case study of amazon basin

Living World - Amazon Case Study

The Amazon is the largest tropical rainforest on Earth. It sits within the Amazon River basin, covers some 40% of the South American continent and as you can see on the map below includes parts of eight South American countries: Brazil, Bolivia, Peru, Ecuador, Colombia, Venezuela, Guyana, and Suriname. The actual word “Amazon” comes from river.

Amazing Amazon facts; • It is home to 1000 species of bird and 60,000 species of plants • 10 million species of insects live in the Amazon • It is home to 20 million people, who use the wood, cut down trees for farms and for cattle. • It covers 2.1 million square miles of land • The Amazon is home to almost 20% of species on Earth • The UK and Ireland would fit into the Amazon 17 times!

The Amazon caught the public’s attention in the 1980s when a series of shocking news reports said that an area of rainforest the size of Belgium was being cut down and subsequently burnt every year. This deforestation has continued to the present day according to the Sao Paulo Space Research Centre. In 2005 they had lost 17% of Amazon rainforest or 650000 square kilometres. Their satellite data is also showing increased deforestation in parts of the Amazon. The process of deforestation The Amazon helps a Newly Emerging Economy(NEE), Brazil, to make money. They build roads into the forest, logging firms then go in and take out valuable hard woods such as mahogany and cedar, worth thousands of pounds in richer economies like Europe. Then farmers, often cattle ranchers from big companies, burn the rest to make way for cattle pasture. 75% of cleared areas are used in this way. This is clearly shown on the map on figure 22 in red. Many of the deforested areas follow roads and branch off from there.  Deforestation is also worse in the South and South East of the Amazon basin, closer to major centres of population in Brazil.

© WWF   Source  Used with permission.

The causes of deforestation 1. Subsistence and commercial farming – subsistence farming is where poor farmers occupy plots of the forest to grow food to feed themselves and their families. They clear forest and then burn it, hence the name slash and burn.  They grow crops until the soil is exhausted and then move on.  This contributes to deforestation but not as much as commercial farming (Farming to sell produce for a profit to retailers or food processing companies). The Brazilian region of Mato Grosso was affected by deforestation in the 1980s and 1990s. 43% of rainforest losses were in this region, and area almost ½ the size of France. It has been replaced by fields for grain and cattle. This has allowed Brazil to overtake Australia as the largest exporter of beef in the world. The land is also flat and easy to farm. It also has high temperatures and lots of rainfall.

2. Logging – This involves cutting down trees for sale as timber or pulp.  The timber is used to build homes, furniture, etc. and the pulp is used to make paper and paper products.  Logging can be either selective or clear cutting. Selective logging is selective because loggers choose only wood that is highly valued, such as mahogany. Clear-cutting is not selective.  Loggers are interested in all types of wood and therefore cut all of the trees down, thus clearing the forest, hence the name- clear-cutting.

3. Road building – trees are also clear for roads.  Roads are an essential way for the Brazilian government to allow development of the Amazon rainforest.  However, unless they are paved many of the roads are unusable during the wettest periods of the year.  The Trans Amazonian Highway has already opened up large parts of the forest and now a new road is going to be paved, the BR163 is a road that runs 1700km from Cuiaba to Santarem. The government planned to tarmac it making it a superhighway. This would make the untouched forest along the route more accessible and under threat from development.

4. Mineral extraction – forests are also cleared to make way for huge mines. The Brazilian part of the Amazon has mines that extract iron, manganese, nickel, tin, bauxite, beryllium, copper, lead, tungsten, zinc and gold! 

The Belo Monte dam site under construction, copyright  Used with the kind permission of Phil Clarke-Hill  - His website is amazing, click here to see it.

5. Energy development – This has focussed mainly on using Hydro Electric Power, and there are 150 new dams planned for the Amazon alone.  The dams create electricity as water is passed through huge pipes within them, where it turns a turbine which helps to generate the electricity.  The power in the Amazon is often used for mining.  Dams displace many people and the reservoirs they create flood large area of land, which would previously have been forest.  They also alter the hydrological cycle and trap huge quantities of sediment behind them. The huge Belo Monte dam started operating in April 2016 and will generate over 11,000 Mw of power.  A new scheme the 8,000-megawatt São Luiz do Tapajós dam has been held up because of the concerns over the impacts on the local Munduruku people.

6. Settlement & population growth – populations are growing within the Amazon forest and along with them settlements.  Many people are migrating to the forest looking for work associated with the natural wealth of this environment. Settlements like Parauapebas, an iron ore mining town, have grown rapidly, destroying forest and replacing it with a swath of shanty towns. The population has grown from 154,000 in 2010 to 220,000 in 2012. The Brazilian Amazon’s population grew by a massive 23% between 2000 and 2010, 11% above the national average.

Impacts of deforestation – economic development, soil erosion, contribution to climate change. • Every time forest is cleared species are lost – so we lose BIODIVERSITY • Climate Change - Burning the forest releases greenhouse gasses like CO2.  This contributes to the warming of our planet via climate change and global warming.  In addition, the loss of trees prevents CO2 being absorbed, making the problem worse. The Amazon also helps to drive the global atmospheric system. There is a lot of rainfall there and changes to the Amazon could disrupt the global system. • Economic development – Brazil has used the forests as a way to develop their country.  The forest has many natural riches that can be exploited.  In addition, Brazil has huge foreign debt and lots of poor people to feed, so they want to develop the forest. May Brazilians see deforestation as a way to help develop their country and improve people’s standard of living. • Soil erosion - the soils of the Amazon forest are not fertile and are quickly exhausted once the forest is cleared. The farmers now artificially fertilise the soil when in the past the nutrient cycle would have done this naturally.  In addition, the lack of forest cover means that soils are exposed to the rainfall.  This washes huge amounts of soil into rivers in the process of soil erosion.

NEXT TOPIC - Living World - Sustainable Forest Management

©2015 Cool Geography

• Copyright Policy
• Privacy & Cookies
• Testimonials
• Feedback & support

• Life in the Amazon basin

Human beings are the most brained creatures. As humans, we excel in exploring the mother Earth and using it for our best purposes. This skill of ours has given us the advantage of interacting with the environment, and Amazon basin is the best living example of that. Amazon basin is the result of the many tributaries joining the Amazon river. Let’s explore the human capacity to interact with the environment.

Suggested Videos

Life in amazon basin.

We humans, are dependent on nature and interact with it for various reasons. Our interaction with the environment is endless and so is our ability to use it. Being the best example of human interaction with the environment, the Amazon basin has been exploited since time immemorial. Amazon basin is located in  South America at 10° N and 10° S of the tropical region.

This region is also referred as the equatorial region. The Amazon river flows through the region and reaches the Atlantic Ocean through the mountains in the west.

Before reading further about Amazon basin it is important that we know the following definitions:

• River’s Mouth: The place where a river flows into another water body is called the river’s mouth.
• Tributaries: When a river or a stream flows into a larger river or lake then that river or lake is called the tributary of the larger river.

The Climate

Amazon basin is situated in the equatorial region which is hot and humid all through the year. Days and nights both are equally hot and wet. Nights are comparatively less hot but the humidity level remains unchanged. Due to the humid conditions here, it rains almost every day.

Browse more Topics under Human Environment Interactions

• Life in the Ganga – Brahmaputra Basin

Read more about Life in Ganga Brahmaputra Basin here in detail.

The Rainforest

As it rains throughout the year the forests here are dense. The trees form a dense roof of leaves that do not even let sunlight penetrate into the forest area. Also, the surface of the earth is damp and dark. So shade tolerant vegetation is present in abundance here. Prominent plant parasites found here are Bromeliads and Orchids. The rainforests of Amazon basin are flocked with a variety of fauna as well. As a result, you can find the rarest of species loitering in the forests here.

Hummingbird, Toucans, Amazon Kingfisher, Hyacinth Macaw, Blue-fronted Amazon are some of the bird species that are present here. And animals like Sloth, Capybara, monkeys, ant-eating tapirs, poison dart frogs are present all over the rainforests of Amazon. The list does not end here! You get a glimpse of the grandest of the reptiles as well.

Crocodiles and snakes like Pythons and Anacondas are common here. Apart from these aquatic animals like the Piranha and Giant Otter can be sighted in the river basin. The list of fauna and flora in Amazon Basin is endless.

People here cut a few trees and cultivate the land according to their needs and requirements. Men have occupations like fishing and hunting, while it’s the women who take care of the crops and fields. The land being near the Amazon river is very fertile which makes it a good for farming. People here generally grow crops like Pineapple, Tapioca, Sweet Potato, Cassava (manioc), Coffee, maize, and cocoa. We call them the Cash crops .

As already said, men practice fishing and hunting which are uncertain means of living. It is the women of Amazon basin who are the major bread earners of the family. From taking care of fields to feeding their families with the vegetables that they cultivate, women are responsible for their families well-being.

They practice slash and burn technique of agriculture. In slash and burn agriculture system, we clear the required forest land for farming. We slash or cut down the trees and bushes. As soon as the fertility of land degrades, women proceed to clear a new piece of land.

The old land eventually gains back its fertility with trees and bushes growing back on them. Manioc or Cassava is the staple food while queen ants and egg sacs are the other savouries of people near the Amazon river.

Settlements of People

People live in special kind of houses called the Maloca here. These houses have steep slant roofs and are large and apartment like in shape. People also reside in houses that are identical to beehives and have thatched roofs over them. Since the settlements here are in close proximity of forests, people find wood in abundance for their personal use.

With pacing technology and modernism, the life of people is slowly changing here. As it is the human nature to evolve and enjoy facilities to their best the advent of transportation has helped in easy navigation through the basin. An area which once was navigable only through the Amazon river can today be explored through Trans-Amazon Highway. Moreover, with aeroplanes and helicopters, it has become easier and faster to reach various places around the basin.

Human Interaction with environment benefits humans, but after an extent depletes the environment. The regular felling of trees in the Amazon basin has resulted in a change of ecology here. The developmental activities near the Amazon river, eventually, have resulted in the destruction of the rainforests. The effects may not be visible today, but in the near future, they surely will. The map below signifies the change human interaction has brought to the environment:

Amazon Basin is the richest example of human interaction with the environment. To let it stay rich with vegetation it has now become imminent to use the resources from the basin intelligently. We humans interact with the environment for our benefits but doing so at the cost of ecology can be fatal for our generations to come.

Solved Questions For You

Q1: Define the major difference between a hamlet and a village.

A) Population      B) Group of Houses      C) Pollution       D) None of the Above

Solution: A) Villages are larger than hamlets. They have a greater population than hamlets. Hamlets are small towns and community while as villages are mostly large suburbs and crossroads. Hence, Hamlet settlements are also less polluted as a result of a smaller population.

Q2: From where the Urban settlements have evolved?

A) Rural Settlements      B) Scattered Settlements      C)  Nucleated Settlements            D) None of the Above.

Solution: A) Rural settlements were the first step towards a stable life in the human history. Urban settlements have evolved through the growth and expansion of rural settlements. Increase in population of the rural settlements marks the beginning of urbanisation.

Customize your course in 30 seconds

Which class are you in.

Human Environment Interactions

• Life in Ganga Brahmaputra Basin

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Download the App

Case Study: The Amazonian Road Decision

The proposed Pucallpa–Cruzeiro do Sul will connect the Amazon’s interior to urban centers and export markets in Peru and Brazil. However, critics are worried that the road will also create new opportunities for illegal logging and infringe on the territory of indigenous communities and wildlife.

Biology, Geography, Human Geography

Loading ...

On the western edge of the Amazon River, there is a proposal to construct a road. This road would connect the remote town of Cruzeiro do Sul, Brazil, with the larger city of Pucallpa, Peru. The construction of the road has become a subject of contentious debate. Proponents of the road claimed that it would provide an efficient way for rural farmers and tradesmen to get their goods to city markets. They claimed it would also allow loggers to more easily transport timber from the depths of the Amazon rainforest to sawmills. From the sawmills in Pucallpa, goods could be transported to Peru’s Pacific coast and shipped to international buyers. Critics of the Pucallpa-Cruzeiro do Sul road, however, argue that it would cut right through traditional territories of the Ashéninka, an indigenous people of eastern Peru. Many leaders fear the road will increase access to previously undeveloped rainforest, threatening the ecosystem and the Ashéninka way of life. Large trees, such as mahogany, for example, will catch the eye of illegal loggers because of their high market value. The great mahogany trees also serve as protection to the Ashéninka from the outside world and are essential for the health of the Amazonian rainforest. The trees provide shelter, food, and nesting grounds that sustain the vast biodiversity within the ecosystem, an ecosystem the Ashéninka have come to depend on for their own food, shelter, and life sustenance. Geography The Amazon Basin is located in South America, covering an area of seven million square kilometers (2.7 million square miles). Nearly 70 percent of the basin falls within Brazil with remaining areas stretching into parts of Peru, Ecuador, Bolivia, Colombia, Venezuela, and Guyana. The Amazon’s massive drainage basin is made of dozens of smaller watersheds , including the Tamaya. Its watershed lies at the headwaters of the Purus and Juruá Rivers, near the border of Peru and Brazil. The Ashéninka people have lived in this region for centuries, surviving on game, fish, and cultivated crops, such as yucca roots, sweet potato, corn, coffee, and sugar cane. Background The rainforest surrounding the Amazon is the largest on the entire planet. In addition to 33 million human inhabitants, including 385 distinct Indigenous groups, it hosts the greatest diversity of plant and animal life in the world. More than two million species of insects are native to the region, including many tree-living species and hundreds of spiders and butterflies. Primates are abundant—including howler, spider, and capuchin monkeys—along with sloths, snakes, and iguanas. Brightly colored parrots, toucans, and parakeets are just some of the region’s native birds. Many of these species are unique to the Amazon rainforest, which means they cannot be found anywhere else in the world. At a global level, the Amazon rainforest helps to regulate climate and acts as a carbon sink for greenhouse gases . At a national level, the Amazon is considered a source of energy and income, based on production and commercialization of raw materials. Some of the most valued tree species in the world thrive in the rainforest. Mahogany is one of the most valuable resources from the Amazon forest. The tree’s rich, red grain and durability make it one of the most coveted building materials in the world. A single mahogany tree can fetch thousands of U.S. dollars on the international market. Even though logging is prohibited in much of the Amazon River, it is legal in some areas in large part because the sale of the wood is so lucrative. The high demand for mahogany has left many of Peru’s watersheds—such as the Tamaya—stripped of their most valuable trees. Without large trees, and their roots, the watershed risks heavy flooding and soil erosion. Conflict The Pucallpa-Cruzeiro do Sul road is part of a larger development plan to link South America’s remote, isolated economies through new transportation, energy, and telecommunications projects. Tension exists between communities that favor developing the rural economies of the Amazon Basin and those who favor preserving its forested areas and diversity of life. The Initiative for the Integration of the Regional Infrastructure of South America (IIRSA) is a proposal for the construction of several highways throughout the continent, five of them within the western Amazon Basin. The Pucallpa-Cruzeiro do Sul road is one such proposed highway. Supporters of the Pucallpa-Cruzeiro do Sul road say international demand for Amazonian resources could help develop the rural economies that are scattered throughout the basin. In addition to providing a route of access for rural goods to enter the global market, the road will allow members of rural communities to access better health care, education, and welfare. This could lead to improved living conditions, healthier lifestyles, and longer life spans. Conservationists are concerned that infrastructure such as the Pucallpa-Cruzeiro do Sul road will devastate an already weakened Amazonian ecosystem, as road access is highly correlated with  deforestation . In Brazil, for instance, 80 percent of deforestation occurs within 48.28 kilometers (30 miles) of a road. Critics argue that the construction of a road along the Brazil-Peru corridor will provide easier access for loggers to reach mahogany and other trees. Indigenous communities like the Ashéninka will also be affected. These communities have largely chosen to maintain a traditional way of life, and conservationists are concerned that the Pucallpa-Cruzeiro do Sul road may expose them to disease and land theft. Identification of Stakeholders Indigenous Communities:  Members of the Ashéninka community are trying to protect the forest and their native lands. Yet, like other Indigenous communities in the area, they are in turmoil, largely divided between those favoring conservation and those seeking greater economic opportunities. While the Ashéninka want to preserve their culture and connections to the forest, they also need access to things like clothes, soap, and medicine. The road could establish trade routes that make these goods more accessible. However, isolated peoples could be exposed to disease and land theft. Wildlife:  The proposed Pucallpa-Cruzeiro do Sul road runs through Serra do Divisor National Park, Brazil, and other reserves that are home to threatened and rare species, including mammals, reptiles, and birds. For some of these species, such as the spider monkey and red howler monkey, the construction of the road could make their populations vulnerable to fragmentation and more visible to hunters. As mahogany and other canopy giants are removed, any wildlife that relies on the trees for shelter, nesting, or food will need to relocate. Amazonian Ecosystem:  In addition to the detrimental effects to the flora and fauna in the area, the construction of the Pucallpa-Cruzeiro do Sul road could accelerate erosion, reduce water quality, and increase deforestation for agriculture and timber extraction. Tropical forest accounts for 40 percent of the global terrestrial carbon sink. A reduced number of trees could exacerbate global warming. Fewer forests means larger amounts of greenhouse gases entering the atmosphere. Logging Companies:  If a road is constructed, loggers will have easier access to mahogany and other trees, allowing them to generate more income and provide a higher standard of living for their families and communities. A higher standard of living might include expanded educational opportunities, improved healthcare facilities, and the chance to participate in political debate. Residents of Rural Communities:  The Pucallpa-Cruzeiro do Sul road would allow local farmers and business people to transfer goods from the Amazonian interior to Peru’s Pacific coast. Right now, merchants who want to travel between Cruzeiro do Sul and Pucallpa must do so by plane. A reliable road would improve basic infrastructure, transportation, and communication for greater commercial and social integration between Peru and Brazil, which meets part of the larger objective of the Initiative for the Integration of Regional Infrastructure in South America. International Consumers:  The global demand for mahogany makes it a multimillion dollar business. Mahogany is used to create bedroom sets, cabinets, flooring, and patio decks throughout the world, mostly in the United States and Europe. Conflict Mitigation Groups are seeking to mitigate conflict in the Pucallpa-Cruzeiro do Sul road conflict through dialogue and alternate infrastructure plans. Environmental conservation groups have suggested that the Pucallpa-Cruzeiro do Sul road be removed from the list of approved projects until the community engages in greater communication surrounding two aspects of the project. First, conservationists are seeking more information on the environmental impact of the construction. This discussion involves local environmental groups, government representatives, and businesses. Second, conservationists are seeking full consent to the project from indigenous communities. Some critics of the Pucallpa-Cruzeiro do Sul road argue that roads are not the only option for the Pucallpa business community to extend its commerce. Traditional river systems are already in place. These critics think the fluvial network should be explored as a viable alternative to road construction. The Upper Amazon Conservancy is working with indigenous peoples to help protect their native territories. One initiative involves organizing community “vigilance committees” that consist of members of indigenous peoples who help park services by patrolling the edges of national parks and keeping illegal loggers out.

Media Credits

The audio, illustrations, photos, and videos are credited beneath the media asset, except for promotional images, which generally link to another page that contains the media credit. The Rights Holder for media is the person or group credited.

Research Manager

Educator reviewer, expert reviewer, last updated.

October 19, 2023

User Permissions

For information on user permissions, please read our Terms of Service. If you have questions about how to cite anything on our website in your project or classroom presentation, please contact your teacher. They will best know the preferred format. When you reach out to them, you will need the page title, URL, and the date you accessed the resource.

If a media asset is downloadable, a download button appears in the corner of the media viewer. If no button appears, you cannot download or save the media.

Text on this page is printable and can be used according to our Terms of Service .

Interactives

Any interactives on this page can only be played while you are visiting our website. You cannot download interactives.

Related Resources

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 01 September 2020

Amplified seasonal cycle in hydroclimate over the Amazon river basin and its plume region

• Yu-Chiao Liang   ORCID: orcid.org/0000-0002-9347-2466 1 ,
• Min-Hui Lo   ORCID: orcid.org/0000-0002-8653-143X 2 ,
• Chia-Wei Lan   ORCID: orcid.org/0000-0002-9650-8460 2 ,
• Hyodae Seo   ORCID: orcid.org/0000-0002-4352-5080 1 ,
• Caroline C. Ummenhofer   ORCID: orcid.org/0000-0002-9163-3967 1 ,
• Stephen Yeager   ORCID: orcid.org/0000-0003-0268-9895 3 ,
• Ren-Jie Wu 2 &
• John D. Steffen 1  

Nature Communications volume  11 , Article number:  4390 ( 2020 ) Cite this article

5782 Accesses

70 Citations

4 Altmetric

Metrics details

• Climate sciences

The Amazon river basin receives ~2000 mm of precipitation annually and contributes ~17% of global river freshwater input to the oceans; its hydroclimatic variations can exert profound impacts on the marine ecosystem in the Amazon plume region (APR) and have potential far-reaching influences on hydroclimate over the tropical Atlantic. Here, we show that an amplified seasonal cycle of Amazonia precipitation, represented by the annual difference between maximum and minimum values, during the period 1979–2018, leads to enhanced seasonalities in both Amazon river discharge and APR ocean salinity. An atmospheric moisture budget analysis shows that these enhanced seasonal cycles are associated with similar amplifications in the atmospheric vertical and horizontal moisture advections. Hierarchical sensitivity experiments using global climate models quantify the relationships of these enhanced seasonalities. The results suggest that an intensified hydroclimatological cycle may develop in the Amazonia atmosphere-land-ocean coupled system, favouring more extreme terrestrial and marine conditions.

Similar content being viewed by others

Seasonally distinct contributions of greenhouse gases and anthropogenic aerosols to historical changes in Arctic moisture budget

Hwa-Jin Choi, Seung-Ki Min, … Baek-Min Kim

Observed poleward freshwater transport since 1970

Taimoor Sohail, Jan D. Zika, … John A. Church

Recent upper Arctic Ocean warming expedited by summertime atmospheric processes

Zhe Li, Qinghua Ding, … Axel Schweiger

Introduction

The Amazon river basin (delineated by the black contour in Fig.  1a ) receives ~2000 mm of rainfall annually 1 . This vast amount of precipitation feeds the Amazon river, ranked as the world’s largest river in terms of annual discharge 2 . The Amazon river discharge contributes ~17% of global river freshwater input to the ocean 2 , significantly affecting the physical and biogeochemical upper ocean properties in the coastal and neighboring oceans 3 , 4 , 5 , 6 , 7 . One prominent feature is the so-called Amazon plume region (APR), which is characterized by relatively low ocean salinity 3 , 4 , 8 and high nutrients brought by the Amazon river discharge 3 , 4 , 9 , 10 . Observational evidence suggests that high nutrient contents from the Amazon river discharge help sustain high marine productivity in the APR, with the maximum chlorophyll content concentrated in the upper 5-m ocean 3 . In addition, the mixing of supersaturated Amazon freshwater with undersaturated surface ocean water results in a net sink of atmospheric carbon dioxide within the APR 11 , 12 . Therefore, the variability of Amazon river discharge can impact marine biogeochemistry, productivity, and the carbon cycle within the APR and surrounding areas 13 .

a The geographic domain of Amazon river basin (black contour line) and APR (red box). The color shading over the ocean represents annual mean 5-m ocean salinity with 34.5 PSU countered as magenta, and the black arrows denote annual mean 5-m ocean current velocities. The magenta and blue stars denote the location of the Obidos and Ciudad Bolivar gauge stations, where Amazon and Orinoco river discharges were recorded, respectively. b Long-term mean (1979–2018) of observed precipitation averaged over the Amazon river basin in each month. Note that the mean TRMM precipitation is averaged over 1998–2018. c Long-term mean of Amazon river discharge (1979–2018) at Obidos and Dai and Trenberth river discharge (1979–2014) in each month. d Long-term mean of 5-m ECCO4 (1992–2017), GECCO2 (1979–2016), ORAS5 (1979–2018), SODA3.3.1 (1980–2015), and EN4 (1979–2018) ocean salinity averaged over the APR in each month. The error bars in b , c , and d indicate the standard deviations of each month throughout the analysis period. The geographic map is produced by Python Cartopy package 77 .

The low salinity water in the APR also significantly increases the upper ocean stratification, creating a thick barrier layer that inhibits the mixing of cold thermocline water into the surface waters 12 . As a consequence of reduced mixing, more heat is trapped in the upper ocean 4 , 8 , 14 , 15 , 16 . Due to the APR’s areal extent spanning from the Amazon river mouth near the Equator to the East Caribbean Sea (red box in Fig.  1a , see “Methods” for the box definition), the enhanced near-surface heat storage in the APR associated with the barrier layer dynamics provides favorable surface conditions for hurricane genesis over the broad regions of the tropical western Atlantic 8 , 15 . Moreover, the variability of Amazon freshwater and resultant ocean salinity changes have been suggested to affect tropical Atlantic air–sea interactions 17 , 18 and the variability of the Atlantic intertropical convergence zone (ITCZ) 19 , regional sea-level height changes 20 , 21 , 22 , as well as to exert potentially far-reaching impacts on the Atlantic meridional overturning circulation (AMOC) 23 . Thus, understanding the mechanisms for changes in the Amazon river discharge and the associated upper ocean stratification in the APR is important not only for the Atlantic hurricane forecasts but also for improved understanding of basin-scale climate variability.

Recent studies have found that the Amazonia hydroclimatological cycle, manifested as the seasonality changes in precipitation and Amazon river discharge, has become intensified during the past few decades 24 , 25 , 26 and resulted in increased likelihood of extreme terrestrial events, such as droughts and floods 24 , 26 , 27 . Ocean salinity in the APR has also been affected by the seasonality changes in the Amazonia hydroclimatological cycle 4 , 7 , 28 . However, it remains unclear if the enhanced precipitation seasonal cycle has intensified the seasonalities of the river discharges and APR ocean salinity by increasing the peaks and deepening the troughs. This study aims to examine the causal link between the changes in the amplitude of the seasonality of the Amazonia hydroclimatological system and APR ocean salinity during the period 1979–2018, using observations, reanalysis, and ocean-state estimate products. The effect of seasonality changes is further quantified using a hierarchical global climate modeling approach.

Enhanced seasonalities in observations and reanalysis data

We first examine the monthly climatological values of precipitation, Amazon river discharge, and APR ocean salinity during the period 1979–2018 (Fig.  1b–d ) to illustrate their seasonal cycle linkage. The area-averaged Amazonia precipitation shows the highest values during January–February–March and the lowest during July–August–September (Fig.  1b ). Only small differences appear among different observational precipitation datasets (Fig.  1b ), indicating the robustness of the estimated annual cycle for the Amazonia precipitation reported in this study.

Following the peak precipitation in March, the Amazon river discharge (expressed as volume transport in m 3  s −1 ), observed at Obidos station (magenta star in Fig.  1a ), reaches its highest value in June (Fig.  1c ). Similarly, the lowest precipitation in August leads to the lowest level of river discharge in November. This 3-month delayed response of the river discharge to precipitation is a prominent hydrological feature in the Amazon river basin 2 .

The seasonal cycle of the near-surface (~5 m below the sea surface) ocean salinity in the APR from multiple ocean-state estimate products shows the freshest values in May–June–July and the saltiest in December–January–February (Fig.  1d ), which largely follows the seasonal cycle of the Amazon river discharge (Fig.  1c ). Although the magnitude of the seasonal cycle varies with the chosen datasets, these ocean-state estimate datasets agree that the APR ocean salinity exhibits a similar seasonal evolution to the Amazon river discharge with no apparent lag. The seasonal cycles of APR ocean near-surface salinity from the 6-year soil moisture and ocean salinity (SMOS, 2011–2016) 29 and 3-year Aquarius (2012–2014) 30 satellite observations have similar characteristics (Supplementary Fig.  1 ).

The above results suggest that the atmosphere, land, and ocean within and near the Amazon river basin are closely connected through their seasonal cycle characteristics and lag relationships. The annual maximum and minimum values of Amazon precipitation during 1979–2018 indicate that the wet seasons have become wetter and the dry seasons’ drier (Fig.  2a ). A significant increase in the maximum values and decrease in the minimum values over the analysis period result in overall significant (at 5% level) increasing trends in the precipitation seasonality (Fig.  2b ). The increasing trend of the seasonality averaged across the Global Precipitation Climatology Project (GPCP) 31 , Global Precipitation Climatology Centre (GPCC) 32 , and Precipitation Reconstruction over Land (PREC/L) 33 observational datasets is +0.35 (±0.05) mm day −1 decade −1 . This represents ~6% of the mean seasonality in the Amazon precipitation (~6 mm/day, Fig.  1b ). Similar increasing trends of seasonality can be found using Tropical Rainfall Measuring Mission (TRMM) 34 and Climate Prediction Center Merged Analysis of Precipitation (CMAP) 35 datasets (Supplementary Fig.  2a–d ).

a Amazonia precipitation during the period 1979–2018. b The seasonality of Amazonia precipitation (maximum minus minimum values) during the period 1979–2018. c , d , e , f , and g , h are similar to a , b , but for Amazon river discharge and APR ocean salinity, respectively. Note that ECCO4 salinity data only cover the period 1992–2017; GECCO2 the period 1979–2016; and SODA3.1.3 the period 1980–2015. The solid (dashed) lines are the linear fits used to determine the trends.

Similarly, increased seasonality in the Amazon river discharge, due to the increased maximum and decreased minimum values, is also found (Fig.  2c, d ). The trend of the increased seasonality in Amazon river discharge is ~1.3 × 10 4  m 3  s −1  decade −1 , representing about 9% of its mean seasonality (~1.4 × 10 5  m 3  s −1 , Fig.  1c ). We also examine thirteen other river discharge datasets available within the Amazon river basin (Supplementary Fig.  3 ), and ten of them show increasing trends throughout the Amazon sub-basins (Supplementary Fig.  4 ), although the temporal coverages of some river discharge data are too short of providing robust trend estimates (e.g., Supplementary Fig.  4e, i ).

Following the enhanced seasonality in the Amazon river discharge, the seasonality trends in APR 5-m ocean salinity (averaged over the red box in Fig.  1a ) have also increased by ~2.89 × 10 −2 , 1.82 × 10 −2 , and 1.23 × 10 −1 PSU decade −1 for the German contribution to ECCO version 2 (GECCO2) 36 , Estimating the Circulation and Climate of the Ocean project version 4 (ECCO4) 37 , and Simple Ocean Data Assimilation version 3.1.1 (SODA3.3.1) 38 products, respectively (Fig.  2f, h ). However, we find decreasing trends in the Ocean Reanalysis/analysis version 5 (ORAS5) 39 and EN4 40 products (Fig.  2h ). The discrepancy among ocean salinity products is likely related to different data-processing or assimilation procedures, and quality and sampling biases of input data (see discussion in “Methods”). The trend averaged over five products is ~1.44 × 10 −2 PSU decade −1 , which accounts for only ~1% of the mean seasonality (~1.38 PSU, Fig.  1d ). However, this trend increases to 5.66 × 10 −2 PSU decade −1 , ~4% of the mean seasonality, when the three products that show an increasing seasonality trend are averaged (though this estimate is dominated by the increasing trend in the SODA3.3.1 product, red line in Fig.  2h ). We also used five gridded ARGO products 41 to determine recent (post-2001) changes in APR ocean salinity seasonality (see Supplementary Fig.  5b ); the average of the five available gridded ARGO datasets shows an unambiguous increasing trend (rightmost bar in Supplementary Fig.  5b ).

To understand the underlying mechanisms for the seasonal cycle changes, we consider the vertically integrated atmospheric moisture budget (see “Methods”), averaged over the Amazon river basin using multiple reanalysis products. The net increasing trend in precipitation seasonality (Fig.  2b ) is found in all the reanalysis datasets (Fig.  3a ), which are largely attributed to the enhanced vertical moisture advection (i.e., \(- \left\langle {\omega \frac{{\partial q}}{{\partial p}}} \right\rangle\) , Fig.  3d ) and secondarily to the enhanced horizontal moisture advection (i.e., \(- \left\langle {{\vec{\mathbf{v}}} \cdot \nabla q} \right\rangle\) , Fig.  3c ). In contrast, the evapotranspiration (i.e., E , Fig.  3b ) and residuals (i.g., δ , Fig.  3e ) counteract the advection effects. The trend toward increasing seasonality in vertical moisture advection is ~0.52 ± 0.15 mm day −1 decade −1 , which contributes to precipitation of ~0.36 ± 7.15 mm day −1 decade −1 and is larger than the horizontal moisture advection of 0.16 ± 0.06 mm day −1 decade −1 . Further decomposition of the vertical moisture advection shows that the dynamical component ( \(- \left\langle {\omega^{ \prime} \frac{{\partial q}}{{\partial p}}} \right\rangle\) , 0.45 ± 0.15 mm day −1 decade −1 , Fig.  3g ) contributes more strongly than the thermodynamic component ( \(- \left\langle {\omega \frac{{\partial q^\prime }}{{\partial p}}} \right\rangle\) , 0.07 ± 0.03 mm day −1 decade −1 , Fig.  3f ). Such increased seasonality in the dynamic component is related to the vertical motion (Fig.  3i ) that favors increased seasonality in convective activity above the Amazon river basin, while that in the thermodynamic component is associated with the increasing atmospheric moisture content (Fig.  3j ).

The seasonality of reanalysis precipitation ( a ), evapotranspiration ( E ) ( b ), horizontal moisture advection ( \(- \left\langle {{\vec{\mathbf{v}}} \cdot \nabla q} \right\rangle\) ) ( c ), vertical moisture advection ( \(- \left\langle {\omega \frac{{\partial q}}{{\partial p}}} \right\rangle\) ) ( d ), residual ( δ ) ( e ), thermodynamic component ( \(- \left\langle {\bar \omega \frac{{\partial q^\prime }}{{\partial p}}} \right\rangle\) ) ( f ), dynamical component ( \(- \left\langle {\omega ^{\prime} \frac{{\partial \bar q}}{{\partial p}}} \right\rangle\) ) ( g ), nonlinear component ( \(- < \omega ^{\prime} \frac{{q^\prime }}{{\partial p}}\) ) ( h ), vertical velocity ( ω ) ( i ), and total moisture ( q ) ( j ) over time. The blue shadings are the range among reanalysis products, and the red lines are the linear fits used to determine trends. Note different y -axis ranges.

The precipitation and evapotranspiration seasonality changes over the APR region should also affect the local ocean salinity. Thus, we perform the same moisture budget analysis over the APR (red box in Fig.  1a ), but find no robust and consistent trends in seasonalities of precipitation and evapotranspiration compared to that of the APR ocean salinity (Supplementary Fig.  6 ). The precipitation seasonality has a negative trend, while that of evapotranspiration has weakly increased, neither of which can account for the observed robust increasing trend in the APR ocean salinity seasonality. This leaves the increased seasonality in river discharge as a sole source for the observed change in ocean salinity seasonality, although it could be modulated by ocean advection and vertical mixing processes. In addition, the Orinoco river discharge may influence the APR ocean salinity 8 ; however, we find its effect is less important than that of the Amazon river discharge, as the seasonality trend in the Orinoco river discharge has decreased before 2000 and weakly increased (2.1 × 10 3  m 3  s −1  decade −1 ) after mid-2000 (Supplementary Fig.  7 ).

Climate model sensitivity experiments

We conduct two sets of historical global climate model experiments during 1979–2009 (the availability of the Coordinated Ocean-ice Reference Experiments Phase 2 project, CORE-II, forcing 42 limits the simulation period, see “Methods”) to single out the effects of Amazonia precipitation and Amazon river discharge seasonality changes. The experiments also help address the causality. The first set uses a global land model with increased seasonality in the precipitation forcing by a factor of 1.5 and 1.75 during the 1979–2009 period over the Amazon river basin (Fig.  4a and see “Methods”). In response to the enhanced precipitation seasonality trend, we find a nearly linear response in the seasonality trend of the Amazon river discharge (Fig.  4b ). The relative change in the river discharge seasonality is slightly higher than that in precipitation seasonality; that is, the precipitation seasonality increases by a factor of 1.75, whereas that of the river discharge increases by a factor of 1.87 (Fig.  4b ). This runoff intensification effect is likely attributed to nonlinear river discharge responses to precipitation intensity identified in a previous study 27 .

a The seasonality in the precipitation forcing used in the global land model control and experimental simulations. b Seasonality changes in Amazon river discharge in the land model experiments. c similar to a , but for Amazon river runoff forcing used to force the ocean model experiments. In a , c , the dashed lines are the linear fits used to determine trends. d similar to b , but for seasonality changes in APR ocean salinity in the ocean model experiments. The black star is the APR salinity seasonality trend, averaged over five spin-up cycles.

To test the robustness of the ocean salinity seasonality response, given the discrepancy shown in the ocean-state estimate products (Fig.  2f, h ), we conduct the second set of experiments using a global ocean model forced with varied seasonality in Amazon river discharge (Fig.  4c and see “Methods”). The APR 5-m ocean salinity seasonality (averaged over the red box in Fig.  1a ) also increases nearly linearly as the river discharge seasonality increases (Fig.  4d ). It should be noted that the total amount of Amazon freshwater input in each experiment remains the same by our experimental design (see “Methods”); that is, no additional freshwater is added to the model when changing its seasonality. We further analyze 12 CORE-II ocean-only hindcast simulations forced with the same CORE-II forcings, including precipitation and river discharge from 1979 to 2009 (see “Methods”), and obtain overall increased APR ocean salinity seasonality trends (Supplementary Fig.  5a ). The above hierarchical historical climate model experiments and CORE-II simulations, therefore, lend support to the increased APR salinity seasonality trends found in GECCO2, ECCO4, and SODA3.1.1 ocean-state estimate products.

The enhanced seasonality in the ocean salinity can affect the ocean physics and dynamics within the APR. Thus, we further examine the trends in the seasonalities of the APR area (Fig.  5a , defined as the area where ocean salinity are less than 34.5 as denoted by the magenta contour line in Fig.  1a ), the 5-m ocean temperature (Fig.  5b ), the upper ocean stratification (i.e., buoyancy frequency,  N 2 , which also indicates the strength of vertical mixing, Fig.  5c ), and the barrier-layer thickness (Fig.  5d ) from the ocean model experiments. In order to better characterize the localized changes, the averaged values shown in Fig.  5 are taken over only the regions where the 5-m ocean salinity is <34.5 PSU (an alternative definition of APR area) rather than averaged over the APR box as those presented in Fig.  4 ). However, both the areal metrics produce very similar results (c.f., Supplementary Fig.  8 and Fig.  4d ). The seasonality trends of the APR area and vertical mixing strength again respond nearly linearly to that of the Amazon river discharge, whereas the trends of the 5-m ocean temperature and barrier-layer thickness seasonalities show more sensitivity, with larger trend increases from ×1.5 to ×1.75 experiments. These features indicate that the vertical mixing process is dominated by the effect of ocean salinity change associated with the Amazon river freshwater change, manifested as more linear behaviors, but the ocean temperature and barrier-layer thickness are also affected by other factors. It is possible that the temperature responses are more affected by the variability of internal ocean dynamics (e.g., baroclinic eddies or the bifurcation of the North Brazilian Current) 43 , 44 manifested as the larger spreads among five ensembles in each experiment (Fig.  5b ). In addition, the Amazon river discharge temperature is not accounted for in the ocean model configuration, which may also contribute to the APR ocean temperature response.

a Seasonality changes in the size of APR, defined as the region where 5-m ocean salinity is <34.5 PSU. b Seasonality changes in the 5-m ocean temperature averaged over the region where 5-m ocean salinity is <34.5 PSU. c , d similar to b , but for seasonality changes in buoyancy frequency and barrier-layer thickness.

This study finds that an amplified seasonal cycle of Amazonia precipitation during the period 1979–2018 leads to enhanced seasonalities in both Amazon river discharge and APR ocean salinity, using a combination of observations and reanalysis datasets. Hierarchical climate model experiments support the observed seasonality changes and shed light on the sole effects of changing seasonalities in the Amazonia precipitation and Amazon river discharge. While previous studies mainly focused on specific dry or wet seasons without taking into account the seasonality changes in a comprehensive fashion 24 , 25 , 26 , our results provide a new route to further study the Amazonia hydroclimatology and the occurrence of extreme events in the Amazon river basin and APR.

Within the APR, we find the enhanced seasonality of ocean salinity is tied closely with the enhanced seasonalities in the plume area, upper ocean stratification, near-surface ocean temperature, and barrier-layer thickness in our ocean model experiments (Fig.  5 ). These changing ocean properties are important in that they could affect the carbon cycle and marine biogeochemistry within the APR more significantly as a consequence of increased seasonality of Amazon river discharge 7 . It is noted that the increasing precipitation trend in the wet season (mostly January–February–March) contributes more than that in the dry season (mostly July–August–September) to the increasing seasonality trend (Fig.  2a ), which is also the case for Amazon river discharge (Fig.  2c ) and APR ocean salinity (Fig.  2e, g ). Presumably, warmer near-surface ocean temperature and thicker barrier layer in the APR in the “fresher” season (mostly May–June–July) could offer favorable surface ocean conditions for hurricane genesis via barrier layer dynamics 4 , 8 , 14 , 15 . Previous studies using statistical and dynamical hurricane forecast framework showed that the inclusion of upper ocean heat content provides longer and better predictability of hurricane intensity 45 , 46 , 47 . The results presented in this study, therefore, have important implications for hurricane forecasting. However, the near-linear relationship in our modeling results does not include the atmosphere–land–ocean feedback processes. There may exist limitations to tie the seasonality changes of Amazon river discharge and APR ocean salinity, and unrealistic seasonality responses. In addition, previous studies have shown that the Amazon river discharge can affect tropical Atlantic air–sea interactions 17 , regional sea-level height 20 , 21 , 22 , and have potentially far-reaching impacts on the AMOC 23 .

The atmospheric moisture budget analysis reveals that the “wet-get-wetter-and-dry-get-drier” phenomenon in the tropical precipitation due to anthropogenic global warming 48 may contribute to the seasonality change. The “wet-get-wetter-and-dry-get drier” precipitation signal can result in a host of consistent seasonality responses in the Amazonia atmosphere–land–ocean coupled system. The enhanced vertical velocity seasonality (Fig.  3i ) may reflect changes in the location and strength of the Atlantic ITCZ that are related to changes in the local sea-surface temperature gradient in the tropical Atlantic 49 .

It should be noted that the decadal and multidecadal natural modes of variability, such as Pacific decadal variability (PDV), interdecadal Pacific variability (IPV), or Atlantic multidecadal variability (AMV), are possible large-scale drivers of the precipitation seasonality changes at longer timescales 50 , 51 , which have been shown by some studies to be more influential than anthropogenic forcing in the Amazon river basin in observations and climate model simulations 52 , 53 . For example, recent dry season droughts across the Amazon river basin have been attributed to the AMV 54 , 55 . We find that the AMV, PDV, and IPV indices and the seasonality of the Amazon river discharge time series, after applying 11-year running average, are correlated after 1970 ( R  = 0.58 for AMV index, R  = −0.81 for PDV index, R  = −0.64 for IPV, all of which are larger than 0.41, critical value at 99% significance level, see Supplementary Fig.  9 ), indeed suggesting that part of the increased Amazon river discharge seasonality trend in the past 30 years can be attributed to low-frequency Atlantic and Pacific sea-surface temperature variations. In addition, changes in hydropower dam construction 56 , deforestation 57 , and groundwater dynamics 58 may have also affected the hydroclimatology of Amazonia and consequently river discharge seasonality. All these effects are not considered in this study. To quantify the relative and combined effect of all local versus remote forcings and natural versus anthropogenic factors would require significant modeling efforts, including a series of well-designed global climate model experiments. This will be left for a future study.

APR and the Amazon river basin

The APR is chosen according to the extent to which the effect of Amazon river freshwater can reach in terms of overall mean state 8 . Previous studies showed that the size of the freshwater plume is determined by the combination of the region of relatively low salinity extending from the Amazon river mouth (magenta contour line in Fig.  1a ) and strength of the prevailing North Brazilian Current (denoted by black arrows near coasts in Fig.  1a ); it carries the freshwater released at the Amazon river mouth northwestward to the Caribbean 4 , 7 , though sometimes the North Brazilian Current turns eastward to bring freshwater eastward 43 . The APR we used in this study (red box in Fig.  1a ) covers not only the near-coastal area but also this bifurcation branch of ocean circulation. The APR is used to calculate the area-averaged near-surface ocean salinity time series. We also consider the region with ocean salinity <34.5 PSU following a previous study 8 to define APR (magenta contour line in Fig.  1a ), which produces similar results (c.f., Fig.  4d and Supplementary Fig.  8 ).

We define the Amazon river basin (black contour line in Fig.  1a ) as the catchment upstream of the Obidos station based on ArcGISV10.1, which is different from the conventional Amazon river basin based on the Amazon river mouth near the Equator (black contour in Supplementary Fig.  3 ). Because the conventional definition includes river discharge downstream of the Obidos station, this adjusted river basin is used to more accurately calculate the area-averaged Amazonia precipitation time series consistent with river discharge observed at the Obidos station. We compare area-averaged precipitation annual cycles using this adjusted and conventional river basin (black contour line in Supplementary Figs.  3 and 10a ). Slightly less precipitation using the adjusted river basin occurs in the wet season, and more precipitation from spring to fall. We also compare the absolute difference ratio between them and find the ratio ranges from 2.5% to 21% (Supplementary Fig.  10b ). The correlation coefficient between their monthly time series during 1979–2018 is as high as 0.99. These results using the adjusted river basin largely capture the amount and variability of those using the conventional Amazon river basin.

The observational, reanalysis, and ocean-state estimate datasets

The monthly Amazon river discharge observed at Obidos gauge station (#17050001, 1.9225°S, and 55.6753°W, magenta star in Fig.  1a ) for 1968–2018 and the Orinoco river discharge data observed at Ciudad Bolivar gauge station (#408000000, 08.1536°N, and 063.5361°W, blue star in Fig.  1a ) for 2003–2018 are obtained from SO HYBAM material transport datasets (formerly Environmental Research Observatory, http://www.ore-hybam.org/ ). Other river discharge data within the Amazon river basin are also obtained from the HYBAM website (Supplementary Figs.  3 and 4 ). We also use the Amazon river and Orinoco river discharge data from the Global River Flow and Continental Discharge Data Set during 1979–2018 ( http://www.cgd.ucar.edu/cas/catalog/surface/dai-runoff/ ) 59 , which provides river discharge data for the world’s 925 largest rivers primarily based on gauge observations with the assistance of model simulations.

For the observational monthly precipitation datasets, we use GPCP version 6 ( https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html ) 31 , 60 , GPCC ( https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html ) 32 , and Precipitation Reconstruction over Land (PREC/L, https://www.esrl.noaa.gov/psd/data/gridded/data.precl.html ) 33 for 1979–2019. We also use observational precipitation datasets from the TRMM version 7 ( https://pmm.nasa.gov/data-access/downloads/trmm ) 34 and the Climate Prediction CMAP ( https://www.esrl.noaa.gov/psd/data/gridded/data.cmap.html ) 35 . Multiple reanalysis products during 1979–2018 are used when calculating the atmospheric water moisture budget (see Supplementary Tables  1 and 4 – 8 ). We have compared the variability of reanalysis precipitation datasets with the observed ones. Their characteristics are very similar (c.f., Figs.  2b and 3a ).

When calculating the effect of evapotranspiration 61 , 62 , we use multiple reanalysis products (Supplementary Table  4 ). We also use global monthly evapotranspiration fields from the Global Land Evaporation Amsterdam Model (GLEAM, during 1980–2018, https://www.gleam.eu/ ) 63 , 64 , which assimilates a series of land surface and satellite observations. Their results are largely similar (c.f. Fig.  3b and Supplementary Fig.  2f ).

Several monthly observational and ocean-state estimate products for the salinity field at the surface and 5 m below the ocean surface used in this study include the SMOS (during 2011–2016, https://www.esa.int/Our_Activities/Observing_the_Earth/SMOS ) 29 and the U.S./Argentina Aquarius/SACD (during 2012–2014, https://aquarius.oceansciences.org/cgi/index-noflash.htm ) 30 , Estimating the Circulation and Climate of the Ocean project version 4 (ECCO4, during 1992–2017, www.ecco-group.org ) 37 and the German contribution to ECCO version 2 (GECCO2, during 1979–2016, https://icdc.cen.uni-hamburg.de/1/daten/reanalysis-ocean/gecco2.html ) 36 , EN4.2.1 (EN4 hereafter, during 1979–2018, https://www.metoffice.gov.uk/hadobs/en4/en4-0-2-profile-file-format.html ) 40 , Ocean Reanalysis/analysis version 5 (ORAS5, during 1979–2018, https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis ) 39 , and Simple Ocean Data Assimilation version 3.1.1 (SODA3.1.1, during 1980–2015, http://www.soda.umd.edu/ ) 38 . We also use five interpolated ARGO products (see the first four bars from the left in Supplementary Fig.  5b , http://www.argo.ucsd.edu/Gridded_fields.html ) 41 .

We examine the data quality of EN4 according to its salinity observation weight and uncertainty in the APR (Supplementary Fig.  11 ). Despite the increasing observation weight and decreasing salinity uncertainty after the late 1990s in the APR; low observation weight and large uncertainty before the mid-1990s may contribute to the discrepancy of APR salinity seasonality between EN4 and other products. In addition, different assimilation process in generating the products may also be a factor.

Before calculating the area-averaged values, we only regrid the field from ORAS5 and GECCO2 product to ~1° × 1° using nearest interpolation because the former is output in T-grid and the latitude grid of the latter varies in different variables. We do not perform interpolation for other datasets.

Atmospheric moisture budget analysis

We utilize a vertically integrated moisture budget analysis to explore the mechanisms behind the enhanced precipitation seasonality. A similar analysis has been performed in many studies to examine global and regional precipitation changes 27 , 65 on various timescales (i.e., daily, monthly, and interannually). The moisture budget is formulated as

where P denotes precipitation, \({\vec{\mathbf{v}}}\) the horizontal velocity field, q specific humidity, E evapotranspiration, δ the residual term, ω is the pressure velocity, and <> mass integration throughout the atmospheric layers (surface to model top). The first term on the right-hand side ( \(- \left\langle {\omega \frac{{\partial q}}{{\partial p}}} \right\rangle\) ) represents the vertical moisture advection, while the second term ( \(- \left\langle {{\vec{\mathbf{v}}} \cdot \nabla q} \right\rangle\) ) represents the horizontal moisture advection ( \(- \left\langle {{\vec{\mathbf{v}}} \cdot \nabla q} \right\rangle\) ). When vertical integration is performed on \(- \left\langle {\omega \frac{{\partial q}}{{\partial p}}} \right\rangle\) , the pressure velocities at the surface and at the model top are assumed to be zero. Note that the residual term ( δ ) includes transient eddy and nonlinear effects.

The vertical moisture advection can be further divided into:

where \(\overline {()}\) indicates seasonal averaging from 1980 to 2018 and ()′ denotes the seasonal anomaly from the seasonal mean in the wet and dry seasons. We disregard the nonlinear term \(- \left\langle {\omega ^{\prime} \frac{{\partial q^\prime }}{{\partial p}}} \right\rangle\) . This decomposition allows us to examine the dynamical and thermodynamical contributions to precipitation changes. The first term of the above equation on the right-hand side represents the thermodynamical term, while the second term, the dynamical term respectively follows previous studies 65 , 66 , 67 . The unit of each term is kg s −1  m −2 , which is equivalent to ml s −1 .

Seasonality calculation and enhancement

Since this study focuses on the seasonal averages of precipitation, river discharge, and APR ocean salinity, we take 3-month averages before calculating their seasonality. The seasonality of the time series with 3-monthly averaging is defined as the difference of its maximum value minus its minimum value within 1 year in this study. Similar results can be obtained without taking a 3-month average. We also calculate seasonality with the difference between fixed wet and dry seasons (e.g., January–February–March averaged precipitation minus July–August–September averaged precipitation based on the climatological seasonal cycle, Fig.  1b ), and obtain very similar results.

To enhance the seasonality for a given forcing field in climate model experiments, we use a fast Fourier transformation (FFT) approach. We first apply FFT on a target time series, and before applying inverse FFT to retrieve the resultant time series, we multiply a targeted factor to enhance its amplitude. To double the amplitude, for example, we choose the factor as 2. To demonstrate, we consider a simple combined sine wave, \(\sin (\frac{{2\pi }}{{360}}x) + \sin \left( {\frac{{4\pi }}{{360}}x} \right) + 5\) , which is shown as blue line in Supplementary Fig.  12 , while the time series that has been amplified by a factor of 2 is shown as the red line. It is noted that the mean of the two time series is exactly the same.

Global land model historical experiments

To test the sensitivity and quantify the seasonality of Amazon river discharge changes in response to Amazonia precipitation seasonality changes, we use the Community Land Model version 4.5 (CLM4.5) 63 under the Community Earth System Model (CESM) framework to conduct land-only experiments with varying precipitation forcing seasonality. The atmospheric conditions used to force the CLM4.5 hindcast experiments are constructed following a previous study 64 using observational and reanalysis datasets from 1948 to 2009. For the control simulation, we conduct a 62-year simulation from 1948 to 2009 with corresponding forcings prescribed. We then repeat four cycles with the same forcings to generate a total of five ensemble members. For the “x1.5” (“x1.75”) experiment, we conduct another 62-year simulation from 1948 to 2009 cycling five times using the precipitation forcing seasonality increased by a factor of 1.5 (1.75) above the Amazon river basin. We only analyze the results from 1979 to 2009.

When we construct precipitation forcing with enhanced seasonality, due to the fact that precipitation forcing is given in 6-h time intervals, we first take the monthly average from the 6-h precipitation field and then perform FFT on the monthly field to enhance the seasonality. We then add increased or decreased values in 1 month back to the original 6-hourly precipitation forcing for the experimental simulations. However, some resultant 6-h values can be less than zero, which is not reasonable, so we set all negative values to zero. Although in this way, the mean of the resultant precipitation forcing is not exactly the same as that of the original precipitation forcing, we compare their mean values and find only a small difference.

Global ocean model historical experiments

We conduct an ocean-only experiment similar to the land-only experiment described above, but with the river runoff forcing seasonality changed at the Amazon river mouth, to examine the sensitivity of ocean salinity in the APR. We use the Parallel Ocean Program version 2 (POP2) under the CESM framework. The boundary conditions used to force POP2 are prepared according to the Coordinated Ocean-ice Reference Experiments Phase 2 project (CORE-II) 42 , which spans from 1948 to 2009. In the global river runoff forcing field to drive the ocean model, we increase the river runoff at the grid, where largest annual mean river runoff occurs in the South American continent, by a factor of 1.5 and 1.75 to construct runoff forcings for the “x1.5” experiment and the “x1.75” experiment, respectively, using the FFT approach as well. It is noted that the global river runoff forcing is constructed based on the Dai-Trenberth’s dataset 59 , which is analyzed in Figs.  1 and 2 .

Due to the fact that the ocean model requires a longer time to reach quasi-equilibrium and to effectively reduce model drift in the historical ocean-only simulations, a five-cycle spin-up simulation was suggested by previous studies 68 , 69 . Therefore, we conduct five-cycle spin-up simulations with repeating boundary conditions from 1948 to 2009 (black star in Fig.  4d ). The control simulation is continued from the spin-up run for another five more cycles to generate five ensemble members, whereas the “x1.5” and the “x1.75” experiments are continued from the spin-up runs for another five more cycles given river runoff forcings with Amazon river runoff forcing seasonalities increased by a factor of 1.5 and 1.75, respectively. We only analyze results from 1979 to 2009.

The original runoff and amplified seasonalities of river runoff are shown in Fig.  4c . We choose the amplification factors in ocean model experiments in order to prevent the amplified runoff value from being smaller than zero. It is noted that the total river runoff released into the ocean is exactly the same in each experiment because the monthly mean of runoff fields is the same, which is a direct result of the FFT approach described above.

Coordinated Ocean-ice Reference Experiments Phase 2

Coordinated Ocean-ice Reference Experiments Phase 2 (CORE-II) entails a set of coordinated historical global ocean model experiments using different state-of-the-art global ocean models developed by different modeling groups. The models are prescribed with common forcings, including precipitation and river runoff from 1948 to 2009 42 , 70 . The boundary fluxes are computed following the same bulk formulae 42 . CORE-II simulations provide a framework to investigate mechanisms of significant ocean phenomena and their seasonal and decadal variabilities, including both forced and internal variability. Therefore, CORE-II fits well for this study to examine whether the increasing seasonality trend of near-surface ocean salinity in the APR is a common feature in response to the observed precipitation and subsequent river runoff seasonality changes in the Amazon river basin.

Supplementary fig.  5a shows that the near-surface ocean seasonalities in 12 CORE-II simulations are overall enhanced during 1979–2007 (we drop the last two years because some models do not provide simulated results in 2008 and 2009), which is consistent with the result found in the GECCO4, ECCO, and SODA3.1.1 ocean-state estimate products. The multi-model mean trend is about 0.054 PSU per decade. The values of the increased seasonality trend are comparable to those in the ocean state estimate products (~0.04 PSU per decade for GECCO2 and ~0.03 PSU per decade for ECCO4), indicating that CORE-II simulations reasonably capture the increased trends of ocean salinity seasonality and support our findings based on ocean-state estimate products. For a comparison of interannual variability between simulated long-term mean seasonal cycles, one is referring to a previous study 42 . CORE-II simulation results are downloaded from the NCAR/NCEP Research Data Archive ( https://rda.ucar.edu/datasets/ds262.0/ ).

Natural variability indices

To assess potential linkages between the seasonality in the Amazonia hydroclimatological system and natural variability, we consider the AMV 71 , PDV 72 , and IPV 73 , as shown in Supplementary Fig.  9 . The time series are shown as 11-year running average to illustrate their decadal variability. The AMV index and a tripole index representing IPV are downloaded from ESRL Physical Science Division ( https://www.esrl.noaa.gov/psd/data/timeseries/AMO/ , and https://www.esrl.noaa.gov/psd/data/timeseries/IPOTPI/ ), and the PDV index is obtained from the Joint Institute for the Study of the Atmosphere and Ocean ( http://research.jisao.washington.edu/pdo/ ).

Near-surface ocean mixing and barrier-layer calculations

In order to examine the responses of near-surface ocean physics and dynamics to Amazon river discharge in the APR, we calculate the barrier-layer thickness and potential energy following a previous study 74 . The barrier-layer thickness is defined as the isothermal layer depth (ILD) minus the mixed layer depth (MLD) when the former is deeper than the latter. If ILD is shallower than MLD, the barrier-layer thickness at this grid is not considered. The MLD is calculated as σ ref  + Δ σ , where σ ref is chosen as 5 m and Δ σ 0.1 kg m −3 . The ILD is computed using the temperature difference equivalence to 0.1 kg m −3 of density increase from the reference depth with the salinity at the reference 5-m depth. We only consider grid points where the 5-m ocean salinity is <35.4 PSU within the APR to better characterize the freshwater plume following a previous study 8 , but similar results can be obtained without this constraint. To characterize the near-surface mixing processes, we also calculate the squared buoyancy frequency (in the unit of s −2 ), defined as:

where g is the gravitational constant, ρ 0 is a reference density (1025 kg m −3 ), ρ is density, and z is depth.

Statistical significance test

For a given time series, the statistical significance of its trend is determined based on a Student’s t test with a null hypothesis that the trend is zero 75 . If the P value is <0.05, the null hypothesis can be rejected with 5% significance, and the trend is considered significant at the 5% level. We consider the effective sample size when performing the t test to take into account the effect of serial correlation. The effective sample size (ESS) is given as:

where N is the length of time series, and R x and R y are the lag-1 autocorrelations of time series x and y , respectively 76 .

Data availability

The monthly Amazon river discharge observed at Obidos gauge station and the Orinoco river discharge data observed at Ciudad Bolivar gauge station are obtained from SO HYBAM material transport datasets (formerly Environmental Research Observatory, http://www.ore-hybam.org/ ). We also use the Amazon river and Orinoco river discharge data from the Global River Flow and Continental Discharge Data Set ( http://www.cgd.ucar.edu/cas/catalog/surface/dai-runoff/ ). For the observational monthly precipitation datasets, we use Global Precipitation Climatology Project version 6 ( https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html ), Global Precipitation Climatology Centre ( https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html ), and Precipitation Reconstruction over Land ( https://www.esrl.noaa.gov/psd/data/gridded/data.precl.html ). We also use observational precipitation datasets from the TRMM version 7 ( https://pmm.nasa.gov/data-access/downloads/trmm ) and the Climate Prediction Center Merged Analysis of Precipitation ( https://www.esrl.noaa.gov/psd/data/gridded/data.cmap.html ). Multiple reanalysis datasets are used: ERAI is obtained from ECMWF ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ), MERRA from NASA ( https://gmao.gsfc.nasa.gov/reanalysis/MERRA/ ), JRA and JRA-55 from the Japan Meteorological Agency and the Central Research Institute of Electric Power Industry ( https://jra.kishou.go.jp/JRA-55/index_en.html ), NCEP_R2 from ESRL ( https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html ), and NCEP_CFSR from NCAR/UCAR ( https://climatedataguide.ucar.edu/climate-data/climate-forecast-system-reanalysis-cfsr ). The Global Land Evaporation Amsterdam Model data are downloaded from https://www.gleam.eu/ . Several monthly observational and ocean-state estimate products for the salinity field at the surface and 5 m below the ocean surface used in this study include the soil moisture and ocean salinity ( https://www.esa.int/Our_Activities/Observing_the_Earth/SMOS ) and the U.S./Argentina Aquarius/SACD ( https://aquarius.oceansciences.org/cgi/index-noflash.htm ), Estimating the Circulation and Climate of the Ocean project version 4 ( www.ecco-group.org ) and the German contribution to ECCO version 2 ( https://icdc.cen.uni-hamburg.de/1/daten/reanalysis-ocean/gecco2.html ), EN4.0.2 ( https://www.metoffice.gov.uk/hadobs/en4/en4-0-2-profile-file-format.html ), Ocean Reanalysis/analysis version 5 ( https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis ), and Simple Ocean Data Assimilation version 3.1.1 ( http://www.soda.umd.edu/ ). We also use five interpolated ARGO products ( http://www.argo.ucsd.edu/Gridded_fields.html ). Ocean-only simulations of Coordinated Ocean-ice Reference Experiments Phase 2 are obtained from NCAR/UCAR ( https://rda.ucar.edu/datasets/ds262.0/ ). The AMV index and a tripole index are downloaded from ESRL Physical Science Division ( https://www.esrl.noaa.gov/psd/data/timeseries/AMO/ , and https://www.esrl.noaa.gov/psd/data/timeseries/IPOTPI/ ), and the PDV index is obtained from the Joint Institute for the Study of the Atmosphere and Ocean ( http://research.jisao.washington.edu/pdo/ ). CORE-II simulation results are downloaded from the NCAR/NCEP Research Data Archive ( https://rda.ucar.edu/datasets/ds262.0/ ). ERAI and ERA5 datasets are downloaded from ECMWF ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ). JRA-55 data are downloaded from JRA Project ( https://jra.kishou.go.jp/JRA-55/index_en.html#download ). NCEP_R1 and NCEP_R2 are downloaded from NOAA’s Physical Sciences Laboratory ( https://psl.noaa.gov/data/gridded/index.html ). The sensitivity climate model simulations are compiled on the Zenodo data repository ( https://doi.org/10.5281/zenodo.3939611 ). The geographic maps in Fig.  1a and Supplementary Fig.  3 are produced by Python Cartopy package ( https://scitools.org.uk/cartopy/docs/latest/# ) 77 .

Code availability

The codes that analyze the data and make figures are available on Y.-C. L.’s GitHub website ( https://github.com/yuchiaol/Amazon_river_seasonality ).

Henderson, A. The Palms of the Amazon (Oxford Univ. Press, Oxford, 1995).

Google Scholar  

Dai, A. & Trenberth, K. E. Estimates of freshwater discharge from continents: latitudinal and seasonal variations. J. hydrometeorol. 3 , 660–687 (2002).

ADS   Google Scholar  

Smith, W. O. Jr & Demaster, D. J. Phytoplankton biomass and productivity in the Amazon river plume: correlation with seasonal river discharge. Cont. Shelf Res. 16 , 291–319 (1996).

Coles, V. J. et al. The pathways and properties of the Amazon river plume in the tropical North Atlantic Ocean. J. Geophys. Res.: Oceans 118 , 6894–6913 (2013).

Mouyen, M. et al. Assessing modern river sediment discharge to the ocean using satellite gravimetry. Nat. Commun. 9 , 3384 (2018).

ADS   PubMed   PubMed Central   Google Scholar  

Oliveira, J. C., Aguiar, W., Cirano, M., Genz, F. & de Amorim, F. N. A climatology of the annual cycle of river discharges into the Brazilian continental shelves: from seasonal to interannual variability. Environ. Earth Sci. 77 , 192 (2018).

Gouveia, N. A., Gherardi, D. F. M. & Aragão, L. E. O. C. The role of the Amazon river plume on the intensification of the hydrological cycle. Geophys. Res. Lett . 46 , 12221–12229 (2019).

Ffield, A. Amazon and Orinoco river plumes and NBC rings: bystanders or participants in hurricane events? J. Clim. 20 , 316–333 (2007).

Hu, C., Montgomery, E. T., Schmitt, R. W. & Muller-Karger, F. E. The dispersal of the Amazon and Orinoco river water in the tropical Atlantic and Caribbean Sea: observation from space and S-PALACE floats. Deep-Sea Res. Pt II 51 , 1151–1171 (2004).

ADS   CAS   Google Scholar  

Stukel, M. R., Coles, V. J., Brooks, M. T. & Hood, R. R. Top-down, bottom-up and physical controls on diatom-diazotroph assemblage growth in the Amazon river plume. Biogeosci. 11 , 3259–3278 (2013).

Körtzinger, A. A significant CO 2 sink in the tropical Atlantic Ocean associated with the Amazon river plume. Geophys. Res. Lett . 30 , 2287 (2003).

Lukas, R. & Lindstrom, E. The mixed layer of the western equatorial Pacific. J. Geophys. Res. 96 , 3343–3357 (1991).

Ibánhez, J. S. P., Diverrès, D., Araujo, M. & Lefèvre, N. Seasonal and interannual variability of sea‐air CO 2 fluxes in the tropical Atlantic affected by the Amazon river plume. Glob. Biogeochem. Cy 29 , 1640–1655 (2015).

Vizy, E. K. & Cook, K. H. Influence of the Amazon/Orinoco plume on the summertime Atlantic climate. J. Geophys. Res.: Atmos. 115 , D21112 (2010).

Grodsky, S. A. et al. Haline hurricane wake in the Amazon/Orinoco plume: AQUARIUS/SACD and SMOS observations. Geophys. Res. Lett . 39 , L20603 (2012).

Grodsky, S. A., Reverdin, G., Carton, J. A. & Coles, V. J. Year-to-year salinity changes in the Amazon plume: contrasting 2011 and 2012 Aquarius/SACD and SMOS satellite data. Remote Sens. Environ. 140 , 14–22 (2014).

Rudzin, J. E., Shay, L. K. & Jaimes de la Cruz, B. The impact of the Amazon–Orinoco river plume on enthalpy flux and air-sea interaction within Caribbean Sea tropical cyclones. Mon. Weather Rev. 147 , 931–950 (2019).

Masson, S. & Delecluse, P. Influence of the Amazon river runoff on the tropical Atlantic. Phys. Chem. Earth, Part B: Hydrol., Oceans Atmos. 26 , 137–142 (2001).

Jahfer, S., Vinayachandran, P. N. & Nanjundiah, R. S. The role of Amazon river runoff on the multidecadal variability of Atlantic ITCZ. Environ. Res. Lett . 15 , 054013 (2020).

Durand, F. et al. Impact of continental freshwater runoff on coastal sea level. Surv. Geophys. 40 , 1437–1466 (2019).

Giffard, P., Llovel, W., Jouanno, J., Morvan, G. & Decharme, B. Contribution of the Amazon river discharge to regional sea level in the tropical Atlantic Ocean. Water 11 , 2348 (2019).

Piecuch, C. G. & Wadehra, R. Dynamic sea level variability due to seasonal river discharge: a preliminary global ocean model study. Geophys. Res. Lett. 47 , e2020GL086984 (2020).

Jahfer, S., Vinayachandran, P. N. & Nanjundiah, R. S. Long-term impact of Amazon river runoff on northern hemispheric climate. Sci. Rep. 7 , 10989 (2017).

ADS   CAS   PubMed   PubMed Central   Google Scholar  

Duffy, P. B., Brando, P., Asner, G. P. & Field, C. B. Projections of future meteorological drought and wet periods in the Amazon. Proc. Natl Acad. Sci. USA 112 , 13172–13177 (2015).

ADS   CAS   PubMed   Google Scholar  

Almeida, C. T., Oliveira-Júnior, J. F., Delgado, R. C., Cubo, P. & Ramos, M. C. Spatiotemporal rainfall and temperature trends throughout the Brazilian Legal Amazon, 1973–2013. Int. J. Climatol. 37 , 2013–2026 (2017).

Barichivich, J. et al. Recent intensification of Amazon flooding extremes driven by strengthened Walker circulation. Sci. Adv. 4 , eaat8785 (2018).

Lan, C. W., Lo, M. H., Chou, C. & Kumar, S. Terrestrial water flux responses to global warming in tropical rainforest areas. Earth’s Future 4 , 210–224 (2016).

Durack, P. J., Wijffels, S. E. & Matear, R. J. Ocean salinities reveal strong global water cycle intensification during 1950 to 2000. Science 336 , 455–458 (2012).

Boutin, J. et al. First assessment of SMOS data over open ocean: part II-sea surface salinity. IEEE Trans. Geosci. Remote Sens 50 , 1662–1675 (2012).

Lagerloef, G. et al. Aquarius satellite mission provides new, detailed view of sea surface salinity. Bull. Am. Meteorol. Soc. 93 , S70–S71 (2012).

Adler, R. F. et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeorol. 4 , 1147–1167 (2003).

Becker, A. et al. A description of the global land-surface precipitation data products of the global precipitation climatology centre with sample applications including centennial (trend) analysis from 1901-present. Earth Syst. Sci. Data 5 , 71–99 (2013).

Chen, M., Xie, P., Janowiak, J. E. & Arkin, P. A. Global land precipitation: A 50-yr monthly analysis based on gauge observations. J. Hydrometeorol. 3 , 249–266 (2002).

Huffman, G. J. et al. The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 8 , 38–55 (2007).

Xie, P. & Arkin, P. A. Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Am. Meteor. Soc. 78 , 2539–2558 (1997).

Köhl, A. Evaluation of the GECCO2 ocean synthesis: transports of volume, heat and freshwater in the Atlantic. Q. J. R. Meteorol. Soc. 141 , 166–181 (2015).

Forget, G. et al. ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation. Geosci. Model Dev. 8 , 3071–3104 (2015).

Carton, J. A., Chepurin, G. A. & Chen, L. SODA3: a new ocean climate reanalysis. J. Clim. 31 , 6967–6983 (2018).

Zuo, H., Balmaseda, M. A., Mogensen, K. & Tietsche, S. OCEAN5: the ECMWF Ocean Reanalysis System and its Real-Time Analysis Component (European Centre for Medium-Range Weather Forecasts, 2018).

Good, S. A., Martin, M. J. & Rayner, N. A. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res.: Oceans 118 , 6704–6716 (2013).

Argo. Argo float data and metadata from Global Data Assembly Centre (Argo GDAC) . SEANOE https://doi.org/10.17882/42182 (2000).

Griffies, S. M. et al. Datasets and protocol for the CLIVAR WGOMD coordinated ocean-sea ice reference experiments (COREs). WCRP Rep. 21 , 1–21 (2012).

Hallberg, R. Using a resolution function to regulate parameterizations of oceanic mesoscale eddy effects. Ocean Model. 72 , 92–103 (2013).

Goes, M., Molinari, R., da Silveira, I. & Wainer, I. Retroflections of the north brazil current during February 2002. Deep-Sea Res. Pt I. 52 , 647–667 (2005).

DeMaria, M. & Kaplan, J. A statistical hurricane intensity prediction scheme (SHIPS) for the Atlantic basin. Weath. Forcast 9 , 209–220 (1994).

DeMaria, M., Mainelli, M., Shay, L. K., Knaff, J. A. & Kaplan, J. Further improvement to the statistical hurricane intensity prediction scheme (SHIPS). Weath. Forecast 20 , 531–543 (2005).

Mainelli, M., DeMaria, M., Shay, L. K. & Goni, G. Application of oceanic heat content estimation to operational forecasting of recent Atlantic category 5 hurricanes. Weath. Forecast 23 , 3–16 (2008).

Chou, C. et al. Increase in the range between wet and dry season precipitation. Nat. Geosci. 6 , 263 (2013).

Chiang, J. C., Kushnir, Y. & Giannini, A. Deconstructing Atlantic intertropical convergence zone variability: influence of the local cross-equatorial sea surface temperature gradient and remote forcing from the eastern equatorial Pacific. J. Geophys. Res.: Atmos. 107 , ACL-3 (2002).

García-García, D. & Ummenhofer, C. C. Multidecadal variability of the continental precipitation annual amplitude driven by AMO and ENSO. Geophys. Res. Lett. 42 , 526–535 (2015).

Jones, C. & Carvalho, L. M. The influence of the Atlantic multidecadal oscillation on the eastern Andes low-level jet and precipitation in South America. npj Clim. Atmos. Sci. 1 , 40 (2018).

Marengo, J. A. Long‐term trends and cycles in the hydrometeorology of the Amazon basin since the late 1920s. Hydrol. Process. 23 , 3236–3244 (2009).

Fernandes, K., Giannini, A., Verchot, L., Baethgen, W. & Pinedo-Vasquez, M. Decadal covariability of Atlantic SSTs and western Amazon dry-season hydroclimate in observations and CMIP5 simulations. Geophys. Res. Lett. 42 , 6793–6801 (2015).

Lewis, S. L., Brando, P. M., Phillips, O. L., van der Heijden, G. M. F. & Nepstad, D. The 2010 Amazon drought. Science 331 , 554 (2011).

Marengo, J. A., Tomasella, J., Soares, W. R., Alves, L. M. & Nobre, C. A. Extreme climatic events in the Amazon basin. Theor. Appl. Climatol. 107 , 73–85 (2012).

Latrubesse, E. et al. Damming the rivers of the Amazon basin. Nature 546 , 363–369 (2017).

Khanna, J., Medvigy, D., Fueglistaler, S. & Walko, R. Regional dry-season climate changes due to three decades of Amazonian deforestation. Nat. Clim. Change 7 , 200–204 (2017).

Lin, Y.-H., Lo, M.-H. & Chou, C. Potential negative effects of groundwater dynamics on dry season convection in the Amazon river basin. Clim. Dyn. 46 , 1001–1013 (2016).

Dai, A. & Trenberth, K. Global river flow and continental discharge dataset. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/D6V69H1T (2016).

Schneider, U. et al. GPCC full data reanalysis version 6.0 (at 0.5°, 1.0°, 2.5°): Monthly land-surface precipitation from rain-gauges built on GTS-based and historic data. Offenbach/Main, Germany  https://doi.org/10.5676/DWD_GPCC/FD_M_V6_100 (2011).

Miralles, D. G. et al. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 15 , 453–469 (2011).

Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10 , 1903–1925 (2017).

Oleson, K. W. et al. Technical Description of version 4.0 of the Community Land Model (CLM) (No. NCAR/TN-478+STR). University Corporation for Atmospheric Research. https://doi.org/10.5065/D6FB50WZ (2010).

Viovy, N. CRUNCEP version 7—atmospheric forcing data for the community land model. in Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory . https://doi.org/10.5065/PZ8F-F017 (2018).

Chou, C. & Lan, C. W. Changes in the annual range of precipitation under global warming. J. Clim. 25 , 222–235 (2012).

Held, I. M. & Soden, B. J. Robust responses of the hydrological cycle to global warming. J. Clim. 19 , 5686–5699 (2006).

Chou, C., Neelin, J. D., Chen, C. A. & Tu, J. Y. Evaluating the “rich-get-richer” mechanism in tropical precipitation change under global warming. J. Clim. 22 , 1982–2005 (2009).

Danabasoglu, G. et al. North Atlantic simulations in coordinated ocean-ice reference experiments phase II (CORE-II). Part II: Inter-annual to decadal variability. Ocean Model. 97 , 65–90 (2016).

Karspeck, A. R. et al. Comparison of the Atlantic meridional overturning circulation between 1960 and 2007 in six ocean reanalysis products. Clim. Dyn. 49 , 957–982 (2017).

Griffies, S. M. et al. Coordinated ocean-ice reference experiments (COREs). Ocean Model. 26 , 1–46 (2009).

Enfield, D. B., Mestas-Nuñez, A. M. & Trimble, P. J. The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental US. Geophys. Res. Lett. 28 , 2077–2080 (2001).

Mantua, N. J., Hare, S. R., Zhang, Y., Wallace, J. M. & Francis, R. C. A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Am. Meteorol. Soc. 78 , 1069–1080 (1997).

Henley, B. J. et al. A tripole index for the interdecadal Pacific oscillation. Clim. Dyn. 45 , 3077–3090 (2015).

Chi, N.-H., Lien, R.-C., D’Asaro, E. A. & Ma, B. B. The surface mixed layer heat budget from mooring observations in the central Indian Ocean during Madden–Julian Oscillation events. J. Geophys. Res.: Oceans 119 , 4638–4652 (2014).

Von Storch, H. & Zwiers, F. W. Statistical Analysis in Climate Research (Cambridge University Press, 2001).

Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M. & Bladé, I. The effective number of spatial degrees of freedom of a time-varying field. J. Clim. 12 , 1990–2009 (1999).

Met Office. Cartopy: a carographic python library with a Matplotlib interface. Exeter, Devon (2010–2015). https://scitools.org.ul/cartopy (2010).

Download references

Acknowledgements

M.-H.L., C.-W.L., and R.-J.W. are supported by the Ministry of Science and Technology in Taiwan under grant 106-2111-M-002-010-MY4. H.S. and J.D.S. are grateful for support from NOAA NA19OAR4310376 and NA17OAR4310255. C.C.U. acknowledges support from the U.S. National Science Foundation under grant OCE-1663704. The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the US National Science Foundation (NSF) under Cooperative Agreement No. 1852977. We thank Dr. Young-Oh Kwon at Woods Hole Oceanographic Institution and Dr. Who Kim at NCAR for discussions about the ocean model experiment design. We thank Dr. Mehnaz Rashid at National Taiwan University and Wen-Yin Wu at the University of Texas at Austin in helping generate the high-resolution Amazon river mask. We also thank Dr. Gael Forget at Massachusetts Institue of Technology for comments on using ECCO and other ocean-state estimate products.

Author information

Authors and affiliations.

Woods Hole Oceanographic Institution, Woods Hole, MA, USA

Yu-Chiao Liang, Hyodae Seo, Caroline C. Ummenhofer & John D. Steffen

Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan

Min-Hui Lo, Chia-Wei Lan & Ren-Jie Wu

National Center for Atmospheric Research, Boulder, CO, USA

Stephen Yeager

You can also search for this author in PubMed   Google Scholar

Contributions

The paper was conceived and written by Y.-C.L. and M.-H.L. with inputs from C.-W.L., H.S., C.C.U., S.Y., R.-J.W., and J.D.S. The global ocean and land model experiments were conducted by Y.-C.L. and R.-J.W. C.-W.L. performed the atmospheric moisture budget analysis. J.D.S. performed the analyses on surface ocean properties and barrier-layer thickness.

Corresponding author

Correspondence to Yu-Chiao Liang .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Peer review information Nature Communications thanks Xianfeng Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, peer review file, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Liang, YC., Lo, MH., Lan, CW. et al. Amplified seasonal cycle in hydroclimate over the Amazon river basin and its plume region. Nat Commun 11 , 4390 (2020). https://doi.org/10.1038/s41467-020-18187-0

Download citation

Received : 08 January 2020

Accepted : 27 July 2020

Published : 01 September 2020

DOI : https://doi.org/10.1038/s41467-020-18187-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Modern anthropogenic drought in central brazil unprecedented during last 700 years.

• Nicolas Misailidis Stríkis
• Plácido Fabrício Silva Melo Buarque
• Valdir Felipe Novello

Nature Communications (2024)

Observed variability and trends in global precipitation during 1979–2020

• Robert F. Adler

Climate Dynamics (2023)

Precipitation patterns over northern Brazil basins: climatology, trends, and associated mechanisms

• M. H. Shimizu
• J. A. Anochi
• M. T. Kayano

Theoretical and Applied Climatology (2022)

Coupled atmosphere-ice-ocean dynamics during Heinrich Stadial 2

• Gayatri Kathayat

Nature Communications (2022)

Onset and termination of Heinrich Stadial 4 and the underlying climate dynamics

• R. Lawrence Edwards

Communications Earth & Environment (2021)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Case Study: The Amazon Basin

• Cattle ranching in Brazil
• Highway construction e.g Marshal Rondon Highway Mineral mining in Carajas
• Since 1970, over 232,000 square miles of Amazon rainforest have been destroyed
• 1995-1998 government granted land in the Amazon to150,000 families
• The Balbina dam flooded some 920 square miles of rainforest when it was completed

Our websites may use cookies to personalize and enhance your experience. By continuing without changing your cookie settings, you agree to this collection. For more information, please see our University Websites Privacy Notice .

UConn Today

• School and College News
• Arts & Culture
• Community Impact
• Entrepreneurship
• Health & Well-Being
• Research & Discovery
• UConn Health
• University Life
• UConn Voices
• University News

September 7, 2021 | Combined Reports - UConn Communications

Study Shows the Impacts of Deforestation and Forest Burning on Biodiversity in the Amazon

Since 2001, between 40,000 and 73,400 square miles of Amazon rainforest have been impacted by fires

Ring of fire: Smoke rises through the understory of a forest in the Amazon region. Plants and animals in the Amazonian rainforest evolved largely without fire, so they lack the adaptations necessary to cope with it. (Credit: Paulo Brando)

A new study, co-authored by a team of researchers including UConn Ecology and Evolutionary Biology researcher Cory Merow provides the first quantitative assessment of how environmental policies on deforestation, along with forest fires and drought, have impacted the diversity of plants and animals in the Amazon. The findings were published in the Sept. 1 issue of Nature .

Researchers used records of more than 14,500 plant and vertebrate species to create biodiversity maps of the Amazon region. Overlaying the maps with historical and current observations of forest fires and deforestation over the last two decades allowed the team to quantify the cumulative impacts on the region’s species.

They found that since 2001, between 40,000 and 73,400 square miles of Amazon rainforest have been impacted by fires, affecting 95% of all Amazonian species and as many as 85% of species that are listed as threatened in this region. While forest management policies enacted in Brazil during the mid-2000s slowed the rate of habitat destruction, relaxed enforcement of these policies coinciding with a change in government in 2019 has seemingly begun to reverse this trend, the authors write. With fires impacting 1,640 to 4,000 square miles of forest, 2019 stands out as one of the most extreme years for biodiversity impacts since 2009, when regulations limiting deforestation were enforced.

“Perhaps most compelling is the role that public pressure played in curbing forest loss in 2019,” Merow says. “When the Brazilian government stopped enforced forest regulations in 2019, each month between January and August 2019 was the worse month on record (e.g. comparing January 2019 to previous January’s) for forest loss in the 20-year history of available data. However, based on international pressure, forest regulation resumed in September 2019, and forest loss declined significantly for the rest of the year, resulting in 2019 looking like an average year compared to the 20-year history.  This was big: active media coverage and public support for policy changes were effective at curbing biodiversity loss on a very rapid time scale.”

The findings are especially critical in light of the fact that at no point in time did the Amazon get a break from those increasing impacts, which would have allowed for some recovery, says senior study author Brian Enquist, a professor in UArizona’s Department of Ecology and Evolutionary Biology .

“Even with policies in place, which you can think of as a brake slowing the rate of deforestation, it’s like a car that keeps moving forward, just at a slower speed,” Enquist says. “But in 2019, it’s like the foot was let off the brake, causing it to accelerate again.”

Known mostly for its dense rainforests, the Amazon basin supports around 40% of the world’s remaining tropical forests. It is of global importance as a provider of ecosystem services such as scrubbing and storing carbon from the atmosphere, and it plays a vital role in regulating Earth’s climate. The area also is an enormous reservoir of the planet’s biodiversity, providing habitats for one out of every 10 of the planet’s known species. It has been estimated that in the Amazon, 1,000 tree species can populate an area smaller than a half square mile.

“Fire is not a part of the natural cycle in the rainforest,” says study co-author Crystal N. H. McMichael at the University of Amsterdam. “Native species lack the adaptations that would allow them to cope with it, unlike the forest communities in temperate areas. Repeated burning can cause massive changes in species composition and likely devastating consequences for the entire ecosystem.”

Since the 1960s, the Amazon has lost about 20% of its forest cover to deforestation and fires. While fires and deforestation often go hand in hand, that has not always been the case, Enquist says. As climate change brings more frequent and more severe drought conditions to the region, and fire is often used to clear large areas of rainforest for the agricultural industry, deforestation has spillover effects by increasing the chances of wildfires. Forest loss is predicted reach 21 to 40% by 2050, and such habitat loss will have large impacts on the region’s biodiversity, according to the authors.

“Since the majority of fires in the Amazon are intentionally set by people, preventing them is largely within our control,” says study co-author Patrick Roehrdanz, senior manager of climate change and biodiversity at Conservation International. “One way is to recommit to strong antideforestation policies in Brazil, combined with incentives for a forest economy, and replicate them in other Amazonian countries.”

Policies to protect Amazonian biodiversity should include the formal recognition of Indigenous lands, which encompass more than one-third of the Amazon region, the authors write, pointing to previous research showing that lands owned, used or occupied by Indigenous peoples have less species decline, less pollution and better-managed natural resources.

The authors say their study underscores the dangers of continuing lax policy enforcement. As fires encroach on the heart of the Amazon basin, where biodiversity is greatest, their impacts will have more dire effects, even if the rate of forest burning remains unchanged.

The research was made possible by strategic investment funds allocated by the Arizona Institutes for Resilience at UArizona and the university’s Bridging Biodiversity and Conservation Science group. Additional support came from the National Science Foundation’s Harnessing the Data Revolution program . Data and computation were provided through the Botanical Information and Ecology Network , which is supported by CyVerse , the NSF’s data management platform led by UArizona.

Recent Articles

March 22, 2024

Exploring Career Pathways and Leadership Opportunities in Nursing

Read the article

Huntington’s Disease Center of Excellence in Connecticut Designated at UConn Health

Tadinada Receives National Recognition for Student Mentorship

• ORIENTATION
• ASSIGNMENTS
• Program Home Page
• LIBRARY RESOURCES
• Getting Help
• Engaging Course Concepts

Case Study: The Amazon Rainforest

The Amazon in context

Tropical rainforests are often considered to be the “cradles of biodiversity.” Though they cover only about 6% of the Earth’s land surface, they are home to over 50% of global biodiversity. Rainforests also take in massive amounts of carbon dioxide and release oxygen through photosynthesis, which has also given them the nickname “lungs of the planet.” They also store very large amounts of carbon, and so cutting and burning their biomass contributes to global climate change. Many modern medicines are derived from rainforest plants, and several very important food crops originated in the rainforest, including bananas, mangos, chocolate, coffee, and sugar cane.

In order to qualify as a tropical rainforest, an area must receive over 250 centimeters of rainfall each year and have an average temperature above 24 degrees centigrade, as well as never experience frosts. The Amazon rainforest in South America is the largest in the world. The second largest is the Congo in central Africa, and other important rainforests can be found in Central America, the Caribbean, and Southeast Asia. Brazil contains about 40% of the world’s remaining tropical rainforest. Its rainforest covers an area of land about 2/3 the size of the continental United States.

There are countless reasons, both anthropocentric and ecocentric, to value rainforests. But they are one of the most threatened types of ecosystems in the world today. It’s somewhat difficult to estimate how quickly rainforests are being cut down, but estimates range from between 50,000 and 170,000 square kilometers per year. Even the most conservative estimates project that if we keep cutting down rainforests as we are today, within about 100 years there will be none left.

How does a rainforest work?

Rainforests are incredibly complex ecosystems, but understanding a few basics about their ecology will help us understand why clear-cutting and fragmentation are such destructive activities for rainforest biodiversity.

High biodiversity in tropical rainforests means that the interrelationships between organisms are very complex. A single tree may house more than 40 different ant species, each of which has a different ecological function and may alter the habitat in distinct and important ways. Ecologists debate about whether systems that have high biodiversity are stable and resilient, like a spider web composed of many strong individual strands, or fragile, like a house of cards. Both metaphors are likely appropriate in some cases. One thing we can be certain of is that it is very difficult in a rainforest system, as in most other ecosystems, to affect just one type of organism. Also, clear cutting one small area may damage hundreds or thousands of established species interactions that reach beyond the cleared area.

Pollination is a challenge for rainforest trees because there are so many different species, unlike forests in the temperate regions that are often dominated by less than a dozen tree species. One solution is for individual trees to grow close together, making pollination simpler, but this can make that species vulnerable to extinction if the one area where it lives is clear cut. Another strategy is to develop a mutualistic relationship with a long-distance pollinator, like a specific bee or hummingbird species. These pollinators develop mental maps of where each tree of a particular species is located and then travel between them on a sort of “trap-line” that allows trees to pollinate each other. One problem is that if a forest is fragmented then these trap-line connections can be disrupted, and so trees can fail to be pollinated and reproduce even if they haven’t been cut.

The quality of rainforest soils is perhaps the most surprising aspect of their ecology. We might expect a lush rainforest to grow from incredibly rich, fertile soils, but actually, the opposite is true. While some rainforest soils that are derived from volcanic ash or from river deposits can be quite fertile, generally rainforest soils are very poor in nutrients and organic matter. Rainforests hold most of their nutrients in their live vegetation, not in the soil. Their soils do not maintain nutrients very well either, which means that existing nutrients quickly “leech” out, being carried away by water as it percolates through the soil. Also, soils in rainforests tend to be acidic, which means that it’s difficult for plants to access even the few existing nutrients. The section on slash and burn agriculture in the previous module describes some of the challenges that farmers face when they attempt to grow crops on tropical rainforest soils, but perhaps the most important lesson is that once a rainforest is cut down and cleared away, very little fertility is left to help a forest regrow.

What is driving deforestation in the Amazon?

Many factors contribute to tropical deforestation, but consider this typical set of circumstances and processes that result in rapid and unsustainable rates of deforestation. This story fits well with the historical experience of Brazil and other countries with territory in the Amazon Basin.

Population growth and poverty encourage poor farmers to clear new areas of rainforest, and their efforts are further exacerbated by government policies that permit landless peasants to establish legal title to land that they have cleared.

At the same time, international lending institutions like the World Bank provide money to the national government for large-scale projects like mining, construction of dams, new roads, and other infrastructure that directly reduces the forest or makes it easier for farmers to access new areas to clear.

The activities most often encouraging new road development are timber harvesting and mining. Loggers cut out the best timber for domestic use or export, and in the process knock over many other less valuable trees. Those trees are eventually cleared and used for wood pulp, or burned, and the area is converted into cattle pastures. After a few years, the vegetation is sufficiently degraded to make it not profitable to raise cattle, and the land is sold to poor farmers seeking out a subsistence living.

Regardless of how poor farmers get their land, they often are only able to gain a few years of decent crop yields before the poor quality of the soil overwhelms their efforts, and then they are forced to move on to another plot of land. Small-scale farmers also hunt for meat in the remaining fragmented forest areas, which reduces the biodiversity in those areas as well.

Another important factor not mentioned in the scenario above is the clearing of rainforest for industrial agriculture plantations of bananas, pineapples, and sugar cane. These crops are primarily grown for export, and so an additional driver to consider is consumer demand for these crops in countries like the United States.

These cycles of land use, which are driven by poverty and population growth as well as government policies, have led to the rapid loss of tropical rainforests. What is lost in many cases is not simply biodiversity, but also valuable renewable resources that could sustain many generations of humans to come. Efforts to protect rainforests and other areas of high biodiversity is the topic of the next section.

Effective Geography

Dolly e sequeira, p s lathika, beeta publications.

• Representation of Geographical Features
• Major Landforms
• Water Bodies
• Agriculture
• North America: Location and Physical Features
• Case Study: Lumbering in Canada
• South America: Location and Physical Features

Case Study: Life in the Amazon River Basin

Available answers.

Fill in the blanks:

• The Amazon Basin is spread over ________ countries.
• The Amazon Basin is covered by equatorial _________.
• The native Americans are called ________.
• _______ is a deadly fish found in the waters of Amazon Basin.
• _______ is a port on the river Amazon in Peru.

Match the following:

Where is Amazon Basin located? How many countries are part of the Amazon Basin?

• 4. What type of climate is found in the Amazon Basin?
• 5. Why does the Amazon Basin have dense forests?
• 6. How do the native Americans obtain their food?