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This chapter constitutes an updated review of long-term climate variability and change in the Amazon region, based on observational data spanning more than 50 years of records and on climate-change modeling studies. We start with the early experiments on Amazon deforestation in the late 1970s, and the evolution of these experiments to the latest studies on greenhouse gases emission scenarios and land use changes until the end of the twenty-first century. The "Amazon dieback" simulated by the HadCM3 model occurs after a "tipping point" of CO2 concentration and warming. Experiments on Amazon deforestation and change of climate suggest that once a critical deforestation threshold (or tipping point) of 40-50% forest loss is reached in eastern Amazonia, climate would change in a way which is dangerous for the remaining forest. This may favor a collapse of the tropical forest, with a substitution of the forest by savanna-type vegetation. The concept of "dangerous climate change," as a climate change, which induces positive feedback, which accelerate the change, is strongly linked to the occurrence of tipping points, and it can be explained as the presence of feedback between climate change and the carbon cycle, particularly involving a weakening of the current terrestrial carbon sink and a possible reversal from a sink (as in present climate) to a source by the year 2050. We must, therefore, currently consider the drying simulated by the Hadley Centre model(s) as having a finite probability under global warming, with a potentially enormous impact, but with some degree of uncertainty.
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3
DANGEROUS CLIMATE CHANGE IN BRAZIL
April 2011
A collaborative project between the
Centro de Ciência do Sistema Terrestre (CCST) of the
Instituto Nacional de Pesquisas Espaciais (INPE), Brazil,
and the Met Office Hadley Centre, UK
Dangerous Climate
A BrAzil-UK AnAlysis of ClimAte ChAnge
And deforestAtion impACts in the AmAzon
Change in Brazil
4DANGEROUS CLIMATE CHANGE IN BRAZIL
AUTHORS . BRAZIL
Jose A. Marengo ( Coordinator )
Ph.D, CCST-INPE, São Paulo, Brazil
jose.marengo@inpe.br
Carlos A. Nobre
Ph.D, CCST-INPE, CEPED-MCT, São Paulo, Brazil
carlos.nobre@inpe.br
Sin Chan Chou
Ph.D, CPTEC-INPE, São Paulo, Brazil
chou.sinchan@cptec.inpe.br
Javier Tomasella
Ph.D, CCST-INPE, São Paulo, Brazil
javier.tomasella@inpe.br
Gilvan Sampaio
Ph.D, CCST-INPE, São Paulo, Brazil
gilvan.sampaio@inpe.br
Lincoln M. Alves
M.S, CCST-INPE, São Paulo, Brazil
lincoln.alves@inpe.br
Guillermo O. Obregón
Ph.D, CCST-INPE, São Paulo, Brazil
guilermo.obregon@inpe.br
Wagner R. Soares
Ph.D, CCST-INPE, São Paulo, Brazil
wagner.soares@cptec.inpe.br
DANGEROUS CLIMATE CHANGE IN BRAZIL
AUTHORS . UK
Richard Betts ( Coordinator )
Ph.D, Met Office Hadley Centre
richard.betts@metoffice.gov.uk
Gillian Kay
Ph.D, Met Office Hadley Centre
gillian.kay@metoffice.gov.uk
COVER
Ana Cíntia Guazzelli (WWF)
www.metoffice.gov.uk
www.inpe.br
www.ccst.inpe.br
5
DANGEROUS CLIMATE CHANGE IN BRAZIL
07 Preface
John Hirst, UK Met Office
08 Preface
Gilberto Câmara, INPE
09 Preface
Carlos A. Nobre, MCT
10 Foreword
12 Part 1 | Context
1. Introduction ....................................................................................................... 17
2. Observed climate variability and tendencies ...................................................... 19
3. Seasonal extremes: droughts of 2005 and 2010, and floods of 2009 ............... 21
4. Global and regional climate change ................................................................... 25
31 Part 2 | New science and scientific development
1. How we model climate ...................................................................................... 33
2. Future climate and assessment of climate change uncertainty in Amazonia ..... 39
3. Deforestation, land use change and climate ...................................................... 43
4. Summary and conclusions................................................................................. 48
Photo: Eduardo Arraut / INPE
6DANGEROUS CLIMATE CHANGE IN BRAZIL
Photo: Laura Borma / INPE
7
DANGEROUS CLIMATE CHANGE IN BRAZIL
John Hirst
Chief Executive
UK Met Office
Agreement for the UK and Brazil to work together on climate-change
issues was reached when President Lula visited the UK in March 2006.
Today, our two countries still work together, with the same sense of
urgency his visit inspired, to assess the impacts of climate change on
Brazil and the effects of deforestation on the Brazilian climate. This report
highlights what has been achieved so far through the cooperation and
expertise of INPE and the Met Office.
Global climate change is not in doubt, but of key importance for nations,
communities and people everywhere is how the climate could be affected
in their part of the world. In this project, INPE and the Met Office have
combined their expertise in climate modelling and in the climate of
Brazil to deepen understanding of how this may change in the future.
Results show that there may be substantial increases in temperature and
significant decreases in rainfall over large swathes of Brazil, including
Amazonia. Among other possible impacts, this has the potential to exert
pressure on the tropical forest. The threat of climate change cannot
be understated, but a more immediate concern is the deforestation of
Amazonia.
Forests around the world store huge amounts of carbon which is released
to the atmosphere when they are cleared and burnt, accelerating climate
change. Deforestation is the third largest cause of emissions after energy
production and industry, placing it ahead of the transport sector. However,
the Amazon forest is worth far more than the sum total of its carbon.
Across the globe, we need to value our forests for all of the services they
provide. A critical part of this process is developing a fuller understanding
of the role of forests within the climate system, which forms a significant
scientific challenge.
The INPE–Met Office collaboration has taken strides in addressing this
question for Brazil by studying the effects of the loss of the Amazon
forest on temperature and rainfall in the region. Model results suggest
that deforestation could cause temperatures to warm over Amazonia,
while the effect on rainfall could be towards drier conditions than those
currently experienced. Importantly, a changing climate could interact with
a fragmented and weakened forest to magnify these impacts.
The collaboration between INPE and the Met Office is critical to advancing
understanding of the dual effects of climate change and deforestation
in Brazil, and how these may impact upon ecosystems on which we
all depend. Using this project as a foundation, together we continue to
conduct cutting-edge science towards achieving these aims. Through
shared research such as this, scientific challenges can be taken on and
fresh insight brought to support decision-making today and for tomorrow.
8DANGEROUS CLIMATE CHANGE IN BRAZIL
The UK-Brazil collaborative project on climate change
in the Amazon is a prime example of the importance
of international cooperation in 21st century science.
Launched in 2006, through the joint efforts of the
Hadley Centre and INPE, the project has produced
significant results. Its research points out the Amazon
rain forest is sensitive to climate change forces.
Increases in temperature and decreases in rainfall may
be higher in Amazonia than the average expected global
variation.
The studies show the importance of Amazonia for
the global climate and as a provider of environmental
services for Brazil. They provide evidence about a
tipping point in the rain forest ecosystem, beyond
which there may be a partial collapse. INPE thanks
the coordinators (Jose Marengo and Carlos Nobre from
Brazil and Richard Betts from the UK) that motivated a
dedicated team of scientists from the UK and Brazil.
Since the project started in 2006, deforestation in
Amazonia changed. Through improved monitoring,
strong legal actions and responsible market practices,
forest clearing in Amazonia fell from 27,000 km2
in 2004 to 6,500 km2 in 2010. In the Copenhagen
climate conference in 2009, the Brazilian government
made an unconditional pledge to curb deforestation in
Amazonia by 80% in 2020, compared to 2005. Recent
data released by INPE shows that Brazil is keeping to its
commitments.
By reducing deforestation in Amazonia, Brazil has
averted an immediate threat. As shown by the project’s
results, had the pace of deforestation continued the
trend of the early 2000s, a medium-term collapse could
have followed. However, Amazonia faces a menace that
Brazil alone cannot avoid. If developed nations do not
assume their historical responsibilities and reduce their
per-capita greenhouse gas emissions, the Amazonian
ecosystem could break down. The report thus carries a
strong message and provides further evidence we must
act to stop dangerous climate change.
Gilberto Câmara
General Director of Brazil’s National
Institute for Space Research, INPE, Brazil
Photo: Laura Borma / INPE
9
DANGEROUS CLIMATE CHANGE IN BRAZIL
The Dangerous Climate Change in Brazil project represents a very worthwhile
example of successful collaboration between the National Institute for Space
Research (INPE) from Brazil and the UK Met Office-Hadley Centre. Throughout
this project, we were able to build capacity for state-of-the-art climate change
projections, directed to raising awareness among key stakeholders (research
scientists and policy makers) about the impacts of climate change in Brazil. The
aim is to empower policy makers with scientific evidence of climate change and
its possible impacts in Brazil, South America and elsewhere in the world.
The experience of the UK Met Office Hadley Centre’s world leading in climate
modeling, together with the experience of INPE in climate change studies on
South America, have been combined in a way that allowed to identify possible
climate change scenarios and impacts, making pioneering projections of the
effects of anthropogenic climate change across South America. These early
results indicated the likelihood of significant increases in drought conditions
across parts of Brazil. Based on the new knowledge generated by this project,
INPE has been developing efforts in South America to improve regional climate
change scenarios, for applications in vulnerability and adaptation studies
The project made three crucial contributions in support of Brazilian involvement
in the international climate change negotiations and in support of INPE´s
research endeavors:
• Building capacity within Brazil for policy-relevant climate change assessments.
• Generation of specific policy-relevant information relating to issues of adapting
to climate change and assessing risks of dangerous climate change across Brazil,
both for the 2nd National Climate Change Communication and international
negotiations and conventions
• Improving the scientific collaboration on assessing the impacts of climate
change in key sectors of society and economy.
Although the climate change projections generated by this collaboration covered
all Brazil, the focus of this report is on Amazonia, a region of national, regional,
and global concern.
As a legacy, this project has generated new methods for assessing the impacts of
both climate change and the direct human impacts on the landscape and ecology
of Brazil, and also a new land cover dataset for use in regional climate modeling
was produced. This work will be continued as part of the scientific agendas
of the National Institute of Science and Technology for Climate Change (INCT-
Climate Change) from the Brazilian Research Council (CNPq), and the Brazilian
Climate Change Network (Rede-CLIMA). Last, but not least, the project helped to
strengthen scientific and cultural ties between the UK and Brazil.
Carlos A. Nobre
National Secretary for R&D Policies
and Programs. Ministry of Science and
Technology of Brazil, MCT, Brasilia, Brazil
10 DANGEROUS CLIMATE CHANGE IN BRAZIL
Scientific
and Societal
Contexts
Photo: Laura Borma / INPE
11
DANGEROUS CLIMATE CHANGE IN BRAZIL
According to the Fourth Assessment
Report of the Intergovernmental Panel
on Climate Change (IPCC AR4 2007),
it is very likely that the rise in global
average temperatures observed over
the last 50 years were caused mainly by
anthropogenic increases in greenhouse
gas concentrations. This change has been
affecting climate, the hydrological cycle and
extremes, with impacts on the availability
of global and regional water resources. The
Amazon forest plays a crucial role in the
climate system, helping to drive atmospheric
circulation in the tropics by absorbing energy
and recycling about half of the rainfall that
falls on it. Previous studies have characterized
the variability of water resources over
Amazonia and their dynamics with time and
distribution over the region, but only due to
natural climate variations and on interannual
and decadal time scales. Furthermore, human
economic activities such as urbanization,
cattle growing and ranching, as well as
agricultural development have affected
vegetation coverage, and the changes in land
use and land cover due to intensive large
scale deforestation could have impacts on the
regional and global climate.
As the agricultural front expands, changing
land use leads to the alteration of Amazonian
ecosystems. Deforestation and subsequent
biomass burning result in the injection of
large volumes of greenhouse gases and
aerosols, and could exacerbate the changes
already produced by natural climate variability.
In addition to the foreseeable increased
deforestation, the following are also a threat:
extinction and/or reduced diversity of fish
species in an area considered a fisheries
hotspot; the accumulation in reservoirs of
sediments and toxic levels of mercury; impacts
on riverbank dwellers and indigenous peoples,
as well as urban communities.
Amazonia can be categorized as a region at great
risk due to climate variability and change. The
risk is not only due to projected climate change
but also through synergistic interactions with
existing threats not related to climate change,
such as land clearance, forest fragmentation
and fire. Some model projections have shown
that over the next several decades there is a
risk of an abrupt and irreversible change over a
part or perhaps the entirety of Amazonia, with
forest being replaced by savanna-type vegetation
with large-scale loss of biodiversity and loss of
livelihoods for people in the region, and with
impacts of climate in adjacent regions and
worldwide. However, the uncertainties of this
kind of modelling are still high.
The Earth System Science Center (CCST) of the
Brazilian National Institute for Space Research
(INPE) and the UK’s Met Office-Hadley Centre are
working together on assessing the implications
of global climate change for Brazil. They are
also assessing the impact of deforestation on
the Brazilian climate. The Dangerous Climate
Change in Brazil project (DCC) uses a set of
global and regional climate models developed by
the Met Office and INPE to project the effects of
greenhouse gas emissions on climate worldwide,
and provide finer detail over Brazil. Although
the projections covered all of Brazil, the focus of
this report is on Amazonia, a region of national,
regional, and global concern. The report is
divided into two sections: the first providing
context to the work, and the second detailing
new science carried out and looking forward to
further policy and planning-relevant scientific
developments. The DCC project was funded by
the UK Government’s Strategic Programme Fund,
the former Global Opportunity Fund (GOF), and
this work is continued as part of the scientific
agendas of the National Institute of Science and
Technology for Climate Change (INCT-Climate
Change) from the Brazilian Research Council
(CNPq), and the Brazilian Climate Change
Network (Rede-CLIMA).
J. Marengo, R. Betts, C. Nobre, G. Kay, S. C. Chou, G. Sampaio
12 DANGEROUS CLIMATE CHANGE IN BRAZIL
Photo: Eduardo Arraut / INPE
13
DANGEROUS CLIMATE CHANGE IN BRAZIL
Context
14 DANGEROUS CLIMATE CHANGE IN BRAZIL
Brazil-UK partnership
in climate science
The Earth System Science
Center (CCST) of the Brazilian
National Institute for Space
Research (INPE) and the UK’s
Met Office Hadley Centre
have been working together
on assessing the implications
of global climate change for
Brazil, and for Amazonia
in particular – a region of
national, regional and global
concern. They have also
assessed how deforestation
within the Amazon may affect
the local and regional climate.
The project has used a set
of climate models developed
by the Met Office and INPE
to project the effects of
greenhouse gas emissions and
deforestation on the climate
of Brazil. The Met Office
global climate model was used
to project climate changes
worldwide, and the INPE
regional climate model then
provided finer detail over Brazil
and Amazonia for different
levels of global warming.
Regional climate models were
also used to assess the effects
of deforestation in the Amazon
on the local and regional
climate.
Executive
Summary
Climate extremes and
impacts in Amazonia
The experience of the past
five years alone has seen two
intense droughts and one
extreme flooding event in
Amazonia. Indications are that
these extremes in rainfall may
have been related to conditions
in the tropical Atlantic Ocean,
although other events in recent
years are likely to have been
related to conditions in the
Pacific Ocean. The very high
rainfall of 2009 and the low
rainfall of 2005 and 2010 were
subsequently felt in the river
levels in the Amazon basin.
A record high in river level at
Manaus in 2009 (Fig. ES1) was
followed the very next year by
a record low in 2010 (Fig. ES2).
The impacts of such events
were severe and extended
across the varied spheres of
human life and livelihoods,
including the ecosystems that
support them. Agriculture,
transport, hydropower and
public health were among the
sectors that were affected,
with significant consequences
for the economy. If the risk of
climate extremes is expected
to increase with a warming
climate, measures must be
taken in order to mitigate the
impacts of these events. There
are positive indications that
government action and new
legislation can be effective in
doing so.
Fig ES1: Floods in Amazonia,
neighborhoods flooded in the city
of Manaus, October 2009
(Folha de São Paulo)
Fig ES2: Drought in Amazonia,
dry bed of the Rio Negro in
Manaus, October 2010
(Folha de São Paulo)
15
DANGEROUS CLIMATE CHANGE IN BRAZIL
Climate change in
Amazonia: impact of
different emissions
scenarios
The global average temperature
rose by approximately 0.7°C
over the last century, and this
warming will continue as a
result of historical and ongoing
greenhouse gas (GHG) emissions.
The Met Office-INPE climate
model projections are for large
increases in air temperatures and
percentage decreases in rainfall
in Amazonia, with the changes
becoming more prominent after
2040 (Fig ES3). The projected
decreases in rainfall may be as
a result of warmer waters in
the Atlantic and Pacific Oceans
causing changes in wind patterns
and the transport of moisture
across South America. This
could lead to major economic
impacts in Brazil: more than 70%
of Brazil’s energy is derived from
hydroelectric sources, so reduced
rainfall may limit electricity
supplies, affecting the industrial
activities in the economically
most important regions of Brazil.
However, these impacts can be
mitigated if action is taken now
to reduce emissions. Smaller
increases in GHGs in the
atmosphere lead to relatively
lower levels of warming both
globally and in Brazil, and to
smaller impacts on rainfall and
river flow. This provides further
scientific evidence for the need to
stabilise GHGs in the atmosphere.
RAINFALL CHANGE (%)
ANNUAL MEAN
TEMPERATURE CHANGE ( ºC )
Global Brazil
6.2
+7.7
+
4.8
+6.0
+
1.8
+2.0
+
Figure ES3. Projected climate change over Brazil by the 2080s relative
to 1961-1990 associated with different levels of global warming. These
projections used the Met Office global climate model and INPE regional
climate model driven by different emissions scenarios using different model
variants to assess uncertainties in climate response. Projected global
warming is within the range projected by other models, and the projection of
faster warming over Brazil in comparison to the global average warming is also
made by other models. Regional rainfall responses to global warming vary
widely between different models. If the general pattern is for global warming
to decrease rainfall in Amazonia (as shown here for the December-January-
February season), greater global warming results in greater reductions in
rainfall. From top to bottom, the emissions scenarios are the IPCC SRES
scenarios A1FI, A1B, and B1; the B1 projection shown here uses a model
with lower climate sensitivity.
16 DANGEROUS CLIMATE CHANGE IN BRAZIL
Impacts of
deforestation on
Brazilian climate
While climate change is a
threat to the Amazon forest
in the long term, through
warming and potential rainfall
reductions, deforestation is a
more immediate threat. The
Amazon is important globally
for taking in and storing
carbon from the atmosphere,
and it also plays a crucial
role in the climate of South
America through its effect on
the local water cycle.
The forest interacts with
the atmosphere to regulate
moisture within the Amazon
basin itself, but its influence
is thought to extend far
beyond its boundaries to other
parts of the continent. INPE
has been studying this since
the 1980s, and observations
and models suggest that
large-scale deforestation could
cause a warmer and somewhat
drier climate by altering the
regional water cycle. Model
results suggest that when
more than 40% of the original
extent of the Amazon forest
becomes deforested, rainfall
decreases significantly
across eastern Amazonia.
Complete deforestation could
cause eastern Amazonia
to warm by more than 4°C,
and rainfall from July to
November could decrease by
up to 40%. Crucially, these
changes would be in addition
to any change resulting from
global warming. Reducing
deforestation could minimise
these impacts as well as
reducing emissions of
greenhouse gases.
Figure ES4: Simulated impacts of deforestation on rainfall in Amazonia. The curves
show the fraction of rainfall in eastern Amazonia for different levels of deforestation
across the whole of Amazonia, compared to the original forest extent, for each
season. In the model, deforested land was converted to soybean plantations. These
results were generated with the INPE global climate model which has a low resolution;
the Met Office’s regional climate model PRECIS is being used to repeat this study
at higher resolution, and to assess the resulting impacts on the remaining areas of
intact forest and water resources. Source: Sampaio et al. 2007.
It has been suggested that
40% deforestation may be a
“tipping point” beyond which
forest loss causes climate
impacts which cause further
forest loss. 3°C to 4°C global
warming may also lead to
a similar tipping point (Fig.
ES4). Although the existence
of these tipping points still
requires clarification, interac-
tions between climate change
and deforestation may make
them more likely. Importantly,
the impacts of deforestation
are greater under drought
conditions, as fires set for
forest clearance burn larger
areas. Reducing deforestation
may help to maintain a more
resilient forest under a chang-
ing climate. The INPE-Met
Office collaboration will con-
tinue to examine these critical
issues for South America and
the globe.
17
DANGEROUS CLIMATE CHANGE IN BRAZIL
Introduction
With global temperatures projected to increase
over the coming century,1 the associated
impacts of climate change will be felt around
the world and are likely to have profound
implications for human populations. A priority
therefore is to develop understanding of how
regional climate may change, and assess
regional climate change risk associated with
different levels of greenhouse gas emissions.
This information is critical to support decision-
making systems for mitigation strategy and
adaptation planning.
Existing global climate change projections
indicate that like most regions of the world,
Brazil will be exposed to a changing climate.
With Brazilian population and activities
already sensitive to the climate, the nature and
degree of changes in the future could be very
important to life in the country. Some studies
have shown that changes in climate could
possibly lead to a die-back of the Amazon
rainforest, that rich centre of biodiversity,
oxygen, and fresh water. However, the regional
signature of global climate change is not the
only process to act upon the forest. Direct
deforestation is a more immediate threat, and
may have implications for the climate within
the Amazon basin and beyond.
(J. Marengo, R. Betts - coordinators of the GOF DCC Project)
The Amazon in the regional and
global earth systems
The Amazon is important to the global carbon
budget through its role in taking in and storing
carbon from the atmosphere within the trees
and the soil. The global forestry industry
currently accounts for approximately 17% of
greenhouse gas emissions, behind only energy
supply (26%) and industry (19%).2 But it is not
just at the global scale that it is important. The
Amazon forest also plays a crucial role in the
climate of South America through its effect on
the regional water cycle. The forest interacts
with the atmosphere to regulate moisture
within the basin. Moisture is transported into
the Amazon region from the tropical Atlantic
by the trade winds. After the rain falls, intense
evaporation and recycling of moisture is
performed by the tropical forest, and then a
large part of this evaporation is returned to the
Amazon region as rain (Fig. 1). It is estimated
that between 30% and 50% of the rainfall
within the Amazon Basin to consist of recycled
evaporation.3 Furthermore, moisture originating
in the Amazon basin is transported by the winds
to other parts of the continent, and is thought
to be important in feeding rainfall in regions
remote from the Amazon itself.4
1. IPCC 2007a
2. IPCC 2007b
3. Molion 1975; Salati 1987; Eltahir and Bras 1998
4. Marengo et al. 2004
18 DANGEROUS CLIMATE CHANGE IN BRAZIL
Both direct deforestation and climate change
have the potential to seriously hamper
the functioning of the Amazon as a forest
ecosystem, reducing its capacity to retain
carbon, disrupting the regional water
cycle, increasing its soil temperature and
eventually forcing the Amazon through a
gradual process of savannization. The issue of
Amazon die-back leapt from climate change
projection to global environmental concern
with the intense Amazonian droughts of
2005 and 2010. Droughts and floods are
part of the natural climate variability of the
Amazon Basin and individual events cannot
be attributed directly to climate change or
assumed to be a consequence of large scale
deforestation in the basin.
However, these droughts and floods and
associated loss of life and livelihoods serve as
reminders of why research such as the DCC
project is crucially important.
Figure 1: Regional hydrological cycle in the Amazon region
The forest-climate
system is complex and inter-
connected, and demands a
better understanding of how
it functions, and how that may
change in the future in the
face of human action including
climate and land use change.
Only then can informed
decisions be made.
19
DANGEROUS CLIMATE CHANGE IN BRAZIL
Observed climate variability
and tendencies
Brazil has warmed by about 0.7 °C over the last
50 years, which is higher than the best estimate
of the global average increase of 0.64 °C. 5 Con-
sidering the trend in the Brazilian winter season
temperatures alone, the trend is even greater at
1 °C. For the Amazon region, where observations
are available, increasing temperatures have
similarly been measured in day- and night-time
temperatures. The exact trends vary depending
on the beginning and end of the observing pe-
riod,6 but all records show a detectable increase.
Observational research has shown no clear signs
of negative trends in rainfall in Amazonia,7
although one study8 did detect a significant
trend towards drier conditions in the southern
Amazon region over the last thirty years of the
20th century. However, the detection of any unidi-
rectional trend may depend of the length of time
series. Figure 2 shows annual rainfall trends
in some stations in the Amazon region using
records from stations for which data were avail-
able: 1951-2005 and 1981-2005. It is difficult
to detect trends at regional level, but from what
these data show, at a local, station scale, there
are more cases where a slight increase in rain-
fall has been measured since 1980 in northern
Amazonia, while a rainfall decrease is more of
a feature in southern Amazonia (Fig. 2b). These
trends are consistent with previous studies.9
Over the longer term, 1951-2005 (Fig. 2a), the
sparse nature of the measurements as well as
the mixture in tendency towards wetter or drier
(G. Obregon, J. Marengo)
5. IPCC 2007a
6. Victoria et al.1998; Marengo 2003
7. Marengo 2004; 2009; Obregon and Marengo 2007; Satyamurty et al. 2009
8. Li et al. 2008
9. Marengo 2004; 2009; Obregon and Marengo 2007; Satyamurty et al. 2009
Figure 2: Trends of rainfall in Amazonia. a) Annual rainfall
in percent, related to their average value, for 1951-2005;
b) Annual rainfall in percent, related to their average value,
for 1981-2005. Black edging to the triangles indicates
statistically significant trends at the 95% confidence level.
Note that the scale is different in the two diagrams.
conditions make it difficult to draw conclusions
about trends across Amazonia.
20 DANGEROUS CLIMATE CHANGE IN BRAZIL
The studies demonstrate that there is no
consistent signal towards either wetter or drier
conditions over the Amazon region over the
observational record. In general, the size and
direction of the trends depend on the rainfall data
sets: how long they are, if there are breaks in the
recording, and if and how they are aggregated. In
a region where measurements are very scarce, the
uncertainty in the size and direction of any trends
must be high.
10. Marengo 2004; 2009
11. Li et al. 2008
12. Obregón and Nobre 2003; Marengo 2004
13. Obregón and Nobre 2003; Marengo 2004
14. Ronchail et al. 2002; Marengo 2004; Marengo et al. 2008a
15. INPE 2010
16. Cox et al. 2008; Good et al. 2008; Marengo et al. 2008a; b;
Tomasella et al. 2010a; b
17. Fu et al. 2001
18. Chen et al. 2001
19. Collins et al. 2010
20. Cox et al. 2008; Good et al. 2008; Marengo et al. 2008a; b;
Tomasella et al. 2010a; b
Other studies have suggested that for Amazonia,
more important than any linear trend is the
presence of decade to decade variations in
the rainfall,10 known as decadal scale rainfall
variability. Decadal variability may help to
explain some of the tendencies towards wetter
or drier conditions that have been recorded. For
example, the period 1945-1976 was relatively
wet, and 1977-2000 relatively dry in Amazonia.
Measurements taken over this period would show
a transition from wetter to drier conditions over
this period, and may help to explain the apparent
short-term drying trend in southern Amazonia
in the study described above.11 It has been shown
that the strong rainfall reductions over western
Amazonia observed between 1951 and 1990 was
modulated by a decadal oscillation.12 Variations
in rainfall such as these are thought to be related
to decadal scale climate variability in the Pacific
Ocean,13 which affects rainfall in the Amazon
through changes to the atmospheric circulation.
Decadal variability in climate occurs naturally in
the absence of human-induced changes to climate
or to the land.
As well as decadal variability in rainfall in the
Amazon, there are also year to year variations,
known as interannual climate variability. At
interannual time scales, the El Niño-Southern
Oscillation (ENSO) phenomenon, which is centred
in the tropical Pacific Ocean but has worldwide
reach, has been recognized as one of the major
patterns that affect climate in Amazonia. Droughts
have been reported during some intense El Niño
Obtaining reliable estimates
of the size and direction of trends
in rainfall across Amazonia is a
significant challenge in a region
where measurements are very scarce.
events, as in 1912, 1926, 1983 and 199814. The
2010 drought began during an El Niño event in
early austral summer of 2010 and then became
more intense during a La Niña event. It was the
below average summer rainfall, which may be
associated with the El Niño, that caused the low
river levels experienced in the austral autumn.15
However, during the 2010 drought, there were also
higher than normal sea surface temperatures in
the tropical North Atlantic, which have previously
been associated with drought events that occurred
during non-El Niño years such as 1964 and
2005.16 The Amazon is connected to, influences,
and is influenced by the global climate system.
Climate variability in other parts of the planet,
but particularly in the tropical Pacific or Atlantic
Oceans, can potentially force variations in the
climate of the Amazon.17
It is still unclear whether these naturally-occurring
variations in the climate of the Amazon can
offset or overshadow the effects of deforestation
or human-induced climate change.18 There is
no reason to expect the naturally-occurring
variations to operate independently of human-
induced climate change. It could be that the
natural variations are superimposed upon a
trend in climate, or that climate change could
affect the characteristics of the cycles of climate
variability. For example, climate change is likely
to affect the processes that control the behaviour
of ENSO, which could modify aspects such as the
magnitude, the frequency or the timing of El Niño/
La Niña episodes. Climate change could also affect
the manner in which remote influences such as
ENSO connect with rainfall over the Amazon.
However, the ways in which the processes that
control ENSO behaviour and impacts interact are
complex, and may enhance or counterbalance
each other. As yet, it is not clear how ENSO will
behave in the future.19 The relationships between
climate change and systems of climate variability,
as well as their impacts on drought behaviour in
Amazonia,20 for example, are questions that are
the subject of ongoing research.
21
DANGEROUS CLIMATE CHANGE IN BRAZIL
Seasonal extremes: droughts of 2005
and 2010, and floods of 2009
(J. Marengo, J. Tomasella, L. Alves)
21. Zeng et al. 2008; Marengo et al. 2008 a, b; Cox et al. 2008
22. Saleska et al. 2007; Philips et al. 2009; Samanta et al. 2010
23. Tomasella et al. 2010a
24. Brown et al. 2006; Aragão et al. 2008; Boyd 2008; Tomasella et al. 2010b
Drought of 2005
The 2005 drought has been
studied from meteorological,21
ecological,22 hydrological23
and human perspectives.24
Large sections of southwestern
Amazonia experienced one of
the most intense droughts of
the last hundred years. The
drought did not affect central
or eastern Amazonia, a pattern
different from the El Niño-
related droughts of 1926, 1983
and 1997/1998, and instead
has been related to high sea
temperatures in the tropical
North Atlantic, which effectively
pull the trade winds — and all of
the moisture they carry — to the
north, away from the Amazon.
Figure 3 shows that rainfall
anomalies in western and
southern Amazonia approached
100 mm per month below the
long term average of 200-400
mm/month during the austral
summer of 2005 in southern
Amazonia, while in the same
region, excesses of above 100
mm per month were detected
during the extreme wet summer
of 2009 (Fig. 4).
Figure 3: Monthly rainfall anomalies (in mm/month, difference from 1961-2009
long-term average) during drought of November 2004 to October 2005. Red colours
indicate drier conditions than normal; blue colours indicate wetter conditions. Source:
GPCC
22 DANGEROUS CLIMATE CHANGE IN BRAZIL
25. Marengo et al. 2008b
Figure 4: Monthly rainfall anomalies (in mm/month, difference from 1961-2009
long-term average) during the floods of November 2008 to October 2009. Red colours
indicate drier conditions than normal; blue colours indicate wetter conditions. Source:
GPCC
Floods of 2009
The floods were the result of
unusually heavy rains across
northern Brazil, which were
probably associated with
the warmer than normal
sea surface temperatures in
the tropical South Atlantic
Ocean, approximately opposite
conditions to those during
the drought of 2005. These
unusually warm waters kept
in place for longer a band of
convection and rainfall, called
the Intertropical Convergence
Zone (ITCZ), which brings
moisture to the Amazon basin.
In this way, intense moisture
transport from the tropical
Atlantic into the Amazon
region persisted for longer.
Rainfall over the central and
western Amazonia (Fig. 4) was
almost 100% above normal
during 2009 austral summer
and part of the autumn, which
then produced the extreme
high river levels in autumn
and winter25 (Fig. 6).
Drought of 2010
Following only five years
after the event of 2005,
another intense drought
struck Amazonia in 2010.
The drought of 2010 affected
a large area covering the
northwest, central and
southwest Amazon, including
parts of Colombia, Peru and
northern Bolivia. Fewer clouds
and less rain also translate
into higher temperatures, and
water levels in the primary
tributary Rio Negro — or Black
River — are at historic lows.
The droughts of 2005 and
2010 were similar in terms
of meteorological severity,
however the hydrological
impacts on water levels of the
later event was more severe.
In a similar way to 2005,
there are some indications
that the 2010 drought could
have been associated with
warmer surface temperatures
in the Atlantic Ocean north
of the equator. The droughts
were similar, too, in terms
of meteorological severity,
although the hydrological
impacts on water levels of
the latter event were more
severe. Likewise, surface
air temperatures over the
Amazon during both years
were warmer than average
(though were substantially
higher in 2010). However,
the spatial characteristics
of the 2010 drought (Fig. 5)
were different from those of
2005 (Fig. 3). In 2005, the
drying was more intense in
southwestern Amazonia, while
in 2010 the dry conditions
were more pronounced in
a region extending from
western Amazonia into eastern
Amazonia.
The 2005 and the 2010
droughts align well with
longer-term projections by
some climate models for a
drying out and warming of
the Amazon by the end of
the 21st century.
23
DANGEROUS CLIMATE CHANGE IN BRAZIL
Figure 5: Monthly rainfall anomalies (in mm/month, difference from 1961-2009
long-term average) during the drought of November 2009 to October 2010. Red
colours indicate drier conditions than normal; blue colours indicate wetter conditions.
Source: GPCC
Figure 6: Annual values of the levels of the Rio Negro in Manaus, Brazil (in meters), for
some extreme dry years (1964, 2005, 1998, and 2010) as compared to the long term
average 1903-1986. Source: CPRM
Impacts of these
extremes
In July of 2009, flooding in
the Brazilian Amazon reached
an all-time high since records
began in 1903, displacing
thousands of people across
the region. Water levels were
measured at 29.75m at a station
on the Rio Negro in Manaus,
the Amazon’s largest city, which
exceeded the previous record
of 29.69m set in 1953.26 The
2009 flooding came just five
years after the severe 2005
drought, where low levels
of the Rio Negro in Manaus
were reported (Fig. 6). The
communities living on the river
banks or in the urban areas of
cities like Manaus suffered the
direct and delayed impacts of
the rising waters on their lives,
their health, and the economy.
There were severe public health
issues such as leptospirosis and
water-borne diseases, damage to
infrastructure and property, and
education suffered as children
and teachers were unable to get
to school. Affected also was the
biodiversity of the Amazon and
many endangered species were
put under pressure.27
The very next year, 2010,
brought another intense
drought, and from its record
high in 2009, the level of the
Rio Negro fell to an all time low
of 13.63 m at Manaus on 24
October, falling just further than
the previous record low of 13.64
m in 1963.28 Fishing activity
and water supplies in the region
were seriously affected due to
the extreme low river levels.
Local newspapers reported that
fishing production, which is
normally about 10 Tons/month,
dropped to 1 Ton/month due to
the drought. Studies analysing
the impacts of the drought
of 2010 are ongoing, but if
26. Marengo et al. 2010a
27. INPE 2010
28. CPRM 2010
24 DANGEROUS CLIMATE CHANGE IN BRAZIL
29. Tomasella et al. 2010b
30. Negrón Juárez et al. 2010
31. Marengo et al. 2008b
32. Marengo et al. 2010a
33. IPCC 2007c
The 2005 drought left
thousands of people in want of
food. Transportation networks,
agriculture and livelihoods
were seriously affected,
and hydropower generation
compromised. The drought had
immediate impacts, but also
brought indirect and delayed
problems to the populations and
ecosystems.
the experience of the 2005
drought can be regarded as
an indicator, the impacts are
likely to have been substantial.
The drought of 2005 had
devastating effects upon the
human populations along the
main channel of the Amazon
River and its western and
southwestern tributaries: the
Solimões (also known as the
Amazon River in the other
Amazon countries) and the
Madeira Rivers, respectively.
The river levels fell to historic
lows and navigation along
these channels had to be
suspended. The drop in river
levels and drying of floodplain
lakes led to high fish mortality,
which then affected local
populations for whom fishing
forms an important component
of their livelihoods. The 2005
drought was more severe
in this respect than that
associated with the 1997/98 El
Niño, because the underlying
meteorological conditions
favoured more intense
evaporation, enhancing the
desiccation of the lakes.29
The very dry conditions had
direct impact on the Amazon
forest itself, causing tree
mortality, but degradation of
the forest caused by climate
extremes could then be
exacerbated by increased
vulnerability to stresses
such as wind, storm or fire
damage. To give one example,
a cluster of storms travelling
across Amazonia in 2005
was estimated to have killed
between 0.3 and 0.5 million
trees in the Manaus region
alone, equivalent to 30% of
the observed deforestation
reported in 2005 over the
same area.30 In addition, the
dry conditions were ideal
Comparing the drought events
of 2005 and 2010 with a
previous one in 1996/97, it
has been apparent that the
social and economical impacts
on the local population of the
more recent droughts have
been less intense (although
the full impacts of the
2010 drought are yet to be
comprehensively assessed).
This may be attributed to
more effective government
action and new legislation. For
effective management, there
must be good information
about the regional climate now
and how it may change in the
future.
for the spread of wildfires,
which destroyed hundreds
of thousands of hectares of
forest. The extensive smoke
emanating from the fires
caused health problems in
people and closed airports.31
The 2005 drought left
thousands of people in want of
food. Transportation networks,
agriculture and livelihoods
were seriously affected,
and hydropower generation
compromised.32 The drought
had immediate impacts, but
also brought indirect and
delayed problems to the
populations and ecosystems.
In sum, the Amazon region
has experienced two extreme
dry spells in just 5 years. This
does not include the drought
of 2006-2007, which affected
only the southeastern Amazon
and which left 10 thousand
km2 of forest scorched in
the region (Tomasella et al
2010a). Within the same
period the population has
also had to contend with
the record flooding of 2009.
The Amazon is periodically
subject to floods and droughts,
but these recent examples
highlight the vulnerability to
today’s extremes of climate
of the human populations
and the ecosystems upon
which they depend. If the
risk of climate extremes is
expected to increase with a
warming climate, discussed
in greater detail in Section 4,
the kinds of impacts outlined
here would be expected on
a more frequent basis.33
However, the magnitude of
an event does not necessarily
map to a set of impacts in a
straightforward manner. Aside
from the particular physical
characteristics of an event
(magnitude, spatial signature,
preceding conditions etc.), the
severity of impacts can depend
on the structures put in place
to manage the event and its
aftermath.
25
DANGEROUS CLIMATE CHANGE IN BRAZIL
Global and regional climate change
(C. Nobre, J. Marengo, G. Sampaio, R. Betts, G. Kay)
What is climate change?
Throughout history, the Earth’s climate has
been changing as a result of natural processes
such as orbital variations, volcanic eruptions
and changes in solar output. And even if
these factors were constant, there would still
be variability in the climate system. There is
natural variability in climate on time scales from
seasons to centuries – such as the droughts
and floods described in the previous section
– which means that we never expect one year
or decade to be the same as the next. But in
the last century or so there have been rapidly
increasing levels of greenhouse gases in the
atmosphere. The ‘greenhouse effect’ is a natural
process. After absorbing energy from the sun,
the earth emits heat towards space, some of
which is absorbed by gases in the atmosphere.
Without this natural greenhouse effect, global
average temperatures would be much colder
than they are today, and life on this planet
would not exist as we know it. Human activities
such as power generation based on fossil fuels
and deforestation have enhanced this natural
process by introducing extra greenhouse
gases into the atmosphere, which then absorb
more heat. So, with rising concentrations
of greenhouse gases in the atmosphere,
global temperatures have likewise increased.
Because of the longevity of previously-emitted
greenhouse gases in the atmosphere, as well
as some inertia within the earth system, there
is already a commitment to some level of
climate change into the future regardless of how
emissions evolve. If emissions continue, larger
climate changes may be expected.
Climate models are the most credible tools
available for making projections of the future
climate. They enable projections to be made not
only of how global average temperatures may
rise over the 21st century, but also how these
changes may play out in the climates across the
globe.
Photo: Stock.xchng
26 DANGEROUS CLIMATE CHANGE IN BRAZIL
Future climate
change
The Intergovernmental Panel
on Climate Change (IPCC)
Fourth Assessment Report
(AR4, 2007) brought together
projections from more than
twenty state-of-the-art climate
models, which were developed
by institutions around the
world. The models were
run according to different
scenarios of greenhouse
gases concentrations in the
atmosphere – from high
emissions to low (IPCC
Special Report on Emissions
Scenarios,34 SRES). Because
we cannot predict the future
greenhouse gas emissions
trajectory – which will
depend on factors such as
demographic change and
34. Naki enovi et al. 2000
energy production decisions
– we must rely on scenarios,
which represent different
emissions pathways. Each
climate model is different
and therefore simulates a
different version of a potential
future climate. However, they
demonstrate that under higher
concentrations of greenhouse
gases, larger changes may be
expected and these are hence
likely to lead to more severe
impacts.
All models simulate increases
in global temperatures over
the coming century. There
are some noteworthy broad
patterns of change that are
common to each emissions
scenario, but differ in intensity.
For example, the Polar Regions
are projected to warm more
than other parts, owing to
radiation-ice feedbacks and
atmospheric responses. Land
masses are understood to
warm more rapidly than the
oceans due to the different
radiative balance of land and
water, and so we can generally
expect any individual
country - such as Brazil - to
warm more than the global
average. Projections of future
rainfall present a rather more
complicated picture, as there
is some disagreement between
the models as to the patterns
or even, in some places, the
direction of change. However,
they do indicate that the
changes will not be uniform
across the globe, with modified
circulation patterns leading to
wetter conditions simulated
in some areas, and drier in
others.
Photo: Stock.xchng
27
DANGEROUS CLIMATE CHANGE IN BRAZIL
Climate change and
Amazonia
Using the same models, but by
focusing on Amazonia, we can
gain more information about
how global climate change
may be manifest in climate
changes in the Amazon region
(Fig. 7). Again, the models
are all different, and so the
level of warming in Amazonia
varies between the models.35
The IPCC’s best estimate of
the increase in temperature
between the end of the 20th
century (1980–1999) and the
end of the 21st century (2090–
2099) for the low emission
scenario (SRES ‘B1’) is 2.2 °C
(likely range is 1.8 °C to 2.6
°C), and the best estimate for
the high scenario (SRES ‘A2’) is
Figure 7: Changes in rainfall (top right) and temperature (bottom left) for the periods 2020-2029, 2050-2059 and 2090-99 with respect
to the 1961-1990 average, simulated by 15 different climate models submitted to the IPCC AR4 for a high (red) and low (black) (SRES
A2 and B1) scenarios. The projected changes were averaged over Amazonia (box in map). The bold lines show the average of the 15
models included in this study for each scenario, and the broken lines show individual model projections. These scenarios neglect the
possibility of climate-carbon cycle feedbacks which lead to accelerated climate change – this is an important point when comparing
with coupled climate-carbon cycle models.
4.5 °C (likely range is 3.9 °C to
5.1 °C).
The projections of temperature
over Amazonia (Fig. 7, bottom
left), show that there is a range
described by the individual
models in the magnitude of
warming. However, all of the
models project increasing
temperatures, and they clearly
demonstrate the effect – larger
increases - of following a
higher emissions scenario
(red lines are for projections
under the higher emissions
scenario). As described above,
projections of rainfall across
the globe are more mixed
between the models than
for temperature, and this
is the case for the Amazon
region. The multi-model
averages show very small
changes (bold lines in Fig. 7,
top right), not because none
of the models are projecting
large changes, but because
some are for wetter conditions
in the future and others for
drier. This is true regardless
of the emissions scenario.
Unlike for temperature, the
rainfall projections appear
to be emissions scenario-
independent for this multi-
model ensemble.
The Met Office Hadley Centre
HadCM3 global models display
strong warming and drying
of the climate in Amazonia
during the 21st century.
Besides the direct implications
of higher temperatures
and lower rainfall on the
28 DANGEROUS CLIMATE CHANGE IN BRAZIL
Figure 8: Percentage change in forest
cover by late 21st century compared
with pre-industrial conditions, as
modelled using Hadley Centre coupled
climate-carbon model HadCM3LC with
a ‘business as usual’ greenhouse gas
concentration scenario. Red colours
indicate a reduction in forest cover.
It demonstrates the ‘die-back’ of the
forest resulting from simulated warmer
and drier climate in the future. After
Cox et al. 2000
35. Cox et al. 2000, 2004
36. Betts et al. 2004, 2008
37. Cox et al. 2008
38. Marengo et al 2010a, b
population, it is possible that
there may be implications for
the continued viability of the
Amazon rainforest, and in
turn, upon the regional and
global climate.
A further version of the
Hadley Centre model, called
HadCM3LC, includes carbon
cycle feedbacks and dynamic
vegetation.35 This allows the
climate to affect the forest, and
any subsequent changes in the
vegetation – such as release of
carbon following tree death – to
feed back to the global carbon
budget and global and regional
climate change. In this model,
the projected changes in
climate caused some initial
forest death within the model,
which then released into the
atmosphere additional carbon
that had been stored by the
trees and soil. Furthermore,
less forest was subsequently
available to take up carbon
from the atmosphere.
In all, this led to higher
concentrations of atmospheric
carbon dioxide (CO2)
in
the model, which further
enhanced the greenhouse
effect and associated changes
in climate around the world.
Over Brazil these in turn led
to further forest death in a
positive feedback loop (Fig.
8).36 The loss of forest also
had effects on the local and
regional climate, as described
in Section 1.
It is not only how average
temperatures and average
rainfall may change in the
future that is of interest, but
also the extreme events that
have large impacts. Climate
change is expected to increase
the frequency and intensity
of extreme rainfall events in
Amazonia by the end of the
21st century,37particularly in
western Amazonia38
. Using a Hadley Centre
climate model projection, one
study has estimated how the
probability of a ‘2005-like’
year in Amazonia changes over
time. It suggests that under
present conditions, 2005 was
an approximately 1-in-20-year
event (one drought like 2005
would be expected in a 20-year
period), but may become a
1-in-2-year event by 2025 and
a 9-in-10-year event by 2060.
In other words it may become
the norm rather than extreme.
If severe droughts like that of
29
DANGEROUS CLIMATE CHANGE IN BRAZIL
If severe droughts like
that of 2005 and 2010 become
more frequent in the future, this
demands adaptation measures
to avoid the impacts felt that
year happening more frequently
with equal devastation. There is
positive evidence that effective
measures can be put in place by
decision-makers to mitigate the
effects of meteorological drought.
It should be kept in mind that
these are projections only,
and do not reflect a definitive
outcome of climate change
and impacts in Amazonia. The
strong increase in tempera-
ture and decrease in rainfall
in the Hadley Centre HadCM3
models that could bring about
die-back are not clear in other
climate models; indeed, some
models indicate that condi-
tions are likely to get wetter
in Amazonia in the future. It
should be recognized, how-
ever, that the Hadley Centre
models are among the best in
simulating the climate of the
present day and the recent
past in the South America re-
gion, and therefore the drying
and warming of the climate
that is projected for Amazonia
must be regarded as plausible.
But any projection of climate
change is just that: a projec-
tion, and must be treated with
caution.
A further point to be taken into
account is that the integration
of vegetation models into full
climate models is relatively
immature and they provide a
fairly crude representation of
vegetation. The models that
contributed to the IPCC Fourth
Assessment Report did not
include integrated dynamic
vegetation models and only
very few submitted to the
next Assessment Report will
incorporate this component.
However, integrated carbon
cycle models (that do not in-
clude dynamic vegetation) are
becoming standard for state-
of-the-art earth system models,
and some further integration
of dynamic vegetation models
should follow. An assessment
of the behaviour of the Ama-
zon rainforest and interaction
with the global carbon budget
and regional climate in models
from other centres will be very
informative.
2005 do become more frequent
in the future, this demands
adaptation measures to avoid
the impacts felt that year
happening more frequently
with equal devastation. There
is positive evidence that
effective measures can be put
in place by decision-makers,
as discussed with respect to
drought in Amazonia (Section
3). But in addition, cumulative
impacts may build up. For
example, it is possible that
the process of ‘savannization’
which begins in eastern
Amazonia could extend more
rapidly into a drought-stricken
western Amazonia.
30 DANGEROUS CLIMATE CHANGE IN BRAZIL
Photo: Stock.xchng
31
DANGEROUS CLIMATE CHANGE IN BRAZIL
New science
and scientific
development
32 DANGEROUS CLIMATE CHANGE IN BRAZIL
How we model climate
Global climate modelling
Climate models are the key tools for making
projections of future climate. They represent
numerically the climate system and inputs into
that system from the sun and other sources.
In a climate model, the world is divided into
grid boxes, which extend across the surface
of the planet, up through the atmosphere and
down into the oceans. On this grid the model
makes mathematical calculations based on
well established physical laws that describe
the movement of air, changes in pressure,
temperature, the formation of rain. In other
words: the weather and climate. In tandem with
improvements in computational performance,
climate models have been increasing in
complexity over the years as more and more
components are included, such as ocean
dynamics, land surface exchanges and aerosols.
Even so, it is not possible to represent all the
detail that exists in the real world, and so
certain processes have to be included in the
model through approximations based on expert
knowledge.
Many institutions around the world have
developed climate models. Variations in
configuration between the different models lead
to differences in their simulations of climate
variability and change as described in Section
4. Climate models are assessed on their ability
to simulate current and past climate, with
regards to average conditions and in variations
in these. If a model simulates well the climate of
(R. Betts, C. Nobre, G. Kay, G. Sampaio, S. Chou)
the 20th century and up to the present day, the
future climate projections may be regarded as
plausible.
Regional climate modelling
To simulate the complex climate system, a
climate model requires a very large amount of
computer resources, which places a limit on the
number of calculations that can be made and
hence the size of the grid. Grid boxes within a
global climate model are currently fairly coarse
- to the order of 100-300 km square. Even at this
resolution, they give a valuable picture of how
large-scale changes may be manifest. But to see
how country-level changes may occur, and how
different levels of concentrations of greenhouse
gases may affect any changes, there is a need
for finer-scale information. One way this can
be achieved is through increasing the spatial
resolution of the climate model in the region
of interest, such as South America, which is
computationally feasible because of the limited
size of the region. The finer spatial resolution
allows a more realistic representation of features
such as the coastline and mountains, and of
smaller-scale atmospheric processes. Therefore
there should be an improvement in the
representation of a particular country’s climate
in a regional climate model over a global model.
The finer-scale regional model is ‘nested’ in
the global climate model (Fig. 9) and requires
driving data from the GCM at the boundaries of
the regional domain. Through this project, sets
33
DANGEROUS CLIMATE CHANGE IN BRAZIL
39. Chou et al. 2002
40. Seluchi and Chou 2001; Chou et al. 2005; Bustamante et al. 2006
Understanding possible impacts of
climate change under different emissions
scenarios at a fine, regional scale is
recognised to be fundamental if action is
to be taken to mitigate climate change, as
well as for informing adaptation planning.
of boundary data from the Met Office Hadley
Centre global models have been prepared and
made available for running INPE’s Eta-CPTEC
regional model39 up to the year 2100. The
Eta-CPTEC regional model has been used as
the operational weather and seasonal climate
forecast model at INPE40 for several years. For
the DCC project, some modifications were
made to Eta-CPTEC to adapt it for climate
change runs and allow the carbon dioxide
(CO2) to vary in accordance with the driving
model. This process provides projections
of climate change over Brazil at the greatly
enhanced resolution of 40km in the Eta-
CPTEC regional model.
It should be noted that the performance of a
regional climate model is strongly dependent
upon the performance of the ‘parent’ global
model. If that global model does not simulate
well important large-scale processes, then the
regional model will not be able to correctly
capture the finer-scale climate. Adding
regional detail to a global model projection of
climate change, whether that is by regional
climate modelling - as in this project - or by
statistical techniques, then adds a further
layer of complexity and uncertainty to the
projections. Even so, understanding possible
impacts of climate change at the regional
scale is recognised to be fundamental if action
is to be taken to mitigate climate change, as
well as for informing adaptation planning.
Figure 9: The high-resolution regional climate model
is ‘nested’ in the global climate model, taking the
data from the global model around the boundaries.
Assessing climate change
uncertainty
It is not possible to be certain of a future climate
outcome produced by any climate model. This is
because of a number of reasons, which can be
divided into the following broad categories:
• Emissions uncertainty: We cannot
know how emissions of greenhouse gases
will change in the future. This depends
on a whole array of socioeconomic
factors including demographic change,
future energy source composition, and
development path.
• Greenhouse gas concentrations:
Emissions do not equate in a simple
manner to concentrations in the
atmosphere. CO2 does not undergo
chemical reactions in the atmosphere,
which means it is relatively long-lived
and is removed only by the carbon ‘sinks’
34 DANGEROUS CLIMATE CHANGE IN BRAZIL
– the oceans and vegetation. Therefore,
projecting future concentrations of
greenhouse gases depend on historical as
well as future emissions, the modelling
of carbon flows and sinks, and how these
may change.
• Natural variability in weather and
climate: The atmospheric system is
chaotic in nature, meaning that it is
sensitive to very small changes, which
may not be measureable. How natural
variations in climate develop within a
model depend very much upon the precise
conditions that initialise the climate
model, which cannot be perfectly known.
However, as we move further through
the coming century, the precise starting
point becomes unimportant with respect
to the climate relative to the changes
brought by increases in greenhouse gas
concentration.
• Modelling uncertainty: Our knowledge
and understanding of the climate system,
and our ability to model it, is incomplete.
Models constructed in different ways – for
example in grid configuration or input
parameters - produce different climate
change magnitudes and patterns. Equally,
making modifications to how processes
are represented in a single model can
produce a range of different climate
futures.
These factors are termed ‘uncertainties’ by
the scientific community, and are ubiquitous
components of any projection of climate change.
It is therefore important to assess the effects
of the uncertainties listed above upon the
magnitude and/or patterns of climate change. A
way to do this is through designing or utilizing
existing suites of model simulations – called
‘ensembles’ – through which the effects of
different sources of uncertainty can be explored.
In this project, the focus has been on assessing
the effects on the climate over Brazil of following
different emissions scenarios, and in modelling
uncertainty.
‘Uncertainties’ are ubiquitous
components of any projection of
climate change. It is therefore
important to assess the effects of
uncertainties upon the magnitude
and/or patterns of climate change.
The ‘Special Report Emission
Scenarios-SRES’ Emission
Scenarios
Of key relevance for future climate change is
the quantity of greenhouse gas emissions. This
will depend on the population, their lifestyle,
and the way this is supported by the production
of energy and the use of the land. These
factors could vary in a multitude of ways; the
international community is already examining
how energy demand and production can be
modified to cause lower emissions, but the
implementation of this will depend on both the
international political process and the actions
of individuals. Even if no specific action is
taken to reduce emissions, the future rates
of emissions are uncertain since the future
changes in population, technology and economic
state are difficult if not impossible to forecast.
Therefore, rather than make predictions of
future emissions, climate science examines a
range of plausible scenarios in order to explore
the implications of each scenario and inform
decisions on reducing emissions and/or dealing
with their consequences.
The IPCC’s climate models have generally
used a set of scenarios known as ‘SRES’
(Special Report on Emissions Scenarios41).
These scenarios were grounded in plausible
storylines of the human socio-economic future,
with differences in economy, technology, and
population but no explicit inclusion of emissions
reductions policies. These scenarios extend
out to 2100 and vary widely in their projected
41. Naki enovi et al. 2000
35
DANGEROUS CLIMATE CHANGE IN BRAZIL
emissions by that time (Fig. 10, left). The A1FI
scenario describes a future world of very rapid
economic growth, global population that peaks
in mid-century and declines thereafter, with
convergence amongst regions and decreasing
global differences in per capita income. New
technologies are introduced rapidly, but with a
continued intensive use of fossil fuels. The A1B
and B1 scenarios describes the same pattern of
population change as A1FI, but while under the
A1B scenario development is based on a balance
across different energy sources, the B1 scenario
has much greater emphasis on clean and
resource-efficient technologies. A1FI emissions
evolve most rapidly over the 21st century, B1
emissions are relatively low, and A1B lies
between. The effect of following these different
emissions scenarios (i.e. forcing climate models
with GHG concentrations, converted from the
emissions scenarios to concentrations by carbon
cycle models) leads to different projected
increases in global average surface temperature
over the 21st century (Fig. 10, right).
Figure 10: Left Panel: Global GHG emissions (in GtCO2-equivalent) in the absence of climate policies: six illustrative SRES marker
scenarios (coloured lines) and the 80th percentile range of recent scenarios published since SRES (post-SRES) (grey shaded area).
Dashed lines show the full range of scenarios developed post-SRES. The emissions include CO2, methane, nitrous oxide and F-gases.
Right Panel: Solid lines are multi-model global averages of surface warming for scenarios A2, A1B and B1, shown as continuations of
the 20th century simulations. These projections also take into account emissions of short-lived GHGs and aerosols. The pink line is not
a scenario, but is for GCM simulations where atmospheric concentrations are held constant at year 2000 values. The bars at the right
of the figure indicate the best estimate (solid line within each bar) and the likely range assessed for the six SRES marker scenarios at
2090-2099. All temperatures are relative to the period 1980-1999. Source: IPCC AR4 Synthesis Report, their Figure SPM.5.
Modelling uncertainty
A way to understand the range in possible
future climates resulting from different model
formulations has been exemplified by the IPCC
process, which effectively created an ensemble
of models from different climate research
centres around the world. Each climate centre
develops its models in different ways, such as
in the representation of model physics or in
grid resolution. The resulting projections can be
compared and/or combined to understand how
these differences affect the simulation of climate
and climate change across the globe.
In the Met Office Hadley Centre, as well
as simulating future climate according to
different SRES scenarios of greenhouse gas
concentrations and participating in the IPCC
multi-model ensembles, it has been a world
leader in developing ‘Perturbed Physics
Ensembles’ (PPEs). This is an innovative
approach designed to systematically assess
modelling uncertainties. This is different from
36 DANGEROUS CLIMATE CHANGE IN BRAZIL
the IPCC process, which can be regarded as a
more opportunistic way to explore uncertainty.
Each PPE is composed of variants of a single
global model. As stated previously, not all
processes can be simulated in detail within
a climate model, but their overall effects
have to be approximated. A process (e.g. rate
of ice fall through a cloud) is represented
by a parameter which is defined by experts
as a particular value, but in reality could
lie within a range of plausible values. In a
PPE, which is a particularly computationally-
intense experimental design, the values of key
parameters are adjusted within their plausible
ranges, giving different parameter combinations.
The effect of running the model with these
different combinations results in variations in
the projections of climate change. The model
variants that are more sensitive to increasing
greenhouse gas concentrations simulate
larger increases in global temperature than
the lower-sensitivity variants. This means that
for a single SRES scenario of greenhouse gas
concentrations, there is a range in level of global
warming (Fig. 11).
Figure 11: Global average temperature increase (in °C, relative to a 1961-90 baseline) under three emissions scenarios: B1
(left), A1B (centre) and A1FI (right). The historical portion of the simulations is identical in all three cases: emissions scenarios
are applied from the beginning of the 21st century. The individual lines indicate models run with different parameter combinations.
There are 17 variants of the same climate model (HadCM3), and each of these was run under the three emissions scenarios.
Some variants display higher sensitivity (i.e. greater warming given the same greenhouse gas forcing) than others, producing this
spread in warming. Under higher concentration scenarios, global average temperature changes are greater than under the lower
concentration scenarios.
Each Met Office PPE comprises the standard
HadCM3 climate model together with 16
variants of this, providing 17-member
ensembles. Three ensembles were produced,
run according to a low (SRES B1), a medium
(SRES A1B) and a high (SRES A1FI) greenhouse
gas concentration scenario. Through this
experiment design, uncertainty in both
emissions trajectory and in model parameter
settings can be explored.
The recognition and inclusion of uncertainties
in projections of climate change does not negate
their utility. On the contrary, they provide very
valuable information if they are communicated
effectively to users. Decision-makers routinely
have to work with information that is uncertain
or incomplete. For informed decisions to be
made, it is therefore important that the sources
of uncertainty are better understood. In addition,
support should be supplied in assessing effects
of these uncertainties, generating bounds upon
the range of possible climate futures in order to
express climate risk. Not only does including
uncertainty represent more fairly the current
state of knowledge about the future climate,
but it provides the basis for making mitigation
decisions as well as a framework for adaptation
planning.
37
DANGEROUS CLIMATE CHANGE IN BRAZIL
Assessing uncertainty in regional
model projections
An Eta-CPTEC regional model simulation, driven
by the Hadley Centre global model HadCM3,
provides a plausible projection of climate
change in the region at a spatial resolution that
has the potential to be valuable for impacts
assessments. The next stage is to consider the
effects of known uncertainties on the climate
change projections for Brazil.
One way to qualitatively assess the effects
of uncertainties on the projections is to run
ensembles of regional climate models. However,
there are strong constraints on doing this
associated with computational expense. In
addition, because the regional model requires
driving data from global models around the
boundaries, it is reliant upon appropriate data at
the correct temporal resolution being available.
Through the DCC project, a subset of four global
models was selected from the Hadley Centre
global model HadCM3 A1B PPE to drive the
Eta-CPTEC regional model. These were selected
during a visit by an INPE scientist to the Hadley
Centre. First of all, they were selected from the
A1B scenario only because driving data from
the other scenarios were not available. Given
that only one emissions scenario was available,
it was important to choose models that spanned
the range of uncertainty within that ensemble
(Fig. 8), while still simulating reasonably well
the present-day climate of Brazil. To this end,
high-, medium-, and low-sensitivity models were
chosen, along with the standard ‘unperturbed’
model.
Including uncertainty
represents more fairly the
current state of knowledge
about the future climate, and
also it provides the basis for
making mitigation decisions
as well as a framework for
adaptation planning.
Pattern Scaling: Assessing implications of
uncertainty in emissions and climate sensitivity
Alongside having a small ensemble of Eta-
CPTEC regional model projections run according
to the SRES A1B emissions scenario, this
project sought to develop a way to place bounds
upon the regional model projections that
encompassed the full range of uncertainty in
the global model PPEs. To do this, an efficient
approach was adopted and developed in
the uncertainty assessment of the regional
projections of change. Termed ‘pattern scaling’,
it is premised on the assumption that a regional
pattern of change in some climate variable of
interest – such as temperature or rainfall - is
related to global average temperature change.42
Thus, if we change the level of global average
warming, we can scale the regional response
accordingly. It should be kept in mind that
as a statistical technique, pattern scaling
has shortcomings. One of these is that it may
not reflect the range in regional response,
and another is that it may not capture large
nonlinearities or threshold behaviour in the
earth system that might occur under global
warming, such as large-scale land surface-
atmosphere feedbacks. However, the use of
pattern scaling techniques is growing, their
applications are being defined and refined,
and they are set to be used heavily in the next
report of the IPCC (Fifth Assessment Report) to
interpolate between global model simulations.
Available to this project was a range of
global temperature changes from the three
Met Office global model PPEs, which span
uncertainty in emissions scenarios and in
model parameter settings (Fig. 12). One of
these global models (forced with the A1B
greenhouse gas concentration scenario) was
used to drive the regional model, and using the
global temperature change in that model along
with the regional changes simulated by Eta-
CPTEC, a pattern of change that connects the
two was derived. Next, that pattern was scaled
to the global warming in the other models. This
process, summarised in Figure 10, provides
42. Huntingford and Cox 2000; Mitchell 2003;
Harris et al. 2006; Giorgi 2008
38 DANGEROUS CLIMATE CHANGE IN BRAZIL
three sets (high, medium and
low emissions scenarios) of
17 scaled regional projections
of change.
Because it relies on scaling
one regional response to
different levels of global
Figure 12: Schematic outlining the pattern scaling approach developed for this project. First, data from GCM 1 (Met Office Hadley
Centre) is used to drive the high-resolution RCM (Eta-CPTEC), which simulates climate changes over the 21st century. The relationship
between the regional changes and the large-scale warming in GCM 1 (in this example, 3.0 °C) is summarised through calculating a
‘Pattern of Regional Change’. Once this is established, the Pattern of Regional Change can be applied to the warming in the other GCMs,
to produce a range ofscaled regional changes. The values of global warming are illustrative only.
warming, this pattern scaling
technique cannot replace
the capability of the GCM-
RCM pairings for simulating
possible variations in
regional response. However,
it can be viewed as a valuable
compliment that enables
assessment of uncertainty
resulting from different
emissions scenarios and levels
of global warming. The result
is a range in projections of
climate change required to
assess climate risk.
39
DANGEROUS CLIMATE CHANGE IN BRAZIL
Future climate and assessment of climate
change uncertainty in Amazonia
(J. Marengo, S. Chou, G. Kay, L. Betts, L. Alves)
Projections of
climate change in
Amazonia
Changes in rainfall and
temperature in the South
America region projected
from the Eta-CPTEC high-
resolution climate model over
the 21st century are shown
in Figure 13. As we move
through the century, the
projected changes become
larger. Over the South
America domain, there are
areas predicted to become
wetter in the future and other
regions that are predicted
to become drier (Fig. 13a-c).
Over Amazonia, projections
are for large percentage
decreases in rainfall and
increases in air temperatures,
with the changes becoming
more pronounced after 2040.
For temperature (Fig. 13 d-f)
the projected warming in the
tropical regions varies from
1-2 °C in 2010-40 to 6-8 °C
by 2071-2100, with increases
being largest in the Amazon
region.
Figure 13: Changes in rainfall (a-c, %) and in air temperature (d-f, °C) in South America
for December-January-February 2010-40 (column 1), 2041-70 (column 2) and 2071-
2100 (column 3) relative to 1961-90 derived from the downscaling of HadCM3 using
the Eta-CPTEC 40 km regional model. Maps represent the mean of 4 of the 17 scaled
regional projections of change. Source: Marengo et al. 2010b.
Over Amazonia, projections are for large percentage
decreases in rainfall and increases in air temperatures,
with the changes becoming more pronounced after 2040.
40 DANGEROUS CLIMATE CHANGE IN BRAZIL
Assessment of climate change
uncertainty
The pattern scaling approach to assessing
uncertainty described in Section 5 is applied
here to Eta-CPTEC projections of climate
change averaged over the Brazilian Amazon
hydrological basin (Fig. 14).
Figure 14: The Brazilian Amazon river basin, over which
the uncertainty analysis of climate change projections was
conducted.
The analysis yields four sets of 17 projections
over the 21st century for the Brazilian Amazon
basin. The diagram below (Fig. 15) shows chang-
es in annual average, maximum and minimum
temperatures relative to the average conditions
simulated over the years 1961-90.
The examples presented here are changes in
the annual average temperature, and increases
are simulated in all cases for every season of
the year. Maximum daytime temperatures are
shown to increase more than minimum night
time temperatures. Larger rises in temperature
can be expected under the higher emissions
scenarios than the lower. There is a certain
degree of overlap between the projection
‘plumes’ (Fig. 15), meaning that the higher-
sensitivity models of a lower emissions scenario
give similar changes as the lower-sensitivity
models of a higher emissions scenario. However,
increasing the greenhouse gas concentrations
should be regarded as effecting a shift in the
whole set of projections.
Figure 15: Projected change in a) annual average tem-
perature (°C), b) average daily maximum temperature and
c) average daily minimum temperature in the Amazon river
basin over the 21st century expressed relative to the 1961-
90 baseline. The blue plume shows the range given by the
17 models of the low (B1) emissions scenario ensemble, the
orange plume shows the medium (A1B) emissions scenario
and the red plume shows the high (A1FI) emissions scenario.
The bars at the side represent the range in uncertainty of
projections at the end of the 21st century, with the darker
horizontal line indicating the ensemble average value.
a)
b)
c)
41
DANGEROUS CLIMATE CHANGE IN BRAZIL
Table 1: Lower and upper limits of range in projected increases in
annual average temperature (°C) in Amazonia by the 2090s with
respect to the 1961-90 baseline under each emissions scenario, as
displayed in Fig. 15 (a).
SCENARIO MINIMUM WARMING MAXIMUM WARMING
B1 2.3 4.8
A1B 3.6 7.0
A1Fl 4.9 8.9
Taking the example of increases in annual
average temperature in the Amazon basin, the
uncertainty in projected changes from model
physics and emissions scenario together gives
a range in possible increases by the end of the
century of just over 2 °C above the baseline at
the low end and 9 °C at the upper end (Table 1).
Increases in temperature can begin to impact
upon human activities and wellbeing at different
thresholds, such as in health, infrastructure and
electricity demand.
The analysis here gives a range
in possible warming in Amazonia of
just over 2 °C above the baseline by
the end of the century at the low end
and 9 °C at the upper end. Increases in
temperature can begin to impact upon
human activities and wellbeing at
different thresholds, such as in health,
infrastructure and electricity demand.
In addition to changes in temperature,
information about possible future changes in
rainfall with its implications for water resources
is critically important in climate change
management decisions. The direct output
from this particular model (Fig. 13) indicates
substantial percentage decreases in summer
(December-February) rainfall by the end of
the 21st century. However, decreases in rainfall
are projected throughout the year, not just in
summer. It is always important to put the results
in the context of other model projections, it
should be noted that the HadCM3 driving model
simulates strong drying over Amazonia over the
21st century, while other GCMs do not. As Figure
7 demonstrates, the uncertainty in rainfall
projections for Amazonia is large, ranging from
large increases in rainfall, to large decreases.
HadCM3 lies on the extreme drying end of the
multi-model group of projections.
Table 2: Table 2. Lower and upper limits of range in projected
percentage changes in annual average rainfall in Amazonia by the
2090s with respect to the 1961-90 baseline under each emissions
scenario.
SCENARIO MINIMUM %
RAINFALL CHANGE
MAXIMUM %
RAINFALL CHANGE
B1 -11.4 -22.2
A1B -17.0 -31.8
A1Fl -22.5 -40.6
In the Amazon, decreases in annual rainfall lie
between approximately 10% and 20% by the last
decade of the century under the low emissions
scenario. With A1FI scenario greenhouse gas
concentrations, these numbers rise to between
around 20% and 40% decreases in rainfall
(Table 2). Figure 16 shows rainfall changes in
Amazonia by the 2090s in a high sensitivity
model (top) and a low sensitivity model (bottom)
from the three ensembles (high, medium and
low emissions scenarios) of scaled projections.
These are displayed alongside the projection of
global warming from the global model ensemble,
and the corresponding scaled increase in
temperature across Brazil. The notion described
above of a shift in the ensemble of projections
under a different emissions scenario is evident,
with high-sensitivity models projecting larger
changes within each emissions scenario than
low-sensitivity models. Together, the figures
demonstrate full range in the uncertainty
explored in this work: from the ‘best case
scenario’ (B1 scenario, low sensitivity model) to
the ‘worst case scenario’ (A1FI scenario, high
sensitivity model).
42 DANGEROUS CLIMATE CHANGE IN BRAZIL
a)
Figure 16: Projected annual mean climate change over Brazil by
the 2090s relative to 1961-1990 in a a) high- and b) low-sensitivity
model associated with different emissions scenarios: high (A1FI, row
1), medium (A1B, row 2) and low (B1, row 3).
b)
These projected changes could have profound
implications for future water resources, fire
occurrence and spread, and related impacts in
Brazil.
This information provides support for decision-
making systems. The range within one emission
scenario provides bounds on possible changes
that can act as a framework for planning
different response actions. For example, various
sectors such as energy, industry or health may
have sensitivities to certain characteristics or
thresholds in the climate state. Hence providing
a range in possible climate futures allows
careful consideration of adaptation measures
appropriate to the level of change.
As greenhouse gas concentrations in the
atmosphere are increased under the higher
emissions scenarios, the climate changes
projected over Brazil become more pronounced.
The differences in response to the greenhouse
gas concentrations under each emissions
scenario become marked only in the second half
of the century (Fig. 15). This suggests that the
benefits of mitigation decisions taken now may
not be realised until later on in the century.
The strength in making projections of future
climate that include uncertainty is twofold in
terms of informing management decisions. First,
they demonstrate high- and low-end plausible
climate futures, which could inform mitigation
policy. Second, the range delivers a structure
upon which a suite of adaptation strategies,
designed to be appropriate to the level of climate
response, could potentially be developed.
As greenhouse gas concentrations
in the atmosphere are increased under
the higher emissions scenarios, the
climate changes projected over Brazil
become greater.
RAINFALL
CHANGE (%)
ANNUAL MEAN
TEMPERATURE
CHANGE ( ºC )
Global Brazil
+7.7
EMISSIONS
SCENARIO
+6.2A1F l
+6.0+4.8A1B
+3.8+3.3B1
RAINFALL
CHANGE (%)
ANNUAL MEAN
TEMPERATURE
CHANGE ( ºC )
Global Brazil
+4.1
EMISSIONS
SCENARIO
+3.4A1F l
+3.1+2.6A1B
+2.0+1.8B1
43
DANGEROUS CLIMATE CHANGE IN BRAZIL
Deforestation, land use change
and climate
Climate change,
Amazon die-back and
impacts
As the results of the DCC
project described above
show, climate change has
the potential to have severe
consequences for the Amazon
forest and the populations –
both local and remote – that
it supports. Previous work
has suggested that under
climate change, the forest
could die back and be replaced
with a different vegetation
type. These experiments
have been done in different
ways. As described in Section
4, integrating a dynamic
vegetation model into the
climate model is emerging
science, and as more of the
new generation of models
include this component,
further progress can be
made in understanding
climate change-vegetation
dynamics. Other studies
have used climate change
projections as inputs in stand-
alone vegetation models,
(C. Nobre, G. Sampaio, G. Kay, R. Betts)
The results of the DCC
project show that climate
change has the potential to
have severe consequences
for the Amazon forest and
the populations – both local
and remote – that it supports.
Figure 17 shows the results
from one such study,43
which used the INPE-CPTEC
Potential Vegetation Model
(CPTEC-PVM) driven with
climate projections from three
different climate models (to
sample uncertainty in the
model projections; refer to
Section 5: Assessing climate
change uncertainty) from a
high (SRES A2) emissions
scenario. It compares the 43. Salazar 2009
distribution of vegetation
types simulated under today’s
climate with that of the end
of the century (2070-2099).
All of these models show that
under the new climate state,
tropical forest (green colour,
Fig. 17) is lost in Amazonia
and replaced by savanna (pink
colour), with changes in some
models more extensive than
in others. The changes in
these models can be explained
by the effects of increases
in CO2 concentration and
temperature, and reductions
in rainfall such that the dry
season becomes longer. Under
these conditions, the tropical
forest becomes less viable
and is replaced in the model
by savanna-type vegetation.
However, this vegetation model
does not include the fertilizing
effect of CO2.
to determine what sort of
vegetation we should expect
– the ‘potential vegetation’ –
under a new future climate.
44 DANGEROUS CLIMATE CHANGE IN BRAZIL
Figure 17: Projected distribution of biomes in South America for 2070-2099 from output from three climate models: ETA CCS,
RegCM3 and HadRM3P models run under the A2 emission scenario. The top left plot represents the current potential biomes (biomes
in equilibrium with observed climate). Source: Salazar, 2009.
The interactions between
forest, climate and CO2 are
complex. Indications are
that over recent decades,
the forest has been gaining
biomass, possibly because of
fertilization of the vegetation
under higher atmospheric
concentrations of CO2.44
Further research, updating the
experiments described above
using a new version of the
vegetation model (CPTEC-PV2)
driven by a range of GCMs,
indicates that the effects
of CO2 fertilization may be
large.45 The new study shows
that when CO2 fertilization is
included along with changes
in climate, the resultant
simulated biome distributions
are not considerably different
from the present day. However,
where dry season length
is simulated to exceed four
months, as is the case for the
HadCM3 driving model, the
Amazon rainforest is largely
replaced by drier biomes
such as savanna or shrubland
irrespective of the fertilizing
effect of CO2. The Hadley
Centre model that projected
the Amazon die-back,
HadCM3LC, which has an
integrated dynamic vegetation
model, shows that the forest
is likely to continue to gain
biomass into the future for a
time as CO2 concentrations
continue to increase. However,
the projections in this
particular model indicate that
the climate changes caused by
the greenhouse gas emissions
then start to override this
fertilization effect, and tree
mortality commences (Fig. 8).
44. Phillips et al. 2008
45. Lapola et al. 2009
An Amazon Forest
degraded or diminished
through climate change
is likely to have serious
consequences for the
inhabitants of the region
and beyond – through loss
of biodiversity, regulation of
rainfall, influence over the
global carbon budget, and all
of the ecosystem services that
the forest provides.
45
DANGEROUS CLIMATE CHANGE IN BRAZIL
An Amazon Forest degraded
or diminished through climate
change is likely to have
serious consequences for
the inhabitants of the region
and beyond – through loss
of biodiversity, regulation of
rainfall, influence over the
global carbon budget, and
all of the ecosystem services
that the forest provides
(Section 1). It should always
be remembered, however, that
these climate and vegetation
models are subject to large
uncertainties, and while the
Met Office Hadley Centre
HadCM3 models tend towards
strong warming and drying
over Amazonia, other models
do not.
Climate change may
have serious – though
uncertain - detrimental
effects to the Amazon forest
in the long term, but direct
deforestation poses an
immediate threat.
Climate change may have
serious – though uncertain
- detrimental effects to the
Amazon forest in the long
term, but direct deforestation
poses an immediate threat.
Deforestation in the
Amazon
A reduction in deforestation
would see immediate benefits
in mitigation of global
greenhouse gas emissions.
In addition, similar effects
on the regional climate that
are possible under die-back
scenarios may apply for direct
deforestation. As well as the
influence over the regional
water cycle, the removal of
large areas of forest would
change the surface energy
exchanges, such that changes
in surface temperature would
also occur. Both observations
and modelling studies indicate
that large-scale deforestation
could cause a warmer and
somewhat drier regional
climate. Model results46
suggest that when more than
40% of the original extent
of the Amazon forest is lost,
rainfall decreases significantly
across eastern Amazonia (Fig.
18). Complete deforestation
could cause eastern Amazonia
to warm by more than 4
°C, and rainfall from July to
November could decrease by
up to 40%.
Crucially, these changes would
be in addition to any change
resulting from global warming.
It has been suggested that 40%
deforestation (Fig. 18) may be
a ‘tipping point’ beyond which
forest loss causes climate
impacts which in turn lead
to further forest loss.47 Global
warming of 3 °C to 4 °C may
also lead to a similar tipping
point.48 Although the existence
of these tipping points
still requires clarification,
interactions between climate
change and deforestation may
make them more likely.
Photo: Stock.xchng
46. Sampaio et al. 2007; Sampaio 2008
47. Sampaio et al. 2007
48. Nobre and Borma 2009
46 DANGEROUS CLIMATE CHANGE IN BRAZIL
Figure 18: Simulated impacts of deforestation on rainfall in Amazonia. The curves
show the fraction of rainfall in eastern Amazonia for different levels of deforestation
across the whole of Amazonia, compared to the original forest extent, for each
season. In the model, deforested land was converted to soybean plantations. Source:
Sampaio et al. 2007.
40% deforestation may be
a ‘tipping point’ beyond which
forest loss causes climate impacts
which in turn lead to further
forest loss. Global warming of
3 °C to 4 °C may also lead to a
similar tipping point.
Through the DCC project, a
vegetation model has been
integrated into a regional
climate model for the first time.
This was based on the global
model that gave the Amazon
forest die-back result (Section
4), and includes a new land-
surface model and dynamic
vegetation. That is, instead of
having one land type assigned
to each grid box, there can
be up to nine, comprising
five vegetation and four non-
vegetation classes. Each of
these has its own properties
and fluxes between the land
surface, the subsurface and
the atmosphere. With this
arrangement, vegetation no
longer has to remain fixed
as the vegetation types can
compete and change from
one to another as the climatic
conditions change, making
one type more or less viable.
This makes it possible to
assess potential effects of
fine-scale climate change
on vegetation, which can
then go on to feed back upon
and modify the regional
climate. Furthermore, it
allows realistic deforestation
scenarios,49 supplied through
the DCC project, to be
imposed on the model, and
the effects of deforestation
on the regional climate and
remaining vegetation to be
investigated.
Loss of the Amazon either
in the short term through
direct deforestation or in the
long term through climate
change could have widespread
impacts, some of which have
the potential to exacerbate the
changes in climate or in forest
cover in a positive feedback
loop (Fig. 19). Furthermore,
these two drivers of change in
forest cover are unlikely to act
independently of one another. 49. Soares-Filho et al. 2006
50. Hirota et al. 2010
Deforestation and
climate synergies
An additional environmental
driver of change in Amazonia
associated with deforestation
would be an increase in
vulnerability of a broken forest
to ‘edge effects’ such as strong
winds, and especially forest
fires. In this project, there has
been no explicit modelling of
effects of direct deforestation
combined with climate change.
However, it can be conjectured
that climate changes acting on
a region already fragmented by
deforestation could have larger
effects than on continuous
forest. Forest fragmentation
opens up the forest to points
of ignition, which are in the
main supplied by human
action: deliberate or otherwise.
Of course, natural fires do
occur, and have been shown to
influence the forest-savanna
transition. A simplified
climate-vegetation-natural fire
model50 estimated that under
current climate conditions, the
tropical forest would penetrate
200 km into the savanna in the
absence of lightning-triggered
fires.
Climate changes acting on
a region already fragmented by
deforestation could have larger
effects than on continuous forest.
A broken forest would be more
vulnerable to forest fires, and
human activity is likely to supply
the ignition. A changing climate
may lead to heightened fire risk,
allowing fires to catch and spread
more readily.
47
DANGEROUS CLIMATE CHANGE IN BRAZIL
If the conditions become more suitable for
fire ignition and spread in the regions where
deforestation is also projected to take place,
then fire has the potential to play a potent role
in further deforestation and degradation (Fig.
19). 51 In drought conditions, fires set for forest
clearance burn larger areas. Forest fires, drought
and logging increase susceptibility to further
burning while deforestation and smoke can
inhibit rainfall, exacerbating the heightened
fire risk, as well as harming human health and
disrupting transport (as experienced during
the Amazon drought of 2005, Section 3). It has
been estimated that if the large-scale patterns
of climate variability in the tropical Pacific and
Atlantic Oceans continue to be associated with
Amazon drought in the future, approximately
55% of the forests of the Amazon will be cleared,
logged, damaged by drought or burned over the
next 20 years.52 Reducing deforestation may
help to maintain a more resilient forest under
51. Golding and Betts 2008
52. Nepstad et al. 2008
Reducing deforestation may help to
maintain a more resilient forest under
drought conditions, be they associated with
a gradually warming and drying climate,
climate variability, or local changes brought
about by land-use change.
Through the DCC project, partnerships and
modelling capacity have been developed to
allow the synergies between climate change,
deforestation and fire to be explored in an
integrated way in the future.
Figure 19: Simplified potential mechanisms of Amazon ‘die-back’. CO2 is not the only greenhouse gas emitted, but is highlighted
here because of its importance in climate change, its role in the earth’s carbon budget, and effects on plant physiology relevant to the
Amazon rainforest. Through feedbacks on the global and regional climates, loss of the Amazon forest may also have implications for
the climate, ecosystems and populations lying outside the Amazon basin.
drought conditions, be they associated with a
gradually warming and drying climate, climate
variability, or local changes brought about by
land-use change.
48 DANGEROUS CLIMATE CHANGE IN BRAZIL
The Amazon forest plays a significant role in
regulating the local, regional and even the global
climate system. It provides a host of ecosystem
services that underpin human activities and
well-being in regions both local and remote.
Therefore, any changes within the basin – be
they climate changes, land use changes, or a
combination of the two – are likely to have far-
reaching consequences for the operation of
natural systems and the people they support.
Understanding how the Amazon functions as
an integrated part of the Earth system and the
risks of how that may change in the future is a
prerequisite to producing optimal development
strategies.
This DCC project has allowed high-resolution
projections of climate change to be made over
the Brazil region along with an assessment of
uncertainty in these simulations. The projections
are for large increases in temperature and
decreases in rainfall during this century. Other
studies have shown that in addition to these
changes, the risk of extreme events such as the
drought of 2005 would become more frequent
in the future. As well as these changes that
would directly affect human systems that are
vulnerable to climate, there could be impacts on
the continued viability of the Amazon forest. In
turn, loss of forest through a changing climate
is likely to affect the regional climate through
the forest’s role in the recycling of rainfall
within the basin and beyond. Economically
important regions of agribusiness, hydropower
and industry of Brazil and other South American
countries lie to the south of the Amazon, and
are estimated to generate some US$1.5 trillion,
or 70% of the combined GDP these countries.
The extent to which moisture transported from
the Amazon contributes to the economic well-
being of the South American continent is as yet
unquantified.
Summary and conclusions
(J. Marengo, C. Nobre, R. Betts, G. Kay)
Until the Amazon forest ecosystem
services are integrated into policy and financial
frameworks, the forest will be regarded as
worth more dead than standing.
It is clearly acknowledged that there are
large uncertainties in the strong tendency
displayed by the Met Office HadCM3 models
towards drier future conditions, any ‘die-
back’ of the forest, and the timing of such
changes. However we know that deforestation
presents a more immediate threat to the
Amazon. Studies of the hydrological cycle
in the Amazon suggest that it recycles as
much as 50% of its rainfall, and that if as little
as 30% of the Amazon is cleared, it will be
unable to generate enough rainfall to sustain
itself, leading to a positive feedback loop of
more forest loss and less rainfall. Rainfall in
other words is essential for sustaining the
Amazonian ecosystems and all the ecosystem
services they generate, and the value of the
Amazon as a water-regulating eco-utility
becomes indistinguishable from the value
of all ecosystem services provided by the
Amazon. As deforestation approaches this
critical threshold, the marginal value of the
forest ecosystem can be expected to rise
rapidly, approaching the infinite if we believe
that the loss of the Amazon ecosystem is
unacceptable. Compounding the uncertainty
of how much forest loss the climate system
can tolerate before it can no longer generate
adequate rainfall to sustain itself, climate
change is likely to have substantial impacts on
such thresholds.
49
DANGEROUS CLIMATE CHANGE IN BRAZIL
53. Hall 2008
The Reducing Emissions in
Deforestation and Degradation
(REDD) mechanism, which
has risen rapidly up the
political agenda particularly
through the Conferences of
Parties COP-15 in Copenhagen
in December 2009 and COP
16 in Cancún in December
2010, is currently the focus
of this new effort. With the
global forestry industry
contributing just below 20%
of greenhouse gas emissions,
reducing deforestation would
confer immediate benefits
on the global carbon budget,
and hence upon the levels
Photo: Eduardo Arraut / INPE
of global warming. It aims
to compensate indigenous
populations for contributing to
the preservation of the forest
for carbon sequestration and
storage in the mitigation of
climate change.53 The role of
the forest in the global carbon
budget is one – albeit very
important – ecosystem service
provided by the Amazon.
Further research is needed
to elucidate the role of the
forest in the economic well-
being of the South American
continent and to integrate this
information into policies and
practical activities to conserve
the Amazon and provide
benefits to its inhabitants.
The DCC Brazil project has
enabled close collaborative
scientific research and
exchange of expertise between
INPE and the Met Office. The
work has fully utilized and
built upon the experience
and capacity in both Brazilian
and UK institutions. The
collaborative ties between
INPE and the Met Office have
been strengthened and the
foundations have been put in
place to enable cutting-edge
research to continue beyond
the lifetime of the DCC project.
50 DANGEROUS CLIMATE CHANGE IN BRAZIL
We would like to thank M. Sumire, D. Grabois, L. Carrijo, R.
Ferreira, from the climate team in the UK Embassy in Brasilia
for their help in designing and implementing the DCC project.
Further thanks go to M. Valverde and E. Andrade who worked
to make this project a success, and to J Miguez and H. Machado
Filho from the National Coordination of Climate Change of
the Ministry of Science and Technology from Brazil for their
support and help in making this project possible. Special thanks
go to INPE´s director, Gilberto Câmara, for all the facilities in
developing the project at the CCST INPE.
Additional thanks go to UNDP Project BRA/05/G31 and the FCO
GOF-Dangerous Climate Change DCC project from the UK. JM
and SC were funded by the Brazilian National Research Council
CNPq. Additional funds came from the Brazilian programs
Rede-CLIMA, the National Institute of Science and Technology
for Climate Change (INCT-CC) from the CNPq, and the FAPESP-
Program on Global Climate Change, Project Assessment of Impacts
and Vulnerability to Climate Change in Brazil and Strategies for
Adaptation Options.
Photo: Laura Borma / INPE
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56 DANGEROUS CLIMATE CHANGE IN BRAZIL
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... Projections indicate that the Amazon will experience an increase of 4 • C in annual mean surface temperature by 2070 [15][16][17][18]. Changes in rainfall are projected to vary by region in the Amazon: the eastern Amazon is projected to become much drier [15,19] and the western Amazon is projected to become substantially wetter [18,20]. Furthermore, projections indicate that the Amazon will also be subject to more frequent and more severe high impact weather events as climate change progresses [19,[21][22][23]. ...
... Continued deforestation may lead to an "Amazon tipping point", after which the ecological system could be irreversibly altered [62,[92][93][94]. Projection models using data from the past three decades in the area suggest that large-scale deforestation could substantially impact the water cycle, resulting in a warmer climate with a decrease in precipitation of up to 40% [20]. For the Shawi, these changes have already had impacts on their water security. ...
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Climate change impacts on water systems have consequences for Indigenous communities. We documented climatic changes on water systems observed by Indigenous Shawi and resultant impacts on health and livelihoods, and explored adaptation options and challenges in partnership with two Indigenous Shawi communities in the Peruvian Amazon. Qualitative data were collected via PhotoVoice, interviews, focus group discussions, and transect walks, and analyzed using a constant comparative method and thematic analysis. Quantitative data were collected via a household survey and analyzed descriptively. Households observed seasonal weather changes over time (n = 50; 78%), which had already impacted their family and community (n = 43; 86%), such as more intense rainfall resulting in flooding (n = 29; 58%). Interviewees also described deforestation impacts on the nearby river, which were exacerbated by climate-related changes, including increased water temperatures (warmer weather, exacerbated by fewer trees for shading) and increased erosion and turbidity (increased rainfall, exacerbated by riverbank instability due to deforestation). No households reported community-level response plans for extreme weather events, and most did not expect government assistance when such events occurred. This study documents how Indigenous peoples are experiencing climatic impacts on water systems, and highlights how non-climatic drivers, such as deforestation, exacerbate climate change impacts on water systems and community livelihoods in the Peruvian Amazon.
... The Amazon basin has already been affected by a warming trend of 0.63 • C over the 20th century (Victoria et al., 1998). Under climate change conditions, precipitation in the Amazon varied more strongly regionally and seasonally (IPCC, 2014a;Marengo et al., 2009). The period 1950-1970 was wet in the northern Amazon, but since 1977 this region has become drier (IPCC, 2014a; Marengo et al., 2009). ...
... Under climate change conditions, precipitation in the Amazon varied more strongly regionally and seasonally (IPCC, 2014a;Marengo et al., 2009). The period 1950-1970 was wet in the northern Amazon, but since 1977 this region has become drier (IPCC, 2014a; Marengo et al., 2009). General circulation models project a regional temperature increase of 2-4 • C by 2100 and a decrease in precipitation specifically during dry seasons (IPCC, 2014a;2014b). ...
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Logging is widespread in tropical regions, with approximately 50% of all humid tropical forests (1.73 × 10⁹ ha) regarded as production forests. To maintain the ecosystem functions of carbon sequestration and timber supply in tropical production forests over a long term, forest management must be sustainable under changing climate conditions. Individual-based forest models are useful tools to enhance our understanding about the long-term effects of harvest and climate change on forest dynamics because they link empirical field data with simulations of ecological processes. The objective of this study is to analyze the combined effects of selective logging and climate change on biomass stocks and timber harvest in a tropical forest in French Guiana. By applying a forest model, we simulated natural forest dynamics under the baseline scenario of current climate conditions and compared the results with scenarios of selective logging under climate change. The analyses revealed how substantially forest dynamics are altered under different scenarios of climate change. (1) Repeated logging within recovery times decreased biomass and timber harvest, irrespective of the intensity of climate change. (2) With moderate climate change as envisaged by the 5th IPCC Assessment Report (representative concentration pathway 2.6), the average biomass remained the same as in the baseline scenario (−1%), but with intensive climate change (RCP 8.5), the average biomass decreased by 12%. (3) The combination of selective logging and climate change increased the likelihood of changes in forest dynamics, driven mainly by rising temperatures. Under RCP 8.5, the average timber harvest was almost halved, regardless of the logging cycle applied. An application-oriented use of forest models will help to identify opportunities to reduce the effects of unwanted ecosystem changes in a changing environment. To ensure that ecosystem functions in production forests are maintained under climate change conditions, appropriate management strategies will help to maintain biomass and harvest in production forests.
... Considering that Ae. aegypti is the vector of several diseases, and that the Intermediate SCCS is not so far from becoming a reality given the nature of mitigation measures taking place and the speed of their implementation, it is important to carry out this species might behave in the face of SCCS. As the climate change patterns across Amazonia will not be homogeneous [45], it is important to carried out such experiments in ways that incorporate the nature of regional differences. There are many uncertainties in how combined effects of biotic and abiotic factors may influence Ae. aegypti larval life-history characteristics; our results add new pieces to this puzzle. ...
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Climate change affects individual life-history characteristics and species interactions, including predator-prey interactions. While effects of warming on Aedes aegypti adults are well known, clarity the interactive effects of climate change (temperature and CO 2 concentration) and predation risk on the larval stage remains unexplored. In this study, we performed a microcosm experiment simulating temperature and CO 2 changes in Manaus, Amazonas, Brazil, for the year 2100. Simulated climate change scenarios (SCCS) were in accordance with the Fourth Assessment Report of Intergovernmental Panel on Climate Change (IPCC). Used SCCS were: Control (real-time current conditions in Manaus: average temperature is 25.76˚C ± 0.71˚C and~477.26 ± 9.38 parts per million by volume (ppmv) CO 2); Light: increase of~1,7˚C and~218 ppmv CO 2 ; Intermediate: increase of~2.4˚C and~446 ppmv CO 2 ; and Extreme: increase of~4.5˚C and~861 ppmv CO2, all increases were relative to a Control SCCS. Light, Intermediate and Extreme SCCS reproduced, respectively, the B1, A1B, and A2 climatic scenarios predicted by IPCC (2007). We analyzed Aedes aegypti lar-val survivorship and adult emergence pattern with a factorial design combining predation risk (control and predator presence-Toxorhynchites haemorrhoidalis larvae) and SCCS. Neither SCCS nor predation risk affected Aedes aegypti larval survivorship, but adult emergence pattern was affected by SCCS. Accordingly, our results did not indicate interactive effects of SCCS and predation risk on larval survivorship and emergence pattern of Aedes aegypti reared in SCCS in western Amazonia. Aedes aegypti is resistant to SCCS conditions tested, mainly due to high larval survivorship, even under Extreme SCCS, and warmer scenarios increase adult Aedes aegypti emergence. Considering that Aedes aegypti is a health problem in western Amazonia, an implication of our findings is that the use of preda-tion cues as biocontrol strategies will not provide a viable means of controlling the accelerated adult emergence expected under the IPCC climatic scenarios. PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.
... Species usually live within the specific defined climatic conditions. Thus, change in temperature and precipitation in any amount could collapse the ecosystem balance by any means (Marengo et al., 2009). Due to climate change, any species could disperse, adapt, extinct (Peterson et al., 2001), or migrate (Werneck et al., 2011) with the severity or rate of change. ...
Chapter
Full-text available
Tropical dry forests is one of the most unique forest types. It differs from other tropical forests with its climatic behavior like a prominent dry period, little annual rainfall, and high evapotranspiration. Out of six global bioclimatic zones, the forests are distributed in four. Climate change is now the most challenging issue regarding the fate of tropical dry forests. A severe climatic change is estimated to occur between 2040 and 2069 that could drastically change the precipitation pattern, temperature, aridity, and distribution of biodiversity. It could alter the forest type permanently. With a large number of heattolerant species, tropical dry forests have a great potentiality to conservationists with the prediction of a large area that could attain the climatic condition favorable for extension of tropical dry forests. But many of the species of tropical dry forests could be extinct due to changing climate at the same time. Proper adaptation and mitigation techniques could minimize the severity of climate change effects.
... Seasonality of rainfall is related to conditions in the tropical Atlantic and movement of the intertropical convergence zone leading to seasonal precipitation maxima in the northern Amazon from March to May and in the southern Amazon from January to April. Further information about the climate system in the Amazon basin is summarized in Marengo et al. (2009), Melack and Coe (2012), and Nobre et al. (2009). ...
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Full-text available
Floodplain lakes represent important aquatic ecosystems, and field-based estimates of their water budgets are difficult to obtain, especially over multiple years. We examine the hydrological fluxes for an Amazon floodplain lake connected to the Solimões River using a process-based hydrologic model. Water exchanges between the river and lake agree well with field estimates, including the timing of different hydrological phases. However, beyond available field data, modeling results show that the seven simulated years all differed from each other. These interannual differences were caused by the interplay between phases when water levels were rising with river-water flowing into the lake (RWRI), versus rising with lake-water flowing out to the river (RWLO). This exchange determines the river-water content in the lake (C L ). Maximum C L occurred before river levels peaked because local catchment contributions can be sufficient to push lake-water out to the river, even as river levels rise. Numerical experiments show that the seasonal distribution of local rainfall, local catchment size, and interannual variability in both climate and river stage can contribute to differing dynamics of C L in a floodplain lake. Their impacts vary among phases: river-rise dominates the RWRI, whereas local hydrological processes dominate the RWLO and receding-water phases. Intermediate-to-long-term rainfall accumulation controls C L during the RWLO phase, whereas annual precipitation accumulation is important for C L during low water. Our model generalizes beyond limited available field studies and offers potential to better understand floodplain lakes in other areas and how regional versus local changes in climate may affect their hydrological dynamics.
... Long-lived northerly squall lines are associated with low level jets (LLJs), a phenomenon expected to be more frequent under a warming climate [79]. LLJs may also trigger SSLs, given that they are essential synoptic-scale features that propagate cold fronts toward Southeast Brazil. ...
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Windthrows are a recurrent disturbance in Amazonia and are an important driver of forest dynamics and carbon storage. In this study, we present for the first time the seasonal and interannual variability of windthrows, focusing on Central Amazonia, and discuss the potential meteorological factors associated with this variability. Landsat images over the 1998–2010 time period were used to detect the occurrence of windthrows, which were identified based on their spectral characteristics and shape. Here, we found that windthrows occurred every year but were more frequent between September and February. Organized convective activity associated with multicell storms embedded in mesoscale convective systems, such as northerly squall lines (that move from northeast to southwest) and southerly squall lines (that move from southwest to northeast) can cause windthrows. We also found that southerly squall lines occurred more frequently than their previously reported ~50 year interval. At the interannual scale, we did not find an association between El Niño-Southern Oscillation (ENSO) and windthrows.
Article
Eta Regional Model of CPTEC-INPE is used to obtain intraseasonal (30-day) 8-member ensemble forecasts over the Madeira River basin for the period 2002–2012. The initial and boundary conditions are taken from Atmospheric General Circulation Global Model in six members and from Global Coupled Ocean-Atmosphere Model in two members. The intraseasonal forecasts produced by dynamic downscaling with Eta Regional model ensemble have satisfactory skill. The skill of the ensemble mean is better than the individual members up to 15-days lead time forecasts. The ensemble mean reproduces the seasonal cycle and spatial distribution of the hydrological variables. Members with the relaxation technique of Betts-Miller-Janjic produced better results. The forecasts by the members that used Kain-Fritsch scheme presented larger deviations from observations. Substantial improvements in skill are obtained through bias correction. This is the first work to attempt dynamic downscaling over the Madeira Basin in the intraseasonal time scale for a period of 10 years. The ensemble downscaled products have potential to be fed into surface hydrological models for forecasting droughts and floods and related hydrological variables over the basin.
Chapter
Tropical dry forests is one of the most unique forest types. It differs from other tropical forests with its climatic behavior like a prominent dry period, little annual rainfall, and high evapotranspiration. Out of six global bioclimatic zones, the forests are distributed in four. Climate change is now the most challenging issue regarding the fate of tropical dry forests. A severe climatic change is estimated to occur between 2040 and 2069 that could drastically change the precipitation pattern, temperature, aridity, and distribution of biodiversity. It could alter the forest type permanently. With a large number of heat-tolerant species, tropical dry forests have a great potentiality to conservationists with the prediction of a large area that could attain the climatic condition favorable for extension of tropical dry forests. But many of the species of tropical dry forests could be extinct due to changing climate at the same time. Proper adaptation and mitigation techniques could minimize the severity of climate change effects.
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More frequent and stronger flood hazards in the last two decades have caused considerable environmental and socioeconomic losses in many regions of the Amazon basin. It is therefore critical to advance predictions for flood levels, with adequate lead times, to provide more effective and earlier warnings to safeguard lives and livelihoods. Water-level variations in large, low-lying, free-flowing river systems in the Amazon basin, such as the Negro River, follow large-scale precipitation anomalies. This offers an opportunity to predict maximum water levels using observed antecedent rainfall. This study aims to investigate possible improvements in the performance and extension of the lead time of existing operational statistical forecasts for annual maximum water level of the Negro River at Manaus, occurring between May and July. We develop forecast models using multiple linear regression methods, to produce forecasts that can be issued in March, February and January. Potential predictors include antecedent catchment rainfall and water levels, large-scale modes of climate variability and the long-term linear trend in water levels. Our statistical models gain one month of lead time against existing models for same skill level, but are only moderately better than existing models at similar lead times. All models lose performance at longer lead times, as expected. However, our forecast models can issue skilful operational forecasts in March or earlier. We show the forecasts for the Negro River maximum water level at Manaus for 2020 and 2021. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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We employ the approach of Roderick and Farquhar (2011) to assess the sensitivity of runoff (R) given changes in precipitation (P), potential evapotranspiration (E p), and other properties that change the partitioning of P (n) by estimating coefficients that predict the weight of each variable in the relative change of R. We use this framework using different data sources and products for P, actual evapotranspiration (E), and E p within the Amazon River basin to quantify the uncertainty of the hydrologic response at the sub-catchment scale. We show that when estimating results from the different combinations of datasets for the entire river basin (at Óbidos), a 10% increase in P would increase R on average 16%, while a 10% increase in E p would decrease R about 6%. In addition, a 10% change in the parameter n would affect the hydrological response of the entire basin around 5%. However, results change from catchment to catchment and are dependent on the combination of datasets. Finally, results suggest that enhanced estimates of E and E p are needed to improve our understanding of the future scenarios of hydrological sensitivity with implications for the quantification of climate change impacts at the regional (sub-catchment and sub-basin) scale in Amazonia.
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An analysis of decadal and long-term patterns of rainfall has been carried out using a combination of raingauge and gridded rainfall datasets, for the entire Amazon basin and for its northern and southern sub-basins. The study covers the period 1929–98. Rainfall variability and variations in circulation and sea surface temperature fields have been analysed in more detail for the period 1950–98. Negative rainfall trends were identified for the entire Amazon basin, while at the regional level there is a negative trend in northern Amazonia and positive trend in southern Amazonia. Decadal time scale variations in rainfall have been discovered, with periods of relatively drier and wetter conditions, with different behaviour in northern and southern Amazonia. Spectral analyses show decadal time scale variations in southern Amazonia, while northern Amazonia exhibits both interannual and decadal scale variations. Shifts in the rainfall regime in both sections of the Amazon basin were identified as occurring in the mid-1940s and 1970s. After 1975–76, northern Amazonia received less rainfall than before 1975. Changes in the circulation and oceanic fields after 1975 suggest an important role of the warming of the tropical central and eastern Pacific on the decreasing rainfall in northern Amazonia, due to more frequent and intense El Niño events during the relatively dry period 1975–98.
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Water availability on the continents is important for human health, economic activity, ecosystem function and geophysical processes. Because the saturation vapour pressure of water in air is highly sensitive to temperature, perturbations in the global water cycle are expected to accompany climate warming. Regional patterns of warming-induced changes in surface hydroclimate are complex and less certain than those in temperature, however, with both regional increases and decreases expected in precipitation and runoff. Here we show that an ensemble of 12 climate models exhibits qualitative and statistically significant skill in simulating observed regional patterns of twentieth-century multidecadal changes in streamflow. These models project 10-40% increases in runoff in eastern equatorial Africa, the La Plata basin and high-latitude North America and Eurasia, and 10-30% decreases in runoff in southern Africa, southern Europe, the Middle East and mid-latitude western North America by the year 2050. Such changes in sustainable water availability would have considerable regional-scale consequences for economies as well as ecosystems.
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Amazon forests are a key but poorly understood component of the global carbon cycle. If, as anticipated, they dry this century, they might accelerate climate change through carbon losses and changed surface energy balances. We used records from multiple long-term monitoring plots across Amazonia to assess forest responses to the intense 2005 drought, a possible analog of future events. Affected forest lost biomass, reversing a large long-term carbon sink, with the greatest impacts observed where the dry season was unusually intense. Relative to pre-2005 conditions, forest subjected to a 100-millimeter increase in water deficit lost 5.3 megagrams of aboveground biomass of carbon per hectare. The drought had a total biomass carbon impact of 1.2 to 1.6 petagrams (1.2 × 1015 to 1.6 × 1015 grams). Amazon forests therefore appear vulnerable to increasing moisture stress, with the potential for large carbon losses to exert feedback on climate change.
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From mid-July to mid-October 2005, an environmental disaster unfolded in the trinational region of Madre de Dios, Peru; Acre, Brazil; and Pando, Bolivia (the MAP region), in southwestern Amazonia. A prolonged dry season and human-initiated fires resulted in smoke pollution affecting more than 400,000 persons, fire damage to over 300,000 hectares of rain forest, and over US$50 million of direct economic losses. Indicatorrs suggest that anomalous drought conditions could occur again this year. In May 2005, river levels, were the lowest in 34 years in Rio Branco,Acre, Brazil, signaling that the subsequent dry season would be unusual. Rainfall became virtually absent for several months, not only in eastern Acre but also in the neighboring Bolivian department of Pando and the Peruvian region of Madre de Dios. This enhanced dry season extended over much of western Amazonia with severe societal impact; by October 2005, regional governments had declared states of emergency in Pando, Acre, and Amazonas, an area covering more than a million square kilometers. Whereas previous droughts could be linked to El Niño events [Williams et al., 2005; Marengo, 2004]], J. A. Marengo et al. (The drought of Amazonia in 2005, manuscript in preparation, 2006) suggest that this drought was not related to El Niño but was instead associated with anomalously warm surface water in the tropical North Atlantic, similar to a previous drought in 1963–1964.
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Projections of changes in climate extremes are critical to assessing the potential impacts of climate change on human and natural systems. Modeling advances now provide the opportunity of utilizing global general circulation models (GCMs) for projections of extreme temperature and precipitation indicators. We analyze historical and future simulations of ten such indicators as derived from an ensemble of 9 GCMs contributing to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR4), under a range of emissions scenarios. Our focus is on the consensus from the GCM ensemble, in terms of direction and significance of the changes, at the global average and geographical scale. The climate extremes described by the ten indices range from heat-wave frequency to frost-day occurrence, from dry-spell length to heavy rainfall amounts. Historical trends generally agree with previous observational studies, providing a basic sense of reliability for the GCM simulations. Individual model projections for the 21st century across the three scenarios examined are in agreement in showing greater temperature extremes consistent with a warmer climate. For any specific temperature index, minor differences appear in the spatial distribution of the changes across models and across scenarios, while substantial differences appear in the relative magnitude of the trends under different emissions rates. Depictions of a wetter world and greater precipitation intensity emerge unequivocally in the global averages of most of the precipitation indices. However, consensus and significance are less strong when regional patterns are considered. This analysis provides a first overview of projected changes in climate extremes from the IPCC-AR4 model ensemble, and has significant implications with regard to climate projections for impact assessments.
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Long-term measurements of ecosystem-atmosphere exchanges of carbon, water, and energy, via eddy flux towers, give insight into three key questions about Amazonian forest function. First, what is the carbon balance of Amazon forests? Some towers give accurate site-specific carbon balances, as validated by independent methods, but decisive resolution of the large-scale question will also require integration of remote sensing techniques (to detect and encompass the distribution of naturally induced disturbance states across the landscape of old growth forests) with eddy flux process studies (to characterize the association between carbon balance and forest disturbance states). Second, what is the seasonality of ecosystem metabolism in Amazonian forests? Models have historically simulated dry season declines in photosynthetic metabolism, a consequence of modeled water limitation. Tower sites in equatorial Amazonian forests, however, show that photosynthetic metabolism increases during dry seasons ("green up"), perhaps because deep roots buffer trees from dry season water stress, while phenological rhythms trigger leaf flush, associated with increased solar irradiance. Third, how does ecosystem metabolism vary across biome types and land use patterns? As dry season length increases from equatorial forest, to drier southern forests, to savanna, fluxes show seasonal patterns consistent with increasing water stress, including a switch from dry season green up to "brown down." Land use change in forest ecosystems removes deep roots, artificially inducing the same trend toward brown down. In the final part, this review suggests that eddy tower network and satellite-based insights into seasonal responses provide a model for detecting responses to extreme interannual climate variations that can test whether forests are vulnerable to model-simulated Amazonian forest collapse under climate change.