# Leveraging AI for Climate Resilience in Africa: Challenges, Opportunities,
and the Need for Collaboration

Rendani Mbuvha $\psi$111Queen Mary University of London corresponding email:
<EMAIL_ADDRESS>Yassine Yaakoubi$\psi$222McGill University John Bagiliko
$\psi$333University of Rwanda and African Institute for Mathematical Sciences
Santiago Hincapie Potes$\psi$ 444Google Research Amal Nammouchi
$\psi$555Karlstad University Sabrina Amrouche $\psi$666ZYTLYN
$\psi$ AfriClimate AI

###### Abstract

As climate change issues become more pressing, their impact in Africa calls
for urgent, innovative solutions tailored to the continent’s unique
challenges. While Artificial Intelligence (AI) emerges as a critical and
valuable tool for climate change adaptation and mitigation, its effectiveness
and potential are contingent upon overcoming significant challenges such as
data scarcity, infrastructure gaps, and limited local AI development. This
position paper explores the role of AI in climate change adaptation and
mitigation in Africa. It advocates for a collaborative approach to build
capacity, develop open-source data repositories, and create context-aware,
robust AI-driven climate solutions that are culturally and contextually
relevant.

## AI for Climate Resilience in Africa

The latest report from the Intergovernmental Panel on Climate Change (IPCC)
sixth assessment unequivocally declares that human-induced climate change
manifests in many extreme weather and climate phenomena globally IPCC, (2022).
Particularly troubling is the disproportionate impact on vulnerable
communities, many of which have contributed minimally to the problem. Regions
such as Central, East, and West Africa have emerged as hotspots of
vulnerability, where climate-related mortality rates in high vulnerability
areas can be as much as 15 times higher than in less vulnerable areas (IPCC,,
2022). Moreover, Africa has experienced an accelerated rate of warming, with
temperatures rising by +0.3 °C per decade from 1991 to 2022, compared to +0.2
°C per decade from 1961 to 1990 (Ranasinghe et al.,, 2021). Most African
countries are projected to encounter unprecedented high temperatures earlier
this century than wealthier, higher-latitude nations. This warming trend has
coincided with below-average rainfall across much of the continent,
exacerbating drought conditions, particularly in the Horn of Africa, including
Ethiopia, Kenya, and Somalia (World Meteorological Organization,, 2022).
Meanwhile, other parts of Africa, such as the Sahel and southern regions, have
faced the opposite extreme, with intense rainfall events leading to
catastrophic flooding. Africa’s livelihoods and national economies are closely
intertwined with climate conditions, with agriculture alone supporting 55%

Artificial Intelligence (AI) is rapidly gaining traction as an essential tool
for adaptation and mitigation to climate change. AI applications for climate
action are diverse, encompassing climate modelling and forecasting,
sustainable energy, transportation, and infrastructure development (Rolnick et
al.,, 2022). These applications clearly demonstrate AI’s ability to enhance
climate resilience by improving predictive capabilities and enabling adaptive
responses to climate change. However, the effectiveness of AI is highly
contingent upon the availability of robust datasets that capture the
complexity of local environmental conditions. Additionally, familiarity with
both the downstream application domain and specific regional contexts plays a
vital role in the modelling stage.

For example, understanding and modelling bimodal rainfall patterns in East
Africa is critical for agricultural planning and water resource management.
These patterns have significant implications for the livelihoods of millions
of people who depend on rain-fed agriculture and pastoralism (James et al.,,
2018). Similarly, the African Easterly Jet plays a significant role in shaping
the climate of West Africa, particularly affecting the monsoon system and
consequently agricultural production and food security in the region
(Nicholson,, 2009). Despite significant advances in climate modelling in
recent decades, none of the current suites of General Circulation Models
(GCMs) is built by centres on the African continent; thus, representation of
the above critical processes is often not a priority (James et al.,, 2018).

Consequently, the lack of comprehensive and quality-controlled weather station
data in Africa limits the ability to evaluate and improve climate models and
tailor them to the unique environmental conditions found across the continent
(James et al.,, 2018). While covering one-fifth of the world’s land area,
Africa has the least developed land-based weather station network of any
continent, with a density of weather stations that is only one-eighth of the
World Meteorological Organization (WMO) requirement (World Meteorological
Organization,, 2019). This network has also been declining in terms of quality
and number of stations over time (World Meteorological Organization,, 2019;
Dinku,, 2019).

This lack of data is compounded by the fact that most climate research funding
and AI development have historically been concentrated outside of Africa,
using non-African datasets that may not be fully applicable or transferable to
Africa’s environmental and socio-economic conditions (Niang et al.,, 2014;
Dinku et al.,, 2018). More specifically, IPCC (Barros et al.,, 2014) shows
that only 3.8% of global funding for climate research was directed towards
Africa, despite the continent’s high vulnerability to the impacts of climate
change. Moreover, most of this funding originated outside Africa and was
allocated to non-African research institutions, which underscores data
availability and research funding as critical limitations.

As highlighted by Kull et al., (2021), the performance of Numerical Weather
Prediction models, which form the basis of modern forecasting, heavily relies
on the availability of comprehensive and quality-controlled meteorological
observations. The sparsity of weather stations across the continent leads to
significant gaps in understanding atmospheric conditions. This, in turn,
translates to lower forecast accuracy, particularly for short-range
predictions crucial for disaster preparedness and daily decision-making in
weather-sensitive sectors like agriculture, energy, and transportation.
Consequently, African nations face increased vulnerability to extreme weather
events and struggle to optimise resource management and economic productivity,
further exacerbating challenges related to sustainable development and climate
change adaptation.

## Opportunities to Drive AI for a Climate Resilient Africa

While there are challenges to using AI for climate action in Africa, there are
also opportunities to overcome these barriers. One such opportunity is to
leverage grassroots movements for decentralised research and collaboration.
These movements facilitate community involvement, garner local knowledge, and
promote transdisciplinary collaboration. By integrating grassroots initiatives
with traditional agricultural practices and local ecological knowledge, AI for
climate action can be effectively implemented.

Grassroots movements have demonstrated success in other areas where Africa is
underrepresented in technology. Examples include
Masakhane777https://www.masakhane.io/, a movement with over 2000 members
actively contributing to the advancement of Natural Language Processing by
creating and curating novel datasets and benchmarks that incorporate African
languages (Orife et al.,, 2020). Another initiative, AfriClimate
AI888https://www.africlimate.ai/, is a newly formed grassroots movement
focused on addressing Climate Action through open-source data curation,
capacity building, and collaboration in pan-African AI-based climate research.
The support and success of such movements are core to developing context-aware
AI-driven solutions across disciplines.

Open-source climate information services (CIS), such as those provided by the
European Centre for Medium-Range Weather Forecasts (ECMWF), have been
fundamental in driving exciting advances in climate informatics, as recent
studies have shown. These advances include the development of state-of-the-art
models such as IceNet (Andersson et al.,, 2021), GraphCast (Lam et al.,,
2022), and Neural GCM (Kochkov et al.,, 2023).

Numerous opportunities arise in the African CIS landscape for collaborative
continental efforts. These opportunities include building and strengthening
weather and climate observation infrastructure and networks for medium and
long-term planning, improving the uptake and effectiveness of CIS through
coordinated implementation and sustainability measures, and building capacity
in the generation, uptake, and effective use of CIS among various stakeholders
(African Union,, 2023).

In conclusion, we advocate for the creation of a Pan-African roadmap for AI
and Climate Science. This roadmap should streamline collaboration among
researchers, practitioners, policymakers, and grassroots communities to tackle
the aforementioned challenges. This furthers the proposition by James et al.,
(2018), which stresses the need to include African experts in climate model
development. It should recognise the opportunity that AI offers to craft
context-aware solutions tailored to the continent’s specific needs and
challenges. In addition to placing a significant emphasis on developing and
implementing context-aware climate information services that are fit for
purpose, this roadmap is envisaged to cultivate an environment conducive to
implementing mitigation and adaptation policies that promote a sustainable,
prosperous, and climate-resilient Africa.

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