Interannual variability of seasonal rainfall in Cordoba, Argentina, evaluated from ENSO and ENSO Modoki signals and verified with MODIS NDVI data

Abstract This study examines space–time characteristics of seasonal rainfall predictability in a tropical region by analyzing observed data and model simulations over Senegal. Predictability is analyzed in terms of the spatial coherence of observed interannual variability at the station scale, and within-ensemble coherence of general circulation model (GCM) simulations with observed sea surface temperatures (SSTs) prescribed. Seasonal mean rainfall anomalies are decomposed in terms of daily rainfall frequency and daily mean intensity. The observed spatial coherence is computed from a 13-station network of daily rainfall during the July–September season 1961–98 in terms of (i) interannual variability of a standardized anomaly index (i.e., the average of the normalized anomalies of each station), (ii) the external variance (i.e., the fraction of common variance among stations), and (iii) the number of spatiotemporal degrees of freedom. Spatial coherence of interannual anomalies across stations is found to be much stronger for seasonal rainfall amount and daily occurrence frequency, compared with daily mean intensity of rainfall. Combinatorial analysis of the station observations suggests that, for occurrence and seasonal amount, the empirical number of spatial degrees of freedom is largely insensitive to the number of stations considered, and is between 3 and 4 for Senegal. For daily mean intensity, by contrast, each station is found to convey almost independent information, and the number of degrees of freedom would be expected to increase for a denser network of stations. The GCM estimates of potential predictability and skill associated with the SST forcing are found to be remarkably consistent with those inferred from the observed spatial coherence: there is a moderate-to-strong skill at reproducing the interannual variations of seasonal amounts and rainfall occurrence, whereas the skill is weak for the mean intensity of rainfall. Over Senegal during July–September, it is concluded that (i) regional-scale seasonal amount and rainfall occurrence frequency are predictable from SSTs, (ii) daily mean intensity of rainfall is spatially incoherent and largely unpredictable at the regional scale, and (iii) point-score estimates of seasonal rainfall predictability and skill are subject to large sampling variability.

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Using seasonal rainfall clusters to explain the interannual variability of the rain belt over the Greater Horn of Africa

International Journal of Climatology ◽

10.1002/joc.6802 ◽

2020 ◽

Author(s):

Larisa S. Seregina ◽

Andreas H. Fink ◽

Roderick Linden ◽

Chris Funk ◽

Joaquim G. Pinto

Keyword(s):

Interannual Variability ◽

Seasonal Rainfall ◽

Horn Of Africa ◽

Rain Belt ◽

Greater Horn Of Africa

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Interannual variability of seasonal rainfall over the Cape south coast of South Africa and synoptic type association

Climate Dynamics ◽

10.1007/s00382-015-2836-2 ◽

2015 ◽

Vol 47 (1-2) ◽

pp. 295-313 ◽

Cited By ~ 20

Author(s):

Christien J. Engelbrecht ◽

Willem A. Landman

Keyword(s):

South Africa ◽

Interannual Variability ◽

South Coast ◽

Seasonal Rainfall ◽

Synoptic Type

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Interannual Variability of Seasonal Rainfall and Associated Circulations over Gambella, Ethiopia

International Journal of Environmental Monitoring and Analysis ◽

10.11648/j.ijema.20210903.13 ◽

2021 ◽

Vol 9 (3) ◽

pp. 67

Author(s):

Waza Mulualem Abera ◽

Wang Wen

Keyword(s):

Interannual Variability ◽

Seasonal Rainfall

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Regularity of rainfall timing across Ethiopia: implications for crop production

10.5194/egusphere-egu21-9279 ◽

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

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.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.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.

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