Comparing Greenland Ice Sheet Melt Variability From Different Satellite Passive Microwave Remote Sensing Products Over a Common 5-year Record

Satellite microwave brightness temperature (Tb) observations over the Greenland Ice Sheet permit determination of melted/frozen snow conditions at spatial and temporal scales that are uniquely suited for climate model validation and metrics of ice sheet change. Strong microwave sensitivity to the presence of liquid water in the snowpack is clear. Yet, a host of unique microwave-derived melt products covering the ice sheet are available, each based on different methodology, and with unknown inter-product agreement. Here, we compared five different published microwave melt products over a common 5-year (2003–2007) record to establish compatibility between products and agreement with in situ observations from a network of on-ice weather stations (AWS) spanning the ice sheet. A sixth product, leveraging both Tb seasonal trends and diurnal variability, was also introduced and included in the comparison. We found variable agreement between products and observations, with melt estimates based on microwave emissions modeling and the newly presented Adaptive Threshold (ADT) algorithm showing the best performance for AWS sites with more than 1-day average annual melt period (e.g., 68.9% of ADT melt days consistent with AWS observations; 31.1% of ADT frozen days contrasting with AWS observed melt). Spatial patterns of melting also varied between products. The different products showed substantial spread in melt occurrence even for products with the best AWS agreement. Product differences were generally larger under higher melt conditions; whereby, the fraction of the ice sheet experiencing ≥25 days of melting each year ranged from 4 to 25% for different products. While long-term satellite records have consistently shown increasing decadal trends in melt extent, our results imply that the melt frequency at any given location, particularly in the ice sheet interior where melting is less prevalent, is still subject to significant uncertainty.

Measuring N2O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model

Nitrous oxide (N2O) emissions from agricultural soil are substantially influenced by nitrogen (N) and field management practices. While routinely soil chambers have been used to measure emissions from small plots, measuring field-scale emissions with micrometeorological methods has been limited. This study implemented a backward Lagrangian stochastic (bLS) technique to simultaneously and near-continuously measure N2O emissions from four adjacent fields of approximately 1 ha each. A scanning open-path Fourier-transform infrared spectrometer (OP-FTIR), edge-of-field gas sampling and measurement, locally measured turbulence, and bLS emissions modeling were integrated to measure N2O emissions from four adjacent fields of maize production using different management in 2015. The maize N management treatments consisted of 220 kg NH3-N ha−1 applied either as one application in the fall after harvest or spring before planting or split between fall after harvest and spring before planting. The field preparation treatments evaluated were no-till (NT) and chisel plow (ChP). This study showed that the OP-FTIR plus bLS method had a minimum detection limit (MDL) of ±1.2 µg m−2 s−1 (3σ) for multi-source flux measurements. The average N2O emission of the four treatments ranged from 0.1 to 2.3 µg m−2 s−1 over the study period of 01 May to 11 June after the spring fertilizer application. The management of the full-N rate applied in the fall led to higher N2O emissions than the split-N rates applied in the fall and spring. Based on the same N application, the ChP practice tended to increase N2O emissions compared with NT. Advection of N2O from adjacent fields influenced the estimated emissions; uncertainty (1σ) in emissions was 0.5 ± 0.3 µg m−2 s−1 if the field of interest received a clean measured upwind background air, but increased to 1.1 ± 0.5 µg m−2 s−1 if all upwind sources were advecting N2O over the field of interest. Moreover, higher short-period emission rates (e.g., half-hour) were observed in this study by a factor of 1.5~7 than other micrometeorological studies measuring N2O-N loss from the N-fertilized cereal cropping system. This increment was attributed to the increase in N fertilizer input and soil temperature during the measurement. We concluded that this method could make near-continuous “simultaneous” flux comparisons between treatments, but further studies are needed to address the discrepancies in the presented values with other comparable N2O flux studies.

Observed Characteristics and Modeled Emissions of Transit Buses on Ramps

On-ramps and off-ramps that serve as connections between high-speed facilities and arterials are potential hotspots for vehicle emissions. The engine load associated with grade and acceleration on uphill ramps can lead to significant emissions of criteria pollutants and greenhouse gases (GHGs) over a short distance. This study explores transit bus operations and emissions at ramps using Global Positioning System (GPS) data collected from Detroit transit buses. Ramp-associated operating data are extracted from the vehicle traces using ArcGIS and assigned to the applicable United States Environmental Protection Agency’s emission rates, i.e., EPA’s Motor Vehicle Emission Simulator (MOVES). The results show that transit bus emission rates for on-ramp operations at 40 mph (64.37 km/h) are about double the average emission rate on the MOVES highway cycles. For lower on-ramp speeds (< 64.37 km/h), as average speeds decrease, on-ramp emission rates drop roughly to the highway emission rates given the less aggressive acceleration noted in the data. Off-ramp emission rates are approximately half of the highway emission rates. The study also finds that post-ramp acceleration, right after buses enter the highway from the on-ramp, contributes to high emissions, because of the high-speed and high-power operations. This is true for the loop on-ramp, where the bus emission rate after entering the highway is higher than the emissions associated with driving on the ramp. On-ramp emissions are found to vary across a wide range of conditions, indicating that further study and more data are needed to explore the overall impacts of on-ramp and post-ramp activity in emissions modeling. A sensitivity analysis of ramp grade effect on emission indicates that ramp grade should be specifically considered in project-level analyses. The research results are useful for understanding ramp driving characteristics, the potential impacts of ramp grade on emissions, and the ramp hotspot analysis.

The importance of management information and soil moisture representation for simulating tillage effects on N₂O emissions in LPJmL5.0-tillage

No-tillage is often suggested as a strategy to reduce greenhouse gas emissions. Modeling tillage effects on nitrous oxide (N2O) emissions is challenging and subject to large uncertainties, as the processes producing the emissions are complex and strongly non-linear. Previous findings have shown deviations between the LPJmL5.0-tillage model and results from meta-analysis on global estimates of tillage effects on N2O emissions. Here we tested LPJmL5.0-tillage at four different experimental sites across Europe and the USA, to verify whether deviations in N2O emissions under different tillage regimes result from a lack of detailed information on agricultural management and/or the representation of soil water dynamics. Model results were compared to observational data and outputs from field-scale DayCent simulations. DayCent has been successfully applied for the simulation of N2O emissions and provides a richer data base for comparison than non-continuous measurements at the experimental sites. We found that adding information on agricultural management improved the simulation of tillage effects on N2O emissions in LPJmL. We also found that LPJmL overestimated N2O emissions as well as the effects of no-tillage on N2O emissions, whereas DayCent tended to underestimate the emissions of no-tillage treatments. LPJmL showed a general bias to over-estimate soil moisture content. Modifications of hydraulic properties in LPJmL in order to match properties assumed in DayCent, as well as of the parameters related to residue cover, improved the overall simulation of soil water as well as the N2O emissions simulated under tillage and no-tillage separately. However, the effects of no-tillage (shifting from tillage to no-tillage) did not improve. Advancing the current state of information on agricultural management as well as improvements in soil moisture highlight the potential to improve LPJmL5.0-tillage and global estimates of tillage effects on N2O emissions.