Browsing by Author "Chen, FL"

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    Changes in below‐cloud evaporation affect precipitation isotopes during five decades of warming across China
    (American Geophysical Union, 2021-03-28) Wang, SJ; Jiao, R; Zhang, MJ; Crawford, J; Hughes, CE; Chen, FL
    Based on daily meteorological records for 651 sites across China during the period 1960–2018, we estimated the changes in isotopic variations in raindrops as they descend from cloud base to ground over past decades, and tested the sensitivity of isotopic variations to climate parameters like air temperature and relative humidity. Air temperature correlates positively and relative humidity correlates negatively with below‐cloud isotopic variation. Generally, the below‐cloud evaporation effect on precipitation isotopes in the arid and semi‐arid regions of China is much greater than that in the humid and semi‐humid regions, although the impact might be reduced under cold‐arid or hot‐humid conditions. With aridity increasing with distance from the coast, the continental effect of precipitation isotopes is modified due to the below‐cloud evaporation. The seasonal pattern of the measured isotopic composition in precipitation near the ground and estimated at cloud base, is still similar in most regions, although the seasonal range is higher at the ground. During the last five decades, the below‐cloud evaporation effect has enhanced for the cold and arid regions of China especially across Qinghai‐Tibet Plateau and Inner Mongolia, due to combined effects of increasing air temperature and decreasing relative humidity. Although the below‐cloud evaporation effect is not always the dominant factor influencing the variability of stable isotopes, it needs to be considered as one of the contributing factors. This enhanced effect may impact the interpretation of past climate based on stable water isotopes, particularly in paleoclimate studies using speleothems and tree rings. © 2021. American Geophysical Union
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    Factors controlling stable isotope composition of precipitation in arid conditions: an observation network in the Tianshan Mountains, central Asia
    (Taylor & Francis Group, 2016-02-01) Wang, SJ; Zhang, MJ; Hughes, CE; Zhu, XF; Dong, L; Ren, ZG; Chen, FL
    Approximately one-third of the Earth's arid areas are distributed across central Asia. The stable isotope composition of precipitation in this region is affected by its aridity, therefore subject to high evaporation and low precipitation amount. To investigate the factors controlling stable water isotopes in precipitation in arid central Asia, an observation network was established around the Tianshan Mountains in 2012. Based on the 1052 event-based precipitation samples collected at 23 stations during 2012–2013, the spatial distribution and seasonal variation of δD and δ18O in precipitation were investigated. The values of δD and δ18O are relatively more enriched in the rainfall dominant summer months (from April to October) and depleted in the drier winter months (from November to March) with low D-excess due to subcloud evaporation observed at many of the driest low elevation stations. The local meteoric water line (LMWL) was calculated to be δD=7.36δ18O – 0.50 (r2=0.97, p<0.01) based on the event-based samples, and δD=7.60δ18O+2.66 (r2=0.98, p<0.01) based on the monthly precipitation-weighted values. In winter, the data indicate an isotopic rain shadow effect whereby rainout leads to depletion of precipitation in the most arid region to the south of the Tianshan Mountains. The values of δ18O significantly correlate with air temperature for each station, and the best-fit equation is established as δ18O=0.78T – 16.01 (r2=0.73, p<0.01). Using daily air temperature and precipitation derived from a 0.5° (latitude)×0.5° (longitude) gridded data set, an isoscape of δ18O in precipitation was produced based on this observed temperature effect. © 2016 S. Wang et al. (Open Access)
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    Meteoric water lines in arid Central Asia using event-based and monthly data
    (Elsevier B. V., 2018-07) Wang, SJ; Zhang, MJ; Hughes, CE; Crawford, J; Wang, GF; Chen, FL; Du, MX; Qui, X; Zhou, S
    The local meteoric water line (LMWL) reflects the relationship between stable oxygen and hydrogen isotopes in precipitation, and is usually calculated using an ordinary least squares regression (OLSR). When event-based data are used to calculate a LMWL, the differences in precipitation amount of samples are not considered using OLSR, which in turn may influence the representativeness of the LMWL for local hydrology. Small rain events occur widely in arid Central Asia (annual mean precipitation <150 mm), and where smaller precipitation has lower deuterium excess, this results in LMWLs with slopes and intercepts lower than the global average. Based on an observation network of isotopes in precipitation across the Tianshan Mountains in arid Central Asia, LMWLs for 23 stations are calculated from event-based data from 2012 to 2013 (n = 978), using ordinary least squares, reduced major axis and major axis regressions and their precipitation-weighted counterparts. For the northern slope and mountainous areas, the LMWL slope and intercept are close to the Global Meteoric Water Line (GMWL), but the slope and intercept are lower for the southern slope indicating the greater dominance of sub-cloud evaporation. The effect of moisture recycling in the irrigated areas on the northern slope also can be seen where the LMWL slopes are >8. Using a precipitation weighted regression method with event-based data (especially precipitation-weighted reduced major axis regression, PWRMA) is generally consistent with the OLSR regression using monthly data. However, event-based datasets provide a wider range of values to better constrain the regression than can be achieved using monthly data over a short period, providing a sounder basis for determining LMWLs for relatively short-term sampling campaigns in an arid setting. The use of the PWRMA regression is preferred for determining the LMWL for the Tianshan Mountains, and results in a regional meteoric water line of δD = 7.9δ18O + 10.16. © 2021 Elsevier B.V.