ABSTRACT
Frontal boundaries have been shown to cause large changes in CO2 mole-fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO2 dry air mole-fraction (XCO2) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO2 frontal contrasts (ΔXCO2, i.e., warm minus cold sector average of XCO2). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO2 during four seasonal field campaigns of the Atmospheric Carbon and Transport-America (ACT-America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting--Chemistry (WRF-Chem) showed that (a) all products had a similar sign of ΔXCO2 though with different levels of agreement in ΔXCO2 magnitudes among seasons; (b) ΔXCO2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO2 frontal contrasts. A linear regression analyses between ΔXCO2 for MFLL versus GMAO, and MFLL versus WRF-Chem for summer-2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO2 variability among four seasons provide guidance to the spatial structures of XCO2 transport errors in models and satellite measurements of XCO2 in synoptically-active weather systems.
ABSTRACT
The field of precision agriculture has brought the concept for "big data" to farming by bringing sensor technology into the field allowing growers to make more efficient management decisions. However much of the research and practice of precision agriculture has focused on soil-related issues while sub-field microclimates have been mostly unstudied despite their known importance to crop production. This study sought to explore the differences in temperature at a sub-field level during an entire season using weather microsensors recording data every minute from 11 Dec 2017 to 11 Apr 2018. Twenty-two cost-effective sensors were placed within a ~ .5 ha area satsuma orange (Citrus unshiu) grove along the Gulf Coast on Baldwin County, Alabama. The sensors were placed in aerated housings in a vertical column on the west face of eleven trees at a height of 1 and 2 m off the ground. We focus on several events where temperatures hovered near 0 °C or near - 7 °C, a temperature known to damage satsuma trees and find that temperatures can vary by as much as 1.5 to 2 °C at the same moment in the same grove. Extreme cold events were also found to be non-uniform within the grove, and the response was seen on a tree-by-tree basis where increased exposure to < - 7 °C temperatures led to increase defoliation (r2 = 0.92) and lower fruit count in the following year (r2 = 0.71). We discuss the implication of these differences in temperature and what it may mean for the future of precision agriculture.
