Energy Exchange and Evapotranspiration over the Ejina Oasis Riparian Forest Ecosystem with Different Land-Cover Types

Investigating the energy and water vapor exchange in oasis riparian forest ecosystems is of significant importance to improve scientific understanding of land surface processes in extreme arid regions. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) provided many observations of water vapor and heat fluxes from riparian forest ecosystem by using a network of eddy-covariance (EC) systems installed over representative surfaces in the Ejina Oasis, which is located in the downstream areas of the Heihe River Basin, northwestern China. Based on EC flux measurements and meteorological data performed at five stations and covering representative surface types of Populus euphratica tree with associated Tamarix chinensis shrub, Tamarix chinensis shrubland, cantaloupe cropland, and barren-land, this study explored the spatio-temporal patterns of heat and water vapor fluxes over the Ejina Oasis riparian forest ecosystem with five different surface types over the course of a growing season in 2014. Energy balance closure of the flux data was evaluated; footprint analysis for each EC site was also performed. Results showed that energy balance closure for the flux data was reasonably good, with average energy balance ratio (EBR) of 1.03. The seasonal variations in net radiation (Rn), latent (LE), and sensible heat flux (H) over the five contrasting surfaces were similar, and a reverse seasonal change was observed in energy partitioning into LE and H. Remarkable differences in Rn, LE, and H between the five surfaces were explored preliminarily, associated closely with the soil properties and foliage phenology. Over the growing season (May–October) in 2014, the total ET ranged 622–731 mm for mixed forest of P. euphratica trees with associated T. chinensis shrubs with average daily ET of 3.6–4.2 mm; ET from T. chinensis shrubland was about 541 mm, with average daily ET of 3.6 mm. ET for barren-land was 195 mm. The total ET in irrigated cantaloupe cropland with plastic mulch was 431 mm for its four-month growing period with a total average of 3.8 mm d−1. Determination of ET over riparian forest ecosystem helps to improve reasonable use of limited water resource in the Ejina Oasis.

Utilizing a novel mobile diagnostics lab to validate the impact of vegetative wall coverings in building cooling load reduction

Building cooling loads are driven by heat gains through enclosures. This research identifies possible ways of reducing the building cooling loads through vegetative shading. Vegetative shading reduces heat gains by blocking radiation and by evaporative air cooling. Few measured data exist, so we gathered thermal data from a vegetative wall grown in front of a Mobile Diagnostics Lab (MDL), a trailer with one conditioned room with instrumentation that collects thermal data from heat flux sensors and thermistors within its walls. In spring 2020 a variety of plants were cultivated in a greenhouse and planted in front of the south façade of the MDL, which was placed in direct sunlight to collect heat flux data. The plants acted as a barrier for solar radiation and reduced the amount of thermal energy affecting the trailer surface. Data were collected through the use of 16 heat flux sensors and development of continuous infrared (IR) images indicating surface temperature with and without plant cover. The façade surface beneath the plants was 10-30 °C cooler than exposed façade areas. In further analyses, the heat-flux data were compared to IR temperature data.

Radiative Energy Flux Variation from 2001–2020

Radiative energy flux data, downloaded from CERES, are evaluated with respect to their variations from 2001 to 2020. We found the declining outgoing shortwave radiation to be the most important contributor for a positive TOA (top of the atmosphere) net flux of 0.8 W/m2 in this time frame. We compare clear sky with cloudy areas and find that changes in the cloud structure should be the root cause for the shortwave trend. The radiative flux data are compared with ocean heat content data and analyzed in the context of a longer-term climate system enthalpy estimation going back to the year 1750. We also report differences in the trends for the Northern and Southern hemisphere. The radiative data indicate more variability in the North and higher stability in the South. The drop of cloudiness around the millennium by about 1.5% has certainly fostered the positive net radiative flux. The declining TOA SW (out) is the major heating cause (+1.42 W/m2 from 2001 to 2020). It is almost compensated by the growing chilling TOA LW (out) (−1.1 W/m2). This leads together with a reduced incoming solar of −0.17 W/m2 to a small growth of imbalance of 0.15 W/m2. We further present surface flux data which support the strong influence of the cloud cover on the radiative budget.

Evaluation of F10.7, Sunspot Number and Photon Flux Data for Ionosphere TEC Modeling and Prediction Using Machine Learning Techniques

Considering the growing volumes and varieties of ionosphere data, it is expected that automation of analytical model building using modern technologies could lead to more accurate results. In this work, machine learning techniques are applied to ionospheric modeling and prediction using sun activity data. We propose Total Electron Content (TEC) spectral analysis, using discrete cosine transform (DCT) to evaluate the relation to the solar features F10.7, sunspot number and photon flux data. The ionosphere modeling procedure presented is based on the assessment of a six-year period (2014–2019) of data. Different multi-dimension regression models were considered in experiments, where each geographic location was independently evaluated using its DCT frequency components. The features correlation analysis has shown that 5-year data seem more adequate for training, while learning curves revealed overfitting for polynomial regression from the 4th to 7th degrees. A qualitative evaluation using reconstructed TEC maps indicated that the 3rd degree polynomial regression also seems inadequate. For the remaining models, it can be noted that there is seasonal variation in root-mean-square error (RMSE) clearly related to the equinox (lower error) and solstice (higher error) periods, which points to possible seasonal adjustment in modeling. Elastic Net regularization was also used to reduce global RMSE values down to 2.80 TECU for linear regression.

Representativeness assessment of the pan-Arctic eddy-covariance site network, and optimized future enhancements

Large changes in the Arctic carbon balance are expected as warming linked to climate change threatens to destabilize ancient permafrost carbon stocks. The eddy covariance (EC) method is an established technique to quantify net losses and gains of carbon between the biosphere and atmosphere at high spatio-temporal resolution. Over the past decades, a growing network of terrestrial EC tower sites has been established across the Arctic, but a comprehensive assessment of the network’s representativeness within the heterogeneous Arctic region is still lacking. This creates additional uncertainties when integrating flux data across sites, for example when upscaling fluxes to constrain pan-Arctic carbon budgets, and changes therein. This study provides an inventory of Arctic (here >= 60° N) EC sites, which has also been made available online (https://cosima.nceas.ucsb.edu/carbon-flux-sites/). Our database currently comprises 120 EC sites, but only 83 are listed as active, and just 25 of these active sites remain operational throughout the winter. To map the representativeness of this EC network, based on 18 bioclimatic and edaphic variables, we evaluated the similarity between environmental conditions observed at the tower locations and those within the larger Arctic study domain. With the majority of sites located in Fennoscandia and Alaska, these regions were assigned the highest level of network representativeness, while large parts of Siberia and patches of Canada were classified as under-represented. This division between regions is further emphasized for wintertime and methane flux data coverage. Across the Arctic, particularly mountainous regions were poorly represented by the current EC observation network. We tested three different strategies to identify new site locations, or upgrades of existing sites, that optimally enhance the representativeness of the current EC network. While 15 new sites can improve the representativeness of the pan-Arctic network by 20 percent, upgrading as few as 10 existing sites to capture methane fluxes, or remain active during wintertime, can improve their respective network coverage by 28 to 33 percent. This targeted network improvement could be shown to be clearly superior to an unguided selection of new sites, therefore leading to substantial improvements in network coverage based on relatively small investments.

A global dataset of atmospheric ⁷Be and ²¹⁰Pb measurements: annual air concentration and depositional flux

7Be and 210Pb air concentration and depositional flux data provide key information on the origins and movements of air masses, as well as atmospheric deposition processes and residence time of aerosols. After their deposition onto the Earth's surface, they are utilized for tracing soil redistribution processes on land, particle dynamics in aquatic systems, and mixing processes in open ocean. Here we present a global dataset of air concentration and depositional flux measurements of atmospheric 7Be and 210Pb made by a large number of global research communities. Data were collected from published papers between 1955 and early 2020. It includes the annual surface air concentration data of 7Be from 367 sites and 210Pb from 270 sites, the annual depositional flux data of 7Be from 279 sites and 210Pb from 602 sites. When available, appropriate metadata have also been summarized, including geographic location, sampling date, methodology, annual precipitation, and references. The dataset is archived at https://doi.org/10.5281/zenodo.4785136 (Zhang et al., 2021) and is freely available for the scientific community. The purpose of this paper is to provide an overview of the scope and nature of this dataset and its potential utility as baseline data for future research.

BAWLD-CH₄: A Comprehensive Dataset of Methane Fluxes from Boreal and Arctic Ecosystems

Methane (CH4) emissions from the Boreal and Arctic region are globally significant and highly sensitive to climate change. There is currently a wide range in estimates of high-latitude annual CH4 fluxes, where estimates based on land cover inventories and empirical CH4 flux data or process models (bottom-up approaches) generally are greater than atmospheric inversions (top-down approaches). A limitation of bottom-up approaches has been the lack of harmonization between inventories of site-level CH4 flux data and the land cover classes present in high-latitude spatial datasets. Here we present a comprehensive dataset of small-scale, surface CH4 flux data from 540 terrestrial sites (wetland and non-wetland) and 1247 aquatic sites (lakes and ponds), compiled from 189 studies. The Boreal-Arctic Wetland and Lake Methane Dataset (BAWLD-CH4) was constructed in parallel with a compatible land cover dataset, sharing the same land cover classes to enable refined bottom-up assessments. BAWLD-CH4 includes information on site-level CH4 fluxes, but also on study design (measurement method, timing, and frequency) and site characteristics (vegetation, climate, hydrology, soil, and sediment types, permafrost conditions, lake size and depth, and our determination of land cover class). The different land cover classes had distinct CH4 fluxes, resulting from definitions that were either based on or co-varied with key environmental controls. Fluxes of CH4 from terrestrial ecosystems were primarily influenced by water table position, soil temperature, and vegetation composition, while CH4 fluxes from aquatic ecosystems were primarily influenced by water temperature, lake size, and lake genesis. Models could explain more of the between-site variability in CH4 fluxes for terrestrial than aquatic ecosystems, likely due to both less precise assessments of lake CH4 fluxes and fewer consistently reported lake site characteristics. Analysis of BAWLD-CH4 identified both land cover classes and regions within the Boreal and Arctic domain where future studies should be focused, alongside methodological approaches. Overall, BAWLD-CH4 provides a comprehensive dataset of CH4 emissions from high-latitude ecosystems that are useful for identifying research opportunities, for comparison against new field data, and model parameterization or validation. BAWLD-CH4 can be downloaded from https://doi.org/10.18739/A27H1DN5S.