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INTRODUCTION

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

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INTRODUCTION

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  1. 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

  2. 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

  3. 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 climatechange scenarios at CPTEC-Brazil.

  4. 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.

  5. Air temperature trends 1961-2010 (IPCC SRES) Global A2 A1 Air temperature (C ) Anomaly (C ) B2 B1 Brazil A2 A1 Anomaly (C ) Air temperature (C ) B2 B1

  6. 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)

  7. Interdecadal rainfall variability: CRU Rainfall anomalies during 1929-45, 1946-75 and 1976-98, using 1961-90 nas reference period. Red/Blue represent negative/positive anomalies. Color bar is show below the panels (Marengo. 2003).

  8. Rainfall anomalies CRU (mm/day) Decade of 1960’s Decade of 1990’s

  9. 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).

  10. 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) PL94a DHS88 +1 +0.5 0 -0.5 -1 -1.5 LR97 Changes in precipitation (mm/day) COS20 LE96 WARMER/DRIER PL94b MAN96 LR93 SUD96 HD95 HAH97 LW89 DK92 SHU96 SUD90 HS93 NEP99 -1 0 +1 +2 +3 SHN91 Changes in temperature (C ) Deforestation Deforestation+2C02

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

  12. DJF rainfall (color) and rainfall anomalies (numbers) Projections are from the HadCM3. A2 2020 2050 2080 B2

  13. SON air temperatures (color) and air temperature anomalies (numbers) Projections are from the HadCM3. A2 2020 2050 2080 B2

  14. 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 Rainfall anomalies (colors) and anomalies (numbers)

  15. CCCMA-A2-2020 CSIRO-A2-2020 ECHAM4-A2-2020 NCARA2-2020 DJF Air temperature (colors) and anomalies (numbers) CCCMA-B2-2020 CSIRO-B2-2020 ECHAM4-B2-2020 NCARB2-2020

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

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

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

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

  20. Vegetation type in South America (Hadley Centre Model with MOSES iterative vegetation scheme “Amazon Dieback” Forced by Climate Change? pre-industrial present 2100

  21. 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

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

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

  24. Global Coupled model HadCM3 of the Hadley Centre 19 vertical levels- atmosphere 300 km 2.5 lat 3.75 long 1.25 km Regional model Eta/CPTEC, 40-10 km, 38 vertical levels 1.25 km 20 vertical Levels-soil -5km

  25. 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) Regionalized climate change Scenarios (XXI Century) IPCC Global models 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

  26. GENERAL METHODOLOGY Paleoclimates Government,Private sector Brazil?Choice? Atlas Meteorological database 20th century Drainage basins 20th century Climate Hydrology 20th century Hydroclimate trends 20th century Hydrological database 20th century Validate Government,Private sector AIACC AOGCM Regional Climate models 21st century Climatology Hydrology 21st century Applications SRES-scenarios Statistical downscaling Database- Fauna- Flora Primary natural and human biomes in the 20th century Biomass21st century Remote sensing techniques - Other Indicators

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

  28. Site of the study region around Salinopolis

  29. Erosion on Torotama Island Erosion in the inner estuary

  30. The RS coastal plain with the Patos-Mirim lagoon system

  31. (a) (b) 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 .

  32. Area affected by a 2-m increase in sea level in the city of Rio de Janeiro, Brazil

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