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Are the Effects of Global Warming Really that Bad?

Short answer: Yes. Even a seemingly slight average temperature rise is enough to cause a dramatic transformation of our planet.

The Missouri River encroaches on homes in Sioux City, Iowa, during a 2011 flood

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Five and a half degrees Fahrenheit. It may not sound like much—perhaps the difference between wearing a sweater and not wearing one on an early-spring day. But for the world in which we live—which climate experts project will be at least 5.7 degrees Fahrenheit warmer by 2100 , relative to pre-industrial levels (1850–1900), should global emissions continue on their current path—this small rise will have grave consequences. These impacts are already becoming apparent for every ecosystem and living thing, including us.

Human influences are the number one cause of global warming , especially the carbon pollution we cause by burning fossil fuels and the pollution capture we prevent by destroying forests. The carbon dioxide, methane, soot, and other pollutants we release into the atmosphere act like a blanket, trapping the sun's heat and causing the planet to warm. Evidence shows that the 2010s were hotter than any other decade on record —and every decade since the 1960s has averaged hotter than the previous one. This warming is altering the earth's climate system, including its land, atmosphere, oceans, and ice, in far-reaching ways.

More frequent and severe weather

Higher temperatures are worsening many types of disasters, including storms, heat waves, floods, and droughts. A warmer climate creates an atmosphere that can collect, retain, and unleash more water, changing weather patterns in such a way that wet areas become wetter and dry areas drier.

According to the National Oceanic and Atmospheric Administration, in 2021, there were 20 weather and climate disaster events in the United States—including severe storms, floods, drought, and wildfires—that individually caused at least $1 billion in losses . “Disasters in 2021 had a staggering total price tag of $145 billion—and that’s an underestimate because it excludes health damages,” says Vijay Limaye , senior scientist at NRDC. “These climate and weather disasters endanger people across the country throughout the entire year. In fact, more than 4 in 10 Americans live in a county that was struck by climate-related disasters in 2021.”

The increasing number of droughts, intense storms, and floods we're seeing as our warming atmosphere holds—and then dumps—more moisture poses risks to public health and safety too. Prolonged dry spells mean more than just scorched lawns. Drought conditions jeopardize access to clean drinking water, fuel out-of-control wildfires, and result in dust storms, extreme heat events, and flash flooding in the States. Elsewhere around the world, lack of water is a leading cause of death and serious disease and is contributing to crop failure. At the opposite end of the spectrum, heavier rains cause streams, rivers, and lakes to overflow, which damages life and property, contaminates drinking water, creates hazardous-material spills, and promotes mold infestation and unhealthy air. A warmer, wetter world is also a boon for foodborne and waterborne illnesses and disease-carrying insects, such as mosquitoes, fleas, and ticks.

Higher death rates

Today's scientists point to climate change as the biggest global health threat of the 21st century. It's a threat that impacts all of us—especially children, the elderly, low-income communities, and minorities—and in a variety of direct and indirect ways. As temperatures spike, so does the incidence of illness, emergency room visits, and death.

"There are more hot days in places where people aren't used to it," Limaye says. "They don't have air-conditioning or can't afford it. One or two days isn't a big deal. But four days straight where temperatures don't go down, even at night, leads to severe health consequences." In the United States, hundreds of heat-related deaths occur each year due to direct impacts and the indirect effects of heat-exacerbated, life-threatening illnesses, such as heat exhaustion, heatstroke, and cardiovascular and kidney diseases. Indeed, extreme heat kills more Americans each year, on average, than hurricanes, tornadoes, floods, and lightning combined.

Dirtier air

Rising temperatures also worsen air pollution by increasing ground-level ozone smog, which is created when pollution from cars, factories, and other sources react to sunlight and heat. Ground-level ozone is the main component of smog, and the hotter things get, the more of it we have. Dirtier air is linked to higher hospital admission rates and higher death rates for asthmatics. It worsens the health of people suffering from cardiac or pulmonary disease. And warmer temperatures also significantly increase airborne pollen , which is bad news for those who suffer from hay fever and other allergies.

Higher wildlife extinction rates

As humans, we face a host of challenges, but we're certainly not the only ones catching heat. As land and sea undergo rapid changes, the animals that inhabit them are doomed to disappear if they don't adapt quickly enough. Some will make it, and some won't. According to the Intergovernmental Panel on Climate Change's Sixth Assessment Report , the risk of species extinction increases steeply with rises in global temperature —with invertebrates (specifically pollinators) and flowering plants being some of the most vulnerable. Moreover, a 2015 study showed that vertebrate species (animals with backbones, like fish, birds, mammals , amphibians, and reptiles) are also disappearing more than 100 times faster than the natural rate of extinction, due to human-driven climate change, pollution, and deforestation.

More acidic oceans

The earth's marine ecosystems are under pressure as a result of climate change. Oceans are becoming more acidic, due in large part to their absorption of some of our excess emissions. As this acidification accelerates, it poses a serious threat to underwater life, particularly creatures with calcium carbonate shells or skeletons, including mollusks, crabs, and corals. This can have a huge impact on shellfisheries . In total, the U.S. shellfish industry could lose more than $400 million annually by 2100 due to impacts of ocean acidification.

Higher sea levels

The polar regions are particularly vulnerable to a warming atmosphere. Average temperatures in the Arctic are rising twice as fast as they are elsewhere on earth, and the world's ice sheets are melting fast. This not only has grave consequences for the region's people, wildlife, and plants; its most serious impact may be on rising sea levels. By 2100, it's estimated our oceans will be 1.6 to 6.6 feet higher, threatening coastal systems and low-lying areas, encompassing entire island nations and the world’s largest cities, including Los Angeles, Miami, and New York City, as well as Mumbai, India; Rio de Janeiro; and Sydney, Australia.

But this isn’t the end of the story

There’s no question: Unchecked climate change promises a frightening future, and it's too late to fully turn back the clock. We've already taken care of that by pumping a century's worth of pollution into the atmosphere. “Even if we stopped all carbon dioxide emissions tomorrow, we'd still see some dangerous effects,” Limaye says. That, of course, is the bad news.

But there's also good news. By aggressively reducing our global emissions now, “we can avoid a lot of the severe consequences that climate change would otherwise bring,” says Limaye. While change must happen at the highest levels of government and business, your voice matters too: to your friends, to your families, and to your community leaders. Together, we can envision a safer, healthier, more equitable future—and build toward it. You can join with millions of people around the world fighting climate change and even work to reduce fossil fuels in your own life .

This story was originally published on March 15, 2016, and has been updated with new information and links.

This NRDC.org story is available for online republication by news media outlets or nonprofits under these conditions: The writer(s) must be credited with a byline; you must note prominently that the story was originally published by NRDC.org and link to the original; the story cannot be edited (beyond simple things such as grammar); you can’t resell the story in any form or grant republishing rights to other outlets; you can’t republish our material wholesale or automatically—you need to select stories individually; you can’t republish the photos or graphics on our site without specific permission; you should drop us a note to let us know when you’ve used one of our stories.

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• Published: 29 March 2011

Climate, health, agricultural and economic impacts of tighter vehicle-emission standards

• Drew Shindell 1 ,
• Greg Faluvegi 1 ,
• Michael Walsh 2 ,
• Susan C. Anenberg 3 , 4 ,
• Rita Van Dingenen 5 ,
• Nicholas Z. Muller 6 ,
• Jeff Austin 7 ,
• Dorothy Koch 1   nAff8 &
• George Milly 1  

Nature Climate Change volume  1 ,  pages 59–66 ( 2011 ) Cite this article

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Non-CO 2 air pollutants from motor vehicles have traditionally been controlled to protect air quality and health, but also affect climate. We use global composition–climate modelling to examine the integrated impacts of adopting stringent European on-road vehicle-emission standards for these pollutants in 2015 in many developing countries. Relative to no extra controls, the tight standards lead to annual benefits in 2030 and beyond of 120,000–280,000 avoided premature air pollution-related deaths, 6.1–19.7 million metric tons of avoided ozone-related yield losses of major food crops, $US0.6–2.4 trillion avoided health damage and $US1.1–4.3 billion avoided agricultural damage, and mitigation of 0.20 (+0.14/−0.17) °C of Northern Hemisphere extratropical warming during 2040–2070. Tighter vehicle-emission standards are thus extremely likely to mitigate short-term climate change in most cases, in addition to providing large improvements in human health and food security. These standards will not reduce CO 2 emissions, however, which is required to mitigate long-term climate change.

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Acknowledgements

We thank the NASA Applied Sciences program, the ClimateWorks Foundation and the California Air Resources Board for supporting this work. We also thank T. Bond for gridding the emissions, M. Brauer for providing the PM2.5-measurement database, J. West for assistance with the population projection, B. Croes and D. Luo at CARB for their assistance and the UNEP/WMO Integrated Assessment of Black Carbon and Tropospheric Ozone team for discussions. Conclusions expressed in this article are the authors and do not necessarily represent those of their employers.

Author information

Dorothy Koch

Present address: Present address: Department of Energy, Washington, District of Columbia 20585, USA,

Authors and Affiliations

NASA Goddard Institute for Space Studies and Columbia Earth Institute, Columbia University, New York, New York 10025, USA

Drew Shindell, Greg Faluvegi, Dorothy Koch & George Milly

International Council for Clean Transportation, San Francisco, California 94104, USA

Michael Walsh

US Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA

Susan C. Anenberg

Environmental Sciences and Engineering Department, The University of North Carolina at Chapel Hill, North Carolina 27599, USA

European Commission, Joint Research Centre, Institute for Environment and Sustainability, Ispra I-21027, Italy

Rita Van Dingenen

Department of Economics, Middlebury College, Middlebury, Vermont 05753, USA

Nicholas Z. Muller

California Air Resources Board, Sacramento, California 95814, USA

Jeff Austin

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Contributions

D.S. planned and led the work and writing of the paper. G.F. carried out the composition–climate modelling. M.W. carried out the emissions analyses. S.C.A. and J.A. carried out the health analyses. R.V.D. carried out the crop-yield and valuation analysis. N.Z.M. carried out the health valuation analysis. D.K. provided input on aerosol modelling. G.M. analysed the composition–climate model output. All contributed to writing the paper.

Corresponding author

Correspondence to Drew Shindell .

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Shindell, D., Faluvegi, G., Walsh, M. et al. Climate, health, agricultural and economic impacts of tighter vehicle-emission standards. Nature Clim Change 1 , 59–66 (2011). https://doi.org/10.1038/nclimate1066

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Accepted : 28 February 2011

Published : 29 March 2011

Issue Date : April 2011

DOI : https://doi.org/10.1038/nclimate1066

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Welcome to the United Nations

Global warming: severe consequences for Africa

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Record global greenhouse gas emissions are putting the world on a path toward unacceptable warming, with serious implications for development prospects in Africa.

“Limiting warming to 1.5° C is possible within the laws of chemistry and physics, but doing so would require unprecedented changes,” said Jim Skea, cochair of the Intergovernmental Panel on Climate Change (IPCC) Working Group III.

But IPCC, the world’s foremost authority for assessing the science of climate change, says it is still possible to limit global temperature rise to 1.5° C—if, and only if, there are “rapid and far-reaching transitions in land, energy, industry, buildings, transport, and cities.”

For sub-Saharan Africa, which has experienced more frequent and more intense climate extremes over the past decades, the ramifications of the world’s warming by more than 1.5° C would be profound.

Temperature increases in the region are projected to be higher than the global mean temperature increase; regions in Africa within 15 degrees of the equator are projected to experience an increase in hot nights as well as longer and more frequent heat waves.

The odds are long but not impossible, says the IPCC. And the benefits of limiting climate change to 1.5° C are enormous, with the report detailing the difference in the consequences between a 1.5° C increase and a 2° C increase. Every bit of additional warming adds greater risks for Africa in the form of greater droughts, more heat waves and more potential crop failures.

Recognizing the increasing threat of climate change, many countries came together in 2015 to adopt the historic Paris Agreement, committing themselves to limiting climate change to well below 2° C. Some 184 countries have formally joined the agreement, including almost every African nation, with only Angola, Eritrea and South Sudan yet to join. The agreement entered into force in November 2016.

In December 2018 countries met in Katowice, Poland, for the Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC)—known as COP24—to finalise the rules for implementation of the agreement’s work programme.

As part of the Paris Agreement, countries made national commitments to take steps to reduce emissions and build resilience. The treaty also called for increased financial support from developed countries to assist the climate action efforts of developing countries.

But even at the time that the Paris Agreement was adopted, it was recognized that the commitments on the table would not be enough. Even if the countries did everything they promised, global temperatures would rise by 3° C this century.

According to the IPCC, projections show that the western Sahel region will experience the strongest drying, with a significant increase in the maximum length of dry spells. The IPCC expects Central Africa to see a decrease in the length of wet spells and a slight increase in heavy rainfall.

West Africa has been identified as a climate-change hotspot, with climate change likely to lessen crop yields and production, with resultant impacts on food security.

Southern Africa will also be affected. The western part of Southern Africa is set to become drier, with increasing drought frequency and number of heat waves toward the end of the 21st century.

A warming world will have implications for precipitation. At 1.5° C, less rain would fall over the Limpopo basin and areas of the Zambezi basin in Zambia, as well as parts of Western Cape in South Africa.   

But at 2° C, Southern Africa is projected to face a decrease in precipitation of about 20% and increases in the number of consecutive dry days in Namibia, Botswana, northern Zimbabwe and southern Zambia. This will cause reductions in the volume of the Zambezi basin projected at 5% to 10%.

If the global mean temperature reaches 2° C of global warming, it will cause significant changes in the occurrence and intensity of temperature extremes in all sub-Saharan regions.

West and Central Africa will see particularly large increases in the number of hot days at both 1.5° C and 2° C. Over Southern Africa, temperatures are expected to rise faster at 2° C, and areas of the southwestern region, especially in South Africa and parts of Namibia and Botswana, are expected to experience the greatest increases in temperature.

Perhaps no region in the world has been affected as much as the Sahel, which is experiencing rapid population growth, estimated at 2.8% per year, in an environment of shrinking natural resources, including land and water resources.

Inga Rhonda King, President of the UN Economic and Social Council, a UN principal organ that coordinates the economic and social work of UN agencies, told a special meeting at the UN that the region is also one of the most environmentally degraded in the world, with temperature increases projected to be 1.5 times higher than in the rest of the world.

Largely dependent on rain-fed agriculture, the Sahel is regularly hit by droughts and floods, with enormous consequences to people’s food security. As a result of armed conflict, violence and military operations, some 4.9 million people have been displaced this year, a threefold increase in less than three years, while 24 million people require humanitarian assistance throughout the region.

Climate change is already considered a threat multiplier, exacerbating existing problems, including conflicts. Ibrahim Thiaw, special adviser of the UN Secretary-General for the Sahel, says the Sahel region is particularly vulnerable to climate change, with 300 million people affected.

Drought, desertification and scarcity of resources have led to heightened conflicts between crop farmers and cattle herders, and weak governance has led to social breakdowns, says Mr. Thiaw. The shrinking of Lake Chad is leading to economic marginalization and providing a breeding ground for recruitment by terrorist groups as social values and moral authority evaporate.     

Also in this issue

Towards a safe and orderly migration

Africa Dialogue Series launched

Ending violence against women and girls in the Sahel: crucial for sustainable development

Plaudits for the man who mends women

Risky journey to Europe

A life on the move

Confronting the challenges of migration in West and Central Africa

African migrants keen to retain their cultural values abroad

Migration can be a catalyst for economic growth

Uganda stands out in refugees hospitality

2019: Year of return for African Diaspora

Egypt ‘Ify’ Ufele

A double challenge for the disabled

Africa’s freedom struggles and the Universal Declaration of Human Rights

Nigeria needs free, fair and credible elections

Crisis worsens in Cameroon

After making peace, Ethiopia and Eritrea now focus on development

Fighting ‘hidden hunger’ with fortified foods

Blue economy can be a lifeline for Africa

Kofi Annan: tribute to a rare gentleman

Book review: living together, living apart social cohesion in a future south africa, un appointments.

Africa to push development agenda at upcoming climate summit

Responding to the Climate Threat: Essays on Humanity’s Greatest Challenge

A new book co-authored by MIT Joint Program Founding Co-Director Emeritus Henry Jacoby

From the Back Cover

This book demonstrates how robust and evolving science can be relevant to public discourse about climate policy. Fighting climate change is the ultimate societal challenge, and the difficulty is not just in the wrenching adjustments required to cut greenhouse emissions and to respond to change already under way. A second and equally important difficulty is ensuring widespread public understanding of the natural and social science. This understanding is essential for an effective risk management strategy at a planetary scale. The scientific, economic, and policy aspects of climate change are already a challenge to communicate, without factoring in the distractions and deflections from organized programs of misinformation and denial. 

Here, four scholars, each with decades of research on the climate threat, take on the task of explaining our current understanding of the climate threat and what can be done about it, in lay language―importantly, without losing critical  aspects of the natural and social science. In a series of essays, published during the 2020 presidential election, the COVID pandemic, and through the fall of 2021, they explain the essential components of the challenge, countering the forces of distrust of the science and opposition to a vigorous national response.  

Each of the essays provides an opportunity to learn about a particular aspect of climate science and policy within the complex context of current events. The overall volume is more than the sum of its individual articles. Proceeding each essay is an explanation of the context in which it was written, followed by observation of what has happened since its first publication. In addition to its discussion of topical issues in modern climate science, the book also explores science communication to a broad audience. Its authors are not only scientists – they are also teachers, using current events to teach when people are listening. For preserving Earth’s planetary life support system, science and teaching are essential. Advancing both is an unending task.

About the Authors

Gary Yohe is the Huffington Foundation Professor of Economics and Environmental Studies, Emeritus, at Wesleyan University in Connecticut. He served as convening lead author for multiple chapters and the Synthesis Report for the IPCC from 1990 through 2014 and was vice-chair of the Third U.S. National Climate Assessment.

Henry Jacoby is the William F. Pounds Professor of Management, Emeritus, in the MIT Sloan School of Management and former co-director of the MIT Joint Program on the Science and Policy of Global Change, which is focused on the integration of the natural and social sciences and policy analysis in application to the threat of global climate change.

Richard Richels directed climate change research at the Electric Power Research Institute (EPRI). He served as lead author for multiple chapters of the IPCC in the areas of mitigation, impacts and adaptation from 1992 through 2014. He also served on the National Assessment Synthesis Team for the first U.S. National Climate Assessment.

Ben Santer is a climate scientist and John D. and Catherine T. MacArthur Fellow. He contributed to all six IPCC reports. He was the lead author of Chapter 8 of the 1995 IPCC report which concluded that “the balance of evidence suggests a discernible human influence on global climate”. He is currently a Visiting Researcher at UCLA’s Joint Institute for Regional Earth System Science & Engineering.

Access the Book

View the book on the publisher's website  here .

Order the book from Amazon  here . 

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• ENVIRONMENT

How global warming is disrupting life on Earth

The signs of global warming are everywhere, and are more complex than just climbing temperatures.

Our planet is getting hotter. Since the Industrial Revolution—an event that spurred the use of fossil fuels in everything from power plants to transportation—Earth has warmed by 1 degree Celsius, about 2 degrees Fahrenheit.  

That may sound insignificant, but 2023 was the hottest year on record , and all 10 of the hottest years on record have occurred in the past decade.  

Global warming and climate change are often used interchangeably as synonyms, but scientists prefer to use “climate change” when describing the complex shifts now affecting our planet’s weather and climate systems.  

Climate change encompasses not only rising average temperatures but also natural disasters, shifting wildlife habitats, rising seas , and a range of other impacts. All of these changes are emerging as humans continue to add heat-trapping greenhouse gases , like carbon dioxide and methane, to the atmosphere.

What causes global warming?

When fossil fuel emissions are pumped into the atmosphere, they change the chemistry of our atmosphere, allowing sunlight to reach the Earth but preventing heat from being released into space. This keeps Earth warm, like a greenhouse, and this warming is known as the greenhouse effect .  

Carbon dioxide is the most commonly found greenhouse gas and about 75 percent of all the climate warming pollution in the atmosphere. This gas is a product of producing and burning oil, gas, and coal. About a quarter of Carbon dioxide also results from land cleared for timber or agriculture.  

Methane is another common greenhouse gas. Although it makes up only about 16 percent of emissions, it's roughly 25 times more potent than carbon dioxide and dissipates more quickly. That means methane can cause a large spark in warming, but ending methane pollution can also quickly limit the amount of atmospheric warming. Sources of this gas include agriculture (mostly livestock), leaks from oil and gas production, and waste from landfills.  

What are the effects of global warming?  

One of the most concerning impacts of global warming is the effect warmer temperatures will have on Earth's polar regions and mountain glaciers. The Arctic is warming four times faster than the rest of the planet. This warming reduces critical ice habitat and it disrupts the flow of the jet stream, creating more unpredictable weather patterns around the globe.  

( Learn more about the jet stream. )

A warmer planet doesn't just raise temperatures. Precipitation is becoming more extreme as the planet heats. For every degree your thermometer rises, the air holds about seven percent more moisture. This increase in moisture in the atmosphere can produce flash floods, more destructive hurricanes, and even paradoxically, stronger snow storms.  

The world's leading scientists regularly gather to review the latest research on how the planet is changing. The results of this review is synthesized in regularly published reports known as the Intergovernmental Panel on Climate Change (IPCC) reports.  

A recent report outlines how disruptive a global rise in temperature can be:

• Coral reefs are now a highly endangered ecosystem. When corals face environmental stress, such as high heat, they expel their colorful algae and turn a ghostly white, an effect known as coral bleaching . In this weakened state, they more easily die.  
• Trees are increasingly dying from drought , and this mass mortality is reshaping forest ecosystems.
• Rising temperatures and changing precipitation patterns are making wildfires more common and more widespread. Research shows they're even moving into the eastern U.S. where fires have historically been less common.
• Hurricanes are growing more destructive and dumping more rain, an effect that will result in more damage. Some scientists say we even need to be preparing for Cat 6 storms . (The current ranking system ends at Cat 5.)

How can we limit global warming?  

Limiting the rising in global warming is theoretically achievable, but politically, socially, and economically difficult.  

Those same sources of greenhouse gas emissions must be limited to reduce warming. For example, oil and gas used to generate electricity or power industrial manufacturing will need to be replaced by net zero emission technology like wind and solar power. Transportation, another major source of emissions, will need to integrate more electric vehicles, public transportation, and innovative urban design, such as safe bike lanes and walkable cities.  

( Learn more about solutions to limit global warming. )

One global warming solution that was once considered far fetched is now being taken more seriously: geoengineering. This type of technology relies on manipulating the Earth's atmosphere to physically block the warming rays of the sun or by sucking carbon dioxide straight out of the sky.

Restoring nature may also help limit warming. Trees, oceans, wetlands, and other ecosystems help absorb excess carbon—but when they're lost, so too is their potential to fight climate change.  

Ultimately, we'll need to adapt to warming temperatures, building homes to withstand sea level rise for example, or more efficiently cooling homes during heat waves.  

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Open Access

Peer-reviewed

Research Article

Observed trends and projections of temperature and precipitation in the Olifants River Catchment in South Africa

Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations South African Weather Service, Pretoria, South Africa, School for Health Systems and Public Health, University of Pretoria, Pretoria, South Africa, Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa

Roles Data curation, Formal analysis, Writing – original draft, Writing – review & editing

Affiliations South African Weather Service, Pretoria, South Africa, Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa

Roles Formal analysis, Writing – review & editing

Affiliation South African Weather Service, Pretoria, South Africa

Affiliations South African Weather Service, Pretoria, South Africa, Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu‐Natal, Westville Campus, Durban, South Africa

• Abiodun Morakinyo Adeola, 
• Andries Kruger, 
• Thabo Elias Makgoale, 
• Joel Ondego Botai

• Published: August 9, 2022
• https://doi.org/10.1371/journal.pone.0271974
• Peer Review
• Reader Comments

Among the projected effects of climate change, water resources are at the center of the matrix. Certainly, the southern African climate is changing, consequently, localized studies are needed to determine the magnitude of anticipated changes for effective adaptation. Utilizing historical observation data over the Olifants River Catchment, we examined trends in temperature and rainfall for the period 1976–2019. In addition, future climate change projections under the RCP 4.5 and RCP 8.5 scenarios for two time periods of 2036–2065 (near future) and 2066–2095 (far future) were analysed using an ensemble of eight regional climate model (RCA4) simulations of the CORDEX Africa initiative. A modified Mann-Kendall test was used to determine trends and the statistical significance of annual and seasonal rainfall and temperature. The characteristics of extreme dry conditions were assessed by computing the Standardized Precipitation Index (SPI). The results suggest that the catchment has witnessed an increase in temperatures and an overall decline in rainfall, although no significant changes have been detected in the distribution of rainfall over time. Furthermore, the surface temperature is expected to rise significantly, continuing a trend already evident in historical developments. The results further indicate that the minimum temperatures over the Catchment are getting warmer than the maximum temperatures. Seasonally, the minimum temperature warms more frequently in the summer season from December to February (DJF) and the spring season from September to November (SON) than in the winter season from June to August (JJA) and in the autumn season from March to May (MAM). The results of the SPI affirm the persistent drought conditions over the Catchment. In the context of the current global warming, this study provides an insight into the changing characteristics of temperatures and rainfall in a local context. The information in this study can provide policymakers with useful information to help them make informed decisions regarding the Olifants River Catchment and its resources.

Citation: Adeola AM, Kruger A, Elias Makgoale T, Ondego Botai J (2022) Observed trends and projections of temperature and precipitation in the Olifants River Catchment in South Africa. PLoS ONE 17(8): e0271974. https://doi.org/10.1371/journal.pone.0271974

Editor: Mou Leong Tan, Universiti Sains Malaysia, MALAYSIA

Received: September 28, 2021; Accepted: July 11, 2022; Published: August 9, 2022

Copyright: © 2022 Adeola et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data process is done using R Software which includes several packages for mapping NetCDF data. Sample data and scripts used for this study are made available on Open Science Framework at https://osf.io/8rhn2 and https://osf.io/3d9j4 .

Funding: Partial funding for the background research was received from the South32 mining company. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Sub-Saharan Africa (SSA) is more likely to be affected by climate change than any other region because of its high exposure and limited capability for adaptation [ 1 ]. Climate change is expected to exacerbate current food insecurity, health challenges, poverty, and development challenges in SSA [ 2 ]. SSA is one of the regions where adaptation planning has been hindered by the complexity and relative uncertainty surrounding climate change impacts [ 3 ]. While uncertainties surround the understanding of future climate impacts in most regions of the globe, the SSA has among the lowest levels of confidence due to fewer research studies conducted using fine resolution climate models [ 1 ]. Modelling with a finer resolution will contribute to enhancing the level of certainty which is crucial to planning an effective adaptation strategy [ 1 ].

Southern Africa is particularly shown to be susceptible to climate change because of its significant economic inequality and strong dependency on rain-fed agriculture [ 4 ]. In the face of global warming caused by emissions of greenhouse gases, it is widely anticipated that extreme weather events will become more frequent, intense and severe [ 5 ]. South Africa, like other countries in the southern region, has recently witnessed rises in the frequency and intensity of extreme weather conditions such as droughts and floods. These weather systems left behind them huge traces of economic destruction including losses of life and properties [ 6 ].

The impacts of climate change are expected to worsen current challenges in many climate-sensitive sectors, increasing their vulnerability as well as the human systems that depend on them [ 7 , 8 ]. Consequently, studies have been done on analysing possible future projections of climatic variables over the near- to far-term [ 9 ]. As the climate warms from current levels to near, medium, and long-term futures, it is crucial to understand the extent to which climate and extreme weather events will change at the local level for better preparedness and the formulation of effective policy [ 9 ]. Globally, the overall impacts of climate change on freshwater resources are projected to be negative.

Climate change, coupled with increasing population, and the demand for social and economic equity all present challenges that need to be identified and addressed. Hence, by understanding the dynamics of current climate variability and future change, effective resource management, planning, and adaptation can be achieved [ 10 ]. In order to achieve enhanced capacity and adaptation strategies to cope with these inherent dynamics, climate change studies are needed at a local scale (fine resolution) [ 11 – 13 ]. This builds the capacity to adapt to future climate change and strengthens resilience to current climate challenges [ 10 ].

Studies have found that to make the most effective decisions for policy formulation on climate change adaptation and climate services, short-term and mid-term climate projections at a local scale are needed [ 14 ]. Climate change projections have so far been conducted at the regional scale in Southern Africa and SSA in particular, using general circulation models (GCMs) [ 15 , 16 ]. Studies have demonstrated that GCMs are capable of reproducing temperature distributions realistically. However, they are liable to overestimating precipitation over Southern Africa for all seasons [ 17 , 18 ]. Furthermore, whilst GCMs are relatively good at producing projections over a wide region, the determination of local impacts and the development of effective adaptation measures requires a thorough knowledge of the local conditions [ 19 ]. Consequently, studies have begun using regional downscaling techniques as a means of achieving greater levels of detail and improving projection accuracy [ 20 – 22 ]. For instance, [ 17 ] demonstrated that Regional Climate Models (RCMs) can improve the accuracy of climate projections, especially in places where the terrain is highly heterogeneous with the small-scale climatic system. In addition, [ 23 , 24 ] showed that an ensemble of ten RCMs effectively simulates precipitation distribution patterns over Southern and Eastern Africa respectively.

Further, [ 22 ] indicated that the downscaled Unified Model by the Met Office over the Africa domain enabled the inclusion of additional details, such as higher resolutions and convection parametrizations. In their study, they observed that the addition of convection enhanced rainfall simulation from June to August. Typical GCM-based models have inherent biases within their boundary conditions; conversely, [ 18 ] demonstrated that forcing the GCMs with local climate data reduces these biases over the African region. Additionally, studies have shown that RCMs also provide an improved representation of annual cycles in Southern Africa with a greater level of detail in projections [ 12 , 18 , 25 ].

This study aims at investigating the trends in historical maximum and minimum temperature and rainfall, the estimation of plausible future climate changes, as well as the magnitude of future occurrences in the variables to inform adaptation initiatives over the Olifants River Catchment. As mentioned, this analysis can provide a basis for future impact analysis and the development of effective adaptation strategies for various climate-sensitive sectors within the catchment.

Materials and methods

The Olifants River Catchment is located in the Limpopo River Basin, a regional drainage basin extending across South Africa, Mozambique, Zimbabwe and Botswana ( Fig 1 ). About 3.2 million people live in the basin’s 54,475 km 2 , of whom two-thirds live in rural areas that transverse the parts of Ekurhuleni, Sedibeng and City of Tshwane districts of Gauteng province, the Mopani, Capricorn, Waterberg and Sekhukhune districts of Limpopo province and Gert Sibande, Nkangala and Ehlanzeni districts of Mpumalanga province [ 26 ]. About 40% of the water in the Limpopo River originates from the Olifants River, making it an integral part of the drainage system. The river flows for about 560 km from Gauteng, through the Mpumalanga and Limpopo Provinces of South Africa through Mozambique and into the Indian Ocean. Mining, industry, and agriculture are major economic activities within the catchment and account for about 6% of the gross domestic product of South Africa [ 27 ]. A large portion of the catchment receives an average annual rainfall of 500 to 800 mm from October to April but reaches more than1000 mm along the escarpment [ 28 ]. Elevations within the catchment range from 200m to about 2300m above sea level, and therefore year-round temperatures have a large range, between about 0°C to 35°C.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

The inset map indicates the location of Olifants River Catchment in South Africa. The Place, Station and Rainfall District shapefile in the Figure were acquired database of the South African Weather Service. The River, Dam, Water management shapefile were collected from the South Africa National Department of Water and Sanitation available from [ 29 ]. The Geographic administrative shapefile was collected from the Demarcation board of South Africa available from [ 30 ]. The elevation data was downloaded from [ 31 ].

https://doi.org/10.1371/journal.pone.0271974.g001

The climate data was acquired from the South African Weather Service (SAWS) climate database. There are four climate stations within the proximity of the catchment which measure temperature and rainfall and have sufficiently long data records, i.e., 1976–2019. The data were quality controlled such that any station with 20% or more of the total possible values missing, was excluded from the analysis. Consequently, two stations with data for the period of 1976–2019 were deemed sufficient for analysis over the study area given the data quality. The climate data analysed include the daily rainfall, minimum temperature (Tmin) and maximum temperature (Tmax). Locations of the stations within the study area and the completeness of their records are shown in Fig 1 and Table 1 .

https://doi.org/10.1371/journal.pone.0271974.t001

In addition, the SAWS district rainfall dataset was analysed. The dataset is a delineation of areas with homogeneous rainfall, in which the entire nation is divided into a total of 94 rainfall districts [ 32 ], with the relevant districts indicated in Fig 1 . These districts are mainly defined by the annual march of monthly maximum rainfall, the boundaries between winter and summer rainfall regions as well as the topography. A district rainfall total is determined by taking the average of the daily rainfall values of available or operational rainfall stations in the district monthly [ 33 ]. For this study, the relevant rainfall districts can be considered to be topographically less complex than most of the rainfall districts in South Africa, which makes the reliability of the monthly statistics relatively representative. Consequently, rainfall totals over two rainfall districts (34 and 63) in which the selected climate stations are located, were analysed. Data sets are available upon request at https://www.weathersa.co.za/home/equiries_climatedata .

Additionally, an ensemble of eight individual Global Climate Models (GCMs) of the Fifth Phase of Coupled Model Inter-comparison Project (CMIP5) ( Table 2 ) was dynamically downscaled to a spatial resolution (0.44° x 0.44°) by the Rossby Centre regional (RCA4) model was used for the future projections. The RCA4 simulated projections are part of the Coordinated Regional Climate Downscaling Experiment (CORDEX) [ 34 , 35 ]. For this study, the selection of the eight models is based on computational resources and data completeness.

https://doi.org/10.1371/journal.pone.0271974.t002

In this study, the climate change projections were performed based on two climate change scenarios. The Representative Concentration Pathways (RCP) 4.5 indicates a 4.5 watts per metre squared–W/m 2 forcing increase relative to pre-industrial conditions and the RCP8.5 indicates a 8.5 watts per metre squared–W/m 2 forcing increase relative to pre-industrial conditions. The RCP4.5 assumes a medium stabilization scenario while the RCP8.5 assumes a scenario that includes no policy-driven mitigation (also refers to as a “business-as-usual” scenario) [ 44 ].

Statistical and trend analysis.

An upward or a downward trend is indicated when S is positive or negative respectively. To determine whether a trend is statistically significant, the Z value is evaluated. Z values in the positive range indicate upward trends and Z values in the negative range indicate downward trends. The 95% level of confidence was used to test whether there was an upward or downward monotone trend (a two-tailed test). Hence, for statistical significance, the probability estimate must be equal to or less than 0.05 in a 95% confidence interval.

The change per unit time (annual or seasonal) within the time series was estimated using Sen’s nonparametric approach with an assumption that the trend is linear [ 46 ]. The magnitude of the trend is predicted by Sen’s estimator. The slope is computed using Eq 2 . A positive or negative value of Q i indicates an increasing or decreasing rate, respectively.

Where, x j and x k are the annual or seasonal values in years j and k respectively.

Standardized Precipitation Index (SPI).

https://doi.org/10.1371/journal.pone.0271974.t003

Model evaluation.

For a given linear analysis of the model, a model with low variance and low bias is considered the ideal model and most suitable for projection while a model with high variance and low bias is considered an overfitting model; and a model with low variance and high bias is typically regarded as an underfitting model. In addition, a model with high variance and high bias will highly probably give the greatest prediction error.

Climate change projections.

Daily simulated total rainfall, and maximum and minimum temperature averages are used to generate projections of annual and 3-month seasonal change. Future projections of rainfall, Tmax and Tmin are presented for the two 30-year periods of 2036–2065 (near future) and 2066–2095 (far future) under the RCP 4.5 and RCP8.5 scenarios. Projected changes are expressed relative to the historical 30-year period of 1976 to 2005. The seasons considered are December-January-February (DJF), March-April-May (MAM), June-July-August (JJA) and September-October-November (SON). Additionally, the Probability Distribution Function was used to estimate the occurrence (number of possible events) of both the historical and projected Tmax, Tmin and rainfall generated over the entire catchment.

Historical variations

Fig 2 shows box plots of Tmax, Tmin and rainfall indicating the values for minimum, first quartile, median, third quartile, and maximum for the two weather stations and corresponding homogenous rainfall districts for 1976–2019. At a monthly average temperature (Tmean) of 25.9 and 24.5°C January are the hottest month of the year over the northern part (Hoedspruit station) and southern part (Oudestad station) of the catchment respectively. At 17.3 and 13.2°C on average, the month of July is the coldest of the year over the northern part and southern part of the catchment respectively. The southern part of the catchment is wetter with larger rainfall received in January. The difference in rainfall between the wettest and driest months is 80.0 mm and 103.6 mm over Hoedspruit and Oudestad stations respectively ( Table 4 ).

Boxplot of minimum temperatures (A top; B top) and maximum temperature (A bottom; B bottom) over Hoedspruit (A) and Oudestad (B) stations and rainfall (C) top District 34 and bottom District 63.

https://doi.org/10.1371/journal.pone.0271974.g002

https://doi.org/10.1371/journal.pone.0271974.t004

The descriptive statistics and historical trends of annual and seasonal rainfall, Tmax and Tmin for the two climate stations and corresponding homogenous rainfall districts with long term data are presented on seasonal and annual bases from 1976–2019 in Table 5 . In the northern part of the catchment at Hoedspruit station, Tmean ranged between 27.3 to 30.2°C with an average value of 28.8 ± 0.7°C for the period 1976 to 2019. The Mann-Kendall trend analysis showed that test Z was +0.201 and Sen’s slope estimator was +0.018, which indicated that the mean annual Tmax significantly increased at the rate of 0.018°C per year at the 95% confidence level over the analysis period. On the other hand, the mean annual Tmin varied between 14.3 to 17.8°C with a mean value of 15.9 ± 0.7°C ( Table 5 ). The trend analysis of mean annual Tmin showed that it decreased with a Z = -0.086 at the rate of -0.01°C per year for the period of study. Therefore, unlike Tmax with abs(Z) < 0.05, there is no significant trend in Tmin. Seasonally, Tmax shows an increasing trend on an annual basis. Seasonally, however, the increasing trend is only statistically significant in winter and spring. A decreasing trend in Tmin is evident for all the seasons except for winter, however, only the summer trend of Tmin is statistically significant. Overall, Tmean has increased by 0.4°C in the northern part. Although the rainfall shows a decreasing trend, the annual and seasonal trends are not statistically significant.

https://doi.org/10.1371/journal.pone.0271974.t005

In the southern part of the catchment at the Oudestad climate station, the mean annual Tmax ranged between 26.1 to 31.3°C with an average value of 27.9 ± 1.1°C for the period 1976 to 2019. The Mann-Kendall trend analysis showed that test Z was +0.53 and Sen’s slope estimator was +0.055, which indicated that the mean annual Tmax significantly increased at the rate of 0.055°C per year at the 95% confidence level for the period under study. On the other hand, the mean annual Tmin varied between 9.5 to 13.9°C with a mean value of 11.9 ± 0.7°C ( Table 4 ). The trend analysis of mean annual Tmin showed that it increased with a Z = 0.18 at the rate of 0.018°C per year for the period of study. However, the rate of increase of mean annual Tmin was not significant at the 95% confidence level. Seasonally, both Tmin and Tmax show increasing trends across all seasons. The increasing trends are statistically significant for Tmax in all four seasons but statistically not significant for Tmin in all four seasons at the 95% confidence level. In general, mean daily Tmax and Tmin have increased by 0.42 and 0.27°C respectively in the southern part of the Catchment.

The rainfall does not show a clear signal for an increasing or decreasing trend. A decreasing trend in total annual and seasonal rainfall in the northern (district 34) of the study area is indicated by the results. However, the trends are not statistically significant at the 95% confidence level. The mean total annual rainfall varied from 176.9 to 988.1 mm with a mean of 446.6 mm±160.4. In the southern (district 63) parts, the mean total annual rainfall varied from 385.6 to 852.7 mm with a mean of 620.0 mm±96.1 The Mann-Kendall trend analysis showed that test Z was +0.066 with a Sen’s slope estimator of +0.007, which indicated that total annual rainfall has increased over the study period. In addition, the results indicate an increasing trend in total seasonal rainfall in summer. However, the trends are not significant at the 95% confidence level.

As shown in Fig 3 the SPI values suggest that the Catchment is characterized by drought conditions with extremely dry category (≤-2.0) occurring in 1992 and 2004 in both districts. The figure further suggests that drought has recently persisted over the Catchment particularly in the northern part (DS34) experiencing moderate to severe drought conditions. The results of the SPI affirm the historical wetter conditions of the southern part of the Catchment. The persistent drought conditions over the southern region can be deduced to be agricultural and hydrological droughts.

The Standardized Precipitation Index for (A) 2-, (B) 6-, and (C) 12-month accumulation period for two homogenous rainfall regions districts 34 and 63 of the Olifant River Catchment from 1976–2020.

https://doi.org/10.1371/journal.pone.0271974.g003

Model evaluation

The results of the effectiveness of the model to simulate historical temperatures and rainfall are shown in Fig 4 as an example for Hoedspruit and summarized in Table 6 . Statistics for eight models were calculated with each model and the ensemble gave a colour code. The location of each colour on the plot indicates how correlated the model’s simulated variables (rainfall and temperatures) match the observed pattern. The contours indicate the RMSE. The RMSE is the difference between the simulated and observed patterns which is proportional to the distance to the point on the x-axis, while the SD of the simulated pattern is proportional to the radial distance from the origin. The ensemble mean for Tmax has a pattern correlation with an observation of about 0.88. The RMSE between the simulated and observed patterns of maximum temperature is about 0.5°C/day while the SD of the simulated pattern for Tmax is about 0.8°C/day which less than the observed SD of 1.0°C/day. Overall, the CanESM2 and CSIRO models generally agree best with the observed maximum temperature, with both having RMSE within 0.65°C/day and 0.7°C/day respectively. However, the CSIRO model has a slightly higher correlation with observations and has the same standard deviation as the observed maximum temperature, whereas CanESM2 has an SD of 1.1°C/day compared to the observed value of 1.0°C/day.

Taylor diagrams for (A) maximum temperature, (B) minimum temperature and (C) precipitation, comparing observations with CMIP5 models and ensemble mean simulations for Olifant River Catchment for the period 1976–2005.

https://doi.org/10.1371/journal.pone.0271974.g004

https://doi.org/10.1371/journal.pone.0271974.t006

On the other hand, the ensemble mean for Tmin gives a higher pattern correlation with an observation of about 0.97. The RMSE between the simulated and observed patterns of Tmin is about 0.2°C/day while the SD of the simulated pattern is about 0.9 compared to the observed SD of 1.0°C/day. All models present a high correlation with observation. The CanESM2 and CSIRO models generally agree best with Tmin observations, with both having RMSE within 0.38 and 0.37 respectively and the same standard deviation as the observed minimum temperature.

While the ensemble mean of the member models produces a higher correlation of about 0.8 between the observed and model simulated annual total rainfall, there is largely less agreement between individual models and the observations. All models indicate a high RMSE and the spatial variability is higher with an average SD of 1.2 mm/day compared to the observed value of 1.0 mm/day except for the CanESM2 model. The Taylor diagram thus suggests that the ensemble models have a relatively high confidence level for temperature projections, whereas it has low confidence for precipitation projections.

Climate change projections for Olifants River Catchment.

Projected changes in the annual values of total rainfall, mean Tmax and Tmin over the Olifants River Catchment for the periods 2036–2065 (near future) and 2066–2095 (far future) under RCP 4.5 and RCP8.5 scenarios, relative to the reference period 1976–2005 are presented in Figs 5 and 6 respectively, with a summary in Table 7 . At mid-century 2050 (2036–2065), under the RCP4.5 condition, Tmax is projected to increase between a minimum of about 1.8 and a maximum of 2.0°C. In addition, Tmax is likely to increase by a minimum of 2.2 and 2.6°C under the RCP4.5 condition by the end of the century ( Fig 5 ).

Projected changes in annual (A) mean maximum, (B) minimum temperature and (C) total rainfall, for 2036–2065 (2nd column) and 2066–2095 (3rd column) periods under scenarios of the RCP4.5 relative to the baseline period 1976–2005 (1st column) over the Olifants River Catchment. "Basemap (Water management shapefile was collected from the South Africa National Department of Water and Sanitation on http://www.dwa.gov.za/iwqs/gis_data/ " [ 29 ].

https://doi.org/10.1371/journal.pone.0271974.g005

Projected changes in annual (A) mean maximum, (B) minimum temperature and (C) total rainfall, for 2036–2065 (2nd column) and 2066–2095 (3rd column) periods under scenarios of the RCP8.5 relative to the baseline period 1976–2005 (1st column) over the Olifants River Catchment. "Basemap (Water management shapefile was collected from the South Africa National Department of Water and Sanitation on http://www.dwa.gov.za/iwqs/gis_data/ " [ 29 ].

https://doi.org/10.1371/journal.pone.0271974.g006

https://doi.org/10.1371/journal.pone.0271974.t007

Under the RCP8.5 scenario, shown in Fig 6 and summarized in Table 7 , Tmax is predicted to increase by a minimum of about 2.2 and a maximum of 2.5°C by mid-century and is further projected to increase between a minimum of 3.9 and 4.6°C by the end of the century. In general, Tmax is averagely projected to be warmer by about 2.2°C in the northern part (Mopani, Ehlanzeni districts) and by 2.5°C in the southern part (Ekurhuleni, Gert Sibande, Sedibeng and City of Tshwane districts) of the catchment under RCP4.5 and to increase by about 4.3°C in the southern part and 3.8°C in the northern part under RCP8.5 compared to the baseline period of 1976–2005. On the other hand, Tmin is projected to increase between 1.7–2.0°C and between 2.1–2.6°C under RCP4.5 by mid-century and end of the century respectively. Higher Tmin values are projected under the RCP8.5 scenario with an increase between 2.1–2.6°C and 3.7–4.5°C by mid-century and end of the century respectively. Averagely, Tmin is projected to increase by about 1.6°C in the northern and central parts, and by about 2.0°C in the southwest including the Waterberg district under the RCP4.5 scenario and estimated to increase by about 3.8°C in the southwestern part and by about 3.6°C in the northern and central parts under the RCP8.5 scenario, relative to the baseline period of 1976–2005 ( Fig 6 ). Generally, the projected changes in temperature and rainfall vary substantially across the catchment. These variations can be attributed to the complexity of the landscape and the associated climate fluctuations as shown in the historical trend analysis.

It is important to note that there is high uncertainty in the direction and amount of change in total annual rainfall across the ensemble models. The projection suggests a decrease in total annual rainfall by about 5–30%, especially over the eastern to central parts of the catchment by mid-2050 under both RCP4.5 and 8.5 scenarios. Regions with less rainfall historically are projected to have marginal increases in rainfall. The southern region of the catchment is projected to receive about a 20mm p.a. (5%) increase in rainfall. The results indicate a further decrease in rainfall (about 10–40%) over a large portion of the catchment going into the far future. The central interior part of the catchment is projected to experience a significant decrease in annual rainfall up to about 25%, with increased drying over time. Despite predictions of general drying conditions over most of the catchment, slight to moderate rainfall increases are projected over the south-western parts of Olifants River Catchment in spring and summer, although statistically not significant.

The results of the occurrence of both the historical and projected Tmax, Tmin and rainfall generated using the probability distribution function over the entire catchment are shown in Figs 7 – 11 . The figures present the averages of the variables as well as the lower and upper extreme boundaries of the projected climate. As shown in Fig 7 , the number of days (events) with an annual maximum temperature > 32°C is projected to increase by 2036–2065 ( Fig 7E ).

Probability distribution estimation of daily seasonal A) Summer, B) Autumn, C) Winter, D) Spring and E) annual maximum temperatures Over Olifants Catchment (1976–2005 vs. 2036–2065).

https://doi.org/10.1371/journal.pone.0271974.g007

Probability distribution estimation of daily seasonal A) Summer, B) Autumn, C) Winter, D) Spring and E) annual maximum temperatures Over Olifants Catchment (1976–2005 vs. 2066–2095).

https://doi.org/10.1371/journal.pone.0271974.g008

Probability distribution estimation of daily seasonal A) Summer, B) Autumn, C) Winter, D) Spring and E) annual minimum temperatures Over Olifants Catchment (1976–2005 vs. 2036–2065).

https://doi.org/10.1371/journal.pone.0271974.g009

Probability distribution estimation of daily seasonal A) Summer, B) Autumn, C) Winter, D) Spring and E) annual minimum temperatures Over Olifants Catchment (1976–2005 vs. 2066–2095).

https://doi.org/10.1371/journal.pone.0271974.g010

https://doi.org/10.1371/journal.pone.0271974.g011

Seasonally, mean Tmax is expected to increase above 31°C in summer, 29°C in autumn, 26°C in winter and 31°C in spring by the mid-2050s ( Fig 7A–7D respectively). These maximum temperature values are projected to further increase in the far future reaching above 33°C in summer, 31°C in autumn, 26°C in winter and 32°C in spring ( Fig 8A–8D respectively) with an increasing number of occurrences.

On the other hand, Tmin > 17°C is projected to increase over the catchment ( Fig 9E ) in the mid-2050s. Seasonally, the number of events of Tmin > 18°C is projected to increase in the summer months. In general, all seasons are projected to experience higher Tmin under the RCP8.5 scenario by the mid-2050s ( Fig 9A–9D respectively).

By the mid-2080s minimum temperature values are further projected to increase above 19°C with increased days of occurrence in summer ( Fig 10A ). Average Tmin values are projected to increase above 17°C, 11°C and 16°C in autumn, winter and spring respectively ( Fig 9B–9D ).

Fig 11 presents the histograms of annual and seasonal monthly total rainfall for the historical period (1976–2005) and projected (2036–2065) indicating the expected changes in the annual and seasonal rainfall distributions over the Olifants River Catchment, as predicted by the CORDEX multi-model mean. The Fig shows that annual total rainfall is projected to reduce in amount and the number of rainfall events ( Fig 11E ). The declining trend in total rainfall is the same across the season except for a projected increase in the events of rainfall amount > 450 mm in summer and > 300 mm in spring.

The historical analysis of available temperature and rainfall time series in the Olifants River Catchment indicates increasing trends in temperature and a generally decreasing trend in rainfall. Specifically, climate projections anticipate that temperature values will further increase, and rainfall decrease under both the RCP4.5 and RCP8.5 scenarios. The results indicate an increasing trend in historical and projected Tmax and Tmin between 1–5°C, particularly in the southern part of the catchment and a decreasing trend in both historical and future rainfall of about 6–35% across the catchment as a whole. Specifically, a major decline in rainfall is projected over the central part of the catchment.

General trends in temperature and rainfall distribution patterns

Regarding the mean surface temperature, historically, the northern parts of the catchment, including the sections of the Mopani and Ehlanzeni districts of the Catchment, are warmer while the southern parts include sections of Ekurhuleni, Gert Sibande, Sedibeng and City of Tshwane districts are relatively cooler. However, the projected climate change under the two scenarios of RCP 4.5 and 8.5, suggests that the southern parts of the catchment are likely to get warmer than the northern parts in general. The findings of this study agree with the findings of [ 49 ] who reported significant increasing trends in warm temperatures over individual stations located in the northern sections of South Africa.

A change in the general rainfall distribution patterns is also expected. The central region of the catchment that includes the sections of the eastern part of Sekhukhune and the northern part of Ehlanzeni districts with historically relatively higher rainfall is projected to experience a greater reduction of rainfall received, at about 30%. In general, the results indicate that the changes will be more apparent after about mid-century. In addition, the SPI analysis affirms the persistence of frequent drought conditions over the Catchment, particularly over the southern region which recently experienced drought. The results of the decreasing trends in observed rainfall are consistent with the individual station analysis conducted by [ 33 ], which found long-term increases in rainfall in the southern interior and decreases in the far north-east, but no significant trends in annual and seasonal rainfall over most of the remainder of South Africa.

The spatial variability in rainfall can be due to varying weather systems that characterize the study area. Rainfall District 34 falls in the Lowveld, and although drying is evident in the Mopani district portion of the catchment, the rainfall statistics show no drying trend in the Lowveld. The Lowveld is very dependent on moisture from the ridging of the Indian Ocean High, and with the expected strengthening of the subtropical high-pressure belt, the influx of moist air from the east will probably not diminish.

Model simulations vs. observations

Although there is a moderate correlation between simulated and observed rainfall, climate models are still useful as they are largely designed to predict the trend in climatic change over time rather than to predict specific weather events. However, we suggest that interpretation and usage of the results of rainfall trends should be guided by historical understanding. However, the results show a strong correlation between simulated and observed temperatures. Hence, the utility of temperature projections is more reliable, especially if used to estimate the rate of change in temperature instead of absolute values.

Probable impacts of climate change

Given the historical increasing drying and a warming trend in rainfall and temperatures over the catchment respectively, climate change is expected to increase the probability of shortages in water supply with both direct and indirect impacts on water-dependent sectors. As temperatures rise and with possible reductions in rainfall, crop yield could be affected, and food security could be compromised. Specifically, the prediction points to a likely scenario of increasing temperatures together with more dry periods which in turn points to an increased likelihood of severe droughts. Particularly, farmers who rely heavily on rain-fed agriculture will be the most adversely affected by climate change. Obviously, the projected climate changes in the catchment will have varying impacts on various sectors. For example, human health could be impacted by an increased incidence of malaria, cholera, and diarrhoea [ 50 ], with an indirect consequent negative impact on tourism. The probable impacts should take into account the demographical and socio-economic situation in the catchment, both current and projected, as in the approach in the Green Book [ 51 ].

CMIP 6 projections

Since the CMIP6 results have become available it will be useful to make a brief comparison between the results in the study and the latest trend projections [ 52 ]. The CMIP6 used the Shared Socialeconomic Pathways (SSPs) as against the RCPs in CMIP5. The CMIP6 projection of the average temperature increase for the Catchment at SSP3-4.5 is an increase of approximately 3°C from the base period of 1961 to 1990 to the projected period of 2081–2100. At SSP5-8.5 the increase is projected at 5–6°C ( https://interactive-atlas.ipcc.ch/ ). Therefore, the CMIP6 temperature projections are within range of the projections derived in this study. The CMIP6 rainfall projections at both SSP3-4.5 and SSP5-8.5 indicate a non-significant 4–6% decrease in rainfall, which is close to the previous CMIP5 projections. These projections are in contrast with the results of this study which indicate reductions of up to 35%, illustrating the need for downscaling of the CMIP6 projections and the interrogation of individual ensemble members to obtain a clearer idea of probable worst-case scenarios to be expected at catchment-scale.

Conclusion and recommendations

Climate-induced threats are anticipated to increase in duration, frequency, and severity under climate change. This study examined trends in historical annual and seasonal temperatures and rainfall and investigated future projections over the Olifants River Catchment to understand the region’s vulnerability to climate change. Daily values of minimum and maximum temperature from two ground weather stations and corresponding SAWS’ district of homogenous monthly rainfall data were used. An ensemble of eight global climate model simulations of the CORDEX Africa forced with CMIP5 models was used to make future climate change projections under the RCP4.5 and RCP 8.5 climate scenarios for two time periods of 2036–2065 (near future) and 2066–2095 (far future). The ensemble of the RCMs is well able to depict the spatial distribution of temperatures and rainfall when compared with historical records indicating that it outperformed the individual models. However, uncertainties are associated with rainfall direction and the amount of change. While CMIP5 and CMIP6 results are comparable, the need for updated RCM outputs based on CMIP6 and the analysis of outputs of individual ensemble members are needed, considering the larger reductions in rainfall at the higher-resolution regional scale compared to the average of the GCM outputs in the same region.

The findings of these analyses are essential for developing adaptation strategies for the various economic sectors functioning in the Olifants River Catchment. In particular, the Ehlanzeni, Gert Sibande, and Sedibeng portions of the catchment which are projected to have higher values of increasing temperatures and decreasing rainfall will need the development of effective climate change adaptation tools. With vast economic activities such as mining and agriculture in the catchment largely dependent on water resources (rainfall), the results of this research are important for developing an impact assessment and adaptation strategies to ensure that the region continues to contribute to the national GPD.

We suggest that the government through the relevant structure should facilitate the provision and access to simplified information on anticipated changes in climatic variables and plausible impacts for varying climate-sensitive sectors, particularly agriculture and water. In addition, the provision of essential tools for decision making for present and future management and practices among direct users such as farmers are imperative for climate change adaptation purposes. Furthermore, given the results, the achievement of various sustainable development goals such as Good Health and Well-being, Clean Water and Sanitation, Zero Hunger, Decent Work and Economic Growth, and No Poverty are likely to be further challenged by climate change in South Africa.

Acknowledgments

The authors present their warm thanks to South32 for inspiring the research through a climate change assessment consultation.

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Scientists agree: Climate change is real and caused by people

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The scientific consensus that climate change is happening and that it is human-caused is strong. Scientific investigation of global warming began in the 19th century , and by the early 2000s, this research began to coalesce into confidence about the reality, causes, and general range of adverse effects of global warming. This conclusion was drawn from studying air and ocean temperatures, the atmosphere’s composition, satellite records, ice cores, modeling, and more.

In 1988 the United Nations and World Meteorological Organization founded the Intergovernmental Panel on Climate Change, IPCC, to provide regular updates on the scientific evidence on global warming. In a 2013 report , the IPCC concluded that scientific evidence of warming is “unequivocal” and that the largest cause is an increase of carbon dioxide in the atmosphere as a result of humans burning fossil fuels. The IPCC continues to assess this science, periodically issuing new reports.

Climate change is real and caused by humans

The IPCC is not the only scientific group that has reached a clear consensus on the scientific evidence of human-caused global warming. As this NASA page points out, 200 global scientific organizations, 11 international science academies, and 18 American science associations have released statements in alignment with this consensus.

Amanda Staudt is the senior director for climate, atmospheric and polar sciences at the National Academies of Science, Engineering and Medicine, where she has worked since 2001. The Academies, she said, first began studying climate change in 1979, researching how much warming would likely happen if the amount of carbon dioxide concentrations in the atmosphere were doubled.

Four decades later, those findings have held up and have been strengthened based on scores of continued studies and analysis. “The remarkable thing about that study,” she said, “is that they basically got the right answer” from the start. This 1979 study by the National Research Council, Staudt said, led to investment in climate science in the U.S. 

Though this consensus has been thoroughly established, scientific research and new findings continue. Staudt said countless attempted rebuttals of climate science findings have been researched and disproved.

“We did a lot of studies in that time period, looking at those questions,” she said, ”and one by one, putting them to bed and convincing ourselves over and over again, that humans were affecting climate, and that we could document that effect.”

At the National Academies, reaching consensus requires open sessions and dialogue with scientists and agreement from committees, which typically consist of 12-15 experts. Their draft reports go through peer review, and reviewers’ concerns are resolved before publication is approved. The goal is for the complex science of climate change to become as thoroughly researched and substantiated as possible.

“One of the things I think about scientists is that we’re all inherently skeptics at some level,” Staudt said. “That’s what drives us to science, that we have questions about the world around us. And we want to prove that for ourselves.”

Scientists consistently reaffirm evidence that climate change is happening

Climate scientists worldwide go through similar processes before their findings are published. And their research papers, too, show a strong consensus about global warming. As NASA states on its website , “Multiple studies published in peer-reviewed scientific journals show that 97 percent or more of actively publishing climate scientists agree: Climate-warming trends over the past century are extremely likely due to human activities.” (By sound practice, scientists resist saying science is for all times “certain” or that its findings are “final,” and the “extremely likely” language respects that practice.)

One of the studies about the consensus was led by John Cook, a fellow at the Climate Change Communication Research Hub at Monash University in Melbourne, Australia. Cook and colleagues reviewed nearly 12,000 scientific papers to examine how aligned published research is on major findings on climate change. That study found that 97 percent of scholarly papers that take a position on climate change do endorse the consensus. The papers that rejected the consensus position contained errors, according to subsequent research .

In reviewing the papers, Cook has said he and his colleagues found the consensus to have been so widely accepted by 2013 that many researchers by then no longer felt a need to mention or reaffirm it in their research papers.

Also see: Causes of global warming: How scientists know that humans are responsible

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Samantha Harrington, director of audience experience for Yale Climate Connections, is a journalist and graphic designer with a background in digital media and entrepreneurship. Sam is especially interested... More by Samantha Harrington

ENCYCLOPEDIC ENTRY

Global warming.

The causes, effects, and complexities of global warming are important to understand so that we can fight for the health of our planet.

Earth Science, Climatology

Tennessee Power Plant

Ash spews from a coal-fueled power plant in New Johnsonville, Tennessee, United States.

Photograph by Emory Kristof/ National Geographic

Global warming is the long-term warming of the planet’s overall temperature. Though this warming trend has been going on for a long time, its pace has significantly increased in the last hundred years due to the burning of fossil fuels . As the human population has increased, so has the volume of fossil fuels burned. Fossil fuels include coal, oil, and natural gas, and burning them causes what is known as the “greenhouse effect” in Earth’s atmosphere.

The greenhouse effect is when the sun’s rays penetrate the atmosphere, but when that heat is reflected off the surface cannot escape back into space. Gases produced by the burning of fossil fuels prevent the heat from leaving the atmosphere. These greenhouse gasses are carbon dioxide , chlorofluorocarbons, water vapor , methane , and nitrous oxide . The excess heat in the atmosphere has caused the average global temperature to rise overtime, otherwise known as global warming.

Global warming has presented another issue called climate change. Sometimes these phrases are used interchangeably, however, they are different. Climate change refers to changes in weather patterns and growing seasons around the world. It also refers to sea level rise caused by the expansion of warmer seas and melting ice sheets and glaciers . Global warming causes climate change, which poses a serious threat to life on Earth in the forms of widespread flooding and extreme weather. Scientists continue to study global warming and its impact on Earth.

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Human, economic, environmental toll of climate change on the rise: WMO

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The relentless advance of climate change brought more drought, flooding and heatwaves to communities around the world last year, compounding threats to people’s lives and livelihoods, the UN’s World Meteorological Organization ( WMO ) said on Friday.

WMO latest State of the Global Climate report shows that the last eight years were the eight warmest on record , and that sea level rise and ocean warming hit new highs . Record levels of greenhouse gases caused “planetary scale changes on land, in the ocean and in the atmosphere”.

#ClimateChange shocks increased in 2022. Ocean heat and sea level rise at record levels. Antarctic sea ice hit a new low. Extreme glacier melt in Europe. #StateOfClimate report highlights the huge socio-economic cost of droughts, floods, and heatwaves.🔗 https://t.co/yipNQtrK12 https://t.co/Vnrbe9M8Xl World Meteorological Organization WMO April 21, 2023

The organization says its report, released ahead of this year’s Mother Earth Day , echoes UN Secretary-General António Guterres ’ call for “ deeper, faster emissions cuts to limit global temperature rise to 1.5 degree Celsius”, as well as “ massively scaled-up investments in adaptation and resilience, particularly for the most vulnerable countries and communities who have done the least to cause the crisis”.

WMO Secretary-General, Prof. Petteri Taalas, said that amid rising greenhouse gas emissions and a changing climate, “populations worldwide continue to be gravely impacted by extreme weather and climate events ”. He stressed that last year, “continuous drought in East Africa, record breaking rainfall in Pakistan and record-breaking heatwaves in China and Europe affected tens of millions, drove food insecurity, boosted mass migration, and cost billions of dollars in loss and damage.”

WMO highlights the importance of investing in climate monitoring and early warning systems to help mitigate the humanitarian impacts of extreme weather. The report also points out that today, improved technology makes the transition to renewable energy “cheaper and more accessible than ever” .

Warmest years on record

The State of the Global Climate report complements the Intergovernmental Panel on Climate Change ( IPCC ) Sixth Assessment report released a month ago, which includes data up to 2020.

WMO’s new figures show that global temperatures have continued to rise, making the years 2015 to 2022 the eight warmest ever since regular tracking started in 1850. WMO notes that this was despite three consecutive years of a cooling La Niña climate pattern.

WMO says concentrations of the three main greenhouse gases, which trap heat in the atmosphere – carbon dioxide, methane, and nitrous oxide – reached record highs in 2021, which is the latest year for which consolidated data is available , and that there are indications of a continued increase in 2022.

Indicators ‘off the charts’

According to the report, “melting of glaciers and sea level rise - which again reached record levels in 2022 - will continue to up to thousands of years ”. WMO further highlights that “Antarctic sea ice fell to its lowest extent on record and the melting of some European glaciers was, literally, off the charts”.

Sea level rise, which threatens the existence of coastal communities and sometimes entire countries, has been fuelled not only by melting glaciers and ice caps in Greenland and Antarctica, but also by the expansion of the volume of oceans due to heat. WMO notes that ocean warming has been “particularly high in the past two decades”.

Deadly consequences

The report examines the many socio-economic impacts of extreme weather, which have wreaked havoc in the lives of the most vulnerable around the world . Five consecutive years of drought in East Africa, in conjunction with other factors such as armed conflict, have brought devastating food insecurity to 20 million people across the region.

Extensive flooding in Pakistan caused by severe rainfall in July and August last year killed over 1,700 people, while some 33 million were affected. WMO highlights that total damage and economic losses were assessed at $30 billion, and that by October 2022, around 8 million people had been internally displaced by the floods.

The report also notes that in addition to putting scores of people on the move, throughout the year, hazardous climate and weather-related events “worsened conditions” for many of the 95 million people already living in displacement .

Threat to ecosystems

Environmental impacts of climate change are another focus of the report, which highlights a shift in recurring events in nature, “such as when trees blossom, or birds migrate”. The flowering of cherry trees in Japan has been tracked since the ninth century, and in 2021 the date of the event was the earliest recorded in 1,200 years .

As a result of such shifts, entire ecosystems can be upended . WMO notes that spring arrival times of over a hundred European migratory bird species over five decades “show increasing levels of mismatch to other spring events”, such as the moment when trees produce leaves and insects take flight, which are important for bird survival.

The report says these mismatches “are likely to have contributed to population decline in some migrant species , particularly those wintering in sub-Saharan Africa”, and to the ongoing destruction of biodiversity.

Ending the ‘war on nature’

In his message on Earth Day, UN chief Mr. Guterres warned that “ biodiversity is collapsing as one million species teeter on the brink of extinction ”, and called on the world to end its “relentless and senseless wars on nature”, insisting that “we have the tools, the knowledge, and the solutions” to address climate change.

Last month, Mr. Guterres convened an Advisory Panel of top UN agency officials, private sector and civil society leaders, to help fast track a global initiative aiming to protect all countries through life-saving early warning systems by 2027. Stepped up coordinated action was announced, initially in 30 countries particularly vulnerable to extreme weather, including Small Island Developing States and Least Developed Countries.

Early Warnings for All

WMO Secretary-General Prof. Petteri Taalas said on Friday that some one hundred countries currently do not have adequate weather services in place, and that the UN Early Warnings for All Initiative “ aims to fill the existing capacity gap to ensure that every person on earth is covered by early warning services”.

Mr. Taalas explained that “achieving this ambitious task requires improvement of observation networks, investments in early warning, hydrological and climate service capacities.” He also stressed the effectiveness of collaboration among UN agencies in addressing humanitarian impacts of climate events, especially in reducing mortality and economic losses. 

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• climate action

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Essay on Global Warming

• Updated on  
• Nov 23, 2023

Being able to write an essay is an integral part of mastering any language. Essays form an integral part of many academic and scholastic exams like the SAT , and UPSC amongst many others. It is a crucial evaluative part of English proficiency tests as well like IELTS , TOEFL , etc. Major essays are meant to emphasize public issues of concern that can have significant consequences on the world. To understand the concept of Global Warming and its causes and effects, we must first examine the many factors that influence the planet’s temperature and what this implies for the world’s future. Here’s an unbiased look at the essay on Global Warming and other essential related topics.

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Since the industrial and scientific revolutions, Earth’s resources have been gradually depleted. Furthermore, the start of the world’s population’s exponential expansion is particularly hard on the environment. Simply put, as the population’s need for consumption grows, so does the use of natural resources , as well as the waste generated by that consumption.

Climate change has been one of the most significant long-term consequences of this. Climate change is more than just the rise or fall of global temperatures; it also affects rain cycles, wind patterns, cyclone frequencies, sea levels, and other factors. It has an impact on all major life groupings on the planet.

Also Read: World Population Day

What is Global Warming?

Global warming is the unusually rapid increase in Earth’s average surface temperature over the past century, primarily due to the greenhouse gases released by people burning fossil fuels . The greenhouse gases consist of methane, nitrous oxide, ozone, carbon dioxide, water vapour, and chlorofluorocarbons. The weather prediction has been becoming more complex with every passing year, with seasons more indistinguishable, and the general temperatures hotter. The number of hurricanes, cyclones, droughts, floods, etc., has risen steadily since the onset of the 21st century. The supervillain behind all these changes is Global Warming. The name is quite self-explanatory; it means the rise in the temperature of the Earth.

Also Read: What is a Natural Disaster?

According to recent studies, many scientists believe the following are the primary four causes of global warming:

• Deforestation 
• Greenhouse emissions
• Carbon emissions per capita

Extreme global warming is causing natural disasters , which can be seen all around us. One of the causes of global warming is the extreme release of greenhouse gases that become trapped on the earth’s surface, causing the temperature to rise. Similarly, volcanoes contribute to global warming by spewing excessive CO2 into the atmosphere.

The increase in population is one of the major causes of Global Warming. This increase in population also leads to increased air pollution . Automobiles emit a lot of CO2, which remains in the atmosphere. This increase in population is also causing deforestation, which contributes to global warming.

The earth’s surface emits energy into the atmosphere in the form of heat, keeping the balance with the incoming energy. Global warming depletes the ozone layer, bringing about the end of the world. There is a clear indication that increased global warming will result in the extinction of all life on Earth’s surface.

Also Read: Land, Soil, Water, Natural Vegetation, and Wildlife Resources

Of course, industries and multinational conglomerates emit more carbon than the average citizen. Nonetheless, activism and community effort are the only viable ways to slow the worsening effects of global warming. Furthermore, at the state or government level, world leaders must develop concrete plans and step-by-step programmes to ensure that no further harm is done to the environment in general.

Although we are almost too late to slow the rate of global warming, finding the right solution is critical. Everyone, from individuals to governments, must work together to find a solution to Global Warming. Some of the factors to consider are pollution control, population growth, and the use of natural resources.

One very important contribution you can make is to reduce your use of plastic. Plastic is the primary cause of global warming, and recycling it takes years. Another factor to consider is deforestation, which will aid in the control of global warming. More tree planting should be encouraged to green the environment. Certain rules should also govern industrialization. Building industries in green zones that affect plants and species should be prohibited.

Also Read: Essay on Pollution

Global warming is a real problem that many people want to disprove to gain political advantage. However, as global citizens, we must ensure that only the truth is presented in the media.

This decade has seen a significant impact from global warming. The two most common phenomena observed are glacier retreat and arctic shrinkage. Glaciers are rapidly melting. These are clear manifestations of climate change.

Another significant effect of global warming is the rise in sea level. Flooding is occurring in low-lying areas as a result of sea-level rise. Many countries have experienced extreme weather conditions. Every year, we have unusually heavy rain, extreme heat and cold, wildfires, and other natural disasters.

Similarly, as global warming continues, marine life is being severely impacted. This is causing the extinction of marine species as well as other problems. Furthermore, changes are expected in coral reefs, which will face extinction in the coming years. These effects will intensify in the coming years, effectively halting species expansion. Furthermore, humans will eventually feel the negative effects of Global Warming.

Also Read: Concept of Sustainable Development

Sample Essays on Global Warming

Here are some sample essays on Global Warming:

Global Warming is caused by the increase of carbon dioxide levels in the earth’s atmosphere and is a result of human activities that have been causing harm to our environment for the past few centuries now. Global Warming is something that can’t be ignored and steps have to be taken to tackle the situation globally. The average temperature is constantly rising by 1.5 degrees Celsius over the last few years. The best method to prevent future damage to the earth, cutting down more forests should be banned and Afforestation should be encouraged. Start by planting trees near your homes and offices, participate in events, and teach the importance of planting trees. It is impossible to undo the damage but it is possible to stop further harm.

Also Read: Social Forestry

Over a long period, it is observed that the temperature of the earth is increasing. This affected wildlife , animals, humans, and every living organism on earth. Glaciers have been melting, and many countries have started water shortages, flooding, and erosion and all this is because of global warming. No one can be blamed for global warming except for humans. Human activities such as gases released from power plants, transportation, and deforestation have increased gases such as carbon dioxide, CFCs, and other pollutants in the earth’s atmosphere. The main question is how can we control the current situation and build a better world for future generations. It starts with little steps by every individual. Start using cloth bags made from sustainable materials for all shopping purposes, instead of using high-watt lights use energy-efficient bulbs, switch off the electricity, don’t waste water, abolish deforestation and encourage planting more trees. Shift the use of energy from petroleum or other fossil fuels to wind and solar energy. Instead of throwing out the old clothes donate them to someone so that it is recycled. Donate old books, don’t waste paper.  Above all, spread awareness about global warming. Every little thing a person does towards saving the earth will contribute in big or small amounts. We must learn that 1% effort is better than no effort. Pledge to take care of Mother Nature and speak up about global warming.

Also Read: Types of Water Pollution

Global warming isn’t a prediction, it is happening! A person denying it or unaware of it is in the most simple terms complicit. Do we have another planet to live on? Unfortunately, we have been bestowed with this one planet only that can sustain life yet over the years we have turned a blind eye to the plight it is in. Global warming is not an abstract concept but a global phenomenon occurring ever so slowly even at this moment. Global Warming is a phenomenon that is occurring every minute resulting in a gradual increase in the Earth’s overall climate. Brought about by greenhouse gases that trap the solar radiation in the atmosphere, global warming can change the entire map of the earth, displacing areas, flooding many countries, and destroying multiple lifeforms. Extreme weather is a direct consequence of global warming but it is not an exhaustive consequence. There are virtually limitless effects of global warming which are all harmful to life on earth. The sea level is increasing by 0.12 inches per year worldwide. This is happening because of the melting of polar ice caps because of global warming. This has increased the frequency of floods in many lowland areas and has caused damage to coral reefs. The Arctic is one of the worst-hit areas affected by global warming. Air quality has been adversely affected and the acidity of the seawater has also increased causing severe damage to marine life forms. Severe natural disasters are brought about by global warming which has had dire effects on life and property. As long as mankind produces greenhouse gases, global warming will continue to accelerate. The consequences are felt at a much smaller scale which will increase to become drastic shortly. The power to save the day lies in the hands of humans, the need is to seize the day. Energy consumption should be reduced on an individual basis. Fuel-efficient cars and other electronics should be encouraged to reduce the wastage of energy sources. This will also improve air quality and reduce the concentration of greenhouse gases in the atmosphere. Global warming is an evil that can only be defeated when fought together. It is better late than never. If we all take steps today, we will have a much brighter future tomorrow. Global warming is the bane of our existence and various policies have come up worldwide to fight it but that is not enough. The actual difference is made when we work at an individual level to fight it. Understanding its import now is crucial before it becomes an irrevocable mistake. Exterminating global warming is of utmost importance and each one of us is as responsible for it as the next.  

Always hear about global warming everywhere, but do we know what it is? The evil of the worst form, global warming is a phenomenon that can affect life more fatally. Global warming refers to the increase in the earth’s temperature as a result of various human activities. The planet is gradually getting hotter and threatening the existence of lifeforms on it. Despite being relentlessly studied and researched, global warming for the majority of the population remains an abstract concept of science. It is this concept that over the years has culminated in making global warming a stark reality and not a concept covered in books. Global warming is not caused by one sole reason that can be curbed. There are multifarious factors that cause global warming most of which are a part of an individual’s daily existence. Burning of fuels for cooking, in vehicles, and for other conventional uses, a large amount of greenhouse gases like carbon dioxide, and methane amongst many others is produced which accelerates global warming. Rampant deforestation also results in global warming as lesser green cover results in an increased presence of carbon dioxide in the atmosphere which is a greenhouse gas.  Finding a solution to global warming is of immediate importance. Global warming is a phenomenon that has to be fought unitedly. Planting more trees can be the first step that can be taken toward warding off the severe consequences of global warming. Increasing the green cover will result in regulating the carbon cycle. There should be a shift from using nonrenewable energy to renewable energy such as wind or solar energy which causes less pollution and thereby hinder the acceleration of global warming. Reducing energy needs at an individual level and not wasting energy in any form is the most important step to be taken against global warming. The warning bells are tolling to awaken us from the deep slumber of complacency we have slipped into. Humans can fight against nature and it is high time we acknowledged that. With all our scientific progress and technological inventions, fighting off the negative effects of global warming is implausible. We have to remember that we do not inherit the earth from our ancestors but borrow it from our future generations and the responsibility lies on our shoulders to bequeath them a healthy planet for life to exist. 

Also Read: Essay on Disaster Management

One good action in a day is to combat the heat.

Global Warming and Climate Change are two sides of the same coin. Both are interrelated with each other and are two issues of major concern worldwide. Greenhouse gases released such as carbon dioxide, CFCs, and other pollutants in the earth’s atmosphere cause Global Warming which leads to climate change. Black holes have started to form in the ozone layer that protects the earth from harmful ultraviolet rays. Human activities have created climate change and global warming. Industrial waste and fumes are the major contributors to global warming. Another factor affecting is the burning of fossil fuels, deforestation and also one of the reasons for climate change.  Global warming has resulted in shrinking mountain glaciers in Antarctica, Greenland, and the Arctic and causing climate change. Switching from the use of fossil fuels to energy sources like wind and solar. When buying any electronic appliance buy the best quality with energy savings stars. Don’t waste water and encourage rainwater harvesting in your community. 

Also Read: Essay on Air Pollution

Writing an effective essay needs skills that few people possess and even fewer know how to implement. While writing an essay can be an assiduous task that can be unnerving at times, some key pointers can be inculcated to draft a successful essay. These involve focusing on the structure of the essay, planning it out well, and emphasizing crucial details. Mentioned below are some pointers that can help you write better structure and more thoughtful essays that will get across to your readers:

• Prepare an outline for the essay to ensure continuity and relevance and no break in the structure of the essay
• Decide on a thesis statement that will form the basis of your essay. It will be the point of your essay and help readers understand your contention
• Follow the structure of an introduction, a detailed body followed by a conclusion so that the readers can comprehend the essay in a particular manner without any dissonance.
• Make your beginning catchy and include solutions in your conclusion to make the essay insightful and lucrative to read
• Reread before putting it out and add your flair to the essay to make it more personal and thereby unique and intriguing for readers  

Relevant Blogs

Ans. Both natural and man-made factors contribute to global warming. The natural one also contains methane gas, volcanic eruptions, and greenhouse gases. Deforestation , mining , livestock raising, burning fossil fuels, and other man-made causes are next.

Ans. The government and the general public can work together to stop global warming. Trees must be planted more often, and deforestation must be prohibited. Auto usage needs to be curbed, and recycling needs to be promoted.

Ans. Switching to renewable energy sources , adopting sustainable farming, transportation, and energy methods, and conserving water and other natural resources.

We hope this blog gave you an idea about how to write and present an essay on global warming that puts forth your opinions. The skill of writing an essay comes in handy when appearing for standardized language tests . Thinking of taking one soon? Leverage Edu provides the best online test prep for the same via Leverage Live . Register today to know more!

Digvijay Singh

Having 2+ years of experience in educational content writing, withholding a Bachelor's in Physical Education and Sports Science and a strong interest in writing educational content for students enrolled in domestic and foreign study abroad programmes. I believe in offering a distinct viewpoint to the table, to help students deal with the complexities of both domestic and foreign educational systems. Through engaging storytelling and insightful analysis, I aim to inspire my readers to embark on their educational journeys, whether abroad or at home, and to make the most of every learning opportunity that comes their way.

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This was really a good essay on global warming… There has been used many unic words..and I really liked it!!!Seriously I had been looking for a essay about Global warming just like this…

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I want to learn how to write essay writing so I joined this page.This page is very useful for everyone.

Hi, we are glad that we could help you to write essays. We have a beginner’s guide to write essays ( https://leverageedu.com/blog/essay-writing/ ) and we think this might help you.

It is not good , to have global warming in our earth .So we all have to afforestation program on all the world.

thank you so much

Very educative , helpful and it is really going to strength my English knowledge to structure my essay in future

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Global warming is the increase in 𝓽𝓱𝓮 ᴀᴠᴇʀᴀɢᴇ ᴛᴇᴍᴘᴇʀᴀᴛᴜʀᴇs ᴏғ ᴇᴀʀᴛʜ🌎 ᴀᴛᴍᴏsᴘʜᴇʀᴇ

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Modelling Malaria Incidence in the Limpopo Province, South Africa: Comparison of Classical and Bayesian Methods of Estimation

Malaria infects and kills millions of people in Africa, predominantly in hot regions where temperatures during the day and night are typically high. In South Africa, Limpopo Province is the hottest province in the country and therefore prone to malaria incidence. The districts of Vhembe, Mopani and Sekhukhune are the hottest districts in the province. Malaria cases in these districts are common and malaria is among the leading causes of illness and deaths in these districts. Factors contributing to malaria incidence in Limpopo Province have not been deeply investigated, aside from the general knowledge that the province is the hottest in South Africa. Bayesian and classical methods of estimation have been applied and compared on the effect of climatic factors on malaria incidence. Credible and confidence intervals from a negative binomial model estimated via Bayesian estimation and maximum likelihood estimation, respectively, were utilized in the comparison process. Overall assumptions underpinning each method were given. The Bayesian method appeared more robust than the classical method in analysing malaria incidence in Limpopo Province. The classical method identified rainfall and temperature during the night to be significant predictors of malaria incidence in Mopani, Vhembe and Waterberg districts. However, the Bayesian method found rainfall, normalised difference vegetation index, elevation, temperatures during the day and night to be the significant predictors of malaria incidence in Mopani, Sekhukhune and Vhembe districts of Limpopo Province. Both methods affirmed that Vhembe district is more susceptible to malaria incidence, followed by Mopani district. We recommend that the Department of Health and Malaria Control Programme of South Africa allocate more resources for malaria control, prevention and elimination to Vhembe and Mopani districts of Limpopo Province.

1. Introduction

Malaria is a mosquito borne disease caused by five protozoan species, namely: Plasmodium Falciparum, Plasmodium vivax , Plasmodium malariae and related species of Plasmodium ovale and Plasmodium knowlesi ([ 1 ]). The protozoa are transmitted to humans through the bites of infected female Anopheles mosquitos (mosquitos carrying protozoa). Plasmodium falciparum is known to account for many malaria cases globally and is therefore regarded as a threat to public health worldwide ([ 1 , 2 ]). Malaria incidence refers to the commonness of malaria occurrence. When the incidence rates are high, transmission and prevalence of malaria are also high. This exposes the vulnerability and danger of the disease to society.

The symptoms of malaria include fever (>37.5 °C), headache, rigors, muscle pains, diarrhea, nausea, vomiting, loss of appetite, inability to feed babies, dizziness and sore throat. Based on history, malaria has infected and taken the lives of millions of individuals. This disease remains a major cause of human morbidity and mortality in most of the developing countries in Africa. Young children, pregnant women, and elderly individuals are groups of people at higher risk of malaria transmission ([ 3 ]). Sachs and Malaney [ 4 ] outlined the factors that contribute to increased malaria cases. These encompassed changing agricultural practices, building of more dams, poor irrigation skills, deforestation, poor public health services and long-term climate change causes such as El Nino and global warming. Hay et al. [ 5 ] found seasonal climatic change to be an important determinant of malaria incidence since variations in climate conditions could increase mosquito vector dynamics and parasite development rates ([ 6 , 7 ]). Indeed, malaria incidence has been found to be generally low during dry-hot season when vector populations are reduced and spatially restricted. As a result, several of studies on malaria incidence tend to focus on the peak transmission season, which is often the rainy season, whereas the epidemiological picture during the dry-hot season is often neglected ([ 7 , 8 ]).

According to Blumberg and Frean [ 9 ], there has been great progress in malaria control globally. This progress is attributed to increased funding, improved use of life-saving interventions and more countries pursuing malaria elimination measures. Although the progress achieved in countries such as Sri Lanka and some Sub-Saharan African countries has been considerable, South Africa remains among countries with high risk of malaria transmission, especially the northern part of the country ([ 7 , 8 , 10 ]). Raman et al. [ 10 ] further outlined that South Africa officially transitioned from controlling malaria to the goal of eliminating the disease in 2012. However, malaria cases have increased from 6811 in 2013 to 11,711 in 2014, with many cases reported in the Mpumalanga and Limpopo provinces of South Africa ([ 7 ]). It will therefore be expedient to model malaria incidence in Limpopo Province because it is amongst the provinces that account for most malaria cases in South Africa ([ 7 , 8 ]). The present study will employ both classical and Bayesian methods of estimation to assess the effect of climatic factors such as temperature, rainfall, elevation and normalised difference vegetation index on malaria incidence. The results of the present study may well assist in malaria control programs for inspection, control, prevention and possible elimination of malaria in Limpopo Province.

This study is crucial because there are still arguments concerning association between climatic factors and malaria incidences ([ 11 ]). Yé et al. [ 11 ] highlighted that effects of climatic factors on malaria transmission are not efficiently assessed, specifically at local levels. Yé et al. [ 11 ] further outlines that data used in many studies are proxy meteorological data obtained through satellites or interpolated from a different scale. To the best of our knowledge limited or no prior study on malaria prevalence in the province or the country has employed local scale data. Hence, in this present study, a local scale data from a malaria control institution in Limpopo province will be used. Indeed, the gap that we seek to address in this paper does not only lie in the data used in fitting the model but also in the methodology. For instance, environmental factors vary overtime hence classical methods may not do “justice” to the data, hence the introduction of the Bayesian methods. This is absent in many of the previous studies cited earlier.

In several studies that modelled malaria counts, methods such as Poisson, negative binomial, hurdle, quasi-Poisson and dynamic computable general equilibrium (DCGE) models have been applied. For instance, Shimaponda-Mataa et al. [ 12 , 13 , 14 , 15 ] modelled the environmental factors and assessed their relationships with malaria incidence through the development of Poisson regression models. Kazembe [ 16 ] conducted a research on malaria incidence and found negative binomial regression model to provide a better fit compared to Poisson regression model. In other separate studies, Shimaponda-Mataa et al. [ 12 , 17 , 18 ] found a positive relationship between rainfall and malaria risk. On the contrary, Zayeri et al. [ 14 ] found a negative relationship between rainfall and malaria incidence. Studies by Shimaponda-Mataa et al. [ 12 , 18 ] further revealed a positive relationship between minimum temperature and malaria risk. Gerritsen et al. [ 14 , 19 ] provided evidence that adults are more susceptible to malaria transmission than children. There are also studies that modelled malaria incidence using other different methods. These methods include the time series data analysis methods employed in the studies of Adeola et al. [ 20 , 21 ], and the qualitative retrospective descriptive method employed in the study of Machimana [ 22 ]. The studies by Adeola et al. [ 20 , 21 ] found that malaria cases have a positive relationship with both temperature and rainfall. These results support the findings of Machimana [ 22 ], which revealed that malaria transmission in South Africa is associated with climate. Machimana [ 22 ] study further revealed that malaria cases are highly seasonal, with higher number of cases in January to April and October. The findings of the study by Machimana [ 22 ] also indicated that persons between the ages 16 to 40 years and males are more susceptible to malaria transmission.

Abiodun et al. [ 8 ] conducted a study on the resurgence of malaria prevalence in South Africa between 2015 and 2018. Their study concentrated on reviewing several malaria-related research articles that were published using Arksey and O’Malley framework. Out of a total of 534 malaria related articles that were reviewed, very few of them made use of Bayesian estimations. The argument in the present study is that climatic variables in relation to malaria prevalence are dynamic and classical statistics estimators such as maximum likelihood methods will probably not be efficient compared to Bayesian estimation, where climatic variables are treated as random variables with some underlying distribution. In another study, Abiodun et al. [ 7 ] conducted a study on malaria incidence in Limpopo Province using dynamical and zero-inflated negative binomial regression models. Results from their study revealed the effect of rainfall and average temperature on malaria incidence. The present paper is different from several previous papers especially in terms modelling and parameter estimation.

Another point of departure is that many of the data sets employed in most of these previous studies in one way or the other have used environmental factors and other predictors of malaria. Environmental factors such as elevation, temperature, normalised difference vegetation index (NDVI) are dynamic and their effect on malaria incidence cannot be fixed, although they are unknown as claimed by the classical approach. A Bayesian estimation therefore will be adopted in the present study to model these factors on malaria incidence because it considers these environmental factors as random variables with some probability distribution. This has made the use of Bayesian estimation methods very popular. In classical paradigm, parameters of a model are unknown, but fixed constants, while in Bayesian estimation the parameters are random, with knowledge about the parameters described in the form of a probability distribution. We seek to explore these two methods and compare them in the context of malaria incidence in Limpopo Province of South Africa. This is limited in a number of studies conducted on malaria incidence, especially in Southern Africa.

In this paper, classical and Bayesian estimation methods will be utilised and compared in the context of modelling malaria incidence. The relation between the two statistical estimations are from the fact that the posterior distribution in the Bayesian approach is proportional to the likelihood function times the prior distribution. Whereas maximum likelihood estimation (MLE) uses asymptotic distributional assumptions in classical statistics, the uncertainty about model parameters in the Bayesian approach is expressed through the prior distributions. Combining the prior distribution and likelihood (data), researchers are able to update the knowledge about the model parameters. This is done through the posterior distribution from which we can infer the estimates of the model parameters and relevant quantities like credible intervals.

The rest of the paper is outlined as follows: Section 2 describes materials and methods while Section 3 presents the discourse on the results. Discussion and concluding remarks are presented in Section 4 and Section 5 , respectively.

2. Materials and Methods

2.1. study frame and data collection.

This study models malaria incidence in Limpopo Province of South Africa. Limpopo Province consists of five districts: Capricorn, Mopani, Sekhukhune, Vhembe, and Waterberg. Malaria incidence or cases data were provided by Malaria Control Center located in Tzaneen. The population data were provided by Statistics South Africa (StatsSA) [ 23 ]. Environmental factors: rainfall, temperature, elevation, and normalised difference vegetation index data were obtained from Ecoverb. The data were collected monthly from January 2014 to June 2015. R software was used in analysing the data while the bar charts were done in Excel.

2.1.1. Classical Methods

Poisson regression model.

The Poisson distribution is probably the most used discrete distribution because of its simplicity. The Poisson probability mass function is given by:

The mean and variance of the Poisson distribution are equal to λ . Hence, for Poisson regression, we have:

Consequently, the Poisson regression model is:

From (3), the mean and variance of the Poisson regression model are equal to μ ( x i ) . Thus, the Poisson regression is apt for count data where the mean and variance are numerically identical. The values of a count response variable y are non-negative. Therefore, the mean function μ ( x i ) safeguards the non-negative nature of the response variable y .

Negative Binomial (NB) Model

The use of negative binomial (NB) model in count data modelling, as it is the case in this study, often comes up when there is over-dispersion. While the Poisson distribution is often first to be considered for fitting count data such as malaria incidence, nevertheless, if the mean is very much less than the variance of the data, then there is over-dispersion in the data. An alternative approach to correct over-dispersion is to fit a NB regression model to the over-dispersed Poisson regression model. The probability mass function for the NB distribution is given by:

The mean and variance of the above model are respectively given by μ = λ [ α ( 1 − λ ) ] and σ 2 = [ λ α [ 1 − λ ] ] 2 . From the mean and variance, we can envisage that the NB distribution is over-dispersed since the variance surpasses the mean. Assume the mean of the NB distribution depends on some predictor variable x i , then we can write the mean μ ( x i ) = λ [ α ( 1 − λ ) ] from which λ is obtained as λ = α μ ( x i ) [ 1 + α μ ( x i ) ] . Hence, the NB regression model can be expressed as:

The mean and variance of the NB regression model are correspondingly given by E ( Y ) = μ ( x i ) and V ( Y ) = μ ( x i ) [ 1 + α μ ( x i ) ] . The NB regression model reduces to the Poisson regression model when the dispersion parameter α = r goes to zero ([ 24 ]).

Maximum Likelihood Estimation

Assume we observe y i ( i = 1 , 2 , … , n ) , count response variables, each with predictor variables x i 1 , x i 2 , … , x i , k − 1 . The likelihood function for the Poisson regression model is obtained by multiplying the respective probabilities in (3) to obtain:

Taking the log of (6) gives the log-likelihood function as:

Similarly, the log-likelihood function for NB regression model can also be estimated via maximum likelihood. Cameron and Trivedi [ 24 ] gives the logarithm function as:

A common goodness-of-fit statistic for count regression models is the Pearson’s χ 2 statistic defined by:

where v ( μ i ) ∧ is the variance function assessed at the estimated mean. The log-likelihood in (7) and (8) can be used as goodness-of-fit statistic. However, the log-likelihood and the Pearson’s chi-square displayed in Equation (9) do not take into account the number of estimated parameters in the model, hence the use information criteria such as Akaike Information Criterion (AIC) become necessary. From the NB regression model, the number of estimated parameters is given by p * = p + 1 . The extra 1 is from the dispersion parameter in NB regression model. In this study, we use the AIC as a goodness-of -fit statistic, which take into consideration p * , the number of parameters. Generally, the higher the number of parameters, the greater the log-likelihood, while AIC penalizes for the number of parameters and it is given by:

2.1.2. Bayesian Approach

Bayesian statistics to a large extent can be attributed to Reverend Thomas Bayes (1701–1761), who developed Bayes’ theorem. Bayes’ theorem expresses the conditional probability, or posterior probability, of an event A after B as observed in terms of the prior probability of A, prior probability of B, and the conditional probability of B given A. The foundation for Bayesian inference is defined from Bayes theorem and is given as follows:

By substituting B with observations y , A with parameter set or space Θ and probabilities P r   with densities p , Equation (11) becomes:

where p ( y ) is the marginal likelihood of y , p ( Θ ) is the set of prior distributions of parameter set Θ before y is observed, p ( y | Θ ) is the likelihood of y under a model, and p ( Θ | y ) is the joint posterior distribution of the parameter set or space Θ that expresses the uncertainty about j parameter set Θ after taking the prior and the data into account. Recall that Θ = θ 1 , … ,   θ j with denominator expressed as:

where defines the marginal likelihood of   y or the prior predictive of y , and may be set into an unidentified constant c resulting in the following:

The presence of the marginal distribution likelihood of y normalises the joint posterior distribution   p ( Θ | y ) , ensuring it is a proper distribution and integrates to one. Eliminating the constant   c from Equation (13) will result in some changes in the relationship from the use of the equal sign to the constant of proportionality, resulting in the following:

2.1.3. Computation of NB Using Bayesian Estimation

Through the Markov chain Monte Carlo (MCMC), we use a Gibbs sampler for the NB regression model. This model is derived from the Poisson regression model to account for over-dispersion, which usually happens or occurs in count data. Suppose the response are independent, then:

where Y i is the response variable for i = 1 , 2 , … , n ; r is the over-dispersion parameter. The expectation is modelled as:

which implies that:

where X is the matrix of regressors and β → is the parameter vector.

The conditional likelihood of Y i given w i is defined as:

where k i = y i − r 2 . Now, exploiting property 1 of the poly-Gamma (PG) distribution, Equation (18) can be written as:

where η i = X i T β → . Suppose ψ i is distributed according to poly-Gamma (PG) ~ ( Y i + r , η i ) , then following Scott and Pillow (2012), the conditional probability for β → is given by:

where Y → * is equal to the n * 1 subvector of Y → corresponding to w i ; n * = ∑ i = 1 n w i is the number of individuals in risk class; ψ → is a vector of length n * with elements z i = y i − r 2 ψ i ; Ω d i a g ( ψ 1 , … , ψ n ) = n * n is the precision matrix and X * = N * × P matrix. From Equation (20), it is clear that z → is normally distributed with mean η → = X * β → and diagonal covariance matrix Ω − 1 . Therefore, it is reasonable to assume a conditional Gaussian prior for β → denoted by:

The conjugate prior full conditional distribution for β → given z → and Ω follows N p ( μ → , ∑ ) , where ∑ = ( ∑ 0 − 1 + X * T Ω X * ) − 1 and μ → = ∑ ( ∑ 0 − 1 + X * T Ω z → ) . Therefore given the current values for β → , w → and r , the Gibbs sampler is given as follows:

• for w i , draw ψ i from its PG ~ ( Y i + r , η i ) distribution
• for w i , define z i = y i − r 2 ψ i ,
• update β → from N ( μ → , ∑ ) distribution,
• update r using a random-walk Metropolis-Hastings algorithm.

This section presents and discusses the results for fitting count regression models, namely; the Poisson regression model and NB model estimated with maximum likelihood, and Bayesian estimation methods. The variables used in fitting both models are described in Table 1 , with descriptive statistics presented in Table 2 . The distribution of malaria incidence in Limpopo province is presented in Figure 1 . The posterior results for the Bayesian method are presented in Appendix A .

The distribution of malaria incidence in the Limpopo province.

Variable description.

Descriptive Statistics.

The histogram displayed in Figure 1 shows that the distribution of malaria incidence is skewed to the right. This implies that it takes a lopsided mound shape with its tail going off to the right. The shape of this histogram is similar to the shape of a Poisson distribution. Hence we model the data using Poisson regression model.

According to Figure 2 , the transmission rate of malaria was high in 2014 than in 2015. This probably may be due to various effects of environmental factors as they may differ in each year and it may also indicate the success of malaria control, prevention and elimination methods that are used currently in Limpopo Province.

The distribution of malaria incidence in 2014 and 2015.

As shown in Figure 3 , Vhembe district is depicted to have the highest rate of malaria incidence, followed by Mopani district as compared to all the other districts. Capricorn district has the lowest rate of malaria incidence. The high rate of malaria incidence in Mopani and Vhembe districts could be attributed to the high temperatures in the two districts.

The distribution of malaria incidence across the districts of Limpopo province.

The p -values for all the covariates as displayed in Table 3 are less than the level of significance, 0.05. This suggests evidence against the null hypothesis of no relationship between the covariates and malaria incidence. Rainfall and elevation estimate values are negative, suggesting that they have a negative relationship with malaria incidence. Table 3 also depicts a positive relationship between temperature, NDVI and malaria incidence. When the ratio of the deviance statistic and its degrees of freedom is significantly larger than 1, then there is evidence of lack of fit in the model developed. The ratio of the deviance statistic and its degrees of freedom is 12.753, which is significantly larger than 1. Hence there is evidence of lack of fit for the model presented in Table 3 .

Parameter estimates for Poisson regression model under classical approach encompassing all the variables.

NB: ** Indicates the significance of the variable within [95%] confidence interval for the five districts in Limpopo province where the study was conducted.

Detection of Over-Dispersion

The Pearson’s chi-square is considered to be robust in detecting over-dispersion in Poisson models. If the ratio of the residual deviance and the degree of freedom is significantly larger than 1, then the probability that the developed model is over-dispersed is high. Based on the Poisson model, the ratio of the residual deviance and the degrees of freedom is 12.753. This implies that the probability that the selected Poisson model is over-dispersed is very high. To validate that the Poisson model selected may be over-dispersed, we check if the response variable satisfies the Poisson assumption of an equality between the mean and the variance. Table 2 shows that the mean for the response variable is 23.95 and the variance is found to be 3822.5. Therefore, the condition of equal mean and variance for a Poisson distribution is violated. We can then conclude that the Poisson model presented by Table 3 is over-dispersed.

Table 4 presents the NB model developed to correct the overdispersed Poisson model presented by Table 3 . According to Table 4 , the 95% confidence interval for the covariate rain does not include 0, which implies that it is significant at 5% level of significance. Hence, there is a relationship between rainfall and malaria incidence. The coefficient estimate of rain is negative. This implies that the relationship between rainfall and malaria incidence is negative. That is, malaria transmission rate increases with a decreasing amount of rainfall. The p -values for Mopani, Vhembe and Waterberg are very close to 0. These p -values implies that there is a certain pattern of malaria transmission between these districts and Capricorn district (the reference category). The coefficient estimates for Mopani, Vhembe and Waterberg are positive. These estimates entail that if malaria incidence increases in each of these districts, then it also increases in Capricorn district (the reference category). We are using the odds ratio, e β to find the precise pattern of malaria incidence amongst the districts. The odds ratio in this case is the ratio of the odds of the reference category (Capricorn) and each of the districts Mopani, Vhembe and Waterberg. If there is an increase in malaria incidence, the increase is e β times more in Mopani, Vhembe and Waterberg than in Capricorn district. The Greek letter β in the odds ratio e β represents the regression coefficient. Table 4 provides evidence that malaria incidence increases by e 2.215 ≈ 9 times in Mopani, e 2.848 ≈ 17 times in Vhembe and e 0.8711 ≈ 2 times in Waterberg than in Capricorn district. A unit increase in temperature during the night increases the incidence of malaria by e 0.2537 ≈ 1 unit. There is no evidence of an existing association between malaria incidence and the covariates, temperature during the day, elevation and NDVI according to Table 4 . The ratio of the deviance statistic and its degrees of freedom is 1.148, which is significantly close to 1 compared to the ratio of the deviance statistic and its degrees of freedom for the Poisson model presented in Table 3 . Hence there is an evidence of good fit in the model presented in Table 4 .

Parameter estimates for negative binomial (NB) model under classical approach encompassing all the variables.

NB: ** Indicates the significance of the variable within [95%] confidence interval for the five districts in Limpopo Province where the study was conducted.

All the 95% credible intervals presented in Table 5 do not include zero, which indicates that all the variables are significant, except for Waterberg. However, the variable of NDVI is extremely significant while other parameters are moderately significant. This implies that malaria incidence is affected more by NDVI than other environmental factors. Both 95% highest posterior density (HPD) credible intervals for the regression coefficients of the covariates rainfall and elevation are negative.

Parameter estimates for negative binomial (NB) model with Bayesian approach encompassing all the variables.

NB: ** Indicates the significance of the variable within [95%] HPD credible interval for the five districts in Limpopo province where the study was conducted.

This implies that there is a very high probability that the estimates of these regression coefficients are negative. Therefore, we can conclude that the relationship between malaria incidence and each of the covariates rain and elevation is negative. That is, an increase in rainfall leads to a decrease in malaria incidence and an increase in elevation above sea level leads to a decrease in malaria incidence.

All the 95% HPD credible intervals for temperature during the night, temperature during the day and NDVI are positive, which indicate that there is a very high probability that the estimates of these regression coefficients are positive. Therefore, we can conclude that the relationship between each of these covariates and malaria incidence is positive. That is, an increase in temperature during the night, temperature during the day and NDVI results in an increase in malaria incidence.

The 95% HPD credible intervals for Mopani, Sekhukhune and Vhembe districts are positive, which indicate that as malaria incidence increase in each of these districts, it also increases in Capricorn district (reference variable). However, both the 95% HPD credible intervals for Waterberg are negative. This implies that if malaria incidence increases in Capricorn district, then it decreases in Waterberg district. We can then conclude, according to the MCMC estimation methods applied to obtain the model in Table 5 that there is a relationship between malaria incidence and each of the environmental factors included in this study.

4. Discussion

Both the Bayesian and classical methods revealed a positive relationship between malaria incidence and temperature during the night. That is, an increase in temperature during the night results in an increase in malaria incidence. Therefore, we can conclude that the risk of malaria transmission is high during warm nights, which are usually the nights of summer seasons. The Bayesian and classical frameworks produced similar results about the relationship between malaria incidence and rainfall, which was found to be negative. Therefore, we can conclude that an increase in the amount of rainfall results in a decrease in malaria incidence. The classical framework does not provide any evidence of an existing relationship between malaria incidence and either elevation, temperature during the day or NDVI. However, the Bayesian framework revealed that an increase in NDVI and temperature during the day lead to increased malaria incidence while an increase in elevation above sea level leads to decreased malaria incidence. Malaria incidence increases in Mopani and/or Vhembe districts, then it also increases in Capricorn district. The classical framework revealed no pattern of malaria incidence between Capricorn and Sekhukhune districts while the Bayesian framework suggests that if malaria incidence increases in Sekhukhune district, then it also increases in Capricorn district. The Bayesian framework also suggests that if malaria incidence decreases in Waterberg (though not significant) district then it increases in Capricorn district while in contrast, the classical framework suggest that if malaria incidence increases in Waterberg district then it also increases in Capricorn district. Both methods affirm that Vhembe district is more susceptible to malaria incidence, followed by Mopani district, confirming the results of Abiodun et al. [ 7 , 8 ]. The classical method did not identify any particular trend of malaria incidence over the period of study. However, the Bayesian method identified an upward trend of malaria incidence over the period of the study. Again, the MLE method generated more errors and wider intervals while the Bayesian estimation method generated fewer errors and narrower intervals. Therefore, we can conclude that the Bayesian method of estimation outperforms the classical method of estimation.

5. Conclusions

This study was limited to one South African province, Limpopo. However, there is another province, Mpumalanga which is also known to account for high malaria cases in South Africa. This study only made use of non-informative priors. However, future research may consider Bayesian estimation under dissimilar prior distributions such as improper, conjugate and Jeffrey’s priors in the model fitting process. The spatial dimension in the context of Bayesian estimation may be considered in analysing the prevalence of diseases such as malaria in future research.

Based on the findings from this study, we recommend that the Department of Health and Malaria Control Programme of South Africa allocate more resources for malaria prevention, control and elimination to Vhembe and Mopani districts in Limpopo Province. We also recommend that the government provide educational seminars to educate the South African communities on how to prevent malaria transmission, especially during the warm summer nights.

Acknowledgments

We are grateful to Malaria Control Division in Tzaneen and Statistics South Africa [ 23 ] for providing us with data for this research. We are also thankful to the National Research Foundation (NRF) for providing funds for this research. We also like to acknowledge the anonymous reviewers for their useful comments.

The posterior distributions for the various variables used in the analysis are presented in this Appendix.

Author Contributions

M.A.S. and A.B. conceptualised the research. M.A.S. collected and cleaned the data. M.A.S., A.B. and D.M. analysed the data with R software and drafted the paper. M.A.S., A.B. and D.M. reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

This research received no external funding. The APC was funded by University of Limpopo, Research Administration and Development.

Conflicts of Interest

The authors declare no conflict of interest.