Climate projection for the period 2006-2075 for the Jauja Valley, simulated by the intercomparison of coupled models CSIRO Mk 3.0, MIROC-ESM and CNRM phase 5 (CMIP5)
DOI:
https://doi.org/10.26439/ing.ind2022.n.5813Keywords:
climate models, temperature, precipitationAbstract
The climatic data for the Jauja valley, Junín region, central highlands of Peru for the xxi century were evaluated, as simulated by the climatic models used in phase 5 by the Intercomparison of coupled models (CMP5). The models used were three (03): CSIRO Mk 3.6, MIROC ESM and CNRM CM5, respectively, at a spatial resolution of 1,875 × 1.86°, 2,81 × 1.87° and 1,41 × 1,40°, built by observed meteorological data in the Jauja valley during the 1975-2005 period, using the IPCC RCP2.6 and 8.5 scenario. The present work is to provide local climate projections for this area, generating a first future climate database for the region, as a decision-making tool for farmers and other users of the basin. The climatic projections show a significant warming from 2,0 degrees of temperature for the RCP2.6 scenario to 3,5 degrees of temperature for the RCP8.5 scenario expected in the entire evaluated area of the Jauja valley for the next fifty years, together with a decrease in precipitation. Precipitation projections are dependent on horizontal resolution, suggesting the need for additional simulations at higher resolution, especially for adequate representation of extreme weather events.
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References
Andersen, L. E., Breisinger, C., Jemio, L. C., Mason-D’Croz, D., Ringler, C., Robertson, R. D., Verner, D., & Wiebelt, M. (2016). Climate change impacts: prospects for 2050 in Brazil, Mexico, and Peru. International Food Policy Research Institute. https://doi.org/10.2499/9780896295810
Aumont, O., & Bopp, L. (2006). Globalizing results from ocean in situ iron fertilization studies. Global Biogeochemical Cycles, 20(2), 1-15. https://doi.org/10.1029/2005GB002591
Autoridad Nacional del Agua. (2010). Proyecto “Obras de control y medición de agua por bloques de riego en el valle Guadalupito”: estudio de preinversión a nivel de perfil. MINAG/ANA. https://repositorio.ana.gob.pe/handle/20.500.12543/2233
Autoridad Nacional del Agua. (2014). Inventario nacional de glaciares y lagunas. http://www.ana.gob.pe/media/981508/glaciares.pdf
Brunet, M., Saladié, O., Jones, P., Sigró, J., Aguilar, E., Moberg, A., Lister, D., Walther, A. M., Lopez, D., & Almarza, C. (2006). The development of a new dataset of Spanish daily adjusted temperature series (SDATS) (1850-2003). International Journal of Climatology, 26, 1777-1802. https://doi.org/10.1002/joc.1338
Cariolle, D., & Teyssèdre, H. (2007). A revised linear ozone photochemistry parameterization for use in transport and general circulation models: multi-annual simulations. Atmospheric Chemistry and Physics, 7(9), 2183-2196. https://doi.org/10.5194/acp-7-2183-2007
Chaudhary, D. P., Ashwani K., Mandhania, S. S., Srivastava, P., & Kumar, R. S. (2012). Maize as fodder? An alternative approach [Boletín técnico, 04]. Directorate of Maize Research.
Côté, M., & Texeira, S. (2012). Integración del cambio climático en los procesos nacionales de desarrollo y en la programación de países de las Naciones Unidas. Programa de las Naciones Unidas para el Cambio Climático. http://www.undp.org/content/dam/undp/library/Environment and Energy/Climate Change/Capacity Development/PNUD-GuíaCambioClimáticoES-Web.pdf
Eisner, S., Voss, F., & Kynast, E. (2012). Statistical bias correction of global climate projections – consequences for large scale modeling of flood flows. Advances in Geosciences, 31, 75-82. https://doi.org/10.5194/adgeo-31-75-2012
Fang, G. H., Yang, J., Chen, Y. N., & Zammit, C. (2015). Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Hydrology and Earth System Sciences, 19(6), 2547-2559. https://doi.org/10.5194/hess-19-2547-2015
Frölicher, T. L., Sarmiento, J. L., , Paynter, D. J., Dunne, J. P., Krasting, J. P., & Winton, M. (2015). Dominance of the Southern Ocean in anthropogenic carbon and heat uptake in CMIP5 models. Journal of Climate, 28(2), 862-886. https://doi.org/10.1175/JCLI-D-14-00117.1
Gordon, H. B., Rotstayn, L. D., McGregor, J. L., Dix, M. R., Kowalczyk, E. A., O’Farrell, S. P., Waterman, L. J., Hirst, A. C., Wilson, S. G., Collier, M. A., Watterson, I. G., & Elliott, T. I. (2002). The CSIRO Mk3 climate system model [Informe]. CSIRO Atmospheric Research. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:The+CSIRO+Mk3+Climate+System+Model#0
Instituto Geofísico del Perú. (s. f.). El clima en el Perú. Jauja. http://met.igp.gob.pe/clima/HTML/jauja.html
Instituto Geofísico del Perú. (2005). Atlas climático de precipitación y temperatura del aire en la cuenca del río Mantaro. https://repositorio.igp.gob.pe/handle/20.500.12816/714
Instituto Nacional de Estadística e Informática. (2017). Perú: evolución de los indicadores de empleo e ingresos por departamennto, 2007-2016. https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1441/libro.pdf
Instituto Nacional de Estadística e Informática. (2019). Perú: evolución de los indicadores de empleo e ingresos por departamento, 2007-2018. https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1678/libro.pdf
Intergovernmental Panel on Climate Change. (2014). Fifth Assessment Report (AR5). https://archive.ipcc.ch/report/ar5/index.shtml
Intergovernmental Panel on Climate Change. (2018). Global warming of 1.5°C [Informe especial]. https://www.ipcc.ch/sr15/
Jain, T. B., Pilliod, D. S., Graham, R. T., Lentile, L. B., & Sandquist, J. E. (2012). Index for characterizing post-fire soil environments in temperate coniferous forests. Forests, 3, 445-466. https://doi.org/10.3390/f3030445
Janes, T., McGrath, F., Macadam, I., & Jones, R. (2019). High-resolution climate projections for South Asia to inform climate impacts and adaptation studies in the Ganges-Brahmaputra-Meghna and Mahanadi Deltas. Science of the Total Environment, 650, 1499-1520. https://doi.org/10.1016/j.scitotenv.2018.08.376
Kumar, M., Bhatt, V. P., & Rajwar, G. S. (2006). Plant and soil diversities in a sub tropical forest of the Garhwal Himalaya. Ghana Journal of Forestry, 19-20, 1-19. https://doi.org/10.4314/gjf.v19i1.36907
Kumar, V., & Ladha, J. K. (2011). Chapter six – Direct seeding of rice: recent developments and future research needs. En Advances in agronomy. Volume 111 (D. L. Sparks, Ed.; pp. 297-413). Academic Press. http://dx.doi.org/10.1016/B978-0-12-387689-8.00001-1.
Laufkötter, C., Vogt, M., Gruber, N., Aita-Noguchi, M., Aumont, O., Bopp, L., Buitenhuis, E., Doney, S. C., Dunne, J., Hashioka, T., Hauck, J., Hirata, T., John, J., Le Quéré, C., Lima, I. D., Nakano, H., Seferian, R., Totterdell, I., Vichi, M., & Völker, C. (2015). Drivers and uncertainties of future global marine primary production in marine ecosystem models. Biogeosciences, 12(23), 6955-6984. https://doi.org/10.5194/bg-12-6955-2015
Lenderink, G., Buishand, A., & Van Deursen, W. (2007). Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrology and Earth System Sciences, 11(3), 1145-1159. https://doi.org/10.5194/hess-11-1145-2007
Mello, C. R., Ávila, L. F., Viola, M. R., Curi, N., & Darrel, N. Ll. (2015). Assessing the climate change impacts on the rainfall erosivity throughout the twenty-first century in the Grande River Basin (GRB) headwaters, Southeastern Brazil. Environmental Earth Sciences, 73(12), 8683-8698. http://dx.doi.org/10.1007/s12665-015-4033-3
Ministerio del Ambiente. (2010). El Perú y el cambio climático. Segunda comunicación nacional del Perú a la Convención Marco de las Naciones Unidas sobre Cambio Climático. https://sinia.minam.gob.pe/documentos/segunda-comunicacion-nacional-peru-convencion-marco-las-naciones
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., Van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., & Wilbanks, T. J. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747-756. http://dx.doi.org/10.1038/nature08823
Ramos Jáuregui, L. Y. (2014). Estimación del efecto del cambio climático en la precipitación en la costa norte del Perú usando simulaciones de modelo climáticos globales [Tesis de licenciatura, Universidad Agraria La Molina]. Repositorio Geofísico Nacional. http://repositorio.igp.gob.pe/handle/IGP/84
Ravindranath, N., Rao, S., Sharma, N., Nair, M., Gopalakrishnan, R., Rao, A., Malaviya, S., Tiwari, R., Sagadevan, A., Munsi, M., Krishna, N., & Govindasamy, B. (2011). Climate change vulnerability profiles for North East India. Current Science, 101, 384-394. https://ssrn.com/abstract=2140671
Schwinger, J., Tjiputra, J. F., Heinze, C., Bopp, L., Christian, J. R., Gehlen, M., Ilyina, T., Jones, C. D., Salas-Mélia, D., Segschneider, J., Séférian, R., & Totterdell, I. (2014). Nonlinearity of ocean carbon cycle feedbacks in CMIP5 Earth system models. Journal of Climate, 27(11), 3869-3888. https://doi.org/10.1175/JCLI-D-13-00452.1
Séférian, R., Bopp, L., Gehlen, M., Orr, J. C., Ethé, C., Cadule, P., Aumont, O., Salas-Mélia, D., Voldoire, A., & Madec, G. (2013). Skill assessment of three Earth system models with common marine biogeochemistry. Climate Dynamics, 40(9-10), 2549-2573. https://doi.org/10.1007/s00382-012-1362-8
Séférian, R., Ribes, A., & Bopp, L. (2014). Detecting the anthropogenic influences on recent changes in ocean carbon uptake. Geophysical Research Letters, 41(16), 5968-5977. https://doi.org/10.1002/2014GL061223
Sharmila, S., Joseph, S., Sahai, A. K., Abhilash, S., & Chattopadhyay, R. (2015). Future projection of Indian summer monsoon variability under climate change scenario: an assessment from CMIP5 climate models. Global and Planetary Change, 124, 62-78. https://doi.org/10.1016/j.gloplacha.2014.11.004
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93(4), 485-498. https://doi.org/10.1175/BAMS-D-11-00094.1
Thomas, D. S. G., Twyman, Ch., Osbahr, H. & Hewitson, B. (2011). Adaptation to climate change and variability: farmer responses to intra-seasonal precipitation trends in South Africa. En C. Williams & D. Kniveton (Eds.), African Climate and Climate Change. Advances in Global Change Research (vol. 43, pp. 155-178). Springer. https://doi.org/10.1007/978-90-481-3842-5_7
Thomson, A. M., Calvin, K. V., Smith, S. J., Kyle, G. P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M. A., Clarke, L. E., & Edmonds, J. A. (2011). RCP4.5: a pathway for stabilization of radiative forcing by 2100. Climatic Change, 109(1), 77-94. https://doi.org/10.1007/s10584-011-0151-4
Voldoire, A., Sánchez-Gómez, E., Salas-Mélia, D., Decharme, B., Cassou, C., Sénési, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernández, H., Madec, G., Maisonnave, E., Moine, M.-P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, … Chauvin, F. (2013). The CNRM-CM5.1 global climate model: description and basic evaluation. Climate Dynamics, 40(9-10), 2091-2121. https://doi.org/10.1007/s00382-011-1259-y
Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., & Kawamiya, M. (2011). MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geoscientific Model Development, 4(4), 845-872. https://doi.org/10.5194/gmd-4-845-2011
Wild, M., Folini, D., Schär, C., Loeb, N., Dutton, E. G., & König-Langlo, G. (2013). The global energy balance from a surface perspective. Climate Dynamics, 40(11-12), 3107-3134. https://doi.org/10.1007/s00382-012-1569-8
