Research

Aerosol transport over the gangetic basin during isro-gbp land campaign-ii. Aloysius (2008). Vol. vol. 26, pp. 431.

Eolian dust and climate: relationships to sources, tropospheric chemistry, transport and deposition. Arimoto (2001). Earth Sci. Rev., Vol. 54, (1–3), pp. 29.
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Cas fgoals-f3-h and cas fgoals-f3-l outputs for the high-resolution model intercomparison project simulation of cmip6. Bao (2020). Atmosph. Oceanic Sci. Lett., Vol. 13, (6), pp. 576.
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Root mean square error (rmse) or mean absolute error (mae)?–arguments against avoiding rmse in the literature. Chai (2014). Geosci. Model Develop., Vol. 7, (3), pp. 1247.
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Identification of deficiencies in seasonal rainfall simulated by cmip5 climate models. Dunning (2017). Environ. Res. Lett., Vol. 12, (11), pp. 114001.
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Overview of the coupled model intercomparison project phase 6 (cmip6) experimental design and organization. Eyring (2016). Geosci. Model Develop., Vol. 9, (5), pp. 1937.
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Taking climate model evaluation to the next level. Eyring (2019). Nat. Climate Change, Vol. 9, (2), pp. 102.
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Understanding the large scale driving mechanisms of rainfall variability over central africa. Farnsworth (2011). pp. 101.

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Rmets national meeting-forecasting for africa. Fitzpatrick (2015). Weather, Vol. 70, (6), pp. 176.
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Warming of the indian ocean threatens eastern and southern african food security but could be mitigated by agricultural development. Funk (2008). Proc. Nat. Acad. Sci., Vol. 105, (32), pp. 11081.
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A global perspective on african climate. Giannini (2008). Climatic Change, Vol. 90, (4), pp. 359.
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Sources and distributions of dust aerosols simulated with the gocart model. Ginoux (2001). J. Geophys. Res.: Atmosph., Vol. 106, (D17), pp. 20255.
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Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Gupta (1999). J. Hydrol. Eng., Vol. 4, (2), pp. 135.
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A new approach of moving average method in time series analysis. Hansun (2013). pp. 1.

The era5 global reanalysis. Hersbach (2020). Quart. J. Roy. Meteorol. Soc., Vol. 146, (730), pp. 1999.
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Mesoscale convective systems. Houze (2004). Rev. Geophys., Vol. 42, (4).
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Mesoscale convective systems over western equatorial africa and their relationship to large-scale circulation. Jackson (2009). Mon. Weather Rev., Vol. 137, (4), pp. 1272.
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Changes in temperature and precipitation extremes in the cmip5 ensemble. Kharin (2013). Climatic Change, Vol. 119, (2), pp. 345.
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Evaluation of precipitation and temperature simulation performance of the cmip3 and cmip5 historical experiments. Koutroulis (2016). Clim. Dyn., Vol. 47, (5), pp. 1881.
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Regional and seasonal intercomparison of cmip3 and cmip5 climate model ensembles for temperature and precipitation. Kumar (2014). Climate Dyn., Vol. 43, (9), pp. 2491.
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Evaluation of temperature and precipitation trends and long-term persistence in cmip5 twentieth-century climate simulations. Kumar (2013). J. Clim., Vol. 26, (12), pp. 4168.
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Fluctuation of rainfall time series in malawi: an analysis of selected areas. Kumbuyo (2014). Geofizika, Vol. 31, (1), pp. 13.
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Mesoscale convective complexes in africa. Laing (1993). Mon. Weather Rev., Vol. 121, (8), pp. 2254.
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Comparison of rainfall products over sub-saharan africa. Le Coz (2020). J. Hydrometeorol., Vol. 21, (4), pp. 553.
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Predictor selection associated with statistical downscaling of precipitation over zambia. Libanda (2016). Asian J. Phys. Chem. Sci., pp. 1.

Skill of cmip5 models in simulating rainfall over malawi. Libanda (2019). Model. Earth Syst. Environ., Vol. 5, (4), pp. 1615.
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Variability of extreme wet events over malawi. Libanda (2017). Geographica Pannonica, Vol. 21, (4).
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Understanding recent eastern horn of africa rainfall variability and change. Liebmann (2014). J. Clim., Vol. 27, (23), pp. 8630.
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Evaluation of cmip5 retrospective simulations of temperature and precipitation in northeastern argentina. Lovino (2018). Int. J. Climatol., Vol. 38, pp. e1158.
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A recent and abrupt decline in the east african long rains. Lyon (2012). Geophys. Res. Lett., Vol. 39, (2).
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Evaluation of cmip5 continental precipitation simulations relative to satellite-based gauge-adjusted observations. Mehran (2014). J. Geophys. Res.: Atmosph., Vol. 119, (4), pp. 1695.
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Identification of sources and sinks of atmospheric aerosols and their impact on east african rainfall. Mmame (2023). Acta Geophys., pp. 1.

Gauging the performance of cmip5 historical simulation in reproducing observed gauge rainfall over kenya. Mumo (2020). Atmos. Res., Vol. 236, pp. 104808.
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Evaluation of spatial and temporal characteristics of rainfall in malawi: a case of data scarce region. Ngongondo (2011). Theoret. Appl. Climatol., Vol. 106, (1), pp. 79.
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Evaluation of cmip5 model precipitation using persiann-cdr. Nguyen (2017). J. Hydrometeorol., Vol. 18, (9), pp. 2313.
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A detailed rainfall climatology for malawi, southern africa. Nicholson (2014). Int. J. Climatol., Vol. 34, (2), pp. 315.
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Climate and climatic variability of rainfall over eastern africa. Nicholson (2017). Rev. Geophys., Vol. 55, (3), pp. 590.
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The relationship of rainfall variability in western equatorial africa to the tropical oceans and atmospheric circulation. part i: The boreal spring. Nicholson (2013). J. Climate, Vol. 26, (1), pp. 45.
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The seasonal evolution of the atmospheric circulation over west africa and equatorial africa. Nicholson (2003). J. Climate, Vol. 16, (7), pp. 1013.
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Aerosols’ variability and their relationship with climatic parameters over west africa. Ochei (2023). Environ. Monit. Assess., Vol. 195, (6), pp. 672.
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Era5: The new champion of wind power modelling?. Olauson (2018). Renew. Energy, Vol. 126, pp. 322.
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Evaluation of cmip5 twentieth century rainfall simulation over the equatorial east africa. Ongoma (2019). Theoret. Appl. Climatol., Vol. 135, (3), pp. 893.
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Present-day and future climate over central and south america according to cmip5/cmip6 models. Ortega (2021). Int. J. Climatol., Vol. 41, (15), pp. 6713.
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Cmip5 simulated climate conditions of the greater horn of africa (gha). part 1: contemporary climate. Otieno (2013). Climate Dyn., Vol. 41, (7), pp. 2081.
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Determining the anthropogenic greenhouse gas contribution to the observed intensification of extreme precipitation. Paik (2020). Geophys. Res. Lett., Vol. 47, (12).
https://doi.org/10.1029/2019GL086875

Modelling the diurnal cycle of tropical convection across the ’grey zone’. Pearson (2014). Quart. J. Roy. Meteorol. Soc., Vol. 140, (679), pp. 491.
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Quantifying and minimizing uncertainty of climate forcing by anthropogenic aerosols. Penner (1994). Bull. Am. Meteorol. Soc., Vol. 75, (3), pp. 375.
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From rain to famine: assessing the utility of rainfall observations and seasonal forecasts to anticipate food insecurity in east africa. Coughlan de Perez (2019). Food Sec., Vol. 11, (1), pp. 57.
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Process-based model evaluation and projections over southern africa from coordinated regional climate downscaling experiment and coupled model intercomparison project phase 5 models. Pinto (2018). Int. J. Climatol., Vol. 38, (11), pp. 4251.
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Global aerosol source/sink map. Prijith (2013). Atmos. Environ., Vol. 80, pp. 533.
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Role of circulation parameters in long range aerosol transport: evidence from winter-icarb. Prijith (2012). J. Atmospheric Sol.-terrest. Phys., Vol. 77, pp. 144.
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Mineral and sea salt aerosol concentrations in various ocean regions. Prospero (1979). J. Geophys. Res.: Oceans, Vol. 84, (C2), pp. 725.
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An observational constraint on cmip5 projections of the east african long rains and southern indian ocean warming. Rowell (2019). Geophys. Res. Lett., Vol. 46, (11), pp. 6050.
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Evaluation and projection of extreme precipitation indices in the eastern mediterranean based on cmip5 multi-model ensemble. Samuels (2018). Int. J. Climatol., Vol. 38, (5), pp. 2280.
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Migrations and dynamics of the intertropical convergence zone. Schneider (2014). Nature, Vol. 513, (7516), pp. 45.
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North american climate in cmip5 experiments. part i: Evaluation of historical simulations of continental and regional climatology. Sheffield (2013). J. Clim., Vol. 26, (23), pp. 9209.
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Projected changes in mean and extreme precipitation in africa under global warming. part i: Southern africa. Shongwe (2009). J. Clim., Vol. 22, (13), pp. 3819.
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Climate extremes indices in the cmip5 multimodel ensemble: Part 1. model evaluation in the present climate. Sillmann (2013). J. Geophys. Res.: Atmosph., Vol. 118, (4), pp. 1716.
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Evaluation and projected changes in daily rainfall characteristics over central africa based on a multi-model ensemble mean of cmip5 simulations. Sonkoué (2019). Theoret. Appl. Climatol., Vol. 137, (3), pp. 2167.
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Seasonal variation of the itcz and its characteristics over central africa. Suzuki (2011). Theoret. Appl. Climatol., Vol. 103, (1), pp. 39.
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Summarizing multiple aspects of model performance in a single diagram. Taylor (2001). J. Geophys. Res.: Atmosph., Vol. 106, (D7), pp. 7183.
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An overview of cmip5 and the experiment design. Taylor (2012). Bull. Am. Meteorol. Soc., Vol. 93, (4), pp. 485.
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’eastern african paradox’rainfall decline due to shorter not less intense long rains. Wainwright (2019). npj Clim. Atmosph. Sci., Vol. 2, (1), pp. 1.

Understanding processes that control dust spatial distributions with global climate models and satellite observations. Wu (2020). Atmos. Chem. Phys., Vol. 20, (22), pp. 13835.
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The rainfall annual cycle bias over east africa in cmip5 coupled climate models. Yang (2015). J. Clim., Vol. 28, (24), pp. 9789.
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Evaluation of cmip6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison. Yazdandoost (2021). Atmos. Res., Vol. 250, pp. 105369.
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Assessment of cmip5 multimodel mean for the historical climate of africa. Zebaze (2019). Atmosph. Sci. Lett., Vol. 20, (8), pp. e926.
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How well do the cmip6 models simulate dust aerosols?. Zhao (2022). Atmos. Chem. Phys., Vol. 22, (3), pp. 2095.
https://doi.org/10.5194/acp-22-2095-2022

Original language	en
Pages (from-to)	3096-3108
Volume	72
Issue number	8
Publication status	Published - 2023

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