38Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely 39 correspond to the limited capability of current earth system models to capture and simulate 40 land-atmosphere feedback. To facilitate its realistic simulation in next generation models, an 41 improved process understanding of the related complex interactions is essential. To this end, 42 accurate 3D observations of key variables in the land-atmosphere (L-A) system with high 43 vertical and temporal resolution from the surface to the free troposphere are indispensable. 44Recently, we developed a synergy of innovative ground-based, scanning active remote sens-45 ing systems for 2D to 3D measurements of wind, temperature, and water vapor from the sur-46 face to the lower troposphere that is able to provide comprehensive data sets for characteriz-47 Motivation 71The land-atmosphere (L-A) system includes the soil, the land cover such as vegetation, and 72 the overlying atmosphere. The interaction of variables, e.g. related to the water and energy 73 budgets, results in characteristic natural variabilities and regimes as well as their changes due 74 to anthropogenic influences. The planetary boundary layer (PBL) is part of the L-A system 75 and represents the interface between the land surface and the free troposphere. Through an 76 exchange of momentum, energy and water, the dynamics, the thermodynamic structure, and 77 the evolution of the PBL affect the formation of shallow and deep clouds, convection initia-78 tion, and thus precipitation (Sherwood et al. 2010, Behrendt et al. 2011, Santanello et al. 79 2011, van den Hurk et al. 2011. One of the most complex feedback 80 loops is between soil moisture and precipitation (Seneviratne et al. 2010, Guillod et al. 2015, 81 Santanello et al. 2017). Precipitation can be influenced directly by the surface fluxes (Ek and 82Holtslag 2004), and indirectly via PBL dynamics and mesoscale circulations (Taylor et al. 83 2012). 84The PBL state and its evolution are strongly influenced by non-linear feedbacks in the L-A 85 system. These are due to two-way interactions between radiation, soil, vegetation, and atmos-86 pheric variables, which result in the diurnal cycles of surface fluxes. The feedbacks are rele-87 vant from local to global scales (Mahmood et al. 2013, Stéfanon et al. 2014, and their 88 strength varies both regionally and seasonally in dependence of soil moisture, advection, and 89 climate regimes. In locations where these feedbacks play an important role, it is likely that 90 they will become even more important due to anthropogenic climate change (Dirmeyer et al. 91 2012). Thus, to improve our understanding of the state and the evolution of the L-A system as 92 well as the dynamics and thermodynamics of the PBL, it is critical that feedbacks and fluxes 93 between the different components, including entrainment at the top of the PBL, are well char-94 4 acterized and appropriately represented in weather, climate, and earth system models (e.g., 95 Se...

Abstract. Measurements of net ecosystem CO2 exchange (NEE) and energy balance were made using chamber-, tower-, and aircraft-based measurement techniques in Alaskan arctic tundra ecosystems during the 1994-1995 growing seasons (June-August). One of our objectives was to quantify the interrelationships between the NEE and the energy balance measurements made from different sampling techniques. Qualitative and quantitative intercomparisons revealed that on average the correspondence between the mass and energy fluxes measured by these sampling methods was good despite potential spatial and temporal mismatches in sampling scale. Quantitative comparisons using least squares linear regression analyses with the tower-based measurements of NEE as the independent variable indicate that the chamber-and aircraft-based NEE measurements were generally lower relative to the tower-based measurements (

Summary Net CO2 flux measurements conducted during the summer and winter of 1994–96 were scaled in space and time to provide estimates of net CO2 exchange during the 1995–96 (9 May 1995–8 May 1996) annual cycle for the Kuparuk River Basin, a 9200 km2 watershed located in NE Alaska. Net CO2 flux was measured using dynamic chambers and eddy covariance in moist‐acidic, nonacidic, wet‐sedge, and shrub tundra, which comprise 95% of the terrestrial landscape of the Kuparuk Basin. CO2 flux data were used as input to multivariate models that calculated instantaneous and daily rates of gross primary production (GPP) and whole‐ecosystem respiration (R) as a function of meteorology and ecosystem development. Net CO2 flux was scaled up to the Kuparuk Basin using a geographical information system (GIS) consisting of a vegetation map, digital terrain map, dynamic temperature and radiation fields, and the models of GPP and R. Basin‐wide estimates of net CO2 exchange for the summer growing season (9 May−5 September 1995) indicate that nonacidic tundra was a net sink of −31.7 ± 21.3 GgC (1 Gg = 109 g), while shrub tundra lost 32.5 ± 6.3 GgC to the atmosphere (negative values denote net ecosystem CO2 uptake). Acidic and wet sedge tundra were in balance, and when integrated for the entire Kuparuk River Basin (including aquatic surfaces), whole basin summer net CO2 exchange was estimated to be in balance (−0.9 ± 50.3 GgC). Autumn to winter (6 September 1995–8 May 1996) estimates of net CO2 flux indicate that acidic, nonacidic, and shrub tundra landforms were all large sources of CO2 to the atmosphere (75.5 ± 8.3, 96.4 ± 11.4, and 43.3 ± 4.7 GgC for acidic, nonacidic, and shrub tundra, respectively). CO2 loss from wet sedge surfaces was not substantially different from zero, but the large losses from the other terrestrial landforms resulted in a whole basin net CO2 loss of 217.2 ± 24.1 GgC during the 1995–96 cold season. When integrated for the 1995–96 annual cycle, acidic (66.4 + 25.25 GgC), nonacidic (64.7 ± 29.2 GgC), and shrub tundra (75.8 ± 8.4 GgC) were substantial net sources of CO2 to the atmosphere, while wet sedge tundra was in balance (0.4 + 0.8 GgC). The Kuparuk River Basin as a whole was estimated to be a net CO2 source of 218.1 ± 60.6 GgC over the 1995–96 annual cycle. Compared to direct measurements of regional net CO2 flux obtained from aircraft‐based eddy covariance, the scaling procedure provided realistic estimates of CO2 exchange during the summer growing season. Although winter estimates could not be assessed directly using aircraft measurements of net CO2 exchange, the estimates reported here are comparable to measured values reported in the literature. Thus, we have high confidence in the summer estimates of net CO2 exchange and reasonable confidence in the winter net CO2 flux estimates for terrestrial landforms of the Kuparuk river basin. Although there is larger uncertainty in the aquatic estimates, the small surface area of aquatic surfaces in the Kuparuk river basin (≈ 5%) presumably reduces the potential f...

Small environmental research aircraft (ERA) are becoming more common for detailed studies of airsurface interactions. The Sky Arrow 650 ERA, used by multiple groups, is designed to minimize the complexity of high-precision airborne turbulent wind measurement. Its relative wind probe, of a nine-port design, is furthermore used with several other airplanes. This paper gives an overview of 1) calibration of the model that converts the probe's raw measurements to meteorological quantities; 2) quality control and assurance (QC-QA) in postprocessing of these quantities to compute fluxes; and 3) sensitivity of fluxes to errors in calibration parameters. The model, an adapted version of standard models of potential flow and aerodynamic upwash, is calibrated using an integrated method to derive a globally optimum set of parameters from in-flight maneuvers. Methods of QC-QA from the tower flux community are adopted for use with airborne flux data to provide more objective selection criteria for large datasets. Last, measurements taken from a standard operational flight are used to show fluxes to be most sensitive to calibration parameters that directly affect the vertical wind component. In another test with the same data, varying all calibration parameters simultaneously by 610% of their optimum values, the model computes a response in the fluxes smaller than 10%, though a larger response may occur if only a subset of parameters is perturbed. A MATLAB toolbox has been developed that facilitates the procedures presented here.