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ISC licensed. United States: N. Copy to clipboard. United States. View Conference 5. Other availability. Please see Document Availability for additional information on obtaining the full-text document. The evidence for the long-term effects of global climate change on the spatiotemporal precipitation characteristics in Central Asia CA is plentiful 16 , 17 , 18 , 19 because CA has a unique ecological pattern with the coexistence of deserts, oases, mountains, snow and glaciers, which are extraordinarily sensitive to the warming temperature 20 , 21 , Precipitation is generally rare with limited wet season rainfall and snow in CA.
In recent years, extreme precipitation events exhibited an upward trend, suggesting an increasing contribution to the total precipitation, which have greatly increased the risk of water-related disasters and affected water resources from mountains 22 , Many efforts have been made to evaluate the precipitation variability on different scales in CA, based on the sparse gauge precipitation data, GCM simulations and fusion remote sensing data which indicate an overall rise in the spatial diversity and heterogeneity of the precipitation extremes during past decades 24 , 25 , 26 , 27 , 28 , 29 and future periods 30 , For these changing patterns, some authors have meanwhile investigated the possible influence from humidity, the atmospheric circulation and other natural factors more specifically 27 , Also, several studies have analyzed and distinguished the degree extent of natural and anthropogenic forcing and suggest that the latter plays an important role in the precipitation variability of CA However, this type of attribution research is still rare and further studies should be carried out so as to investigate and quantify the individual influence of anthropogenic climate change on the precipitation variation in CA.
Therefore, 4 indices including the total annual wet-day precipitation PRCPTOT , the maximum 5-day precipitation amount RX5day , the simple daily intensity index SDII and the extremely wet days R95p have been selected to evaluate the impact from anthropogenic forcing on the trend and non-uniformity i. Based on the administrative boundaries, societal and geographical conditions and precipitation variability, we divided CA into 4 parts.
This study aims to 1 examine the spatial—temporal variation including the trend in historical precipitation, 2 investigate the human contribution to potential changes in precipitation non-uniformity, and 3 discuss potential influencing factors in the temporal precipitation variability of CA.
Figure S2 shows the CMIP5 performance on the temporal variation of the historical simulations in CA, revealing a reasonable reproducibility in the magnitude and spatial patterns of the temporal variations. An obvious regional characteristic has also been observed and higher Gini-coefficients were mainly found in the southern parts for both the HadEX3 and CMIP5. The highest Gini-coefficient exceeded 0.
Overall, although the CMIP5 historical simulations overestimated the observed Gini-coefficients, they generally captured the spatial patterns of the annually observed precipitation variability in CA and might be used to quantify the anthropogenic contribution to changes in the temporal precipitation variability. In terms of spatial distribution, the simulated trend of the PRCPTOT is larger in the southeastern fringe and Tianshan Mountains of CA than in other regions and we could even see a decreasing trend in the southern edge of the Aral Sea Basin due to anthropogenic forcing Fig.
The subfigures were done in the software R 4. These results indicate that the radiative forcing changes mainly driven by human activities are responsible for the significant rise of the extreme climate indices in CA.
The vertical line indicates the median best estimate trend value. Among these 4 indices, the R95p has the highest mean Gini-coefficient value, exceeding 0. The probability distribution of the averaged bootstrap resampled Gini-coefficients has been calculated from the multi-model ensemble means in CA, which is shown in Fig. The dash- lines in figures a — d represent the median best estimate value of the Gini-coefficients. The solid lines in figures e — h demonstrate the median best estimate value of the RAI and the dash- line indicates the 95th percentile value.
In Fig. In this study, the median RAI value derived from the bootstrapped distribution is determined as the best estimate, which is used to evaluate positive or negative changes. Meanwhile, Fig. On the contrary, Fig. Based on the resampled averaged Gini-coefficients, the boxplots were drawn Fig. The possible reason for this phenomenon might be explained by the fact that there is a bigger precipitation frequency and larger precipitation amounts and rainy months are also noticed in NCA and CCA than in WCA and ECA, reflecting the higher magnitude of temporal variability in historical simulations in WCA and ECA.
Figure 4 i—l also clearly denotes that, in ECA, the NAT simulations have higher median Gini-coefficients than those of the ALL simulations for 4 sub-regions, indicating that the temporal variability of the annual precipitation is likely to decrease due to anthropogenic forcing in ECA. From the figure, we could conclude that the best RAI estimates in CCA and WCA are generally greater than zero, which suggests that anthropogenic forcing in these 2 regions caused more unbalanced extreme precipitation indices from to However, the effects did not pass the significance test.
Hence, some CA regions with significant human activities induced changes in the temporal distribution. Most regions of CA demonstrate however insignificant human activities induced non-uniformity. The bottom panel indicates the corresponding uncertainty range 5th to 95th percentile of the temporal RAI estimates for 4 sub-regions in CA. The horizontal lines in the box demonstrate the best estimate i. The results might reflect that the current GCM simulations show the general weakness of the daily variation of the precipitation prediction in these regions with sparse gauged data 34 , Overall, the CMIP5 ALL simulations could generally capture the spatial pattern of the temporal variations, which prove to be consistent with previous studies.
Compared with previous studies 26 , 36 , the whole of CA suffers from more significant changes in the precipitation extreme events. Another instance of most precipitation indices shows the spatial diversity and heterogeneity and generally the increasing trend of the PRCPTOT, SDII, R95p and RX5day which was substantial in the Tianshan Mountain and hill regions 38 but not obvious in the midland desert and depression areas All these findings comply with the trend variability of the precipitation analyzed in this study, clearly suggesting that anthropogenic forcing has risen the likelihood of heavy precipitation events in CA, especially in the alpine areas.
The results reveal an increase in the temporal equality of the extreme precipitation indices in northwest China, which is consistent with the results analyzed by Sun et al. A possible factor causing this result might be the low value of the precipitation frequency and amount in these regions with a mean annual precipitation of less than mm Although the extreme precipitation indices exhibit a rising tendency with an increase of the precipitation amounts from to 41 , a rise within a certain range will probably trigger a drop in non-uniformity i.
Several studies have explored the potential linkage between the precipitation extremes and the large-scale atmospheric circulation in CA, suggesting that the large-scale atmospheric circulation changes play a vital role in the changes of the precipitation amounts and extreme precipitation events 32 , 42 , 43 , 44 , All these indices have strong effects on the differences in the moisture transport pathways, eventually affecting the precipitation patterns and extremes 36 , 46 , However, the influence of the large-scale atmospheric circulation is rather complex 42 , 43 and further work is still needed in order to quantify the internal regimes more precisely.
The main results and conclusions could be summarized as follows: 1 overall, the CMIP5 ALL simulations demonstrated a little overestimation for the Gini-coefficients but reasonably characterized the spatial pattern; 2 there is a clear signal that the radiative forcing changes mainly driven by human activities have significantly increased the extreme climate indices in CA and the median trend for the PRCPTOT, SDII, R95p and RX5day rose from 0.
Due to the simple ecological structure, fragile ecosystem and weak stability in CA, the changes in the precipitation extremes probably cause a series of ecological, environmental and social sequences. Overall, the meteorological observational network was established during the Soviet era and the network is still unsatisfactory in CA.
After the fall of the Soviet Union, there has been a persistent downward trend in the quantity and quality of the measurements at most meteorological stations. Many meteorological stations were closed due to a lack of funds, causing incomplete long-term datasets at most stations 26 , In this study, the HadEX3 dataset was selected as the observed dataset; this dataset was chosen because it is the latest global dataset of the land surface extreme climate indices derived from the daily station data.
We have checked and confirmed that it generally covers the existing meteorological stations with long-term data in CA. The dataset was first calculated at each station and then interpolated onto a global grid over land with a 1.
In general, the historical experiment has been forced by the observed atmospheric composition changes reflecting the anthropogenic and natural sources and the time-evolving land cover In order to obtain a better comparison with the observed data, the time period of the historical and historicalNat simulations was selected from to In this study, 4 indices, including the total annual wet-day precipitation PRCPTOT , the maximum 5-day precipitation amount RX5day , the simple daily intensity index SDII and the extremely wet days R95p have been selected so as to evaluate the spatial—temporal variability of the extreme precipitation events Table S3.
Of them, the PRCPTOT could be utilized to assess the changes in total precipitation; the SDII represents the precipitation intensity; the R95p is a percentile-based threshold index to evaluate the precipitation events during very wet days and the RX5day demonstrates an absolute index, which is usually applied to describe changes in potential flood risks as heavy rain conditions over several consecutive days which could contribute to flooding The statistical significance for each indice has been checked by using the Mann—Kendall trend test, which is a non-parametric method to effectively evaluate the trend of extreme climatic events The trends were obtained from the arithmetic mean values of the annual extreme precipitation indices, respectively for the ALL and NAT scenario.
The Gini coefficient has been applied as a measure for the income inequality in a society 57 but more recently this has been done with a quantification uniformity in the time series of the climate variables 58 , 59 , calculated by the following equation:. The Gini index ranges from 0 to 1, with 1 associated to the maximal disparity and 0 denoting a complete uniformity.
Therefore, the Gini coefficient with small values suggests more uniformity in the temporal variation of 4 extreme precipitation indices, while the Gini coefficient with higher values indicates a higher non-uniformity in the temporal variation of the precipitation indices.
The diagram of the Gini coefficient is presented in Figure S3. The Gini coefficient is easy to interpret in different geographical environments, other measures for the variability methods, such as the standard deviation, are a possibility distribution of the data and prove to be scale-sensitive In order to compute the regional Gini coefficient, we have considered the average value of the gridded Gini-coefficients in 4 sub-regions and finally, we noticed that each region has a single value of the Gini-coefficient for each CMIP5 historical and historicalNat scenario.
In this study, we obtained 10, spatially averaged Gini coefficients, which could be used to represent the inter-model variability. In order to understand the impact from anthropogenic forcing on the extreme precipitation events in different sub-regions, we calculated the probability distribution functions PDFs for the ALL and NAT simulations of each sub-region. In order to assess the extent of the anthropogenic or natural influences for climate change or extreme events, scientists have developed climate models to simulate the changes in precipitation with and without anthropogenic influences by setting various scenarios More recently, this typically involved estimates of the fractional attributable risk FAR , a common technique to quantify the attributable risk of extreme precipitation events in the model analysis.
The uncertainty of FAR has generally been estimated for its statistical significance by means of the bootstrapping method.
The RAI has been calculated in the following equation 62 :. A negative RAI suggests that human activities lead to an increase in uniformity in the time series of the extreme precipitation indices, while a positive RAI value indicates rather the opposite.
In the context of resource management, a positive RAI value indicates that the challenges faced in water resource and ecosystem management have been increasing, while a negative RAI value means that the variability was reduced, so the water resources are easier to manage as a result. In order to evaluate the RAI uncertainty, the Bootstrapping resampling procedure has been applied to generate 10, sub-samples from 42 CMIP5 realizations and to rank these so as to extract the 5th and 95th percentile RAI values.
Thus, the distribution of 10, RAI values could reflect the uncertainty associated with the use of different models and provides a basis to communicate the RAI ranges. A positive median of the RAI is statistically significant if its 5th percentile value is also positive, while a negative RAI median is statistically significant if its 95th percentile value is also negative.
Finally, the RAI estimates could be applied to reflect the influence of anthropogenic forcing on the precipitation non-uniformity, along with its significance. Panels for Figs. Coumou, D. A decade of weather extremes. Min, S. Nature , — Willett, K. Attribution of observed surface humidity changes to human influence. Van Oldenborgh, G. Attribution of extreme rainfall from hurricane harvey, August Trenberth, K.
Attribution of climate extreme events. Rosier, S. Article Google Scholar.
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