Limitations of Emergent Constraints on Multi-Model Projections: Case Study of Constraining Vegetation Productivity With Observed Greening Sensitivity

Abstract. Recent research on Emergent Constraints (EC) has delivered promising results. The method utilizes a measurable variable (predictor) from the recent historical past to obtain a constrained estimate of change in a difficult-to-measure variable (predictand) at a potential future CO2 concentration (forcing) from multi-model projections. This procedure critically depends on, first, accurate estimation of the predictor from observations and models, and second, on a robust relationship between inter-model variations in the predictor-predictand space. We investigate issues related to these two themes in this article, using vegetation greening sensitivity to CO2 forcing during the satellite era as a predictor of change in Gross Primary Productivity (GPP) of the Northern High Latitudes region (60° N–90° N, NHL) for a doubling of pre-industrial CO2 concentration in the atmosphere. Greening sensitivity is defined as changes in annual maximum of green leaf area index (LAImax) per unit CO2 forcing realized through its radiative and fertilization effects. We first address the question of how to realistically characterize the greening sensitivity of a large area, the NHL, from pixel-level LAImax data. This requires an investigation into uncertainties in LAImax data source and an evaluation of the spatial and temporal variability in greening sensitivity to forcing in both the data and model simulations. Second, the relationship between greening sensitivity and ΔGPP across the model ensemble depends on a strong coupling among simultaneous changes in GPP and LAImax. This coupling depends in a complex manner on the magnitude (level), time-rate of application (scenarios) and effects (radiative and/or fertilization) of CO2 forcing. We investigate how each one of these three aspects of forcing can impair the EC estimate of the predictand (ΔGPP). Accounting for uncertainties in greening sensitivity and stability of the relation between inter-model variations results in a quantitative estimate of the uncertainty (±0.2 Pg C yr−1) on constrained GPP enhancement (ΔGPP = +3.4 Pg C yr−1) for a doubling of pre-industrial atmospheric CO2 concentration in NHL. This ΔGPP is 60 % larger than the conventionally used average of model projections. The illustrated sources of uncertainty and limitations of the EC method go beyond carbon cycle research and are generally relevant for Earth system sciences.

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Investigating the applicability of emergent constraints

Earth System Dynamics ◽

10.5194/esd-10-501-2019 ◽

2019 ◽

Vol 10 (3) ◽

pp. 501-523 ◽

Cited By ~ 1

Author(s):

Alexander J. Winkler ◽

Ranga B. Myneni ◽

Victor Brovkin

Keyword(s):

Carbon Cycle ◽

Observational Data ◽

Temporal Variability ◽

Co2 Concentration ◽

Accurate Estimation ◽

Area Index ◽

High Latitude Region ◽

Emergent Constraints ◽

Data Source ◽

Co2 Forcing

Abstract. Recent research on emergent constraints (ECs) has delivered promising results in narrowing down uncertainty in climate predictions. The method utilizes a measurable variable (predictor) from the recent historical past to obtain a constrained estimate of change in an entity of interest (predictand) at a potential future CO2 concentration (forcing) from multi-model projections. This procedure first critically depends on an accurate estimation of the predictor from observations and models and second on a robust relationship between inter-model variations in the predictor–predictand space. Here, we investigate issues related to these two themes in a carbon cycle case study using observed vegetation greening sensitivity to CO2 forcing as a predictor of change in photosynthesis (gross primary productivity, GPP) for a doubling of preindustrial CO2 concentration. Greening sensitivity is defined as changes in the annual maximum of green leaf area index (LAImax) per unit CO2 forcing realized through its radiative and fertilization effects. We first address the question of how to realistically characterize the predictor of a large area (e.g., greening sensitivity in the northern high-latitude region) from pixel-level data. This requires an investigation into uncertainties in the observational data source and an evaluation of the spatial and temporal variability in the predictor in both the data and model simulations. Second, the predictor–predictand relationship across the model ensemble depends on a strong coupling between the two variables, i.e., simultaneous changes in GPP and LAImax. This coupling depends in a complex manner on the magnitude (level), time rate of application (scenarios), and effects (radiative and/or fertilization) of CO2 forcing. We investigate how each one of these three aspects of forcing can affect the EC estimate of the predictand (ΔGPP). Our results show that uncertainties in the EC method primarily originate from a lack of predictor comparability between observations and models, the observational data source, and temporal variability of the predictor. The disagreement between models on the mechanistic behavior of the system under intensifying forcing limits the EC applicability. The discussed limitations and sources of uncertainty in the EC method go beyond carbon cycle research and are generally applicable in Earth system sciences.

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Estimation of nighttime ecosystem respiration over a paddy field in China

Biogeosciences Discussions ◽

10.5194/bgd-7-1201-2010 ◽

2010 ◽

Vol 7 (1) ◽

pp. 1201-1232 ◽

Cited By ~ 1

Author(s):

M. S. Hossen ◽

T. Hiyama ◽

H. Tanaka

Keyword(s):

Paddy Field ◽

Ecosystem Respiration ◽

Co2 Flux ◽

Co2 Concentration ◽

Accurate Estimation ◽

Concentration Profiles ◽

Area Index ◽

Actual Measurement ◽

Paddy Ecosystem ◽

Storage Change

Abstract. Accurate estimation of terrestrial ecosystem respiration is crucial for developing regional- to global-scale carbon budget databases. This study evaluated nighttime ecosystem respiration under low turbulence conditions at a paddy field in China during the 2004 growing season. Data from turbulent flux with storage change and alternatively from CO2 concentration profiles measured from the surface to 32 m height were investigated and compared. Conditions were separated into windy and calm using a friction velocity (u∗) threshold. On calm nights, the vertical gradient of CO2 concentration was higher near the canopy level and decreased with height. No differences were detected in terms of quantity and seasonality between the eddy covariance-observed nighttime ecosystem respiration (Re) and the alternatively calculated Re under calm conditions. Nighttime underestimation of paddy ecosystem respiration was low, even under calm conditions. Under stable atmospheric conditions, nighttime "loss" of CO2 flux may result mainly from CO2 being stored in air below the sensor height, and CO2 drainage loss could be small because advection is small. Because the addition of measurement-height storage change is preferable for reducing nighttime underestimation, u∗ filtering and low turbulence data elimination are not required for the paddy ecosystem. Alternatively, under low turbulence conditions, nighttime flux can be calculated from concentration profiles, but actual measurement of the nocturnal boundary layer height is very important. For gap-filling of nighttime CO2 flux data for a paddy ecosystem, development of multiple regression functions based on the crop biomass/leaf area index in association with field water status is preferable to a single regression function using air/soil temperature.

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Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Plant Methods ◽

10.1186/s13007-020-00693-3 ◽

2020 ◽

Vol 16 (1) ◽

Author(s):

Shanjun Luo ◽

Yingbin He ◽

Qian Li ◽

Weihua Jiao ◽

Yaqiu Zhu ◽

...

Keyword(s):

Precision Agriculture ◽

Growth Stage ◽

Estimation Error ◽

Potato Yield ◽

Hyperspectral Data ◽

Single Stage ◽

Accurate Estimation ◽

Yield Estimation ◽

Area Index ◽

Red Edge

Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

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Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?

Earth System Dynamics ◽

10.5194/esd-11-1233-2020 ◽

2020 ◽

Vol 11 (4) ◽

pp. 1233-1258

Author(s):

Manuel Schlund ◽

Axel Lauer ◽

Pierre Gentine ◽

Steven C. Sherwood ◽

Veronika Eyring

Keyword(s):

Climate Sensitivity ◽

Statistical Significance ◽

Coupled Model ◽

Surface Air Temperature ◽

Co2 Concentration ◽

Intergovernmental Panel ◽

Equilibrium Climate Sensitivity ◽

Emergent Constraints ◽

Statistical Relationships ◽

Emergent Constraint

Abstract. An important metric for temperature projections is the equilibrium climate sensitivity (ECS), which is defined as the global mean surface air temperature change caused by a doubling of the atmospheric CO2 concentration. The range for ECS assessed by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report is between 1.5 and 4.5 K and has not decreased over the last decades. Among other methods, emergent constraints are potentially promising approaches to reduce the range of ECS by combining observations and output from Earth System Models (ESMs). In this study, we systematically analyze 11 published emergent constraints on ECS that have mostly been derived from models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) project. These emergent constraints are – except for one that is based on temperature variability – all directly or indirectly based on cloud processes, which are the major source of spread in ECS among current models. The focus of the study is on testing if these emergent constraints hold for ESMs participating in the new Phase 6 (CMIP6). Since none of the emergent constraints considered here have been derived using the CMIP6 ensemble, CMIP6 can be used for cross-checking of the emergent constraints on a new model ensemble. The application of the emergent constraints to CMIP6 data shows a decrease in skill and statistical significance of the emergent relationship for nearly all constraints, with this decrease being large in many cases. Consequently, the size of the constrained ECS ranges (66 % confidence intervals) widens by 51 % on average in CMIP6 compared to CMIP5. This is likely because of changes in the representation of cloud processes from CMIP5 to CMIP6, but may in some cases also be due to spurious statistical relationships or a too small number of models in the ensemble that the emergent constraint was originally derived from. The emergently- constrained best estimates of ECS also increased from CMIP5 to CMIP6 by 12 % on average. This can be at least partly explained by the increased number of high-ECS (above 4.5 K) models in CMIP6 without a corresponding change in the constraint predictors, suggesting the emergence of new feedback processes rather than changes in strength of those previously dominant. Our results support previous studies concluding that emergent constraints should be based on an independently verifiable physical mechanism, and that process-based emergent constraints on ECS should rather be thought of as constraints for the process or feedback they are actually targeting.

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An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI

Functional Plant Biology ◽

10.1071/fp08045 ◽

2008 ◽

Vol 35 (10) ◽

pp. 1070 ◽

Cited By ~ 46

Author(s):

Sigfredo Fuentes ◽

Anthony R. Palmer ◽

Daniel Taylor ◽

Melanie Zeppel ◽

Rhys Whitley ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Gap Analysis ◽

Digital Camera ◽

Field Measurements ◽

Accurate Estimation ◽

Image Capture ◽

Area Index ◽

Modis Lai ◽

Matlab Programming

Leaf area index (LAI) is one of the most important variables required for modelling growth and water use of forests. Functional–structural plant models use these models to represent physiological processes in 3-D tree representations. Accuracy of these models depends on accurate estimation of LAI at tree and stand scales for validation purposes. A recent method to estimate LAI from digital images (LAID) uses digital image capture and gap fraction analysis (Macfarlane et al. 2007b) of upward-looking digital photographs to capture canopy LAID (cover photography). After implementing this technique in Australian evergreen Eucalyptus woodland, we have improved the method of image analysis and replaced the time consuming manual technique with an automated procedure using a script written in MATLAB 7.4 (LAIM). Furthermore, we used this method to compare MODIS LAI values with LAID values for a range of woodlands in Australia to obtain LAI at the forest scale. Results showed that the MATLAB script developed was able to successfully automate gap analysis to obtain LAIM. Good relationships were achieved when comparing averaged LAID and LAIM (LAIM = 1.009 – 0.0066 LAID; R2 = 0.90) and at the forest scale, MODIS LAI compared well with LAID (MODIS LAI = 0.9591 LAID – 0.2371; R2 = 0.89). This comparison improved when correcting LAID with the clumping index to obtain effective LAI (MODIS LAI = 1.0296 LAIe + 0.3468; R2 = 0.91). Furthermore, the script developed incorporates a function to connect directly a digital camera, or high resolution webcam, from a laptop to obtain cover photographs and LAI analysis in real time. The later is a novel feature which is not available on commercial LAI analysis softwares for cover photography. This script is available for interested researchers.

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Effects of elevated CO2 concentration on photosynthetic acclimation and productivity of two potato cultivars grown in open-top chambers

Functional Plant Biology ◽

10.1071/pp99205 ◽

2000 ◽

Vol 27 (12) ◽

pp. 1119 ◽

Cited By ~ 8

Author(s):

Ad H.C.M. Schapendonk ◽

Marcel van Oijen ◽

Paul Dijkstra ◽

C. Sander Pot ◽

Wilco J.R.M. Jordi ◽

...

Keyword(s):

Elevated Co2 ◽

Potato Cultivar ◽

Co2 Concentration ◽

Growing Season ◽

Photosynthetic Acclimation ◽

Area Index ◽

Open Top Chambers ◽

Fluorescence Measurements ◽

Low Co2 ◽

Rate Of Photosynthesis

In two subsequent years, an early maturing potato cultivar with low leaf area index (LAI) and a late cultivar with high LAI were grown at concentrations of 350 and 700 L CO2 L–1 in open-top chambers. The average increase of tuber dry matter yield by elevated CO2 was 27% in 1995 and 49% in 1996. During the first weeks after planting, elevated CO2 stimulated the light-saturated rate of photosynthesis (Amax) of both cultivars by 80%. However, Amax under elevated CO2 declined to the level of the low-CO2 treatment in the course of the growing season. In 1995 this convergence due to acclimation of photosynthesis was completed within 6 weeks, but in 1996, acclimation proceeded until the end of the growing season. Photosynthetic acclimation was accompanied by a reduced Rubisco content, and was correlated more closely with accumulation of sucrose than of starch. From fluorescence measurements it was concluded that, in contrast to the carboxylation efficiency, the efficiency of photosynthetic reactions centers was not affected by acclimation to elevated CO2. The faster photosynthetic acclimation in 1995 coincided with overall lower values of Amax, crop growth rate and growth response to elevated CO2. It is shown that the indeterminate growth pattern of potato with its large sink capacity does not preclude acclimation. The effect of acclimation on yield was quantified by computer simulations. The simulated results indicated that photosynthetic acclimation reduced the positive effect of elevated CO2 on tuber yield by 50%.

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Patterns of Carbon Sequestration in a Young Forest Ecosystem after Clear-Cutting

Forests ◽

10.3390/f11020126 ◽

2020 ◽

Vol 11 (2) ◽

pp. 126 ◽

Cited By ~ 3

Author(s):

Sille Rebane ◽

Kalev Jõgiste ◽

Andres Kiviste ◽

John A. Stanturf ◽

Marek Metslaid

Keyword(s):

Forest Ecosystem ◽

Net Ecosystem Exchange ◽

Co2 Concentration ◽

Co2 Exchange ◽

Large Area ◽

Clear Cut ◽

Clear Cutting ◽

Young Stand ◽

Young Forest ◽

Measurement Period

A large area of Estonian hemiboreal forest is recovering from clear-cut harvesting and changing carbon (C) balance of the stands. However, there is a lack of information about C- source/sink relationships during recovery of such stands. The eddy covariance technique was used to estimate C-status through net ecosystem exchange (NEE) of CO2 in two stands of different development stages located in southeast Estonia in 2014. Measured summertime (June–September) mean CO2 concentration was 337.75 ppm with mean NEE −1.72 µmol m−2 s−1. June NEE was −4.60 µmol m−2 s−1; July, August, and September NEE was −1.17, −0.77, and −0.25 µmol m−2 s−1, respectively. The two stands had similar patterns of CO2 exchange; measurement period temperature drove NEE. Our results show that after clear-cutting a 6-year-old forest ecosystem was a light C-sink and 8-year-old young stand demonstrated a stronger C-sink status during the measurement period.

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Assessment of Climate Change Impact on Crop yield and Evaluation of Coping Strategy using Crop Growth Simulation Model

Journal of the Institute of Engineering ◽

10.3126/jie.v15i1.27719 ◽

2020 ◽

Vol 15 (1) ◽

pp. 106-122

Author(s):

J. Alam ◽

R. K. Panda

Keyword(s):

Climate Change ◽

Grain Yield ◽

Simulation Model ◽

Leaf Area Index ◽

Leaf Area ◽

Co2 Concentration ◽

Maize Yield ◽

Crop Growth ◽

Climate Change Scenarios ◽

Area Index

Any change in climate will have implications for climate-sensitive systems such as agriculture, forestry and some other natural resources. Changes in solar radiation, temperature and precipitation will produce changes in crop yields and hence economics of agriculture. It is possible to understand the phenomenon of climate change on crop production and to develop adaptation strategies for sustainability in food production, using a suitable crop simulation model. CERES-Maize model of DSSAT v4.0 was used to simulate the maize yield of the region under climate change scenarios using the historical weather data at Kharagpur (1977-2007), Damdam (1974-2003) and Purulia (1986-2000), West Bengal, India. The model was calibrated using the crop experimental data, climate data and soil data for two years (1996-1997) and was validated by using the data of the year 1998 at Kharagpur. The change in values of weather parameters due to climate change and its effects on the maize crop growth and yield was studied. It was observed that increase in mean temperature and leaf area index have negative impacts on maize yield. When the maximum leaf area index increased, the grain yield was found to be decreased. Increase in CO2 concentration with each degree incremental temperature decreased the grain yield but increase in CO2 concentration with fixed temperature increased the maize yield. Adjustments were made in the date of sowing to investigate suitable option for adaptation under the future climate change scenarios. Highest yield was obtained when the sowing date was advanced by a week at Kharagpur and Damdam whereas for Purulia, the experimental date of sowing was found to be beneficial.

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Effects of high spatial and temporal resolution Earth observations on simulated hydrometeorological variables in a cropland (southwestern France)

Hydrology and Earth System Sciences ◽

10.5194/hess-21-5693-2017 ◽

2017 ◽

Vol 21 (11) ◽

pp. 5693-5708 ◽

Cited By ~ 3

Author(s):

Jordi Etchanchu ◽

Vincent Rivalland ◽

Simon Gascoin ◽

Jérôme Cros ◽

Tiphaine Tallec ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Surface ◽

Agricultural Landscapes ◽

Surface Model ◽

Spatial And Temporal Variability ◽

Area Index ◽

The Impact ◽

Sentinel 2

Abstract. Agricultural landscapes are often constituted by a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes and must be taken into account in simulations of land surface and distributed hydrological models. The Sentinel-2 mission allows for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy fluxes via the Interactions between the Surface Biosphere Atmosphere (ISBA) land surface model included in the EXternalized SURface (SURFEX) modeling platform. The study focuses on the effect of the leaf area index (LAI) spatial and temporal variability on these fluxes. We compare the use of the LAI climatology from ECOCLIMAP-II, used by default in SURFEX-ISBA, and time series of LAI derived from the high-resolution Formosat-2 satellite data (8 m). The study area is an agricultural zone in southwestern France covering 576 km2 (24 km  ×  24 km). An innovative plot-scale approach is used, in which each computational unit has a homogeneous vegetation type. Evaluation of the simulations quality is done by comparing model outputs with in situ eddy covariance measurements of latent heat flux (LE). Our results show that the use of LAI derived from high-resolution remote sensing significantly improves simulated evapotranspiration with respect to ECOCLIMAP-II, especially when the surface is covered with summer crops. The comparison with in situ measurements shows an improvement of roughly 0.3 in the correlation coefficient and a decrease of around 30 % of the root mean square error (RMSE) in the simulated evapotranspiration. This finding is attributable to a better description of LAI evolution processes with Formosat-2 data, which further modify soil water content and drainage of soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels with simulations using Formosat-2 data in comparison to the reference simulation values. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

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Towards Resolving Spatial and Temporal Greenhouse Gas Dynamics across a Heterogeneous Arctic Tundra Landscape in the Western Canadian Arctic

10.5194/egusphere-egu2020-19864 ◽

2020 ◽

Author(s):

Carolina Voigt ◽

Gabriel Hould Gosselin ◽

Andrew Black ◽

Charles Chevrier-Dion ◽

Charlotte Marquis ◽

...

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Landscape Scale ◽

The Arctic ◽

Nutrient Concentrations ◽

Small Scale ◽

Active Layer Thickness ◽

Spatial And Temporal Variability ◽

Area Index ◽

Chamber Measurements

The Arctic is currently warming faster than the rest of the world. Warming and associated permafrost thaw in Arctic landscapes may mobilize large pools of carbon (C) and nitrogen (N) and ultimately increase the atmospheric burden of the greenhouse gases (GHGs) carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Arctic GHG dynamics and their environmental and hydrological controls are poorly understood. Whether Arctic landscapes act as a net GHG source or sink depends on the complex and spatially varying interactions between hydrology, active layer thickness, topography, temperature, vegetation, substrate availability and microbial dynamics.Our study site, Trail Valley Creek (68&#176;44&#8217; N, 133&#176;29&#8217; W), is an upland tundra site characterized by small-scale (<10 m) land cover and soil type (mineral and organic) heterogeneity consisting of different land cover types: shrub, tussock and lichen patches, polygonal tundra and thermokarst-affected areas, wetlands, lakes, and streams. To understand the large spatial and temporal variability of GHG dynamics across these terrestrial and aquatic landcover types we use a nested observational approach at plot- (<1 m2), ecosystem- (~10 m2), landscape- (~100 m2) and regional (~50 km2) scale. Existing (since 2013) ecosystem-scale eddy covariance (EC) measurements of net CO2 and CH4 exchanges are complemented with landscape-scale EC measurements and plot-scale automated and manual chamber measurements within the EC tower footprint and beyond. To increase process-based understanding we complement these multi-scale GHG flux observations with a wide array of auxiliary measurements including soil profile dynamics of CO2, CH4, N2O, and oxygen, lake and soil pore nutrient concentrations, soil temperature and moisture profiles, thaw depth, leaf area index (LAI), normalized difference vegetation index (NDVI), lake catchment characteristics, and quality and microbial degradability of aquatic dissolved organic matter.Preliminary results from manual chamber measurements show that tussocks were the largest net CO2 sink during the growing season. While the majority of terrestrial landcover types showed small but consistent and seasonally varying CH4 uptake, lake shore and thermokarst-affected areas displayed high nutrient loads and were hotspots of CH4 emissions. Therefore, capturing the landscape heterogeneity, areal coverage and hydrological connectivity of terrestrial and aquatic landcover types is important and our study highlights the need to combine belowground, plot-, ecosystem- and landscape-scale measurements to understand biosphere-atmosphere interactions in the Arctic.

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