Interactive comment on “ Estimation of the high-resolution variability of extreme wind speeds for a better management of wind damage risks to forest-based bioeconomy ” by Ari K

lakes, clear cut areas etc. The detailed structure of wind flow in this kind of heterogeneous terrain is very complex (e.g. Dupont&Brunet, Forestry, Vol. 81, No. 3, 2008. doi:10.1093/forestry/cpn006); one dominant feature being rapid deceleration of wind when wind encounters forest edge. The main wind damage are found typically within distance less than 50 m from the forest edge (Peltola et al. 1999b). In integration of the so called effective roughness we have applied normal distribution having variance 150 m. With these assumptions the weighting of each grid is as demonstrated in Fig. 1. The weight of the closest grid square is about 11 % and the furthest grid square located 500 m upwind has the weight of 0.04 % only. With no doubt, this formula is a simplification of a very complex issue as the exact impact of roughness elements on wind flow depend beside terrain properties also on the characteristics of prevailing air flow. However, when aiming in computationally light applications all these issues cannot be taken into account and the approach selected here gives a realistic interpretation of the complicated issue. We would be happy to add the Fig. 1. (e.g. to appendix) and explanatory text to the manuscript.

1 Introduction p1.l. 5: could you please add a few examples from the papers you cite which specific risks are impacting on the forests in Finland.p2.l. 35ff: The authors should add one or two sentences on the drawbacks of reanalysis data sets when the network of stations used for assimilation changes in space and time affecting the temporal and spatial covariance patterns.p3.l. 1ff: In my opinion the authors could improve the intro by adding a short paragraph on their downscaling cascade from large to their localized scale.I guess that at least three levels of complexities are involved.An important issue in this context is that a consistent approach is desirable where the subsequent downscaling steps comprise over the at least as complex structure as the preceding.For instance, given the assimilated or GCM derived large-scale circulation shows too strong biases (e.g.blocking frequencies) also the following steps do not compensate this shortcoming but inherent the information from the boundaries.This should at least be kept in mind to consistently interpret results and according uncertainties.

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Printer-friendly version Discussion paper 2 Material and methods 2.2 Estimation of return level for regional maximum wind speeds p4 l. 1ff: I assume that also a seasonal component is into the variability of maximum wind speeds.The authors could add some information which processes drive maximum wind speed during different seasons ( e.g.frontal based cyclonic maximum wind speed in cold fronts during winter half year vs. wind gusts originating from thunder storms that are operating during the summer seasons).
Another important information relates to the temporal basis.As much as I could infer authors use maximum monthly wind speeds.Using maximum daily wind data would provide a better statistical basis.However, in this case one also needs to account for the effect of serial correlated data.
A third issue involved in the analysis of extremes relates to the procedure of averaging -Are the values used for comparisons based on 6(x)hourly means or are they related to certain reading hours, i.e. instantaneous measurements without any temporal averaging ?This could for instance explain already part of the differences between ERA40 and observations.Given the comparable short length of the observational basis with the high value of return period it might also be useful to calculate shorter term return period, i.e. two and five years.In general, on the small scale geographic information is missing at borders.It would also be helpful to include an inset covering the large scale surroundings.In addition, each map should have its own frame with lat/lon coordinates. C3

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Printer-friendly version Discussion paper Table 1: Please include the length of the individual meteorological recordings to better visualize the robustness in the estimation of the 10yr return period.If the length between the ERA40 and the meteorological station varies then only the common overlap period should be used.Another question is whether the direction of strongest wind direction is the same for both, the ERA40 data set and the meteorological observations, respectively.
Appendix Figure 1: For the comparison a similar basis should be used.Obviously the ERA40 data are based on maximum monthly wind speed whereas the boxplot is based on 10min readings.Again, it would be important to know the averaging procedure, especially for the Era40 interim data set.Suggestion for modified abstract: Abstract The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies.In a forested country, such as Finland, over 50% of its current bioeconomy relies on the sustainable management and utilization of forest resources.In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 meter spatial resolution CORINE-land use dataset and high resolution digital elevation data.A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalysed data.These data were [Using a geospatial re-mapping technique the data ] were downscaled to 26 meteorological station locations to represent very diverse environments typical for Finish landscape. C4

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Printer-friendly version Discussion paper Applying a comparison, the downscaled 10-year return levels represent 77% of the observed variation among the stations examined.In addition, the spatial variation of wind multiplier downscaled 10-year return level wind was compared with the WAsPmodel simulated wind.The heterogeneous test area was situated in Northern Finland, and it was found that the major features of the spatial variation were similar, but in the details, there were relatively large differences.The results indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations having the highest forest wind damage risks.It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations.