This revision addresses almost all my original concerns, and is a great improvement. In particular the bug in the attribution calculation was fixed, leading to new and interesting results. However, these new results suggest an alternative way of doing the attribution that might give a simpler and cleaner conclusion (Major comment below.)
Also, the revisions have directly led to a number of new smaller suggestions, including a few cases where a prior point was partially but not fully addressed (168, 183-184, 236-238 under Minor below).
Therefore, I recommend major revisions again before publication, mainly to give the authors a chance to implement my attribution suggestion if they would like to. Regardless, this is an excellent study and should ultimately be published.
Major comment:
Thank you very much for finding the attribution code bug; Fig. 13 now makes a lot more sense! Thank you also for making the caption and figure design clearer (and including Table 4 to drive home the point.)
With these new attribution results, a key take-home conclusion is that the increased specific humidity has canceled much of the PET increase from the direct warming. However, as you allude to at least once in the text, we would expect a specific humidity increase anyway from warming - it’s not some independent factor that’s come in and saved the day, but rather part of the same thermodynamic process of warming and increase of the holding capacity of the atmosphere for vapor (qs). If qs goes up and relative humidity doesn’t change much, then qa also has to go up.
So I think it may be worth doing the attribution using temperature and *relative humidity* instead, which are much more independent on climate-change time scales than temperature and specific humidity. In fact, a baseline expectation for climate change is that RH won’t change much at all, while warming will be substantial (e.g. Held and Soden, 2006, http://doi.org/10.1175/JCLI3990.1 ; Schneider et al, 2010, http://doi.org/10.1029/2009RG000302 ).
Your PET equation would then have qs-qa replaced by qs(1-RH), where you would define and compute your RH as your daily qa divided by your daily qs. This is not quite the daily average of RH (due to nonlinearity) but it gives you an exact equation. You could call it daily effective RH.
Then you could just re-derive the Ta partial-derivative equation (C5) in appendix C using this new form of the PET equation, in which RH is taken to be constant instead of qa constant. And make a new RH derivative equation in appendix C to replace the qa derivative (C4). Compute RH trends rather than (or in addition to) qa trends. Then do the analysis just like before.
I would think this way, you’d have much less cancellation - the Ta derivative and contribution will become much smaller though still positive (since with RH high and constant, qa implicitly still increases and this is folded in) and the RH contribution will also be smaller than the old qa contribution (and may well be of opposite sign). Will simplify your story a lot. In particular the Delta/(qs-q) term on line 749 will just become Delta/qs, which is much smaller.
The inspiration for doing this is Scheff and Frierson (2014, http://doi.org/10.1175/JCLI-D-13-00233.1). They were studying future climate model projections rather than observations, but found that their RH-driven term looked quite independent from their Ta-driven term, with no sense of cancellation. Vicente-Serrano et al. also did this in their recent Spain observed PET analysis. They also found little cancellation when doing it this way.
This change is optional, but should be strongly considered. If you decline to do this, you should be more explicit in the text that the specific-humidity increase is not an independent factor, and that a substantial cancellation is totally expected given that Britain has high RH and that this RH has not changed that much with warming. Again, you already sort-of allude to this, but could be much clearer.
Minor:
General: It’s hard to immediately find the Thompson et al. (1981) MORECS paper, but just googling the title gives this article http://www.sciencedirect.com/science/article/pii/0378377483900173 by Field (1983) in the journal Agricultural Water Management. Is this actually the same as Thompson et al. (1981)? If so, may be better to cite this one, since it’s so much easier to locate. Even if it’s not identical, it still may be useful to cite.
General (but optional): I still think it’s worth changing the trend units throughout the study to (50yr)-1 or (51yr)-1, instead of decade-1, for most natural interpretation of the numbers. (However, I also understand the possible reason for keeping it decade-1, namely for quicker intercomparability with other studies.)
21-22: Because of the choice of different regions here (Great Britain vs. England), the PET trends in the abstract can’t be directly compared to the PETI trends in the abstract. I think it’s worth including at least one matching pair here, so the reader less familiar with PETI can quickly gauge how much of a difference the interception-correction makes. E.g. provide both PET and PETI trend numbers for Great Britain.
54: To be fair, the Princeton forcings are also available at 0.25deg, which is better than 1deg though still much coarser than your 1km.
85-91 vs 91-96: You suddenly shift from talking about AED to talking about PET, without explicitly mentioning that they’re connected, or how. This could be a little odd for readers from outside hydrology. Even replacing “as an input” with “to represent AED” at 92-93 and 95 would go a long way toward fixing this.
99: It’s almost obvious, but writing “1km x 1km” before “dataset” here would make it completely clear what you mean.
125: Section 4 doesn’t just discuss trends in annual means, but also trends in seasonal means - so “annual means of” can be deleted (right?)
168: Now that the units issue is cleared up, it also has to be clarified what lapsing by %/m actually means. E.g. let’s say the MORECS e is 1000 Pa and the MORECS elevation is 500m. Do you just straightforwardly say 500*0.025 = 12.5, so to obtain sea-level e, increase the 1000 by 12.5% and obtain 1125 Pa?
Or do you (more accurately) think of it as a simple differential equation, de/e = -0.00025dz , so esea = eMORECS*exp(0.00025*500) = 1000*exp(0.125) = 1000*(1.133) = 1133 Pa?
The numerical difference isn’t that large (about a percent) but for completeness and reproducibility this has to be specified. I am fine with doing it either way, again the issue is just stating which way you did it.
Also, if you lapsed the first way, how do you then go back up to the 1kmx1km grid elevation at line 177? In our example case here, if you have to convert the 1125 Pa esea back to a 500m elevation grid cell of your new 1kmx1km grid, do you literally decrease it by 12.5%? 1125*0.875 = 984 Pa which is not quite 1000. Or do you instead decrease it by 12.5% *of the original MORECS qa measurement* (i.e. by 125 Pa in our case), which will precisely get you back to 1000. This also needs to be specified; the latter is clearly better. [Note this is not an issue if you lapsed the second way above, since unlike 1.125*0.875, exp(0.125)*exp(-0.125) is exactly 1.]
183-184: The 100000 Pa assumption only matters a few percent to the specific humidity, but what about to the PET or PETI itself? Because the formula for PET involves a small difference between two larger numbers (qsat - qa), an error of only a few percent in qa becomes an error of 10% or more in this difference. So really you should check how this approximation affects your output PET and PETI values, not (just) how it affects your input specific humidity values. This is especially an issue in a wet place like Britain where RH is high and qa is relatively close to qsat.
Note that even if full PET and PETI are not affected as much, EPA could still be affected a lot (since EPA is directly proportional to qsat-qa). So you should especially check if EPA is significantly thrown off (e.g. by ~10% or more.)
236-238: This doesn’t seem like sufficient reason not to interpolate it - you don’t need to be able to correct for elevation to interpolate using the bicubic spline (unless I’m missing something?) At present you have sudden jumps in your 1kmx1km DTR product at each CRU grid cell boundary (Fig 1h), so the LSM output will presumably also have jumps like this. If you use the bicubic spline on the CRU to obtain 1kmx1km values, these artifacts will disappear. There will be no elevation dependence, but there was already no (known) elevation dependence to start with, as you say. So I think you need to either explain why you think this wouldn’t be wise, or else go ahead and implement it.
261: Still very hard to impossible to see the positive correlation between elevation and windspeed in Fig 1d. I can vaguely see some different color in the Scottish highlands but it’s not clear what color it is on the scale. Perhaps the problem is that many different values are mixed together in close proximity in the highlands? In that case I’m not quite sure what to do.
289-292: To be fair, you should add that this implies your PET & PETI (and particularly EPA) products are also likely to be biased high where there is tall vegetation.
313-314, 321, 332: In these calculations, do you use 100000 Pa for p*, or do you use your p* derived in section 2.8? You should be explicit, either way.
373-374: This seems odd - don’t stomata generally close when humidity is too low (to save water loss), not when humidity is too high?
402-405: This implies that you still use PETI correction for multiple days after rain event. Whereas, 385-387 stated that the correction is only applied on the day of a rain event, and not on subsequent days. Which is it? This should be made consistent.
End of 455: Table 2 only has the annual (not seasonal) trends, so this little sentence should be moved back to near the start of 453 (i.e. right before the sentence introducing the seasonal means/trends.)
460: Also except in summer! At least according to Fig 11a. Only in spring and fall are the temperature trends mostly significant at 95%. That’s interesting.
476: Table 2 also includes the aerodynamic/radiative components, so they should be mentioned on this line as well. [In addition, Table 2 has an extra (blank) “PET” line (second from bottom) that can be removed.]
479-483: Since you’re getting more geographically specific (e.g. “north-west Scotland”, “northern England”, etc) you should explicitly call out Fig B2 rather than just mentioning appendix B. I wonder if this is also reason to make Fig B2 a main-text figure rather than an appendix figure.
496-497: Again, except for the English lowlands in autumn.
545 “whole dataset”: More to the point, it assumes that the rate of change is constant over the annual cycle (i.e. that it doesn’t depend on season). This is the key concern. So I think “whole dataset” should be replaced with “annual cycle”, “seasonal cycle” or similar. In any case, I am glad to see that the sensitivity to this assumption is quite low in your study. It is not always true!
562-563: Temperature is seen to decrease the radiative component solely because you’re including the temperature dependence of the outgoing LW in your temperature contribution (beginning of 749), while *not* including the temperature dependence of the downward LW (209-213). I would strongly guess that if both directions were included (or if neither were included), these funny negative temperature contributions would disappear.
So perhaps a fairer way to do this would be to make net-LW the LW variable for the attribution exercise, instead of downward LW. This would essentially move the T^4 upward piece from the temperature contribution into the LW contribution, greatly reducing both the negative temperature contribution and the positive LW contribution in Fig 13b and thus simplifying your story a lot for EPR. However, I suppose you could also leave it as-is but just mention this caveat in the text. Your call.
If you make this change, you should also use net-SW instead of downward SW to be consistent, though you’ll get exactly the same result since they are linear multiples of each other.
563-564: It only partly cancels - in the EPA panels, the downward humidity bars are only about half to 2/3 as big as the upward temperature bars. Only when the wind stilling is included does most of it cancel. So the stilling is key here.
568: The EPA decreases due to wind are a lot larger than the EPR increases due to wind, so I would insert “more strongly” before “decreasing” here, or similar.
582: “Negligible” is too strong a word, particularly for Wales where it’s -38% of the total (Table 4). But your point is that it’s definitely not the main driver, as it was in the Donohue and McVicar studies, and that is an important point. Maybe “has had a negligible influence” -> “has not been a dominant influence”? or similar?
674: “the water cycle is intensified under climate change” is very vague, and means different (and often orthogonal) things to different researchers. I would use a more specific reason like “the air is able to hold more water under climate change” as you used earlier in the paper. [If you use my major suggestion above, this whole part might be written differently anyway.]
729-730: The seasonal cycle of downward SW does not seem to be represented well at site c in Fig A2 - the values in your dataset are much too small compared to the independent observations, particularly in the warm season. This should be mentioned to be fair. (Do you have any insight as to why this is?)
748-749 vs rest of paper: Here the steam-point temperature is called Tsp, but back on lines 316, 317 and 321 it was called Ts. These should be made consistent. I much prefer Tsp since in ET or PET studies Ts ordinarily refers to the land surface skin-temperature, so I recommend you change Ts to Tsp back at 316, 317 and 321.
Beginning of 749: The OLR term should have Ta^3 not Ta^4, since it’s a derivative (right?) Thus the factor of 4 out front? I assume this is just a typo, and not also a code issue, but should check this in the code too.
Table 1, Specific Humidity line: In the “Source Data” cell it says temperature was used. Is this a typo? Temperature isn’t mentioned at all in section 2.2.
Fig A2 caption, lines 1287-1288: I assume “air temperatures” is supposed to be “downward SW radiation”?
Fig B1 (and probably B2 as well): Caption should specify annual mean.
Also Fig B1: Air-pressure panel is missing and so DTR panel is actually g instead of h. (Though, air pressure was just a monthly climatology [right?] so won’t have a trend - so probably should leave the figure as-is and update the caption to remove air pressure and assign DTR to g.)
Typos:
Beginning of 593: “or” should be “of”
1292: “Ration” should be “ratio” |