Transcript elninocyclones - START - SysTem for Analysis Research and

Ministério da Ciência e Tecnologia
Instituto Nacional de Pesquisas Espaciais
Centro de Previsão de Tempo e Estudos Climáticos
Modelling El Niño-Tropical
Cyclones/Hurricanes and Extreme
weather
Should we expect more extreme weather events?
One of the major concerns with a climate change is that an increase in
extreme events might occur. Results of observational studies suggest
that changes in total precipitation are amplified at the tails, and
changes in some temperature extremes have been observed.
Model experiments for future climate change show changes in extreme
events, such as increases in extreme high temperatures, decreases in
extreme low temperatures, and increases in intense precipitation
events. On the other hand, for other variables, such as extra-tropical
storminess or tropical storms not definite trend could be observed so
far.
Issues to be considered in the modelling of climate change:
Predictability, Skill of the models, resolution.
Predictability
Key factors affecting interannual variability / predictability in the region,
applicable to longer time scale climate precistions.
For the oceans How can we better predict the phase and amplitude of SST in key areas
 What are the respective roles of the dynamics (wind forcing) and
thermodynamics (latent heat flux) in the genesis of tropical sea temperature
variability
 How does ENSO intensity and ‘type’ affect predictability
 How is the global ENSO signal transmitted to Africa and at what lead-time
 What is the role of subsurface conditions and thermocline adjustments in
coupling processes
For the atmosphere and land What is the atmospheric response to SST variation in key areas, what are the
preferred response frequencies How is our knowledge affected by model
parameterization
 What local land features / indices modulate climate
 What are the limits of and spatial distribution of predictability over the
continent to assess what components are externally or internally forced
 How can forecasters merge multiple predictions in an optimal way
 What are the decision variables and how can we best accommodate user
needs for climate predictions
Standard deviation for rainfall among ensemble members using CPTEC GCM, 10 years, 9
members
(Larger values—Lower predictability)
DJF
JJA
MAM
SON
Rainfall correlation anomaly using CPTEC GCM, 10 years, 9 members
Green Values-higher predictability
Uncertainties:
Uncertainty in projected climate change arises from three main sources:
Forcing scenarios: The use of a range of forcing scenarios reflects
uncertainties in future emissions and in the resulting greenhouse gas
concentrations and aerosol loadings in the atmosphere.
Model response: The ensemble standard deviation and the range are used
as available indications of uncertainty in model results for a given forcing,
although they are by no means a complete characterisation of the
uncertainty
Missing or misrepresented physics: No attempt has been made to quantify
the uncertainty in model projections of climate change due to missing or
misrepresented physics. Current models attempt to include the dominant
physical processes that govern the behaviour and the response of the
climate system to specified forcing scenarios.
Model resolution and subgrid-scale processes.
Prediction of extremes: Tropical Cyclones and hurricanes
The problem of predicting how tropical cyclone frequency might respond
to climate change can be broken into two parts: predicting how the
prevalence of necessary conditions will change, and predicting how the
frequency and strength of potential triggers will change.
Given increased concentrations of greenhouse gases, theoretical
considerations suggest that the strength of large-scale tropical
circulations such as monsoons and trade winds will increase.
In general, this would be accompanied by an increase in vertical wind
shear, which would hinder the formation of tropical cyclones. On the
other hand, more vigorous large-scale circulation might favor more and
stronger triggers, such as easterly waves. This would favor more tropical
cyclones.
Thus the problem is complex, and simple reasoning produces ambiguous
results.
Problems with simulation of tropical cyclones and their variability
GCMs have been used by a number of groups to explore changes in
tropical cyclone activity in a world with doubled carbon dioxide (CO2). To
date, each group has examined changes in the activity of tropical
cyclones produced explicitly by the models. This approach has some
drawbacks, because neither the spatial resolution nor the physics of
current models is sufficient to accurately simulate tropical cyclones.
While the physics of mature model storms may resemble real tropical
cyclones, it is unlikely that GCMs realistically mimic tropical cyclone
formation, which recent field experiments show to occur on scales as
small as 100 miles. The spatial resolution of GCMs is around 200 miles.
Nevertheless, GCMs do accurately simulate the frequency of tropical
cyclones in the present climate. For climate change scenarios, however,
they produce conflicting results. Some of these discrepancies may result
from inadequate sampling of tropical cyclones in the model climates.
As in the real world, there is much interannual variability in GCM tropical
cyclone statistics, making it difficult to extract a representative sample.
Should we believe in estimates of climate change and impacts on tropical
cyclone activity?
Perhaps a better strategy would be to use GCMs to assess the prevalence
of necessary conditions and of potential triggers. This would circumvent
the need to actually simulate genesis and would be within the bounds of
the models' capabilities. (One would have to exercise some care in doing
this, since some of these conditions can be expected to vary with climate
change. For example, the SST threshold of 26° C would change with
global mean temperature).
At present, however, there is little basis for accepting quantitative
estimates of climate change produced by GCMs, if for no other reason
than that there is no basis for believing that they handle water vapor
correctly.
But there is also good reason to be optimistic about solving the problems
that plague current models, and future GCMs should prove to be valuable
tools for assessing the effects of climate change on tropical cyclone
activity.
Will changes in SST and large scale circulation in climate change
scenarios would affect tropical cyclone activity?
In the current climate, tropical cyclones develop over tropical ocean
waters whose SST exceeds about 26°C. But once developed, they may
move considerably poleward of these zones. An oft-stated misconception
about tropical cyclones is that were the area of 26°C waters to increase,
so too would the area experiencing tropical cyclone formation.
Thus there is little basis for believing that global warming would
substantially expand or contract the area of the world prone to tropical
cyclone formation. This is borne out by GCM simulations that show that
doubling CO2 substantially increases the area of 26°C waters, but causes
no perceptible increase in the area experiencing tropical cyclones.
It is conceivable, though, that changes in the large-scale circulation of
the atmosphere might increase or decrease the rate at which tropical
cyclones move out of their genesis regions and into higher latitudes. It is
also likely that changes in atmospheric circulation and sea surface
temperature distribution within the tropics would be associated with
variations in the distribution of storms.
What is the relationship between greenhouse warming, and El Niño/La
Niña?
There is a lot of confusion about the interrelations connecting climate
phenomena such as El Niño, La Niña and greenhouse effect. Is it true that
a warmer atmosphere is likely to produce stronger or more frequent El
Niños?
It is certainly a plausible hypothesis that global warming may affect El
Niño, since both phenomena involve large changes in the earth's heat
balance. However, GCMs are hampered by inadequate representation of
many key physical processes (such as the effects of clouds on climate
and the role of the ocean).
Also, no computer model yet can reliably simulate BOTH El Niño AND
greenhouse gas warming together. So, depending on which model you
choose to believe, you can get different answers. For example, some
scientists have speculated that a warmer atmosphere is likely to produce
stronger or more frequent El Niños, based on trends observed over the
past 25 years. However, some computer models indicate El Niños may
actually be weaker in a warmer climate.
Changes in Variability
The capability of models to simulate the large-scale variability of climate,
such as the El Niño-Southern Oscillation (ENSO) has improved
substantially in recent years, with an increase in the number and quality
of coupled ocean-atmosphere models and with the running of multicentury experiments and multi-member ensembles of integrations for a
given climate forcing.
There have been a number of studies that have considered changes in
interannual variability under climate change (e.g., Knutson and Manabe, 1994; Knutson
et al., 1997; Tett et al. 1997; Timmermann et al. 1999; Boer et al. 2000b; Collins, 2000a,b).
Other studies have looked at intra-seasonal variability in coupled models
and the simulation of changes in mid-latitude storm tracks (e.g., Carnell et al.
1996; Lunkeit et al., 1996; Carnell and Senior, 1998; Ulbrich and Christoph, 1999), tropical
cyclones (Bengtsson et al., 1996; Henderson-Sellers et al., 1998; Knutson et al., 1998; Krishnamurti et
al., 1998; Royer et al., 1998) or blocking anticyclones (Lupo et al., 1997; Zhang and Wang,
1997; Carnell and Senior, 1998).
The results from these models must still be treated with caution as they cannot
capture the full complexity of these structures, due in part to the coarse
Intra-seasonal variability: Daily precipitation variability
Changes in daily variability of temperature and rainfall are most obviously
manifest in changes in extreme events and much of the work in this area
will be discussed in the extreme events section . However, changes in
short time-scale variability do not necessarily only imply changes in
extreme weather. More subtle changes in daily variability, when integrated
over time, could still have important socio-economic impacts.
The global mean precipitation also increased, by around 10% in both
models, typical of the changes in many mixed-layer models on doubling
CO2. An analysis of changes in daily precipitation variability in a coupled
model (Durman et al., 2001) suggests a similar reduction in wet days over
More recently, there have been several studies looking at changes in
intra-seasonal circulation patterns using higher resolution atmosphereonly models with projected SSTs taken from coupled models at given time
periods in the future. Theere are some changes in extra-tropical storms
on extreme wind and precipitation events and in lower-frequency
variability such as persistent or “blocking” anti-cyclones. Changes in
African easterly waves may be due to a doubling of CO2 in one model.
Standard deviations of Niño-3 SST
anomalies (Unit: °C) as a function of
time during transient greenhouse
warming simulations (black line)
from 1860 to 2100 and for the same
period of the control run (green line).
Minimum and maximum standard
deviations derived from the control
run are denoted by the dashed green
lines. A low-pass filter in the form of
a sliding window of 10 years width
was used to compute the standard
deviations. (a) ECHAM4/OPYC model.
Also shown is the time evolution of
the standard deviation of the
observed from 1860 to 1990 (red
line). Both the simulated and
observed SST anomalies exhibit
trends towards stronger interannual
variability, with pronounced interdecadal variability superimposed,
(reproduced from Timmermann et al.,
1999), (b) HadCM3 (Collins, 2000b).
IInterannual variability and ENSO
Climate models have assessed changes that might occur in ENSO in
connection with future climate warming and in particular, those aspects of
ENSO that may affect future climate extremes.
Firstly, will the long-term mean Pacific SSTs shift toward a more El Niñolike or La Niña-like regime? Since 1995, the analyses of several global
climate models indicate that as global temperatures increase due to
increased greenhouse gases, the Pacific climate will tend to resemble a
more El Niño-like state (1999; Boer et al., 2000b). However, the reasons for
such a response are varied, and could depend on the model
representation of cloud feedback or the stronger evaporative damping of
the warming in the warm pool region.
Secondly, will El Niño variability (the amplitude and/or the frequency of
temperature swings in the equatorial Pacific) increase or decrease?. Hu et
al. (2001) find that the largest changes in the amplitude of ENSO occur on
decadal time-scales with increased multi-decadal modulation of the ENSO
amplitude. Several authors have also found changes in other statistics of
variability related to ENSO. Collins (2000a) finds an increased frequency
of ENSO events and a shift in the seasonal cycle
Finally, how will ENSO’s impact on weather in the Pacific Basin and other
parts of the world change? Some studies indicate that future seasonal
precipitation extremes associated with a given ENSO event are likely to
be more intense due to the warmer, more El Niño-like, mean base state in
a future climate. That is, for the tropical Pacific and Indian Ocean regions,
anomalously wet areas could become wetter and anomalously dry areas
become drier during future ENSO events. Also, in association with
changes in the extra-tropical base state in a future warmer climate, the
teleconnections to mid-latitudes, particularly over North America, may
shift somewhat with an associated shift of precipitation and drought
conditions in future ENSO events (Meehl et al., 1993).
It must be recognised that an “El Niño-like” pattern can apparently occur
at a variety of time-scales ranging from interannual to inter-decadal
(Zhang et al., 1997), either without any change in forcing or as a response
to external forcings such as increased CO2 (Meehl et al., 2000b). Making
conclusions about “changes” in future ENSO events will be complicated
by these factors. Additionally, since substantial internally generated
variability of ENSO statistics on multi-decadal to century time-scales
occurs in long unforced climate model simulations (Knutson et al., 1997),
the attribution of past and future changes in ENSO amplitude and
frequency to external forcing may be quite difficult.
The change in 20-year return
values for daily maximum
(upper panel) and minimum
(lower panel) surface air
temperature (or screen
temperature) simulated in a
global coupled atmosphereocean model (CGCM1) in
2080 to 2100 relative to the
reference period 1975 to 1995
(from Kharin and Zwiers,
2000). Contour interval is
4°C. Zero line is omitted.
Changes of Extreme Events
Models have improved over time, but they still have limitations that affect
the simulation of extreme events in terms of spatial resolution, simulation
errors, and parametrizations that must represent processes that cannot
yet be included explicitly in the models, particularly dealing with clouds
and precipitation. Yet we have confidence in many of the qualitative
aspects of the model simulations since they are able to reproduce
reasonably well many of the features of the observed climate system not
only in terms of means but also of variability associated with extremes
Simulations of 20th century climate have shown that including known
climate forcings (e.g., greenhouse gases, aerosols, solar) leads to
improved simulations of the climate conditions we have already
observed.
Increased intensity of precipitation events in a future climate with
increased greenhouse gases was one of the earliest model results
regarding precipitation extremes, and remains a consistent result in a
number of regions with improved, more detailed models (Hennessy et al.,
1997; Kothavala, 1997; Durman et al., 2001; Yonetani and Gordon, 2001).
Simulating a climatology of tropical cyclones
Because of their relatively small extent (in global modelling terms) and
intense nature, detailed simulation of tropical cyclones for this purpose is
difficult.
Atmospheric GCMs can simulate tropical cyclone-like disturbances which
increase in realism at higher resolution though the intense central core is
not resolved (e.g., Bengtsson et al., 1995; McDonald, 1999). Further
increases of resolution, by the use of RCMs, provide greater realism (e.g.,
Walsh and Watterson, 1997) with a very high resolution regional hurricane
prediction model giving a reasonable simulation of the magnitude and
location of maximum surface wind intensities for the north-west Pacific
basin (Knutson et al., 1998). GCMs generally provide realistic simulation of
the location and frequency of tropical cyclones.
Much effort has gone into obtaining and analysing good statistics on
tropical cyclones in the recent past. The main conclusion is that there is
large decadal variability in the frequency and no significant trend during
the last century. One study looking at the century time-scale has shown an
increase in the frequency of North Atlantic cyclones from 1851 to 1890 and
1951 to 1990 (Fernandez-Partagas and Diaz, 1996).
Tropical cyclones in a warmer climate
Most assessments of changes in tropical cyclone behaviour in a future
climate have been derived from GCM or RCM studies of the climate
response to anthropogenically-derived atmospheric forcings (Walsh
and Katzfey, 2000). Recently, more focused approaches have been
used: nesting a hurricane prediction model in a GCM climate change
simulation (Knutson et al., 1998); inserting idealised tropical cyclones
into an RCM climate change simulation (Walsh and Ryan, 2000).
Frequencies increased in the north-west Pacific, decreased in the North
Atlantic, and changed little in the south-west Pacific. The likely mean
response of tropical Pacific sea surface warming having an El Niño-like
structure suggests that the pattern of tropical cyclone frequency may
become more like that observed in El Niño years.
An indication of the likely changes in maximum intensity of cyclones
will be better provided by models able to simulate realistic tropical
cyclone intensities. A sample of GCM-generated tropical cyclone cases
nested in a hurricane prediction model gave increases in maximum
intensity (of wind speed) of 5 to 11% in strong cyclones over the northwest Pacific for a 2.2°C SST warming (Knutson and Tuleya, 1999). T ).
The very high resolution modelling work suggests that increases in the
intensity of tropical cyclones will be accompanied by increases in mean
and maximum precipitation rates. In the cases studied, precipitation in
the vicinity of the storm centre increased by 20% whereas peak rates
increased by 30%. Part of these increases may be due to the increased
moisture-holding capacity of a warmer atmosphere but nevertheless
point to substantially increasing destructive capacity of tropical
cyclones in a warmer climate.
Areas of deep convection that can be associated with tropical cyclone
formation would not expand with increases in CO2 due to an increase of
the SST threshold for occurrence of deep convection (Dutton et al.,
2000). Additionally, since tropical storm activity in most basins is
modulated by El Niño/La Niña conditions in the tropical Pacific,
projections of future regional changes in tropical storm frequencies may
depend on accurate projections of future El Niño conditions, an area of
considerable uncertainty for climate models.
In conclusion, there is some evidence that regional frequencies of
tropical cyclones may change but none that their locations will change.
There is also evidence that the peak intensity may increase by 5% to
10% and precipitation rates may increase by 20% to 30%. .
Changes in extremes of weather and climate
Although changes in weather and climate extremes are important to
society, ecosystems, and wildlife, it is only recently that evidence for
changes we have observed to date has been able to be compared to
similar changes that we see in model simulations for future climate
(generally taken to be the end of the 21st century).
Though
several
simulations
changes in extremes
of weather
and climate of 20th century climate with various
estimates of observed forcings now exist , few of these have been
analysed for changes in extremes over the 20th century. So far, virtually
all studies of simulated changes in extremes have been performed for
future climate.
The assessment of extremes here relies on very large-scale changes that
are physically plausible or representative of changes over many areas.
There are some regions where the changes of certain extremes may not
agree with the larger-scale changes.
Estimates of confidence in observed and projected changes in extreme weather and climate events.
Confidence in observed changes
(latter half of the 20th century)
Changes in Phenomenon
Confidence in projected changes
(during the 21st century)
Likely
Higher maximum temperatures
and more hot days a over nearly
all land areas
Higher minimum temperatures,
fewer cold days and frost days
over nearly all land areas
Very likely
Increase of heat index b over land
areas
More intense precipitation events
Very likely, over most areas
Very likely
Likely, over many areas
Likely, over many Northern
Hemisphere mid- to high latitude
land areas
Likely, in a few areas
Not observed in the few
analyses available
Insufficient data for assessment
Very likely
Very likely, over many areas
c
Increased summer continental
drying and associated risk of
drought
Likely, over most mid-latitude
continental interiors. (Lack of
consistent projections in other
areas)
Increase in tropical cyclone peak
wind intensities d
Increase in tropical cyclone
mean and peak precipitation
intensities d
Likely, over some areas
Likely, over some areas
Hot days refers to a day whose maximum temperature reaches or exceeds some temperature that is
considered a critical threshold for impacts on human and natural systems. Actual thresholds vary regionally, but
typical values include 32°C, 35°C or 40°C.
b Heat index refers to a combination of temperature and humidity that measures effects on human comfort.
c For other areas, there are either insufficient data or conflicting analyses.
d Past and future changes in tropical cyclone location and frequency are uncertain.
a
Climate variability and extreme events-Global and Regional Climate
modelling
Global models: Enhanced resolution improves many aspects of the
AGCMs’ intra-seasonal variability of circulation at low and intermediate
frequencies. However, in some cases values underestimated at standard
resolution are overestimated at enhanced resolution. Little sensitivity to
resolution in either the interannual or intra-seasonal variability of
circulation and precipitation of the South Asian monsoon in HadAM3a.
Due to the limited number and length of simulations and a lack of
comprehensive analyses, this subject has been almost completely
ignored. The only response in variability or extremes that has received
any attention is that of tropical cyclones.
Regional models: Changes in climate variability between control and
2xCO2 simulations with a nested RCM for the Great Plains of the USA
have been reported. Studies have analysed changes in the frequency of
heavy precipitation events in enhanced GHG climate conditions over the
European region, and suggest an increase of up to several tens of
percentage points in the frequency of occurrence of precipitation events
exceeding 30 mm/day.
Scenario information: Regionalization
Each of the stages of analysis required scenario information to be
provided, including:
•scenarios of carbon dioxide (CO2) concentration, affecting
crop growth and water use, as an input to the crop models;
•climate observations and scenarios of future climate, for the
crop model simulations;
•adaptation scenarios (e.g., new crop varieties, adjusted farm
management) as inputs to the crop models;
•scenarios of regional population and global trading policy as
an input to the trade model.
Regionalisation techniques
Three major techniques (referred to as regionalisation techniques) have
been developed to produce higher resolution climate scenarios:
(1) regional climate modelling (Giorgi and Mearns, 1991; McGregor, 1997;
Giorgi and Mearns, 1999);
(2) statistical downscaling (Wilby and Wigley, 1997; Murphy, 1999); and
(3) high resolution and variable resolution Atmospheric General
Circulation Model (AGCM) time-slice techniques (Cubasch et al., 1995;
Fox-Rabinovitz et al., 1997). The two former methods are dependent
on the large-scale circulation variables from GCMs, and their value as
a viable means of increasing the spatial resolution of climate change
information thus partially depends on the quality of the GCM
simulations.
The variable resolution and high resolution time-slice methods use the
AGCMs directly, run at high or variable resolutions.
Rainfall estimated by satellite in Venezuela
15-17 December 1999
Forecast of rainfall (accumulated 24 hours) for
15 December –Global and regional models
MGC CPTEC/COLA T126
(100 km)
Modelo regional Eta/ (24 horas)
MGC CPTEC/COLA T062
(200 km)
Modelo regional Eta/ (60 horas)
Incorporation of changes in variability: daily to interannual time-scales
Changes in variability have not been regularly incorporated in climate
scenarios because:
(1) less faith has been placed in climate model simulations of changes in
variability than of changes in mean climate;
(2) techniques for changing variability are more complex than those for
incorporating mean changes; and
(3) there may have been a perception that changes in means are more
important for impacts than changes in variability (Mearns, 1995).
Techniques for incorporating changes in variability emerged in the
early 1990s
Other types of variance changes, on an interannual time-scale, based on
changes in major atmospheric circulation oscillations, such as ENSO and
North Atlantic Oscillation (NAO), are difficult to incorporate into impact
assessments. The importance of the variability of climate associated with
ENSO phases for resources systems such as agriculture and water
resources have been well demonstrated (e.g., Cane et al., 1994; Chiew et
al., 1998; Hansen et al., 1998).
Where ENSO signals are strong, weather generators can be successfully
conditioned on ENSO phases; and therein lies the potential for creating
scenarios with changes in the frequency of ENSO events. By conditioning
on the phases, either discretely (Wang and Connor, 1996) or continuously
(Woolhiser et al., 1993), a model can be formed for incorporating changes
in the frequency and persistence of such events, which would then
induce changes in the daily (and interannual) variability of the local
climate sites. However, it must be noted that there remains much
uncertainty in how events such as ENSO might change with climate
change.
Changes in the frequency of more complex extremes are based on
changes in the occurrence of complex atmospheric phenomena (e.g.,
hurricanes, tornadoes, ice storms). Given the sensitivity of many
exposure units to the frequency of extreme climatic events, it would
be desirable to incorporate into climate scenarios the frequency and
intensity of some composite atmospheric phenomena associated with
impacts-relevant extremes.
More complex extremes are difficult to incorporate into scenarios for the
following reasons:
(1) high uncertainty on how they may change (e.g., tropical cyclones);
(2) the extremes may not be represented directly in climate models (e.g.,
ice storms); and
(3) straightforward techniques of how to incorporate changes at a
particular location have not been developed (e.g., tropical cyclone
intensity at Cairns, Australia).
In the case of extremes that are not represented at all in climate models,
secondary variables may sometimes be used to derive them. For example,
freezing rain, which results in ice storms, is not represented in climate
models, but frequencies of daily minimum temperatures on wet days
might serve as useful surrogate variables (Konrad, 1998).
An example of an attempt to incorporate such complex changes into
climate scenarios is the study of McInnes et al. (2000), who developed an
empirical/dynamical model that gives return period versus height for
tropical cyclone-related storm surges for Cairns on the north Australian
coast. To determine changes in the characteristics of cyclone intensity,
they prepared a climatology of tropical cyclones based on data drawn
from a much larger area than Cairns locally. They incorporated the effect
of climate change by modifying the parameters of the Gumbel distribution
of cyclone intensity based on increases in tropical cyclone intensity
derived from climate model results over a broad region characteristic of
the location in question. Estimates of sea level rise also contributed to the
modelled changes in surge height.
Northeast Brazil: Seasonal Rainfall Comparison
Wet Year: 1974
Station
Area Averaged Value = 781.7 mm
Hulme 0.5 deg
Area Averaged Value = 781.6 mm
ECHM overload
the rainfall
amounts
RSM general
pattern is
provided by global
model
RSM
Area Averaged Value = 467.4 mm
ECHAM
Area Averaged Value = 1229.2 mm
ITCZ further
south
ITCZ with strong
convective activity
RSM captured
the gradient
RSM dry too
much
Seasonal Rainfall Comparison
Dry Year: 1983
Station
Area Averaged Value = 343.4 mm
Hulme 0.5 deg
Area Averaged Value = 428.9 mm
ECHM solve
only global
patterns
Again RSM
general pattern
is provided by
global model
RSM
Area Averaged Value = 206.3 mm
ECHAM
Area Averaged Value = 641.1 mm
ITCZ further
north
ITCZ weak
RSM dry
excessively
Seasonal Precipitation Comparison
Dry Year (1983)
Seasonal Rainfall Comparison: Wet minus Dry (1974-1983)
Station
Area Averaged Value = 433.5 mm
Hulme 0.5 deg
Area Averaged Value = 352.7 mm
RSM: Displacement of
ITCZ to the south
RSM
Area Averaged Value = 261.0 mm
ECHAM
Area Averaged Value = 601.7 mm
Better simulation of
the difference between
the Northeast Region
and the Southeast
Region than ECHAM
Zooming in the interest region
Wet Year (1974)
 RSM was able to reproduce qualitatively the rainfall's gradient between the
northwest and southeast
RSM failed positioning the maximum of rain
Zooming in the interest region
Dry Year (1983)
 RSM captured the "idea" of wet in Maranhao against dry southeastward
Number of events that produced more than 10 mm/day
Wet Year(1974)
SUDENE
RSM
FEB
MAR
APR
Number of events that produced more than 10 mm/day
Dry Year(1983)
MAR
APR
SUDENE
RSM
FEB
 Both cases RSM shows a dry month along the season
Daily Evolution
Dry Season (1983)
SUDENE
ECHAM
RSM
Rainfall 24h (mm)
Region 2
Region 2
Region 1
Rainfall 24h (mm)
Region 1
FEB
MAR
APR