scholarly journals Variability of rainfall in Suriname and the relation with ENSO-SST and TA-SST

2006 ◽  
Vol 6 ◽  
pp. 77-82 ◽  
Author(s):  
R. J. Nurmohamed ◽  
S. Naipal
Keyword(s):  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Time Variability ◽  
Spatial Correlations ◽  
Wet Season ◽  
Inter Tropical Convergence Zone ◽  
The Pacific ◽  
Cross Correlation Analysis ◽  
Rainfall Anomalies ◽  
Lagged Correlation

Abstract. Spatial correlations in the annual rainfall anomalies are analyzed using principle component analyses (PCA). Cross correlation analysis and composites are used to measure the influence of sea surface temperatures anomalies (SSTAs) in the tropical Atlantic and tropical Pacific Ocean with the seasonal rainfall in Suriname. The spatial and time variability in rainfall is mainly determined by the meridional movement of the Inter-tropical Convergence Zone (ITCZ). Rainfall anomalies tend to occur fairly uniformly over the whole country. In December-January (short wet season), there is a lagged correlation with the SSTAs in the Pacific region (clag3Nino1+2=-0.63). The strongest correlation between the March-May rainfall (beginning long wet season) and the Pacific SSTAs is found with a correlation coefficient of ckNino1+2=0.59 at lag 1 month. The June-August rainfall (end part of long wet season) shows the highest correlation with SSTAs in the TSA region and is about c=-0.52 for lag 0. In the September-November long dry season there is also a lagged correlation with the TSA SSTAs of about clag3=0.66. The different correlations and predictors can be used for seasonal rainfall predictions.

scholarly journals Accurate Quantification of Seasonal Rainfall and Associated Climate–Wildfire Relationships

2010 ◽  
Vol 49 (12) ◽  
pp. 2559-2573 ◽  
Author(s):  
Matthew G. Slocum ◽  
William J. Platt ◽  
Brian Beckage ◽  
Steve L. Orzell ◽  
Wayne Taylor
Keyword(s):  
Daily Rainfall ◽  
Seasonal Rainfall ◽  
Wet Season ◽  
Rainfall Characteristics ◽  
Central Florida ◽  
South Central ◽  
Multiple Regression Technique ◽  
Precise Quantification ◽  
Rainfall Anomalies ◽  
Force Range

Abstract Wildfires are often governed by rapid changes in seasonal rainfall. Therefore, measuring seasonal rainfall on a temporally finescale should facilitate the prediction of wildfire regimes. To explore this hypothesis, daily rainfall data over a 58-yr period (1950–2007) in south-central Florida were transformed into cumulative rainfall anomalies (CRAs). This transformation allowed precise estimation of onset dates and durations of the dry and wet seasons, as well as a number of other variables characterizing seasonal rainfall. These variables were compared with parameters that describe ENSO and a wildfire regime in the region (at the Avon Park Air Force Range). Onset dates and durations were found to be highly variable among years, with standard deviations ranging from 27 to 41 days. Rainfall during the two seasons was distinctive, with the dry season having half as much as the wet season despite being nearly 2 times as long. The precise quantification of seasonal rainfall led to strong statistical models describing linkages between climate and wildfires: a multiple-regression technique relating the area burned with the seasonal rainfall characteristics had an of 0.61, and a similar analysis examining the number of wildfires had an of 0.56. Moreover, the CRA approach was effective in outlining how seasonal rainfall was associated with ENSO, particularly during the strongest and most unusual events (e.g., El Niño of 1997/98). Overall, the results presented here show that using CRAs helped to define the linkages among seasonality, ENSO, and wildfires in south-central Florida, and they suggest that this approach can be used in other fire-prone ecosystems.

scholarly journals The Hydroclimatology of Kuwait: Explaining the Variability of Rainfall at Seasonal and Interannual Time Scales

2008 ◽  
Vol 9 (5) ◽  
pp. 1095-1105 ◽  
Author(s):  
Marc P. Marcella ◽  
Elfatih A. B. Eltahir
Keyword(s):  
Gradual Increase ◽  
Southern Oscillation ◽  
Transitional Zone ◽  
Rain Gauge ◽  
Annual Rainfall ◽  
Spatial Correlations ◽  
Double Peaks ◽  
Rainfall Anomalies ◽  
The Tropics ◽  
Atmospheric Systems

Abstract This paper presents an analysis of the spatial, seasonal, and interannual variabilities of Kuwaiti rainfall. Based on an analysis of rain gauge, as well as satellite, datasets, it is estimated that about 110–190 mm of rainfall occurs annually in Kuwait, depending on the dataset sampled. The corresponding estimates for the standard deviations of the annual rainfall are about 40–70 mm. Discrepancies between values arise from the different techniques used in constructing each dataset. Moreover, the spatial distribution of annual rainfall features a gradual increase from the southwest to the northeast. A distinct rainy season occurs from November to April, with double peaks in January and March. In addition, the seasonal variability of rainfall is associated with shifts in patterns of midlatitude storm tracks, which propagate southward toward the Middle East during the winter and spring season. These trends are characterized using estimates of the spatial correlations of rainfall in Kuwait with the surrounding region. At the interannual time scale, significant correlation is found between the tropical El Niño–Southern Oscillation (ENSO) and annual rainfall anomalies. Similar weak correlations are found between midlatitude rainfall in Europe and rainfall in Kuwait. The weak connections observed with both tropical and midlatitude atmospheric systems are consistent with the fact that Kuwait is located in the transitional zone between the tropics and midlatitudes.

scholarly journals Reproductive seasonality in African ungulates in relation to rainfall

2014 ◽  
Vol 41 (4) ◽  
pp. 323 ◽  
Author(s):  
Joseph O. Ogutu ◽  
Hans-Peter Piepho ◽  
Holly T. Dublin
Keyword(s):  
Linear Models ◽  
Global Climate ◽  
Eastern Africa ◽  
Annual Rainfall ◽  
Wet Season ◽  
Reproductive Seasonality ◽  
Fecundity Rate ◽  
Juvenile Recruitment ◽  
Cross Correlation Analysis ◽  
African Ungulates

Context Reproductive seasonality in ungulates has important fitness consequences but its relationship to resource seasonality is not yet fully understood, especially for ungulates inhabiting equatorial environments. Aims We test hypotheses concerning synchronisation of conception or parturition peaks among African ungulates with seasonal peaks in forage quality and quantity, indexed by rainfall. Methods We relate monthly apparent fecundity and juvenile recruitment rates to monthly rainfall for six ungulate species inhabiting the Masai Mara National Reserve (Mara) of Kenya, using cross-correlation analysis and distributed lag non-linear models. We compare the phenology and synchrony of breeding among the Mara ungulates with those for other parts of equatorial East Africa, with bimodal rainfall and less seasonal forage variation, and for subtropical southern Africa, with unimodal rainfall distribution and greater seasonal forage variation. Key results Births were more synchronised for topi, warthog and zebra than for hartebeest, impala and giraffe in the Mara, and for impala and hartebeest in southern than in eastern Africa. This pattern is likely to reflect regional differences in climate and plant phenology, hider–follower dichotomy and grazing versus browsing. All six species except the browsing giraffe apparently time the conception to occur in one wet season and births to occur just before the onset or during the next wet season, so as to maximise high-quality forage intake during conception and parturition. Fecundity and recruitment rates among the African ungulates peak at intermediate levels of rainfall and are reduced at low or excessive levels of rainfall. Fecundity rate is most strongly positively correlated with rainfall pre-conception, during conception and during early gestation, followed by rainfall at about the time of parturition for all the grazers. For giraffe, fecundity rate is most strongly correlated with rainfall during the gestation period. Conclusions Rainfall seasonality strongly influences reproductive seasonality and juvenile recruitment among African ungulates. The interaction of the rainfall influence with life-history traits and other factors leads to wide interspecific and regional variation. Implications Global climate change, especially widening annual rainfall variation expected to result from global warming, could reduce the predictability of the timing of peak forage availability and quality based on meteorological cues, the length of time with adequate nutrition or both, and hence reduce reproductive success among tropical ungulates.

scholarly journals Regularity of rainfall timing across Ethiopia: implications for crop production

2021 ◽  
Author(s):  
Mosisa Wakjira ◽  
Darcy Molnar ◽  
Nadav Peleg ◽  
Johan Six ◽  
Peter Molnar
Keyword(s):  
Interannual Variability ◽  
Relative Entropy ◽  
Crop Production ◽  
Crop Yields ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Rainfall Regime ◽  
Cereal Crop ◽  
Wet Season ◽  
The Impact

<p>Rainfall timing is a key parameter that farmers rely on to match the cropping season with the time window over which seasonal precipitation provides adequate soil moisture to meet plant growth demand. The unpredictability of rainfall timing affects the selection of an optimal growing season, and hence crop production in regions where rainfed agriculture (RFA) is practiced. In this study, we (a) map rainfall timing, and its interannual variability and changes over RFA areas across Ethiopia for the period 1981-2010, and (b) explore the impact of variability in rainfall timing on cereal crop production in the period 1995-2010.</p><p>For the mapping of rainfall timing, we used the quasi-global CHIRPS precipitation dataset over Ethiopia. We use information entropy on monthly rainfall to define the rainfall seasonality metrics, i.e. the relative entropy and dimensionless seasonality index, and map them in space. For rainfall timing attributes, we determine the onset, cessation, and length of the wet season from LOESS-smoothed cumulative pentad rainfall anomalies for each hydrological year. Changes in seasonality metrics and rainfall timing attributes are analyzed using non-parametric methods. We show that high seasonality (unimodal rainfall regime) is located in the northern part of the Ethiopian RFA area where high annual rainfall and high relative entropy are coincident, and where the onset of the rainfall season varies between mid-April to late-June and cessation occurs between mid-September to late-October. Low seasonality in the southern part of the Ethiopian RFA area shows low relative entropy regardless of the annual rainfall total. We observed a considerable interannual variability both in seasonality and rainfall timing over the study period, especially in the onset and length of the wet season. The length of the wet season and magnitude of seasonal rainfall are predominantly controlled by the timing of rainfall onset.</p><p>For the impacts of rainfall timing on crop production, we used cereal crop production data from the Central Statistical Agency of Ethiopia for the period 1995-2010 in 45 administrative zones. We carried out a parametric correlation analysis between rainfall timing and rescaled and de-trended crop production anomalies. We observe that anomalies in seasonal cereal crop production in RFA areas are significantly correlated with anomalies in rainfall onset (negatively) and the length of the wet season (positively), with a regional average production loss of 3% per pentad of late rainfall onset, and 2.7% per pentad of shorter length of the wet season. Seasonal rainfall is less strongly correlated with cereal crop production anomalies compared to the rainfall onset. These results show that the interannual variability in rainfall timing (start of the rainy season) even under present climate has strong impacts on crop yields in RFA areas in Ethiopia, and this may be exacerbated in a future climate.</p>

scholarly journals Sources of Tropical Subseasonal Skill in the CFSv2

2020 ◽  
Vol 148 (4) ◽  
pp. 1553-1565 ◽  
Author(s):  
Carl J. Schreck ◽  
Matthew A. Janiga ◽  
Stephen Baxter
Keyword(s):  
Indian Ocean ◽  
Low Frequency ◽  
Equatorial Pacific ◽  
Convectively Coupled Equatorial Waves ◽  
Subseasonal Variability ◽  
The Pacific ◽  
Fourier Filtering ◽  
The Indian Ocean ◽  
Rainfall Anomalies ◽  
Combined Data

Abstract This study applies Fourier filtering to a combination of rainfall estimates from TRMM and forecasts from the CFSv2. The combined data are filtered for low-frequency (LF, ≥120 days) variability, the MJO, and convectively coupled equatorial waves. The filtering provides insight into the sources of skill for the CFSv2. The LF filter, which encapsulates persistent anomalies generally corresponding with SSTs, has the largest contribution to forecast skill beyond week 2. Variability within the equatorial Pacific is dominated by its response to ENSO, such that both the unfiltered and the LF-filtered forecasts are skillful over the Pacific through the entire 45-day CFSv2 forecast. In fact, the LF forecasts in that region are more skillful than the unfiltered forecasts or any combination of the filters. Verifying filtered against unfiltered observations shows that subseasonal variability has very little opportunity to contribute to skill over the equatorial Pacific. Any subseasonal variability produced by the model is actually detracting from the skill there. The MJO primarily contributes to CFSv2 skill over the Indian Ocean, particularly during March–May and MJO phases 2–5. However, the model misses opportunities for the MJO to contribute to skill in other regions. Convectively coupled equatorial Rossby waves contribute to skill over the Indian Ocean during December–February and the Atlantic Ocean during September–November. Convectively coupled Kelvin waves show limited potential skill for predicting weekly averaged rainfall anomalies since they explain a relatively small percent of the observed variability.

scholarly journals Precipitation in the Amazon and its relationship with moisture transport and tropical Pacific and Atlantic SST from the CMIP5 simulation

2015 ◽  
Vol 12 (1) ◽  
pp. 671-704 ◽  
Author(s):  
G. Martins ◽  
C. von Randow ◽  
G. Sampaio ◽  
A. J. Dolman
Keyword(s):  
Moisture Transport ◽  
General Circulation ◽  
Regional Analysis ◽  
Coupled Model ◽  
General Circulation Models ◽  
Moisture Convergence ◽  
Cmip5 Models ◽  
Wet Season ◽  
The Pacific ◽  
Eastern Edge

Abstract. Studies on numerical modeling in Amazonia show that the models fail to capture important aspects of climate variability in this region and it is important to understand the reasons that cause this drawback. Here, we study how the general circulation models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulate the inter-relations between regional precipitation, moisture convergence and Sea Surface Temperature (SST) in the adjacent oceans, to assess how flaws in the representation of these processes can translate into biases in simulated rainfall in Amazonia. Using observational data (GPCP, CMAP, ERSST.v3, ERAI and evapotranspiration) and 21 numerical simulations from CMIP5 during the present climate (1979–2005) in June, July and August (JJA) and December, January and February (DJF), respectively, to represent dry and wet season characteristics, we evaluate how the models simulate precipitation, moisture transport and convergence, and pressure velocity (omega) in different regions of Amazonia. Thus, it is possible to identify areas of Amazonia that are more or less influenced by adjacent ocean SSTs. Our results showed that most of the CMIP5 models have poor skill in adequately representing the observed data. The regional analysis of the variables used showed that the underestimation in the dry season (JJA) was twice in relation to rainy season as quantified by the Standard Error of the Mean (SEM). It was found that Atlantic and Pacific SSTs modulate the northern sector of Amazonia during JJA, while in DJF Pacific SST only influences the eastern sector of the region. The analysis of moisture transport in JJA showed that moisture preferentially enters Amazonia via its eastern edge. In DJF this occurs both via its northern and eastern edge. The moisture balance is always positive, which indicates that Amazonia is a source of moisture to the atmosphere. Additionally, our results showed that during DJF the simulations in northeast sector of Amazonia have a strong bias in precipitation and an underestimation of moisture convergence due to the higher influence of biases in the Pacific SST. During JJA, a strong precipitation bias was observed in the southwest sector associated, also with a negative bias of moisture convergence, but with weaker influence of SSTs of adjacent oceans. The poor representation of precipitation-producing systems in Amazonia by the models and the difficulty of adequately representing the variability of SSTs in the Pacific and Atlantic oceans may be responsible for these underestimates in Amazonia.

scholarly journals Variability and Trends of Temperature and Rainfall Over Three Agro-Ecological Zones in North Shewa, Central Ethiopia

2020 ◽  
Author(s):  
Ashenafi Hailu Shekuru ◽  
Arega Bazezew Berlie ◽  
Yechale Kebede Bizuneh
Keyword(s):  
Crop Production ◽  
Smallholder Farmers ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Upward Trend ◽  
Central Ethiopia ◽  
Ecological Zones ◽  
Annual Minimum Temperature ◽  
Rainfall Anomalies ◽  
Agro Ecological Zones

Abstract This study aims to analyze variability and trends of temperature and rainfall over three agro-ecological zones (AEZs) in Central Ethiopia. Gridded rainfall and temperature data, recorded on daily basis for 35 years (1979 - 2013) at 30 meteorological stations, were used for analysis. While Mann–Kendall test was applied to analyze the trends in rainfall and temperature, Sen’s slope estimator was used to determine the magnitude of change. The study detected an upward trend of 0.07°C/annum (p < 0.001) in mean annual maximum temperature at Kolla AEZ. It also showed an upward trend of 0.06/year (p < 0.001) for both Dega and Woina Dega AEZs. Mean annual minimum temperature exhibited an upward trend of 0.03°C/year at Kolla (p < 0.001), Woina Dega (p < 0.05), and Dega (p < 0.01), signifying a 1.05°C increase between 1979 and 2013. Results from precipitation concentration index (PCI) revealed highest percentage (97.1%) of irregular distributions in annual rainfall pattern at Kolla AEZ, followed by Woina Dega (82.9%). Standardized rainfall anomalies (SRA) computed in the study also showed higher percentage (28.6%) of drought in Kolla AEZ, which experienced drought once in every 3 or 4 years. The study revealed negative annual rainfall anomalies for 18 years in Kolla and 16 years in both Dega and Woina Dega AEZs. The reduced precipitation and rise in temperature could trigger wide-ranging influences on agricultural practices and crop production of smallholder farmers. Policymakers and stakeholders should give priority in designing and introducing pro-poor plus geographically differentiated adaptive strategies.

Extreme Rainfall Events in the Macro-Metropolis of São Paulo: trends and connection with climate oscillations

2021 ◽  
Author(s):  
Carolyne B. Machado ◽  
Thamiris L. O. B. Campos ◽  
Sameh A. Abou Rafee ◽  
Jorge A. Martins ◽  
Alice M. Grimm ◽  
...  
Keyword(s):  
Urban Areas ◽  
Extreme Rainfall ◽  
Kendall Test ◽  
Rain Gauge ◽  
Sao Paulo ◽  
Annual Rainfall ◽  
São Paulo ◽  
Wet Season ◽  
Extreme Rainfall Events ◽  
Climatic Indices

AbstractIn the present work, the trend of extreme rainfall indices in the Macro-Metropolis of São Paulo (MMSP) was analyzed and correlated with largescale climatic oscillations. A cluster analysis divided a set of rain gauge stations into three homogeneous regions within MMSP, according to the annual cycle of rainfall. The entire MMSP presented an increase in the total annual rainfall, from 1940 to 2016, of 3 mm per year on average, according to Mann-Kendall test. However, there is evidence that the more urbanized areas have a greater increase in the frequency and magnitude of extreme events, while coastal and mountainous areas, and regions outside large urban areas, have increasing rainfall in a better-distributed way throughout the year. The evolution of extreme rainfall (95th percentile) is significantly correlated with climatic indices. In the center-north part of the MMSP, the combination of Pacific Decadal Oscillation (PDO) and Antarctic Oscillation (AAO) explains 45% of the P95th increase during the wet season. In turn, in southern MMSP, the Temperature of South Atlantic (TSA), the AAO, the El Niño South Oscillation (ENSO) and the Multidecadal Oscillation of the North Atlantic (AMO) better explain the increase in extreme rainfall (R2 = 0.47). However, the same is not observed during the dry season, in which the P95th variation was only negatively correlated with the AMO, undergoing a decrease from the ‘70s until the beginning of this century. The occurrence of rainy anomalous months proved to be more frequent and associated with climatic indices than dry months.

Mechanisms and effects of dry and wet Sahel epochs

2021 ◽  
Author(s):  
John T Bruun ◽  
Katy Sheen ◽  
Mat Collins
Keyword(s):  
Dominant Frequency ◽  
African Region ◽  
Decomposition Approach ◽  
Inter Tropical Convergence Zone ◽  
Lake Chad ◽  
Agricultural Growth ◽  
Evaporation And Condensation ◽  
The Pacific ◽  
Dry Conditions ◽  
Frequency State

&lt;p&gt;The Sahel is Northern African region between the equator and the Sahara desert. It is home to a belt of semi-arid grassland that stretches from the Atlantic and across the continent westward towards the Red Sea. The monsoon type rainfall season that occurs in this region is influenced by the way that moisture transport along this belt region combines along the Inter Tropical Convergence Zone (ITCZ). The Sahel is one of the most productive crop areas of Africa, and if the rains fail &amp;#8211; it has long lasting implications for its community. Due to its &amp;#160;planetary location dry conditions pervade the Sahel for most of the year, with food production and livelihoods reliant on the summer monsoon rainy season between July and September. In this study we use (where available) up to 100 years of re-analysis records (GPCC rainfall, NCAR wind and HadiSST ocean data) together with an accurate signal decomposition approach (dominant frequency state analysis, DFSA). With this we assess how the teleconnection influence of the Pacific ENSO and the Atlantic dipole mechanisms influence the dry and wet Sahel rain conditions. The severe Sahelian drought of the 1980&amp;#8217;s is shown to be a compounded sequence of drying dynamic effects that combined to occur suddenly over the span of 5-10 years. Our work indicates that dry and wet conditions appear to be related to land-air evaporation and condensation in the vicinity of the Sahel river catchments, with the land locked Lake Chad catchment having a particularly sensitive arid climate. Our latest finding&amp;#8217;s help explain how the Atlantic and Pacific physical mechanism influence the Sahel monsoon and its extremes. With an assessment of agricultural data we also show how agricultural growth in the region is impacted by these factors. We present and discuss Africa dry and wet rainfall epoch forecasts over the next 30 years for Sahel based on stable and altered climate hysteresis scenarios.&lt;/p&gt;

scholarly journals Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand

2016 ◽  
Vol 48 (3) ◽  
pp. 867-882 ◽  
Author(s):  
M. S. Babel ◽  
T. A. J. G. Sirisena ◽  
N. Singhrattna
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Large Scale ◽  
Resource Planning ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Rainfall Forecasting ◽  
Artificial Neural ◽  
Atmospheric Variables

Understanding long-term seasonal or annual or inter-annual rainfall variability and its relationship with large-scale atmospheric variables (LSAVs) is important for water resource planning and management. In this study, rainfall forecasting models using the artificial neural network technique were developed to forecast seasonal rainfall in May–June–July (MJJ), August–September–October (ASO), November–December–January (NDJ), and February–March–April (FMA) and to determine the effects of climate change on seasonal rainfall. LSAVs, temperature, pressure, wind, precipitable water, and relative humidity at different lead times were identified as the significant predictors. To determine the impacts of climate change the predictors obtained from two general circulation models, CSIRO Mk3.6 and MPI-ESM-MR, were used with quantile mapping bias correction. Our results show that the models with the best performance for FMA and MJJ seasons are able to forecast rainfall one month in advance for these seasons and the best models for ASO and NDJ seasons are able do so two months in advance. Under the RCP4.5 scenario, a decreasing trend of MJJ rainfall and an increasing trend of ASO rainfall can be observed from 2011 to 2040. For the dry season, while NDJ rainfall decreases, FMA rainfall increases for the same period of time.

Sign in / Sign up

Export Citation Format

Share Document