• Energy Research

AbstractThe FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

AbstractIt is critical to accurately estimate carbon (C) turnover time as it dominates the uncertainty in ecosystem C sinks and their response to future climate change. In the absence of direct observations of ecosystem C losses, C turnover times are commonly estimated under the steady state assumption (SSA), which has been applied across a large range of temporal and spatial scales including many at which the validity of the assumption is likely to be violated. However, the errors associated with improperly applying SSA to estimate C turnover time and its covariance with climate as well as ecosystem C sequestrations have yet to be fully quantified. Here, we developed a novel model‐data fusion framework and systematically analyzed the SSA‐induced biases using time‐series data collected from 10 permanent forest plots in the eastern China monsoon region. The results showed that (a) the SSA significantly underestimated mean turnover times (MTTs) by 29%, thereby leading to a 4.83‐fold underestimation of the net ecosystem productivity (NEP) in these forest ecosystems, a major C sink globally; (b) the SSA‐induced bias in MTT and NEP correlates negatively with forest age, which provides a significant caveat for applying the SSA to young‐aged ecosystems; and (c) the sensitivity of MTT to temperature and precipitation was 22% and 42% lower, respectively, under the SSA. Thus, under the expected climate change, spatiotemporal changes in MTT are likely to be underestimated, thereby resulting in large errors in the variability of predicted global NEP. With the development of observation technology and the accumulation of spatiotemporal data, we suggest estimating MTTs at the disequilibrium state via long‐term data assimilation, thereby effectively reducing the uncertainty in ecosystem C sequestration estimations and providing a better understanding of regional or global C cycle dynamics and C‐climate feedback.

AbstractIncreasing urbanization causes an urban heat island (UHI) effect and exacerbates health risks of heat waves due to global warming. The surface UHI (SUHI) in large cities has been extensively studied, yet a systematic evaluation on the impacts of urbanization on local‐to global‐scale land surface warming is lacking. We propose a new procedure to quantify the warming effects of urbanization at local, regional, and global scales using high‐resolution satellite observations. We find strong local warming effects for 88% of the urban‐dominated pixels across the globe and cooling effects for the rest of the urban lands on a diurnal mean timescale, with a global urban mean intensity of 1.1°C in 2015. The SUHI effects differ substantially by time of day, season, and climate zone, and are closely related to surface evapotranspiration. By extending local effects to the entire land surface, we estimate a diurnal mean warming of only 0.008°C globally. However, urbanization can have large warming effects regionally, especially in eastern China, the eastern United States, and Europe. In addition, we show that global urban expansion results in over three‐quarters of SUHI effects in 1985–2015, and its effect will likely increase by 50%–200% by the end of this century. The SUHI‐added warming could be up to 0.12°C in summer in Europe by 2100 under a fossil‐fueled development pathway. Our results reveal that urbanization substantially intensifies local and regional land surface warming and that prioritized attention should be given to the SUHI‐added warming in highly or rapidly urbanized regions.