Browsing by Author "Hao, Dalei"

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    Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle
    Jian, Jinshi; Bailey, Vanessa; Dorheim, Kalyn; Konings, Alexandra G.; Hao, Dalei; Shiklomanov, Alexey N.; Snyder, Abigail; Steele, Meredith; Teramoto, Munemasa; Vargas, Rodrigo; Bond-Lamberty, Ben (Springer Nature, 2022-04-01)
    The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, RS), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and RS are irreconcilable. When we estimate GPP based on RS measurements and some assumptions about RS:GPP ratios, we found the resulted global GPP values (bootstrap mean 149+29−23 Pg C yr−1) are significantly higher than most GPP estimates reported in the literature (113+18−18 Pg C yr−1). Similarly, historical GPP estimates imply a soil respiration flux (RsGPP, bootstrap mean of 68+10−8 Pg C yr−1) statistically inconsistent with most published RS values (87+9−8 Pg C yr−1), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global RS:GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget.
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    Predicting Spring Phenology in Deciduous Broadleaf Forests: NEON Phenology Forecasting Community Challenge
    Wheeler, Kathryn I.; Dietze, Michael C.; LeBauer, David; Peters, Jody A.; Richardson, Andrew D.; Ross, Arun A.; Thomas, R. Quinn; Zhu, Kai; Bhat, Uttam; Munch, Stephan; Buzbee, Raphaela Floreani; Chen, Min; Goldstein, Benjamin; Guo, Jessica; Hao, Dalei; Jones, Chris; Kelly-Fair, Mira; Liu, Haoran; Malmborg, Charlotte; Neupane, Naresh; Pal, Debasmita; Shirey, Vaughn; Song, Yiluan; Steen, McKalee; Vance, Eric A.; Woelmer, Whitney M.; Wynne, Jacob H.; Zachmann, Luke (Elsevier, 2024-01-01)
    Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative's National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate when, where, and for what model type phenology forecast skill is highest. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts.