21 June, 2021 (Syed, wr, GRACE_GLDAS) Syed, T. H., Famiglietti, J. S., Rodell, M., Chen, J., and Wilson, C. R. (2008), Analysis of terrestrial water storage changes from GRACE and GLDAS, Water Resour. Res., 44, W02433, doi:10.1029/2006WR005779. Introduction This paper seemed like one of early TWS studies using the GRACE observation. I felt it as a classical one, so decided to read through, focusing on figures and the conclusion. Message: latitudinal dominant TWS component Dominant TWS pool Dominant TWS flux High latitudes Snow water equivalent Snowmelt-derived runoff Mid latitudes Soil moisture Evaporation Low latitudes Soil moisture Precipitation Their message can be summarized with this table.
12 April, 2021 (Vishwakarma, erl, the_TVR_metric) Vishwakarma, B. D., Bates, P., Sneeuw, N., Westaway, R. M., & Bamber, J. L. (2021). Re-assessing global water storage trends from GRACE time series. Environmental Research Letters, 16(3), 034005. https://doi.org/10.1088/1748-9326/abd4a9 Message: the variability should be considered together with trend. The authors suggested to use a new metric, named trend to variability ratio (TVR), when evaluating the severity of the change in the terrestrial water storage observations from GRACE satellite.
07 April, 2021 (Humphrey, Nature, smcAtmFeedback_NeeIav) Humphrey, V., Berg, A., Ciais, P. et al. Soil moisture–atmosphere feedback dominates land carbon uptake variability. Nature 592, 65–69 (2021). https://doi.org/10.1038/s41586-021-03325-5 Message: Soil moisture–atmosphere feedback dominates land carbon uptake variability The same as the title. Novelty: putting the feedback concept in the discussion Many studies have reported various factors such as SMC, Tair, and VPD, as the driving the global NEE IAV. This study reconciles these conflicting results by introducing a new common label to the candidates.
05 March, 2021 (Ahlstrom, science, NBP IAV dominator) Ahlstrom A, Raupach MR, Schurgers G, Smith B, Arneth A, Jung M, et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science. 2015 May 22;348(6237):895–9. https://doi.org/10.1126/science.aaa1668 Message: Semi-arid regions dominate the land-carbon-sink variability The take-home message was simple and strong. Semi-arid regions dominated the global variability of land carbon sink variability (both long-term trend and interannual variability).
27 December, 2020 (Jung, bg, FLUXCOM carbon) Gervasio Piñeiro, Susana Perelman, Juan P. Guerschman, José M. Paruelo, How to evaluate models: Observed vs. predicted or predicted vs. observed?, Ecological Modelling, Volume 216, Issues 3–4, 2008, Pages 316-322, ISSN 0304-3800, https://doi.org/10.1016/j.ecolmodel.2008.05.006. Prediction (x-axis) vs. Observation (y-axis) Martin added a comment to my proposal. He said that I should put prediction on the x-axis, referring to a paper: I have just look over the conclusion of the paper.
Robust linear regression Sujan recommended me to use the robust regression models (RLM) instead of the standard OLS (ordinary least squares). What are the differences? Robust: less affected by outliers A drawback of OLS is that the resulted regression line can be significantly altered by some outliers. As the OLS try to find a best line of the minimum SS, the optimum would likely to have more focus on outliers which have large SS.
27 December, 2020 (Jung, bg, FLUXCOM carbon) Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O’Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G.
knitr::opts_chunk$set(eval = TRUE, echo = TRUE, warning = FALSE) References: solitude: https://github.com/talegari/solitude isofor: https://campus.datacamp.com/courses/anomaly-detection-in-r/isolation-forest # install.packages("mvoutlier")) # install.packages("remotes")) # remotes::install_github("Zelazny7/isofor") #import libraries library(ggplot2) library(ggpubr) library(solitude) # hereafter, "sol" library(isofor) # hereafter, "iso" library(viridis) packageVersion("solitude") #create sample data data("humus", package = "mvoutlier") # 2-dimensional columns_required <- c("Bi", "Cd") humus2 <- humus[ , columns_required] # multi-dimensional columns_required_mul <- setdiff(colnames(humus) , c("Cond", "ID", "XCOO", "YCOO", "LOI") ) humus_mul <- humus[ , columns_required_mul] str(humus2) str(humus_mul) #plot data ggplot(humus2, aes(x = Bi, y = Cd)) + geom_point(shape = 1, alpha = 0.
14 October, 2020 (Gentine, erl, cw coupling) Gentine, P., Green, J.K., Guérin, M., Humphrey, V., Seneviratne, S.I., Zhang, Y., et al. (2019). Coupling between the terrestrial carbon and water cycles—a review. Environ. Res. Lett., 19. https://doi.org/10.1088/1748-9326/ab22d6 Overview The right topic for my project. The authors wrote that this paper would be an introduction of the coupling of carbon and water cycles to people who are not familiar with. Indeed I think they summarized the topics nicely with well-organized structure.
14 October, 2020 (Liu, ncomm, dominant SM effect) Liu, L., Gudmundsson, L., Hauser, M. et al. Soil moisture dominates dryness stress on ecosystem production globally. Nat Commun 11, 4892 (2020). https://doi.org/10.1038/s41467-020-18631-1 Overview Soil moisture (SM) and vapor pressure deficit (VPD) have been regarded as the major driver of dryness stress on the ecosystem productivity. However, it has been difficult to separate each effect due to the strong coupling between SM and VPD, which resulted in a number of model representation for the dryness stress.
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.