the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Unravelling the future role of internal variability in South Asian near-surface wind speed
Abstract. Near-surface wind speed (NSWS) plays a critical role in water evaporation, air quality, and energy production. Despite its importance, NSWS changes in South Asia, a densely populated region, remain underexplored. This study aims to understand and quantify the uncertainties in the projections of NSWS over South Asia, particularly in relation to internal variability. Utilizing a 100-member large ensemble simulation from the Max Planck Institute Earth System Model, we identified the Interdecadal Pacific Oscillation (IPO) as the leading climate mode of internal variability influencing South Asian NSWS in the near future. Our findings reveal that the IPO could significantly impact future NSWS, with its positive phase being linked to strengthened westerly flows and increased NSWS across South Asia. Notably, the study shows that accounting for the impact of IPO could reduce NSWS projection uncertainty by up to 8 % in the near future and 15 % in the far future. This highlights the key role of internal variability, particularly the IPO, in modulating regional NSWS projections. By reducing uncertainties in these projections, our findings can inform climate adaptation strategies for South Asia, helping optimize wind energy assessments in the context of changing winds.
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Status: open (until 17 Jun 2025)
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RC1: 'Comment on egusphere-2025-1156', Peter Pfleiderer, 14 May 2025
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The authors present an interesting study on changes in near surface wind speed over South Asia with a focus on links to internal modes of variability such as IPO and AMO. Most of the results are based on a MPI-ESM large ensemble. In the current state, an evaluation of how well MPI-ESM reproduces crucial physical mechanisms related to NSWS in South Asia is missing. The manuscript is well written and structured and after some revision would be a valuable contribution to the field.
The authors do not sufficiently evaluate how well MPI-ESM reproduces the physical mechanisms related to NSWS (and it's changes). While the NSWS of reanalysis datasets lies well within the MPI-ESM ensemble spread, this might be due to multiple compensating misrepresentations in the model. I would suggest to compare the 850mbar wind climatology as shown in fig 4c to reanalysis datasets. If possible, please also show figures comparable to 4a,b for reanalysis data. If available, it would also be helpful to refer to the literature to evaluate the representation of IPO dynamics in MPI-ESM and projected trends in the Pacific and the Indian Ocean in comparison to reanalysis and other climate models.
Concerning the regression between IPO and NSWS:
Is NSWS averaged over the region of interest?
I do not understand, why you perform the regression over the period 1974-2095. I thought that with this analysis you wanted to estimate how much IPO can influence trends over periods of roughly 30 years. The regression slope between IPO and NSWS over 1974-2095 should be quite weak and does not really represent the influence of IPO on NSWS you are interested (at least in Figure 5 a). Why don't you use a similar timescale for this regression?
Related to the precious comment, I'm wondering whether analyzing the RCP85 scenario is the right choice for studying the link between dominant modes of variability and NSWS. In RCP85 there should are strong forced changes in NSWS as well as SSTs and SST patterns. Therefore both NSWS and IPO (or AMO) are very likely changing over the time frame of a century. I assume that the detrended (part of the signal that is not forced) IPO and NSWS is used for the analysis. Is that the case? Please clarify in the methods section.
Specific comments:
L79: Are the periods for all reanalysis datasets and CRA-40 the same?
L86: Are you sure about 2006 as start date for the projections? In Figure 1 you write about 2015.
L128-129: Why do you frame it as a hiatus? I find the term "hiatus" misleading here, as the MMM should not contain any influence of internal variability, right?
L150: Please explain the "inter-member EOF analysis" in more detail. Is the EOF analysis performed on a combination of NSWS patterns and SST fields? Is it performed on NSWS fields only?
Citation: https://doi.org/10.5194/egusphere-2025-1156-RC1
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