Spatial and temporal variations of precipitation concentration and their relationships with large-scale atmospheric circulations across Northeast China

Accepted Manuscript Spatial and temporal variations of precipitation concentration and their relationships with large-scale atmospheric circulations across Northeast China

Rui Wang, Jiquan Zhang, Enliang Guo, Chunli Zhao, Tiehua Cao PII: DOI: Reference:

S0169-8095(18)31570-9 https://doi.org/10.1016/j.atmosres.2019.02.008 ATMOS 4491

To appear in:

Atmospheric Research

Received date: Revised date: Accepted date:

6 December 2018 15 January 2019 19 February 2019

Please cite this article as: R. Wang, J. Zhang, E. Guo, et al., Spatial and temporal variations of precipitation concentration and their relationships with large-scale atmospheric circulations across Northeast China, Atmospheric Research, https://doi.org/10.1016/ j.atmosres.2019.02.008

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ACCEPTED MANUSCRIPT Spatial and temporal variations of precipitation concentration and their relationships with large-scale atmospheric circulations across Northeast China Rui Wang,a, b, c Jiquan Zhang, a, b, c* Enliang Guo,d, e,f* Chunli Zhaog, Tiehua Caoh

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a. School of Environment, Northeast Normal University, No.2555 Jingyue Street, Nanguan Changchun 130117, P.R. China

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b. State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, No.2555 Jingyue Street, Nanguan, Changchun 130117, P.R. China c. Key Laboratory for Vegetation Ecology, Ministry of Education, No.2555 Jingyue Street, Nanguan, Changchun 130117, P.R. China

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d. College of Geographical Science, Inner Mongolia Normal University, No.81 Zhaowuda, Saihan Hohhot 010022, P. R. China

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e. Inner Mongolia Key Laboratory of Disaster and Ecological Security on the Mongolian Plateau, Inner Mongolia Normal University, No.81 Zhaowuda, Saihan Hohhot 010022, P. R. China f. Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems, Inner Mongolia Normal University, No.81 Zhaowuda, Saihan Hohhot 010022, China

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g. School of Horticulture, Jilin Agricultural University, No.2888 Xincheng Street, Nanguan, Changchun 130117, P.R. China h. Institute of Agricultural Resources and Environment, Jilin Academy of Agricultural Sciences, No.1365 Shengtai Street, Jingyue Economic and Development Zone, Changchun 130117, P.R. China

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* Correspondence to: [email protected]; Tel.: +86-135-9608-6467; Fax: +86-431-8916-5624 and [email protected]; Tel.: +86-13081505377. ABSTRACT: Understanding the trends of precipitation concentration play a key role in watershed development and management. In this study, spatiotemporal and abrupt changes in the concentration index (CI) and their relationships with summer large-scale atmospheric circulations over Northeast China (NEC) were discovered. We used daily precipitation data from 71 meteorological stations and five large-scale atmosphere circulation indices for the period 1961–2016 across NEC. The results show that annual daily precipitation concentration in NEC decreased slightly in most areas. The spatial distribution of the CI ranges from 0.64 to 0.7 and the regions of the highest value were in Liaoning Province. The correlation between CI and different large-scale atmospheric indices show significant differences and mainly depend on the regional differences. Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO) are negatively correlated with the CI in most regions of NEC, in contrast to the East Asian summer monsoon index (EASMI) and Multivariate ENSO Index (MEI). The Southern Oscillation Index (SOI) is negatively correlated with the CI in Liaoning Province and positivity correlated with the CI in Jilin and most of Heilongjiang Province. Compared to before 1975, a Eurasian continent anticyclonic circulation anomaly caused more clear days and higher solar radiation during 1976–2016. Keywords: Concentration index; Large-scale atmosphere circulation; Spatial–temporal patterns; Correlation; Northeast China 1

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1. Introduction Since the 1950s, climatic projections have indicated that many climatic changes were unprecedented, such as the warming of the atmosphere and oceans, rising sea levels, melting glaciers, and increasing greenhouse gases (Trenberth et al., 2013; Schewe et al., 2014; Marshall et al., 2016; Sachindra et al., 2016). It is well established that warmer atmospheres can retain more water vapour and increase the flow rates of various parts of the water cycle on the Earth's surface, sometimes leading to more frequent and intense extreme rainfall events (Kumar, 2013). In this context, irregular temporal patterns of precipitation are of great interest, with the fine structure of precipitation having been altered (Feng et al., 2013; Thomas and Prasannakumar, 2016). Over the 21st century so far, some researches have shown the high percentages of annual total precipitation were concentrated mainly on a limited number of very rainy days, as result of the heightening of the global water cycle and rainfall intensity (Chou et al., 2013; Gloor et al., 2013; Ning et al., 2015; Singh et al., 2013; Wu et al., 2013; Wuebbles et al., 2014). In the future, precipitation patterns are expected to change still further, and extreme weather events are likely to occur more frequently. A significant decrease in the number of rainy days, as well as significantly increasing precipitation intensity values, has been identified in many places of the world (Deng et al., 2018a; Zhang and Hu, 2018). Thus, exploring the characteristics of precipitation concentration has become a key part of monitoring the pace of physical processes in the atmosphere. The decreasing trends for precipitation sometimes do not represent a lower risk because of the occurrence of short, diachronic strong precipitation events. Daily precipitation, as a discrete process, can be described by a negative exponential distribution because of the increased number of null values (Coscarelli and Caloiero, 2012; Cortesi et al., 2012; Martin-Vide, 2004; Monjoa and Martin-Vide, 2016). In order to study the temporal structure of daily precipitation, Martin-Vide (2004) developed the concentration index (CI) to estimate the degree of rainfall concentration and extreme torrential rainfall that can connect levels of precipitation events and their duration. The CI is a proper index that can provide information on the frequency distribution of daily precipitation, focusing on the amounts of precipitation. In other words, it is a dynamic point of view with which to analyse the variability, trends, abrupt changing points, and distributions based on the daily precipitation. The calculation of the CI is similar to that of the Gini index, which is itself closely related to the statistical programs in the determination of the daily intensity of precipitation (Jolliffe and Hope, 1996; Martin-Vide, 2004; Monjo and Martin-Vide, 2016; Peña et al., 2017; Vyshkvarkova et al., 2018). This index has been applied in many locations throughout the world such as the United States (Royéa and Martin-Vide, 2017), China (Li et al., 2011), Spain (Serrano-Notivoli et al., 2017), southern Russia (Vyshkvarkova et al., 2018), Chile (Sarricolea and Martin-Vide, 2014), Italy (Coscarelli and Caloiero, 2012), Algeria (Benhamrouche et al., 2015) and even the entire world (Monjo and Martin-Vide, 2016). The CI can depict the percentage contribution of days with precipitation to the total amount of precipitation and plays a remarkably crucial role in the study of climate change and the forecasting of high-intensity extreme precipitation events (Xiao et al., 2017; Xu et al., 2011). Large-scale atmospheric circulations can provide a circulation background field for climate change. Precipitation variations are closely related atmospheric anomalies that are likely to record different climate changes in response to global climate change (Lu et al., 2014). Previous studies have shown that changes in precipitation are related to large-scale atmospheric circulation changes (Hoerling et al. 2016; Xiao et al. 2017; Mallakpour and Villarini 2016). For instance, Deng et al. 2

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2. Material and methods

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(2018b) analysed the correlation between extreme precipitation and four indices: Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDI), and Dipole Mode Index (DMI). Wang et al. (2016) indicated the East Asian summer monsoon index (EASMI) might be very important for the formation of precipitation. However, few studies have focused on how changes in precipitation concentration are related to the large-scale circulation changes. Meteorologists indicated that the enhancement of the global hydrological cycle will lead to increased rainfall variability by the end of the 21st century (Wibig and Piotrowski, 2018; Meehl et al., 2019). Furthermore, there is evidence of long-term change in increases in precipitation associated with the ocean variability, which is dynamically driven by an atmospheric circulation, and only secondarily with a smaller contribution from anthropogenic climate change (Gu and Adler, 2013). Northeast China (NEC) is an important production base of agriculture, forestry, industry, and animal husbandry, and which lies in both temperate and cold temperate zones. NEC is universally recognised as a sensitive area of climatic change in global climate models because of its continental monsoon climate (Liang et al., 2011). In this context, consecutive dry and wet days, rainstorms, and floods occur more frequently in NEC, which has resulted in numerous negative impacts on the national economy of the region, particularly agricultural production and the ecological environment (Wang et al., 2013a). Therefore, the investigation on the spatial and temporal distribution of precipitation concentration can help improve the risk management and prevention of meteorological disaster in NEC. The objectives of this study are to explore: (a) the temporal variation and abrupt changes of the CI based on the Sen’s slope and Pettitt’s test, respectively. (b) the spatial distributions of annual and seasonal CI values, (c) the teleconnection relationships between the CI and five large-scale atmospheric indices and effects of major summer atmospheric circulation on changes of precipitation.

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2.1. Data sources and study area Datasets of daily precipitation amounts covering the period of 1961–2016 from 71 stations in NEC were used in this study, and were extracted from the Climate Data Center (CDC) of the China Meteorological Administration (CMA) (http://cdc.nmic.cn/). The meteorological stations are shown in Fig. 1. In this network, weather observing data collection began in 1950 and has continued to the present; however, some meteorological stations are missing too many data collected prior to 1960. Thus, the time series in this study was from 1961 to 2016. In addition, stations with poor quality data and insufficient collection periods were excluded when the data for a year were missing on more than 10% of the days, according to the computer program RClimDex and the Rhtest software (http://cccma.seos.uvic.ca/ETCCDI/software.shtml). Finally, 71 stations were selected that have at least 55 years of data following the data quality and homogeneity assessment (attached to supplementary materials).

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Fig. 1. Study area and the precipitation stations Data including the monthly 850hPa geopotential height, the wind and water vapor flux field on 2.5°×2.5°grids from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR). In additions, the correlations between precipitation concentration and five large-scale atmospheric indices were analyzed. The Southern Oscillation Index (SOI) is the index based on the sea level pressure differences, The Multivariate ENSO Index (MEI) composed of sea-level pressure and zonal and meridional components of the surface wind, sea surface temperature, surface air temperature, and total cloudiness fraction of the sky. The El Niño-Southern Oscillation (ENSO) was indicated by the SOI and the MEI. The North Atlantic Oscillation (NAO) index is based on the surface sea level pressure difference between the Subtropical (Azores) High and the Subpolar Low. The Pacific Decadal Oscillation (PDO) index is often described as a long-lived El Niño-like pattern of Pacific climate variability. The above four indices were obtained from National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI) (https://www.ncdc. noaa.gov/teleconnections/). The East Asian summer monsoon index (EASMI) is relative to the land–sea thermal contrast between the Pacific Ocean and the Eurasian continent, which affects precipitations in East Asia. EASMI was collected from the personal home page of Jianping Li. ( http://ljp.gcess.cn/dct/page/1). 2.2. Concentration Index The CI was used in this study to analyse the contribution of the days of greatest rainfall to the total amount and the weight of the daily precipitation events, as proposed by Martin-Vide (2004). The theoretical basis of the CI is that the probability of large daily amounts of precipitation occurring in a given time period and geographical location. Thus, the first step was to arrange the daily precipitation in ascending order and classify the values in 1-mm classes, from [0.1–0.9], [1.0–1.9], etc., until the last class includes the highest daily precipitation value. Then, the accumulated percentages of the precipitation (Y or ΣPi (%) ) contributed by the accumulated percentages of days (X or Σni (%) ) during Y’s occurrence were analysed. The Lorenz curve is a function that can describe the X against the corresponding Y, which is the polygonal line provided 4

ACCEPTED MANUSCRIPT by the empirical cumulative value of a variable according to its cumulative frequency (Benhamrouche et al., 2015), as follows:

Y = aX exp(bX )

(1)

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where a and b are constants. They can be calculated using the least-squares method. For illustrative purposes, Fig. 2 compares the cumulative frequency of days when precipitation occurred from cumulative daily rain amount collected at four stations: Yilan, Yanji, Dalian, and Dandong. The results show that the curves are quite similar for the four stations, but the area between the curve and the bisector of the quadrant in Dandong and Dalian is larger, meaning that in these two regions most rainy days accumulate most of the total annual precipitation. These differences in the annual exponential curves at each station provide a means of studying precipitation regimes. According to the exponential curve, area A can be defined as the definite integral of the aforementioned curve between 0 and 100, as provided by Eq. (2), and the area S is enclosed by the bisector of the quadrant ( Y = X ). The polygonal line provides a measure of the concentration and the value is the difference between 5000 and the value of A, as provided by Eq. (3) 100

(2)

S = 5000 - A

(3)

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1   a bx A =  e ( x - ) b 0 b

Based on the aforementioned values, the CI value can be defined as follows: (4) Note that the CI is a fraction of S and the lower surface of the triangle delimited by the uniform distribution line.

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CI = S / 5000

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 Xi  Xk    ik 

Qi  

i  1,..., N

(5)

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Fig. 2. Example of the exponential curves (cumulative number of precipitation days versus the amount of accumulated precipitation, in %) of the four stations. 2.3. Sen’s slope Sen (1968) developed a non-parametric test to estimate the true slope of a linear trend in a time series. The slope estimating N pairs of data is calculated as follows:

where X i and X k are the data values at times i and k ( i  k ), respectively. Sen’s slope is the median of the N values of Qi . Then, the N values of Qi are ranked from smallest to largest. If N is even, Sen’s slope estimator is computed by Qmed  [QN /2  Q N  2 /2 ] / 2 , and if N is odd, Sen’s slope estimator is computed by Qmed  Q( N 1) / 2 . Finally, Qmed is calculated by a two-sided test at the 100(1-α) % confidence interval. The value of Sen’s slope can reflect the data trend and its steepness. 2.4. Pettitt’s test The non-parametric Pettitt’s test was applied to assess the phenomenon of abrupt changes in the 6

ACCEPTED MANUSCRIPT time series of the climatic data, as described in Pettitt (1979). It can capture the change points in the middle of the time series, which is the least sensitive to outliers and skewed distribution and is more sensitive to breaks (Jaiswal et al., 2015; Wijngaard et al., 2003). Comparing to the Mann-Kendall test (MK test), the Pettitt’s test has a higher power for non-normally distributed data and it is distribution free. Villarini et al. (2009) indicated that Pettitt’s test has better accuracy than other statistical approaches in calculating abrupt changes. For a series of T observations, the K test statistic is computed as follows:

Kt  max1t T Ut ,T t = max(KT ,KT )

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in which:

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1 if ( xt  x j  0)  Dij  0 if ( xt  x j  0) -1 if ( x  x  0) t j 

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U t ,T  it 1Tj i 1Dij

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(7)

(8)

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When the maximum KT occurs, time ( T ) would be the abrupt point. To determine a significance level, α, the P value is defined as follows:

P  exp  6 KT / (T  T ) 

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(9)

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2.5. Correlation analyses Correlation between sets of data is a measure on how well they are related, and Pearson’s correlation coefficient is the most used measure of correlation (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient), which was used to investigate the direction and strength of the relationships between the variables.

3. Results

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3.1. Brief variation characteristics of the CI The annual CI value was calculated for 71 meteorological stations in NEC for the period from 1961 to 2016. The linear trends of the CI value including those in Heilongjiang, Jilin, Liaoning Provinces and NEC as a whole, which are shown in Fig. 3. Annual CI values showed non-significant decreasing trends in Heilongjiang, Jilin, Liaoning and NEC, with the -0.0004 year −1, -0.0004 year −1, -0.0005 year −1and -0.0004 year −1, respectively. This is consistent with Wang et al. (2013a). The regional average CI values in Heilongjiang Province were in decline and varied from 0.64 to 0.7. The highest value was 0.64 occurring in 1965 during the period of 1961–2016. After the 1980s CI increased, indicating that heavy rainfall during these periods was more frequent and occurred more readily at some points (Fig. 3a). The value of CI in Jilin Province ranged from 0.62 to 0.70, having fluctuated greatly over the past 56 years. Particularly during the 1970s, the CI had an obvious decreasing trend, indicating rainfall was more uneven (Fig. 3b). The CI values in Liaoning Province decreased during every generation, ranging from 0.65 to 0.73, and the annual average of CI was the highest at 0.73, in 1965 (Fig. 3c). Taking a broader view of NEC (Fig. 3d), the CI values were higher than the annual average during the 1960s and 1970s and lower following the 1980s, indicating that precipitation was more concentrated prior to the 1970s.

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Fig. 3. Linear trends in CI values in NEC from 1961–2016 3.2. Spatiotemporal characteristics of variation trends of the CI 3.2.1. Spatial distribution characteristics of the annual CI and seasonal CI To analyse the spatial distribution of the precipitation concentration, we calculated the spatial distribution of the annual CI and seasonal CI in NEC (Fig. 4). Fig. 4 (a) shows that the CI values for most regions ranged from 0.65 to 0.68. Lower precipitation levels in these regions, based on historical rainfall data, indicates a higher risk of drought. The highest CI value was in south-eastern Liaoning Province. Coupled with higher precipitation in this region, this increases the risk of disastrous flooding (Fang et al., 2017). The lowest CI values were in eastern Jilin Province, but the generally high precipitation in these regions indicated that annual precipitation showed a more uniform distribution. As Fig. 4 (b) shows, the spatial distribution of spring CI ranged from 0.59 to 0.65. The high-spring CI value regions were all in the southwest and the spring CI values in the east were low. During the summer, the CI ranged from 0.61 to 0.69. The high-value regions were concentrated in most regions of Liaoning Province, and the values in eastern Jilin and central regions of Heilongjiang provinces were obviously lower compared to those of the annual CI. Except for several sporadic high-value regions, the autumn CI values were relatively low, ranging from 0.52 to 0.64. The winter CI values significantly decreased compared to those of the annual CI ranging from 0.52 to 0.64, and the spatial distribution of winter CI increased from west to east. As a whole, a high value of the CI mainly occurred in Liaoning Province, indicating that heavy rain even rainstorms in this area were more frequent.

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Fig. 4. Spatial distributions of the annual and seasonal CI in NEC for 1961-2016 3.2.2. Spatial characteristics of variation trends of the CI In this study, variation trends in the CI during the period 1961–2016 were analysed based on Sen’s slope method (Table 1). A positive value of Sen’s slope shows a growing trend and a negative value shows a decreasing trend, while a zero value represents no trend. The interpolations of Sen’s slope in CI and annual total precipitations at all stations were then calculated using the IDW (Inverse Distance Weighted) method (Fig. 5). As shown in Table 1 and Fig. 5(a), only six (8.45%) stations show increasing trends. The stations were distributed in the southwest regions of Heilongjiang Province (Fuyu, Qiqihar, and Anda), the southern regions of Jilin Province (Huanren and Ji’an), and the western part of Liaoning Province (Xiongyue). These regions were not above the significance level, meaning that the rising trends of the CI were not obvious. The regions greater than 90% in NEC showed decreasing trends. These stations are distributed over large areas of NEC, in particular, the central regions of Heilongjiang Province, the west of Jilin Province, and most parts of Liaoning Province. In additions, there were 12, 14, and 7 stations that exceeded the 99%, 95%, and 90% significance levels accounting for 16.9%, 19.7%, and 9.9% of the total stations, respectively. The CI values decrease significantly in the mid-eastern and mid-southern regions of Liaoning Province, the middle regions of Heilongjiang Province, and the mid-western regions of Jilin Province. Overall, decreasing trends in the CI were mainly concentrated in the north-central and southern regions of NEC. Fig. 5b shows the spatial distribution of the variation trends in precipitation. Decreasing trends 9

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Sen's slope

P value

Station

Sen's slope

P value

Station

Sen's slope

P value

50353

-0.00012 ↓

0.646

50978

-0.00057 ↓

0.036**

54337

-0.00013 ↓

0.636

50468

-0.00042 ↓

0.136

50983

50557

-0.00053 ↓

0.044**

54041

-0.00036 ↓

0.173

54339

-0.00065 ↓

0.030**

-0.00054 ↓

0.037**

54342

-0.00064 ↓

0.106

50564

-0.00069 ↓

0.004***

54049

-0.00076 ↓

0.041**

54346

-0.00046 ↓

0.081*

50656

-0.00054 ↓

0.103

54063

-0.00057 ↓

0.081*

54351

-0.00081 ↓

0.026**

50658

-0.00065 ↓

0.061*

54094

-0.00008 ↓

0.816

54363

-0.00014 ↓

0.548

50742

0.00019 ↑

50745

0.00030 ↑

50756

-0.00075 ↓

50758

-0.00011 ↓

50774

-0.00071 ↓

50788 50844 50853

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are mainly concentrated in the southern regions of the Jilin and Liaoning provinces and the central regions of Heilongjiang Province. The increasing trends were mainly concentrated in the eastern and western regions of Heilongjiang Province and the central regions of Jilin Province. Only one station (Suifenhe) show significantly increasing trends at the significance level of 5%. By comparing Fig. 5a and 5b, we can further understand the characteristics of precipitation in NEC. Precipitation and the CI showed decreasing trends in western and southern regions of Liaoning Province, western regions of Jilin Province and south-central regions of Heilongjiang Province, indicating a high risk of drought. The decreasing magnitudes of the high-intensity precipitation in these regions are relatively large. The decreasing CI and increasing precipitation in the eastern Jilin indicated the increasing magnitude of low-intensity precipitation was greater than the high-intensity precipitation. Precipitations in these regions were relatively evenly distributed. Precipitation and the CI showed increasing trends in western Heilongjiang and eastern Jilin, indicating a high risk of flood (Royéa and Martin-Vide, 2017). In these regions, high-intensity precipitation was greater than those of the low-intensity precipitation. An increasing precipitation and declining CI occurred in Xiongyue of Liaoning Province and Ji’an and Huanren of Jilin Province, which indicate these regions are more readily subject to high-intensity precipitation, and thus heavy rainfall and rainstorms are more frequent. Table 1. Trends in the CI value based on Sen’s slope at all stations in NEC

54096

-0.00058 ↓

0.067*

54365

0.00007 ↑

0.849

0.333

54142

-0.00030 ↓

0.408

54374

-0.00031 ↓

0.151

0.009***

54157

-0.00026 ↓

0.392

54377

0.00002 ↑

0.938

0.740

54161

-0.00048 ↓

0.045**

54386

-0.00075 ↓

0.003***

0.020**

54181

-0.00026 ↓

0.362

54454

-0.00020 ↓

0.606

-0.00056 ↓

0.013**

54186

-0.00059 ↓

0.054*

54455

-0.00092 ↓

0.008***

-0.00013 ↓

0.557

54236

-0.00055 ↓

0.065*

54471

-0.00004 ↓

0.893

-0.00099 ↓

0.000***

54237

-0.00093 ↓

0.007**

54476

0.00010 ↑

0.729

0.00004↑

0.916

54254

-0.00024 ↓

0.408

54486

-0.00109 ↓

0.002***

-0.00066 ↓

0.005***

54259

-0.00007 ↓

0.893

54493

-0.00029 ↓

0.319

50873

-0.00018 ↓

0.425

54266

-0.00075 ↓

0.010***

54497

-0.00063 ↓

0.033**

50877

-0.00020 ↓

0.548

54273

-0.00068 ↓

0.009***

54563

-0.00116 ↓

0.001***

50888

-0.00034 ↓

0.232

54276

-0.00016 ↓

0.596

54584

-0.00078 ↓

0.008***

50936

-0.00063 ↓

0.047**

54284

-0.00028 ↓

0.221

54662

-0.00061 ↓

0.115

50948

-0.00022 ↓

0.502

54285

-0.00033 ↓

0.112

50949

-0.00025 ↓

0.355

54286

-0.00018 ↓

0.511

50953

-0.00077 ↓

0.010***

54292

-0.00042 ↓

0.186

50963

-0.00039 ↓

0.168

54324

-0.00068 ↓

0.078*

50968

-0.00068 ↓

0.014**

54326

-0.00067 ↓

0.019**

50862

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the value at a 90% significant level

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Fig. 5. The variation trends of the annual CI and precipitation based on Sen’s slope at all stations in NEC 3.2.3. Analyses of change points of the CI The change point is the presence of possible abrupt change in precipitation concentration, and it can have large impacts on trend analysis results (Hsu et al., 2014). Fig. 6 shows the change point detection results in CI based on the Pettitt test. In all records, there are 23 stations out of 71 with statistically significant change points in mean at the significant level of 10%. The stations are distributed sporadically over every subregion in NEC. Meanwhile, abrupt changes were more likely to occur during the 1970s, accounting for 32.39% of all stations, followed by those occurring during the 1980s (25.35%). After 1980, the number of stations with a change point is relatively normally distributed in different 10-year periods. The study observed that the timing of change points tended to be later (i.e. more recent) in central and southern Liaoning Province, northern and southern Heilongjiang Province, and northern Jilin Province. Change points in central Heilongjiang and Jilin Province and north-eastern Liaoning Province tended to occur earlier.

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Fig. 6. Change points in annual CI based on the Pettitt test. The colours indicate the time of change point within different periods. The number in the bracket indicates the corresponding number of the station. The diamonds indicate statistically significant change points Besides, the trends of the CI in each station were further analyzed by subdividing the record into two subseries (before and after the change point). Fig. 7 shows the spatial distributions of the trend of the CI with the two subseries. Only three stations showed significantly changing trends before change point and six stations showed significantly changing trends after change point. The signal of variation trend of the CI was weak. In additions, before the year of change point, there are decreasing trends in western Heilongjiang Province and north-eastern Liaoning Province, while they showed increasing trends after the year of change points. The results were consistent with zhang (2017).

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Fig. 7. The variation trends of the annual CI based on Sen’s slope at all stations with change point in NEC 3.3. Correlations between the CI and large-scale atmospheric circulations Fig. 8 shows the variation features of EASMI, NAO, MEI, PDO, SOI and CI from 1961 to 2016. We found that a change point occurred in 1986 for EASMI, with variation decreasing before 1986 and increasing after 1986. There was an abrupt change in 1981 for NAO, the increasing fluctuation of NAO were mitigatory before 1981 and the NAO had a decreasing trend after 1981, with a sharp decrease in 2010. The abrupt change point for MEI occurred in 1976, the signal of abrupt change was weak and the fluctuation frequency of MEI were low before 1976, but after 1976 there had a large fluctuation frequency. The abrupt change for PDO occurred in 1975. Before 1975, the fluctuation frequency and the value were both low with an increasing trend. From 1975 to 1988, the variation trends significantly increased. After 1975, the PDO had a violent fluctuation with a relative high average and the p-value were 0.003 at 99% significant level. The trends of SOI had a small fluctuation frequency. The p-value was 0.237, and though the signal of abrupt change is weak, a sharp decrease occurred from 1975 to 1977. The CI showed two different decreasing stages between 1961-1975 and 1976-2016. It experienced a relatively high average before 1975 and a relatively low average after 1975.

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Fig. 8. Abrupt changes of EASMI, NAO, MEI, PDO, SOI and CI from 1961 to 2016. The vertical red line indicates a possible change point based on the Pettit test. Fig. 9 shows the teleconnection relationships between CI and five climate indices: EASMI, NAO, MEI, PDO and SOI, which clearly shows the spatial influence of atmospheric circulation on the precipitation concentration in NEC. On the whole, most stations were not significantly correlated with the above five indices, but the spatial distributions still can be recognized. The EASMI were positively correlated with CI with the exception of a few scattered stations. The positive correlations between EASMI and CI decreased from the middle to both sides of NEC. There were 8 stations with a significantly positively correlation, distributed in central parts of Heilongjiang and Liaoning Province. These stations included Beian, Suihua, Haerbin, Shangzhi, Kaiyuan, Anshan, Shenyang and Wafangdian. Almost all of the NAO were negatively correlated with the CI. The significant correlations were mainly distributed in western NEC and the negative correlations decreased from western to eastern NEC. As for MEI, there existed obvious spatial diversities. In most regions of Heilongjiang and Jilin Provinces, the MEI were negatively correlated with CI and Tsitsihar, Changchun, Anda, Meihekou and Tonghe had significant negative correlations. Conversely, positive correlations were found between MEI and CI in most regions of 14

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Liaoning Province, especially in Zhuanghe and Dalian. The correlations between MEI and CI were significantly positive in these two cities. In additions, we found that PDO shows a negative correlation with the CI except for at a few scattered stations. There were stations in eastern, western and central Heilongjiang Province with significant negative correlations and the correlations in eastern Jilin Province were both significant. For the correlations between SOI and CI, most regions in Heilongjiang and Jilin Provinces showed positive relations but Sunwu in Heilongjiang Province had a significant negative correlation. Conversely, in most regions of Liaoning Province, SOI and CI were positively correlated, particularly in Zhuanghe and Dalian,. The signal of significant correlation becomes weak for the SOI, and there were no strongly discernible spatial patterns can be identified.

Fig. 9. The statistically correlation between the CI and five climate indices: EASMI, NAO, MEI, PDO and SOI. To quantify the relations between large-scale atmospheric circulation and the CI, we calculated the mean circulation composites and investigated the influences of circulation changes on the trend of precipitation concentration. Firstly, we found the CI has a significant abrupt change point in 1975 (Fig. 8). Then, the change in summer circulation at 850hpa was calculated by subtraction: after and before the abrupt change (Fig. 10). Compared with 1961-1975, the summer geopotential height at 850hPa in 1976-2016 was higher over NEC. The larger variation in summer geopotential height occurred in the east of NEC (120°-125°E, 40°-55°N). Furthermore, a Eurasian continent anticyclonic circulation anomaly prevailed over the west of NEC, affecting the entirety of NEC, which caused anomalous downdraft movement and led to more clear days and higher solar 15

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radiation during 1976–2016. Meanwhile, stronger westerly winds (near 55° –65°N) blocked the cold air flowing into low latitudes from arctic regions. A cyclone was found in the western Pacific (140°-160°E, 30°-35°N), which weakened the east Asian summer monsoon and brought less water vapors from the ocean to the land. Therefore, more frequent rainy days during 1961-1975 occurred over the east of NEC (Fig. 10a). Most areas in NEC showed obvious water vapour divergence anomalies. Compared with 1961-1975, the amount of water vapour in this area was less, and the magnitude of precipitation was also reduced relatively in 1976-2016 (Fig. 10b). An intense anticyclone (positive geopotential height anomaly) centred over northern Europe strengthened the westward movement of water vapour, with water vapour in the Western Pacific and Indian Oceans being transported to the Northeast through the Yellow Sea and Bohai Sea.

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Fig. 10. Spatial changes of differences in the compound field of geopotential height and wind speed (a) and the compound field of water vapor flux and moisture divergence flux (b) between 1976-2016 and 1961-1975 in summers at 850hPa. 4. Discussion Variations of precipitation concentration were the basis of risk mapping and played a vital function in public safety, storm infrastructure and climate changes. The CI is an index related to the rainfall weight in the annual total of a specific fraction of the days with the highest amounts of rainfall, focusing on the more or less regular distribution of the various classes of daily quantities of precipitation. Therefore, it provides new information for the statistical procedures used to determine the daily precipitation intensity. In this paper, the CI can reflect the regional irregular distribution in space and time of strong precipitation events. The CI values in our study were slightly higher than those of Spain (0.50–0.70), Italy (0.43–0.63), and New Zealand (0.47–0.70); much higher than those of Peru (0.42–0.58); and similar to other regions of the world such as Iran (0.59–0.73) and Chile (0.52–0.74) (Alijani et al., 2008; Caloiero, 2014; Coscarelli and Caloiero, 2012; Sarricolea and Martín-Vide, 2014; Zubieta et al., 2016). In China, the CI in Northeast and Northwest China were greater than those in the Yangtze River’s middle and lower reaches and Southeast China. However, the precipitation was less in Northeast and Northwest China (Zhang et al., 2009). This result is because although precipitation in Northeast and Northwest China is low, it is more concentrated on a few heavy precipitation days. NEC is a study area where precipitation dynamics are a key factor in many fields such as agriculture, economics, and urban safety, because not only is NEC a major industrialised region, but also because it is among the vital bases for trading in cereals. The CI value in NEC varied over a small range, between 0.64 and 0.70, which is in agreement with the results of other research in China (Jiang et al., 2016; Shi et al., 2015; Wang et al., 2013b). Recently, Monjo and Martin-Vide (2016) indicated that CI values worldwide 16

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range between 0.38 and 0.87, whereas in NEC they were found to be from 0.6 to 0.77. These results are very similar to our results, and the spatial distributions of the CI in the study area were similar as well. Overall, the regions with higher CI values were in the eastern and southern parts of NEC. The eastern area of Liaoning such as Dalian and Dandong had the highest concentrations, at orographic barriers near the coast, and the lower regions were in the eastern area of Jilin Province, considered inner continental lands. The CI value is an index based on daily precipitation, and the value of precipitations can be affected by changes in Asian monsoon strength as a result of global climatic warming. Most of the focus in assessing China's climate-related problems has been on the effects of the winter and summer monsoon (Böll et al., 2015; Coats et al., 2015). Within this context, this work considered the effects of large-scale atmospheric circulation on precipitation. The anticyclonic circulation anomaly prevailing over NEC resulted in more frequent precipitation, a finding similar to the results of Wang et al (2017). The western Pacific subtropical high is one of the most important circulation systems in the western Pacific and East Asia (Gao and Shi, 2016; Guhathakurta et al., 2017; Wang et al., 2017). The stronger and longer the western Pacific subtropical high, the more frequent and the stronger the precipitation is in eastern China (Ding et al., 2010). The stronger Asian zonal circulation can prevent cold air from flowing into lower latitudes in NEC, resulting in higher summer temperatures in the region (Wang et al., 2012). Therefore, the summer precipitation anomaly is driven by the variation of atmospheric circulation. This is consistent with our findings. There are differences in the correlation between precipitation concentration and large-scale atmospheric circulation indices in different regions of the world (Harding et al., 2013). Our results showed that no significant correlations were detected between CI and five large-scale atmospheric circulation indices, differing from results from Russia (Wright et al., 2014) and eastern China (Kong et al., 2016). This confirms that the effects of some large-scale atmospheric circulation are limited to some regions (Wang et al. 2017). Our studies showed that the NAO were significantly negative correlated with the CI (Fig. 9) because the northerly winds from dry land impeded the water vapor and occurrences of precipitation. Moreover, years linked to abrupt change points in long-term trends of the CI were connected with large-scale atmospheric variations. Before 1981, NAO showed an increasing trend, so the CI had a decreasing trend. However, after 1981, NAO shows a decreasing trend and an increasing trend in the CI could be found in NEC. This research showed that the regional responses of the CI to the large-scale atmospheric indices are different and complex. Some regions are synchronously impacted by the multiple climate indices. For example, the trend of the CI in central parts of Liaoning and Heilongjiang provinces are mainly affected by EASMI; the trend of the CI in western Liaoning and Heilongjiang Provinces are mainly affected by NAO and the trend of the CI in eastern Jilin Province is mainly affected by PDO. However, the variation trend analyses of precipitation concentration show a large uncertainty because of the differences in many factors such as the dataset structures and different study periods and study areas. The statistical analysis of precipitation trends needs a daily resolution that differs highly in terms of monthly or yearly precipitation amounts. Differences in datasets expedite the uncertainly of the research on the CI. Future research would investigate construction based on complete observatories and gridded datasets, as this is also a complex issue to consider during the extrapolation of this information to a wider region. 17

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5. Conclusion In this study, we investigated the spatial and temporal characteristics of CI across NEC, as well as the correlations between CI and large-scale atmospheric circulations over NEC from 1961-2016. The main conclusions are as follows: (1) In NEC, the overall trend of the annual concentration index was to decrease. The value of CI was most likely to undergo abrupt changes in the 70s, based on the Pettitt method, and there are significant changes in the CI in 32% of all of the stations. Although the spatial distributions of change points are discrete, change points tended to occur be more common in Jilin Province and mainly occurred in the 70s. (2) In most parts of NEC, the CI value’s trends were decrease, based on Sen’s slope, exhibiting significant trends in Heilongjiang and Liaoning Province. However, the annual precipitation concentration index tended to increase slightly and did not reach a significant trend in the eastern region of Heilongjiang Province, with the annual rainfall also having an increasing tendency. Therefore, monitoring and control measures in this area should be enhanced. (3) The CI values obtained range between 0.64 and 0.70, with the values in summer and autumn higher and in winter and spring lower. The spatial distributions of CI in spring and summer are similar to these in the yearly time-scale, which decrease from the southwest to the northeast. Overall, the regions with the highest value of CI were in Liaoning Province. (4) The regional responses of annual CI to the large-scale atmospheric indices are different and complex. Some regions are simultaneously affected by the multiple climate indices, but the correlations between the CI and NAO, PDO were mainly negative. There are no strongly discernible spatial patterns. Compared with 1961-1975, during 1976-2016, a larger variation in summer geopotential height occurred in the east of NEC. Also during 1976-2016 a Eurasian continent anticyclonic circulation anomaly caused more clear days and higher solar radiation, with water vapour in the Western Pacific and Indian Oceans being transported to the Northeast through the Yellow Sea and Bohai Sea. Acknowledgements This research is supported by the National Science Foundation of China (Nos. 41571491 and 41371495); The National Science Foundation for Young Scientists of China (No. 41807507); The China Special Fund for Meteorological Research in the Public Interest (No. GYHY201506001-6); The National Key Technology R&D Program of China under Grant (No. 2013BAK05B01); The Fundamental Research Funds for the Central Universities of China (No. 2412016KJ046). References Alijani, B., O’Brien, J., Yarnal, B., 2008. Spatial analysis of precipitation intensity and concentration in Iran. Theor. Appl. Climatol., 94: 107–124. https://doi.org/10.1007/s00704-007-0344-y Benhamrouche, A., Boucherf, D., Hamadache, R., Bendahmane, L., Martin-Vide J., Teixeira Nery, J., 2015. Spatial distribution of the daily precipitation concentration index in Algeria. Nat. Hazards Earth Syst. Sci., 15, 617–625. http://dx.doi.org//10.5194/nhess-15-617-2015 Böll, A., Schulz, H., Munz, P., Rixen, T., Gaye, B., Emeis, K. C., 2015. Contrasting sea surface temperature of summer and winter monsoon variability in the northern Arabian Sea over the last 25 ka. Palaeogeogr. Palaeocl., 426, 10-21. https://doi.org/10.1016/j.palaeo.2015.02.036 Caloiero, T., 2014. Analysis of daily rainfall concentration in new zealand. Natural Hazards, 72(2), 389-404. https://doi.org/10.1007/s11069-013-1015-1 18

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Precipitation concentration was analyzed by concentration index (CI), which was calculated by the contribution of the days of greatest rainfall to the total amount and the weight of the daily precipitation events. Annual CI of more than 90% stations showed decreasing trends and 32.39% of all stations occurred an abrupt change during 1970s. CI values ranged from 0.65 to 0.68. The highest value was in south-eastern Liaoning Province and the lowest value for CI were in eastern Jilin Province. EASMI, MEI and SOI (NAO and PDO) had positive (negative) impacts on CI in most stations. The summer precipitation anomaly is driven by the variation of atmospheric circulation.

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