12 October, 2020 (Green, ng, regional feedbacks) Green, J., Konings, A., Alemohammad, S. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nature Geosci 10, 410–414 (2017). https://doi.org/10.1038/ngeo2957 Overview Here the authors confirm land-atmosphere feedbacks using satellite observations and a statistical method. The feedback from the biosphere to the atmosphere explains 30% of variations in precipitation and photosynthetically active radiation (PAR). They employed a multivariate conditional Granger Causality (MVGC) using vector autoregression models (VAR) to decompose two directions in the feedbacks (i.

21 September, 2020 (Bloom, bgd, Legacy-(NBE IAV)) Bloom, A. A., Bowman, K. W., Liu, J., Konings, A. G., Worden, J. R., Parazoo, N. C., Meyer, V., Reager, J. T., Worden, H. M., Jiang, Z., Quetin, G. R., Smallman, T. L., Exbrayat, J.-F., Yin, Y., Saatchi, S. S., Williams, M., and Schimel, D. S.: Lagged effects dominate the inter-annual variability of the 2010–2015 tropical carbon balance, Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-459, in review, 2020.

18 September, 2020 (Humphrey, nature, TWS-[CO2]) Humphrey, V., Zscheischler, J., Ciais, P. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018). https://doi.org/10.1038/s41586-018-0424-4 Overview This paper provides observational evidence that the inter-annual variation (IAV) of CO2 growth rate (CGR) is strongly coupled to changes in both terrestrial water storage (TWS) and temperature at the global scale. It uses GRACE product, satellite observations of TWS, and measurements of CGR from NOAA.

11 September, 2020 (Green, nature, SMC->NBP) Green, J.K., Seneviratne, S.I., Berg, A.M. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019). https://doi.org/10.1038/s41586-018-0848-x Overview The authors concluded that soil moisture has substantial effect on long-term net biosphere productivity (NBP, a.k.a. NEE?) using a global satellite product of SIF and TWS (GRACE) and multi-model ensemble from the GLACE-CMIP5 project. They explained that the SMC effect was attributable to non-linear response of NBP to SMC.

26 July, 2020 (McDowell, science, Forest dynamics in the future) McDowell, N.G., Allen, C.D., Anderson-Teixeira, K., Aukema, B.H., Bond-Lamberty, B., Chini, L., et al. (2020). Pervasive shifts in forest dynamics in a changing world. Science, 368, eaaz9463. Shifts toward shorter and younger forests. What will be the future state of forests and how will it be different from the current one? Uncertainties In the early stage of the climate change, trees assimilated more carbon than before the change.

23 July, 2020 (Samuels-Crow, JGRBG, SAM->NEE and ET) Samuels‐Crow, K. E., Ogle, K., & Litvak, M. E. (2020). Atmosphere‐Soil Interactions Govern Ecosystem Flux Sensitivity to Environmental Conditions in Semiarid Woody Ecosystems over Varying Timescales. Journal of Geophysical Research: Biogeosciences, 125, e2019JG005554. https://doi.org/10.1029/2019JG005554 Summary Samuels-Crow et al., in this paper, quantified environmental antecedent (past) memory of water (ET) and carbon (NEE) fluxes over two semi-arid sites where two different conifer species dominate each.

6 June, 2020 (Trautmann, HESS, TWS variation across scales) Trautmann, T., Koirala, S., Carvalhais, N., Eicker, A., Fink, M., Niemann, C., et al. (2018). Understanding terrestrial water storage variations in northern latitudes across scales. Hydrology and Earth System Sciences, 22, 4061–4082. Why did I read this paper? This paper contains the description and use of a hydrological model that a group I will join deals with. What they did? The hydrological model is relatively economic, or parsimonious, given small number of internal water storages (soil moisture, snowpack, and land run off), input variables (air temperature, precipitation, and net radiation), and parameters (10 params.

20 May, 2020 (Guo, NPH, SAM->iso/ansiohydry) Guo, J.S., Hultine, K.R., Koch, G.W., Kropp, H. and Ogle, K. (2020), Temporal shifts in iso/anisohydry revealed from daily observations of plant water potential in a dominant desert shrub. New Phytol, 225: 713-726. doi:10.1111/nph.16196 A key reference for applying the SAM framework I have reviewed a paper in which the SAM framework was applied to fluxtower NEE data. I read the paper because I used a similar type of data (i.

1 May, 2020 (Liu, ELE, SAM->NEE across biomes) Liu, Y., Schwalm, C.R., Samuels‐Crow, K.E. and Ogle, K. (2019), Ecological memory of daily carbon exchange across the globe and its importance in drylands. Ecol Lett, 22: 1806-1816. doi:10.1111/ele.13363 Why did I read this paper? To apply the SAM framework for my paper, I needed a reference. Professor Ogle recommended this paper for me as I and Liu et al. have the same type of data.

Log 2020-04-12: 23, 24.6 과학원에서 기상자료 QC하는 R 코드를 짰는데, 꽤 애먹었다. 컴퓨터 메모리는 8GB. 인풋으로 사용하는 csv 파일이 최대 2GB 정도로 크긴 했지만 메모리에는 문제가

Intro Near-term Ecological Forecasting Initiative (NEFI) Summer Course 2020. 책 저자이자 보스턴 대학교(Boston University)에 재직중인 Dietze 교수는 매년 Bayesian application to ecological forecasting을 가르치는 summer

Intro Kallestinova E. D. (2011). How to write your first research paper. The Yale journal of biology and medicine, 84(3), 181–190. Confused by now with encountering the start of a new research, I read this paper to seek for avenues. The author, Elena D. Kallestinova, provided some pragmatic rules with the relevant explanation. Rather than procastinating, it might be more helpful for my writing to dive directly into my data, models, and codes.

27 February, 2020 (Holgate, RSE, SMC) Holgate, C.M., De Jeu, R.A., van Dijk, A.I.J.M., Liu, Y.Y., Renzullo, L.J., Dharssi, I., Parinussa, R.M., Van Der Schalie, R., Gevaert, A., Walker, J., 2016. Comparison of remotely sensed and modelled soil moisture data sets across Australia. Remote Sensing of Environment 186, 479–500. Summary This paper compared time series pattern and temporal anomaly of diverse soil moisture content (SMC) products with in-situ measurements. The methodologies included the Pearson correlation coefficient, an anomaly index and the cluster analysis.