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.
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.
22 February, 2020 (Eller, NPH, JULES-SOX) Eller, C.B., Rowland, L., Mencuccini, M., Rosas, T., Williams, K., Harper, A., Medlyn, B.E., Wagner, Y., Klein, T., Teodoro, G.S., Oliveira, R.S., Matos, I.S., Rosado, B.H.P., Fuchs, K., Wohlfahrt, G., Montagnani, L., Meir, P., Sitch, S. and Cox, P.M. (2020), Stomatal optimization based on xylem hydraulics (SOX) improves land surface model simulation of vegetation responses to climate. New Phytol. doi:10.1111/nph.16419 Summary Many researchers, including me, who use ecosystem models have recognized that the beta function approach for representing the change in plant photosynthesis to soil moisture stress needs improvement.
15 February, 2020 (Ogle, CHANCE, SAM) Kiona Ogle & Jarrett J. Barber (2016) Plant and Ecosystem Memory, CHANCE, 29:2, 16-22, DOI: 10.1080/09332480.2016.1181961 Why did I read this paper? It is often easier to get knowledge (i.e., model, concept, equation, and so forth) not from the original descriptive paper, but from other papers using the knowledge because they introduce the knowledge after masticating it, not as it is. This paper masticates the stochastic antecedent modelling (SAM) framework for beginners.
7 February, 2020 (Reichstein, Nature, Deep learning) Reichstein, M., Camps-Valls, G., Stevens, B. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019). https://doi.org/10.1038/s41586-019-0912-1 Question: How have machine learning algorithms, especially deep learning, been applied to Earth system science and what is the future direction of the application? Context: Our ability to produce a deluge of data outpaces our ability to assimilate the information.
4 February, 2020 (Jung, Nature, Compensatory) Jung, M., Reichstein, M., Schwalm, C. et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature 541, 516-520 (2017). https://doi.org/10.1038/nature20780 Question: How do changes in temperature and water availability effect on gross primary productivity (GPP), terrestrial ecosystem respiration (TER), and net ecosystem exchange (NEE) at local and global scales? Context: Despite temperature is known to contribute to the inter annual variation (IAV) of the terrestrial carbon cycle, several studies propose that water availability play an important role in the carbon cycle relates such as the carbon balance of semi-tropics and the sensitivity to IAV.