Search for: All records

Robust carbon monitoring systems are needed for land managers to assess and mitigate the changing effects of ecosystem stress on western United States forests, where most aboveground carbon is stored in mountainous areas. Atmospheric carbon uptake via gross primary productivity (GPP) is an important indicator of ecosystem function and is particularly relevant to carbon monitoring systems. However, limited ground-based observations in remote areas with complex topography represent a significant challenge for tracking regional-scale GPP. Satellite observations can help bridge these monitoring gaps, but the accuracy of remote sensing methods for inferring GPP is still limited in montane evergreen needleleaf biomes, where (a) photosynthetic activity is largely decoupled from canopy structure and chlorophyll content, and (b) strong heterogeneity in phenology and atmospheric conditions is difficult to resolve in space and time. Using monthly solar-induced chlorophyll fluorescence (SIF) sampled at ∼4 km from the TROPOspheric Monitoring Instrument (TROPOMI), we show that high-resolution satellite-observed SIF followed ecological expectations of seasonal and elevational patterns of GPP across a 3000 m elevation gradient in the Sierra Nevada mountains of California. After accounting for the effects of high reflected radiance in TROPOMI SIF due to snow cover, the seasonal and elevational patterns of SIF were well correlated with GPP estimates from a machine-learning model (FLUXCOM) and a land surface model (CLM5.0-SP), outperforming other spectral vegetation indices. Differences in the seasonality of TROPOMI SIF and GPP estimates were likely attributed to misrepresentation of moisture limitation and winter photosynthetic activity in FLUXCOM and CLM5.0 respectively, as indicated by discrepancies with GPP derived from eddy covariance observations in the southern Sierra Nevada. These results suggest that satellite-observed SIF can serve as a useful diagnostic and constraint to improve upon estimates of GPP toward multiscale carbon monitoring systems in montane, evergreen conifer biomes at regional scales.

 

In situattenuated total reflection surface enhanced infrared absorption spectroscopy (ATR‐SEIRAS) is often used to investigate the near‐surface electrocatalytic reaction environment. However, there is a gap in directly correlating the near‐surface reaction environment with electrocatalytic reaction rates. To that end, we designed an electrochemical flow reactor foroperandoelectrochemical ATR‐SEIRAS and demonstrate its capability with the CO₂reduction reaction (CO₂RR). Roughened gold catalyst thin films are prepared on ATR silicon crystals as a model system to probe local species under CO₂RR conditions in 0.1 M KHCO₃. We measured changes in the interfacial CO₂concentration as a function of applied potential and electrolyte flow rate inoperando, allowing us to correlate the changes in reaction rates with the observed CO₂concentration. Including the choice of the catalyst and electrolyte, coupling hydrodynamic control with ATR‐SEIRAS in this platform enables investigations of how the local microenvironment affects the activity and selectivity of electrochemical reactions.

 

As the majority of fossil fuel carbon dioxide (CO₂) emissions originate from cities, the use of novel techniques to leverage available satellite observations of CO₂and proxy species to constrain urban CO₂is of great importance. In this study, we seek to empirically determine relationships between satellite observations of CO₂and the proxy species nitrogen dioxide (NO₂), applying these relationships to NO₂fields to generate NO₂‐derived CO₂fields (NDCFs) from which CO₂emissions can be estimated. We first establish this method using simulations of CO₂and NO₂for the cities of Buenos Aires, Melbourne, and Mexico City, finding that the method is viable throughout the year. For the same three cities, we next calculate empirical relationships (slopes) between co‐located observations of NO₂from the Tropospheric Monitoring Instrument and Snapshot Area Mode observations of CO₂from Orbiting Carbon Observatory‐3. Applying varying combinations of slopes to generate NDCFs, we evaluate methodological uncertainties for each slope application method and use a simple mass balance method to estimate CO₂emissions from NDCFs. We demonstrate monthly urban CO₂emissions estimates that are comparable to emissions inventory estimates. We additionally prove the utility of our method by demonstrating how large uncertainties at a grid cell level (equivalent to ∼1–3 ppm) can be reduced substantially when aggregating emissions estimates from NDCFs generated from all NO₂swaths (about 1%–6%). Rather than rely on prior knowledge of emission ratios, our method circumvents such assumptions and provides a valuable observational constraint on urban CO₂emissions.

 

Despite the need for researchers to understand terrestrial biospheric carbon fluxes to account for carbon cycle feedbacks and predict future CO₂ concentrations, knowledge of these fluxes at the regional scale remains poor. This is particularly true in mountainous areas, where complex meteorology and lack of observations lead to large uncertainties in carbon fluxes. Yet mountainous regions are often where significant forest cover and biomass are found – i.e., areas that have the potential to serve as carbon sinks. As CO₂ observations are carried out in mountainous areas, it is imperative that they are properly interpreted to yield information about carbon fluxes. In this paper, we present CO₂ observations at three sites in the mountains of the western US, along with atmospheric simulations that attempt to extract information about biospheric carbon fluxes from the CO₂ observations, with emphasis on the observed and simulated diurnal cycles of CO₂. We show that atmospheric models can systematically simulate the wrong diurnal cycle and significantly misinterpret the CO₂ observations, due to erroneous atmospheric flows as a result of terrain that is misrepresented in the model. This problem depends on the selected vertical level in the model and is exacerbated as the spatial resolution is degraded, and our results indicate that a fine grid spacing of ∼ 4 km or less may be needed to simulate a realistic diurnal cycle of CO₂ for sites on top of the steep mountains examined here in the American Rockies. In the absence of higher resolution models, we recommend coarse-scale models to focus on assimilating afternoon CO₂ observations on mountaintop sites over the continent to avoid misrepresentations of nocturnal transport and influence. 

The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is an opportunity for data assimilation systems that combine land surface models and satellite‐derived observations to provide improved carbon monitoring. Here, we use the Data Assimilation Research Testbed to adjust the Community Land Model (CLM5.0) with remotely sensed observations of leaf area and above‐ground biomass. The adjusted simulation significantly reduced the above‐ground biomass and leaf area, leading to a reduction in both photosynthesis and respiration fluxes. The reduction in the carbon fluxes mostly offset, thus both the adjusted and free simulation projected a weak carbon sink to the land. This result differed from a separate observation‐constrained model (FLUXCOM) that projected strong carbon uptake to the land. Simulation diagnostics suggested water limitation had an important influence upon the magnitude and spatial pattern of carbon uptake through photosynthesis. We recommend that additional observations important for water cycling (e.g., snow water equivalent, land surface temperature) be included to improve the veracity of the spatial pattern in carbon uptake. Furthermore, the assimilation system should be enhanced to maximize the number of the simulated state variables that are adjusted, especially those related to the recommended observed quantities including water cycling and soil carbon.

 

The 2085 km²Jordan River Basin, and its seven sub‐catchments draining the Central Wasatch Range immediately east of Salt Lake City, UT, are home to an array of hydrologic, atmospheric, climatic and chemical research infrastructure that collectively forms the Wasatch Environmental Observatory (WEO). WEO is geographically nested within a wildland to urban land‐use gradient and built upon a strong foundation of over a century of discharge and climate records. A 2200 m gradient in elevation results in variable precipitation, temperature and vegetation patterns. Soil and subsurface structure reflect systematic variation in geology from granitic, intrusive to mixed sedimentary clastic across headwater catchments, all draining to the alluvial or colluvial sediments of the former Lake Bonneville. Winter snowfall and spring snowmelt control annual hydroclimate, rapid population growth dominates geographic change in lower elevations and urban gas and particle emissions contribute to episodes of severe air pollution in this closed‐basin. Long‐term hydroclimate observations across this diverse landscape provide the foundation for an expanding network of infrastructure in both montane and urban landscapes. Current infrastructure supports both basic and applied research in atmospheric chemistry, biogeochemistry, climate, ecology, hydrology, meteorology, resource management and urban redesign that is augmented through strong partnerships with cooperating agencies. These features allow WEO to serve as a unique natural laboratory for addressing research questions facing seasonally snow‐covered, semi‐arid regions in a rapidly changing world and an excellent facility for providing student education and research training.