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 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 (IPCC SRES) Global Brazil Anomaly (C ) Air temperature (C ) B1 B2 A1 A2 A1 B2 B1

Changes in temperature and precipitation (mean ) 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 , and , using 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 , and , using nas reference period. Red/Blue represent positive/positive anomalies. Color bar is show below the panels (Marengo 2003).

Changes in temperature (C ) Changes in precipitation (mm/day) PL94a Deforestation Deforestation+2C02 SUD90 WARMER/DRIER NEP99 LR93 SHU96 LR97 COS20 DHS88 LW89 LE96 SHN91 SUD96 HS93 PL94b MAN96 HAH97 DK92 HD95 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)

MediumPredictability Low Predictability Higher predictability Medium predictability Climate Predictability in South America (for rainfall)

DJF rainfall (color) and rainfall anomalies (numbers) Projections are from the HadCM A2 B2

A2 B2 SON air temperatures (color) and air temperature anomalies (numbers) Projections are from the HadCM3.

CCCMA-A2-2020CSIRO-A2-2020ECHAM4-A2-2020NCAR-A CCCMA-B2-2020CSIRO-B2-2020ECHAM4-B2-2020NCAR-B DJF Rainfall anomalies (colors) and anomalies (numbers)

CCCMA-A2-2020CSIRO-A2-2020ECHAM4-A2-2020NCARA CCCMA-B2-2020CSIRO-B2-2020ECHAM4-B2-2020NCARB DJF Air temperature (colors) and anomalies (numbers)

B2- HadCM3 rainfall (2020) A2- HadCM3 rainfall (2020) DJF MAM JJA SON

B2- HadCM3 Air temperature (2020) A2- HadCM3 air temperature (2020) DJF MAM JJA SON

CSIRO A2 CCMa B2 HadCM3 B2 NCAR B2 CCMa A2 ECHAM4 A2 HadCM3 A2 NCAR A2 ECHAM4 B2 CSIRO B2 Air temperature trends in Manaus from A2 and B2 IPCC SRES scenarios

ECHAM4 B2 HadCM3 B2 NCAR B2 CSIRO B2 CCMa A2 NCAR A2 HadCM3 A2 ECHAM4 A2 CSIRO A2 CSIRO B2 NCAR B2 HadCM3 B2 ECHAM4 B2 CCMa B2 Precipitation trends in Manaus from A2 and B2 IPCC SRES scenarios

“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

Activities related to climate change to be developed at CPTEC Paleoclimates and present climate (mechanisms and feedbacks) Climate change (prediction and future scenarios) Emissions Concentrations (Carbon CO2, CH4, aerosols..) Global climate change (temperature, rain, sea level) Regional details (mountain effects, islands, valleys..) Assessments of impacts Impacts (Natural ecosystems, water resources) POLICY MAKERS-GOVERNMENT

Dynamic Downscaling: Climate change scenarios (A1, A2, B1, B2) Climate run of the Eta/CPTEC regional model SRES IPCC scenarios-HadCM3H Coupling HadCM3- Eta/CPTEC Validations predictability assessments model skill assessments Climate change studies, impacts and vulnerability assessments Analysis and validation of the HadCM3 climatology Statistical downscaling

19 vertical levels- atmosphere 20 vertical Levels-soil 2.5 lat 3.75 long 1.25 km 300 km -5km Global Coupled model HadCM3 of the Hadley Centre Regional model Eta/CPTEC, km, 38 vertical levels

Global and Regional Climate Change? Present time climate and Hydrology Climate variability and trends IPCC Global models Downscaling using the Eta/CPTEC regional model nested on the global HadCM3H model Climate modeling SRES-IPCC Scenarios Regionalized climate change Scenarios (XXI Century) Data Bank Observations climate-hydrology (global and regional) Paleoclimates Applications: -Water Resources -Natural ecosystems -Agriculture -Health… Previous studies ( ex. Deforestation )

Meteorological database 20th century Hydrological database 20th century Drainage basins 20th century Climate Hydrology 20th century Hydroclimate trends 20th century Validate Regional Climate models 21st century AOGCM SRES- scenarios Primary natural and human biomes in the 20th century Remote sensing techniques - Other Database - Fauna - Flora Biomass 21st century Indicators Climatology Hydrology 21st century Brazil? Choice? Atlas Government, Private sector GENERAL METHODOLOGY Statistical downscaling Applications Paleoclimates Government, Private sector AIACC

Case study: “Impacts of Global Climate Changes on the Brazilian Ecosystems”

Site of the study region around Salinopolis

Erosion in the inner estuary Erosion on Torotama Island

The RS coastal plain with the Patos-Mirim lagoon system

(a) (b) Evidence of erosive processes at the central portion of the barrier island. a) Conceição Lighthouse in 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