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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 Forestry Natural Disasters (droughts and floods).

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slide1

El Niño Southern Oscillation-Tropical Cyclones/Hurricanes and Extreme weather

(Observational aspects and modeling)

José. A. Marengo

CPTEC/INPE

slide9

SECTORS AFFECTED BY EL NIÑO 1997-98

IN MESOAMERICA (Source: V. Magaña)

Agriculture

Forestry

Natural Disasters

(droughts and floods)

Agriculture

Forestry

Fishery

Natural Disasters

(drought)

Mexico

Agriculture

Forestry

Natural disasters

(drought)

Agriculture

Forestry

Fishery

Natural disasters

(drought)

Health

Belize

Guatemala

Honduras

El Salvador

Nicaragua

Panama

Costa Rica

Agriculture

Health

Communications

Electricity generation

Agriculture

Electricity generation

Natural disasters

(drought)

summers during el ni o featured severe droughts in most of mexico source v maga a
Summers during El Niño featured severe droughts in most of Mexico (Source. V. Magaña)

Summers during La Niña, back to

Normal or above normal rain

El Niño

La Niña

Rains in NW Mexico show little

Association with EL Nino (lower

Predictability).

slide11

¿ How much did El Niño 1997-98 cost in Mexico?

Fuente: Magaña et. al. 1999

Bolivia 527

Colombia 564

Ecuador 2882

Perú 3498

Venezuela 72

Costa Rica 82

Argentina 2500

In other countries:

(millions of dollars)

Source: CEPAL, CAF

slide13

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.

slide14

Questions:

  • 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.
  • 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)
  • What extremes are we talking about?, Events that occurs with a high resolution in time-scale: Hurricanes and tropical cyclones..
  • 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)
  • What is the state-of-the are in modeling climate extremes?: Good for weather forecast, improvement for climate forecast.
  • Is the the current generation of climate models successful in reproducing climate extremes?. Yes, but moderate success for El Niño non-related variability only.
  • What about modeling climate change scenarios?,See answers 1-6
slide15

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.

slide16

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
slide17

DJF

MAM

Rainfall

CPTEC

GCM

OBSV

(Xie Arkin)

Model-Obsv

slide18

Rainfall correlation anomaly using CPTEC GCM, 10 years, 9 members

Green Values-higher predictability

DJF

MAM

slide19

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 (?)

slide20

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.
slide21

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.

slide22

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 Niño-like 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.

slide23

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 inter-decadal, 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.

slide26

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.

slide27

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.

slide28

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.

slide31

Location of meteorological and oceanographic parameters used in the Atlantic seasonal forecasts by W. Gray (CSU).

slide36

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.

slide37

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.

slide38

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.

slide39

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 intohigher latitudes and their variations.

slide41

Extreme rainfall event:

Venezuela in December 1999

Related to El Niño or climate change (global warming?).

Noclimate + anthropogenic

slide42

3-D Projection of Caracas and

Coastal line of Venezuela

N

Cerro El Avila

Mar Caribe

Caracas DF

N

La Guaira

Maiquetia

Region affected by intense

rainfall, landslides, and floods

slide45

Rainfall estimated by satellite in Venezuela

15-17 December 1999 (+30,000 people death)

experiences on GCM with higher resolution)

slide46

Forecast of rainfall (accumulated 24 hours) for

15 December –Global and regional models

GCM CPTEC/COLA T126

(100 km)

GCM CPTEC/COLA T062 (200 km)

Regional Eta/CPTEC 40 km/ (60 h)

Regional Eta/CPTEC 40 km (24 h)

slide47

Downscaling and regionalisation techniques:

climate prediction in Northeast Brazil

slide48

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.

slide49

Regionalisation techniques

  • Three major techniques (referred to as regionalisation techniques) have been developed to produce higher resolution climate scenarios:
  • regional climate modelling;
  • statistical downscaling, and
  • 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 theAGCMs directly, run at high or variable resolutions.
  • Example: Northeast Brazil, GCM: ECHAM Max Planck, Regional Spectral Model (RSM), 1983 (dry El Niño year)
slide50

Seasonal Rainfall Comparison

Dry Year: FMA 1983

Hulme 0.5 deg

Area Averaged Value = 428.9 mm

Station

Area Averaged Value = 343.4 mm

ECHAM

Area Averaged Value = 641.1 mm

RSM

Area Averaged Value = 206.3 mm

slide51

Region 2

Region 1

OBSV

ECHAM

RSM

Daily Evolution

Dry year (1983)

Region 2

Rainfall 24h (mm)

MAR

FEB

APR

OBSV

ECHAM

RSM

Region 1

Rainfall 24h (mm)

MAR

FEB

APR

climate driven amazon dieback

Climate-driven Amazon Dieback

Broadleaf tree in South America

pre-industrial present 2100

slide53

Paleoclimates

Tendencies and

variability

Climate-hydrology

presentc

Observations climate-hydrology

(regional, global data)

Global and regional

climate change?

Hydrological and climatic data,

data processing and quality control,

Climate Scenarios

XXI century

IPCC global models

Climate models

Regional Downscaling

Eta/CPTEC Regional model

Nested on HadCM3H global model

Water resources

XXI century

SRES-IPCC

scenarios

DIS

Applications