A long-term (2005–2016) dataset of hourly integrated land–atmosphere interaction observations on the Tibetan Plateau

Abstract

Abstract. The Tibetan Plateau (TP) plays a critical role in influencing regional and global climate, via both thermal and dynamical mechanisms. Meanwhile, as the largest high-elevation part of the cryosphere outside the polar regions, with vast areas of mountain glaciers, permafrost and seasonally frozen ground, the TP is characterized as an area sensitive to global climate change. However, meteorological stations are biased and sparsely distributed over the TP, owing to the harsh environmental conditions, high elevations, complex topography and heterogeneous surfaces. Moreover, due to the weak representation of the stations, atmospheric conditions and the local land–atmosphere coupled system over the TP as well as its effects on surrounding regions are poorly quantified. This paper presents a long-term (2005–2016) in situ observational dataset of hourly land–atmosphere interaction observations from an integrated high-elevation and cold-region observation network, composed of six field stations on the TP. These in situ observations contain both meteorological and micrometeorological measurements including gradient meteorology, surface radiation, eddy covariance (EC), soil temperature and soil water content profiles. Meteorological data were monitored by automatic weather stations (AWSs) or planetary boundary layer (PBL) observation systems. Multilayer soil temperature and moisture were recorded to capture vertical hydrothermal variations and the soil freeze–thaw process. In addition, an EC system consisting of an ultrasonic anemometer and an infrared gas analyzer was installed at each station to capture the high-frequency vertical exchanges of energy, momentum, water vapor and carbon dioxide within the atmospheric boundary layer. The release of these continuous and long-term datasets with hourly resolution represents a leap forward in scientific data sharing across the TP, and it has been partially used in the past to assist in understanding key land surface processes. This dataset is described here comprehensively for facilitating a broader multidisciplinary community by enabling the evaluation and development of existing or new remote sensing algorithms as well as geophysical models for climate research and forecasting. The whole datasets are freely available at the Science Data Bank (https://doi.org/10.11922/sciencedb.00103; Ma et al., 2020) and additionally at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Meteoro.tpdc.270910, Ma 2020).

Long-term variations in actual evapotranspiration over the Tibetan Plateau

“…The instrumental setup at each site consists of an EC system comprising a sonic anemometer (CSAT3, Campbell Scientific Inc) and an open-path gas analyzer (LI-7500, LI-COR); a fourcomponent radiation flux system (CNR-1, Kipp & Zonen), installed at a height of 1.5 m; a soil heat flux plate (Hukseflux, HFP01), buried in the soil at a depth of 0.1 m; and soil moisture and temperature probes, buried at a depth of 0.05, 0.10, and 0.15 m, respectively (Han et al, 2017). The EC data were processed with the EC software package TK3 (Mauder and Foken, 2015). The main post-processing procedures of the EC raw data were as follows: spike detection, coordinate rotation, spectral loss correction, frequency response corrections (Moore, 1986), and corrections for density fluctuations (Webb et al, 1980).…”

Toward a definition of Essential Mountain Climate Variables

“…Opportunities to remotely estimate surface soil moisture conditions are increasing, but their coarse spatial resolution remains an issue in areas of rugged topography. Generally speaking, more intense and comprehensive monitoring of land-atmosphere interactions in such regions (see, e.g., Ma and co-workers 100 and van den Bergh and co-workers 101 ) could prove many valuable insights.…”

Improved parameterization of snow albedo in Noah coupled with Weather Research and Forecasting: applicability to snow estimates for the Tibetan Plateau

“…Much research has been carried out to identify the factors that influence these changes, including the effects of terrain shielding, altitude, sky conditions, vege-L. Liu et al: Improved parameterization of snow albedo in Noah coupled with WRF tation, and snow properties such as grain size, liquid water content, depth, and impurities (Warren and Wiscombe, 1980;Wiscombe and Warren, 1980;Aoki et al, 2003;Jonsell et al, 2003;Hansen and Nazarenko, 2004;Liang et al, 2005;Wang et al, 2015;He et al, 2018a). This body of research has led to the development of many parameterization schemes for surface albedo (Oerlemans and Knap, 1998;Wang et al, 2007;Bao et al, 2008;Li and Hu, 2009;Gardner and Sharp, 2010;Kuipers Munneke et al, 2011;Malik et al, 2014;Dang et al, 2015;He et al, 2017He et al, , 2018bMeng and Li, 2019;Saito et al, 2019;Wang et al, 2020). Most snow albedo parameterization schemes depend on statistical empirical formulas and constant parameters rather than on representing physical snow albedo feedback processes.…”