Predictability of NDVI in semi-arid African regions

In semi-arid Africa, rainfall variability is an important issue for ecosystems and agricultural activities. However, due to its discrete nature in time and space, rainfall is difficult to measure, quantify, and predict. In the dry tropics, a good proxy for rainfall is vegetation activity since this parameter is well correlated with rainfall variations. In this study, over 20 years of Normalized Difference Vegetative Index (NDVI) data from the Advanced Very High Resolution Radiometers are used. The goal is to assess the skill of linear statistical models in estimating regional NDVI interannual variability based on ocean and atmospheric fields (but not rainfall) and then to hindcast it with a 1- to 2-month lead-time. Three semi-arid areas of ～150 000 km² located in Western, Southern, and Eastern Tropical Africa are considered for this purpose. The predictors are: the Niño3.4 sea surface temperature index, the main modes of National Center for Environmental Prediction (NCEP) surface temperature variability in a window centered over Africa, and regional-scale indices based on NCEP surface temperatures and atmospheric variables (relative humidity, geopotential heights, and winds). The regional indices, which are physically and statistically robust, are generally asynchronous with the NDVI predictand. The statistical models, based on linear multiple regressions, give significant results, and the correlation between observed and cross-validated NDVI is 0.67 in Southern Africa, 0.76 for the long rains and 0.83 for the short rains in Eastern Africa, and 0.88 in Western Africa. The results have implications for (1) better understanding the role of El Niño/Southern Oscillation in semi-arid Africa, and (2) highlight the importance of regional climate processes for vegetation growth at these scales, notably the role played by the Mediterranean Sea and its influence on the West African monsoon. The predictability of NDVI over these African regions is discussed.     

North African climate variability. Part 3: Resource prediction

Summary Tropical north Africa depends on rain-fed agriculture as the main economic driver. The variability of climate-sensitive resources is investigated with a goal to develop statistical long-lead prediction models with reasonable skill. Climate data from NCEP is analysed in conjunction with agricultural and economic production in various sectors, in addition to the traditional climatic indices: temperature and rainfall. Key predictors for statistical models include the lower-level zonal wind over the Atlantic and Pacific Oceans. These exhibit a ‘memory’ that is consistent with sea surface temperatures (SST) through equatorial upwelling dynamics. Kinematic predictors outperform SST in hindcast fit by an average 33% with respect to various tropical north African resource indices. A multi-decadal oscillation induces long-term trends in rainfall that contribute to apparently skilful forecasts based on the interaction of Pacific ENSO and the Atlantic zonal overturning circulation. The skill of statistical forecasts is lower when the drying trend is removed.     

基于5分钟雷达资料降水估测模型的比较

For the Z-R relationship in radar-based rainfall estimation, the distribution of corresponding R values for a given Z value （or the corresponding Z value for a given R value） may be highly skewed. However, the traditional power-law model is physically deduced and fitted under the normal-distribution presumption of radar wave echoes associated with a rain rate value, and it may not be very appropriate. Considering this problem, the authors devised several generalized linear models with different forms and distribution presumptions to represent the Z-R relationship. Radar-reflectivity scans observed by a CINRAD/SC Doppler radar and 5-minute rainfall accumulation recorded by 10 ground gauges were used to fit these models. All data used in this study were collected during some large rainfalls of the period from 2005 to 2007. The radar and all gauges were installed in the catchment of the Yishu River, a branch of the Huaihe River in China. Three models based on normal distribution and a dBZ presumption of gamma distribution were fitted using maximum-likelihood techniques, which were resolved by genetic algorithms. Comparisons of estimated maximized likelihoods based on assumptions of gamma and normal distribution showed that all generalized linear models （GLMs） of presumed gamma distribution were better fitted than GLMs based on normal distribution. In a comparison of maximum-likelihood, the differences between these three models were small. Three error statistics were used to assess the agreement between radar estimated rainfall and gauge rainfall： relative bias （B）, root mean square error （RMSE）, and correlation coefficient （r）. The results showed that no one model was excellent in all criteria. On the whole, the GLM-based models gave smaller relative bias than the traditional power-law model. It is suggested that validations conducted in many previous works should have been made against a specific criterion but overlooked others.     

Influence of spatial and temporal scales on statistical analyses of rainfall variability in the River Nile basin

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.     

Storylines: an alternative approach to representing uncertainty in physical aspects of climate change

Investigating the relationships between climate extremes and crop yield can help us understand how unfavourable climatic conditions affect crop production. In this study, two statistical models, multiple linear regression and random forest, were used to identify rainfall extremes indices affecting wheat yield in three different regions of the New South Wales wheat belt. The results show that the random forest model explained 41–67% of the year-to-year yield variation, whereas the multiple linear regression model explained 34–58%. In the two models, 3-month timescale standardized precipitation index of Jun.–Aug. (SPI_JJA), Sep.–Nov. (SPI_SON), and consecutive dry days (CDDs) were identified as the three most important indices which can explain yield variability for most of the wheat belt. Our results indicated that the inter-annual variability of rainfall in winter and spring was largely responsible for wheat yield variation, and pre-growing season rainfall played a secondary role. Frequent shortages of rainfall posed a greater threat to crop growth than excessive rainfall in eastern Australia. We concluded that the comparison between multiple linear regression and machine learning algorithm proposed in the present study would be useful to provide robust prediction of yields and new insights of the effects of various rainfall extremes, when suitable climate and yield datasets are available.     

Validating the dynamic downscaling ability of WRF for East Asian summer climate

This work aims, as a first step, to analyze rainfall variability in Northern Algeria, in particular extreme events, during the period from 1940 to 2010. Analysis of annual rainfall shows that stations in the northwest record a significant decrease in rainfall since the 1970s. Frequencies of rainy days for each percentile (5th, 10th, 25th, 50th, 75th, 90th, 95th, and 99th) and each rainfall interval class (1–5, 5–10, 10–20, 20–50, and ≥50 mm) do not show a significant change in the evolution of daily rainfall. The Tenes station is the only one to show a significant decrease in the frequency of rainy days up to the 75th percentile and for the 10–20-mm interval class. There is no significant change in the temporal evolution of extreme events in the 90th, 95th, and 99th percentiles. The relationships between rainfall variability and general atmospheric circulation indices for interannual and extreme event variability are moderately influenced by the El Niño-Southern Oscillation and Mediterranean Oscillation. Significant correlations are observed between the Southern Oscillation Index and annual rainfall in the northwestern part of the study area, which is likely linked with the decrease in rainfall in this region. Seasonal rainfall in Northern Algeria is affected by the Mediterranean Oscillation and North Atlantic Oscillation in the west. The ENSEMBLES regional climate models (RCMs) are assessed using the bias method to test their ability to reproduce rainfall variability at different time scales. The Centre National de Recherches Météorologiques (CNRM), Czech Hydrometeorological Institute (CHMI), Eidgenössische Technische Hochschule Zürich (ETHZ), and Forschungszentrum Geesthacht (GKSS) models yield the least biased results.     

Changes in the interannual SST-forced signals on West African rainfall. AGCM intercomparison

Rainfall over West Africa shows strong interannual variability related to changes in Sea Surface Temperature (SST). Nevertheless, this relationship seem to be non-stationary. A particular turning point is the decade of the 1970s, which witnessed a number of changes in the climatic system, including the climate shift of the late 1970s. The first aim of this study is to explore the change in the interannual variability of West African rainfall after this shift. The analysis indicates that the dipolar features of the rainfall variability over this region, related to changes in the Atlantic SST, disappear after this period. Also, the Pacific SST variability has a higher correlation with Guinean rainfall in the recent period. The results suggest that the current relationship between the Atlantic and Pacific El Ni?o phenomena is the principal responsible for these changes. A fundamental goal of climate research is the development of models simulating a realistic current climate. For this reason, the second aim of this work is to test the performance of Atmospheric General Circulation models in simulating rainfall variability over West Africa. The models have been run with observed SSTs for the common period 1957?C1998 as part of an intercomparison exercise. The results show that the models are able to reproduce Guinean interannual variability, which is strongly related to SST variability in the Equatorial Atlantic. Nevertheless, problems in the simulation of the Sahelian interannual variability appear: not all models are able to reproduce the observed negative link between rainfall over the Sahel and El Ni?o-like anomalies in the Pacific, neither the positive correlation between Mediterranean SSTs and Sahelian rainfall.     

Determination of seasonal rainfall variability, onset and cessation in semi-arid Tharaka district, Kenya

The study quantified rainfall variability for March–May (MAM) and October–December (OND) seasons in Tharaka district, Kenya. The parameters analysed were inter-annual variability of seasonal rainfall, onset and cessation using daily rainfall data in three agro-ecological zones’ stations. Percentage mean cumulative method was used to determine onset and cessation, and seasonal variability was estimated using rainfall variability indices. Although both seasons are highly variable, OND has been persistently below mean over time while MAM shows high within-season variability. Despite the near uniformity in the mean onset and cessation dates, the former is highly variable on an inter-annual scale. The two rainfall seasons are inherently dissimilar and therefore require specific cropping in agro-ecological zone LM4 and LM4-5. It is possible that farmers in IL5 are missing an opportunity by under-utilising MAM rainfall. The results should be incorporated in implications of climate variability and vulnerability assessment in semi-arid Tharaka district.     

Assessment of projected agro-climatic indices over Awun river basin,Nigeria for the late twenty-first century

This paper examines changes in rainfall effectiveness indices of the Awun basin in Nigeria during the late twenty-first century for agricultural applications with outputs from high-resolution regional climate model (RCM) simulations. The RCM simulations are driven by two global climate models for a reference period (1985–2004) and a future period (2080–2099) and for RCP4.5 (a scenario with some mitigation) and RCP8.5 (a business as usual scenario) forcings. Simulations are provided for the control (1985–2004) and scenario (2080–2099) periods. Observations from synoptic station are used for bias-correction. Three indices being local onset date, seasonality index (SI), and hydrologic ratio (HR) are analyzed. Onset and HR are tested with two evapotranspiration (ET_p) models. Farmers’ perceptions are also collected to validate trends of rainfall indices for the present-day climate. We found that onset dates do not depend much on the ET_p models used, and farmers’ perceptions are consistent with predicted rainfall patterns. Present-day climate trend shows an early onset. However, onset is projected to be late in future and the delay will be magnified under the business as usual scenario. Indeed, average onset date is found on the 5th May for present-day while in the future, a delay about 4 and 8 weeks is projected under RCP4.5 and RCP8.5 scenarios respectively. SI is between 0.80 and 0.99, and HR is less than 0.75 for all scenarios, meaning respectively that (i) the rainy season will get shorter and (ii) the area will get drier in the future compared to the present-day. Local stakeholders are forewarned to prepare for potential response strategies. A continuous provision of forecast-based rainfall indices to support farmer’s decision making is also recommended.     

A timescale decomposed threshold regression downscaling approach to forecasting South China early summer rainfall

A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.     

Intraseasonal oscillation of the rainfall variability over Rwanda and evaluation of its subseasonal forecasting skill

非洲中东部地区的经济主要依靠自给农业支撑,该地区农业经济对降水的变化尤为敏感.本文以卢旺达为例,观测分析指出卢旺达的次季节降雨主要集中在10-25天;根据次季节尺度降水变率的单点相关方法,发现卢旺达的次季节降水变率和周围区域变化一致;进一步合成结果显示该地区次季节降水变率与异常西风有关,这可追溯到赤道地区西传的赤道Rossby波.最后,本文评估了当前动力模式ECMWF对 卢旺达地区(即非洲中东部)次季节降水变率的预报能力,发现EC模式在对该区域降水和相关风场指数的预报技巧都在18天左右,且预报技巧表现出一定的年际差异,这可能与热带太平洋的背景海温信号有关.该工作增进了当 前对非洲中东部地区的次季节降水变率和预测水平的认知,并且对该地区国家粮食安全和防灾减灾具有启示性意义.     

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.     

Interannual variability of rainfall over the Sahel based on multiple regional climate models simulations

We analyse the interannual variability of the averaged summer monsoon rainfall over the Sahel from multiple regional climate models driven by the ERA-interim reanalysis and seek to provide effective information for future modelling work. We find that the majority of the models are able to reproduce the rainfall variability with correlation coefficient exceeding 0.5 compared with observations. This is due to a good representation of the dynamics of the main monsoon features of the West African climate such as the monsoon flux, African Easterly Jet (AEJ) and Tropical Easterly Jet (TEJ). Among the models, only HIRHAM fails to reproduce the rainfall variability exhibiting hence a correlation coefficient of ?0.2. This deficiency originates from the fact that HIRHAM does not properly capture the variability of monsoon flow and the relationship between rainfall and the AEJ dynamic. We conclude that a good performance of a regional climate model in simulating the monsoon dynamical features variability is of primary importance for a better representation of the interannual variability of rainfall over the Sahel.     

Volatility modeling of rainfall time series

Networks of rain gauges can provide a better insight into the spatial and temporal variability of rainfall, but they tend to be too widely spaced for accurate estimates. A way to estimate the spatial variability of rainfall between gauge points is to interpolate between them. This paper evaluates the spatial autocorrelation of rainfall data in some locations in Peninsular Malaysia using geostatistical technique. The results give an insight on the spatial variability of rainfall in the area, as such, two rain gauges were selected for an in-depth study of the temporal dependence of the rainfall data-generating process. It could be shown that rainfall data are affected by nonlinear characteristics of the variance often referred to as variance clustering or volatility, where large changes tend to follow large changes and small changes tend to follow small changes. The autocorrelation structure of the residuals and the squared residuals derived from autoregressive integrated moving average (ARIMA) models were inspected, the residuals are uncorrelated but the squared residuals show autocorrelation, and the Ljung–Box test confirmed the results. A test based on the Lagrange multiplier principle was applied to the squared residuals from the ARIMA models. The results of this auxiliary test show a clear evidence to reject the null hypothesis of no autoregressive conditional heteroskedasticity (ARCH) effect. Hence, it indicates that generalized ARCH (GARCH) modeling is necessary. An ARIMA error model is proposed to capture the mean behavior and a GARCH model for modeling heteroskedasticity (variance behavior) of the residuals from the ARIMA model. Therefore, the composite ARIMA–GARCH model captures the dynamics of daily rainfall in the study area. On the other hand, seasonal ARIMA model became a suitable model for the monthly average rainfall series of the same locations treated.     

Recent disruptions in the timing and intensity of precipitation in Calakmul,Mexico

This study addresses changes in the timing and intensity of precipitation from 1982 to 2016 from three meteorological stations around Calakmul, Mexico, a landscape balancing biodiversity conservation and smallholder agricultural production. Five methods were used to assess changes in precipitation: the Mann-Kendall test of annual and wet season trends; a fuzzy-logic approach to determine the onset of the rainy season; the Gini Index and Precipitation Concentration Index (PCI) to evaluate the temporal distribution of precipitation; Simple Precipitation Intensity Index (SDII) to evaluate precipitation intensity; and the Rainfall Anomaly Index (RAI) to identify the deficit or surplus of rainfall compared with the long-term mean. Overall, rainfall trends in Calakmul over this period indicate a slight increase, though results of the indices (Gini, SDII, PCI) all indicate that rainfall has become more intense and more unevenly distributed throughout the year. There was no significant trend in the onset date of rainfall or the RAI overall, though there were more pronounced crests and troughs from 2004 to 2016. Higher interannual variability and more pronounced rainfall anomalies, both positive and negative, suggest that rainfall in the Calakmul region has become more extreme. This research informs for management and livelihood strategies in the local region and offers insights for analyses of regional patterns of seasonal precipitation events in tropical landscapes worldwide.     

Intraseasonal oscillation of the southwest monsoon over Sri Lanka and evaluation of its subseasonal forecast skill

斯里兰卡的雨季发生于5-9月间,主要受西南季风的控制.本文发现该地区的西南季风降水存在很强的次季节变率,主导周期为10-35天.降水的季节内变化与西传的异常气旋有关.进一步,利用S2S比较计划中欧洲中心的数值预报模式(ECMWF)提供的回报试验数据,评估了当今动力模式对斯里兰卡西南季风次季节变化的预报技巧.结果显示,对季风指数的预测技巧超过30天,而对降水指数的预测技巧大约两周,且模式的预报技巧具有明显的年际差异.分析表明,能否正确模拟出大尺度环流对热带对流的响应是影响斯里兰卡降水预测的重要因子.     

Modeling olive-crop forecasting in Tunisia

Tunisia is the world’s second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia’s five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993–2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.     

Changes in rainfall regime over Burkina Faso under the climate change conditions simulated by 5 regional climate models

Sahelian rainfall has recorded a high variability during the last century with a significant decrease (more than 20 %) in the annual rainfall amount since 1970. Using a linear regression model, the fluctuations of the annual rainfall from the observations over Burkina Faso during 1961–2009 period are described through the changes in the characteristics of the rainy season. The methodology is then applied to simulated rainfall data produced by five regional climate models under A1B scenario over two periods: 1971–2000 as reference period and 2021–2050 as projection period. As found with other climate models, the projected change in annual rainfall for West Africa is very uncertain. However, the present study shows that some features of the impact of climate change on rainfall regime in the region are robust. The number of the low rainfall events (0.1–5 mm/d) is projected to decrease by 3 % and the number of strong rainfall events (>50 mm/d) is expected to increase by 15 % on average. In addition, the rainy season onset is projected by all models to be delayed by one week on average and a consensus exists on the lengthening of the dry spells at about 20 %. Furthermore, the simulated relationship between changed annual rainfall amounts and the number of rain days or their intensity varies strongly from one model to another and some changes do not correspond to what is observed for the rainfall variability over the last 50 years.     

东亚季风指数的定义及其与中国气候的关系

利用NCEP/NCAR 850 hPa月平均风场再分析资料,在客观地选定定义地区范围的基础上;定义了一组新的东亚季风指数:西南季风面积和强度指数,东南季风面积和强度指数,偏北季风面积和强度指数.研究了各季风指数的相互关系、季节变化和年际变异.这6个东亚季风指数突出反映了东亚西南季风、东南季风及偏北季风3支季风气流强度和范围变化的特征.分析表明,它们相互之间既有一定联系,又有独立性.各季风指数存在明显的季节变化和年际变化.另外,分析了各季风指数与中国夏季降水和冬季气温的联系,以考察其解释我国气候异常分布的能力.结果表明,这些指数与我国夏季降水和冬季气温有很好的关系,并各自对应有一定的降水和气温分布.特别是西南季风与东南季风影响我国夏季降水的地区有很大差异.因此,我们指出,研究东亚夏季风时,区别西南季风与东南季风是很有必要的,用单一指数不足以表征它们不同的变化.     

Spatial and temporal variability of precipitation indices during 1961–2010 in Hunan Province,central south China

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.