|Second review of Yin and Roderick|
“Inter-annual variability of the global terrestrial cycle“
The paper has overall improved as the authors have addressed many of
the concerns raised by me and the other reviewers.
However, one important issue, and several minor points remain unresolved.
As mentioned in my previous review, I think it is critical for this study to show that
the discovered patterns are not just implemented in the model used to derive the CDR dataset.
It has to be shown that similar patterns are present across independent datasets,
as only this can indicate that nature is indeed operating this way.
I appreciate efforts in this direction made by the authors, namely the consideration
of the LandFlux-EVAL dataset, the Jung et al. dataset, and the ERA5 reanalysis.
But I believe that these analyses need to be expanded before the paper can be published:
I understand that the authors do not want to use GLEAM as a reference dataset as this
was used in the derivation of the CDR reanalysis. But instead the Jung et al. dataset should
be updated to the 2019 version (Jung et al. 2019).
The authors stated in their response: 'We could replace the MPI we used with the updated database
(Jung et al., 2019) but we do not see how that would alter the results.'
This is not about altering the results, but about using state-of-the-art alternative
datasets to illustrate the robustness of the CDR-based results.
I do not see the point in using an almost 10-year old dataset while updated and much evolved
I also appreciate the ERA5-based analyses which the authors have done in response to
my previous comments. I share their conclusion that this dataset is not suitable to be used
in the context of this study.
However, this way the runoff results remain not confirmed with independent data.
Therefore I suggest to use the E-RUN gridded runoff dataset (Gudmundsson and Seneviratne 2016)
for this purpose.
I do not wish to remain anonymous - Rene Orth.
This statement somewhat ignores the efforts leading to the ERA-Land (Balsamo et al. 2013) and MERRA-Land (Reichle et al. 2011) datasets.
line 75: 'the various ... databases' - after only reading the text up to this point it is not clear what is meant here
line 78: it should be 'these variabilities'
lines 88/89: 'in time step t' - these are all fluxes which are accumulated during time steps t-1 and t; also, I would mention here that the time step considered in this study is 1 year
lines 91/92: 'Eq (1) is the familiar...' - this sentence is an unnecessary repetition
line 96: known here?
line 103: The SRB dataset only extents until 2007 (if I am not mistaken) while the analyses in this study consider a time period until 2010. How can you still use the SRB data then?
lines 105/106: Sentence is hard to understand, please rephrase.
line 160: Please comment on the offset.
line 177: I would replace 'trend' with 'pattern'
line 180: not clear what is meant here with 'physics of runoff generation'
lines 178-181: Padron et al. 2017 is relevant in this context, and could be cited.
lines 188, 203, 223: 'very different' is not obvious to me from the comparison of Figs 1 and 3. Please clarify.
lines 225-226: This is an important finding which should be highlighted in the abstract and/or conclusions.
lines 294-303: If the main conclusion is that things are complex, and there is no particular lesson learned here, then I would suggest to remove this section. It confuses readers and distracts from the relevant main messages of the study.
lines 307-328: It feels inconsistent that in addition to the wet and hot grid cell
no wet and cold grid cell has been selected as a case study (as was done in the case of high and low water storage capacity).
- While this study is performed at annual time scales, the authors could add some outlook/clarification
that the revealed variability propagation across the water cycle might behave differently at shorter time scales
- Figures 2,5, and others display physically implausible values - please comment on this
- It is not intuitive that non-consistent (logarithmic/non-lagarithmic) axes are used for E0/P across different figures
Balsamo, G., C. Albergel, A. Beljaars, S. Boussetta, E. Brun, H. Cloke, D. Dee, E. Dutra, J. Muñoz-Sabater, F. Pappenberger, P. de Rosnay, T. Stockdale, and F. Vitart, 2013: ERA Interim Land: a global land water resources dataset. Hydrol. Earth Syst. Sci., 19, 389–407.
Gudmundsson, L., and S.I. Seneviratne, 2016: Observation-based gridded runoff estimates for Europe (E-RUN version 1.1). Earth Syst. Sci. Data, 8 (2), 279–295.
Jung, M., S. Koirala, U. Weber, K. Ichii, F. Gans, G. Camps-Valls, D. Papale, C. Schwalm, G. Tramontana, and M. Reichstein, 2019: The FLUXCOM ensemble of global land-atmosphere energy fluxes. Scientific Data, 6 (74).
Padron, R.S., L. Gudmundsson, P. Greve, and S.I. Seneviratne, 2017: Large‐Scale Controls of the Surface Water Balance Over Land: Insights From a Systematic Review and Meta‐Analysis. Water Res. Resour., 53 (11), 9659-9678.
Reichle, R.H., R.D. Koster, G.J.M.D. Lannoy, B.A. Forman, Q. Liu, S.P.P. Mahanama, and A. Toure, 2011: Assessment and enhancement of MERRA land surface hydrology estimates. J. Clim., 24, 6322–6338,