Improving estimates of net ecosystem CO2 exchange between the Arctic land surface and the atmosphere

Feedbacks between the climate system and the high-latitude carbon cycle will substantially influence the intensity of future climate change. It is therefore crucial that the net ecosystem exchange of CO2 (NEE) between the high-latitude land surface and the atmosphere is accurately quantified, where NEE refers to the difference between ecosystem respiration (R) and photosynthesis (gross ecosystem exchange, GEE): NEE=-GEE+R in umol/m^2/s. NEE can only be directly measured over areas of 1 km^2 through eddy covariance, and modeling approaches such as the Vegetation Photosynthesis Respiration Model (VPRM) are required to upscale NEE. VPRM is a remote sensing based model that calculates R as a linear function of air temperature (Ta) when air temperature is above a given threshold (Tlow), and sets respiration to a constant value when Ta<Tlow. GEE is estimated according to remote sensing observations of vegetation indices, shortwave radiation, air temperature, and soil moisture. Although in situ findings have shown that snow and Arctic species composition have a substantial influence on high-latitude NEE, model estimates of high-latitude NEE have typically been generated without Arctic-specific vegetation classes, and without using remote sensing observations to represent the effects of snow on NEE. The hypothesis driving this work was therefore that uncertainty in estimates of high-latitude NEE could be reduced by representing the influences of Arctic vegetation classes and snow. The central objectives were to determine feasible approaches for reducing uncertainty in VPRM estimates of NEE by representing the influences of snow and Arctic vegetation, create PolarVPRM accordingly, and analyze inter-annual variability in PolarVPRM estimates of high-latitude North American NEE (2001-2012). The associations between snow and NEE, and the potential to describe these influences on NEE using remote sensing observations, were examined using time lapse camera observations of snow cover area (SCA) and eddy covariance measurements of NEE from Daring Lake, Northwest Territories, Canada. Analyses indicated good agreement between SCA derived from camera, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) observations. SCA was also found to influence the timing and magnitude of NEE. MODIS SCA was therefore incorporated into VPRM, and VPRM was calibrated using eddy covariance and meteorological observations collected in 2005 at Daring Lake. VPRM was run through years 2004-2007 over both Daring Lake and Ivotuk, Alaska, USA, using four model formulations, three of which represented the effects of SCA on respiration and/or photosynthesis, and another which did not use MODIS SCA. Comparisons against eddy covariance observations indicated that uncertainty was reduced in VPRM estimates of NEE when respiration was calculated as a linear function of soil temperature when SCA>50%, and as a linear function of air temperature when SCA<50%, thereby reflecting the influence of snow on decoupling soil/air temperatures. Representing the effect of SCA on NEE therefore reduced uncertainty in VPRM estimates of NEE. In order to represent spatial variability in high-latitude estimates of NEE due to vegetation type, Arctic-specific vegetation classes were created for PolarVPRM by combining and aggregating two existing vegetation classifications: the Synergetic Land Cover Product and the Circumpolar Arctic Vegetation Map. Levene's test indicated that the PolarVPRM vegetation classes divided the pan-Arctic region into heterogeneous distributions in terms of net primary productivity, and passive microwave derived estimates of snow and growing season influences on NEE. A non-parametric statistical approach of Alternating Conditional Expectations found significant, non-linear associations to exist between passive microwave derived estimates of snow and growing season drivers of NEE. Furthermore, the shape of these associations varied according to the vegetation class over which they were examined. Further support was therefore provided to the idea that uncertainty in model estimates of NEE could be reduced by calculating snow and growing season NEE separately within each vegetation class. PolarVPRM estimates of NEE in 2001-2012 were generated at a three hourly and 1/6 x 1/4 degree resolution across polar North America (55-170 W, 55-83 N). Model calibration was conducted over three sites: Daring Lake, Ivotuk, and Atqasuk, Alaska, USA. Model validation was then conducted by comparing PolarVPRM estimates of year-round daily average NEE to non-gap-filled eddy covariance observations of daily average NEE acquired over the three calibration sites, as well as six other Arctic sites. PolarVPRM performed well over all sites, with an average mean absolute error (MAE) of 0.20 umol/m^2/s, and had diminished error rates when the influence of SCA on respiration was explicitly represented. Error analysis indicated that peak growing season GEE was underestimated at Barrow because GEE at this site showed a stronger response to the amount of incoming shortwave radiation than at the calibration site, suggesting that PolarVPRM may underestimate GEE over wetland and barren vegetated regions. Despite these uncertainties, PolarVPRM was found to generate more accurate estimates of monthly and three-hourly NEE relative to eddy covariance observations than two established models, FLUXNET Model-Tree Ensemble (MTE) and CarbonTracker. Relative to eddy covariance observations and PolarVPRM estimates, MTE tended to overestimate snow season respiration, and CarbonTracker tended to overestimate the amount of midday photosynthesis. Analysis of PolarVPRM output across North America (north of 55 N) found an increase in net annual carbon efflux over over time (2001-2012). Specifically, increased rates of respiration are estimated when soil and air temperatures are warmer. Although increases in growing season vegetation indices and air temperature enable greater photosynthetic uptake by Arctic vegetation, forests and shrublands uptake less CO2 in the middle of the growing season when air temperatures rise above the physiological optima for photosynthesis. As a result, PolarVPRM estimated a decline in net photosynthetic uptake over time. Overall, PolarVPRM output indicates that North American regions north of 55 N are losing strength as a carbon sink in response to rising air temperatures.1 yea

Analyzing pan-Arctic 1982–2006 trends in temperature and bioclimatological indicators (productivity, phenology and vegetation indices) using remote sensing, model and field data

Warming induced changes in Arctic vegetation have to date been studied through observational and experimental field studies, leaving significant uncertainty about the representativeness of selected field sites as well as how these field scale findings scale up to the entire pan-Arctic. The purposes of this thesis were therefore to 1) analyze remotely-sensed/modeled temperature, Normalized Difference Vegeta- tion Indices (NDVI) and plant Net Primary Productivity (NPP) to assess coarse- scale changes (1982–2006) in vegetation; and 2) compare field, remote sensing and model outputs to estimate limitations, challenges and disagreements between data formats. The following data sources were used: • Advanced Very High Resolution Radiometer Polar Pathfinder Extended (APP- x, temperature & albedo) • Moderate Resolution Imaging Spectroradiometer (MODIS, Normalized Dif- ference Vegetation Index (NDVI) & Enhanced Vegetation Index (EVI) ) • Landsat Enhanced Thematic Mapper (Landsat ETM, NDVI) • Global Inventory Modeling and Mapping Studies (GIMMS, NDVI) • Global Productivity Efficiency Model (GloPEM, Net Primary Productivity (NPP)) Over the pan-Arctic (1982-2007), increases in temperature, total annual NPP and maximum annual NDVI were observed. Increases in NDVI and NPP were found to be closely related to increases in temperature according to non-parametric Sen’ slope and Mann Kendall tau tests. Variations in phenology were largely non- significant but related to increases in growing season temperature. Snow melt onset and spring onset correspond closely. MODIS, Landsat and GIMMS NDVI data sets agree well, and MODIS EVI and NDVI are very similar for spring and summer at Fosheim Peninsula. GloPEM NPP and field estimates of NPP are poorly correlated, whereas GIMMS NDVI and GloPEM NPP are well correlated, indicating a need for better calibration of model NPP to field data. In summary, increases in pan-Arctic biological productivity indicators were ob- served, and were found to be closely related to recent circumpolar warming. How- ever, these changes appear to be focused in regions from which recent field studies have found significant ecological changes (Alaska), and coarse resolution remote sensing estimates of ecological changes have been less marked in other regions. Dis- crepancies between results from model, field data and remote sensing, as well as central questions remaining about the impact of increases in productivity on soil- vegetation-atmosphere feedbacks, indicate a clear need for continued research into warming induced changes in pan-Arctic vegetation

Economic Implications of Environmental Sustainability for Companies: A Case Study of 3M

As awareness of sustainability grows, firms are being pressured to adopt social and environmental practices to keep pace with ethical standards and consumer demand. Firms must adapt to a changing marketplace, and new management strategies are being developed. Our central purpose in this paper is therefore to explore the economic implications of enhanced environmental sustainability through a case study of 3M, a chemical company that has been implementing sustainable solutions for over 30 years. We begin our case study by analyzing the effectiveness of the lifecycle management approach (LCM) currently advocated to businesses in search of sustainability. Although the LCM methodology is still developing at this stage, it has yielded great results for 3M when combined with employee expertise. We will then go on to analyze why these increases in sustainability have increased profits, and what effect tighter environmental legislation would have on competitive markets. The final section of this paper will analyze the performance of environmentally responsible firms on the stock market to determine whether increased sustainability makes firms more desirable to investors. Our critical analysis of the multi-faceted economic implications of enhanced environmental sustainability will therefore allow us to determine 1) the effectiveness of current approaches to sustainability; 2) the economic implications of enhanced corporate responsibility and legislation, and 3) the impact of enhanced sustainability on the performance of companies on the stock market

Long-term drainage reduces CO2 uptake and increases CO2 emission on a Siberian floodplain due to shifts in vegetation community and soil thermal characteristics

With increasing air temperatures and changing precipitation patterns forecast for the Arctic over the coming decades, the thawing of ice-rich permafrost is expected to increasingly alter hydrological conditions by creating mosaics of wetter and drier areas. The objective of this study is to investigate how 10 years of lowered water table depths of wet floodplain ecosystems would affect CO2 fluxes measured using a closed chamber system, focusing on the role of long-term changes in soil thermal characteristics and vegetation community structure. Drainage diminishes the heat capacity and thermal conductivity of organic soil, leading to warmer soil temperatures in shallow layers during the daytime and colder soil temperatures in deeper layers, resulting in a reduction in thaw depths. These soil temperature changes can intensify growing-season heterotrophic respiration by up to 95 %. With decreased autotrophic respiration due to reduced gross primary production under these dry conditions, the differences in ecosystem respiration rates in the present study were 25 %. We also found that a decade-long drainage installation significantly increased shrub abundance, while decreasing Eriophorum angustifolium abundance resulted in Carex sp. dominance. These two changes had opposing influences on gross primary production during the growing season: while the increased abundance of shrubs slightly increased gross primary production, the replacement of E. angustifolium by Carex sp. significantly decreased it. With the effects of ecosystem respiration and gross primary production combined, net CO2 uptake rates varied between the two years, which can be attributed to Carex-dominated plots' sensitivity to climate. However, underlying processes showed consistent patterns: 10 years of drainage increased soil temperatures in shallow layers and replaced E. angustifolium by Carex sp., which increased CO2 emission and reduced CO2 uptake rates. During the non-growing season, drainage resulted in 4 times more CO2 emissions, with high sporadic fluxes; these fluxes were induced by soil temperatures, E. angustifolium abundance, and air pressure.Peer reviewe

Carbon Dioxide Sources from Alaska Driven by Increasing Early Winter Respiration from Artic Tundra

High-latitude ecosystems have the capacity to release large amounts of carbon dioxide (CO2) to the atmosphere in response to increasing temperatures, representing a potentially significant positive feedback within the climate system. Here, we combine aircraft and tower observations of atmospheric CO2 with remote sensing data and meteorological products to derive temporally and spatially resolved year-round CO2 fluxes across Alaska during 2012-2014. We find that tundra ecosystems were a net source of CO2 to the atmosphere annually, with especially high rates of respiration during early winter (October through December). Long-term records at Barrow, AK, suggest that CO2emission rates from North Slope tundra have increased during the October through December period by 73% ± 11% since 1975, and are correlated with rising summer temperatures. Together, these results imply increasing early winter respiration and net annual emission of CO2in Alaska, in response to climate warming. Our results provide evidence that the decadal-scale increase in the amplitude of the CO2 seasonal cycle may be linked with increasing biogenic emissions in the Arctic, following the growing season. Early winter respiration was not well simulated by the Earth System Models used to forecast future carbon fluxes in recent climate assessments. Therefore, these assessments may underestimate the carbon release from Arctic soils in response to a warming climate

Investigating Alaskan Methane and Carbon Dioxide Fluxes Using Measurements from the CARVE Tower

Northern high-latitude carbon sources and sinks, including those resulting from degrading permafrost, are thought to be sensitive to the rapidly warming climate. Because the near-surface atmosphere integrates surface fluxes over large ( ∼ 500–1000 km) scales, atmospheric monitoring of carbon dioxide (CO2) and methane (CH4) mole fractions in the daytime mixed layer is a promising method for detecting change in the carbon cycle throughout boreal Alaska. Here we use CO2 and CH4 measurements from a NOAA tower 17 km north of Fairbanks, AK, established as part of NASA\u27s Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), to investigate regional fluxes of CO2 and CH4 for 2012–2014. CARVE was designed to use aircraft and surface observations to better understand and quantify the sensitivity of Alaskan carbon fluxes to climate variability. We use high-resolution meteorological fields from the Polar Weather Research and Forecasting (WRF) model coupled with the Stochastic Time-Inverted Lagrangian Transport model (hereafter, WRF-STILT), along with the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM), to investigate fluxes of CO2 in boreal Alaska using the tower observations, which are sensitive to large areas of central Alaska. We show that simulated PolarVPRM–WRF-STILT CO2 mole fractions show remarkably good agreement with tower observations, suggesting that the WRF-STILT model represents the meteorology of the region quite well, and that the PolarVPRM flux magnitudes and spatial distribution are generally consistent with CO2 mole fractions observed at the CARVE tower. One exception to this good agreement is that during the fall of all 3 years, PolarVPRM cannot reproduce the observed CO2 respiration. Using the WRF-STILT model, we find that average CH4 fluxes in boreal Alaska are somewhat lower than flux estimates by Chang et al. (2014) over all of Alaska for May–September 2012; we also find that enhancements appear to persist during some wintertime periods, augmenting those observed during the summer and fall. The possibility of significant fall and winter CO2 and CH4 fluxes underscores the need for year-round in situ observations to quantify changes in boreal Alaskan annual carbon balance

Replication Data for: Assessing biotic contributions to CO2 fluxes in Northern China using the Vegetation, Photosynthesis and Respiration Model (VPRM-CHINA) and observations from 2005 to 2009.

Calibration data, gridded vegetation CO2 fluxes, model code, and documentation for associated publication

Decadal biomass increment in early secondary succession woody ecosystems is increased by CO2 enrichment

Increasing atmospheric CO2 stimulates photosynthesis which can increase net primary production (NPP), but at longer timescales may not necessarily increase plant biomass. Here we analyse the four decade-long CO2-enrichment experiments in woody ecosystems that measured total NPP and biomass. CO2 enrichment increased biomass increment by 1.05 +/- 0.26 kg C m(-2) over a full decade, a 29.1 +/- 11.7% stimulation of biomass gain in these early-secondary-succession temperate ecosystems. This response is predictable by combining the CO2 response of NPP (0.16 +/- 0.03 kg C m(-2) y(-1)) and the CO2-independent, linear slope between biomass increment and cumulative NPP (0.55 +/- 0.17). An ensemble of terrestrial ecosystem models fail to predict both terms correctly. Allocation to wood was a driver of across-site, and across-model, response variability and together with CO2-independence of biomass retention highlights the value of understanding drivers of wood allocation under ambient conditions to correctly interpret and predict CO2 responses.Peer reviewe