Municipal Temperature and Heatwave Predictions as a Tool for Integrated Socio-Environmental Impact Analysis in Brazil
Author(s)
D. Costa1,2*,
S. Hacon2,
A. S. P. Siqueira2,
S. L. L. A. Pinheiro2,
K. S. Gonçalves2*,
A. Oliveira2,
P. Cox1
Affiliation(s)
1 College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK. 2 National School of Public Health/Oswaldo Cruz Foundation (FIOCRUZ)/ENSP, Rio de Janeiro, Brazil.
1 College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK. 2 National School of Public Health/Oswaldo Cruz Foundation (FIOCRUZ)/ENSP, Rio de Janeiro, Brazil.
ABSTRACT
Numerical climate models render data in a gridded format which is often problematic for integrated analysis with other kinds of data in jurisdictional formats. In this paper a joint analysis of municipal Gross Domestic Product per capita (GDPc) and predicted temperature increase was undertaken in order to estimate different levels of human and economic exposure. This is based on a method of converting model outputs into a country municipal grid which enabled depicting climate predictions from the Eta-Hadgem2-ES Regional Climate Model (RCM) into the municipal level in Brazil. The conversion to country municipality grid was made using a combination of interpolation and buffering techniques in ArcGIS for two emission scenarios (RCP 4.5 and 8.5) and three timeframes (2011-2040, 2041-2070, 2071-2100) for mean temperature increase and number of heatwave days (WSDI). The results were used to support the Third National Communication (TCN) of Brazil to the United Nations Framework Convention on Climate Change (UNFCCC) and show a coherent matching of the gridded output from the original RCM. The joint climate and GDPc analysis show that in the beginning of the century the more severe warming is centred over regions where GDPc is generally higher (Centre-West and Southeast). At the end of the century, critical levels of warming spread north and northeastwards where municipalities have the lowest GDPc levels. In the high emission scenario (RCP 8.5), the strongest warming and the spreading over poorer regions are anticipated to the mid-century. These results are the key to further explore solutions for climate change adaptation based on current resources and prepare in different sectors, for long-term risk management and climate adaptation planning strategies.
Numerical climate models render data in a gridded format which is often problematic for integrated analysis with other kinds of data in jurisdictional formats. In this paper a joint analysis of municipal Gross Domestic Product per capita (GDPc) and predicted temperature increase was undertaken in order to estimate different levels of human and economic exposure. This is based on a method of converting model outputs into a country municipal grid which enabled depicting climate predictions from the Eta-Hadgem2-ES Regional Climate Model (RCM) into the municipal level in Brazil. The conversion to country municipality grid was made using a combination of interpolation and buffering techniques in ArcGIS for two emission scenarios (RCP 4.5 and 8.5) and three timeframes (2011-2040, 2041-2070, 2071-2100) for mean temperature increase and number of heatwave days (WSDI). The results were used to support the Third National Communication (TCN) of Brazil to the United Nations Framework Convention on Climate Change (UNFCCC) and show a coherent matching of the gridded output from the original RCM. The joint climate and GDPc analysis show that in the beginning of the century the more severe warming is centred over regions where GDPc is generally higher (Centre-West and Southeast). At the end of the century, critical levels of warming spread north and northeastwards where municipalities have the lowest GDPc levels. In the high emission scenario (RCP 8.5), the strongest warming and the spreading over poorer regions are anticipated to the mid-century. These results are the key to further explore solutions for climate change adaptation based on current resources and prepare in different sectors, for long-term risk management and climate adaptation planning strategies.
KEYWORDS
Climate Change, Municipality, Integrated Analysis, Spatial Interpolation, Climate Impacts, Socio-Environmental Impact Analysis
Climate Change, Municipality, Integrated Analysis, Spatial Interpolation, Climate Impacts, Socio-Environmental Impact Analysis
Cite this paper
Costa, D. , Hacon, S. , Siqueira, A. , Pinheiro, S. , Gonçalves, K. , Oliveira, A. and Cox, P. (2015) Municipal Temperature and Heatwave Predictions as a Tool for Integrated Socio-Environmental Impact Analysis in Brazil. American Journal of Climate Change, 4, 385-396. doi: 10.4236/ajcc.2015.44031.
Costa, D. , Hacon, S. , Siqueira, A. , Pinheiro, S. , Gonçalves, K. , Oliveira, A. and Cox, P. (2015) Municipal Temperature and Heatwave Predictions as a Tool for Integrated Socio-Environmental Impact Analysis in Brazil. American Journal of Climate Change, 4, 385-396. doi: 10.4236/ajcc.2015.44031.
References
[1] Vaughan, C. and Dessai, S. (2014) Climate Services for Society: Origins, Institutional Arrangements, and Design Elements for an Evaluation Framework. Wiley Interdisciplinary Reviews-Climate Change, 5, 587-603.
http://dx.doi.org/10.1002/wcc.290 [2] Flato, G.M. (2011) Earth System Models: An Overview. Wiley Interdisciplinary Reviews-Climate Change, 2, 783-800.
http://dx.doi.org/10.1002/wcc.148 [3] Chou, S.C., Marengo, J.A., Lyra, A.A., Sueiro, G., Pesquero, J.F., Alves, L.M., Kay, G., Betts, R., Chagas, D.J., Gomes, J.L., Bustamante, J.F. and Tavares, P. (2012) Downscaling of South America Present Climate Driven by 4-Member HadCM3 Runs. Climate Dynamics, 38, 635-653.
http://dx.doi.org/10.1007/s00382-011-1002-8 [4] Pesquero, J.F., Chou, S.C., Nobre, C.A. and Marengo, J.A. (2010) Climate Downscaling over South America for 1961-1970 Using the Eta Model. Theoretical and Applied Climatology, 99, 75-93.
http://dx.doi.org/10.1007/s00704-009-0123-z [5] Chou, S.C., Lyra, A., Mourão, C., Dereczynski, C., Pilotto, I., Gomes, J., Bustamante, J., Tavares, P., Silva, A., Rodrigues, D., Campos, D., Chagas, D., Sueiro, G., Siqueira, G. and Nobre, P. (2014) Evaluation of the Eta Simulations Nested in Three Global Climate Models. American Journal of Climate Change, 3, 438-454.
http://dx.doi.org/10.4236/ajcc.2014.35039 [6] Adger, W.N. (2006) Vulnerability. Global Environmental Change, 16, 268-281.
http://dx.doi.org/10.1016/j.gloenvcha.2006.02.006 [7] Marengo, J., Aragao, L., Cox, P., Costa, D., Smith, L., Alves, L., Betts, R., Kaye, N., Hacon, S.S. and Reis, V. (2015) Impacts of Climate Extremes in Brazil: The Development of a Web Platform for Understanding. Bulletin of the American Meteorological Society (submitted). [8] Aragão, L.E.O.C.A., Marengo, J.L., Cox, P., Costa, D., Smith, L., Betts, R., Kaye, N., et al. (2015) Climate Impacts on Ecosystems and Human-Health in Brazil Quantified Using a Web Platform for Decision-Making. Environmental Research Letters (submitted). [9] Pinheiro, S.L.L.A., Hacon, S.S., Siqueira, A.S.P., Gonçalves, K.S., Costa, D. and Oliveira, A. (2015a) Health Vulnerability to Climate Change in Brazil: Mapping Composite Indexes Based on Principal Component Analysis. EcoHealth (submitted). [10] Pinheiro, S.L.L.A., Hacon, S.S., Siqueira, A.S.P., Gonçalves, K.S., Costa, D. and Oliveira, A. (2015) Heat Waves and Health: Vulnerabilities, Risk and Impacts on Mortality Considering Climate Change Scenarios in Brazil. Weather and Climate Extremes, In Preparation. [11] Nairn, J., Fawcett, R. and Ray, D. (2009) CAWCR Technical Report No. 071: Modelling and Understanding High Impact Weather. Proceedings of the Third CAWCR Modelling Workshop, Melbourne, 30 November-2 December 2009, 83-86. [12] Perkins, S.E. and Alexander, L.V. (2013) On the Measurement of Heat Waves. Journal of Climate, 26, 4500-4517.
http://dx.doi.org/10.1175/JCLI-D-12-00383.1 [13] Fischer, E.M. and Schär, C. (2010) Consistent Geographical Patterns of Changes in High-Impact European Heatwaves. Nature Geoscience, 3, 398-403.
http://dx.doi.org/10.1038/ngeo866 [14] Meehl, G.A. and Tebaldi, C. (2004) More Intense, More Frequent, and Longer Lasting Heat Waves in the 21st Century. Science, 305, 994-997.
http://dx.doi.org/10.1126/science.1098704 [15] Huth, R., Kysely, J. and Pokorná, L. (2000) A GCM Simulation of Heat Waves, Dry Spells, and Their Relationships to Circulation. Climatic Change, 46, 29-60.
http://dx.doi.org/10.1023/A:1005633925903 [16] Alexander, L.V. and Arblaster, J.M. (2009) Assessing Trends in Observed and Modelled Climate Extremes over Australia in Relation to Future Projections. International Journal of Climatology, 29, 417-435.
http://dx.doi.org/10.1002/joc.1730 [17] Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Tank, A.K., Peterson, T.C., Trewin, B. and Zwiers, F.W. (2011) Indices for Monitoring Changes in Extremes Based on Daily Temperature and Precipitation Data. Wiley Interdisciplinary Reviews: Climate Change, 2, 851-870.
http://dx.doi.org/10.1002/wcc.147 [18] Sillmann, J., Kharin, V.V., Zwiers, F.W., Zhang, X. and Bronaugh, D. (2013) Climate Extremes Indices in the CMIP5 Multi-Model Ensemble: Part 2. Future Climate Projections. Journal of Geophysical Research: Atmospheres, 118, 2473-2493.
http://dx.doi.org/10.1002/jgrd.50188 [19] Fischer, E.M., Seneviratne, S.I., Lüthi, D. and Schär, C. (2007) Contribution of Land-Atmosphere Coupling to Recent European Summer Heat Waves. Geophysical Research Letters, 34, 1-6.
http://dx.doi.org/10.1029/2006GL029068 [20] Oliveira, A., Hacon, S.S., Costa, D., Pinheiro, S., Pereira, C., et al. (2015) Impacto das projeções de temperatura ambiente na diarreia infantil no Brasil. Revista Pan Americana de Salud Publica (submitted). [21] Oulton, N. (2012) Hooray for GDP! Centre for Economic Performance Occasional Papers, CEPOP30. Centre for Economic Performance, London School of Economics and Political Science, London.
[1] Vaughan, C. and Dessai, S. (2014) Climate Services for Society: Origins, Institutional Arrangements, and Design Elements for an Evaluation Framework. Wiley Interdisciplinary Reviews-Climate Change, 5, 587-603.
http://dx.doi.org/10.1002/wcc.290 [2] Flato, G.M. (2011) Earth System Models: An Overview. Wiley Interdisciplinary Reviews-Climate Change, 2, 783-800.
http://dx.doi.org/10.1002/wcc.148 [3] Chou, S.C., Marengo, J.A., Lyra, A.A., Sueiro, G., Pesquero, J.F., Alves, L.M., Kay, G., Betts, R., Chagas, D.J., Gomes, J.L., Bustamante, J.F. and Tavares, P. (2012) Downscaling of South America Present Climate Driven by 4-Member HadCM3 Runs. Climate Dynamics, 38, 635-653.
http://dx.doi.org/10.1007/s00382-011-1002-8 [4] Pesquero, J.F., Chou, S.C., Nobre, C.A. and Marengo, J.A. (2010) Climate Downscaling over South America for 1961-1970 Using the Eta Model. Theoretical and Applied Climatology, 99, 75-93.
http://dx.doi.org/10.1007/s00704-009-0123-z [5] Chou, S.C., Lyra, A., Mourão, C., Dereczynski, C., Pilotto, I., Gomes, J., Bustamante, J., Tavares, P., Silva, A., Rodrigues, D., Campos, D., Chagas, D., Sueiro, G., Siqueira, G. and Nobre, P. (2014) Evaluation of the Eta Simulations Nested in Three Global Climate Models. American Journal of Climate Change, 3, 438-454.
http://dx.doi.org/10.4236/ajcc.2014.35039 [6] Adger, W.N. (2006) Vulnerability. Global Environmental Change, 16, 268-281.
http://dx.doi.org/10.1016/j.gloenvcha.2006.02.006 [7] Marengo, J., Aragao, L., Cox, P., Costa, D., Smith, L., Alves, L., Betts, R., Kaye, N., Hacon, S.S. and Reis, V. (2015) Impacts of Climate Extremes in Brazil: The Development of a Web Platform for Understanding. Bulletin of the American Meteorological Society (submitted). [8] Aragão, L.E.O.C.A., Marengo, J.L., Cox, P., Costa, D., Smith, L., Betts, R., Kaye, N., et al. (2015) Climate Impacts on Ecosystems and Human-Health in Brazil Quantified Using a Web Platform for Decision-Making. Environmental Research Letters (submitted). [9] Pinheiro, S.L.L.A., Hacon, S.S., Siqueira, A.S.P., Gonçalves, K.S., Costa, D. and Oliveira, A. (2015a) Health Vulnerability to Climate Change in Brazil: Mapping Composite Indexes Based on Principal Component Analysis. EcoHealth (submitted). [10] Pinheiro, S.L.L.A., Hacon, S.S., Siqueira, A.S.P., Gonçalves, K.S., Costa, D. and Oliveira, A. (2015) Heat Waves and Health: Vulnerabilities, Risk and Impacts on Mortality Considering Climate Change Scenarios in Brazil. Weather and Climate Extremes, In Preparation. [11] Nairn, J., Fawcett, R. and Ray, D. (2009) CAWCR Technical Report No. 071: Modelling and Understanding High Impact Weather. Proceedings of the Third CAWCR Modelling Workshop, Melbourne, 30 November-2 December 2009, 83-86. [12] Perkins, S.E. and Alexander, L.V. (2013) On the Measurement of Heat Waves. Journal of Climate, 26, 4500-4517.
http://dx.doi.org/10.1175/JCLI-D-12-00383.1 [13] Fischer, E.M. and Schär, C. (2010) Consistent Geographical Patterns of Changes in High-Impact European Heatwaves. Nature Geoscience, 3, 398-403.
http://dx.doi.org/10.1038/ngeo866 [14] Meehl, G.A. and Tebaldi, C. (2004) More Intense, More Frequent, and Longer Lasting Heat Waves in the 21st Century. Science, 305, 994-997.
http://dx.doi.org/10.1126/science.1098704 [15] Huth, R., Kysely, J. and Pokorná, L. (2000) A GCM Simulation of Heat Waves, Dry Spells, and Their Relationships to Circulation. Climatic Change, 46, 29-60.
http://dx.doi.org/10.1023/A:1005633925903 [16] Alexander, L.V. and Arblaster, J.M. (2009) Assessing Trends in Observed and Modelled Climate Extremes over Australia in Relation to Future Projections. International Journal of Climatology, 29, 417-435.
http://dx.doi.org/10.1002/joc.1730 [17] Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Tank, A.K., Peterson, T.C., Trewin, B. and Zwiers, F.W. (2011) Indices for Monitoring Changes in Extremes Based on Daily Temperature and Precipitation Data. Wiley Interdisciplinary Reviews: Climate Change, 2, 851-870.
http://dx.doi.org/10.1002/wcc.147 [18] Sillmann, J., Kharin, V.V., Zwiers, F.W., Zhang, X. and Bronaugh, D. (2013) Climate Extremes Indices in the CMIP5 Multi-Model Ensemble: Part 2. Future Climate Projections. Journal of Geophysical Research: Atmospheres, 118, 2473-2493.
http://dx.doi.org/10.1002/jgrd.50188 [19] Fischer, E.M., Seneviratne, S.I., Lüthi, D. and Schär, C. (2007) Contribution of Land-Atmosphere Coupling to Recent European Summer Heat Waves. Geophysical Research Letters, 34, 1-6.
http://dx.doi.org/10.1029/2006GL029068 [20] Oliveira, A., Hacon, S.S., Costa, D., Pinheiro, S., Pereira, C., et al. (2015) Impacto das projeções de temperatura ambiente na diarreia infantil no Brasil. Revista Pan Americana de Salud Publica (submitted). [21] Oulton, N. (2012) Hooray for GDP! Centre for Economic Performance Occasional Papers, CEPOP30. Centre for Economic Performance, London School of Economics and Political Science, London.