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1.
This paper presents the methods, procedure and results in studying spatial and temporal characteristics of rainfall in Malawi, a data scarce region, between 1960 and 2006. Rainfall variables and indicators from rainfall readings at 42 stations in Malawi, excluding Lake Malawi, were analysed at monthly, seasonal and annual scales. In the study, the data were firstly subjected to quality checks through the cumulative deviations test and the standard normal homogeneity test. Spatial rainfall variability was investigated using the spatial correlation function. Temporal trends were analysed using Mann?CKendall and linear regression methods. Heterogeneity of monthly rainfall was investigated using the precipitation concentration index (PCI). Finally, inter-annual and intra-annual rainfall variability were tested using normalized precipitation anomaly series of annual rainfall series (|AR|) and the PCI (|APCI|), respectively. The results showed that (1) most stations revealed statistically non-significant decreasing rainfall trends for annual, seasonal, monthly and the individual months from March to December at the 5% significance level. The months of January and February (the highest rainfall months), however, had overall positive but statistically non-significant trends countrywide, suggesting more concentration of the seasonal rainfall around these months. (2) Spatial analysis results showed a complex rainfall pattern countrywide with annual mean of 1,095?mm centred to the south of the country and mean inter-annual variability of 26%. (3) Spatial correlation amongst stations was highest only within the first 20?km, typical of areas with strong small-scale climatic influence. (4) The country was further characterised by unstable monthly rainfall regimes, with all PCIs more than 10. (5) An increase in inter-annual rainfall variability was found.  相似文献   

2.
In this study, empirical orthogonal function was applied to analyze rainfall variability in the Nile basin based on various spatio-temporal scales. The co-occurrence of rainfall variability and the variation in selected climate indices was analyzed based on various spatio-temporal scales. From the highest to the lowest, the cumulative amount of variance explained by the first two principal components (PCs) for any selected size of the spatial domain was obtained for the annual, seasonal, and monthly rainfall series respectively. The variability in the annual rainfall of 1° × 1° spatial coverage explained by only the first PC was about 55% on average. However, this percentage reduced to about 40% on average across the study area when the size of the spatial domain was increased from 1° × 1° to 10° × 10°. The variation in climate indices was shown to explain rainfall variability more suitably at a regional than location-specific spatial scale. The magnitudes and sometimes signs of the correlation between rainfall variability and the variation in climate indices tended to vary from one time scale to another. These findings are vital in the selection of spatial and temporal scales for more considered attribution of rainfall variability across the study area.  相似文献   

3.
A standard principal component analysis has been performed over the Mediterranean and over the larger European region on monthly precipitation anomalies for the winters between 1979 and 1995. The main centres of action of the associated EOFs are very similar for the two regions and the two sets of PCs are highly correlated with each other. Focusing on the Mediterranean region, the same analysis has been performed using 500?hPa geopotential height monthly anomalies taken from the operational NCEP analysis. Comparing the two sets of PCs associated with upper-air and surface data, a strong correlation has been found suggesting the presence of a two-way link between regional precipitation patterns and large-scale circulation anomalies. For both fields, the largest fraction of variance is explained by the North Atlantic Oscillation, while smaller but still substantial fractions are explained by other known patterns of large-scale variability such as the Eastern Atlantic pattern and the Euro-Atlantic blocking. No detectable connection has been found between Mediterranean precipitation patterns and El Niño SST anomalies during winter. With respect to temporal variability, significant trends have been found over most European areas during the winters considered. The associated pattern is characterised by a substantial increase of precipitation over western Scandinavia and a general decrease over southern Europe. This result is confirmed by analysing data from stations located in northern Italy.  相似文献   

4.
In this study, statistical techniques are employed to decompose climate signals around southern Africa into the dominant temporal frequencies, with the aim of modelling and predicting area-averaged rainfall. In the rainfall time series over the period 1900–1999, the annual cycle accounts for 83% of variance. Residual spectral energy cascades from biennial (42%) to interannual (20%) to decadal bands (3%). Regional climate signals are revealed through a multi-taper singular value decomposition analysis of sea surface temperature and sea level pressure fields over the Atlantic and Indian Oceans, in conjunction with southern Africa rainfall. Rossby wave action in the South Indian Ocean dominates the biennial scale variability. El Niño-Southern Oscillation (ENSO) and related Indian Ocean dipole patterns are important for interannual variability. Significant sea temperature and pressure fluctuations occurring 6–12 months prior to rainfall contribute biennial and interannual indices to a multi-variate model that demonstrates useful predictive skill.  相似文献   

5.
It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UK Meteorological Office Hadley Centre’s climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with extremes from the MIRA dataset. The results suggest that the model reproduces the number and spatial distribution of rainfall extremes with some accuracy, but that mean rainfall and rainfall variability is under-estimated (over-estimated) over wet (dry) regions of southern Africa.  相似文献   

6.
A methodology has been applied to investigate the spatial variability and trends existent in a mid-twentieth century climatic time series (for the period 1943–1977) recorded by 58 climatic stations in the Albert–Victoria water management area in Uganda. Data were subjected to quality checks before further processing. In the present work, temporal trends were analyzed using Mann–Kendall and linear regression methods. Heterogeneity of monthly rainfall was investigated using the precipitation concentration index (PCI). Results revealed that 53 % of stations have positive trends where 25 % are statistically significant and 45 % of stations have negative trends with 23 % being statistically significant. Very strong trends at 99 % significance level were revealed at 12 stations. Positive trends in January, February, and November at 40 stations were observed. The highest rainfall was recorded in April, while January, June, and July had the lowest rainfall. Spatial analysis results showed that stations close to Lake Victoria recorded high amounts of rainfall. Average annual coefficient of variability was 19 %, signifying low variability. Rainfall distribution is bimodal with maximums experienced in March–April–May and September–October–November seasons of the year. Analysis also revealed that PCI values showed a moderate to seasonal rainfall distribution. Spectral analysis of the time components reveals the existence of a major period around 3, 6, and 10 years. The 6- and 10-year period is a characteristic of September–October–November, March–April–May, and annual time series.  相似文献   

7.
Daily precipitation totals at 55 sites were used to investigate geographic variability in winter (DJF) rainfall over Cumbria, NW England, over an 11-year period. Winter is the wettest season (>800?mm in the mountainous Lake District), with rainfall mechanisms closely linked to North Atlantic forcing. The Lamb weather type catalogue was used to identify rainfall distributions under different wind directions. Precipitation magnitude over Cumbria is much more sensitive to a change in wind direction than the geographic pattern in rainfall, with southwesterly (easterly) winds producing the highest (lowest) spatially averaged daily rainfall totals of 8.2?mm (0.6?mm). S-mode principal components analysis was used to identify the main patterns of precipitation variability. Three principal components (PCs) were retained as being statistically significant (cumulative explained variance for unrotated PCs?=?84.3%), with a correlated PC structure (direct oblimin rotation) best describing the spatial variance in rainfall. PC 1 has a very high index of strength (variance measure?=?40.9), indicating that there is one dominant rainfall pattern. PC 1 shows a gradient between wetter conditions in southwest Cumbria and over the central Lake District and drier conditions in NE Cumbria, and is usually caused by active zonal west to southwest flows. Almost of equal importance to PC 1 is PC 3 (variance measure?=?39.3), which has a more uniform rainfall distribution than PC 1 and is usually caused by fronts stalling over the region. PC 2, which shows an east to west decline in rainfall totals, is much less important than PCs 1 and 3 (variance measure?=?18.6). PC 2??s rainfall pattern can be caused by easterly flows with high pressure over Scandinavia and low pressure over the Continent, or by strong southwesterly flows, with depressions often centred over Scotland. Finally, cluster analysis was carried out to identify precipitation regions for all days and for each wind direction. Clusters were found to be largely stable to changes in wind direction, with stations in the central Lake District often clustered together, thus highlighting the importance of orographic enhancement of rainfall in this region.  相似文献   

8.
Summary Spatial scales of variability in seasonal rainfall over Africa are investigated by means of statistical and numerical techniques. In the statistical analysis spatial structure is studied using gridded 0.5° resolution monthly data in the period 1948–1998. The de-seasonalized time series are subjected to successive principal component (PC) analysis, allowing the number of modes to vary from 10 to 24, producing cells of varying dimension. Then the original rainfall data within each cell are cross-correlated (internal), then averaged and compared with the adjacent cells (external) for each PC solution. By considering the ratio of internal to external correlation, the spatial scales of rainfall variability are evaluated and an optimum solution is found whose cell dimensions are approximately 106 km2. The aspect of scale is further studied for southern Africa by consideration of numerical model ensemble simulations over the period 1985–1999 forced with observed sea surface temperatures (SSTs). The hindcast products are compared with observed January to March (JFM) rainfall, based on a station-satellite merged analysis of precipitation (CMAP) data at 2.5° resolution. Validations for different sized areas indicate that cumulative standardized errors are greatest at the scale of a single grid cell (104 km2) and decrease 20–30% by averaging over successively larger areas (106 km2).  相似文献   

9.
Six in situ precipitation time series of varying time periods in the northwestern region and the Global Precipitation Climatology Centre (GPCC) v6 0.5° monthly dataset (1901–2010) were statistically examined for monotonic trends in Trinidad. The Pettit test was used to investigate the abrupt changes in the mean while the Mann–Kendall test was employed to assess the monotonic trends. It was found that three in situ stations and the six grids experienced abrupt changes in the rainfall patterns and that there was an apparent shift in the seasons. In addition, for five out of the six in situ stations no monotonic change was detected in the monthly, seasonal, and annual rainfall patterns. Gradual decreases were detected in the calculated weighted area average for five stations, the GPCCv6 dataset and St. Ann’s time series. The GPCCv6 data indicated that the dry season in the southern Trinidad is becoming drier. Results also suggested that the range between the greatest and lowest recorded rainfall values for some months have increased while others decreased. The gridded dataset appears to give a good representation of the dry season (January to May) rainfall compared with the wet season (June to December) and was found to be negatively biased for the north-western region but may not necessarily be so for the entire island. The results suggested that in the north-western region mirco-climates may exist. It is recommended that further investigations are needed using in situ data.  相似文献   

10.
Summary The spatial organization of Monsoon rainfall over Sri Lanka is examined using Orthogonal Factor Analysis (OFA) on long-term mean monthly rainfall data. Three types of orthogonal structure of Monsoon regime in Sri Lanka have been identified. Interpretation of orthogonal factor scores revealed that a large amount of rainfall occurs from March to October in the southwestern parts of Sri Lanka, from December to February in the eastern parts, and in November in the northern and mid-western parts which are all represented by high positive factor scores. Orthogonal factor scores for the first three factors account for 93.6% of the total variance of mean monthly rainfall and clearly indicate that the southeast and northwest parts of the country with lowest rainfall, resulting from lack of Monsoons, are represented by negative factor scores. The three orthogonal factors identified different rainfall maxima in different time periods and, additionally, significant spatial differences between regions. Seasonal changes in the Monsoon wind system, ITCZ weather phenomena, and topography were the main factors which influence the spatial structure of Monsoon rainfall over Sri Lanka.With 4 Figures  相似文献   

11.
Monthly temperature series for Central Europe back to AD 1500 are developed from documentary index series from Germany, Switzerland and the Czech Republic (1500–1854) and 11 instrumental temperature records (1760–2007). Documentary evidence from the Low Countries, the Carpathian Basin and Poland are used for cross-checking for earlier centuries. The instrumental station records are corrected for inhomogeneities, including insufficient radiation protection of early thermometers and the urban heat island effect. For overlapping period (1760–1854), the documentary data series correlate with instrumental temperatures, most strongly in winter (86% explained variance in January) and least in autumn (56% in September). For annual average temperatures, 81% of the variance is explained. Verification statistics indicate high reconstruction skill for most months and seasons. The last 20 years (since 1988) stand out as very likely the warmest 20-year period, accounting for the calibration uncertainty and decreases in proxy data quality before the calibration period. The new reconstruction displays a previously unobserved long-term decrease in DJF, MAM and JJA temperature variability over last five centuries. Compiled monthly, seasonal and annual series can be used to improve the robustness of gridded large-scale European temperature reconstructions and possible impact studies. Further improvement of the reconstruction would be achieved if documentary data from other European countries are further developed.  相似文献   

12.
Based on daily precipitation records at 75 meteorological stations in Hunan Province, central south China, the spatial and temporal variability of precipitation indices is analyzed during 1961–2010. For precipitation extremes, most of precipitation indices suggest that both the amount and the intensity of extreme precipitation are increasing, especially the mean precipitation amount on a wet day, showing a significant positive trend. Meanwhile, both of the monthly rainfall heterogeneity and the contribution of the days with the greatest rainfall show an upward trend. When it comes to rainfall erosivity, most of this province is characterized by high values of annual rainfall erosivity. Although the directions of trends in annual rainfall erosivity at most stations are upward, only 6 of the 75 stations have significant trends. Furthermore, the spatial and temporal variation of dryness/wetness has been assessed by the standardized precipitation index (SPI). The principal component analysis (PCA) was applied to the SPI series computed on 24-month time scales. The results demonstrated a noticeable spatial variability with three subregions characterized by different trends: a remarkable wet tendency prevails in the central and southern areas, while the northern areas are dominated by a remarkable dry tendency.  相似文献   

13.
Spatial and temporal precipitation variability in Chhattisgarh State in India was examined by using monthly precipitation data for 102 years (1901–2002) from 16 stations. The homogeneity of precipitation data was evaluated by the double-mass curve approach and the presence of serial correlation by lag-1 autocorrelation coefficient. Linear regression analysis, the conventional Mann–Kendall (MK) test, and Spearman’s rho were employed to identify trends and Sen’s slope to estimate the slope of trend line. The coefficient of variation (CV) was used to analyze precipitation variability. Spatial interpolation was done by a Kriging process using ArcGIS 9.3. Results of both parametric and non-parametric tests and trend tests showed that at 5 % significance level, annual precipitation exhibited a decreasing trend at all stations except Bilaspur and Dantewada. For both annual and monsoon precipitation, Sen’s test showed a decreasing trend for all stations, except Bilaspur and Dantewada. The highest percentage of variability was observed in winter precipitation (88.75 %) and minimum percentage variability in annual series (14.01 %) over the 102-year periods.  相似文献   

14.
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.  相似文献   

15.
Summary Along with averages, rainfall variability and distribution are important climatological information. In this study, using 114 years (1871–1984) data of 306 stations, it is demonstrated that the variability and spatial distribution of annual, summer monsoon and monthly rainfall are highly dependent upon the respective period mean rainfall variation over India. The magnitude of three selected absolute measures of variability, e.g. standard deviation, absolute mean deviation and mean absolute interannual variability is found to increase linearly with mean rainfall.In order to describe the relation between the rainfall frequency distribution and the mean rainfall, a linear regression between the rainfall amount expected with a specified exceedance/non-exceedance probability and the mean rainfall amount is presented. Highly significant linear curves for a large number of probabilities specified in an average probability diagram clearly demonstrate the dependence of the rainfall frequency distribution on mean rainfall over India.With 8 Figures  相似文献   

16.
Summary The variability and extreme wet anomalies in the Greater Horn of Africa (GHA) climate are investigated based on a multi-year National Center for Atmospheric Research (NCAR) AGCM ensemble data. While the GCM ensemble average reproduces realistic inter-annual variability of rainfall pattern over the GHA sub-region compared to observations, there is a distinct northward shift in the simulated regions of rainfall maxima throughout the season. However, in agreement with observations and many previous studies, the inter-annual variability derived from leading mode of EOF analysis is dominated by ENSO-related fluctuations. On the other hand, the spatial pattern corresponding to the second mode (EOF2) exhibits a unique dipole rainfall anomaly pattern (wet/dry conditions) over the northern/southern halves of our domain during all the three months of the short rains season. When the 3–10 year periodicity is filtered out from the 40-year EOF2 time series of the ensemble mean data, three distinct quasi-decadal regimes in the rainfall anomalies is exhibited for both monthly and seasonal mean data. It is also evident from our results that a combination of anomalous surface and mid-tropospheric flow from northwestern and eastern Atlantic Ocean and easterly flow from the Indian Ocean played a significant role in setting up the non-ENSO related 1961 floods. Coversely, during the ENSO-related 1997 floods, the mid-troposheric flow was characterized by anomalous westerly flow originating from the Congo rainforest that converged with the flow from Indian Ocean along the East Africa coast and over eastern/northeastern Kenya. The anomalous moisture flux convergence/divergence in both the ensemble and NCEP reanalysis is also consistent with the mid-trospheric flow anomalies that are associated with the two wet events.  相似文献   

17.
The spatial and temporal structures of the intraseasonal atmospheric variability over central Africa is investigated using 2.5°?×?2.5° daily outgoing longwave radiation (OLR) and National Centers for Environmental Prediction (NCEP) Reanalysis zonal winds for the period 1980–2010. The study begins with an overview of the Central African rainfall regime, noting in particular the contrast amongst Western and Eastern parts, with different topography and surface conditions features. The annual mean rainfall and OLR over the region revealed a zone of intense convective activity centered on the equator near 30°E, which extends southward and covers almost all the Congo forest. The annual cycle of rainfall reflects the classical bi-annual shift of Inter-Tropical Convergence Zone across the equatorial belt, between 10°S and 10°N. The result of the empirical orthogonal functions (EOFs) analysis has shown that the three leading EOF modes explain about 45?% of total intraseasonal variability. The power spectra of all the three corresponding principal components (PCs) peak around 45–50?days, indicating a Madden–Julian Oscillation (MJO) signal. The first mode exhibits high positive loadings over Northern Congo, the second over Southern Ethiopia and the third over Southwestern Tanzania. The PCs time series revealed less interannual modulation of intraseasonal oscillations for the Congo mode, while Ethiopian and Tanzanian modes exhibit strong interannual variations. H?vmoller plots of OLR, 200 and 850?hPa NCEP zonal winds found the eastward propagating features to be the dominant pattern in all the three times series, but this propagation is less pronounced in the OLR than in the 850 and 200?hpa zonal wind anomalies. An index of MJO strength was built by averaging the 30–50?day power for each day. A plot of MJO indices and El Ni?o Southern Oscillation (ENSO) cycle confirm a strong interannual modulation of MJO over Eastern central Africa partially linked with the ENSO events (El Ni?o and La Ni?a). Strong MJO activity is observed during La Ni?a years or during ENSO-neutral years, while weak or absent MJO activity is typically associated with strong El Ni?o episodes.  相似文献   

18.
我国夏季降水与青藏高原春季NDVI的关系   总被引：6，自引：1，他引：5       下载免费PDF全文
利用1982年1月-2001年12月NDVI资料、台站降水资料和NCEP/NCAR再分析资料, 通过相关分析和合成分析方法, 初步分析了我国夏季降水与青藏高原春季植被的关系及可能机理。结果发现:青藏高原春季NDVI与我国夏季降水相关系数从南到北呈西北-东南向“ + - +”带状分布。合成分析也表明:青藏高原春季NDVI大、小值年降水年内分布也存在明显差异。降水的上述差异, 可能是由于青藏高原春季NDVI变化导致热源效应改变, 引起大气环流变化造成的。对环流分析也发现:大气环流的变化特征与降水变化表现出很好的一致性。  相似文献   

19.
India’s annual weather cycle consists mainly of wet and dry periods with monsoonal rains being one of the significant wet periods that shows strong spatiotemporal variability. This study includes the climatological characteristics, fluctuation features, and periodic cycles of annual, seasonal, and monthly rainfall of seven river basins across the eastern Gangetic Plain (EGP) using the longest possible instrumental area-averaged monthly rainfall series (1829–2012). Understanding the relationships between these parameters and global tropospheric temperature changes and El Niño and La Niña climatic signals is also attempted.

Climatologically, mean annual rainfall in the EGP varies from 1070.5?mm in the Tons River basin to 1508.6?mm in the Subarnarekha River basin. The highest rainfall in the EGP occurs during monsoon (1188?mm). The annual rainfall in all river basins and monsoon rainfall in four river basins is normally distributed. Annual and monsoonal rainfall in the Brahmani and Son River basins show a significant decreasing long-term trend. Over the last 20 years, annual rainfall in all river basins and monsoonal rainfall in five river basins show a decreasing trend. The power spectra for all rainfall series are characterized by consistent significant wavelength peaks at 3–5 years, 10–20 years, 40 years, and more than 80 years. Short-term fluctuations with a period less than 10 years is the major contributor to total variance in annual and/or monsoon rainfall (77.6%), followed by decadal variations with a period of 10–30 years (13.1%) and a long-term trend with a period greater than 30 years (9.3%).Temperature and thickness gradients from the Tibet–Himalaya–Karakoram–Hindu Kush highlands to eight strong highs show a significant correlation with rainfall during the onset and withdrawal phases of summer monsoon in the EGP.  相似文献   

20.
Summary ?Nepal, lying in the southern periphery of the Tibetan Plateau receives about 80% of the total annual rainfall during summer monsoon (June–September). Rainfall analysis shows that summer monsoon is more active in the southern part of Nepal but in the high Himalayas and Trans-Himalayan region other weather systems like western disturbances are also as effective as monsoon in giving rainfall. The influence of Southern Oscillation (SO) in Nepal monsoon rainfall is found to be very significant. The years with significant negative (positive) Southern Oscillation Index (SOI) have less (more) rainfall in most of the cases during the 32-year period. This relationship is also found to vary with time. The years with deficient rainfall are associated most of the times with negative departure of SOI and the composite chart during these occasions shows about 95% area of Nepal experiencing below normal rainfall. Likewise at the time of positive departure of SOI, most of the region (94%) experienced above normal rainfall. There is a good relation between SOI and rainfall over Nepal during monsoon. The correlation coefficient between Nepal monsoon rainfall and monthly SOI shows a statistically significant in-phase relationship during and after monsoon but poor relation during the months prior to monsoon season. These results suggest that monsoon plays an active and effective role on the interannual variability including SOI. Received December 28, 1999/Revised May 22, 2000  相似文献   

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