous assessment on a temperate lake (Guseva et al, 2020).…”

“…As such, improving the capability of 1-D lake models in representing the thermal regimes of diverse lake systems is of great value. Previous lake model validations mostly focused on individual lakes because data for calibration and verification of a large variety of lakes are not readily available (Guseva et al, 2020;Stepanenko et al, 2010). Also, previous studies did not identify which parameters or model processes should be the focus for improving model performance at the global scale, making it difficult to determine whether the developed numerical treatments are appropriate for regional or global applications is unclear.…”

Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication

“…These multi-model ensembles offer the oppor-tunity to inter-compare models for an improved understanding of process representation and inter-model differences as well as for model improvement. Some MIPs examples include FireMIP for the fire regime and its drivers (Rabin et al, 2017); CMIP for past, present, and future climate changes and their drivers (Eyring et al, 2016;Kageyama et al, 2018); LakeMIP for physical and biogeochemical processes of lakes (Stepanenko et al, 2010;Thiery et al, 2014); AgMIP for crop growth (Rosenzweig et al, 2013); and WaterMIP or ISIMIP for the water cycle (Haddeland et al, 2011;Frieler et al, 2017).…”

“…Nevertheless, MIPs have underlined the need to go beyond good overall model performance and to improve process representation in the models (Guseva et al, 2020), integrate missing processes (Friend et al, 2014), and reduce uncertainties (Warszawski et al, 2014). MIPs showed that robust similarities exist among models, and as a result models are not strictly independent of each other given previous and legacy versions, and there are existing links among modelling communities who indirectly transfer some models' strengths and weaknesses by sharing their ideas and codes (Masson and Knutti, 2011;Knutti et al, 2013).…”

Effects of dimensionality on the performance of hydrodynamic models

“…A large variety of different numerical models have been used for simulating temperature and hydrodynamics in lakes and reservoirs, as well as the biogeochemical and ecological processes that depend on it (e.g. Dissanayake et al, 2019;Guseva et al, 2020;Wang et al, 2020;Xu et al, 2021). While the mechanistic description of underlying physical processes is identical or at least similar in all models, they differ in their dimensionality, i.e.…”