Transcript Regional climate change scenario supporting activities for studies

Regional Climate Change Scenario supporting
activities for studies on detection, impacts
assessments and mitigation
Jose A. Marengo
Climate Studies Group
CPTEC/INPE
São Paulo, Brazil
INTRODUCTION
-The release of the IPCC Third Assessment Report has
brought to attention the possible impacts of the increase
in the concentration of greenhouse gases in climate
change in the world, and in South America these changes
are beside the possible effect of regional deforestation on
climate.
-New models and new developments have allowed some
new insight on climate change scenarios in Latin America,
as compared to the SAR-IPCC released in 1996
-There is a need for climate change scenarios downscaled
in space (regional climate change) and time (extreme
events).
-There is a need for “common criteria” to define and to
analyze extremes from different AIACC projects, so we
can analyzed all of them for the entire LAC region
Current situation:
-Concern on regional and federal governments about possible impacts of
climate change
at regional levels on sectors such as natural
ecossistems, agriculture, water resources availability, human health, etc.
-Current projections of climate change scenarios for century XXIClimate
models from I PCC TAR (2001)Need for a regional details of climate
predictiondownscaling. Need for assessments on extreme events.
-One question:
How climate change will affect human activities,
bidiversity and natural ecosystems and due to potential climate change?.
-Major objective: To know and better understand patterns of climate
change and its impacts on human activities, biodiversity and natural
ecosystems, in the context of impacts of climate change and the
development of policies and strategies for conservation and management
of natural resources and human activities:
-Development of regional modeling/statistical downscaling capacity for
climate change scenarios at CPTEC-Brazil.
OBJECTIVES
1-Characterization of biodiversity and biomes distribution, water
resources availability, agriculture, health on present times;
2-Characterization of present climate (observations, statistics,
projections, socio-economic data, model climatology of present
climates);
3-Assessments of climate change scenarios at global and regional
scales, using technique of downscaling, and its impacts on human
activities and natural ecosystems. This should be linked to the
development and implementation of technical capacity and
formation of humam resouces for studies and monitoring.
Uncertainties in future climate projections for Brazildifferences
among climate modelslack of regional climate change
projectionneed for a strategy for climate change studies and
developing of regional climate modeling of climate change.
Air temperature trends 1961-2010 (IPCC SRES)
Global
A2
A1
B2
B1
Brazil
A2
A1
B2
B1
Changes in temperature and precipitation (mean 1961-90)
For 2050, scenarios B2-low e A2-high. Each dot represents
Different models, and the error bars represent natural
climate variability (Carter and Hulme 2000)
Interdecadal rainfall variability: CRU Rainfall anomalies during 1929-45, 1946-75 and 197698, using 1961-90 nas reference period. Red/Blue represent negative/positive anomalies.
Color bar is show below the panels (Marengo. 2003).
Rainfall anomalies CRU (mm/day)
Decade of 1960’s
Decade of 1990’s
Interdecadal temperature variability: CRU Temperature anomalies during 1929-45, 1946-75
and 1976-98, using 1961-90 nas reference period. Red/Blue represent positive/positive
anomalies. Color bar is show below the panels (Marengo 2003).
Scatter Plot of changes in temperature
and precipitation due to deforestation
in the Amazon basin (from modeling
experiments: Marengo and Nobre 2001,
D’Almeida 2002)
MAN96
LE96
PL94b
PL94a
DHS88
LR97
COS20
WARMER/DRIER
HD95
DK92
HS93
LR93
HAH97
SUD96
LW89
SHU96
SUD90
NEP99
SHN91
Deforestation
Deforestation+2C02
Climate Predictability in South America (for rainfall)
Higher predictability
Medium predictability
Low Predictability
Medium
Predictability
Medium predictability
DJF rainfall (color) and rainfall anomalies (numbers) Projections are from
the HadCM3.
A2
2020
B2
2050
2080
SON air temperatures (color) and air temperature anomalies (numbers)
Projections are from the HadCM3.
A2
2020
B2
2050
2080
DJF Rainfall anomalies (colors) and anomalies (numbers)
CCCMA-A2-2020
CSIRO-A2-2020
ECHAM4-A2-2020
NCAR-A2-2020
CCCMA-B2-2020
CSIRO-B2-2020
ECHAM4-B2-2020
NCAR-B2-2020
DJF Air temperature (colors) and anomalies (numbers)
CCCMA-A2-2020
CSIRO-A2-2020
ECHAM4-A2-2020
NCARA2-2020
CCCMA-B2-2020
CSIRO-B2-2020
ECHAM4-B2-2020
NCARB2-2020
A2- HadCM3 rainfall (2020)
DJF
MAM
JJA
SON
MAM
JJA
SON
B2- HadCM3 rainfall (2020)
DJF
A2- HadCM3 air temperature (2020)
DJF
MAM
JJA
SON
JJA
SON
B2- HadCM3 Air temperature (2020)
DJF
MAM
Air temperature trends in Manaus from A2 and B2 IPCC SRES scenarios
CCMa A2
CCMa B2
ECHAM4 A2
ECHAM4 B2
HadCM3 A2
NCAR A2
CSIRO A2
HadCM3 B2
NCAR B2
CSIRO B2
Precipitation trends in Manaus from A2 and B2 IPCC SRES scenarios
CCMa A2
ECHAM4 A2
HadCM3 A2
NCAR A2
CSIRO A2
CCMa B2
ECHAM4 B2
ECHAM4 B2
HadCM3 B2
HadCM3 B2
NCAR
NCARB2
B2
CSIRO B2
B2
CSIRO
“Amazon Dieback” Forced by Climate Change?
Vegetation type in South America (Hadley Centre
Model with MOSES iterative vegetation scheme
pre-industrial
present
2100
NEEDS:
-These projections exhibit a degree of uncertainty due
the differences between models, since some of them
exhibit problems in representing the temporal and spatial
distribution of temperature and rainfall.
-Global models produce projections with some regional
details missing since there is not an availability of
downscaled climate change scenarios valid for the
different sections of the basinbetter global models.
-Need for downscaled climate change scenarios:
Regional climate models (dynamic) (up to 10 km) or
statistical downscaling.
-If regional models are usedneed for “multi model
ensemble” using various regional models.
-Identify regions with better model skill and higher
climate predictabilityreduce uncertainty on climate
change simulations
Paleoclimates and present climate
(mechanisms and feedbacks)
Emissions
Concentrations
(Carbon CO2, CH4, aerosols..)
Global climate change
Climate change
(prediction and
future scenarios)
(temperature, rain, sea level)
Regional details
(mountain effects, islands, valleys..)
Impacts
(Natural ecosystems, water resources)
POLICY MAKERS-GOVERNMENT
Assessments of
impacts
Activities related to climate change to be developed at
CPTEC
Statistical
downscaling
SRES IPCC
scenarios-HadCM3H
Coupling HadCM3Eta/CPTEC
Climate run of the
Eta/CPTEC regional
model
Analysis and validation
of the HadCM3
climatology
Climate change studies,
impacts
and
vulnerability
assessments
Dynamic Downscaling:
Climate change
scenarios (A1, A2, B1,
B2)
Validations
predictability assessments
model skill assessments
Global Coupled
model HadCM3 of
the Hadley Centre
19 vertical levelsatmosphere
300 km
2.5
lat
3.75
long
1.25 km
1.25 km
20 vertical
Levels-soil
-5km
Regional model
Eta/CPTEC, 40-10
km, 38 vertical
levels
Previous studies (ex. Deforestation)
Paleoclimates
Climate variability
and trends
Present time climate
and Hydrology
Global and Regional
Climate Change?
Observations climate-hydrology
(global and regional)
IPCC Global models
Regionalized climate
change Scenarios
(XXI Century)
Climate
modeling
Downscaling using the
Eta/CPTEC regional model
nested on the global
HadCM3H model
Applications:
-Water Resources
-Natural ecosystems
-Agriculture
-Health…
SRES-IPCC
Scenarios
Data
Bank
GENERAL METHODOLOGY
Paleoclimates
Brazil?
Choice?
Government,
Private sector
Atlas
Meteorological
database 20th
century
Hydrological
database
20th century
Drainage
basins
20th century
Climate
Hydrology
Hydroclimate
trends
20th century
20th
century
Validate
AOGCM
21st
century
SRESscenarios
Primary natural and
human biomes in the
20th century
Regional
Government,
Private sector
AIACC
Climate
models
Statistical
downscaling
Remote sensing
techniques - Other
Climatology
Hydrology
21st century
Database
- Fauna
- Flora
Indicators
Applications
Biomass
21st
century
Case study:
“Impacts of Global Climate Changes on the
Brazilian Ecosystems”
Site of the study
region around
Salinopolis
Erosion on Torotama Island
Erosion in the inner estuary
The RS coastal plain
with the Patos-Mirim
lagoon system
(b)
(a)
Evidence of erosive processes at the central portion of the barrier island. a)
Conceição Lighthouse in 1993. b) remains of lighthouse after a 1993 storm
surge. The rate of erosion at this site is 2.3 m/year .
Area affected by
a 2-m increase in
sea level in the
city of Rio de
Janeiro, Brazil