| 1. |
- Mbufong, H. N., et al.
(författare)
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Assessing the spatial variability in peak season CO2 exchange characteristics across the Arctic tundra using a light response curve parameterization
- 2014
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Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4189. ; 11:17, s. 4897-4912
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Tidskriftsartikel (refereegranskat)abstract
- This paper aims to assess the spatial variability in the response of CO2 exchange to irradiance across the Arctic tundra during peak season using light response curve (LRC) parameters. This investigation allows us to better understand the future response of Arctic tundra under climatic change. Peak season data were collected during different years (between 1998 and 2010) using the micrometeorological eddy covariance technique from 12 circumpolar Arctic tundra sites, in the range of 64-74 degrees N. The LRCs were generated for 14 days with peak net ecosystem exchange (NEE) using an NEE-irradiance model. Parameters from LRCs represent site-specific traits and characteristics describing the following: (a) NEE at light saturation (F-csat), (b) dark respiration (Rd), (c) light use efficiency (alpha), (d) NEE when light is at 1000 mu molm(-2) s(-1) (F-c1000), (e) potential photosynthesis at light saturation (P-sat) and (f) the light compensation point (LCP). Parameterization of LRCs was successful in predicting CO2 flux dynamics across the Arctic tundra. We did not find any trends in LRC parameters across the whole Arctic tundra but there were indications for temperature and latitudinal differences within sub-regions like Russia and Greenland. Together, leaf area index (LAI) and July temperature had a high explanatory power of the variance in assimilation parameters (F-csat, F-c1000 and P-sat), thus illustrating the potential for upscaling CO2 exchange for the whole Arctic tundra. Dark respiration was more variable and less correlated to environmental drivers than were assimilation parameters. This indicates the inherent need to include other parameters such as nutrient availability, substrate quantity and quality in flux monitoring activities.
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| 2. |
- Dengel, S., et al.
(författare)
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Testing the applicability of neural networks as a gap-filling method using CH4 flux data from high latitude wetlands
- 2013
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Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4189. ; 10, s. 8185-8200
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Tidskriftsartikel (refereegranskat)abstract
- Since the advancement in CH4 gas analyser technology and its applicability to eddy covariance flux measurements, monitoring of CH4 emissions is becoming more widespread. In order to accurately determine the greenhouse gas balance, high quality gap-free data is required. Currently there is still no consensus on CH4 gap-filling methods, and methods applied are still study-dependent and often carried out on low resolution, daily data. In the current study, we applied artificial neural networks to six distinctively different CH4 time series from high latitudes, explain the method and test its functionality. We discuss the applicability of neural networks in CH4 flux studies, the advantages and disadvantages of this method, and what information we were able to extract from such models. Three different approaches were tested by including drivers such as air and soil temperature, barometric air pressure, solar radiation, wind direction (indicator of source location) and in addition the lagged effect of water table depth and precipitation. In keeping with the principle of parsimony, we included up to five of these variables traditionally measured at CH4 flux measurement sites. Fuzzy sets were included representing the seasonal change and time of day. High Pearson correlation coefficients (r) of up to 0.97 achieved in the final analysis are indicative for the high performance of neural networks and their applicability as a gap-filling method for CH4 flux data time series. This novel approach which we show to be appropriate for CH4 fluxes is a step towards standardising CH4 gap-filling protocols.
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| 3. |
- Fisher, J. B., et al.
(författare)
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Carbon cycle uncertainty in the Alaskan Arctic
- 2014
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Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4189. ; 11:15, s. 4271-4288
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Tidskriftsartikel (refereegranskat)abstract
- Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for the Alaskan Arctic from four recent model intercomparison projects - NACP (North American Carbon Program) site and regional syntheses, TRENDY (Trends in net land atmosphere carbon exchanges), and WETCHIMP (Wetland and Wetland CH4 Inter-comparison of Models Project) - we provide a baseline of terrestrial carbon cycle uncertainty, defined as the multi-model standard deviation (sigma) for each quantity that follows. Mean annual absolute uncertainty was largest for soil carbon (14.0+/-9.2 kgCm(-2)), then gross primary production (GPP) (0.22+/-0.50 kgCm(-2) yr(-1)), ecosystem respiration (Re) (0.23+/-0.38 kgCm(-2) yr(-1)), net primary production (NPP) (0.14+/-0.33 kgCm(-2) yr(-1)), autotrophic respiration (Ra) (0.09+/-0.20 kgCm(-2) yr(-1)), heterotrophic respiration (Rh) (0.14+/-0.20 kgCm(-2) yr(-1)), net ecosystem exchange (NEE) (-0.01+/-0.19 kgCm(-2) yr(-1)), and CH4 flux (2.52+/-4.02 g CH4 m(-2) yr(-1)). There were no consistent spatial patterns in the larger Alaskan Arctic and boreal regional carbon stocks and fluxes, with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as carbon neutral. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic and larger boreal region.
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| 4. |
- McGuire, A. D., et al.
(författare)
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An assessment of the carbon balance of Arctic tundra: comparisons among observations, process models, and atmospheric inversions
- 2012
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Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4189. ; 9:8, s. 3185-3204
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Tidskriftsartikel (refereegranskat)abstract
- Although Arctic tundra has been estimated to cover only 8% of the global land surface, the large and potentially labile carbon pools currently stored in tundra soils have the potential for large emissions of carbon (C) under a warming climate. These emissions as radiatively active greenhouse gases in the form of both CO2 and CH4 could amplify global warming. Given the potential sensitivity of these ecosystems to climate change and the expectation that the Arctic will experience appreciable warming over the next century, it is important to assess whether responses of C exchange in tundra regions are likely to enhance or mitigate warming. In this study we compared analyses of C exchange of Arctic tundra between 1990 and 2006 among observations, regional and global applications of process-based terrestrial biosphere models, and atmospheric inversion models. Syntheses of flux observations and inversion models indicate that the annual exchange of CO2 between Arctic tundra and the atmosphere has large uncertainties that cannot be distinguished from neutral balance. The mean estimate from an ensemble of process-based model simulations suggests that Arctic tundra has acted as a sink for atmospheric CO2 in recent decades, but based on the uncertainty estimates it cannot be determined with confidence whether these ecosystems represent a weak or a strong sink. Tundra was 0.6 A degrees C warmer in the 2000s compared to the 1990s. The central estimates of the observations, process-based models, and inversion models each identify stronger sinks in the 2000s compared with the 1990s. Some of the process models indicate that this occurred because net primary production increased more in response to warming than heterotrophic respiration. Similarly, the observations and the applications of regional process-based models suggest that CH4 emissions from Arctic tundra have increased from the 1990s to 2000s because of the sensitivity of CH4 emissions to warmer temperatures. Based on our analyses of the estimates from observations, process-based models, and inversion models, we estimate that Arctic tundra was a sink for atmospheric CO2 of 110 Tg C yr(-1) (uncertainty between a sink of 291 Tg C yr(-1) and a source of 80 Tg C yr(-1)) and a source of CH4 to the atmosphere of 19 Tg C yr(-1) (uncertainty between sources of 8 and 29 Tg C yr(-1)). The suite of analyses conducted in this study indicate that it is important to reduce uncertainties in the observations, process-based models, and inversions in order to better understand the degree to which Arctic tundra is influencing atmospheric CO2 and CH4 concentrations. The reduction of uncertainties can be accomplished through (1) the strategic placement of more CO2 and CH4 monitoring stations to reduce uncertainties in inversions, (2) improved observation networks of ground-based measurements of CO2 and CH4 exchange to understand exchange in response to disturbance and across gradients of climatic and hydrological variability, and (3) the effective transfer of information from enhanced observation networks into process-based models to improve the simulation of CO2 and CH4 exchange from Arctic tundra to the atmosphere.
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| 5. |
- Watts, J. D., et al.
(författare)
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A satellite data driven biophysical modeling approach for estimating northern peatland and tundra CO2 and CH4 fluxes
- 2014
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Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4189. ; 11:7, s. 1961-1980
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Tidskriftsartikel (refereegranskat)abstract
- The northern terrestrial net ecosystem carbon balance (NECB) is contingent on inputs from vegetation gross primary productivity (GPP) to offset the ecosystem respiration (R-eco) of carbon dioxide (CO2) and methane (CH4) emissions, but an effective framework to monitor the regional Arctic NECB is lacking. We modified a terrestrial carbon flux (TCF) model developed for satellite remote sensing applications to evaluate wetland CO2 and CH4 fluxes over pan-Arctic eddy covariance (EC) flux tower sites. The TCF model estimates GPP, CO2 and CH4 emissions using in situ or remote sensing and reanalysis-based climate data as inputs. The TCF model simulations using in situ data explained >70% of the r(2) variability in the 8 day cumulative EC measured fluxes. Model simulations using coarser satellite (MODIS) and reanalysis (MERRA) Records accounted for approximately 69% and 75% of the respective r(2) variability in the tower CO2 and CH4 records, with corresponding RMSE uncertainties of <= 1.3 gCm(-2) d(-1) (CO2) and 18.2 mg Cm-2 d(-1) (CH4). Although the estimated annual CH4 emissions were small (<18 gCm(-2) yr(-1)) relative to R-eco (>180 gCm(-2) yr(-1)), they reduced the across-site NECB by 23% and contributed to a global warming potential of approximately 165 +/- 128 gCO(2)eqm(-2) yr(-1) when considered over a 100 year time span. This model evaluation indi-cates a strong potential for using the TCF model approach to document landscape-scale variability in CO2 and CH4 fluxes, and to estimate the NECB for northern peatland and tundra ecosystems.
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