| 1. |
- Jansen, Joachim, 1989-, et al.
(author)
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Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
- 2023
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In: Scientific Data. - : Springer Nature. - 2052-4463. ; 10:1
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Journal article (peer-reviewed)abstract
- Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.
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| 2. |
- Guseva, S., et al.
(author)
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Bulk Transfer Coefficients Estimated From Eddy-Covariance Measurements Over Lakes and Reservoirs
- 2023
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In: Journal of Geophysical Research - Atmospheres. - : American Geophysical Union (AGU). - 2169-897X .- 2169-8996. ; 128:2
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Journal article (peer-reviewed)abstract
- The drag coefficient, Stanton number and Dalton number are of particular importance for estimating the surface turbulent fluxes of momentum, heat and water vapor using bulk parameterization. Although these bulk transfer coefficients have been extensively studied over the past several decades in marine and large-lake environments, there are no studies analyzing their variability for smaller lakes. Here, we evaluated these coefficients through directly measured surface fluxes using the eddy-covariance technique over more than 30 lakes and reservoirs of different sizes and depths. Our analysis showed that the transfer coefficients (adjusted to neutral atmospheric stability) were generally within the range reported in previous studies for large lakes and oceans. All transfer coefficients exhibit a substantial increase at low wind speeds (<3 m s(-1)), which was found to be associated with the presence of gusts and capillary waves (except Dalton number). Stanton number was found to be on average a factor of 1.3 higher than Dalton number, likely affecting the Bowen ratio method. At high wind speeds, the transfer coefficients remained relatively constant at values of 1.6.10(-3), 1.4.10(-3), 1.0.10(-3), respectively. We found that the variability of the transfer coefficients among the lakes could be associated with lake surface area. In flux parameterizations at lake surfaces, it is recommended to consider variations in the drag coefficient and Stanton number due to wind gustiness and capillary wave roughness while Dalton number could be considered as constant at all wind speeds. Plain Language Summary In our study, we investigate the bulk transfer coefficients, which are of particular importance for estimation the turbulent fluxes of momentum, heat and water vapor in the atmospheric surface layer, above lakes and reservoirs. The incorrect representation of the surface fluxes above inland waters can potentially lead to errors in weather and climate prediction models. For the first time we made this synthesis using a compiled data set consisting of existing eddy-covariance flux measurements over 23 lakes and 8 reservoirs. Our results revealed substantial increase of the transfer coefficients at low wind speeds, which is often not taken into account in models. The observed increase in the drag coefficient (momentum transfer coefficient) and Stanton number (heat transfer coefficient) could be associated with the presence of wind gusts and capillary waves. In flux parameterizations at lake surface, it is recommended to consider them for accurate flux representation. Although the bulk transfer coefficients were relatively constant at high wind speeds, we found that the Stanton number systematically exceeds the Dalton number (water vapor transfer coefficient), despite the fact they are typically considered to be equal. This difference may affect the Bowen ratio method and result in biased estimates of lake evaporation.
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| 3. |
- Jansen, Joachim, 1989-
(author)
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Carbon trace gas dynamics in subarctic lakes
- 2020
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Doctoral thesis (other academic/artistic)abstract
- Northern lakes are important sources of greenhouse gases carbon dioxide and methane to the atmosphere. Emissions are expected to increase as the climate continues to warm. Even so, lake carbon budgets are currently poorly constrained. This is in part because of a limited understanding of the processes that govern the flux. This thesis focuses on the physical and biogeochemical drivers of carbon trace gas emissions from three small, post-glacial lakes situated within the Stordalen Mire, a subarctic peatland underlain by thawing permafrost in northern Sweden. A unique, multiyear dataset is used to quantify the importance of different emission pathways – ebullition, turbulence-driven diffusion and release from storage – on short and long timescales. In summer and on seasonal to interannual timescales, emissions are robust functions of thermal energy input. Short-term storage-and-release cycles are governed by kinetic drivers, such as turbulence fuelled by wind shear and, to a lesser extent, by thermal convection. In winter, when the lakes are ice-covered, persistent anoxia and density-driven currents enable methane accumulation at rates exceeding summer emissions. Release at ice-off in spring can constitute the majority of annual methane emissions and scales predictably with ice-cover season length, except in warm winters when snowmelt displaces lake water. Most lake flux studies focus on the warmest summer months and omit the spring efflux, as well as emissions in the colder ice-free months which, because of the well-known temperature-dependency of carbon cycling processes, tend to be low. The latter sampling bias may lead to a substantial overestimation of the ice-free flux in regional and global lake emission budgets. Temperature proxies, potentially combined with gas transfer models, can efficiently gap-fill colder months to arrive at a more representative flux estimate, but important feedbacks, such as lake degassing with increasing wind speed, must be taken into account. The mechanisms emerging from intense study of the Stordalen lakes are likely to be found in a majority of northern lakes, which are small, seasonally ice-covered and of post-glacial origin. However, because gas transfer velocity and temperature sensitivity are spatiotemporally variable, field observations remain essential for the development and calibration of models, and to predict future emissions.
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| 4. |
- Jansen, Joachim, 1989-, et al.
(author)
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Global increase in methane production under future warming of lake bottom waters
- 2022
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In: Global Change Biology. - : John Wiley & Sons. - 1354-1013 .- 1365-2486. ; 28:18, s. 5427-5440
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Journal article (peer-reviewed)abstract
- Lakes are significant emitters of methane to the atmosphere, and thus are important components of the global methane budget. Methane is typically produced in lake sediments, with the rate of methane production being strongly temperature dependent. Local and regional studies highlight the risk of increasing methane production under future climate change, but a global estimate is not currently available. Here, we project changes in global lake bottom temperatures and sediment methane production rates from 1901 to 2099. By the end of the 21st century, lake bottom temperatures are projected to increase globally, by an average of 0.86-2.60 degrees C under Representative Concentration Pathways (RCPs) 2.6-8.5, with greater warming projected at lower latitudes. This future warming of bottom waters will likely result in an increase in methane production rates of 13%-40% by the end of the century, with many low-latitude lakes experiencing an increase of up to 17 times the historical (1970-1999) global average under RCP 8.5. The projected increase in methane production will likely lead to higher emissions from lakes, although the exact magnitude of the emission increase requires more detailed regional studies.
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| 5. |
- Kirchner, Nina, 1972-, et al.
(author)
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Water temperature, mixing, and ice phenology in the arctic-alpine Lake Darfáljávri (Lake Tarfala), northern Sweden
- 2024
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In: Arctic, Antarctic and Alpine research. - : Taylor & Francis. - 1523-0430 .- 1938-4246. ; 56:1
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Journal article (peer-reviewed)abstract
- In the rapidly warming circumpolar Arctic, recent research of lakes has focused on their climatology and ecology but is challenged by sparsity of wintertime data. At the c. 48-m-deep and c. 0.5-km2 large proglacial Darfaljavri (Lake Tarfala), located in an arctic-alpine environment in the Scandinavian Mountains, year-round water temperatures were previously reported for 2016 to 2019. Here, this record is continued for 2019-2020 and 2021-2022, complemented by time-lapse imagery records of the state of the lake surface, as well as degree-day modeling of ice phenology (timing of ice-on and ice-off). Darfaljavri is cryostratified during winter, with interannual variations in the thermocline's thickness and temperature range. The ice season lasts from October to July. Modeled ice-on dates match observed ones reasonably well; however, observed ice-off dates occur much later than modeled ones, likely because of cold impact from Darfaljavri's glacial environment as inferred from a comparison with a close tundra lake. Though new insights into the complex lake mixing and ice phenology are provided, it remains to attribute the characteristics of Darfaljavri's winter stratification to additional potential drivers, such as lake ice thickness, atmospheric heat fluxes, and the water balance of the lake.
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| 6. |
- Weyhenmeyer, Gesa A., et al.
(author)
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Towards critical white ice conditions in lakes under global warming.
- 2022
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1
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Journal article (peer-reviewed)abstract
- The quality of lake ice is of uppermost importance for ice safety and under-ice ecology, but its temporal and spatial variability is largely unknown. Here we conducted a coordinated lake ice quality sampling campaign across the Northern Hemisphere during one of the warmest winters since 1880 and show that lake ice during 2020/2021 commonly consisted of unstable white ice, at times contributing up to 100% to the total ice thickness. We observed that white ice increased over the winter season, becoming thickest and constituting the largest proportion of the ice layer towards the end of the ice cover season when fatal winter drownings occur most often and light limits the growth and reproduction of primary producers. We attribute the dominance of white ice before ice-off to air temperatures varying around the freezing point, a condition which occurs more frequently during warmer winters. Thus, under continued global warming, the prevalence of white ice is likely to substantially increase during the critical period before ice-off, for which we adjusted commonly used equations for human ice safety and light transmittance through ice.
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| 7. |
- Yuan, Kunxiaojia, et al.
(author)
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Causality guided machine learning model on wetland CH4 emissions across global wetlands
- 2022
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In: Agricultural and Forest Meteorology. - : Elsevier. - 0168-1923 .- 1873-2240. ; 324
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Journal article (peer-reviewed)abstract
- Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.
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