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Article

Implications of Solar Radiation Modification on Rainfall and Temperature Patterns over Eastern Africa

by
Alex Nimusiima
1,*,
Godwin Ayesiga
2,
Ronald Ingula Odongo
3,
Catherine Mulinde
1,
Lawrence Aribo
2,
Moses Ojara
2 and
Bob Alex Ogwang
2
1
Department of Geography, Geo-Informatics and Climatic Sciences, Makerere University, Kampala P.O. Box 7062, Uganda
2
Uganda Department of Meteorological Services, Ministry of Water and Environment, Kampala P.O. Box 7025, Uganda
3
Department of Geography, University of Florida, Gainesville, FL 32611, USA
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(6), 646; https://doi.org/10.3390/atmos16060646
Submission received: 20 March 2025 / Revised: 5 May 2025 / Accepted: 15 May 2025 / Published: 27 May 2025
(This article belongs to the Section Climatology)
Browse Figures
Versions Notes

Abstract

:
This study explores the implications of Solar Radiation Modification (SRM) on the rainfall and temperature spatial patterns over the Eastern Africa region. The Geoengineering Model Intercomparison Project (GeoMIP) models under the SRM scenarios of G6solar and G6sulfur are evaluated against the Shared Socioeconomic Pathways (SSPs). First, six GeoMIP models are evaluated against historical data and are found to capture the climate spatial patterns in the region fairly well but with a dry bias for all the models. Secondly, the models are run under both the SRM scenarios and the SSP scenarios for 2051–2080. Results show that G6solar SRM scenario predicts increased annual precipitation in the region compared to SSP245 but predicts reduced annual precipitation compared to SSP585 in the same period. The G6sulfur scenario predicts reduced annual precipitation compared to both SSP245 and SSP585 in most parts of the region with more reductions expected over SSP585 compared to SSP245 in the same period. For temperature, the G6solar scenario predicts a reduced annual mean temperature compared to both SSP245 and SSP585 in most parts of the region, with more reductions in temperature against SSP585 compared to SSP245 in the same period. However, G6sulfur shows some inconsistent results, with some models predicting increased temperatures under SRM compared to SSPs, especially for SSP245, while other models predict reduced temperatures in the same period.

1. Introduction

Climate change and variability are key global issues affecting almost all economic sectors, especially in developing countries. Intergovernmental Panel on Climate Change (IPCC) reports have highlighted anthropogenic greenhouse gases as a major cause of global climate change in the recent past [1]. Global efforts by different bodies such as the United Nations Framework Convention on Climate Change (UNFCCC) have proposed strategies aimed at addressing this climate change problem. For example, the Paris Agreement 2015 aims at reducing the mean global climate to 2 °C below pre-industrial values, with a more ambitious target of keeping mean global temperature increases to within 1.5 °C above pre-industrial values [2].
In line with the Paris Agreement, countries have made pledges to reduce their greenhouse gas emissions through Nationally Determined Contributions (NDCs). Even with this target, the submitted NDCs of greenhouse gas reductions from the various countries are expected to cause an average warming of 2.6 °C to 3.1 °C by 2100, which is far beyond the agreed warming levels [3]. Therefore, the mitigation of climate change by reducing the amount of greenhouse gases in the atmosphere should be supplemented by other initiatives if future environmental sustainability is to be achieved.
Initiatives, such as Solar Radiation Modification (SRM), that enhance the earth’s reflectivity are now being explored as supplementary strategies for reducing the trend of global warming by cooling the environment [4]. Solar Radiation Modification (SRM) has been defined by IPCC as a range of radiation modification measures not related to greenhouse gas (GHG) mitigation that seek to limit global warming [5]. The most common methods of SRM used so far are Stratospheric Aerosol Injection (SAI) and Marine Cloud Brightening (MCB) [4]. SAI involves the injection of highly reflective aerosols (or their precursors) into the stratosphere to deflect more sunlight back to space and cool the climate. It is estimated that SAI can potentially reduce the earth’s energy by a radiative forcing of 2–5 Wm−2, which is equivalent to approximately 1–2% of the total solar radiation absorbed by the earth [4]. MCB on the other hand involves adding suitable Cloud Condensation Nuclei (CCN)—sub-microscopic particles that facilitate the condensation of water vapor in the atmosphere to form cloud droplets, like sea salt, into the low-level (~0–2 km) marine cloud layer. Then, evaporation of the water from the droplets results in suspended sea salt particles that can act as CCN. This increase in CCN would produce both a higher number of cloud droplets and droplets of smaller sizes, which would increase the reflectivity of clouds and therefore scatter more radiation back to space [6].
Global initiatives such as the Geoengineering Model Intercomparison Project (GeoMIP) [7], the Geoengineering Large Ensemble (GLENS) [8], and the Assessing Responses and Impacts of Solar climate intervention on the Earth (ARISE) system [9] have run Global Circulation Models (GCMs) with different SRM scenarios as an approach to manipulate incoming solar radiation. Two SRM simulations, G6solar and G6sulfur, under GeoMIP are considered to represent SRM in this study [7]. The G6solar scenario simulates the effects of geoengineering by increasing the amount of solar radiation reflected away from the Earth. This is achieved theoretically by deploying reflective particles in the atmosphere or increasing the reflectivity of clouds [7]. The G6sulfur scenario simulates the effect of injecting sulfur dioxide (SO2) into the upper atmosphere to form sulfate aerosols [7]. Like the G6solar simulation, the goal is to increase the Earth’s reflectivity, thereby reducing the amount of sunlight that reaches the surface and mitigating the effects of climate change. These global efforts of SRM need to be studied at regional and sub-regional scales to understand how the manipulated radiation budget might influence the climatic patterns at these scales. The need to study SRM at a regional level is particularly important for the Eastern Africa region, which has a rather dynamic climate and whose economic development majorly depends on the variations in climatic parameters [10]. More specifically, the response of SRM to rainfall and temperature over Eastern Africa has not been adequately investigated. This study, therefore, examines GeoMIP simulations with an aim to understand the impact of SRM on Eastern Africa spatial climate patterns. This study addresses the following two research questions: (1) To what extent do GeoMIP GCM models represent historical rainfall and temperature spatial patterns over the Eastern Africa region? (2) How do projected future SRM models compare with Conventional Shared Socioeconomic Pathways (SSPs) in predicting rainfall and temperature spatial patterns over Eastern Africa?

2. Materials and Methods

2.1. Study Area

This study focuses on the Eastern Africa region that comprises eleven countries, including Ethiopia, Eritrea, Djibouti, Sudan, South Sudan, Somalia, Uganda, Burundi, Rwanda, Kenya, and Tanzania (Figure 1). The study region is one of the most vulnerable to the impacts of climate change and variability, especially droughts and floods [11]. The rainfall patterns in the region are mainly influenced by the movement of the Inter-Tropical Convergence Zone (ITCZ), with the southern parts experiencing a bimodal rainfall regime, while the northern parts are dominated by a uni-modal rainfall regime [10]. Teleconnections such as the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) also modulate rainfall patterns in the region, especially in the September to December (SOND) season. The positive modes of ENSO and IOD tend to enhance rainfall in the Eastern Africa region [12]. For example, warm El Niño Southern Oscillation (ENSO) events tend to lead to enhanced rainfall in the second rainfall season of September to November, when the rains extend to even December and sometimes January (the usually known dry months) [10]. On the other hand, cool El Niño Southern Oscillation (ENSO) events tend to lead to suppressed rainfall, especially in the long rainfall season of March, April, and May (MAM) [12]. The Indian Ocean dipole has also been identified as one of the causes of climate variability in the Eastern African region, where the positive phase of the dipole is mainly associated with enhanced rainfall.

2.2. Data Sources

This study considered three different datasets, that is, historical observations, historical model data, and future climate projections. Historical observations of rainfall and temperature were accessed from the Climate Hazards Centre Infrared Temperature (CHIRTS) and Climate Hazards Group Infrared Precipitation (CHIRPS) for the period 1981 to 2010 for temperature and rainfall, respectively, to represent the historical observed climate. These data had a spatial resolution of 0.05° × 0.05° and were downloaded monthly from https://data.chc.ucsb.edu/products/CHIRPS-2.0/ (accessed on 14 May 2025) and https://www.chc.ucsb.edu/data/chirtsmonthly (accessed on 14 May 2025). The CHIRPS and CHIRTS datasets incorporate ground-based observations, as extensively described in [13,14]. The CHIRPS dataset has been validated over the Eastern Africa region, e.g., in [14]. In addition, many studies, such as [15,16,17], have used CHIRPS to understand historical climate patterns in the region. The historical observations were extracted in the form of monthly rainfall and temperature values, from which annual averages of temperature and annual totals of rainfall were computed.
We examined the historical model data, including simulations of monthly precipitation and temperature for the same period 1981–2010. These were obtained from the website for the Coupled Model Intercomparison Project Phase 6 (CMIP6) for six GeoMIP GCMs (https://esgf-node.llnl.gov/search/cmip6/ (accessed on 14 May 2025)), as indicated in Table 1. Future climate projections for the GCM runs were retrieved from the GeoMIP website (https://aims2.llnl.gov/search (accessed on 14 May 2025)). GeoMIP6 consists of SRM simulations that are consistent with CMIP6 and the experiments are designed for analyzing the effects of solar irradiance reduction, an increase in the loading of stratospheric sulfate aerosols, and marine cloud (or sky) brightening [7]. From the GeoMIP simulations, two scenarios of G6solar and G6sulfur were used in this study. The G6solar scenario simulates an idealized SRM approach by uniformly reducing the solar constant over the 21st century. It reduces radiative forcing from a high-tier emission scenario of SSP585 to a medium-tier emission scenario of SSP245, with uniform reduction in the solar constant [7]. On the other hand, the G6sulfur scenario simulates the injection of sulfur dioxide (SO2) into the lower stratosphere, forming sulfate aerosols that reflect sunlight. It reduces radiative forcing from SSP585 to SSP245 levels through stratospheric aerosol injection. Sulfur dioxide particles are injected into the tropical lower stratosphere between longitude bands 10° S and 10° N [7].
The two SRM scenarios of G6solar and G6sulphur were then compared with the conventional greenhouse gas scenarios of SSP245 (average) and SSP585 (high emission) for a future period of 2051–2080 to understand the regional impacts on rainfall and temperature.

2.3. Data Analysis

The GCM datasets were re-gridded from their original resolutions indicated in Table 1 to the CHIRPS resolution of 0.05° using the bilinear interpolation method to enable the assessment of variations between the datasets. Comparisons were conducted at two levels: (1) six GeoMIP models with the historical climate and (2) SRM scenarios (G6solar and G6sulfur) with the conventional Shared Socioeconomic Pathways (SSPs) of SSP245 and SSP585. In terms of climate variables, we focused on both precipitation and temperature.
We used climatological means to assess how well the GeoMIP GCM simulations captured the spatial patterns of annual means for both rainfall and temperature based on CHIRPS. Future simulations of the same GCMs were re-gridded to 0.05° × 0.05° (the same as CHIRPS) for all the scenarios and compared with SSP585 and SSP245 in the 2051–2080 climate period.

3. Results

3.1. Observed Historical Rainfall and Temperature Patterns

Observed average annual mean temperature and mean annual total rainfall are represented in Figure 2. Long-term average total annual rainfall ranges from about 700 mm to 1900 mm, with the lowest rainfall observed in the northern parts (Sudan) and eastern parts (Somalia), while the highest rainfall is mainly in the highland areas of Ethiopia, Kenya, and Uganda. The historical mean annual temperature ranges between 21 °C and 31 °C, with cooler temperatures observed in the highland areas and the highest temperatures observed in the northern parts of the region. The signal shown in Figure 2 is similar to that shown in previous publications, such as [15]. This offers confidence to compare the GeoMIP simulations with the CHIRPS dataset.

3.2. Evaluation of GEOMIP GCMs Against Historical Precipitation

Figure 3 shows the mean total annual rainfall of six GeoMIP GCMs in the historical period 1981 to 2010. All the models capture spatial patterns similar to those shown in CHIRPS (Figure 2), with high rainfall amounts over the Eastern African highlands and the Ethiopian highlands and low rainfall values observed over Sudan. Generally, a mean annual rainfall of between 700 mm and 1700 mm is observed across the GCMs. The UKESM1-0-LL model produces the highest rainfall amounts in the region, with the western parts of the region having slightly more rainfall than the eastern parts of the region. On the other hand, the MPI-ESM1-2-HR simulates the lowest amount of rainfall across the region compared with other models. In comparison with CHIRPS rainfall data over the same period, all models show a less-than-average long-term rainfall compared with CHIRPS (dry bias), as shown in Figure 4. The dry bias is lower in low lying areas such as Sudan and Somalia across all the models. In general, a dry bias of the GeoMIP GCMs is observed in the entire region, with strong signals observed in highland areas, especially in Ethiopia. This might be due to the failure of GCMs to capture the internal local effects of such highlands [18,19].

3.3. Evaluation of GeoMIP GCMs Using Historical Temperature

Figure 5 shows the mean annual temperature of the six GeoMIP GCMs in the historical period 1981 to 2010. Temperatures of between 17 °C and 33 °C can be seen in the region, with highland areas having low values compared with other areas. In comparison with observed spatial patterns, all the models, apart from IPSL-CM6A-LR, which shows lower temperatures than the historical period in the entire region, show an underestimation in the highland areas, while other parts compare well to the historical period, with anomalies ranging between −1 and 1 (Figure 6). Some models, such as IPSL-CM6A-LR, UKESM1-0-LL, and CESM2, underestimate the temperature patterns in the northern parts of the region, especially in Sudan in the northern part of the region. On the other hand, the CNRM_ESM2-1 model overestimates the spatial temperature patterns, especially in southern Sudan and northern Uganda.
From the evaluation of the GeoMIP GCM models against the observations, the models are seen to capture the general expected temperature and rainfall spatial patterns, albeit a dry bias in rainfall and some underestimation in the temperature, especially in the highland areas. There is, therefore, some confidence that the GeoMIP models examined here will also fairly represent future climate spatial patterns in the region.

3.4. Projected Future Rainfall Patterns from SRM Models

Figure 7 and Figure 8 show projected future mean total rainfall for 2051–2080 under two SRM scenarios, G6solar and G6sulfur, respectively. The SRM scenarios are run in the six GeoMIP GCMs and similar spatial patterns like historical data are observed in future projections, with the highest rainfall observed in UKESM1-0-LL and low rainfall observed in MPI-ESM1-2-HR. No major differences are observed in the two SRM scenarios of G6solar and G6sulfur in these projections. Projected mean total annual rainfall ranges from 1000 mm to 2400 mm across the region.

3.5. Comparison of SRM Scenario of G6Sulfur and the Conventional SSP Annual Total Precipitation

We examine the SRM scenarios of G6solar and G6sulfur for the six GeoMIP models and compare them with SSP245 and SSP585, the conventional greenhouse gas scenarios for the same GCMs, over the period 2051 to 2080.

3.5.1. Comparison of SRM Scenario of G6Solar and the Conventional SSP Total Annual Precipitation

Figure 9 and Figure 10 show the precipitation anomalies for the SRM G6solar scenario evaluated against the conventional GHG scenarios of SSP245 and SSP 585, respectively. Analysis of the projections show that three GCMs—CNRM, MPI_LR, and MPI_HR—project that G6solar will lead to increased rainfall compared with SSP245 in most parts of the region. Significant increases in annual precipitation are expected in the northern parts of the region, especially under MPI_LR and MPI_HR GCM. On the other hand, IPSL_CM61_LR and CESM2 models show that SRM under G6solar will lead to a decreased rainfall in most parts of the region compared with SSP245 in the period 2051–2080. Generally, on average, an annual rainfall increase of 4 mm to 16 mm is expected for the G6solar scenario over SSP245 in most parts of the region.
Under SSP585 and SRM G6solar, three models—IPSL_CM61_LR, UKESM1-0-LL, and CESM2—predict less rainfall in the region compared with the SSP585 scenario in the period 2051–2080 (Figure 10). Precipitation decreases of 10 mm to 25 mm are expected in the region from most of the models. Only MPI_LR and MPI_HR GCM show an increase in annual precipitation, especially in the northern parts of the region, compared with SSP585.
Therefore, the G6solar SRM scenario predicts increased annual precipitation in the region compared with SSP245, but predicts reduced annual precipitation in the region compared with SSP585 in the same period 2051–2080.

3.5.2. Comparison of SRM Scenario of G6Sulfur and the Conventional SSP Total Annual Precipitation

Figure 11 and Figure 12 show the precipitation anomalies for the SRM G6sulfur scenario compared with SSP245 and SSP 585, respectively. All models predict reduced annual precipitation in the southern parts of the region and increased rainfall in the northern parts of the region for the G6sulfur scenario compared with SSP245 in the period 2051–2080 (Figure 11). The northern parts are expected to have annual rainfall increases of 3 mm to 9 mm, while the southern parts are expected to have reduced rainfall of the same amount (3 mm to 9 mm) in the same period compared with the conventional SSP245.
For SSP585 and the G6sulfur scenario, all models, apart from CNRM, predict reduced annual precipitation in the region of 12 mm to 24 mm in most parts of the region compared with SSP585 in the period 2051–2080. The CNRM model under the G6sulfur scenario predicts increased rainfall in the region compared with SSP245, with values ranging between 3 mm and 9 mm
Therefore, the G6sulfur SRM scenario predicts reduced annual precipitation compared with both SSP245 and SSP585, especially in the southern parts of the region, with more reduced precipitation over SSP585 compared with SSP245 in the same period.

3.6. Comparison of SRM Scenario of G6Sulfur and the Conventional SSP Annual Mean Temperature

Further, we analyze the SRM scenarios of G6solar and G6sulfur for the six GeoMIP models and compare them with SSP245 and SSP585 (the conventional greenhouse gas scenarios for the same GCMs) during the period 2051 to 2080.

3.6.1. Comparison of SRM Scenario of G6Solar and the Conventional SSP Annual Mean Temperature

Figure 13 and Figure 14 show the temperature anomalies for the SRM G6solar scenario compared with the conventional GHG scenarios of SSP245 and SSP585, respectively. Under G6solar, all the GeoMIP models examined here show that SRM will reduce the mean annual temperature in the region (Figure 13). UKESM1-0-LL and the CNRM_ESM2-1 project that SRM will reduce the mean annual temperature by 0 °C to 1 °C, while the rest of the models project that G6solar will reduce the mean annual temperature in the region by 1 °C to 1.75 °C compared with SSP245.
For SSP585 and SRM G6solar, all models show that SRM will reduce the mean annual temperature in the region compared with SSP585 (Figure 14). Four of the six models show that SRM under G6solar will reduce the annual mean temperature in the highland areas by 1 °C to 1.2 °C, and the rest of the region by 0.75 °C to 1.05 °C compared with the projected SSP585.
Therefore, the G6solar SRM scenario predicts a reduced annual mean temperature compared with both SSP245 and SSP585 in most parts of the region, with a slightly more reduced temperature than SSP585 compared with SSP245 in the same period.

3.6.2. Comparison of SRM Scenario of G6Sulfur and the Conventional SSP Annual Mean Temperature

Figure 15 and Figure 16 show the temperature anomalies for the SRM G6sulfur scenario compared with the conventional GHG scenarios of SSP245 and SSP585, respectively. Under G6sulfur, IPSL-CM6A-LR and CNRM_ESM2-1 models show that SRM will increase the temperature in the region by up to 1.6 °C. Two models—MPI-ESM1-2-HR and MPI-ESM1-2-LR—show that SRM will only reduce the temperature in highland areas by 0 °C to 0.4 °C, with the rest of the region expected to have an increase in temperature of up to 0.8 °C. It is only UKESM1-0-LL that shows that SRM under G6sulfur will lead to reduced annual mean temperatures of 0 °C to 0.8 °C throughout the region compared with SSP245.
For SSP585 and SRM G6sulfur, three models, CNRM_ESM2-1, IPSL-CM6A-LR, and CESM2, show that SRM will increase mean annual temperatures for the southern parts of the region by 0.5 °C–1.5 °C, but, in the northern parts, temperatures are expected to reduce by 0 °C to 1 °C compared with the projected SSP585 temperatures. MPI-ESM1-2-HR and MPI-ESM1-2-LR models show that SRM will reduce mean annual temperatures in the region by 0.5 °C to 1.5 °C, apart from southern Sudan, where SRM is expected to increase mean annual temperatures by 0.5 °C to 1 °C compared with SSP585.
Therefore, unlike the G6solar scenario that predicts reduced mean annual temperatures in the region compared with the SSP scenarios, G6sulfur is showing some inconsistent results, with some models such as IPSL-CM6A_LR and CNRM predicting increased temperatures in the region under SRM compared with, especially, SSP245.

4. Discussion

We explore the potential impact of Solar Radiation Modification (SRM) on the rainfall and temperature characteristics over Eastern Africa. We examine model experiments from the Geoengineering Model Intercomparison Project (GeoMIP) under two SRM scenarios of G6Sulfur and G6Solar and two GHG emission scenarios SSP245 and SSP585 in a climate period 2051 to 2080. The SRM technique deployed in the simulations analyzed in this study is the reduction in the solar constant for G6Solar simulations and the injection of stratospheric aerosols for G6Sulfur simulations. The SRM scenario of G6solar shows a cooling effect in the region compared with the conventional Shared Socioeconomic Pathways (SSPs) of SSP245 and SSP585 in the future period. On the other hand, G6sulfur does not show a clear pattern in the entire region in both scenarios, with half of the models showing a cooling, while the other three show a warming compared with SSP245 in the 2051–2080 period. SRM-induced cooling may lead to a reduction in the radiative forcing of the earth’s surface, which may lead to reduced evaporation at the surface and this will affect atmospheric circulation patterns in the region [20]. SRM is expected to cool the climate since it reduces the amount of solar radiation received on the earth by reflecting it back to space. This assumption, however, is for global simulation and, therefore, there could be regional differences in its impact on different parts of the world.
Research on SRM in different parts of the globe has shown that moderate SRM use can significantly reduce many of the impacts of climate change, including average and extreme temperatures, water availability, and cyclone intensity, but may not perfectly reverse these impacts [21,22]. Even with SRM differences in regional precipitation patterns, cloud cover and atmospheric circulation could persist, with some regions experiencing increased rainfall while others will experience reduced rainfall as a result of SRM. Projections from some of the studies in Africa show less certainty in the expected rainfall changes across the continent, with possible drying in some areas in Sub-Sahara Africa (SSA) and increases in rainfall in other areas of tropical Africa [23].
In Eastern Africa, SRM’s influence on temperature and precipitation is particularly significant due to the region’s reliance on rainfall for agriculture and water resources, which form the backbone of its economies and also provide a livelihood to many of its people [24]. Modeling studies on SRM suggest that, while global temperatures may decrease, regional responses can vary because of the already varying climatic conditions [25]. For example, many areas in Africa have already experienced warming at a rate higher than the global average and future projections show a high certainty of expected temperature in the region [26]. In Eastern Africa, SRM could potentially lead to changes in the hydrological cycle, affecting the onset, duration, and intensity of the rainy seasons. These alterations could have significant implications for agriculture, water resources, and ecosystem services. However, the exact nature of these changes remains uncertain due to the complex interplay of atmospheric dynamics, regional climate systems, and local geographical features [27]. In this study, both the G6solar and G6sulfur SRM scenarios show that temperatures are likely to decrease in most parts of the region compared with the conventional greenhouse gas scenarios of SSP245 and SSP585 in the period 2051–2080.

5. Conclusions

Solar radiation modification has the potential to influence regional temperature and precipitation patterns, including those in East Africa. Historical climate events and modeling studies indicate that changes in solar radiation can lead to significant climatic shifts. This study investigated the impact of two SRM scenarios, G6solar and G6sulfur, from the GEOMIP simulations over the conventional greenhouse gas scenarios of SSP245 and SSP585 in the Eastern Africa region. Results show that no clear trends are observed in SRM rainfall projections over SSPs, with some places expecting an increase while others expect a decrease in rainfall in the 2051–2080 period. For temperature, the G6solar SRM scenario predicts a reduced annual mean temperature compared with both SSP245 and SSP585 in most parts of the region, with a slightly more reduced temperature than SSP585 compared with SSP24. On the other hand, G6sulfur shows some contradicting results, with some models, such as IPSL-CM6A_LR, predicting increased temperatures under SRM compared with SSP245. Therefore, as a whole, SRM is expected to reduce regional temperatures in most parts compared with SSPs, but the trend of rainfall is not very clear, with some areas expected to increase rainfall while others are expected to decrease rainfall compared with SSPs.

Author Contributions

A.N., conceptualization, data analysis, write-up; G.A., conceptualization, literature review, project administration; R.I.O., data analysis and write-up; C.M., conceptualization, literature review, editing; L.A., conceptualization, review, editing; M.O., conceptualization, literature review, editing; B.A.O., conceptualization, funding acquisition, supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Degrees Modelling Fund (DMF) to the East African Research team under the grant DMF23UGA.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original historical meteorological data used are openly available from https://www.chc.ucsb.edu/data/chirps (accessed on 15 May 2024) and GeoMIP GCM data are available from https://esgf-node.llnl.gov/search/cmip6 (accessed on 6 June 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

ARISEAssessing Responses and Impacts of Solar climate intervention on the Earth system
CHIRPSClimate Hazards Group Infrared Precipitation
CMIP6Coupled Model Intercomparison Project Phase 6
CCNCloud Condensation Nuclei
DMFDegrees Modeling Fund
ENSOEl Niño-Southern Oscillation
GCMGeneral Circulation Model
GeoMIPGeoengineering Model Intercomparison Project
GHGGreen House Gas
GLENSGeoengineering Large Ensemble
IPCCIntergovernmental Panel on Climate Change
IODIndian Ocean Dipole
ITCZInter-Tropical Convergence Zone
LTMLong Term Mean
MCBMarine Cloud Brightening
NDCNationally Determined Contribution
SAIStratospheric Aerosol Injection
SONDSeptember, October, November, and December
SRMSolar Radiation Modification
SSPShared Socioeconomic Pathway
UNFCCCUnited Nations Framework Convention on Climate Change

References

  1. IPCC. Summary for Policymakers. In Climate Change 2021. The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., et al., Eds.; Cambridge University Press: Cambridge, UK, 2021. [Google Scholar]
  2. UNFCCC. Paris Agreement. In COP 21; United Nations Framework Convention on Climate Change (UNFCCC): Le Bourget, France, 2015. [Google Scholar]
  3. Rogelj, J.; den Elzen, M.; Höhne, N.; Fransen, T.; Fekete, H.; Winkler, H.; Schaeffer, R.; Sha, F.; Riahi, K.; Meinshausen, M. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 2016, 534, 631–639. [Google Scholar] [CrossRef] [PubMed]
  4. Lawrence, M.G.; Schäfer, S.; Muri, H.; Scott, V.; Oschlies, A.; Vaughan, N.E.; Boucher, O.; Schmidt, H.; Haywood, J.; Scheffran, J. Evaluating climate geoengineering proposals in the context of the Paris Agreement temperature goals. Nat. Commun. 2018, 9, 3734. [Google Scholar] [CrossRef] [PubMed]
  5. IPCC. Annex VII: Glossary. In Climate Change 2021. The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Matthews, J.B.R., Fuglestvedt, J.S., Masson-Delmotte, V., Möller, V., Méndez, C., van Diemen, R., Reisinger, A., Semenov, S., Eds.; Cambridge University Press: Cambridge, UK, 2021. [Google Scholar]
  6. Ahlm, L.; Jones, A.; Stjern, C.W.; Muri, H.; Kravitz, B.; Kristjánsson, J.E. Marine cloud brightening—As effective without clouds. Atmos. Chem. Phys. 2017, 17, 13071–13087. [Google Scholar] [CrossRef]
  7. Kravitz, B.; Robock, A.; Tilmes, S.; Boucher, O.; English, J.M.; Irvine, P.J.; Jones, A.; Lawrence, M.G.; MacCracken, M.; Muri, H.; et al. The Geoengineering Model Intercomparison Project Phase 6 (GeoMIP6): Simulation design and preliminary results. Geosci. Model Dev. 2015, 8, 3379–3392. [Google Scholar] [CrossRef]
  8. Tilmes, S.; MacMartin, D.G.; Lenaerts, J.T.M.; van Kampenhout, L.; Muntjewerf, L.; Xia, L.; Harrison, C.S.; Krumhardt, K.M.; Mills, M.J.; Kravitz, B.; et al. Reaching 1.5 and 2.0 °C global surface temperature targets using stratospheric aerosol geoengineering. Earth Syst. Dynam. 2020, 11, 579–601. [Google Scholar] [CrossRef]
  9. MacMartin, D.G.; Visioni, D.; Kravitz, B.; Richter, J.H.; Felgenhauer, T.; Lee, W.; Morrow, D.; Sugiyama, M. Scenarios for modeling solar geoengineering. Proc. Natl. Acad. Sci. USA 2022, 119, e2202230119. [Google Scholar] [CrossRef]
  10. Nicholson, S.E. An analysis of recent rainfall conditions in eastern Africa. Int. J. Climatol. 2016, 36, 526–532. [Google Scholar] [CrossRef]
  11. Li, C.J.; Chai, Y.Q.; Yang, L.S.; Li, H.R. Spatio-temporal distribution of flood disasters and analysis of influencing factors in Africa. Nat. Hazards 2016, 82, 721–731. [Google Scholar] [CrossRef]
  12. Nicholson, S.E. Long-term variability of the Eastern Africa “short rains” and its links to large-scale factors. Int. J. Climatol. 2015, 35, 3979–3990. [Google Scholar] [CrossRef]
  13. Funk, C.; Peterson, P.; Landsfeld, M.; Pedreros, D.; Verdin, J.; Shukla, S.; Husak, G.; Rowland, J.; Harrison, L.; Hoell, A.; et al. The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci. Data 2015, 2, 150066. [Google Scholar] [CrossRef]
  14. Dinku, T.; Funk, C.; Peterson, P.; Maidment, R.; Tadesse, T.; Gadain, H.; Ceccato, P. Validation of the CHIRPS satellite rainfall estimates over Eastern Africa. Adv. Remote Sens. Rainfall Snowfall 2018, 144 (Suppl. S1), 292–312. [Google Scholar] [CrossRef]
  15. Gebrechorkos, S.H.; Hülsmann, S.; Bernhofer, C. Evaluation of multiple climate data sources for managing environmental resources in East Africa. Hydrol. Earth Syst. Sci. 2018, 22, 4547–4564. [Google Scholar] [CrossRef]
  16. Ayugi, B.; Tan, G.; Ullah, W.; Boiyo, R.; Ongoma, V. Inter-comparison of remotely sensed precipitation datasets over Kenya during 1998–2016. Atmos. Res. 2019, 225, 96–109. [Google Scholar] [CrossRef]
  17. Ocen, E.; de Bie, C.A.J.M.; Onyutha, C. Investigating false start of the main growing season: A case of Uganda in East Africa. Heliyon 2021, 7, e08428. [Google Scholar] [CrossRef]
  18. Mearns, L.O.; Giorgi, F.; McDaniel, L.; Shields, C. Climate Scenarios for the Southeast U.S. Based on GCM and Regional Model Simulations. J. Clim. 2003, 16, 874–885. [Google Scholar]
  19. Ongoma, V.; Chen, H.; Omony, G.W.; Nzioki, R. Variability of extreme weather events over the equatorial East Africa, a case study of rainfall in Kenya and Uganda. Theor. Appl. Climatol. 2018, 131, 295–308. [Google Scholar] [CrossRef]
  20. Trenberth, K.E.; Dai, A. Effects of Mount Pinatubo volcanic eruption on the hydrological cycle as an analog of geoengineering. Geophys. Res. Lett. 2007, 34, L15702. [Google Scholar] [CrossRef]
  21. Curry, C.L.; Sillmann, J.; Bronaugh, D.; Alterskjaer, K.; Cole, J.N.S.; Ji, D.; Kravitz, B.; Kristjánsson, J.E.; Moore, J.C.; Muri, K.; et al. A multimodel examination of climate extremes in an idealized geoengineering experiment. J. Geophys. Res. Atmos. 2014, 119, 3900–3923. [Google Scholar] [CrossRef]
  22. Muthyala, R.; Bala, G.; Nalam, A. Regional scale analysis of climate extremes in an SRM geoengineering simulation, Part 1: Precipitation extremes. Curr. Sci. 2018, 114, 1024. [Google Scholar] [CrossRef]
  23. Dosio, A.; Jones, R.G.; Jack, C.; Lennard, C.; Nikulin, G.; Hewitson, B. What can we know about future precipitation in Africa? Robustness, significance and added value of projections from a large ensemble of regional climate models. Clim. Dyn. 2019, 53, 5833–5858. [Google Scholar] [CrossRef]
  24. Ongoma, V.; Chen, H.; Gao, C. Projected changes in mean rainfall and temperature over East Africa based on CMIP5 models. Int. J. Climatol. 2017, 37, 1375–1392. [Google Scholar] [CrossRef]
  25. Niang, I.; Ruppel, O.; Abdrabo, M.; Essel, A.; Leonard, C.; Padgham, J.; Urquhart, P.; Adelekan, I.O.; Archibald, S.; Balinga, M.; et al. Chapter 22: Africa. In Climate Change 2014 Impacts: Impacts, Adaptation, and Vulnerability; Cambridge University Press: Cambridge, UK, 2014; pp. 1–115. [Google Scholar]
  26. Engelbrecht, F.; Adegoke, J.; Bopape, M.-J.; Naidoo, M.; Garland, R.; Thatcher, M.; McGregor, J.; Katzfey, J.; Werner, M.; Ichoku, C. Projections of rapidly rising surface temperatures over Africa under low mitigation. Environ. Res. Lett. 2015, 10, 85004. [Google Scholar] [CrossRef]
  27. Pinto, I.; Jack, C.; Lennard, C.; Tilmes, S.; Odoulami, R.C. Africa’s Climate Response to Solar Radiation Management with Stratospheric Aerosol. Geophys. Res. Lett. 2020, 47, e2019GL086047. [Google Scholar] [CrossRef]
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Figure 1. Study area and elevation map of Eastern Africa. (a) Location of the study area within the African continent. (b) Detailed map of the Eastern Africa region showing the countries included in the study: Sudan, South Sudan, Eritrea, Djibouti, Ethiopia, Somalia, Uganda, Kenya, Rwanda, Burundi, and Tanzania. Elevation is depicted using a color gradient ranging from low (−151 m) to high (5634 m) altitudes.
Figure 1. Study area and elevation map of Eastern Africa. (a) Location of the study area within the African continent. (b) Detailed map of the Eastern Africa region showing the countries included in the study: Sudan, South Sudan, Eritrea, Djibouti, Ethiopia, Somalia, Uganda, Kenya, Rwanda, Burundi, and Tanzania. Elevation is depicted using a color gradient ranging from low (−151 m) to high (5634 m) altitudes.
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Figure 2. Long-Term Mean (LTM) annual mean temperature (left), and mean total annual rainfall (right) in Eastern Africa for the period 1981 to 2010 based on CHIRPS.
Figure 2. Long-Term Mean (LTM) annual mean temperature (left), and mean total annual rainfall (right) in Eastern Africa for the period 1981 to 2010 based on CHIRPS.
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Figure 3. Historical representation of mean annual rainfall in GeoMIP GCMs for 1981–2010.
Figure 3. Historical representation of mean annual rainfall in GeoMIP GCMs for 1981–2010.
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Figure 4. Historical rainfall anomalies for the GeoMIP GCMs from CHIRPS during the (1981–2010) period.
Figure 4. Historical rainfall anomalies for the GeoMIP GCMs from CHIRPS during the (1981–2010) period.
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Figure 5. Historical representation of mean annual temperature in GeoMIP GCMs for 1981–2010.
Figure 5. Historical representation of mean annual temperature in GeoMIP GCMs for 1981–2010.
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Figure 6. Historical temperature anomalies for the GeoMIP GCMs from CHIRPS in the (1981–2010) period.
Figure 6. Historical temperature anomalies for the GeoMIP GCMs from CHIRPS in the (1981–2010) period.
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Figure 7. Projected average precipitation for GeoMIP models under G6Solar for 2051–2080.
Figure 7. Projected average precipitation for GeoMIP models under G6Solar for 2051–2080.
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Figure 8. Projected average precipitation based on GeoMIP models under G6Sulfur for 2051–2080.
Figure 8. Projected average precipitation based on GeoMIP models under G6Sulfur for 2051–2080.
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Figure 9. Projected precipitation anomalies for SRM G6solar scenario compared with SSP245 GHG scenario for 2051–2080.
Figure 9. Projected precipitation anomalies for SRM G6solar scenario compared with SSP245 GHG scenario for 2051–2080.
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Figure 10. Projected precipitation anomalies for SRM G6solar scenario compared with SSP585 GHG scenario for 2051–2080.
Figure 10. Projected precipitation anomalies for SRM G6solar scenario compared with SSP585 GHG scenario for 2051–2080.
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Figure 11. Projected precipitation anomalies for SRM G6sulfur scenario compared with SSP245 GHG scenario for 2051–2080.
Figure 11. Projected precipitation anomalies for SRM G6sulfur scenario compared with SSP245 GHG scenario for 2051–2080.
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Figure 12. Projected precipitation anomalies for SRM G6sulfur scenario compared with SSP585 GHG scenario for 2051–2080.
Figure 12. Projected precipitation anomalies for SRM G6sulfur scenario compared with SSP585 GHG scenario for 2051–2080.
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Figure 13. G6Solar temperature anomalies in relation to SSP245 for 2051–2080 period.
Figure 13. G6Solar temperature anomalies in relation to SSP245 for 2051–2080 period.
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Figure 14. G6Solar temperature anomalies in relation to SSP585 for 2051–2080.
Figure 14. G6Solar temperature anomalies in relation to SSP585 for 2051–2080.
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Figure 15. G6Sulfur temperature anomalies in relation to SSP245 for 2051–2080.
Figure 15. G6Sulfur temperature anomalies in relation to SSP245 for 2051–2080.
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Figure 16. G6Sulfur temperature anomalies in relation to SSP585 for 2051–2080.
Figure 16. G6Sulfur temperature anomalies in relation to SSP585 for 2051–2080.
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Table 1. GeoMIP GCM models.
Table 1. GeoMIP GCM models.
NoModel NameInstitution/Modeling CentreResolution
1CESM2National Center for Atmospheric Research (NCAR), USA0.5° × 0.5°
2CNRM_ESM2-1Centre de Recherches Meteorologiques (CNRM) in collaboration with the Centre Euopeen de Recherche et de Formation Avancee en Calcul Scientifique (CERFACS), France0.5° × 0.5°
3IPSL-CM6A-LRInstitut Pierre Simon Laplace, Paris, France1.9° × 1.3°
4MPI-ESM1-2-HRMax Planck Institute for Meteorology, Hamburg, Germany2.5° × 1.3°
5MPI-ESM1-2-LRMax Planck Institute for Meteorology, Hamburg, Germany0.9° × 0.9°
6UKESM1-0-LLMet Office Hadley Centre (MOHC), UK1.9° × 1.3°
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Nimusiima, A.; Ayesiga, G.; Odongo, R.I.; Mulinde, C.; Aribo, L.; Ojara, M.; Ogwang, B.A. Implications of Solar Radiation Modification on Rainfall and Temperature Patterns over Eastern Africa. Atmosphere 2025, 16, 646. https://doi.org/10.3390/atmos16060646

AMA Style

Nimusiima A, Ayesiga G, Odongo RI, Mulinde C, Aribo L, Ojara M, Ogwang BA. Implications of Solar Radiation Modification on Rainfall and Temperature Patterns over Eastern Africa. Atmosphere. 2025; 16(6):646. https://doi.org/10.3390/atmos16060646

Chicago/Turabian Style

Nimusiima, Alex, Godwin Ayesiga, Ronald Ingula Odongo, Catherine Mulinde, Lawrence Aribo, Moses Ojara, and Bob Alex Ogwang. 2025. "Implications of Solar Radiation Modification on Rainfall and Temperature Patterns over Eastern Africa" Atmosphere 16, no. 6: 646. https://doi.org/10.3390/atmos16060646

APA Style

Nimusiima, A., Ayesiga, G., Odongo, R. I., Mulinde, C., Aribo, L., Ojara, M., & Ogwang, B. A. (2025). Implications of Solar Radiation Modification on Rainfall and Temperature Patterns over Eastern Africa. Atmosphere, 16(6), 646. https://doi.org/10.3390/atmos16060646

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