Quasi-periodicities in Chinese precipitation time series

Summary Although climate change deeply affects China, climatic time series are expected to show quasi-periodic behavior. This hypothesis has been tested by means of Autocorrelation Spectral Analysis (ASA) to detect quasi-periodicities in precipitation time series of 132 climate stations spread over China for the period from 1951 to 2002. A Continuous Wavelet Transform (CWT) has also been applied in order to examine if these quasi-periods are stable in time. Finally, it has been tested whether precipitation series recorded at neighboring stations show similar spectral behavior and can thus be grouped. All in all, our hypothesis regarding quasi-periodicities has been verified. Most of the time series show significant quasi-periods. A 2–3 year quasi-periodicity is predominant in many different regions of China. The result of ASA is that precipitation series from neighboring stations often show similar quasi-periods and therefore a grouping seems to be justified. However, the outcome of CWT provides more detailed information. In north-eastern China several series show similar quasi-periods which are statistically significant at different times; here, a grouping would be incorrect. In addition, the results of CWT show a less uniform pattern of quasi-periods in the southern inland regions. Whereas ASA shows a relatively uniform pattern of 2–3 year quasi-periods, CWT detects 2–3, 4–5 and 6–7 year quasi-periods. Nevertheless, although the quasi-periods are detected as being unstable, time series from neighboring stations sometimes show analogous significant quasi-periods within the same time frame and can thus be grouped. This can be seen in the northern part of central China, near the border to North Korea and along the coast of the South China Sea. Authors’ addresses: Heike Hartmann, Lorenz King, Department of Geography, Justus Liebig University, Senckenbergstra?e 1, 35390 Giessen, Germany; Stefan Becker, Department of Geography & Urban Planning, University of Wisconsin-Oshkosh, Oshkosh, WI 54901, U.S.A.     

Nonstationary frequency analysis of extreme daily precipitation amounts in Southeastern Canada using a peaks-over-threshold approach

Theoretical and Applied Climatology - In this paper, a statistical inference of Southeastern Canada extreme daily precipitation amounts is proposed using a classical nonstationary...     

Downscaling and projection of precipitation from general circulation model predictors in an equatorial climate region by the automated regression-based statistical method

The authors have applied an automated regression-based statistical method, namely, the automated statistical downscaling (ASD) model, to downscale and project the precipitation climatology in an equatorial climate region (Peninsular Malaysia). Five precipitation indices are, principally, downscaled and projected: mean monthly values of precipitation (Mean), standard deviation (STD), 90th percentile of rain day amount, percentage of wet days (Wet-day), and maximum number of consecutive dry days (CDD). The predictors, National Centers for Environmental Prediction (NCEP) products, are taken from the daily series reanalysis data, while the global climate model (GCM) outputs are from the Hadley Centre Coupled Model, version 3 (HadCM3) in A2/B2 emission scenarios and Third-Generation Coupled Global Climate Model (CGCM3) in A2 emission scenario. Meanwhile, the predictand data are taken from the arithmetically averaged rain gauge information and used as a baseline data for the evaluation. The results reveal, from the calibration and validation periods spanning a period of 40 years (1961–2000), the ASD model is capable to downscale the precipitation with reasonable accuracy. Overall, during the validation period, the model simulations with the NCEP predictors produce mean monthly precipitation of 6.18–6.20 mm/day (root mean squared error 0.78 and 0.82 mm/day), interpolated, respectively, on HadCM3 and CGCM3 grids, in contrast to 6.00 mm/day as observation. Nevertheless, the model suffers to perform reasonably well at the time of extreme precipitation and summer time, more specifically to generate the CDD and STD indices. The future projections of precipitation (2011–2099) exhibit that there would be an increase in the precipitation amount and frequency in most of the months. Taking the 1961–2000 timeline as the base period, overall, the annual mean precipitation would indicate a surplus projection by nearly 14~18 % under both GCM output cases (HadCM3 A2/B2 scenarios and CGCM3 A2 scenario). According to the model simulation, the September–November periods might be the more significant months projecting the increment of the precipitation amount around over 50 %, while the precipitation deficit would be seen in March–May periods.     

Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods

Three statistical downscaling methods are compared with regard to their ability to downscale summer (June–September) daily precipitation at a network of 14 stations over the Yellow River source region from the NCEP/NCAR reanalysis data with the aim of constructing high-resolution regional precipitation scenarios for impact studies. The methods used are the Statistical Downscaling Model (SDSM), the Generalized LInear Model for daily CLIMate (GLIMCLIM), and the non-homogeneous Hidden Markov Model (NHMM). The methods are compared in terms of several statistics including spatial dependence, wet- and dry spell length distributions and inter-annual variability. In comparison with other two models, NHMM shows better performance in reproducing the spatial correlation structure, inter-annual variability and magnitude of the observed precipitation. However, it shows difficulty in reproducing observed wet- and dry spell length distributions at some stations. SDSM and GLIMCLIM showed better performance in reproducing the temporal dependence than NHMM. These models are also applied to derive future scenarios for six precipitation indices for the period 2046–2065 using the predictors from two global climate models (GCMs; CGCM3 and ECHAM5) under the IPCC SRES A2, A1B and B1scenarios. There is a strong consensus among two GCMs, three downscaling methods and three emission scenarios in the precipitation change signal. Under the future climate scenarios considered, all parts of the study region would experience increases in rainfall totals and extremes that are statistically significant at most stations. The magnitude of the projected changes is more intense for the SDSM than for other two models, which indicates that climate projection based on results from only one downscaling method should be interpreted with caution. The increase in the magnitude of rainfall totals and extremes is also accompanied by an increase in their inter-annual variability.     

Probabilistic projections of regional temperature and precipitation extending from observed time series

Probabilistic projections of change in regional temperature and precipitation previously derived allow for the range of sensitivities to global warming simulated by CMIP3 models. However, the changes were relative to an idealized base climate for 1980–1999, disregarding observed trends, such as those in rainfall in some Australian regions. Here we propose a method that represents projections for both forced change and decadal means as time series that extend from the observed series, illustrated using data for central Victoria. The main idea is to estimate the time-evolving underlying (or forced) past climate then convert this to a series of absolute values, by using the mean of the full observational record. We again use the pattern scaling assumption, and combine the CMIP3 sensitivities used for future change with a global warming series beginning at 1900. Like the confidence interval of regression theory, the analysis gives an estimate of the range of the underlying climate at each decade. This range can be augmented to allow for natural variability. A Bayesian theory can be applied to combine the model-based sensitivity with that estimated from observations. The time series are modified and the persistence of current observed anomalies considered, ultimately merging the probabilistic projections with the observed record. For some other cases, such as rainfall in southwest and north Australia and temperature in the state of Iowa, the two sensitivity estimates appear less compatible, and possible additional forcings are considered. Examples of the potential use of such time series are presented.     

Prediction of spring precipitation in China using a downscaling approach

The aim of this paper is to use a statistical downscaling model to predict spring precipitation over China based on a large-scale circulation simulation using Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction (DEMETER) General Circulation Models (GCMs) from 1960 to 2001. A singular value decomposition regression analysis was performed to establish the link between the spring precipitation and the large-scale variables, particularly for the geopotential height at 500?hPa and the sea-level pressure. The DEMETER GCM predictors were determined on the basis of their agreement with the reanalysis data for specific domains. This downscaling scheme significantly improved the predictability compared with the raw DEMETER GCM output for both the independent hindcast test and the cross-validation test. For the independent hindcast test, multi-year average spatial correlation coefficients (CCs) increased by at least ~30?% compared with the DEMETER GCMs’ precipitation output. In addition, the root mean-square errors (RMSEs) decreased more than 35?% compared with the raw DEMETER GCM output. For the cross-validation test, the spatial CCs increased to greater than 0.9 for most of the individual years, and the temporal CCs increased to greater than 0.3 (95?% confidence level) for most regions in China from 1960 to 2001. The RMSEs decreased significantly compared with the raw output. Furthermore, the preceding predictor, the Arctic Oscillation, increased the predicted skill of the downscaling scheme during the spring of 1963.     

Statistical downscaling of daily precipitation over Sweden using GCM output

A classification of Swedish weather patterns (SWP) was developed by applying a multi-objective fuzzy-rule-based classification method (MOFRBC) to large-scale-circulation predictors in the context of statistical downscaling of daily precipitation at the station level. The predictor data was mean sea level pressure (MSLP) and geopotential heights at 850 (H850) and 700 hPa (H700) from the NCEP/NCAR reanalysis and from the HadAM3 GCM. The MOFRBC was used to evaluate effects of two future climate scenarios (A2 and B2) on precipitation patterns on two regions in south-central and northern Sweden. The precipitation series were generated with a stochastic, autoregressive model conditioned on SWP. H850 was found to be the optimum predictor for SWP, and SWP could be used instead of local classifications with little information lost. The results in the climate projection indicated an increase in maximum 5-day precipitation and precipitation amount on a wet day for the scenarios A2 and B2 for the period 2070–2100 compared to 1961–1990. The relative increase was largest in the northern region and could be attributed to an increase in the specific humidity rather than to changes in the circulation patterns.     

Improvement of daily precipitation estimations using PRISM with inverse-distance weighting

Improved daily precipitation estimations were attempted using the parameter-elevation regressions on a parameter-elevation regression on independent slopes model (PRISM) with inverse-distance weighting (IDW) and a precipitation-masking algorithm for precipitation areas. The PRISM (PRISM_ORG) suffers two overestimation problems when the daily precipitation is estimated: overestimation of the precipitation intensity in mountainous regions and overestimation of the local precipitation areas. In order to solve the problem of overestimating the precipitation intensity, we used the IDW technique that employs the same input stations as those used in the PRISM regression (PRISM_IDW). A precipitation-masking algorithm that selectively masks the precipitation estimation grid points was additionally applied to the PRISM_IDW results (PRISM_MSK). For 6 months from March to August 2012, daily precipitation data were produced in a horizontal resolution of 1 km based on the above two experiments and PRISM_ORG. Afterwards, each experiment was evaluated for improvements. The monthly root mean squared errors (RMSEs) of PRISM_IDW and PRISM_MSK were reduced by 0.83 mm/day and 0.86 mm/day, respectively, compared to PRISM_ORG.     

Application of Markov models to area-average daily precipitation series and interannual variability in seasonal totals

This paper examines the success of various Markov-chain models of daily precipitation series in reproducing the characteristics of area-average rainfall in Britain. The first model considered is the standard twos-tate first-order Markov renewal process coupled to an amount model using the incomplete -probability distribution. We find that variability of seasonal totals and autocorrelation of daily amounts are both too small in this model, compared with observations. These are serious deficiencies, often overlooked, and possibly related. We proceed to consider models involving Markov chains of higher (temporal) order and many states, both of which generalizations may increase autocorrelation. A second-order two-state model is no better than the first-order, but a first-order many-state model captures a high fraction of the seasonal variability, because use of many states improves the model's representation of spells of heavy precipitation, which appear to have a considerable influence on the seasonal variance. Better still is a second-order many-state model, a type which, to our knowledge, has not previously been investigated. We suggest that the best model would have a continuum of states, rather than a discrete set. Our conclusion is that a large proportion of seasonal variability may be explained in terms of the average daily structure, but there may be a residual component caused by processes operating on longer time-scales and possibly predictable with reference to these. Reproduction of long-period (e.g. monthly or seasonal) variance and of the structure of daily autocorrelation provide crucial tests of stochastic weather generators, and we recommend that models which fail to simulate these statistics realistically be used only with great caution.     

Detecting inhomogeneity in daily climate series using wavelet analysis

A wavelet method was applied to detect inhomogeneities in daily meteorological series, data which are being increasingly applied in studies of climate extremes. The wavelet method has been applied to a few well- established long-term daily temperature series back to the 18th century, which have been ＂homogenized＂ with conventional approaches. Various types of problems remaining in the series were revealed with the wavelet method. Their influences on analyses of change in climate extremes are discussed. The results have importance for understanding issues in conventional climate data processing and for development of improved methods of homogenization in order to improve analysis of climate extremes based on daily data.     

Climate variability and change within the discharge time series: a statistical approach

The paper contains some results of long-time series analysis of discharges with respect to climate variability and change. The appropriate statistical computations based on data supplied by the Global Runoff Data Center in Koblenz. The computations have been carried out for the case of annual and of monthly (of each month) time series. The verification of the assumed hypotheses has been conducted for 5% significance level.The hypothesis that the mean value and the variance are stationary and ergodic (Kruskal-Wallis test) have to be rejected, respectively: (a) from 10.2% to 21.6% of cases and from 1.7% to 7.4% of cases for monthly discharges; and (b) in 23.3% and 3.4% of cases for annual discharges. Whereas, in case of the Mann-Kendall test trends emerge (a) in the mean value from 25% of cases to 42.6% of cases, and in the variance from 10.2% of cases to 19.3% of cases for monthly discharges; (b) in 42% and 9.1% of cases there occur trends in the mean value and the variance, respectively, for annual discharges. Moreover, results of the tests are presented separately for the time series being independently and dependently distributed in time.     

Complexity analysis of the air temperature and the precipitation time series in Serbia

Regional Atmospheric Modeling System (RAMS) was applied to the study of the effect of the topographical altitude of the Tibetan Plateau (TP) on a severe drought event which took place in eastern China from November 2008 to January 2009. Two simulations of this drought event were conducted: a control simulation (CNTRL run) using original model settings and a sensitive simulation (TOPO run), where no change other than to reduce the TP topography by 50 %. The results show that the CNTRL simulation validates RAMS by reproducing this drought event fairly accurately. However, as part of the TOPO simulation, the total heat flux showed a decrease over most parts of the TP, latent heat flux underwent a significant increase over the southeastern TP, contrary to sensible heat, and a universal decrease over eastern China; this led to an increase in precipitation over the southeastern TP and a decrease in precipitation over eastern China. The decrease of total heat flux over the TP is collocated with an anomalous anticyclonic circulation from the TP to the coasts of southeastern China. Changes in atmospheric circulation and low-level water vapor transport pathways were consistent with changes in precipitation. In general, reducing the topographical altitude of the TP worsens drought in eastern China and moreover causes a significant decrease in precipitation over southern China.     

数据处理方法对中国区域平均降水序列精度的影响

利用地面观测站点资料研究大范围地区的降水长期变化规律,选取不同的指标会使分析结果出现显著差异。利用中国大陆区域内2139个国家站的逐日降水资料,比较不同数据处理方法得到全国和中国西部、中国东部地区的降水量、降水日数和降水强度的区域平均时间序列,探讨对其变化趋势估算的偏差。研究表明,1951—1957年估算的中国区域平均降水量原始值出现虚假的偏高,使趋势估算出现较大误差;1951—2016年中国西部地区平均降水量距平百分率时间序列的波动幅度显著偏大;区域平均降水量、降水日数和降水强度的距平和标准化距平序列较为可信。全国平均降水量、降水日数的原始值和距平值序列都基本反映了中国东部湿润地区降水的变化,降水量距平百分率的变化主要由西部干燥区域的降水变化构成,降水量标准化距平则可综合反映湿润和干燥地区的降水变化。     

Correction of the precipitation time series nonhomogeneity caused by replacement of the Nipher shielded rain gauge by a Tretyakov precipitation gauge

In this paper, a modern techniqne for correction of precipitation measured with a Nipher shielded rain gauge, with the use of the Valdai Control System as an intercomparison reference, is presented. This technique allows obtaining unbiased daily and timed precipitation data not affected by the rain gauge systematic errors. In conjunction with the existing method of the bias correction for precipitation measured with the Tretyakov precipitation gauge, the problem of generation of unbiased precipitation time series, which includes both types of measurements, covers the entire period of measurements, and has any (i.e., daily through yearly) temporal resolution, is solved. The results of correction for nine stations located in different climatic zones of the Russian Federation are shown. The results are summarized and presented in the form of long-term averages. Statistically homogeneous precipitation time series for the period from 1936 to 2000 are obtained. Temporal trends of annual and cold-season precipitation are calculated and analysed, and their statistical significance is estimated.     

A relation between extreme daily precipitation and extreme short term precipitation

The Royal Netherlands Meteorological Institute (KNMI) has published the KNMI’06 climate scenarios in 2006. These scenarios give the possible states of the climate in The Netherlands for the next century. Projections of changes in precipitation were made for a time scale of 1 day. The urban drainage sector is, however, more interested in projections on shorter time scales. Specifically, time scales of 1 h or less. The aim of this research is to provide projections of precipitation at these shorter time scales based on the available daily scenarios. This involves an analysis of climate variables and their relations to precipitation at different time scales. On the basis of this analysis, one can determine a numeric factor to translate daily projections into shorter time scale projections.     

Atmospheric circulation and daily precipitation in Wales

Summary Daily precipitation affinity areas are established for Wales by means of S-mode Principal Components Analysis (PCA) and Cluster Analysis, using data for the autumn-winter (September-January) period, 1982–87, subset according to surface wind direction. Relatively strong PCA simple structures were found for five wind directions clockwise from south to north, using the OBLIMIN rotation for the first four (south to northwest) and the VARIMAX rotation for the last (north). Whilst interpretation of many of the most significant Prinicipal Components (PCs) was difficult in strictly meteorological or climatological terms, the precipitation areas produced by clustering based on the most important PCs, yielded a continuum of change in areal organisation with progressively changing wind direction. Only the far southwest and northwest of the country appear in every area map, although superficially, northeastern, English border and southeastern areas were present on most. The derived areas bear only small overall resemblance to currently used forecasting areas.With 9 Figures