the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Apr 2026
Present and future responses of near-surface wind speed to different land-use and land-cover types in China
Abstract. Near-surface wind speed (NSWS) is highly sensitive to land-use and land-cover change (LULCC). However, previous studies have mainly focused on overall LULCC effects, leaving the attribution of wind variations to individual land transitions and management poorly constrained. Here, we utilize simulations from the latest land-use model intercomparison project (LUMIP) in CMIP6 to disentangle and quantify the responses of NSWS to different LULCC types over China from 1970 to 2014. We find that the primary-to-grazing transition is the dominant factor to LULCC-Induced NSWS variability, followed by fertilizer use, with urbanization contributes the least among the examined types. Future projections further suggest that land-use pathways can substantially perturb wind patterns, with low-emission pathways under minimally regulated land use producing more pronounced alterations than high-emission pathways under sustainable land use. These results highlighting the importance of resolving LULCC in attributing and projecting NSWS changes, with implications for climate modeling, wind-energy assessment, and land-use policy.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-1947', Anonymous Referee #1, 08 Jun 2026
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RC2: 'Comment on egusphere-2026-1947', Anonymous Referee #2, 18 Aug 2026
This manuscript isolates the impact of land-use and land-cover changes LULCC on Near Surface Wind Speeds (NSWS), using ridge regression to determine how the individual components of LULCC contribute to NSWS changes. Differences between simulations with and without the LULCC forcings provide the basis for the NSWS analysis. NSWS changes are further decomposed by individual land use type and land use transitions. The study makes use of ridge regression to reconstruct NSWS stilling observed historically in the model simulations, and again ususes ridge regression to find contributions of each land state and transition to the NSWS.
I believe the research in this paper provides new insight on how LULCC component affect NSWS in future climate simulations, and is publication worthy, pending major revisions as follows:
Major Revisions:
- Throughout the text, quality of writing is inconsistent. There are sections of the paper that appear well-written and only require light revision, but other sections of the paper are very tricky to understand. E.g., lines 81– 86 make use of excessive pronouns, which are vague and unclear, and lines 246-251 seems to take too many words to describe that LULCCs give different wind ranges. While I will outline some of the technical writing errors in the minor revisions section, they are no means exhaustive, and I encourage the authors to consider a careful re-reading of the manuscript to ensure proper grammar and succinct wording.
- The authors do a good job demonstrating that individual models poorly capture the observed historical NSWS trend and that using ridge regression helps to improve this result. However, the authors also state that ridge regression still appears to underestimate the overall mean historical trend. I am curious whether the authors consider this underestimation important to consider when evaluating the LULCC-Induced NSWS changes in the future climate scenario. Furthermore, I believe including simulated future NSWS trends like 1h, for both SSP126 and SSP370, would help the reader more easily understand whether wind speed trends continue to decline overall and whether the trends remain consistent with historical simulation.
- Fig. 4 can be improved with the addition of hatching for statistical significance.
Minor and Technical Revisions:
Make sure spacing after periods is consistent, either 1 or 2 spaces, not a mix of both.
- Line 12: With urbanization contributes… -> While urbanization contributes…
- Line 14: These results highlighting -> These results highlight
- Line 18: Near-surface wind speed (NSWS), typically measured at 10 meters above the ground -> 10-meter near-surface wind speed (NSWS)
- Line 22: Remove “In recent four decades”. Between 1980’s and 2010’s is sufficient.
- Line 25: Remove “where”.
- Line 25: Largest reduction of what?
- Line 26: Unclear of “its” here refers to NSWS or stilling.
- Lines 26-28: Line reads as if stilling refers to both NSWS decline over land and NSWS increase over marine- but “stilling” feels like it should just refer to the NSWS decline?
- Line 31: attributions -> iterations
- Lines 51-54. Run-on sentence, also, very difficult to digest.
- Line 76: Make Table 1’s header “List of CMIP6 experiments used.”
- Line 78: Make Table 2’s header “List of CMPI6 models used.”
- Table 2: Second column should be “Number of Ensemble Members” or “Ensemble Member Count”
- Lines 81-86. Lots of vague wording. Please remove “it” statements, and on line 85, what is “that method”?
- Line 115: “Comparing each swap with its parent scenario”, could you add an e.g. to make it clear what this means? I feel like it would be something like, “e.g., SSP126 and SSP126-SSP370Lu”
- Line 118: Should be difference between SSP370-SSP126Lu and SSP370?
- Section 2.5/2.6, I think at the end of section 2.6 it is implied that the ridge regression analysis is performed on each grid point separately, is that true about ridge regression analysis performed in section 2.5?
- Line 138-140: State instead, “All variables were standardized to the same scale before model fitting to address predictors with different magnitudes. The way to quantify …”
- Remove “This analysis enabled clear interpretation of the proportional impact of each variable”.
- Line 155: Instead of saying “step 1”, better to say Eq. (3)? I understood eventually what this meant but it was tricky to connect the dots when using “step 1”.
- Line 157: What is harvest in this context? I originally thought it referred to crops but it seems to refer to Primary/Secondary land instead?
- State instead, “Here Yi denotes transition or harvest, RCstate the state-level relative contribution, RCstate A to state B the estimated relative contribution for the harvest of the state (if present).
- Line 170: “Table 4: The variables relevant to each state.
- Line 174: “To retain it, also employ…” -> “As such, we apply…”
- Line 184: Describe why the Tibetan Plateau is masked out?
- Line 190: I do not know what the conventional multi-model ensemble is. Please describe it.
- Lines 226-228: Again, vague wording. Avoid “it” statements, also, “It also implies that credible LULCC attribution requires reliable skill.”, but how? And wouldn’t accurate LULCC attribution always require reliable skill?
- Line 234: State instead, “Hatching indicates a statistically significant trend at the 0.01 level.
- Lines 235-237. Rewrite first sentence to: “We next employ ridge regression to historical LULCC-induced NSWS given ridge regression’s superior performance over MME and any single model.
- Lines 247-251: Seems to be a lot of verbiage to describe that different LUCCs give different wind ranges.
- Lines 298-303/309. I feel like the section up to line 303 would be better stated in the data and methods, possibly up to line 309 as well.
- Line 322: “divergence” can be confused with the meteorological parameter here. Consider “differences between periods” instead.
- Line 328: Should it be SSP370-SSP126Lu minus SSP370 instead, according to figure?
Citation: https://doi.org/10.5194/egusphere-2026-1947-RC2
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- 1
This manuscript investigates the present and future responses of near-surface wind speed (NSWS) to different land-use and land-cover change (LULCC) types over China using experiments from the Land-Use Model Intercomparison Project (LUMIP) of Coupled Model Intercomparison Project Phase 6 (CMIP6). The topic is timely and relevant, as LULCC has long been considered an important but not sufficiently resolved driver of terrestrial wind speed changes. A major strength of this study is that it goes beyond treating LULCC as an aggregated factor and attempts to distinguish the roles of individual land-use states, transitions, and management practices. This provides a more detailed and physically informative view of how different LULCC pathways may influence NSWS. I appreciate the effort to evaluate model performance before conducting the attribution analysis. The results are interesting and the manuscript is well written. Overall, I find the manuscript suitable for publication after some revisions. My comments below:
Major comments:
1. It would be helpful to briefly clarify how the penalty parameter is selected and whether any cross-validation or sensitivity test was conducted. This would make the calibration procedure easier to reproduce.
2. The manuscript suggests that fertilization may influence NSWS through vegetation growth, surface albedo, evapotranspiration, boundary-layer stability, and local circulation. This interpretation is reasonable, but the direction of the net effect is not straightforward. A short discussion clarifying why fertilization is associated with increased NSWS over much of China in the results would make this part more convincing.
3. The distinction between SSP126-SSP370Lu and SSP370-SSP126Lu is important for the conclusions, but the current wording may be difficult for readers who are not familiar with LUMIP. I suggest adding one or two clearer sentences explaining what is fixed and what is changed in each comparison.
Minor comments:
1. “with urbanization contributes the least” suggested be revised to “with urbanization contributing the least”. Similar language issues appear in several places and can be addressed through careful editing.
2. In Table 1, the caption “Used experiment of CMIP6” could be revised to “CMIP6 and LUMIP experiments used in this study”.
3. Several abbreviations are used without being defined at their first occurrence. Please provide the full names when these abbreviations first appear in the manuscript.
4. Some inconsistencies in capitalization are present in the tables and headings. Please revise them for consistency.