General Ocean Turbulence Model Research Articles

Evaluating the performance of turbulence closure models for tidal stream resource characterization

Harvesting tidal stream energy from the ocean for electricity generation has been considered as an energy resource alternative to fossil fuels for mitigating the negative impact of climate change and enhancing energy security and coastal resilience. Numerical models have been used extensively to characterize and assess tidal resources at potential tidal energy development sites. Turbulence plays an important role in site selection and tidal turbine farm deployment. However, most of the numerical modeling studies for tidal energy resource characterization do not include turbulence characteristics because of the limitation of Reynolds averaged Navier–Stokes coastal ocean model in resolving the inertial sub-range turbulence scales. However, studies also demonstrated that turbulence properties, such as turbulence intensity and turbulence kinetic energy, simulated by the coastal ocean models based on turbulence closures can be useful in assisting tidal resource characterization at tidal energy sites. In this study, we evaluated four General Ocean Turbulence Model (GOTM) turbulence closure models implemented in a tidal hydrodynamic model –Finite Volume Community Ocean Model (FVCOM) to characterize the tidal energy resource in the Western Passage, Maine, USA – a top ranked tidal energy site. The four turbulence closure models used in this analysis are k–kl (Mellor–Yamada Level 2.5), k-ε, k–ω, and a generic model. Model sensitivities showed that the MY2.5 model performed the best in comparison with the field measurements. In particular, the simulated time series of turbulence intensity and kinetic energy matched the observed data very well in the Western Passage. Detailed analysis was conducted to characterize turbulence properties on the horizontal plane and at selective cross-sections. This study demonstrated that turbulence properties simulated by a coastal ocean model, along with tidal hydrodynamic properties, can be very informative for tidal energy resource characterization and project site selection, as recommended by the International Electrotechnical Commission (IEC) technical specifications (IEC TS 62600-201).

Nonhydrostatic simulation of internal lee wave-induced turbulence mixing: A comparative study of second-moment closure mixing schemes for an idealized marginal sea sill topography

To further understand the processes of wave-turbulence interactions under nonhydrostatic conditions and to gain insight into the selection of second-moment closure models in subsequent nonhydrostatic ocean models, a newly developed nonhydrostatic ocean model is coupled with the General Ocean Turbulence Model (GOTM) to form a robust version of the Marine Environment Research and Forecasting Model (MERF). On this basis, internal lee wave simulation with various turbulent mixing schemes is investigated for an idealized two-dimensional supercritical sea slope topography with high-resolution (100 m). First, the LES-type simulation based on a 3D Smagorinsky diffusivity scheme can reproduce well the characteristics of lee waves, serving as a benchmark for comparison. Second, the Mellor and Yamada (MY) scheme and the quasi-equilibrium scheme are able to effectively simulate the effects of internal waves on large-scale structures, while small-scale dynamic processes are partially suppressed by turbulent mixing. However, the K-ε model and the one-equation model further suppress the small-scale dynamic processes and weaken the propagation of internal lee waves due to overly strong turbulence mixing. Finally, the generation of a lee wave changes the instantaneous temperature field, and the use of the second-moment closure models converts more energy into irreversible mixing. Enhancing the model resolution can slightly improve the accuracy of the simulation in exchange for paying considerable computation.

Modifying the Mixed Layer Eddy Parameterization to Include Frontogenesis Arrest by Boundary Layer Turbulence

Abstract Current submesoscale restratification parameterizations, which help set mixed layer depth in global climate models, depend on a simplistic scaling of frontal width shown to be unreliable in several circumstances. Observations and theory indicate that frontogenesis is common, but stable frontal widths arise in the presence of turbulence and instabilities that participate in keeping fronts at the scale observed, the arrested scale. Here we propose a new scaling law for arrested frontal width as a function of turbulent fluxes via the turbulent thermal wind (TTW) balance. A variety of large-eddy simulations (LES) of strain-induced fronts and TTW-induced filaments are used to evaluate this scaling. Frontal width given by boundary layer parameters drawn from observations in the General Ocean Turbulence Model (GOTM) are found qualitatively consistent with the observed range in regions of active submesoscales. The new arrested front scaling is used to modify the mixed layer eddy restratification parameterization commonly used in coarse-resolution climate models. Results in CESM-POP2 reveal the climate model’s sensitivity to the parameterization update and changes in model biases. A comprehensive multimodel study is in planning for further testing. Significance Statement The ocean surface plays a major role in the climate system, primarily through exchange in properties, such as in heat and carbon, between the ocean and atmosphere. Accurate model representation of ocean surface processes is crucial for climate simulations, yet they tend to be too small, fast, or complex to be resolved. Significant efforts lie in approximating these small-scale processes using reduced expressions that are solved by the model. This study presents an improved representation of the ocean surface in climate models by capturing some of the synergy that has been missing between the processes that define it. Results encourage further testing across a wider range of models to comprehensively evaluate the effects of this adjustment in climate simulations.

Disentangling the effects of climate change and reoligotrophication on primary production in a large lake

Climate change and reduction in nutrient loads have significant effects on primary production and phytoplankton growth dynamics. Since in the last few decades in many regions, nutrients in lakes were reduced simultaneously as the climate changed. Yet, it remains unclear which of the two has impacted primary production the most. In this study, we couple the General Ocean Turbulence Model with the Ecological Regional Ocean Model to disentangle the effects of climate change and reoligotrophication on primary production (PP) in Lake Geneva, Switzerland–France. We apply a data assimilation method to calibrate the model with the observations from the past (1981–1990) and validate it against the in situ data from the present decade (2011–2019). Both decades represent different climate conditions and trophic states of the lake. We show that the model is skilful to reproduce assimilated and unassimilated observations from both periods. According to our results, the effect of reoligotrophication on PP is marginally higher than that of warming, leading to a net decrease in primary production by 10% from the past to the present. The areal phosphorus supply in Lake Geneva, in spite of a decrease by $$\sim$$ 70%, is still characteristic of a meso-to-eutrophic ecosystem. This points towards an incomplete reoligotrophication of the lake. The effects of future climate change on winter mixing and PP dynamics have also been studied. Although there would be a significant reduction in deep mixing, the autotrophic production in Lake Geneva is expected to increase by $$\sim$$ 20% by the end of twenty-first century, largely due to stimulation in biomass build-up of temperature-dependent algae (e.g. dinoflagellates and cyanobacteria). Considering our results to represent other large temperate lakes with similar trophic status and water residence time as Lake Geneva, future climate scenarios are expected to bring back symptoms of eutrophication.

Record‐setting algal bloom in polymictic Lake Balaton (Hungary): A synergistic impact of climate change and (mis)management

Abstract After 25 years of apparently successful eutrophication management, a record‐breaking mixed bloom of Ceratium furcoides and Aphanizomenon flos‐aquae developed unexpectedly in 2019 in the large, polymictic Lake Balaton. The peak concentration of chlorophyll exceeded 300 mg/m3, 1.5 times higher than the pre‐management maxima. The external load was insufficient to provide the phosphorus (P) required to support this bloom, and its taxonomic composition was radically different from blooms in the 1980s. We hypothesised: (1) that unusually long periods of intermittent stratification led to anoxic P release from the sediments, providing the required P; and (2) that the surprising bloom composition indicated a potential regime shift. To test hypothesis 1, we analysed decade‐long high‐frequency data on drivers of lake metabolism recorded at a shallow near‐shore observatory. To explore likely dissolved oxygen (DO) conditions in deep water in 2019, we performed a sensitivity analysis of a one‐dimensional DO model driven by the General Ocean Turbulence Model, the latter being calibrated against high‐frequency hydrometeorological data measured in the open water. Hypothesis 2 was examined by a sequential t‐test analysis applied to multidecadal (1976–2020) data on phytoplankton biomass and composition, nutrient loads and summer mean air temperature. We found that DO depletion (<1 g/m3) was the immediate precursor of the large 2019 bloom. The synergistic impact of a climatic regime shift and multiple coincident management actions provoked a shift in phytoplankton composition in 2013 and forced the ecosystem into the state of internal eutrophication. In the present ecosystem state, similarly large blooms could develop in any summer depending on the sequence of weather events. Within the present, warmer climate regime identified here, the effectiveness of external nutrient control in eutrophication management may be limited by changes in the thermal structure. Therefore, it is vital to supplement external nutrient control with internal nutrient control strategies, such as water level regulation, to manage eutrophication and prevent future blooms. Large, continuous polymictic lakes are susceptible to small changes in water level, because the likelihood of stratification (and thus the likeliness of a eutrophication event) increases steeply and non‐linearly with increasing depth. With a climate that continues to warm, and the associated changes expected in internal lake processes, it is possible that we will see regime shifts (such as that identified in Lake Balaton) across more lakes globally. Managing these conditions will require case‐specific modifications of lake management plans.

Forced vs. Intrinsic Wintertime Submonthly Variability of Sea Surface Temperature in the Midlatitude Western North Pacific

The relative importance of wintertime forced and intrinsic SST variability in the Kuroshio-Oyashio Extension (KOE) region on submonthly timescales (2–10 and 10–30 days) is evaluated based on theoretical, observational, and modeling analysis. It is shown that the theoretical framework extended from the stochastic climate model has difficulties in representing observed SST variability on such short scales. We then employ the single-column General Ocean Turbulence Model (GOTM) to explicitly evaluate the SST variability forced by atmospheric disturbances. Results show that in the KOE region forced SST variability is responsible for a very small fraction of the total variability (<10%) on the submonthly scales, indicating the dominance of intrinsic oceanic processes. Outside the KOE forced variability dominates. By means of sensitivity experiments, the key physical factors are identified: upper ocean vertical mixing, wind stress forcing (mainly for outside KOE), and latent heat flux, the former two of which are not considered in the theoretical framework. The above results are robust against different levels of submonthly SST variability.

Rain-Induced Stratification of the Equatorial Indian Ocean and Its Potential Feedback to the Atmosphere.

Surface freshening through precipitation can act to stably stratify the upper ocean, forming a rain layer (RL). RLs inhibit subsurface vertical mixing, isolating deeper ocean layers from the atmosphere. This process has been studied using observations and idealized simulations. The present ocean modeling study builds upon this body of work by incorporating spatially resolved and realistic atmospheric forcing. Fine‐scale observations of the upper ocean collected during the Dynamics of the Madden‐Julian Oscillation field campaign are used to verify the General Ocean Turbulence Model (GOTM). Spatiotemporal characteristics of equatorial Indian Ocean RLs are then investigated by forcing a 2D array of GOTM columns with realistic and well‐resolved output from an existing regional atmospheric simulation. RL influence on the ocean‐atmosphere system is evaluated through analysis of RL‐induced modification to surface fluxes and sea surface temperature (SST). This analysis demonstrates that RLs cool the ocean surface on time scales longer than the associated precipitation event. A second simulation with identical atmospheric forcing to that in the first, but with rainfall set to zero, is performed to investigate the role of rain temperature and salinity stratification in maintaining cold SST anomalies within RLs. Approximately one third, or 0.1°C, of the SST reduction within RLs can be attributed to rain effects, while the remainder is attributed to changes in atmospheric temperature and humidity. The prolonged RL‐induced SST anomalies enhance SST gradients that have been shown to favor the initiation of atmospheric convection. These findings encourage continued research of RL feedbacks to the atmosphere.

Climate Process Team: Improvement of Ocean Component of NOAA Climate Forecast System Relevant to Madden‐Julian Oscillation Simulations

AbstractGiven the increasing attention in forecasting weather and climate on the subseasonal time scale in recent years, National Oceanic and Atmospheric Administration (NOAA) announced to support Climate Process Teams (CPTs) which aim to improve the Madden‐Julian Oscillation (MJO) prediction by NOAA’s global forecasting models. Our team supported by this CPT program focuses primarily on the improvement of upper ocean mixing parameterization and air‐sea fluxes in the NOAA Climate Forecast System (CFS). Major improvement includes the increase of the vertical resolution in the upper ocean and the implementation of General Ocean Turbulence Model (GOTM) in CFS. In addition to existing mixing schemes in GOTM, a newly developed scheme based on observations in the tropical ocean, with further modifications, has been included. A better performance of ocean component is demonstrated through one‐dimensional ocean model and ocean general circulation model simulations validated by the comparison with in‐situ observations. These include a large sea surface temperature (SST) diurnal cycle during the MJO suppressed phase, intraseasonal SST variations associated with the MJO, ocean response to atmospheric cold pools, and deep cycle turbulence. Impact of the high‐vertical resolution of ocean component on CFS simulation of MJO‐associated ocean temperature variations is evident. Also, the magnitude of SST changes caused by high‐resolution ocean component is sufficient to influence the skill of MJO prediction by CFS.

FABM-NflexPD 1.0: assessing an instantaneous acclimation approach for modeling phytoplankton growth

Abstract. Coupled physical–biogeochemical models can generally reproduce large-scale patterns of primary production and biogeochemistry, but they often underestimate observed variability and gradients. This is partially caused by insufficient representation of systematic variations in the elemental composition and pigment density of phytoplankton. Although progress has been made through approaches accounting for the dynamics of phytoplankton composition with additional state variables, formidable computational challenges arise when these are applied in spatially explicit setups. The instantaneous acclimation (IA) approach addresses these challenges by assuming that Chl:C:nutrient ratios are instantly optimized locally (within each modeled grid cell, at each time step), such that they can be resolved as diagnostic variables. Here, we present the first tests of IA in an idealized 1-D setup: we implemented the IA in the Framework for Aquatic Biogeochemical Models (FABM) and coupled it with the General Ocean Turbulence Model (GOTM) to simulate the spatiotemporal dynamics in a 1-D water column. We compare the IA model against a fully dynamic, otherwise equivalently acclimative (dynamic acclimation; DA) variant with an additional state variable and a third, non-acclimative and fixed-stoichiometry (FS) variant. We find that the IA and DA variants, which require the same parameter set, behave similarly in many respects, although some differences do emerge especially during the winter–spring and autumn–winter transitions. These differences however are relatively small in comparison to the differences between the DA and FS variants, suggesting that the IA approach can be used as a cost-effective improvement over a fixed-stoichiometry approach. Our analysis provides insights into the roles of acclimative flexibilities in simulated primary production and nutrient drawdown rates, seasonal and vertical distribution of phytoplankton biomass, formation of thin chlorophyll layers and stoichiometry of detrital material.

The Influence of Preexisting Stratification and Tropical Rain Modes on the Mixed Layer Salinity Response to Rainfall

AbstractThe freshwater input from rain to the surface ocean is a key component of the global water cycle. Frequent rainfall in the inter‐tropical convergence zone creates regions of strong surface stratification and low salinity, which vary seasonally. We evaluate how variations in rain type and preexisting upper ocean stratification influence the timing and duration of the salinity response to rainfall using the General Ocean Turbulence Model. A series of model simulations was run by prescribing three typical background stratification conditions and idealized rain and wind forcing that was consistent with observed convective, stratiform, and mixed convective and stratiform rainfall. Background stratification was assessed using underway CTD observations and rain forcing was identified from mooring observations collected in the eastern tropical Pacific during the second Salinity Processes in the Upper Ocean Regional Study. Model results show that strong stratification, whether preexisting or from convective rainfall, inhibits downward mixing of freshwater and allows near‐surface salinity anomalies to persist following rain. In contrast, when stratiform rain precedes convective rain, salinity anomalies are quickly mixed downward and longer lasting deeper in the mixed layer. This implies that accurately quantifying the salinity structure following rain should consider preexisting stratification and the type of rainfall. Furthermore, patterns of rainfall and stratification likely affect the bias between salinity observations at the surface and deeper in the mixed layer. Because satellite rain data do not correctly represent the small scales of rain forcing, the small‐scale surface salinity response to rain cannot be predicted from satellite data.

Integrating CVMix into GOTM (v6.0): a consistent framework for testing, comparing, and applying ocean mixing schemes

Abstract. The General Ocean Turbulence Model (GOTM) is a one-dimensional water column model, including a set of state-of-the-art turbulence closure models, and has widely been used in various applications in the ocean modeling community. Here, we extend GOTM to include a set of newly developed ocean surface vertical mixing parameterizations of Langmuir turbulence via coupling with the Community Vertical Mixing Project (CVMix). A Stokes drift module is also implemented in GOTM to provide the necessary ocean surface waves information to the Langmuir turbulence parameterizations, as well as to facilitate future development and evaluation of new Langmuir turbulence parameterizations. In addition, a streamlined workflow with Python and Jupyter notebooks is also described, enabled by the newly developed and more flexible configuration capability of GOTM. The newly implemented Langmuir turbulence parameterizations are evaluated against theoretical scalings and available observations in four test cases, including an idealized wind-driven entrainment case and three realistic cases at Ocean Station Papa, the northern North Sea, and the central Baltic Sea, and compared with the existing general length scale scheme in GOTM. The results are consistent with previous studies. This development extends the capability of GOTM towards including the effects of ocean surface waves and provides useful toolsets for the ocean modeling community to further study the effects of Langmuir turbulence in a broader scope.

Air–Sea Interaction in the Central Mediterranean Sea: Assessment of Reanalysis and Satellite Observations

Air–sea heat fluxes are essential climate variables, required for understanding air–sea interactions, local, regional and global climate, the hydrological cycle and atmospheric and oceanic circulation. In situ measurements of fluxes over the ocean are sparse and model reanalysis and satellite data can provide estimates at different scales. The accuracy of such estimates is therefore essential to obtain a reliable description of the occurring phenomena and changes. In this work, air–sea radiative fluxes derived from the SEVIRI sensor onboard the MSG satellite and from ERA5 reanalysis have been compared to direct high quality measurements performed over a complete annual cycle at the ENEA oceanographic observatory, near the island of Lampedusa in the Central Mediterranean Sea. Our analysis reveals that satellite derived products overestimate in situ direct observations of the downwelling short-wave (bias of 6.1 W/m2) and longwave (bias of 6.6 W/m2) irradiances. ERA5 reanalysis data show a negligible positive bias (+1.0 W/m2) for the shortwave irradiance and a large negative bias (−17 W/m2) for the longwave irradiance with respect to in situ observations. ERA5 meteorological variables, which are needed to calculate the air–sea heat flux using bulk formulae, have been compared with in situ measurements made at the oceanographic observatory. The two meteorological datasets show a very good agreement, with some underestimate of the wind speed by ERA5 for high wind conditions. We investigated the impact of different determinations of heat fluxes on the near surface sea temperature (1 m depth), as determined by calculations with a one-dimensional numerical model, the General Ocean Turbulence Model (GOTM). The sensitivity of the model to the different forcing was measured in terms of differences with respect to in situ temperature measurements made during the period under investigation. All simulations reproduced the true seasonal cycle and all high frequency variabilities. The best results on the overall seasonal cycle were obtained when using meteorological variables in the bulk formulae formulations used by the model itself. The derived overall annual net heat flux values were between +1.6 and 40.4 W/m2, depending on the used dataset. The large variability obtained with different datasets suggests that current determinations of the heat flux components and, in particular, of the longwave irradiance, need to be improved. The ENEA oceanographic observatory provides a complete, long-term, high resolution time series of high quality in situ observations. In the future, more similar sites worldwide will be needed for model and satellite validations and to improve the determination of the air–sea exchange and the understanding of related processes.

Model-based data analysis of the effect of winter mixing on primary production in a lake under reoligotrophication

Nutrient loading, in combination with climate change are important drivers of primary productivity in lakes. Understanding and forecasting future changes in primary production (PP) in response to local and global forcing are major challenges for developing sustainable lake management. The objective of this study is to understand and characterize the mechanisms underlying the large differences in observed PP rates and nutrient concentrations between two consecutive years (2012 and 2013) in Lake Geneva, Switzerland. For this purpose, we apply a one-dimensional (1D) physical–biogeochemical model system. The Framework of Aquatic Biogeochemical models (FABM) interface is used to couple the General Ocean Turbulence Model (GOTM) with a biogeochemical model, the Ecological Regional Ocean Model (ERGOM). We calibrated GOTM, by adjusting physical parameters, with the observed temperature profiles. A model calibration method is implemented to minimize model-data misfits and to optimize the biological parameters related to phytoplankton growth dynamics.According to our results, the simulated surface mixed layer depth is deeper and heat loss from the lake and turbulent kinetic energy in the water column are much higher in winter 2012 than that in 2013, pointing to a cooling-driven, deep mixing in the lake in 2012. We found significant differences in internal phosphorus loads in the epilimnion between the two years, with estimates for 2012 being higher than those for 2013. ERGOM predicts weak nutrient limitation on phytoplankton and higher growth rates in 2012. Apparently, the deep mixing event led to high turnover of nutrients (particularly dissolved inorganic phosphate) to the productive surface layers, and a massive algal bloom developed later in the productive season. In contrary, the turnover of nutrients in 2013 was weak and consequently the PP was low. Our findings demonstrate the utility of a coupled physical–biological model framework for the investigation of the meteorological and physical controls of PP dynamics in aquatic systems.

On the Role of Biogeochemical Coupling Between Sympagic and Pelagic Ecosystem Compartments for Primary and Secondary Production in the Barents Sea

Primary production in the Arctic marine system is principally due to pelagic phytoplankton. In addition, sea-ice algae also make a contribution and play an important role in food web dynamics. A proper representation of sea-ice algae phenology and the linkage with the pelagic and benthic systems is needed, so as to better understand the ecosystem response to warming and shrinking ice cover. Here we describe the extension of the biogeochemical model ECOSMO II to include a sympagic system in the model formulation, illustrated by implementation in the Barents Sea. The new sympagic system formulation includes four nutrients (NO3, NH4, PO4, and SiO2), one functional group for sea-ice algae and one detritus pool, and exchanges with the surface ocean layer. We investigated the effects of linkage between the three systems (sympagic, pelagic and benthic) on the ecosystem dynamic; the contribution of the ice algae to total primary production; and how the changes in ice coverage will affect the lower trophic level Arctic food-web dynamics. To solve the scientific and technical challenges related to the coupling, the model was implemented in a 1-D application of the General Ocean Turbulence Model (GOTM). Results showed that the model simulated the seasonal pattern of the sympagic components realistically when compared to the current knowledge of the Barents Sea. Our results show that the sympagic system influences the timing and the amplitude of the pelagic primary and secondary production in the water column. We also demonstrated that sea-ice algae production leads to seeding of pelagic diatoms and an enhancement of the zooplankton production. Finally, we used the model to explain how the interaction between zooplankton and ice algae can control the pelagic primary production in the Barents Sea.

A marine carbon monoxide (CO) model with a new parameterization of microbial oxidation

Traditionally, marine carbon monoxide (CO) models assume that the microbial oxidation of CO is only dependent on the concentration of CO in the water column. However, CO oxidation rates in the ocean have been reported to vary up to two orders of magnitude both spatially and temporally. Here, we developed a new model assuming that CO microbial oxidation is dependent on bacterial carbon biomass other than CO concentration. In addition to microbial oxidation, the model also describes CO photochemical production, vertical mixing, and air-sea gas exchange. The new CO model has been embedded in the European Regional Seas Ecosystem Model (ERSEM) and coupled with the General Ocean Turbulence Model (GOTM). The CO-GOTM-ERSEM model was implemented at the Bermuda Atlantic Time Series (BATS) station to simulate CO concentrations observed in March 1993 by Kettle (1994). The proposed second-order function describing CO microbial oxidation introduces a new parameter, the bacteria biomass specific oxidation rate, which was estimated to be 5.7 ± 0.2 (μg C m−3)−1 h−1. Statistical metrics indicates that the new CO model performs better than a previously published model with a first-order decay function to describe microbial oxidation, acknowledging the dependence of microbial oxidation on bacterial abundance is realistic. A long-term (1992 - 1994) simulation carried out with CO-GOTM-ERSEM reproduced the spatial and seasonal variability of CO reported in the literature. Our model provides a realistic description of the CO dynamics and is potentially usable in different environmental contexts worldwide.

Simulations of future changes in thermal structure of Lake Erken: proof of concept for ISIMIP2b lake sector local simulation strategy

Abstract. This paper, as a part of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b), assesses the impacts of different levels of global warming on the thermal structure of Lake Erken (Sweden). The General Ocean Turbulence Model (GOTM) one-dimensional hydrodynamic model was used to simulate water temperature when using ISIMIP2b bias-corrected climate model projections as input. These projections have a daily time step, while lake model simulations are often forced at hourly or shorter time steps. Therefore, it was necessary to first test the ability of GOTM to simulate Lake Erken water temperature using daily vs hourly meteorological forcing data. In order to do this, three data sets were used to force the model as follows: (1) hourly measured data, (2) daily average data derived from the first data set, and (3) synthetic hourly data created from the daily data set using generalised regression artificial neural network methods. This last data set is developed using a method that could also be applied to the daily time step ISIMIP scenarios to obtain hourly model input if needed. The lake model was shown to accurately simulate Lake Erken water temperature when forced with either daily or synthetic hourly data. Long-term simulations forced with daily or synthetic hourly meteorological data suggest that by the late 21st century the lake will undergo clear changes in thermal structure. For the representative concentration pathway (RCP) scenario, namely RCP2.6, surface water temperature was projected to increase by 1.79 and 1.36 ∘C when the lake model was forced at daily and hourly resolutions respectively, and for RCP6.0 these increases were projected to be 3.08 and 2.31 ∘C. Changes in lake stability were projected to increase, and the stratification duration was projected to be longer by 13 and 11 d under RCP2.6 scenario and 22 and 18 d under RCP6.0 scenario for daily and hourly resolutions. Model changes in thermal indices were very similar when using either the daily or synthetic hourly forcing, suggesting that the original ISIMIP climate model projections at a daily time step can be sufficient for the purpose of simulating lake water temperature.

Effects of large-scale floating (solar photovoltaic) platforms on hydrodynamics and primary production in a coastal sea from a water column model

Abstract. An improved understanding of the effects of floating solar platforms on the ecosystem is necessary to define acceptable and responsible real-world field implementations of this new marine technology. This study examines a number of potential effects of offshore floating solar photovoltaic (PV) platforms on the hydrodynamics and net primary production in a coastal sea for the first time. Three contrasting locations within the North Sea (a shallow and deeper location with well-mixed conditions and a seasonally stratifying location) have been analysed using a water column physical–biogeochemical model: the General Ocean Turbulence Model coupled with the European Regional Seas Ecosystem Model – Biogeochemical Flux Model (GOTM-ERSEM-BFM). The results show strong dependence on the characteristics of the location (e.g. mixing and stratification) and on the density of coverage with floating platforms. The overall response of the system was separated into contributions by platform-induced light deficit, shielding by the platforms of the sea surface from wind and friction induced by the platforms on the currents. For all three locations, light deficit was the dominant effect on the net primary production. For the two well-mixed locations, the other effects of the platforms resulted in partial compensation for the impact of light deficit, while for the stratified location, they enhanced the effects of light deficit. For up to 20 % coverage of the model surface with platforms, the spread in the results between locations was relatively small, and the changes in net primary production were less than 10 %. For higher percentages of coverage, primary production decreased substantially, with an increased spread in response between the sites. The water column model assumes horizontal homogeneity in all forcings and simulated variables, also for coverage with floating platforms, and hence the results are applicable to very-large-scale implementations of offshore floating platforms that are evenly distributed over areas of at least several hundreds of square kilometres, such that phytoplankton remain underneath a farm throughout several tidal cycles. To confirm these results, and to investigate more realistic cases of floating platforms distributed unevenly over much smaller areas with horizontally varying hydrodynamic conditions, in which phytoplankton can be expected to spend only part of the time underneath a farm and effects are likely to be smaller, spatial detail and additional processes need to be included. To do so, further work is required to advance the water column model towards a three-dimensional modelling approach.

Modeling the Ecological Response of a Temporarily Summer-Stratified Lake to Extreme Heatwaves

Climate extremes, which are steadily increasing in frequency, can have detrimental consequences for lake ecosystems. We used a state-of-the-art, one-dimensional, hydrodynamic-ecosystem model [General Ocean Turbulence Model (GOTM)-framework for aquatic biogeochemical models (FABM)-PCLake] to determine the influence of extreme climate events on a temperate and temporarily summer stratified lake (Lake Bryrup, Denmark). The model was calibrated (eight years data) and validated (two years data), and the modeled variables generally showed good agreement with observations. Then, a span of extreme warming scenarios was designed based on weather data from the heatwave seen over northern Europe in May–July 2018, mimicking situations of extreme warming returning every year, every three years, and every five years in summer and all year round, respectively. We found only modest impacts of the extreme climate events on nutrient levels, which in some scenarios decreased slightly when looking at the annual mean. The most significant impacts were found for phytoplankton, where summer average chlorophyll a concentrations and cyanobacteria biomass peaks were up to 39% and 58% higher than during baseline, respectively. As a result, the phytoplankton to nutrient ratios increased during the heat wave experiments, reflecting an increased productivity and an increased cycling of nutrients in the pelagic. The phytoplankton blooms occurred up to 15 days earlier and lasted for up to half a month longer during heat wave years relative to the baseline. Our extreme scenarios illustrated and quantified the large impacts of a past heat wave (observed 2018) and may be indicative of the future for many temperate lakes.

Historical modelling of changes in Lake Erken thermal conditions

Abstract. Historical lake water temperature records are a valuable source of information to assess the influence of climate change on lake thermal structure. However, in most cases such records span a short period of time and/or are incomplete, providing a less credible assessment of change. In this study, the hydrodynamic GOTM (General Ocean Turbulence Model, a hydrodynamic model configured in lake mode) was used to reconstruct daily profiles of water temperature in Lake Erken (Sweden) over the period 1961–2017 using seven climatic parameters as forcing data: wind speed (WS), air temperature (Air T), atmospheric pressure (Air P), relative humidity (RH), cloud cover (CC), precipitation (DP), and shortwave radiation (SWR). The model was calibrated against observed water temperature data collected during the study interval, and the calibrated model revealed a good match between modelled and observed temperature (RMSE =1.089 ∘C). From the long-term simulations of water temperature, this study focused on detecting possible trends in water temperature over the entire study interval 1961–2017 and in the sub-intervals 1961–1988 and 1989–2017, since an abrupt change in air temperature was detected in 1988. The analysis of the simulated temperature showed that epilimnetic temperature increased on average by 0.444 and 0.792 ∘C per decade in spring and autumn in the sub-interval 1989–2017. Summer epilimnetic temperature increased by 0.351 ∘C per decade over the entire interval 1961–2017. Hypolimnetic temperature increased significantly in spring over the entire interval 1961–2017, by 0.148 and by 0.816 ∘C per decade in autumn in the sub-interval 1989–2016. Whole-lake temperature showed a significant increasing trend in the sub-interval 1989–2017 during spring (0.404 ∘C per decade) and autumn (0.789 ∘C per decade, interval 1989–2016), while a significant trend was detected in summer over the entire study interval 1961–2017 (0.239 ∘C per decade). Moreover, this study showed that changes in the phenology of thermal stratification have occurred over the 57-year period of study. Since 1961, the stability of stratification (Schmidt stability) has increased by 5.365 J m−2 per decade. The duration of thermal stratification has increased by 7.297 d per decade, corresponding to an earlier onset of stratification of ∼16 d and to a delay of stratification termination of ∼26 d. The average thermocline depth during stratification became shallower by ∼1.345 m, and surface-bottom temperature difference increased over time by 0.249 ∘C per decade. The creation of a daily time step water temperature dataset not only provided evidence of changes in Erken thermal structure over the last decades, but is also a valuable resource of information that can help in future research on the ecology of Lake Erken. The use of readily available meteorological data to reconstruct Lake Erken's past water temperature is shown to be a useful method to evaluate long-term changes in lake thermal structure, and it is a method that can be extended to other lakes.

Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators

Abstract. Observation operators (OOs) are a central component of any data assimilation system. As they project the state variables of a numerical model into the space of the observations, they also provide an ideal opportunity to correct for effects that are not described or are insufficiently described by the model. In such cases a dynamical OO, an OO that interfaces to a secondary and more specialised model, often provides the best results. However, given the large number of observations to be assimilated in a typical atmospheric or oceanographic model, the computational resources needed for using a fully dynamical OO mean that this option is usually not feasible. This paper presents a method, based on canonical correlation analysis (CCA), that can be used to generate highly efficient statistical OOs that are based on a dynamical model. These OOs can provide an approximation to the dynamical model at a fraction of the computational cost. One possible application of such an OO is the modelling of the diurnal cycle of sea surface temperature (SST) in ocean general circulation models (OGCMs). Satellites that measure SST measure the temperature of the thin uppermost layer of the ocean. This layer is strongly affected by atmospheric conditions, and its temperature can differ significantly from the water below. This causes a discrepancy between the SST measurements and the upper layer of the OGCM, which typically has a thickness of around 1 m. The CCA OO method is used to parameterise the diurnal cycle of SST. The CCA OO is based on an input dataset from the General Ocean Turbulence Model (GOTM), a high-resolution water column model that has been specifically tuned for this purpose. The parameterisations of the CCA OO are found to be in good agreement with the results from the GOTM and improve upon existing parameterisations, showing the potential of this method for use in data assimilation systems.