The Turbulence Regime of the Atmospheric Surface Layer in the Presence of Shallow Cold Drainage Flows: Application of Laser Scintillometry

The presence of shallow cold flows in the atmospheric boundary layer (ABL) instigates changes in the turbulent regime of the atmospheric surface layer (ASL). This small scale flow circulation introduces radiative cooling controls over large areas in polar latitudes during winter. In this study, microscale dynamic and turbulent variables have been obtained in the framework of the Winter Boundary Layer Experiment in Fairbanks, Alaska, developed during the winters of 2009/2010 and 2010/2011. Multiscale surface turbulence observations based on Eddy covariance and laser scintillometry were combined with Doppler acoustic sounding to document simultaneous changes in the ABL flow and ASL turbulence. We computed changes in momentum and heat fluxes characterizing intermittent and persistent modes of the drainage flow over three study cases. On the basis of laser scintillometry observations, we argue that a significant source of turbulence aiming at the surface fluxes has origins in the upper level shear-induced thermal turbulence at the top of the ABL

Evapotranspiration in Northern Agro-Ecosystems: Numerical Simulation and Experimental Comparison

Evapotranspiration and near-surface soil moisture dynamics are key-entangled variables regulating flux at the surface-atmosphere interface. Both are central in improving mass and energy balances in agro ecosystems. However, under the extreme conditions of high-latitude soils and weather pattern variability, the implementation of such coupled liquid and vapor phase numerical simulation remain to be tested. We consider the nonisothermal solution of the vapor flux equation that accounts for the thermally driven water vapor transport and phase changes. Fully coupled flux model outputs are compared and contrasted against field measurements of soil temperature, heat flux, water content, and evaporation in a subarctic agroecosystem in Alaska. Two well-defined hydro-meteorological situations were selected: dry and wet periods. Numerical simulation was forced by time series of incoming global solar radiation and atmospheric surface layer thermodynamic parameters: surface wind speed, ambient temperature, relative humidity, precipitation, and soil temperature and soil moisture. In this simulation, soil parameters changing in depth and time are considered as dynamically adjusted boundary conditions for solving the set of coupled differential equations. Results from this evaluation give good correlation of modeled and observed data in net radiation (Rnet) (R2 of 0.92, root mean square error (RMSE) of 45 W m−2), latent heat (0.70, RMSE of 53 W m−2), and sensible heat (R2 = 0.63, RMSE = 32 W m−2) during the dry period. On the other hand, a poor agreement was obtained in the radiative fluxes and turbulent fluxes during the wet period due to the lack of representation in the radiation field and differences in soil dynamics across the landscape

Unusually Deep Wintertime Cirrus Clouds Observed over the Alaskan Sub-Arctic

Unusually deep wintertime cirrus clouds at altitudes exceeding 13.0 km above mean sea level (AMSL) were observed at Fairbanks, Alaska (64.86 N, 147.85 W, 0.300 km AMSL) over a twelve hour period, beginning near 1200 UTC 1 January 2017. Such elevated cirrus cloud heights are far more typical of warmer latitudes, and in many instances associated with convective outflow, as opposed to early winter over the sub-Arctic on a day featuring barely four hours of local sunlight. In any other context, they could have been confused for polar stratospheric clouds, which are a more common regional/seasonal occurrence at elevated heights. The mechanics of this unique event are documented, including the thermodynamic and synoptic environments that nurtured and sustained cloud formation. The impact of an unusually deep and broad anticyclone over the wintertime Alaskan sub-Arctic is described. Comparisons with climatological datasets illustrate how unusual these events are regionally and seasonally. The event proves a relatively uncharacteristic confluence of circulatory and dynamic features over the wintertime Alaskan sub-Arctic. Our goal is to document the occurrence of this event within the context of a growing understanding for how cirrus cloud incidence and their physical characteristics vary globally. Cirrus clouds are unique within the earth-atmosphere system. Formed by the freezing of submicron haze particles in the upper troposphere, they are the last primary cloud mechanism contributing to the large scale exchange of the terrestrial water cycle. Accordingly, cirrus clouds are observed globally at all times of the year, exhibiting an instantaneous global occurrence rate near 40%. Radiatively, however, they are even more distinct. During daylight hours, cirrus are the only cloud genus that can induce either positive or negative top-of-the-atmosphere forcing (i.e., heating or cooling; all other clouds induce a negative sunlit cooling effect). Though diffuse compared with low-level liquid water clouds, their significance radiatively and thus within climate, is borne out of their overwhelming relative occurrence rate. This emerging recognition makes understanding cirrus cloud occurrence and physical cloud properties an innovative and exciting element of current climate study. The observations described here contribute to this knowledge, and the apparent potential for anomalous wintertime radiative characteristics exhibited along sub-Arctic latitudes

A combined microbial and biogeochemical dataset from high-latitude ecosystems with respect to methane cycle.

High latitudes are experiencing intense ecosystem changes with climate warming. The underlying methane (CH4) cycling dynamics remain unresolved, despite its crucial climatic feedback. Atmospheric CH4 emissions are heterogeneous, resulting from local geochemical drivers, global climatic factors, and microbial production/consumption balance. Holistic studies are mandatory to capture CH4 cycling complexity. Here, we report a large set of integrated microbial and biogeochemical data from 387 samples, using a concerted sampling strategy and experimental protocols. The study followed international standards to ensure inter-comparisons of data amongst three high-latitude regions: Alaska, Siberia, and Patagonia. The dataset encompasses diferent representative environmental features (e.g. lake, wetland, tundra, forest soil) of these high-latitude sites and their respective heterogeneity (e.g. characteristic microtopographic patterns). The data included physicochemical parameters, greenhouse gas concentrations and emissions, organic matter characterization, trace elements and nutrients, isotopes, microbial quantifcation and composition. This dataset addresses the need for a robust physicochemical framework to conduct and contextualize future research on the interactions between climate change, biogeochemical cycles and microbial communities at highlatitudes

Lidar to determine the fractions of ice, liquid and water vapor in polar tropospheric clouds

A new Lidar combining Raman spectroscopy and linear polarization analysis is presented. This new instrument identifies the fraction of ice, liquid, and water vapor in low level polar tropospheric clouds and provides the polarimetric S and P state of the backscattering 532 nm Lidar signal. An overview of the research applications is given followed by a theoretical estimation of the Lidar returns. The instrument concept and optical characteristics are discussed. First Lidar profiles and instrument evaluations will be provided during the conference

Projections of spring wheat growth in Alaska: Opportunity and adaptations in a changing climate

Recent accelerations of climate warmings can open agricultural opportunities in the region of Interior Alaska. In this paper, a simulation of spring wheat growth forced with projected climate scenarios was conducted by the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. The model was calibrated and validated using experimental data.Using an Alaskan cultivar (cv.) Ingal and a baseline covering 1989–2018, projected changes in days to maturity and yield were simulated following the Representative Concentration Pathways (RCP) 4.5 (medium–low emissions) and RCP8.5 (high emissions) climate change scenarios. For each RCP scenario, spring wheat growth was simulated in the time series covering 2020–2049 (indicated as 2035 s), 2050–2079 (2065 s), and 2080–2099 (2090 s). The baseline value of days to maturity was 69 and yield resulted in 1956 kg ha−1. Results show that under RCP4.5 and RCP8.5 2035 s, 2065 s, and 2090 s scenarios, days to maturity decrease, ranging from 64 to 55 days, and changes in yield range from a 3% increase to a 6% decrease.Adaptation by increasing the cultivar’s growing degree day requirement resulted in 69 and 68 days to maturity in RCP4.5 2035 s and RCP8.5 2035 s, respectively, which in turn increased yields 5% and 7%, respectively. Increased soil water at planting from 80 to 85% field capacity, due to increased annual precipitation, resulted in additional yield increases. This indicates that selecting spring wheat varieties to maintain similar baseline days to maturity and agronomic practices that store fall/winter precipitation are of importance to materialize future spring wheat yield increases of Interior Alaska

Monthly mean meteorological parameters measured at the FEF.

<p>Monthly means calculated between June to September during the 2012^a and 2013^a growing season in comparison with historical data of the climate normal^b in the 30-year time period from 1981–2010 for Fairbanks, Alaska, USA, provided by the National Climatic Data Center. Hist. represents the mean monthly historical climatological data in the 30-year period. The second-four columns indicate mean air temperatures with standard deviation (Std dev) during two summer seasons compared to the 30-year average.</p><p>^a Meteorological station at the study site</p><p>^bThe climate normal (a 30-year mean) at the Fairbanks International Airport (<a href="http://climate.gi.alaska.edu/Climate/Normals" target="_blank">http://climate.gi.alaska.edu/Climate/Normals</a>).</p><p>^C Here the growing period is calculated from 1 June to 20 September</p><p>Monthly mean meteorological parameters measured at the FEF.</p

Time-series of average surface air temperature during growing season 2012 and 2013 compared with the thirty years climate data.

<p>The green line shows the daily mean of air temperature in 2012, the red line shows the daily mean of air temperature in 2013, and the black line shows the 30-year average of air temperature. Shading of each color provides an indication of the confidence range of the air temperature. The horizontal axis represents fractional Julian day in local AKST.</p