The effect of air-sea coupling on the simulated boreal summer intraseasonal oscillation (BSISO) is examined using atmosphere—ocean-mixed-layer coupled (SPCAM3-KPP) and uncoupled configurations of the Super-Parameterized (SP) Community Atmospheric Model, version 3 (SPCAM3). The coupled configuration is constrained to either the observed ocean mean state or the mean state from the SP coupled configuration with a dynamic ocean (SPCCSM3), to understand the effect of mean state biases on the BSISO in the latter. All configurations overestimate summer mean subtropical rainfall and its intraseasonal variance. All configurations simulate realistic BSISO northward propagation over the Indian Ocean and western Pacific, in common with other SP configurations.Constraining SPCAM3-KPP to the SPCCSM3 mean state reduces the overestimated BSISO variability, but also weakens BSISO propagation. Using the SPCCSM3 mean state also introduces a one-month delay to the BSISO seasonal cycle compared to SPCAM3-KPP with the observed ocean mean state, which matches well with the reanalysis. The phase relationship between intraseasonal rainfall and sea surface temperature (SST) is captured by all coupled models, but with a shorter delay between suppressed convection and warm SST relative to the reanalysis. Prescribing the 31-day smoothed SSTs from the SPCAM3-KPP simulations in SPCAM3 worsens the overestimated BSISO variance. This suggests that air-sea coupling improves the amplitude of the simulated BSISO. Based on a Taylor diagram, SPCCSM3 mean state SST biases and air-sea coupling both lead to higher simulated BSISO fidelity, largely due to their ability to suppress the overestimated subtropical BSISO variance.
The simple version has omitted Tensorboard function.
When you want to use the function of Tensorboard,
use New version.
New version is here.https://doi.org/10.6084/m9.figshare.7801154The simple version works faster than The Tensorboard Version.
The PredNet*, written in Keras, was ported to Chainer and reconstructed for convenience.Visualization of internal images in the network models was performed using a self-made converter program and Tesorboard in Tensorflow.
*Lotter, W., Kreiman, G., & Cox, D., Deep predictive coding networks for video prediction and unsupervised learning, arXiv 1605.08104.
This program was used inWatanabe E, Kitaoka A, Sakamoto K, Yasugi M and Tanaka K, Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction. Frontiers in Psychology 9:345. doi: 10.3389/fpsyg.2018.00345 (2018)
The ethnobotanyR package can be used to calculate common quantitative ethnobotany indices to assess the cultural significance of plant species based on informant consensus.
ethnobotanyR v.0.1.6 is a patch with updated quantitative indices and new tools for generating diagrams of uses by species and informants.
Take part in the development:
An implementation of the quantitative ethnobotany indices in R. The goal is to provide an easy- to-use platform for ethnobotanists to assess the cultural significance of plant species based on informant consensus. The package closely follows the paper by Tardio and Pardo-de-Santayana (2008). Tardio, J., and M. Pardo-de-Santayana, 2008. Cultural Importance Indices: A Comparative Analysis Based on the Useful Wild Plants of Southern Cantabria (North- ern Spain) 1. Economic Botany, 62(1), 24-39. .