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International audience; Terrestrial biosphere models (TBMs) play a key role in the detection and attribution of carbon cycle processes at local to global scales and in projections of the coupled carbon-climate system. TBM evaluation commonly involves direct comparison to eddy-covariance flux measurements. We use atmospheric CO2 mole fraction ([CO2]) measured in situ from aircraft and tower, in addition to flux-measurements from summer 2016 to evaluate the Carnegie-Ames-Stanford-Approach (CASA) TBM. WRF-Chem is used to simulate [CO2] using biogenic CO2 fluxes from a CASA parameter-based ensemble and CarbonTracker version 2017 (CT2017) in addition to transport and CO2 boundary condition ensembles. The resulting “super ensemble” of modeled [CO2] demonstrates that the biosphere introduces the majority of uncertainty to the simulations. Both aircraft and tower [CO2] data show that the CASA ensemble net ecosystem exchange (NEE) of CO2 is biased high (NEE too positive) and identify the maximum light use efficiency Emax a key parameter that drives the spread of the CASA ensemble in summer 2016. These findings are verified with flux-measurements. The direct comparison of the CASA flux ensemble with flux-measurements confirms missing sink processes in CASA. Separating the daytime and nighttime flux, we discover that the underestimated net uptake results from missing sink processes that result in overestimation of respiration. NEE biases are smaller in the CT2017 posterior biogenic fluxes, which assimilate observed [CO2]. Flux tower analyses reveal an unrealistic overestimation of nighttime respiration in CT2017 which we attribute to limited flexibility in the inversion strategy.

This study investigates the sources of fine particulate matter (aerodynamic diameter ≤2.5 μm; PM2.5) composition for the Baltimore, Maryland, metropolitan area, covering a 6-year period (2008–2013). Data obtained from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) were used for the identification of eight chemical speciation clusters (factors), which, as a percentage of the average concentration, were identified as secondary sulfate (31.9%), secondary nitrate (14.3%), gasoline (17.4%), diesel (10.1%), soil (4.0%), biomass burning (11%), marine aerosol (4.1%), and industrial processing (7.2%). The results show predominant influence from vehicle emissions transiting major highways I-695 and I-95 located in the vicinity of the sampling site. Strong influence on PM2.5 mass from biomass burning was found in the first 2 years (2008–2009) due to particulate matter remnants from forest fire events in North Carolina and a strong contribution in 2013 that was due mainly to wood burning during winter. Sulfate, nitrate, soil, and marine aerosol fractions registered very low variability over the 6-year period analyzed. In addition, this study shows a significant reduction in particulate matter from industrial origins after a major industrial source in Baltimore shut down. The results obtained from Baltimore were compared with those from the Beltsville, Maryland, sampling station located 25 miles south of Baltimore for 2011 and 2012, where good agreement was found for most of the factors.Implications: This paper presents the first long-term aerosol speciation analysis in a Mid-Atlantic United States metropolitan area, which is essential for the air quality management agencies in order to revise regulations and reduce human exposure to adverse air quality conditions. The results suggest that although a declining trend in the overall PM2.5 was observed, no significant tendency was observed in the identified sources besides exceptional events such as the impact of wildfires on local air quality and downward contribution from industrial fraction of PM2.5 after the Steel Mill at Sparrows Point closure in 2012.

There is enormous potential to harness the kinetic energy of offshore wind and produce power. However significant uncertainties are introduced in the offshore wind resource assessment process, due in part to limited observational networks and a poor understanding of the marine atmosphere’s complexity. Given the cubic relationship between a turbine’s power output and wind speed, a relatively small error in the wind speed estimate translates to a significant error in expected power production. The University of Maryland Baltimore County (UMBC) collected in-situ measurements offshore, within Maryland’s Wind Energy Area (WEA) from July-August 2013. This research demonstrates the ability of Doppler wind lidar technology to reduce uncertainty in estimating an offshore wind resource, compared to traditional resource assessment techniques, by providing a more accurate representation of the wind profile and associated hub-height wind speed variability. The second objective of this research is to elucidate the impact of offshore micrometeorology controls (stability, wind shear, turbulence) on a turbine’s ability to produce power. Compared to lidar measurements, power law extrapolation estimates and operational National Weather Service models underestimated hub-height wind speeds in the WEA. In addition, lidar observations suggest the frequent development of a low-level wind maximum (LLWM), with high turbinelayer wind shear and low turbulence intensity within a turbine’s rotor layer (40m-160m). Results elucidate the advantages of using Doppler wind lidar technology to improve offshore wind resource estimates and its ability to monitor under-sampled offshore meteorological controls impact on a potential turbine’s ability to produce power.

This paper presents coordinated ground-based observations by the NOAA-CREST Lidar Network (CLN) for profiling of aerosols, cloud, water vapor, and wind along the US east coast including Caribbean region at Puerto Rico. The instrumentation, methodology and observation capability are reviewed. The applications to continental and intercontinental-scale transport of smoke and dust plumes, and their large scale regional impact are discussed.