Causal pathways linking different flavours of ENSO with the Greater Horn of Africa short rains

David A Macleod*, Richard Graham Graham, Chris O’Reilly, George Otieno, Martin Todd

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)
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Abstract

There is a strong association between canonical El Niño and a wet Greater Horn of Africa (GHA) short rains. However, the link with Modoki El Niño events appears to be significantly weaker. In order to understand this, we present an analysis of observational data and idealised climate model experiments. Idealised atmospheric simulations isolate the direct influence of Pacific heating on the GHA and reveal that neither the longitudinal position nor the observed weaker magnitude of Modoki Pacific heating anomalies can explain the difference in teleconnections. The direct effect of canonical or Modoki Pacific heating patterns on the GHA is similar and neither reproduces the structure of the full GHA teleconnection: they both generate a wet‐dry dipole over the GHA instead of large‐scale single‐signed wet anomalies. Our results indicate that the strong canonical ENSO influence on GHA is indirect, mediated through its strong relationship with the Indian Ocean Dipole (IOD). By contrast, Modoki ENSO is uncorrelated with the IOD, resulting in weak teleconnection to GHA. Understanding these differences aids seasonal forecast interpretation, whilst their representation in models is likely a prerequisite for making accurate projections of changes in extremes over the GHA and beyond.
Original languageEnglish
Article numbere1015
Number of pages11
JournalAtmospheric Science Letters
Volume22
Issue number2
Early online date28 Oct 2020
DOIs
Publication statusPublished - 1 Feb 2021

Bibliographical note

Funding Information:
We acknowledge ECMWF for provision of computing resources used to run experiments under a ‘Special Project’. Climate model data is archived at ECMWF, is accessible with a free licence, details can be found at www.ecmwf.int (archiving details of the experiments are provided in Table 1 ). We also acknowledge ECMWF for the use of ERA5 reanalysis data and the Climate Hazards Group of UCSB for CHIRPS data. N34 and IOD data was provided by NOAA, available at: https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/ , whilst EMI data was downloaded from JAMSTEC and is available at: http://www.jamstec.go.jp/frsgc/research/d1/iod/modoki_home.html.en . This work was funded by the Science for Humanitarian Emergencies and Resilience (SHEAR) consortium project ‘Towards Forecast‐based Preparedness Action’ (ForPAc), grant numbers NE/P000673/1, NE/P000568/1 and NE/P000428/1. The SHEAR programme is funded by the UK Natural Environment Research Council, the Economic and Social Research Council and the UK Department for International Development.

Funding Information:
We acknowledge ECMWF for provision of computing resources used to run experiments under a ?Special Project?. Climate model data is archived at ECMWF, is accessible with a free licence, details can be found at www.ecmwf.int (archiving details of the experiments are provided in Table 1). We also acknowledge ECMWF for the use of ERA5 reanalysis data and the Climate Hazards Group of UCSB for CHIRPS data. N34 and IOD data was provided by NOAA, available at: https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/, whilst EMI data was downloaded from JAMSTEC and is available at: http://www.jamstec.go.jp/frsgc/research/d1/iod/modoki_home.html.en. This work was funded by the Science for Humanitarian Emergencies and Resilience (SHEAR) consortium project ?Towards Forecast-based Preparedness Action? (ForPAc), grant numbers NE/P000673/1, NE/P000568/1 and NE/P000428/1. The SHEAR programme is funded by the UK Natural Environment Research Council, the Economic and Social Research Council and the UK Department for International Development.

Publisher Copyright:
© 2020 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.

Keywords

  • ENSO
  • IOD
  • seasonal forecasting
  • Greater Horn of Africa

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