Validation of turbulent heat transfer models against eddy covariance flux measurements over a seasonally ice-covered lake

In this study we analyzed turbulent heat fluxes over a seasonal ice cover on a boreal lake located in southern Finland. Eddy covariance (EC) flux measurements of sensible (H) and latent heat (LE) from four ice-on seasons between 2014 and 2019 are compared to three different bulk transfer models: one with a constant transfer coefficient and two with stability-adjusted transfer coefficients: the Lake Heat Flux Analyzer and SEA-ICE. All three models correlate well with the EC results in general while typically underestimating the magnitude and the standard deviation of the flux in comparison to the EC observations. Differences between the models are small, with the constant transfer coefficient model performing slightly better than the stability-adjusted models. Small difference in temperature and humidity between surface and air results in low correlation between models and EC. During melting periods (surface temperature T-0 > 0 degrees C), the model performance for LE decreases when compared to the freezing periods (T-0 < 0 degrees C), while the opposite is true for H. At low wind speed, EC shows relatively high fluxes (+/- 20 W m(-2)) for H and LE due to non-local effects that the bulk models are not able to reproduce. The complex topography of the lake surroundings creates local violations of the Monin-Obukhov similarity theory, which helps explain this counterintuitive result. Finally, the uncertainty in the estimation of the surface temperature and humidity affects the bulk heat fluxes, especially when the differences between surface and air values are small.Peer reviewe

Random uncertainties of flux measurements by the eddy covariance technique

Large variability is inherent to turbulent flux observations. We review different methods used to estimate the flux random errors. Flux errors are calculated using measured turbulent and simulated artificial records. We recommend two flux errors with clear physical meaning: the flux error of the covariance, defining the error of the measured flux as 1 standard deviation of the random uncertainty of turbulent flux observed over an averaging period of typically 30 min to 1 h duration; and the error of the flux due to the instrumental noise. We suggest that the numerical approximation by Finkelstein and Sims (2001) is a robust and accurate method for calculation of the first error estimate. The method appeared insensitive to the integration period and the value 200 s sufficient to obtain the estimate without significant bias for variety of sites and wide range of observation conditions. The filtering method proposed by Salesky et al. (2012) is an alternative to the method by Finkelstein and Sims (2001) producing consistent, but somewhat lower, estimates. The method proposed by Wienhold et al. (1995) provides a good approximation to the total flux random uncertainty provided that independent cross-covariance values far from the maximum are used in estimation as suggested in this study. For the error due to instrumental noise the method by Lenschow et al. (2000) is useful in evaluation of the respective uncertainty. The method was found to be reliable for signal-to-noise ratio, defined by the ratio of the standard deviation of the signal to that of the noise in this study, less than three. Finally, the random uncertainty of the error estimates was determined to be in the order of 10 to 30 % for the total flux error, depending on the conditions and method of estimation.Peer reviewe

Limitations and Challenges of MODIS-Derived Phenological Metrics Across Different Landscapes in Pan-Arctic Regions

Recent efforts have been made to monitor the seasonal metrics of plant canopy variations globally from space, using optical remote sensing. However, phenological estimations based on vegetation indices (VIs) in high-latitude regions such as the pan-Arctic remain challenging and are rarely validated. Nevertheless, pan-Arctic ecosystems are vulnerable and also crucial in the context of climate change. We reported the limitations and challenges of using MODerate-resolution Imaging Spectroradiometer (MODIS) measurements, a widely exploited set of satellite measurements, to estimate phenological transition dates in pan-Arctic regions. Four indices including normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), phenology index (PI), plant phenological index (PPI) and a MODIS Land Cover Dynamics Product MCD12Q2, were evaluated and compared against eddy covariance (EC) estimates at 11 flux sites of 102 site-years during the period from 2000 to 2014. All the indices were influenced by snow cover and soil moisture during the transition dates. While relationships existed between VI-based and EC-estimated phenological transition dates, the R-2 values were generally low (0.01-0.68). Among the VIs, PPI-estimated metrics showed an inter-annual pattern that was mostly closely related to the EC-based estimations. Thus, further studies are needed to develop region-specific indices to provide more reliable estimates of phenological transition dates.Peer reviewe

A revised dry deposition scheme for land-atmosphere exchange of trace gases in ECHAM/MESSy v2.54

Dry deposition to vegetation is a major sink of ground-level ozone and is responsible for about 20 % of the total tropospheric ozone loss. Its parameterization in atmospheric chemistry models represents a significant source of uncertainty for the global tropospheric ozone budget and might account for the mismatch with observations. The model used in this study, the Modular Earth Submodel System version 2 (MESSy2) linked to the fifth-generation European Centre Hamburg general circulation model (ECHAM5) as an atmospheric circulation model (EMAC), is no exception. Like many global models, EMAC employs a "resistance in series" scheme with the major surface deposition via plant stomata which is hardly sensitive to meteorology, depending only on solar radiation. Unlike many global models, however, EMAC uses a simplified high resistance for nonstomatal deposition which makes this pathway negligible in the model. However, several studies have shown this process to be comparable in magnitude to the stomatal uptake, especially during the night over moist surfaces. Hence, we present here a revised dry deposition in EMAC including meteorological adjustment factors for stomatal closure and an explicit cuticular pathway. These modifications for the three stomatal stress functions have been included in the newly developed MESSy VERTEX submodel, i.e. a process model describing the vertical exchange in the atmospheric boundary layer, which will be evaluated for the first time here. The scheme is limited by a small number of different surface types and generalized parameters. The MESSy submodel describing the dry deposition of trace gases and aerosols (DDEP) has been revised accordingly. The comparison of the simulation results with measurement data at four sites shows that the new scheme enables a more realistic representation of dry deposition. However, the representation is strongly limited by the local meteorology. In total, the changes increase the dry deposition velocity of ozone up to a factor of 2 globally, whereby the highest impact arises from the inclusion of cuticular uptake, especially over moist surfaces. This corresponds to a 6 % increase of global annual dry deposition loss of ozone resulting globally in a slight decrease of ground-level ozone but a regional decrease of up to 25 %. The change of ozone dry deposition is also reasoned by the altered loss of ozone precursors. Thus, the revision of the process parameterization as documented here has, among others, the potential to significantly reduce the overestimation of tropospheric ozone in global models.Peer reviewe

Validation of WRF-Chem Model and CAMS Performance in Estimating Near-Surface Atmospheric CO2 Mixing Ratio in the Area of Saint Petersburg (Russia)

Nowadays, different approaches for CO2 anthropogenic emission estimation are applied to control agreements on greenhouse gas reduction. Some methods are based on the inverse modelling of emissions using various measurements and the results of numerical chemistry transport models (CTMs). Since the accuracy and precision of CTMs largely determine errors in the approaches for emission estimation, it is crucial to validate the performance of such models through observations. In the current study, the near-surface CO2 mixing ratio simulated by the CTM Weather Research and Forecasting—Chemistry (WRF-Chem) at a high spatial resolution (3 km) using three different sets of CO2 fluxes (anthropogenic + biogenic fluxes, time-varying and constant anthropogenic emissions) and from Copernicus Atmosphere Monitoring Service (CAMS) datasets have been validated using in situ observations near the Saint Petersburg megacity (Russia) in March and April 2019. It was found that CAMS reanalysis data with a low spatial resolution (1.9° × 3.8°) can match the observations better than CAMS analysis data with a high resolution (0.15° × 0.15°). The CAMS analysis significantly overestimates the observed near-surface CO2 mixing ratio in Peterhof in March and April 2019 (by more than 10 ppm). The best match for the CAMS reanalysis and observations was observed in March, when the wind was predominantly opposite to the Saint Petersburg urbanized area. In contrast, the CAMS analysis fits the observed trend of the mixing ratio variation in April better than the reanalysis with the wind directions from the Saint Petersburg urban zone. Generally, the WRF-Chem predicts the observed temporal variations in the near-surface CO2 reasonably well (mean bias ≈ (−0.3) − (−0.9) ppm, RMSD ≈ 8.7 ppm, correlation coefficient ≈ 0.61 ± 0.04). The WRF-Chem data where anthropogenic and biogenic fluxes were used match the observations a bit better than the WRF-Chem data without biogenic fluxes. The diurnal time variation in the anthropogenic emissions influenced the WRF-Chem data insignificantly. However, in general, the data of all three WRF-Chem model runs give almost the same CO2 temporal variation in Peterhof in March and April 2019. This could be related to the late start of the growing season, which influences biogenic CO2 fluxes, inaccuracies in the estimation of the biogenic fluxes, and the simplified time variation pattern of the CO2 anthropogenic emissions

Validation of WRF-Chem Model and CAMS Performance in Estimating Near-Surface Atmospheric CO2 Mixing Ratio in the Area of Saint Petersburg (Russia)

Nowadays, different approaches for CO2 anthropogenic emission estimation are applied to control agreements on greenhouse gas reduction. Some methods are based on the inverse modelling of emissions using various measurements and the results of numerical chemistry transport models (CTMs). Since the accuracy and precision of CTMs largely determine errors in the approaches for emission estimation, it is crucial to validate the performance of such models through observations. In the current study, the near-surface CO2 mixing ratio simulated by the CTM Weather Research and Forecasting—Chemistry (WRF-Chem) at a high spatial resolution (3 km) using three different sets of CO2 fluxes (anthropogenic + biogenic fluxes, time-varying and constant anthropogenic emissions) and from Copernicus Atmosphere Monitoring Service (CAMS) datasets have been validated using in situ observations near the Saint Petersburg megacity (Russia) in March and April 2019. It was found that CAMS reanalysis data with a low spatial resolution (1.9° × 3.8°) can match the observations better than CAMS analysis data with a high resolution (0.15° × 0.15°). The CAMS analysis significantly overestimates the observed near-surface CO2 mixing ratio in Peterhof in March and April 2019 (by more than 10 ppm). The best match for the CAMS reanalysis and observations was observed in March, when the wind was predominantly opposite to the Saint Petersburg urbanized area. In contrast, the CAMS analysis fits the observed trend of the mixing ratio variation in April better than the reanalysis with the wind directions from the Saint Petersburg urban zone. Generally, the WRF-Chem predicts the observed temporal variations in the near-surface CO2 reasonably well (mean bias ≈ (−0.3) − (−0.9) ppm, RMSD ≈ 8.7 ppm, correlation coefficient ≈ 0.61 ± 0.04). The WRF-Chem data where anthropogenic and biogenic fluxes were used match the observations a bit better than the WRF-Chem data without biogenic fluxes. The diurnal time variation in the anthropogenic emissions influenced the WRF-Chem data insignificantly. However, in general, the data of all three WRF-Chem model runs give almost the same CO2 temporal variation in Peterhof in March and April 2019. This could be related to the late start of the growing season, which influences biogenic CO2 fluxes, inaccuracies in the estimation of the biogenic fluxes, and the simplified time variation pattern of the CO2 anthropogenic emissions

Effects of Climate Change on CO2 Concentration and Efflux in a Humic Boreal Lake : A Modeling Study

Climate change may have notable impacts on carbon cycling in freshwater ecosystems, especially in the boreal zone. Higher atmospheric temperature and changes in annual discharge patterns and carbon loading from the catchment affect the thermal and biogeochemical conditions in a lake. We developed an extension of a one-dimensional process-based lake model MyLake for simulating carbon dioxide (CO2 ) dynamics of a boreal lake. We calibrated the model for Lake Kuivajarvi, a small humic boreal lake, for the years 2013-2014, using the extensive data available on carbon inflow and concentrations of water column CO2 and dissolved organic carbon. The lake is a constant source of CO2 to the atmosphere in the present climate. We studied the potential effects of climate change-induced warming on lake CO2 concentration and air-water flux using downscaled air temperature data from three recent-generation global climate models with two alternative representative concentration pathway forcing scenarios. Literature estimates were used for climate change impacts on the lake inflow. The scenario simulations showed a 20-35% increase in the CO2 flux from the lake to the atmosphere in the scenario period 2070-2099 compared to the control period 1980-2009. In addition, we estimated possible implications of different changes in terrestrial inorganic and organic carbon loadings to the lake. The scenarios with plausible increases of 10% and 20% in CO2 and dissolved organic carbon loadings, respectively, produced increases of 2.1-2.5% and 2.2-2.3% in the annual CO2 flux.Peer reviewe

High-frequency productivity estimates for a lake from free-water CO2 concentration measurements

Lakes are important actors in biogeochemical cycles and a powerful natural source of CO2. However, they are not yet fully integrated in carbon global budgets, and the carbon cycle in the water is still poorly understood. In freshwater ecosystems, productivity studies have usually been carried out with traditional methods (bottle incubations, C-14 technique), which are imprecise and have a poor temporal resolution. Consequently, our ability to quantify and predict the net ecosystem productivity (NEP) is limited: the estimates are prone to errors and the NEP cannot be parameterised from environmental variables. Here we expand the testing of a free-water method based on the direct measurement of the CO2 concentration in the water. The approach was first proposed in 2008, but was tested on a very short data set (3 days) under specific conditions (autumn turnover); despite showing promising results, this method has been neglected by the scientific community. We tested the method under different conditions (summer stratification, typical summer conditions for boreal dark-water lakes) and on a much longer data set (40 days), and quantitatively validated it comparing our data and productivity models. We were able to evaluate the NEP with a high temporal resolution (minutes) and found a very good agreement (R-2 >= 0.71) with the models. We also estimated the parameters of the productivity-irradiance (PI) curves that allow the calculation of the NEP from irradiance and water temperature. Overall, our work shows that the approach is suitable for productivity studies under a wider range of conditions, and is an important step towards developing this method so that it becomes more widely used.Peer reviewe

Varying Vegetation Composition, Respiration and Photosynthesis Decrease Temporal Variability of the CO2 Sink in a Boreal Bog

We quantified the role of spatially varying vegetation composition in seasonal and interannual changes in a boreal bog’s CO2 uptake. We divided the spatially heterogeneous site into six microform classes based on plant species composition and measured their net ecosystem exchange (NEE) using chamber method over the growing seasons in 2012–2014. A nonlinear mixed-effects model was applied to assess how the contributions of microforms with different vegetation change temporally, and to upscale NEE to the ecosystem level to be compared with eddy covariance (EC) measurements. Both ecosystem respiration (R) and gross photosynthesis (PG) were the largest in high hummocks, 894–964 (R) and 969–1132 (PG) g CO2 m−2 growing season−1, and decreased toward the wetter microforms. NEE had a different spatial pattern than R and PG; the highest cumulative seasonal CO2 sink was found in lawns in all years (165–353 g CO2 m−2). Microforms with similar wetness but distinct vegetation had different NEE, highlighting the importance of vegetation composition in regulating CO2 sink. Chamber-based ecosystem-level NEE was smaller and varied less interannually than the EC-derived estimate, indicating a need for further research on the error sources of both methods. Lawns contributed more to ecosystem-level NEE (55–78%) than their areal cover within the site (21.5%). In spring and autumn, lawns had the highest NEE, whereas in midsummer differences among microforms were small. The contributions of all microforms to the ecosystem-level NEE varied seasonally and interannually, suggesting that spatially heterogeneous vegetation composition could make bog CO2 uptake temporally more stable.Peer reviewe