24 April, 2022 (Zhang, gcb, domimant_timescales_and_regions_of_global_NEE) Zhang, X., Wang, Y.-P., Peng, S., Rayner, P. J., Ciais, P., Silver, J. D., Piao, S., Zhu, Z., Lu, X., & Zheng, X. (2018). Dominant regions and drivers of the variability of the global land carbon sink across timescales. Global Change Biology, 24(9), 3954–3968. https://doi.org/10.1111/gcb.14275 Zhang et al. used the Fourier transformation to attribute variance of carbon cycle components to multiple time scales (from interannual to multi-decadal scales).
23 April, 2022 (Green, gcb, lst_over_tair) Green, J. K., Ballantyne, A., Abramoff, R., Gentine, P., Makowski, D., & Ciais, P. (2022). Surface temperatures reveal the patterns of vegetation water stress and their environmental drivers across the tropical Americas. Global Change Biology, 28(9), 2940–2955. https://doi.org/10.1111/gcb.16139 Green et al. introduced the ratio of land surface temperature (LST; temperature of the top canopy) to air temperature as an indicator of vegetation water stress. I was particularly interested in their analysis using random forest and the Shapley value to quantify the effect (e.
19 February, 2022 (Stocker, ngeo, underestimated_drysoil_gpp) Stocker, B.D., Zscheischler, J., Keenan, T.F. et al. Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nat. Geosci. 12, 264–270 (2019). https://doi.org/10.1038/s41561-019-0318-6Introduction Stocker et al. investigated the effect of dry soil on GPP across scales after isolating from the effect of VPD. I read this paper, wondering the global error distribution is associated with my global TWS IAV error map. Message: Soil moisture should be included as a GPP estimator as VPD alone cannot account for dry soil impact on GPP Dry soil effect reduces the global annual GPP by 15% The effect of dry soil decreases with increasing spatial scale, due to compensation from different regions.
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.