Transcript marengo_extremes

El Niño Southern Oscillation-Tropical
Cyclones/Hurricanes and Extreme weather
(Observational aspects and modeling)
José. A. Marengo
CPTEC/INPE
SECTORS AFFECTED BY EL NIÑO 1997-98
IN MESOAMERICA (Source: V. Magaña)
Agriculture
Agriculture
Forestry
Forestry
Natural Disasters
Fishery
(droughts and
Natural Disasters
floods)
(drought)
Mexico
Agriculture
Agriculture
Forestry
Forestry
Natural disasters
Belize
Fishery
(drought)
Guatemala
Honduras
El Salvador Nicaragua
Natural disasters
(drought)
Panama
Costa Rica
Agriculture
Health
Health
Agriculture
Communications
Electricity generation
Electricity generation
Natural disasters
(drought)
Summers during El Niño featured severe droughts
in most of Mexico (Source. V. Magaña)
El Niño
Summers during La Niña, back to
Normal or above normal rain
La Niña
Rains in NW Mexico show little
Association with EL Nino (lower
Predictability).
¿ How much did El Niño 1997-98 cost in Mexico?
Concepto
Pérdidas monetarias*
Incendios forestales
Agricultura
Desastres naturales
Pesca
Total nacional
2 30
14 00
2 00
70
1900
*millones de dólares
Fuente: Magaña et. al. 1999
In other countries:
(millions of dollars)
Source: CEPAL, CAF
Bolivia
Colombia
Ecuador
Perú
Venezuela
Costa Rica
Argentina
527
564
2882
3498
72
82
2500
Modeling El Niño and extremes of climate
Facts:
Once developed, El Niño and La Niña "events" are known to shift
seasonal temperature and precipitation patterns in many different regions
of the world.
In several parts of the tropics, and some areas outside of the tropics,
these seasonal shifts are fairly consistent from one El Niño and La Niña
event to the next.
It is important to remember, however, that no two El Niño or La Niña
events are identical and that the seasonal shifts in temperature and
precipitation patterns associated with them can vary from one event to
the next.
Thus, when an El Niño or La Niña develops, it does not guarantee that
regions which are typically affected by them will be affected, only that
there is enhanced probability that this will be the case.
Questions:
1) How good can we model El Niño’s evolution and regional impacts
and clmate extremes?. It depends on how good is the model in
reproducing climate processes and feedbacks.
2) Do all models do a good job predicting climate in all the planet?, No,
in some regions, yes, in others do not (Model predictability and skill
of the model)
3) What extremes are we talking about?, Events that occurs with a high
resolution in time-scale: Hurricanes and tropical cyclones..
4) How can we distinguish regional high resolution weather and clima
aspects produced by a GCM?, using downscaling (statistical,
regional climate models, or GCM with higher resolution)
5) What is the state-of-the are in modeling climate extremes?: Good for
weather forecast, improvement for climate forecast.
6) Is the the current generation of climate models successful in
reproducing climate extremes?. Yes, but moderate success for El
Niño non-related variability only.
7) What about modeling climate change scenarios?, See answers 1-6
On climate change scenarios: 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 of the
distribution, 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 of clmate, Skill of the models, resolution.
Predictability
Key factors affecting interannual variability / predictability in the region,
applicable to longer time scale climate predictions.
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
 What is the role of subsurface conditions and thermocline adjustments in
coupling processes
For the atmosphere and land-surface
 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
DJF
Rainfall
CPTEC
GCM
OBSV
(Xie Arkin)
Model-Obsv
MAM
Rainfall correlation anomaly using CPTEC GCM, 10 years, 9
members
Green Values-higher predictability
DJF
MAM
Uncertainties:
Uncertainty in projected climate change arises from four 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:. Bias in climate models
may be also reproduced in downscaled scenarios (?)
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.
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
(coupled ocean-atmosphere models, multi-century 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
Other studies have looked at intra-seasonal variability in coupled
models and the simulation of changes in mid-latitude storm tracks,
tropical cyclones or blocking anticyclones
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 resolution in both the atmosphere and oceans of the majority of
the models used.
Interannual 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.
Secondly, will El Niño variability (the amplitude and/or the frequency of
temperature swings in the equatorial Pacific) increase or decrease?. The
largest changes in the amplitude of ENSO occur on decadal time-scales
with increased multi-decadal modulation of the ENSO amplitude.
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.
It must be recognised that an “El Niño-like” pattern can apparently
occur at a variety of time-scales ranging from interannual to interdecadal, either without any change in forcing or as a response to
external forcings such as increased CO2.
Making conclusions about “changes” in future ENSO events will be
complicated by these factors.
Modeling extremes of climate
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.
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.
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. Further increases of resolution, by the use of
RCMs, provide greater realism 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.
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.
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. Recently,
more focused approaches have been used: nesting a hurricane
prediction model in a GCM climate change simulation inserting
idealised tropical cyclones into an RCM climate change simulation.
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.
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 north-west Pacific
for a 2.2°C SST warming.
Tropical Cyclones, Hurricanes and El Niño
Location of meteorological and oceanographic parameters
used in the Atlantic seasonal forecasts by W. Gray (CSU).
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.
Problems with simulation of tropical cyclones and their variability
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.
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. 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.
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 and SST distribution within the tropics might affect the
rate at which tropical cyclones move out of their genesis regions and
into higher latitudes and their variations.
Extreme rainfall event:
Venezuela in December 1999
Related to El Niño or climate change
(global warming?).
Noclimate + anthropogenic
3-D Projection of Caracas and
Coastal line of Venezuela
Cerro El Avila
N
Caracas DF
N
La Guaira
Maiquetia
Region affected by intense
rainfall, landslides, and floods
Rainfall estimated by satellite in Venezuela
15-17 December 1999 (+30,000 people death)
experiences on GCM with higher resolution)
Forecast of rainfall (accumulated 24 hours) for
15 December –Global and regional models
GCM CPTEC/COLA T126
(100 km)
Regional Eta/CPTEC 40 km (24 h)
GCM CPTEC/COLA T062
(200 km)
Regional Eta/CPTEC 40 km/ (60 h)
Downscaling and regionalisation techniques:
climate prediction in Northeast Brazil
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.
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.
Regionalisation techniques
Three major techniques (referred to as regionalisation techniques)
have been developed to produce higher resolution climate scenarios:
(1) regional climate modelling;
(2) statistical downscaling, and
(3) high resolution and variable resolution Atmospheric General
Circulation Model (AGCM) time-slice techniques
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.
Example: Northeast Brazil, GCM: ECHAM Max Planck, Regional Spectral
Model (RSM), 1983 (dry El Niño year)
Seasonal Rainfall Comparison
Dry Year: FMA 1983
Station
Area Averaged Value = 343.4 mm
RSM
Area Averaged Value = 206.3 mm
Hulme 0.5 deg
Area Averaged Value = 428.9 mm
ECHAM
Area Averaged Value = 641.1 mm
Rainfall 24h (mm)
Daily Evolution
Dry year (1983)
OBSV
ECHAM
RSM
Region 2
Region 2
Region 1
Rainfall 24h (mm)
FEB
MAR
APR
OBSV
ECHAM
RSM
Region 1
FEB
MAR
APR
Climate-driven Amazon Dieback
Broadleaf tree in South America
pre-industrial
present
2100
Paleoclimates
Climate-hydrology
presentc
Observations climate-hydrology
(regional, global data)
Tendencies and
variability
Global and regional
climate change?
Hydrological and climatic data,
data processing and quality control,
IPCC global models
Climate
models
Climate Scenarios
XXI century
Regional Downscaling
Eta/CPTEC Regional model
Nested on HadCM3H global
model
SRES-IPCC
scenarios
Water resources
XXI century
DIS
Applications