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Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site-level synthesis
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Black, T.A., Izaurralde, R.C., Lokupitiya, E., Munger, J.W., Schaefer, K., Weng, E., Richardson, A.D., Altaf Arain, M., Luo, Y., Ciais, P., Ricciuto, D.M., Stoy, P.C., Dietze, M.C., Poulter, B., Barr, A.G., Liu, S., Hollinger, D., Tian, H., Suyker, A.E., Verbeeck, H., Price, D.T., Grant, R.F., Peng, C., Baker, I.T., Vargas, R., Anderson, R.S., Tonitto, C., Sahoo, A.K., Chen, J.M., Flanagan, L.B., Riley, W.J., Wang, W., Lafleur, P., Gough, C.M., Verma, S.B., Kucharik, C.J.
Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods,...
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Spring 2022
Reinforcement learning (RL) has shown great success in solving many challenging tasks via the use of deep neural networks. Although the use of deep learning for RL brings immense representational power to the arsenal, it also causes sample inefficiency. This means that the algorithms are...