the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing decadal to centennial scale nonstationary variability in meteorological drought trends
Kyungmin Sung
Max Carl Arne Torbenson
James H. Stagge
Abstract. There are indications that the reference climatology underlying meteorological drought has shown non-stationarity at seasonal, decadal, and centennial time scales, impacting the interpretation of normalized drought indices and potentially producing serious ecological, economic, and social consequences. Analyzing these trends in the meteorological drought climatology beyond the 100-year observation period contributes to a better understanding of the non-stationary changes, ultimately determining whether they are within the range of natural variability or outside this range. To accomplish this, our study introduces a novel approach to incorporate unevenly scaled tree-ring proxy data (NASPA) with instrumental precipitation datasets by first temporal downscaling the proxy data to produce a regular time series, and then modeling climate non-stationarity while simultaneously correcting model induced bias. This new modeling approach was applied to 14 sites across the continental United States using the 3-month Standardized Precipitation Index (SPI) as a basis. Findings showed locations which have experienced recent rapid shifts towards drier or wetter conditions during the instrumental period compared to the past 1000 years, with drying trends generally in the west and wetting trends in the east. This study also found that seasonal shifts have occurred in some regions recently, with seasonality changes most notable for southern gauges. We expect that our new approach provides a foundation for incorporating various datasets to examine non-stationary variability in long-term precipitation climatology and to confirm the spatial patterns noted here in greater detail.
Kyungmin Sung et al.
Status: open (extended)
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RC1: 'Comment on egusphere-2022-476', Anonymous Referee #1, 13 Dec 2022
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General comments
This manuscript presents a description of the model, analysis, and research gap. The writing and derived conclusion are clear. Some assumptions need further analysis to be expressed correctly. The graphs and tables provide valuable information for the analysis but deeper interpretations are needed. The dataset provides multiple ways to formulate the research approach that can be further explored.
Major comments
Line 146. The window of a 3-month moving average is not small? considering tree ring only provide growing/not growing period?
Line 147. The sixth percentile is a bit vague, even though it is a fitted two-parameter Gamma distribution. What is the difference with a 5%-10% threshold for the results?
Line 187. Does the assumption of a better non-linear fitness than linearity come from drought trends' particular behavior? The obtention of the smoothed long-term trend is the reason? (line 195).
Line 232. Which is the adequate NASPA reconstruction skill threshold and why? Table 1, shows a lower score of 0.220.
Line 369. How much can be considered natural variability?
Line 393. Natural variability should be addressed. Also, regarding altered seasonality alteration, is it in the natural variability to state that is drier or wet?
Minor comments
Line 103. Does the application of KNN for downscaling make it a novelty itself, just because it was not performed in tree-ring data?
Line 138. Is it correct to use the n-days distribution to convert accumulated precipitation into the standard normal distribution?
Line 203. Why the parameter's shape can change slowly on a multi-decadal scale?
Figure 5. The SPI +1.5 to -1.5 is only different for the ASO. Is it better to provide another visualization?
Line 327. Which is the order of magnitude improvements of the dsNASPA (of negative bias) over drought severity?
Line 344. Why is the drier trend especially severe? Is it the only one with this trend on the west coast?
Line 378. Is wider uncertainty the correct way to refer to higher uncertainty?
Line 425. Improve writing.
Citation: https://doi.org/10.5194/egusphere-2022-476-RC1
Kyungmin Sung et al.
Kyungmin Sung et al.
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