Search

The continued warming of the Arctic could release vast stores of carbon into the atmosphere from high-latitude ecosystems, especially from thawing permafrost. Increasing uptake of carbon dioxide (CO2) by vegetation during longer growing seasons may partially offset such release of carbon. However, evidence of significant net annual release of carbon from site-level observations and model simulations across tundra ecosystems has been inconclusive. To address this knowledge gap, we combined top-down observations of atmospheric CO2 concentration enhancements from aircraft and a tall tower, which integrate ecosystem exchange over large regions, with bottom-up observed CO2 fluxes from tundra environments and found that the Alaska North Slope is not a consistent net source nor net sink of CO2 to the atmosphere (ranging from -6 to +6TgCyr-1 for 2012-2017). Our analysis suggests that significant biogenic CO2 fluxes from unfrozen terrestrial soils, and likely inland waters, during the early cold season (September-December) are major factors in determining the net annual carbon balance of the North Slope, implying strong sensitivity to the rapidly warming freeze-up period. At the regional level, we find no evidence of the previously reported large late-cold-season (January-April) CO2 emissions to the atmosphere during the study period. Despite the importance of the cold-season CO2 emissions to the annual total, the interannual variability in the net CO2 flux is driven by the variability in growing season fluxes. During the growing season, the regional net CO2 flux is also highly sensitive to the distribution of tundra vegetation types throughout the North Slope. This study shows that quantification and characterization of year-round CO2 fluxes from the heterogeneous terrestrial and aquatic ecosystems in the Arctic using both site-level and atmospheric observations are important to accurately project the Earth system response to future warming.

The continued warming of the Arctic could release vast stores of carbon into the atmosphere from high-latitude ecosystems, especially from thawing permafrost. Increasing uptake of carbon dioxide (CO2) by vegetation during longer growing seasons may partially offset such release of carbon. However, evidence of significant net annual release of carbon from site-level observations and model simulations across tundra ecosystems has been inconclusive. To address this knowledge gap, we combined top-down observations of atmospheric CO2 concentration enhancements from aircraft and a tall tower, which integrate ecosystem exchange over large regions, with bottom-up observed CO2 fluxes from tundra environments and found that the Alaska North Slope is not a consistent net source nor net sink of CO2 to the atmosphere (ranging from −6 to +6 Tg C yr−1 for 2012–2017). Our analysis suggests that significant biogenic CO2 fluxes from unfrozen terrestrial soils, and likely inland waters, during the early cold season (September–December) are major factors in determining the net annual carbon balance of the North Slope, implying strong sensitivity to the rapidly warming freeze-up period. At the regional level, we find no evidence of the previously reported large late-cold-season (January–April) CO2 emissions to the atmosphere during the study period. Despite the importance of the cold-season CO2 emissions to the annual total, the interannual variability in the net CO2 flux is driven by the variability in growing season fluxes. During the growing season, the regional net CO2 flux is also highly sensitive to the distribution of tundra vegetation types throughout the North Slope. This study shows that quantification and characterization of year-round CO2 fluxes from the heterogeneous terrestrial and aquatic ecosystems in the Arctic using both site-level and atmospheric observations are important to accurately project the Earth system response to future warming.

The warming of the Arctic is affecting the carbon cycle of tundra ecosystems. Most research on carbon fluxes from Arctic tundra ecosystems has focused on abiotic environmental controls (e.g., temperature, rainfall, or radiation). However, Arctic tundra vegetation, and therefore the carbon balance of these ecosystems, can be substantially impacted by herbivory. In this study we tested how vegetation consumption by brown lemmings (Lemmus trimucronatus) can impact carbon exchange of a wet-sedge tundra ecosystem near Utqiaġvik, Alaska during the summer and the recovery of vegetation during the following summer. We placed brown lemmings in individual enclosure plots and tested the impact of lemmings' herbivory on carbon dioxide (CO2) fluxes, methane (CH4) fluxes, and the normalized difference vegetation index (NDVI) immediately after lemming removal and during the following growing season. During the first summer of the experiment, lemmings' herbivory reduced plant biomass (as shown by the decrease in the NDVI) and decreased net CO2 uptake while not significantly impacting CH4 emissions. CH4 emissions were likely not significantly affected due to CH4 being produced deeper in the soil and escaping from the stem bases of the vascular plants. The summer following the lemming treatments, NDVI and net CO2 fluxes returned to magnitudes similar to those observed before the start of the experiment, suggesting a complete recovery of the vegetation and a transitory nature of the impact of lemming herbivory. Overall, lemming herbivory has short-term but substantial effects on carbon sequestration by vegetation and might contribute to the considerable interannual variability in CO2 fluxes from tundra ecosystems.

The contribution of soil heterotrophic respiration to the boreal–Arctic carbon (CO2) cycle and its potential feedback to climate change remains poorly quantified. We developed a remote-sensing-driven permafrost carbon model at intermediate scale (∼1 km) to investigate how environmental factors affect the magnitude and seasonality of soil heterotrophic respiration in Alaska. The permafrost carbon model simulates snow and soil thermal dynamics and accounts for vertical soil carbon transport and decomposition at depths up to 3 m below the surface. Model outputs include soil temperature profiles and carbon fluxes at 1 km resolution spanning the recent satellite era (2001–2017) across Alaska. Comparisons with eddy covariance tower measurements show that the model captures the seasonality of carbon fluxes, with favorable accuracy in simulating net ecosystem CO2 exchange (NEE) for both tundra (R>0.8, root mean square error (RMSE – 0.34 g C m−2 d−1), and boreal forest (R>0.73; RMSE – 0.51 g C m−2 d−1). Benchmark assessments using two regional in situ data sets indicate that the model captures the complex influence of snow insulation on soil temperature and the temperature sensitivity of cold-season soil heterotrophic respiration. Across Alaska, we find that seasonal snow cover imposes strong controls on the contribution from different soil depths to total soil heterotrophic respiration. Earlier snowmelt in spring promotes deeper soil warming and enhances the contribution of deeper soils to total soil heterotrophic respiration during the later growing season, thereby reducing net ecosystem carbon uptake. Early cold-season soil heterotrophic respiration is closely linked to the number of snow-free days after the land surface freezes (R=-0.48, p), i.e., the delay in snow onset relative to surface freeze onset. Recent trends toward earlier autumn snow onset in northern Alaska promote a longer zero-curtain period and enhanced cold-season respiration. In contrast, southwestern Alaska shows a strong reduction in the number of snow-free days after land surface freeze onset, leading to earlier soil freezing and a large reduction in cold-season soil heterotrophic respiration. Our results also show nonnegligible influences of subgrid variability in surface conditions on the model-simulated CO2 seasonal cycle, especially during the early cold season at 10 km scale. Our results demonstrate the critical role of snow cover affecting the seasonality of soil temperature and respiration and highlight the challenges of incorporating these complex processes into future projections of the boreal–Arctic carbon cycle.

Wetlands are the largest global natural methane (CH4/ source, and emissions between 50 and 70° N latitude contribute 10-30% to this source. Predictive capability of land models for northern wetland CH4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with siteto regional-scale observations. A comparison of the CH4 fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH4 emissions when compared with CarbonTracker CH4 predictions. CLM4.5 CH4 emission predictions agree well with growing season (May-September) CarbonTracker Alaskan regional-level CH4 predictions and sitelevel observations. However, CLM4.5 underestimated CH4 emissions in the cold season (October-April). The monthly atmospheric CH4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH4 mole fraction measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH4 emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH4 cycle are from the wetland extent, coldseason CH4 production and CH4 transport processes. We recommend more cold-season experimental studies in highlatitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements.

Wetlands are the largest global natural methane (CH4) source, and emissions between 50 and 70° N latitude contribute 10–30 % to this source. Predictive capability of land models for northern wetland CH4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with site- to regional-scale observations. A comparison of the CH4 fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH4 emissions when compared with CarbonTracker CH4 predictions. CLM4.5 CH4 emission predictions agree well with growing season (May–September) CarbonTracker Alaskan regional-level CH4 predictions and site-level observations. However, CLM4.5 underestimated CH4 emissions in the cold season (October–April). The monthly atmospheric CH4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH4 mole fraction measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH4 emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH4 cycle are from the wetland extent, cold-season CH4 production and CH4 transport processes. We recommend more cold-season experimental studies in high-latitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements.

The importance and consequences of extreme events on the global carbon budget are inadequately understood. This includes the differential impact of extreme events on various ecosystem components, lag effects, recovery times, and compensatory processes. In the summer of 2007 in Barrow, Arctic Alaska, there were unusually high air temperatures (the fifth warmest summer over a 65-year period) and record low precipitation (the lowest over a 65-year period). These abnormal conditions were associated with substantial desiccation of the Sphagnum layer and a reduced net Sphagnum CO2 sink but did not affect net ecosystem exchange (NEE) from this wet-sedge arctic tundra ecosystem. Microbial biomass, NH4+ availability, gross primary production (GPP), and ecosystem respiration (Reco) were generally greater during this extreme summer. The cumulative ecosystem CO2 sink in 2007 was similar to the previous summers, suggesting that vascular plants were able to compensate for Sphagnum CO2 uptake, despite the impact on other functions and structure such as desiccation of the Sphagnum layer. Surprisingly, the lowest ecosystem CO2 sink over a five summer record (2005–2009) was observed during the 2008 summer (~70% lower), directly following the unusually warm and dry summer, rather than during the extreme summer. This sink reduction cannot solely be attributed to the potential damage to mosses, which typically contribute ~40% of the entire ecosystem CO2 sink. Importantly, the return to a substantial cumulative CO2 sink occurred two summers after the extreme event, which suggests a substantial resilience of this tundra ecosystem to at least an isolated extreme event. Overall, these results show a complex response of the CO2 sink and its sub-components to atypically warm and dry conditions. The impact of multiple extreme events requires further investigation.

The northern terrestrial net ecosystem carbon balance (NECB) is contingent on inputs from vegetation gross primary productivity (GPP) to offset the ecosystem respiration (Reco) 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 r2 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 r2 variability in the tower CO2 and CH4 records, with corresponding RMSE uncertainties of &leq; 1.3 g C m−2 d−1 (CO2) and 18.2 mg C m−2 d−1 (CH4). Although the estimated annual CH4 emissions were small (< 18 g C m−2 yr−1) relative to Reco (> 180 g C m−2 yr−1), they reduced the across-site NECB by 23% and contributed to a global warming potential of approximately 165 ± 128 g CO2eq m−2 yr−1 when considered over a 100 year time span. This model evaluation indicates 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.

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.

Accurate estimates of annual budgets of methane (CH4) efflux in arctic regions are severely constrained by the paucity of non-summer measurements. Moreover, the incomplete understanding of the ecosystem-level sensitivity of CH4 emissions to changes in tundra moisture makes prediction of future CH4 release from the Arctic extremely difficult. This study addresses some of these research gaps by presenting an analysis of eddy covariance and chamber measurements of CH4 efflux and supporting environmental variables during the autumn season and associated beginning of soil freeze-up at our large-scale water manipulation site near Barrow, Alaska (the Biocomplexity Experiment). We found that the autumn season CH4 emission is significant (accounting for 21–25% of the average growing season emission), and that this emission is mostly controlled by the fraction of inundated landscape, atmospheric turbulence, and the decline in unfrozen water during the period of soil freezing. Drainage decreased autumn CH4 emission by a factor of 2.4 compared to our flooded treatment. Flooding slowed the soil freezing process which has implications for extending elevated CH4 emissions longer into the winter season.

Drained thaw lake basins (DTLB's) are the dominant land form of the Arctic Coastal Plain in northern Alaska. The presence of continuous permafrost prevents drainage and so water tables generally remain close to the soil surface, creating saturated, suboxic soil conditions. However, ice wedge polygons produce microtopographic variation in these landscapes, with raised areas such as polygon rims creating more oxic microenvironments. The peat soils in this ecosystem store large amounts of organic carbon which is vulnerable to loss as arctic regions continue to rapidly warm, and so there is great motivation to understand the controls over microbial activity in these complex landscapes. Here we report the effects of experimental flooding, along with seasonal and spatial variation in soil chemistry and microbial activity in a DTLB. The flooding treatment generally mirrored the effects of natural landscape variation in water-table height due to microtopography. The flooded portion of the basin had lower dissolved oxygen, lower oxidation-reduction potential (ORP) and higher pH, as did lower elevation areas throughout the entire basin. Similarly, soil pore water concentrations of organic carbon and aromatic compounds were higher in flooded and low elevation areas. Dissolved ferric iron (Fe(III)) concentrations were higher in low elevation areas and responded to the flooding treatment in low areas, only. The high concentrations of soluble Fe(III) in soil pore water were explained by the presence of siderophores, which were much more concentrated in low elevation areas. All the aforementioned variables were correlated, showing that Fe(III) is solubilized in response to anoxic conditions. Dissolved carbon dioxide (CO2) and methane (CH4) concentrations were higher in low elevation areas, but showed only subtle and/or seasonally dependent effects of flooding. In anaerobic laboratory incubations, more CH4 was produced by soils from low and flooded areas, whereas anaerobic CO2 production only responded to flooding in high elevation areas. Seasonal changes in the oxidation state of solid phase Fe minerals showed that net Fe reduction occurred, especially in topographically low areas. The effects of Fe reduction were also seen in the topographic patterns of pH, as protons were consumed where this process was prevalent. This suite of results can all be attributed to the effect of water table on oxygen availability: flooded conditions promote anoxia, stimulating dissolution and reduction of Fe(III), and to some extent, methanogenesis. However, two lines of evidence indicated the inhibition of methanogenesis by alternative e- acceptors such as Fe(III) and humic substances: (1) ratios of CO2:CH4 evolved from anaerobic soil incubations and dissolved in soil pore water were high; (2) CH4 concentrations were negatively correlated with the oxidation state of the soluble Fe pool in both topographically high and low areas. A second set of results could be explained by increased soil temperature in the flooding treatment, which presumably arose from the increased thermal conductivity of the soil surface: higher N mineralization rates and dissolved P concentrations were observed in flooded areas. Overall, these results could have implications for C and nutrient cycling in high Arctic areas where warming and flooding are likely consequences of climate change.

Projected increases in air temperature and precipitation due to climate change in Arctic wetlands could dramatically affect ecosystem function. As a consequence, it is important to define controls on evapotranspiration, the major pathway of water loss from these systems. We quantified the multi-year controls on midday Arctic coastal wetland evapotranspiration, measured with the eddy covariance method at two vegetated, drained thaw lake basins near Barrow, Alaska. Variations in near-surface soil moisture and atmospheric vapor pressure deficits were found to have nonlinear effects on midday evapotranspiration rates. Vapor pressure deficits (VPD) near 0.3 kPa appeared to be an important hydrological threshold, allowing latent heat flux to persistently exceed sensible heat flux. Dry (compared to wet) soils increased bulk surface resistance (water-limited). Wet soils favored ground heat flux and therefore limited the energy available to sensible and latent heat flux (energy-limited). Thus, midday evapotranspiration was suppressed from both dry and wet soils but through different mechanisms. We also found that wet soils (ponding excluded) combined with large VPD, resulted in an increased bulk surface resistance and therefore suppressing evapotranspiration below its potential rate (Priestley-Taylor α < 1.26). This was likely caused by the limited ability of mosses to transfer moisture during large atmospheric demands. Ultimately, in addition to net radiation, the various controlling factors on midday evapotranspiration (i.e., near-surface soil moisture, atmospheric vapor pressure, and the limited ability of saturated mosses to transfer water during high VPD) resulted in an average evapotranspiration rate of up to 75% of the potential evapotranspiration rate. These multiple limitations on midday evapotranspiration rates have the potential to moderate interannual variation of total evapotranspiration and reduce excessive water loss in a warmer climate. Combined with the prevailing maritime winds and projected increases in precipitation, these curbing mechanisms will likely prevent extensive future soil drying and hence maintain the presence of coastal wetlands.

Wetlands are the largest global natural methane (CH4) source, and emissions between 50 and 70° N latitude contribute 10-30% to this source. Predictive capability of land models for northern wetland CH4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with siteto regional-scale observations. A comparison of the CH4 fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH4 emissions when compared with CarbonTracker CH4 predictions. CLM4.5 CH4 emission predictions agree well with growing season (May-September) CarbonTracker Alaskan regional-level CH4 predictions and sitelevel observations. However, CLM4.5 underestimated CH4 emissions in the cold season (October-April). The monthly atmospheric CH4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH4 mole fraction measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH4 emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH4 cycle are from the wetland extent, coldseason CH4 production and CH4 transport processes. We recommend more cold-season experimental studies in highlatitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Tropical peatlands are one of the most important terrestrial ecosystems in terms of impact on the atmospheric greenhouse gas composition. Currently, greenhouse gas emissions from tropical peatlands following disturbances due to deforestation, drainage or wildfire are substantial. We quantified in situ nitrous oxide (N2O) fluxes during both dry and wet seasons using a closed chamber method at sites that represented differing land uses and land use change intensities in Central Kalimantan, Indonesia. Cumulative N2O fluxes were compared with carbon dioxide (CO2) and methane (CH4) fluxes. The mean N2O flux rates (N2O-N±SD, mgm-2 h-1) varied as follows: drained forest (0.112±0.293) > agricultural peat at the Kalampangan site (0.012±0.026) > drained burned peat (0.011±0.018) > agricultural peat at the Marang site (0.0072±0.028) > undrained forest (0.0025±0.053) > clear-felled, drained, recovering forest (0.0022±0.021). The widest N2O flux range was detected in the drained forest (max. 2.312 and min. -0.043 mgN2ONm-2 h-1). At the other flux monitoring sites the flux ranges remained at about one tenth that of the drained forest site. The highest N2O emission rates were observed at water tables close to the peat surface where also the flux range was widest. Annual cumulative peat surface N2O emissions (expressed in CO2 equivalents as a percentage of the total greenhouse gas (N2O, CO2 and CH4) emissions) were 9.2% at highest, but typically ~1 %. Average N2O fluxes and also the total of monitored GHG emissions were highest in drainageaffected forest which is characterized by continuous labile nitrogen availability from vegetation, and water tables typically below the surface. [ABSTRACT FROM AUTHOR]

Conversion of tropical peatlands to agriculture leads to a release of carbon from previously stable, longterm storage, resulting in land subsidence that can be a surrogate measure of CO2 emissions to the atmosphere. We present an analysis of recent large-scale subsidence monitoring studies in Acacia and oil palm plantations on peatland in SE Asia, and compare the findings with previous studies. Subsidence in the first 5 yr after drainage was found to be 142 cm, of which 75 cm occurred in the first year. After 5 yr, the subsidence rate in both plantation types, at average water table depths of 0.7 m, remained constant at around 5 cm yr-1. The results confirm that primary consolidation contributed substantially to total subsidence only in the first year after drainage, that secondary consolidation was negligible, and that the amount of compaction was also much reduced within 5 yr. Over 5 yr after drainage, 75% of cumulative subsidence was caused by peat oxidation, and after 18 yr this was 92 %. The average rate of carbon loss over the first 5 yr was 178 t CO2eq ha-1 yr-1, which reduced to 73 t CO2eq ha-1 yr-1 over subsequent years, potentially resulting in an average loss of 100 t CO2eq ha-1 yr-1 over 25 yr. Part of the observed range in subsidence and carbon loss values is explained by differences in water table depth, but vegetation cover and other factors such as addition of fertilizers also influence peat oxidation. A relationship with groundwater table depth shows that subsidence and carbon loss are still considerable even at the highest water levels theoretically possible in plantations. This implies that improved plantation water management will reduce these impacts by 20% at most, relative to current conditions, and that high rates of carbon loss and land subsidence are inevitable consequences of conversion of forested tropical peatlands to other land uses. [ABSTRACT FROM AUTHOR]

In fire-prone environments, the "event-dependent hypothesis" states that plant population changes are driven by the unique set of conditions of a fire (e.g. fire season, climate). Climate variability, in particular changes in rainfall patterns, can be most important for seeder species, since they regenerate after fire from seeds, and for Mediterranean shrublands, given the high yearly variability of rainfall in these ecosystems. Yet, the role of rainfall variability and its interaction with fire characteristics (e.g. fire season) on plant populations has received little attention. Here we investigated the changes in seedling emergence and recruitment of three seeder species (Cistus ladanifer, Erica umbellata and Rosmarinus officinalis) after fires lit during three different years and at two times (early and late) during the fire season. Three plots were burned at each season, for a total of 18 plots burned during the three years. After fire, emerged seedlings were tallied, tagged and monitored during three years (two in the last burning year). Rainfall during the study period was rather variable and, in some years, it was well below average. Postfire seedling emergence varied by a factor of 3 to 12, depending on the species and on the burning year. The bulk of seedling emergence occurred during the first year after fire; seedling recruitment at the end of the study period was tightly correlated with this early emergence. Emergence in Erica and Rosmarinus, but not in Cistus, was correlated with precipitation in the fall and winter immediately after fire, with Erica being the most sensitive to reduced rainfall. Fire season was generally neither an important factor in controlling emergence nor, in particular, recruitment. We discuss how projected changes in rainfall patterns with global warming could alter the balance of species in this shrubland, and could drive some species to near local extinction. [ABSTRACT FROM AUTHOR]

Precipitation and temperature drive many aspects of terrestrial ecosystem function. Climate change scenarios predict increasing precipitation variability and temperature, and long term experiments are required to evaluate the ecosystem consequences of interannual climate variation, increased growing season (intra-annual) rainfall variability, and warming. We present results from an experiment applying increased growing season rainfall variability and year round warming in native tallgrass prairie. During ten years of study, total growing season rainfall varied 2-fold, and we found ~50-200% interannual variability in plant growth and aboveground net primary productivity (ANPP), leaf carbon assimilation (ACO2), and soil CO2 efflux (J CO2) despite only ~40 % variation in mean volumetric soil water content (0-15 cm, Θ15). Interannual variation in soil moisture was thus amplified in most measures of ecosystem response. Differences between years in Θ15 explained the greatest portion (14-52%) of the variation in these processes. Experimentally increased intra-annual season rainfall variability doubled the amplitude of intra-annual soil moisture variation and reduced Θ15 by 15%, causing most ecosystem processes to decrease 8-40% in some or all years with increased rainfall variability compared to ambient rainfall timing, suggesting reduced ecosystem rainfall use efficiency. Warming treatments increased soil temperature at 5 cm depth, particularly during spring, fall, and winter. Warming advanced canopy green up in spring, increased winter JCO2, and reduced summer JCO2 and forb ANPP, suggesting that the effects of warming differed in cooler versus warmer parts of the year. We conclude that (1) major ecosystem processes in this grassland may be substantially altered by predicted changes in interannual climate variability, intra-annual rainfall variability, and temperature, (2) interannual climate variation was a larger source of variation in ecosystem function than intra-annual rainfall variability and warming, and (3) effects of increased growing season rainfall variability and warming were small, but ecologically important. The relative effects of these climate drivers are likely to vary for different ecosystem processes and in wetter or drier ecosystems. [ABSTRACT FROM AUTHOR]

Peatlands are carbon (C) storage ecosystems sustained by a high water table (WT). High WT creates anoxic conditions that suppress the activity of aerobic decomposers and provide conditions for peat accumulation. Peatland function can be dramatically affected by WT drawdown caused by climate and/or land-use change. Aerobic decomposers are directly affected by WT drawdown through environmental factors such as increased oxygenation and nutrient availability. Additionally, they are indirectly affected via changes in plant community composition and litter quality. We studied the relative importance of direct and indirect effects of WT drawdown on aerobic decomposer activity in plant litter at two stages of decomposition (incubated in the field for 1 or 2 years). We did this by profiling 11 extracellular enzymes involved in the mineralization of organic C, nitrogen (N), phosphorus (P) and sulphur. Our study sites represented a three-stage chronosequence from pristine to short-term (years) and long-term (decades) WT drawdown conditions under two nutrient regimes (bog and fen). The litter types included reflected the prevalent vegetation: Sphagnum mosses, graminoids, shrubs and trees. Litter type was the main factor shaping microbial activity patterns and explained about 30% of the variation in enzyme activities and activity allocation. Overall, enzyme activities were higher in vascular plant litters compared to Sphagnum litters, and the allocation of enzyme activities towards C or nutrient acquisition was related to the initial litter quality (chemical composition). Direct effects of WT regime, site nutrient regime and litter decomposition stage (length of incubation period) summed to only about 40% of the litter type effect. WT regime alone explained about 5% of the variation in enzyme activities and activity allocation. Generally, enzyme activity increased following the long-term WT drawdown and the activity allocation turned from P and N acquisition towards C acquisition. This caused an increase in the rate of litter decomposition. The effects of the short-term WT drawdown were minor compared to those of the long-term WT drawdown: e.g., the increase in the activity of C-acquiring enzymes was up to 120% (bog) or 320% (fen) higher after the long-term WT drawdown compared to the short-term WT drawdown. In general, the patterns of microbial activity as well as their responses to WT drawdown depended on peatland type: e.g., the shift in activity allocation to C-acquisition was up to 100% stronger at the fen compared to the bog. Our results imply that changes in plant community composition in response to persistent WT drawdown will strongly affect the C dynamics of peatlands. The predictions of decomposer activity under changing climate and/or land-use thus cannot be based on the direct effects of the changed environment only, but need to consider the indirect effects of environmental changes: the changes in plant community composition, their dependence on peatland type, and their time scale. [ABSTRACT FROM AUTHOR]