Valor agregado del modelamiento regional del clima sobre áreas caracterizadas por terreno complejo: precipitación sobre los Andes Colombianos

ilustraciones, gráficas, mapas, tablas

Autores:
Espitia Rodríguez, Javier Fernando
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/81537
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/81537
https://repositorio.unal.edu.co/
Palabra clave:
550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
Depth-area-duration (hydrometeorology)
Hydrometeorology
Precipitación pluvial
Hidrometeorología
CORDEX
Added value
Regional climate model
Parameterizations
Sensitivity study
Domain
Resolution
Valor agregado
Modelo regional del clima
Parametrizaciones
Estudio de sensibilidad
Dominio
Resolución
Pronóstico meteorológico
Weather forecasting
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional
id UNACIONAL2_08cd17899214600b070a4b5a6fb94084
oai_identifier_str oai:repositorio.unal.edu.co:unal/81537
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Valor agregado del modelamiento regional del clima sobre áreas caracterizadas por terreno complejo: precipitación sobre los Andes Colombianos
dc.title.translated.eng.fl_str_mv Added value of modeling regional climate over areas characterized by complex terrain: precipitation over the Andes Colombians
title Valor agregado del modelamiento regional del clima sobre áreas caracterizadas por terreno complejo: precipitación sobre los Andes Colombianos
spellingShingle Valor agregado del modelamiento regional del clima sobre áreas caracterizadas por terreno complejo: precipitación sobre los Andes Colombianos
550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
Depth-area-duration (hydrometeorology)
Hydrometeorology
Precipitación pluvial
Hidrometeorología
CORDEX
Added value
Regional climate model
Parameterizations
Sensitivity study
Domain
Resolution
Valor agregado
Modelo regional del clima
Parametrizaciones
Estudio de sensibilidad
Dominio
Resolución
Pronóstico meteorológico
Weather forecasting
title_short Valor agregado del modelamiento regional del clima sobre áreas caracterizadas por terreno complejo: precipitación sobre los Andes Colombianos
title_full Valor agregado del modelamiento regional del clima sobre áreas caracterizadas por terreno complejo: precipitación sobre los Andes Colombianos
title_fullStr Valor agregado del modelamiento regional del clima sobre áreas caracterizadas por terreno complejo: precipitación sobre los Andes Colombianos
title_full_unstemmed Valor agregado del modelamiento regional del clima sobre áreas caracterizadas por terreno complejo: precipitación sobre los Andes Colombianos
title_sort Valor agregado del modelamiento regional del clima sobre áreas caracterizadas por terreno complejo: precipitación sobre los Andes Colombianos
dc.creator.fl_str_mv Espitia Rodríguez, Javier Fernando
dc.contributor.advisor.spa.fl_str_mv Baquero Bernal, Astrid
dc.contributor.author.spa.fl_str_mv Espitia Rodríguez, Javier Fernando
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Simulación del Sistema Climático Terrestre
dc.subject.ddc.spa.fl_str_mv 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
topic 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
Depth-area-duration (hydrometeorology)
Hydrometeorology
Precipitación pluvial
Hidrometeorología
CORDEX
Added value
Regional climate model
Parameterizations
Sensitivity study
Domain
Resolution
Valor agregado
Modelo regional del clima
Parametrizaciones
Estudio de sensibilidad
Dominio
Resolución
Pronóstico meteorológico
Weather forecasting
dc.subject.lemb.eng.fl_str_mv Depth-area-duration (hydrometeorology)
Hydrometeorology
dc.subject.lemb.spa.fl_str_mv Precipitación pluvial
Hidrometeorología
dc.subject.proposal.eng.fl_str_mv CORDEX
Added value
Regional climate model
Parameterizations
Sensitivity study
Domain
Resolution
dc.subject.proposal.spa.fl_str_mv Valor agregado
Modelo regional del clima
Parametrizaciones
Estudio de sensibilidad
Dominio
Resolución
dc.subject.unesco.spa.fl_str_mv Pronóstico meteorológico
dc.subject.unesco.eng.fl_str_mv Weather forecasting
description ilustraciones, gráficas, mapas, tablas
publishDate 2021
dc.date.issued.none.fl_str_mv 2021-09
dc.date.accessioned.none.fl_str_mv 2022-06-08T18:43:37Z
dc.date.available.none.fl_str_mv 2022-06-08T18:43:37Z
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/81537
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/81537
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv Aldrian, E., Dumenil, L., Jacob, D., Podzun R., & Gunawan, D. (2004). Long-term simulation of Indonesian rainfall with the MPI regional model. Clim. Dyn., 22:795-814.
Alfaro, S. and Gomes, L. (2001). Modeling mineral aerosol production by wind erosion: emission intensities and aerosol size distributions in source areas. Journal of geophysical research, 106. D16.
Almazroui, M. (2016). RegCM4 in climate simulation over CORDEX-MENA/Arab domain: selection of suitable domain, convection and land-surface schemes. Int. J. Climatol. 36, 236- 251.
Amador, J. A., & Alfaro, E. J. (2009). Métodos de reducción de escala: aplicaciones al tiempo, clima, variabilidad climática y cambio climático. Revista iberoamericana de economía ecológica vol. 11: 39-52.
Ambrizzi, T, Reboita, M., Da Rocha, R., Llopart, M. (2019). The state of the art and fundamental aspects of regional climate modeling in South America. Acad. Sci 1436:98– 120.
Anthes, R., Hsu, E. and Kuo, Y. (1987). Description of the Penn State/NCAR Mesoscale Model version 4 (MM4). NCAR tech. note, NCAR/tn-282+str, 66 pp.
Arakawa, A. & Schubert, W. (1974). Interaction of a cumulus cloud ensemble with the large- scale environment. Part I. J. Atmos. Sci., 31:674-701.
Artale, V., Calmanti, S., Carillo, A., and Dell’Aquila, A. (2010). An Atmosphere–Ocean regional climate model for the Mediterranean area: assessment of a present climate simulation. Clim Dyn 35: 721–740.
Bliss, N. B., and Olsen, L., M. (1996). Development of a 30-arc-second digital elevation model, in Pecora Thirteen, Human Interactions With the Environment-Perspectives from Space, Sioux Falls, South Dakota, 20–22 August.
Bonilla, C., & Mesa S. (2017). Validación de la precipitación estimada por modelos climáticos acoplados del proyecto de intercomparación CMIP5 en Colombia. RACCEFYN, 41(158), 107–118. https://doi.org/10.18257/raccefyn.427.
Bretherton, C., McCaa, J. and Grenier, H. (2004). A new parameterization for shallow cumulus convection and its application to marine subtropical cloud-topped boundary layers. Part I: description and 1d results. monthly weather review, 132:864–882.
Brutsaert, W. (1982). Evaporation into the atmosphere: theory, history and applications, USA, Reidel Himgham mass, 299 pp.
Calderón, S., Romero, G., Ordóñez, A., Álvarez, A., Ludeña, C., Sánchez, L., Miguel, C., Martínez, K. & Pereira, M. (2014). Impactos económicos del cambio climático en Colombia. Washington D.C: Banco Interamericano de Desarrollo. Recuperada del repositorio. Cepal.org/bits-tream/11362/37879/1/s1500268_es.pdf.
Castro, C. L., Pielke, R. A., & Leoncini, G. (2005). Dynamical downscaling: an assessment of value added using a regional climate model, J. Geophys. Res., 110, d05108, doi:10.1029/2004jd004721.
Ceballos, J. (2009). Manifestación del cambio climático “los glaciares en Colombia”. calentamiento global, más ciencia, mejores políticas. Revista la Tadeo 63 ISSN 0120-5250.
Collins, W., Bitz, C., Blackmon, M., Bonan, G., Bretherton, C., Carton, J., Chang, P., Doney, S., Hack, J., Henderson, T., Kiehl, J., Large, W., Mckenna, Santer, D. and Smith, R. (2006). The community climate system model version 3 (CCSM3). Journal of climate, 19:2122-2143.
Collins, W. J., et al. (2011), Development and evaluation of an Earth-System Model- HadGEM2, Geosci. Model Devel., 4, 1051–1075.
Curry, C., Tencer, B., Whan, K., Weaver, A., Giguère, M., & Wiebe, E. (2016). Searching for added value in simulating climate extremes with a high-resolution regional climate model over western Canada, Atmosphere-Ocean, doi:10.1080/07055900.2016.1158146.
De Haan, I., Kanamitsu, M., De Sales, F., and Sun, I. (2014). An evaluation of the seasonal added value of downscaling over the United States using new verification measures. Theoretical and applied climatology, 122(1-2):47–57.
De Sales, F. and Xue, Y. (2011). Assessing the dynamic-downscaling ability over South America using the intensity-scale verification technique. International Journal of Climatology, 31(8):1205–1221.
Dickinson, R. E., Ronald, M. E., Giorgi, F. and Gary, T. (1989). A regional climate model for the western United States. Climatic Change 15, no. 3: 383–422.
Dickinson, R. E., Henderson-Sellers, A., & Kennedy, P. J. (1993). Biosphere-atmosphere transfer scheme (bats) version 1 as coupled to the NCAR community climate model. National Center for Atmospheric Research (NCAR) technical note NCAR/tn-387+str, NCAR, boulder, co, doi: 10.5065/d67w6959.
Di Luca, A., De Elía, R., & Laprise, R. (2012). Potential for added value in precipitation simulated by high resolution nested regional climate models and observations, Clim. Dynam., 38, 1229–1247.
Di Luca, A., De Elía, R., & Laprise, R. (2013). Potential for small scale added value of RCM’s downscaled climate change signal, Clim. Dyn., 40, 1415–1433.
Di Luca, A., de Elía, R., & Laprise, R. (2015). Challenges in the quest for added value of regional climate dynamical downscaling, advances in modeling, 1, 10–21, doi 10.1007/s40641-015-0003-9.
Di Luca, A., Argüeso, D., Evans, J. P., De Elía, R., & Laprise, R. (2016). Quantifying the overall added value of dynamical downscaling and the contribution from different spatial scales, J. Geophys. Res. Atmos., 121, 1575–1590, doi:10.1002/2015jd024009.
Elguindi, N., Bi, X., Giorgi, F., Nagarajan, B., Pal, J., Solmon, F., Rauscher, S., Zakey, A., O’Brien, T., Nogherotto, R. & Giuliani, G. (2014). Regional climate model RegCM: reference manual, version 4.5. The Abdus Salam International Centre for Theoretical Physics. Strada Costiera, 11 I – 34151, Trieste Italy. Earth System Physics Section – esp. Pag. 9.
Emanuel, K. (1991). A scheme for representing cumulus convection in large scale models. J. Atmos. Sci., 48:2313-2335.
Falco, M., Carril, A., Menéndez, C., Zaninelli, P. y Li, L. Assessment of CORDEX simulations over South America: added value on seasonal climatology and resolution considerations. Climate Dynmics, Springer Verlag, 2019, 52 (7-8), pp.4771-4786.
Feser, F. (2006). Enhanced detectability of added value in Limited-Area model results separated into different spatial scales, Mon. Weather Rev., 134, 2180–2197.
Feser, F., Rockel, B., Von-Storch, H., Winterfeldt, J., & Zahn, M. (2011). Regional climate models add value to global model data: a review and select examples. Mon. Wea. Rev., 134, 1181–1192.
Foley, A.M. (2010). Uncertainty in regional climate modelling: a review. Prog. Phys. Geogr. 34: 647–670.
Fritsch, J., & Chappell, C. (1980). Numerical prediction of convection driven mesoscale pressure systems. Part I: convective parameterization. J. Atmos. Sci. 37: 1722–1733.
Funk, C., et al. (2015). The climate hazards infrared precipitation with stations a new environmental record for monitoring e Extremes, Scientific Data. doi: http://dx.doi.org/10.15780/g2rp4q.
Gao, X., Xu, Y., Zhao, Z., Pal, J., and Giorgi, F. (2006). On the role of resolution and topography in the simulation of East Asia precipitation. Theoretical and Applied Climatology, 86(1-4):173–185.
Giorgetta, M., Roeckner, E., Mauritsen, T., Stevens, B., Crueger, T., Esch, M., Rast, S., Kornblueh, l., Schmidt, H., Kinne, S., Möbis, B., Krismer, T., Reick, C., Raddatz, T. and Gayler V. (2012). The atmospheric general circulation model ECHAM6 - Model Description.
Giorgetta, M. A, et al. (2013). Climate and carbon cycle changes from 1850 to 2100 in MPI- ESM simulations for the Coupled Model Intercomparison Project Phase 5. Journal of advances in modelling Earth Systems,5,572-597.
Giorgi, F. (1990). Simulation of regional climate using a Limited-Area model nested in a General Circulation model. Journal of climate 3, no. 9: 941–63.
Giorgi, F., & Mearns, l. (1991). Approaches to regional climate change simulation: a review. Rev. Geophys., 29: 191-216.
Giorgi, F., & Mearns, l. (1999). Introduction to special section: regional climate modeling revisited. Journal of Geophysical Research, Vol. 104, no: d6: 6335–6352.
Giorgi, F., Bi, X., and Gian, Y. (2003). Indirect vs. direct effects of anthropogenic sulfate on the climate of east asia as simulated with a regional coupled climate-Chemistry/Aerosol model. Climatic change, 58:345–376.
Giorgi, F. (2005). Climate Change Prediction. Climatic Change, 73, 239–265.
Giorgi, F. (2006). Regional climate modeling: status and perspectives, J. Phys. IV, 139, 101– 118.
Giorgi, F., Jones, C. and Asrar, G. (2009). Addressing climate information needs at the regional level: the CORDEX framework. World Meteorological Organization (WMO) bulletin, Tomo 58, no 3, Pág. 175.
Giorgi, F. (2010). Uncertainties in climate change predictions, from the global to the regional scale. Epi web conf. 9: 115–129.
Giorgi, F., et al. (2012), RegCM4: model description and preliminary tests over multiple CORDEX domains, Clim. res., 52, 7–29.
Giorgi, F. and Gutowski., W. (2015). Regional dynamical downscaling and the CORDEX initiative. Annual review of environment and resources, 40(1):467–490.
Giorgi, F. and Gutowski., W. (2016). Coordinated experiments for projections of regional climate change. Curr. Clim. Change rep. 2: 202–210.
Giorgi, F. and Gao, X. (2018). Regional earth system modeling: review and future directions, Atmos. Ocean. Sci. Lett., 11, 189–197.
Giorgi, F. (2019). Thirty years of regional climate modeling: ¿where are we and where are we going next? Journal of Geophysical Research: atmospheres, 124, 5696–5723. https:// doi.org/10.1029/2018jd030094.
Grell, G. (1993). Prognostic evaluation of assumptions used by cumulus parameterizations. mon. wea. rev., 121:764-787.
Grell, A., Dudhia, J. and Stauffer, D. (1994). A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR technical note.
Gutowski, J., et al. (2016). WCRP Coordinated Regional Downscaling Experiment (CORDEX): un MPI de diagnóstico para CMPI6, Geosci. Model Dev., 9, 4087–4095.
Guyennon, N., Romano, E., Portoghese, I., Salerno, F., Calmanti, S., Perangeli, A.B., Tartari, G., and Copetti, D. (2013). Benefits from using combined dynamical-statistical downscaling approaches – lessons from a case study in the Mediterranean region. Hydrology and Earth System sciences 17(2), 705-720.
Hackenbruth, J., Schädler, G., & Willem, J. (2016). Added value of high-resolution regional climate simulations for regional impact studies. Meteorologische Zeitschrift, vol. 25, no. 3, 291 -304. doi10.1127/metz/2016/0701.
Hostetler, S., Bates, G. and Giorgi, F. (1993). Interactive nesting of a lake thermal model within a regional climate model for climate change studies. Geophysical Research, 98:5045– 5057.
Hostetler, S., Giorgi, F., Bates, G., & Bartlein, P. (1994). The role of lake‐atmosphere feedbacks in sustaining paleolakes Bonneville and Lahontan 18,000 years ago. Science, 263, 665–668.
Hostetler, S. W., Bartlein, P. J., Clark, P. U., Small, E., & Solomon, A. (2000). Simulated influences of lake Agassiz on the climate of Central North America 11,000 years ago. nature, 405, 334–337.
Holtslag, A. M., De Bruijn, I. F., Pan, H. l. (1990). A high-resolution air mass transformation model for Short-Range weather forecasting. Mon. Weather Rev. 118: 1561–1575.
Hurtado, A. (2009). Estimación de los campos mensuales históricos de precipitación en el territorio colombiano. Tésis de Maestría, Universidad Nacional de Colombia Sede Medellin.
IDEAM, (2005). Clasificaciones climáticas y confort térmico anual. Atlas climatológico de Colombia. capítulo 2, 78-87.
IPCC, (2007). Cambio Climático 2007: informe de síntesis. Contribución de los grupos de trabajo i, ii y iii al cuarto informe de evaluación del Grupo Intergubernamental de Expertos sobre el Cambio Climático. Ginebra, Suiza.
Jones, R., Noguer, M., Hassell, D., Hudson, D., Wilson, S., Jenkins, G., & Mitchell, J. (2004). Generating high resolution climate change scenarios using PRECIS. Hadley Center for Climate Prediction and Research, UK.
Kain, J. and Fritsch J. (1990). A one-dimensional entraining/detraining plume model and its application in convective parameterization, J. Atmos. Sci., 47, 2784–2802.
Kain, J. (2004). The Kain–Fritsch convective parameterization: an update, J. Appl. Meteorol., 43, 170 181.
Kanamitsu, M. and DeHaan, L. (2011). The Added Value Index: A new metric to quantify the added value of regional models. Journal of Geophysical Research, 116(D11):D11106.
Karmacharya, J., New, M., Jones, R., and Levine, R. (2016). Added value of a high- resolution regional climate model in simulation of intraseasonal variability of the south Asian summer monsoon. International Journal of Climatology.
Katzfey, J., Chattopadhyay, M., McGregor, J., Nguyen, K., & Thatcher, M. (2011). The added value of dynamical downscaling. CSIRO Marine and Atmospheric Research.19th International Congress on Modelling and Simulation, Perth, Australia, 12–16, December 2011.
Kerkhoff, C., Kuensch, H. R., & Schaer, C. (2014). Assessment of bias assumptions for climate models, J. Clim., 27, 6799–6818.
Kiehl, J., Hack, J., Bonan, G., Boville, A., Briegleb, B., Williamson, D., and Rasch, P. (1996). Description of the NCAR Community Climate Model (CCM3), technical note NCAR/tn−420+str, 152, 1996.
Laprise, R. (2008). Challenging some tenets of regional climate modelling, Meteorol. Atmos. Phys., 100, 3–22.
Laprise, R. (2014). Comment on “The added value to global model projections of climate change by dynamical downscaling: a case study over the continental U.S. using the GISS- MODELE2 and WRF models” by Racherla et al, J. Geophys. Res. Atmos., 119, 3877–3881, doi: 10.1002/2013jd019945.
Latif, M. (2011). Uncertainty in climate change projections. J. Geochem. Explor. 110: 1–7.
Laurent, B., Bergametti, G., Leon, J. and Mahowald, N. (2008). Modeling mineral dust emissions from the Sahara Desert using new surface properties and soil database. Journal of Geophysical Research, 113. d14218.
Lawrence, P. and Chase, T. (2007). Representing a new model consistent land surface in the Community Land Model (CLM3.0). J. Geophys. Res., 112. g01023.
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson, S. C., Lawrence, P. J., Zeng, X., Y., Zong-Liang, L. S., Sekiguchi, K. B., Gordon, B. S., and Andrew, G. (2011). Parameterization improvements and functional and structural advances in version 4 of the Community Land Model, J. Adv. Model. Earth Syst., 3, m03001, https://doi.org/10.1029/2011ms000045.
León, G. (2000). Tendencia de la temperatura del aire en Colombia. Meteorología colombiana, n. 2, pp. 57-65.
León, G. (2005). Verificación de los modelos meteorológicos. Nota técnica IDEAM, pp.12.
Loveland, T., Reed, B., Brown, J., Ohlen, D., Zhu, J., Yang, l. and Merchant, J. (2000). Development of a global land cover characteristics database and IGBP discover from 1-km AVHRR data. Inter. J. Remote Sensing, 21:1303-1330.
Lucas-Picher, P., Laprise, R., and Winger, K. (2017). Evidence of added value in North American regional climate model hindcast simulations using ever-increasing horizontal resolutions, Clim. Dynam., 48, 2611–2633.
Lupo, A. & Kininmonth., W. (2013). Global climate models and their limitations climate change reconsidered ii: physical science and its summary for policymakers. The heartland institute in September 2013 for the Nongovernmental International Panel on Climate Change (NIPCC).
Martin, G., Bellouin, N., Collins, W., Culverwell, I., Halloran, R., Hardiman, S. (2011). The HadGEM2 family of met office unified model climate configurations, Geoscientific Model Development. 4, 723– 757. doi: 10.5194/gmd-4-723-2011.
MAVDT-IDEAM-PNUD, (2010). Segunda Comunicación de Colombia ante la Convención Marco de Cambio Climático. Instituto de Hidrología, Meteorología y Estudios Ambientales. Bogotá D.C. 443 p.
METED, (2008). Funcionamiento de los modelos de mesoescala. https://www.meted.ucar.edu/education_training/lesson/262.
NOAA, (2012). Climate Model. Top Ten: breakthroughs. accessed 6/20/2013. retrieved from http://celebrating200years.noaa.gov/breakthroughs/climate_model/modeling_sch matic.html.
Oleson, K., Niu, G., Yang, Z., Lawrence, D., Thornton, P., Lawrence, J., Stöckli, R., Dickinson, R., Bonan, G., Levis, S., Dai, A., and Gian, T. (2008). Improvements to the community land model and their impact on the hydrological cycle, J. Geophys. Res., 113, g01021, https://doi.org/10.1029/2007jg000563, 2008.
Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C., Levis, S., Li, F., Riley, W., Subin, Z., Swenson, S., Thornton, E., Bozbiyik, A., Fisher, R., Heald, C., Kluzek, E., Lamarque, J., Lawrence, P., Leung, L., Lipscomb, W., Muszala, S., Ricciuto, D., Sacks, W., Sun, Y., Tang, J., and Yang Z.L. (2013). Technical description of version 4.5 of the Community Land Model (CLM). NCAR technical note NCAR/tn-503+str, National Center for Atmospheric Research, Boulder.
Oviedo, B. & Leon, G. (2010). Guía de procedimientos para la generación de escenarios de cambio climático regional y local a partir de los modelos globales. IDEAM, Bogotá, Colombia.
Pabón, J.D. & Hurtado, G. (2002). Cambios en los patrones de temperatura media anual del aire y precipitación anual en los páramos de Colombia. en: páramos y ecosistemas alto- andinos de Colombia en condiciones Hot Spot & Global Climatic tensor (Castaño-Uribe, editor), 2002, Bogotá D.C., 387 páginas; pp. 242-251.
Pabón, J. (2006). Escenario de cambio climático para Colombia. En: Memorias del IV Encuentro de la Red de Universidades del Pacífico Sur (RUPSUR), 8-10 de noviembre de 2006.
Pabón, J. D. (2012). Cambio climático en Colombia: tendencias en la segunda mitad del siglo XX y escenarios posibles para el siglo XXI. Rev. Acad. Colomb. Cienc. 36 (139): 261- 278. ISSN 0370-3908.
Pal, J., Small, E. and Eltahir, E. (2000). Simulation of regional-scale water energy budgets: representation of subgrid cloud and precipitation processes within RegCM. J. Geophys. Res. 105: 29579–29594.
Pesquero, J. F., Chou, S. C., Nobre, C. A., & Marengo, J. A. (2010). Climate downscaling over South America for 1961-1970 using the ETA Model. Theor. Appl. Climatol. 99, 75-93. DOI 10.1007/S00704-009-0123-Z.
Posada, C. (2007). La adaptación al cambio climático en Colombia. Revista virtual Ledesma Rev. Ing. ISSN. 0121-4993.
Poveda, G. (2009). Evidences of climate and environmental change on water resources and malaria in Colombia, Conf. Series, Earth Environ. Sci., 6, 292054, doi:10.1088/1755- 1307/6/9/292054.
Prein, A. F., Gobietz, A., Truhetzs, H., Keuler, K., Goergens, K., Teichmann, C., Fox Maule, C., Van Meijgaarda, E., Déqué, M., Nikulin, G., Vautard, R., Colette, A., Kjellström, E., & Jacob, D. (2016). Precipitation in the EURO‐CORDEX 0.11° and 0.44° simulations: high resolution, ¿high benefits? Clim Dyn. 46, 383–412. doi 10.1007/s00382-015-2589-y.
Prömmel, K., Geyer, B., Jones, J. M., & Widman, M. (2010). Evaluation of the skill and added value of a reanalysis-driven regional simulation for alpine temperature, Int. J. Climatol., 30, 760–773.
Ratnam, J., Giorgi, F., Kaginalker, A., and Cozzini, S. (2009). Simulation of the Indian monsoon using the RegCM3‐ROMS regional coupled model. Climate dynamics, 33, 119- 139.
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C. and Wang, W. (2002). An improved in situ and satellite SST analysis for climate. Journal of climate, Tomo 15, no 13, pages. 1609-1625.
Roads, J., Chen, S., Cocke, S., Druyan, L., Fulakeza, M., LaRow, T., Lonergan, P., Qian, J., and Zebiak, S. (2003). International Research Institute/Applied Research Centers (IRI/ARCs) regional model intercomparison over South America. Journal of Geophysical Research, 108(D14):4425.
Ruiz, F. (2007). Escenarios de cambio climático, algunos modelos y resultados de lluvia para Colombia bajo el escenario A1B. Nota técnica. IDEAM, Bogotá, Colombia.
Ruiz, F. & Martínez, M. (2007). Report on activities performed in MRI-JAPAN to simulate climate in Colombia and the A1B scenario with the Japanese model using a resolution of 20 x 20 km. Visualizing future climate in Latin America: results from the application of the earth simulator. Latin America and Caribbean region sustainable development working paper 30, 43-59.
Ruiz, F. (2010). Cambio climático en temperatura, precipitación y humedad relativa para Colombia usando modelos meteorológicos de alta resolución (panorama 2011-2100). Nota Técnica de IDEAM, No. IDEAM-METEO/005-2010, Bogotá D. C., 91 páginas.
Rummukainen, M. (2016). Added value in regional climate modeling. Clim. Change. 7, 145–159. doi: 10.1002/wcc.378.
Schneider, U., Becker, A., Finger, P., Rustemeier, E., Ziese, M. (2020). GPCC full data monthly product version 2020 at 0.25°: monthly land-surface precipitation from rain- gauges built on GTS-based and historical data. doi: 10.5676/DWD_GPCC/fd_m_v2020_025.
Simmons, A., Uppala, D., and Kobayashi, S. (2007). Era-Interim: new ECMWF reanalysis products from 1989 onwards, ECMWF news., 110, 29–35.
Smith, S. (1988). Coefficients for sea surface wind stress, heat flux, and wind profiles as a function of wind speedand temperature, J. Geophys. Res., 93, 15467–15472.
Sundqvist, H., Berge, E. and Kristjansson, J. (1989). The effects of domain choice on summer precipitation simulation and sensitivity in a regional climate model, J. Climate, 11, 2698-2712.
Taylor, K. (2001). Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183–7192, doi:10.1029/2000jd900719.
Taylor, K. (2005). Taylor diagram primer: a brief 4-page overview which summarizes the important aspects of these useful plots.
Tiedtke, M. (1989). A comprehensive mass flux scheme for cumulus parameterization in large-scale. Models. Bul. Am. Meteorol. Soc., 117:1779-1800.
Torma, C., Giorgi, F., & Coppola, E. (2015). Added value of regional climate modeling over areas characterized by complex terrain-precipitation over the alps, J. Geophys. Res. Atmos., 120, 3957–3972, doi:10.1002/ 2014jd022781.
Tiwari, P. R., et al. (2015). The role of land surface schemes in the regional climate model (RegCM) for seasonal scale simulations over western Himalaya, Atmosphere, volume 28, ISSUE 2, pages 129-142, ISSN 0187-6236, https://doi.org/10.1016/s0187- 6236(15)30005-9.
Uppala, S., Dee, D., Kobayashi, S., Berrisford, P. and Simmons, A. (2008). Towards a climate data assimilation system: status update of Era-Interim, ECMWF new., 15, 12–18.
Urrea, V., Ochoa, A. & Mesa, O. (2016). Validación de la base de datos de precipitación CHIRPS para Colombia en escala diaria, mensual y anual en el período 1981- 2014. Universidad Nacional de Colombia, sede Medellín, Colombia.
USAID, (2014). A review of downscaling methods for climate change projections. African and latin american resilience to climate change (ARCC).
Vichot, A., Martínez, D., Centella, A., & Bezanilla, A. (2014). Sensibilidad al cambio de dominio y resolución de tres configuraciones del modelo climático regional RegCM 4.3 para la región de América Central y el Caribe. Revista de Climatología, vol. 14, 45-62.
Wang, J. and Kotamarthi, V. R. Downscaling with a nested regional climate model in near-surface fields over the contiguous United States: WRF dynamical downscaling, J. Geophys. Res. Atmos., 119, 8778–8797, 2014.
Wang, G., Yu, M., Pal, J. S., Mei, R., Bonan, G. B., Levis, S., and Thornton, P. E. (2016). On the development of a coupled regional climate vegetation model RCM-CLM-CN- DV and its validation its tropical Africa, Clim. Dynam, 46, 515–539.
Wilby, R. & Dawson, C. (2004). Using SDSM version 3.1- a decision support tool for the assessment of regional climate change impacts. England, UK.
Wilks, D.S. (2006). Statistical methods in the atmospheric sciences. Academic Press, San Diego, pp. 148-151.
Winterfeldt, J., & Weisse, R. (2009). Assessment of value added for surface marine wind speed obtained from two regional climate models, Mon. Weather Rev., 137, 2955– 2965.
Ynoue, R., Ambrizzi, T., Reboita, M. & Da Silva, G. (2017). Meteorología: conceptos básicos. Sao Paulo: taller de textos.
Zeng, X., Zhao, M., & Dickinson, R. E. (1998). Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using toga COARE and TAO data. J. Clim. 11: 2628–2644.
Zeng, X. (2005). A prognostic scheme of sea surface skin temperature for modeling and data assimilation. Geophysical Research Letters, 32. l14605.
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.spa.fl_str_mv xxi, 186 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.coverage.country.spa.fl_str_mv Colombia
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Bogotá - Ciencias - Maestría en Ciencias - Meteorología
dc.publisher.department.spa.fl_str_mv Departamento de Geociencias
dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/81537/3/1067880634.2021.pdf
https://repositorio.unal.edu.co/bitstream/unal/81537/4/license.txt
https://repositorio.unal.edu.co/bitstream/unal/81537/5/1067880634.2021.pdf.jpg
bitstream.checksum.fl_str_mv 8c143461a8a88d68f9198f7308f5729b
8153f7789df02f0a4c9e079953658ab2
0842d0596be96602f79569c1083af481
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
_version_ 1814089430026158080
spelling Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Baquero Bernal, Astrid31d2c064977990ef966c8ebabd2fc841600Espitia Rodríguez, Javier Fernando6c631edaf4051a8c97d02dd6c2115e4bGrupo de Simulación del Sistema Climático Terrestre2022-06-08T18:43:37Z2022-06-08T18:43:37Z2021-09https://repositorio.unal.edu.co/handle/unal/81537Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, mapas, tablasPrimero, se investigó el valor agregado (VA) de la reducción de escala dinámica usando valores simulados de precipitación obtenidos con el modelo regional del clima (MRC) RegCM4 (con dos resoluciones, 0.44 ° y 0.22 °) forzadas por dos modelos globales del clima (MGCs) diferentes (HadGEM2-ES y MPI-ESM-MR) del proyecto de intercomparación de modelos de clima acoplados (CMIP5) para el período 1981-2005, a partir del experimento CORDEX, con enfoque en la región Andina colombiana. Se utilizaron diferentes métricas de VA: patrón espacial de precipitación media, el ciclo anual de precipitación, la fórmula de Di Luca-Dioso y distribución de intensidad de precipitación diaria. La comparación con los datos de referencia CHIRPS mostró que RegCM4 no proporciona VA en las regiones de topografía compleja (Andes colombianos), sin embargo, en las regiones llanas de Colombia, se evidenció VA aportado por RegCM4 con relación a los MGCs. Segundo, se analizó la susceptibilidad de este modelo ante la elección de distintas resoluciones, dimensiones del dominio, MGCs y a la aplicación de diferentes esquemas convectivos y de superficie. En particular, para estos esquemas, se ejecutaron simulaciones de 1999 a 2004 (1999 es el período de arranque) sobre un dominio que incluye el área de estudio (AE) y se utilizó el esquema de superficie BATS/CLM-4.5 junto con cinco esquemas convectivos: GFC, GAS, EMAN, TI y GLEO. Todas las simulaciones fueron forzadas con datos ERA-Interim. Para evaluar esta susceptibilidad se tuvieron en cuenta estadísticos como el RMSE, BIAS, SD y r. Se encontró que RegCM4 en AE: (1) presenta sensibilidad mínima ante el cambio de dominio, (2) exhibe gran sensibilidad ante la selección de los modelos HadGEM2-ES y MPI-ESM-MR, siendo levemente mejor el primero, (3) es altamente susceptible ante el cambio de resolución, y (4) muestra susceptibilidad mínima entre esquemas de superficie, es decir, cuando se selecciona ya sea el esquema BATS o el esquema CLM-4.5 y bastante notable entre esquemas de convección, es decir, cuando se selecciona ya sea EMAN, GAS, GFC, GLEO o TIE. Se encontró que EMAN y TIE presentan el mejor rendimiento junto con el esquema de superficie BATS. (Texto tomado de la fuente).First, the added value (AV) of downscaling was investigated using simulated precipitation values obtained with the regional climate model (RCM) RegCM4 (with two resolutions, 0.44 ° and 0.22 °) forced by two global climate models. (GCMs) different (HadGEM2-ES and MPI-ESM-MR) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) for the period 1981-2005, based on the CORDEX experiment, with a focus on the Colombian Andean region. Different AV metrics were used: spatial pattern of mean precipitation, the annual cycle of precipitation, the Di Luca-Dioso formula and distribution of daily precipitation intensity. Comparison with the CHIRPS reference data showed that RegCM4 does not provide AV in regions with complex topography (Colombian Andes), however, in the flat regions of Colombia, AV contributed by RegCM4 was evidenced in relation to GCM. Second, the susceptibility of this model to the choice of different resolutions, domain dimensions, GCMs and the application of different convective and surface schemes was analyzed. In particular, for these schemes, simulations were run from 1999 to 2004 (1999 is the start-up period) on a domain that includes the study area (AE) and the BATS / CLM-4.5 surface scheme was used together with five schemes. convective: GFC, GAS, EMAN, TI and GLEO. All simulations were forced with ERA-Interim data. To evaluate this susceptibility, statistics such as RMSE, BIAS, SD and r were taken into account. It was found that RegCM4 in AE: (1) presents minimal sensitivity to domain change, (2) exhibits great sensitivity to the selection of the HadGEM2-ES and MPI-ESM-MR models, the former being slightly better, (3) is highly susceptible to change in resolution, and (4) shows minimal susceptibility between surface schemes, that is, when either the BATS scheme or the CLM- 4.5 scheme is selected, and quite noticeable between convection schemes, that is, when Either EMAN, GAS, GFC, GLEO or TIE is selected. EMAN and TIE were found to have the best performance together with the BATS surface scheme.MaestríaMagíster en Ciencias - Meteorologíaxxi, 186 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - MeteorologíaDepartamento de GeocienciasFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá550 - Ciencias de la tierra::551 - Geología, hidrología, meteorologíaDepth-area-duration (hydrometeorology)HydrometeorologyPrecipitación pluvialHidrometeorologíaCORDEXAdded valueRegional climate modelParameterizationsSensitivity studyDomainResolutionValor agregadoModelo regional del climaParametrizacionesEstudio de sensibilidadDominioResoluciónPronóstico meteorológicoWeather forecastingValor agregado del modelamiento regional del clima sobre áreas caracterizadas por terreno complejo: precipitación sobre los Andes ColombianosAdded value of modeling regional climate over areas characterized by complex terrain: precipitation over the Andes ColombiansTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMColombiaAldrian, E., Dumenil, L., Jacob, D., Podzun R., & Gunawan, D. (2004). Long-term simulation of Indonesian rainfall with the MPI regional model. Clim. Dyn., 22:795-814.Alfaro, S. and Gomes, L. (2001). Modeling mineral aerosol production by wind erosion: emission intensities and aerosol size distributions in source areas. Journal of geophysical research, 106. D16.Almazroui, M. (2016). RegCM4 in climate simulation over CORDEX-MENA/Arab domain: selection of suitable domain, convection and land-surface schemes. Int. J. Climatol. 36, 236- 251.Amador, J. A., & Alfaro, E. J. (2009). Métodos de reducción de escala: aplicaciones al tiempo, clima, variabilidad climática y cambio climático. Revista iberoamericana de economía ecológica vol. 11: 39-52.Ambrizzi, T, Reboita, M., Da Rocha, R., Llopart, M. (2019). The state of the art and fundamental aspects of regional climate modeling in South America. Acad. Sci 1436:98– 120.Anthes, R., Hsu, E. and Kuo, Y. (1987). Description of the Penn State/NCAR Mesoscale Model version 4 (MM4). NCAR tech. note, NCAR/tn-282+str, 66 pp.Arakawa, A. & Schubert, W. (1974). Interaction of a cumulus cloud ensemble with the large- scale environment. Part I. J. Atmos. Sci., 31:674-701.Artale, V., Calmanti, S., Carillo, A., and Dell’Aquila, A. (2010). An Atmosphere–Ocean regional climate model for the Mediterranean area: assessment of a present climate simulation. Clim Dyn 35: 721–740.Bliss, N. B., and Olsen, L., M. (1996). Development of a 30-arc-second digital elevation model, in Pecora Thirteen, Human Interactions With the Environment-Perspectives from Space, Sioux Falls, South Dakota, 20–22 August.Bonilla, C., & Mesa S. (2017). Validación de la precipitación estimada por modelos climáticos acoplados del proyecto de intercomparación CMIP5 en Colombia. RACCEFYN, 41(158), 107–118. https://doi.org/10.18257/raccefyn.427.Bretherton, C., McCaa, J. and Grenier, H. (2004). A new parameterization for shallow cumulus convection and its application to marine subtropical cloud-topped boundary layers. Part I: description and 1d results. monthly weather review, 132:864–882.Brutsaert, W. (1982). Evaporation into the atmosphere: theory, history and applications, USA, Reidel Himgham mass, 299 pp.Calderón, S., Romero, G., Ordóñez, A., Álvarez, A., Ludeña, C., Sánchez, L., Miguel, C., Martínez, K. & Pereira, M. (2014). Impactos económicos del cambio climático en Colombia. Washington D.C: Banco Interamericano de Desarrollo. Recuperada del repositorio. Cepal.org/bits-tream/11362/37879/1/s1500268_es.pdf.Castro, C. L., Pielke, R. A., & Leoncini, G. (2005). Dynamical downscaling: an assessment of value added using a regional climate model, J. Geophys. Res., 110, d05108, doi:10.1029/2004jd004721.Ceballos, J. (2009). Manifestación del cambio climático “los glaciares en Colombia”. calentamiento global, más ciencia, mejores políticas. Revista la Tadeo 63 ISSN 0120-5250.Collins, W., Bitz, C., Blackmon, M., Bonan, G., Bretherton, C., Carton, J., Chang, P., Doney, S., Hack, J., Henderson, T., Kiehl, J., Large, W., Mckenna, Santer, D. and Smith, R. (2006). The community climate system model version 3 (CCSM3). Journal of climate, 19:2122-2143.Collins, W. J., et al. (2011), Development and evaluation of an Earth-System Model- HadGEM2, Geosci. Model Devel., 4, 1051–1075.Curry, C., Tencer, B., Whan, K., Weaver, A., Giguère, M., & Wiebe, E. (2016). Searching for added value in simulating climate extremes with a high-resolution regional climate model over western Canada, Atmosphere-Ocean, doi:10.1080/07055900.2016.1158146.De Haan, I., Kanamitsu, M., De Sales, F., and Sun, I. (2014). An evaluation of the seasonal added value of downscaling over the United States using new verification measures. Theoretical and applied climatology, 122(1-2):47–57.De Sales, F. and Xue, Y. (2011). Assessing the dynamic-downscaling ability over South America using the intensity-scale verification technique. International Journal of Climatology, 31(8):1205–1221.Dickinson, R. E., Ronald, M. E., Giorgi, F. and Gary, T. (1989). A regional climate model for the western United States. Climatic Change 15, no. 3: 383–422.Dickinson, R. E., Henderson-Sellers, A., & Kennedy, P. J. (1993). Biosphere-atmosphere transfer scheme (bats) version 1 as coupled to the NCAR community climate model. National Center for Atmospheric Research (NCAR) technical note NCAR/tn-387+str, NCAR, boulder, co, doi: 10.5065/d67w6959.Di Luca, A., De Elía, R., & Laprise, R. (2012). Potential for added value in precipitation simulated by high resolution nested regional climate models and observations, Clim. Dynam., 38, 1229–1247.Di Luca, A., De Elía, R., & Laprise, R. (2013). Potential for small scale added value of RCM’s downscaled climate change signal, Clim. Dyn., 40, 1415–1433.Di Luca, A., de Elía, R., & Laprise, R. (2015). Challenges in the quest for added value of regional climate dynamical downscaling, advances in modeling, 1, 10–21, doi 10.1007/s40641-015-0003-9.Di Luca, A., Argüeso, D., Evans, J. P., De Elía, R., & Laprise, R. (2016). Quantifying the overall added value of dynamical downscaling and the contribution from different spatial scales, J. Geophys. Res. Atmos., 121, 1575–1590, doi:10.1002/2015jd024009.Elguindi, N., Bi, X., Giorgi, F., Nagarajan, B., Pal, J., Solmon, F., Rauscher, S., Zakey, A., O’Brien, T., Nogherotto, R. & Giuliani, G. (2014). Regional climate model RegCM: reference manual, version 4.5. The Abdus Salam International Centre for Theoretical Physics. Strada Costiera, 11 I – 34151, Trieste Italy. Earth System Physics Section – esp. Pag. 9.Emanuel, K. (1991). A scheme for representing cumulus convection in large scale models. J. Atmos. Sci., 48:2313-2335.Falco, M., Carril, A., Menéndez, C., Zaninelli, P. y Li, L. Assessment of CORDEX simulations over South America: added value on seasonal climatology and resolution considerations. Climate Dynmics, Springer Verlag, 2019, 52 (7-8), pp.4771-4786.Feser, F. (2006). Enhanced detectability of added value in Limited-Area model results separated into different spatial scales, Mon. Weather Rev., 134, 2180–2197.Feser, F., Rockel, B., Von-Storch, H., Winterfeldt, J., & Zahn, M. (2011). Regional climate models add value to global model data: a review and select examples. Mon. Wea. Rev., 134, 1181–1192.Foley, A.M. (2010). Uncertainty in regional climate modelling: a review. Prog. Phys. Geogr. 34: 647–670.Fritsch, J., & Chappell, C. (1980). Numerical prediction of convection driven mesoscale pressure systems. Part I: convective parameterization. J. Atmos. Sci. 37: 1722–1733.Funk, C., et al. (2015). The climate hazards infrared precipitation with stations a new environmental record for monitoring e Extremes, Scientific Data. doi: http://dx.doi.org/10.15780/g2rp4q.Gao, X., Xu, Y., Zhao, Z., Pal, J., and Giorgi, F. (2006). On the role of resolution and topography in the simulation of East Asia precipitation. Theoretical and Applied Climatology, 86(1-4):173–185.Giorgetta, M., Roeckner, E., Mauritsen, T., Stevens, B., Crueger, T., Esch, M., Rast, S., Kornblueh, l., Schmidt, H., Kinne, S., Möbis, B., Krismer, T., Reick, C., Raddatz, T. and Gayler V. (2012). The atmospheric general circulation model ECHAM6 - Model Description.Giorgetta, M. A, et al. (2013). Climate and carbon cycle changes from 1850 to 2100 in MPI- ESM simulations for the Coupled Model Intercomparison Project Phase 5. Journal of advances in modelling Earth Systems,5,572-597.Giorgi, F. (1990). Simulation of regional climate using a Limited-Area model nested in a General Circulation model. Journal of climate 3, no. 9: 941–63.Giorgi, F., & Mearns, l. (1991). Approaches to regional climate change simulation: a review. Rev. Geophys., 29: 191-216.Giorgi, F., & Mearns, l. (1999). Introduction to special section: regional climate modeling revisited. Journal of Geophysical Research, Vol. 104, no: d6: 6335–6352.Giorgi, F., Bi, X., and Gian, Y. (2003). Indirect vs. direct effects of anthropogenic sulfate on the climate of east asia as simulated with a regional coupled climate-Chemistry/Aerosol model. Climatic change, 58:345–376.Giorgi, F. (2005). Climate Change Prediction. Climatic Change, 73, 239–265.Giorgi, F. (2006). Regional climate modeling: status and perspectives, J. Phys. IV, 139, 101– 118.Giorgi, F., Jones, C. and Asrar, G. (2009). Addressing climate information needs at the regional level: the CORDEX framework. World Meteorological Organization (WMO) bulletin, Tomo 58, no 3, Pág. 175.Giorgi, F. (2010). Uncertainties in climate change predictions, from the global to the regional scale. Epi web conf. 9: 115–129.Giorgi, F., et al. (2012), RegCM4: model description and preliminary tests over multiple CORDEX domains, Clim. res., 52, 7–29.Giorgi, F. and Gutowski., W. (2015). Regional dynamical downscaling and the CORDEX initiative. Annual review of environment and resources, 40(1):467–490.Giorgi, F. and Gutowski., W. (2016). Coordinated experiments for projections of regional climate change. Curr. Clim. Change rep. 2: 202–210.Giorgi, F. and Gao, X. (2018). Regional earth system modeling: review and future directions, Atmos. Ocean. Sci. Lett., 11, 189–197.Giorgi, F. (2019). Thirty years of regional climate modeling: ¿where are we and where are we going next? Journal of Geophysical Research: atmospheres, 124, 5696–5723. https:// doi.org/10.1029/2018jd030094.Grell, G. (1993). Prognostic evaluation of assumptions used by cumulus parameterizations. mon. wea. rev., 121:764-787.Grell, A., Dudhia, J. and Stauffer, D. (1994). A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR technical note.Gutowski, J., et al. (2016). WCRP Coordinated Regional Downscaling Experiment (CORDEX): un MPI de diagnóstico para CMPI6, Geosci. Model Dev., 9, 4087–4095.Guyennon, N., Romano, E., Portoghese, I., Salerno, F., Calmanti, S., Perangeli, A.B., Tartari, G., and Copetti, D. (2013). Benefits from using combined dynamical-statistical downscaling approaches – lessons from a case study in the Mediterranean region. Hydrology and Earth System sciences 17(2), 705-720.Hackenbruth, J., Schädler, G., & Willem, J. (2016). Added value of high-resolution regional climate simulations for regional impact studies. Meteorologische Zeitschrift, vol. 25, no. 3, 291 -304. doi10.1127/metz/2016/0701.Hostetler, S., Bates, G. and Giorgi, F. (1993). Interactive nesting of a lake thermal model within a regional climate model for climate change studies. Geophysical Research, 98:5045– 5057.Hostetler, S., Giorgi, F., Bates, G., & Bartlein, P. (1994). The role of lake‐atmosphere feedbacks in sustaining paleolakes Bonneville and Lahontan 18,000 years ago. Science, 263, 665–668.Hostetler, S. W., Bartlein, P. J., Clark, P. U., Small, E., & Solomon, A. (2000). Simulated influences of lake Agassiz on the climate of Central North America 11,000 years ago. nature, 405, 334–337.Holtslag, A. M., De Bruijn, I. F., Pan, H. l. (1990). A high-resolution air mass transformation model for Short-Range weather forecasting. Mon. Weather Rev. 118: 1561–1575.Hurtado, A. (2009). Estimación de los campos mensuales históricos de precipitación en el territorio colombiano. Tésis de Maestría, Universidad Nacional de Colombia Sede Medellin.IDEAM, (2005). Clasificaciones climáticas y confort térmico anual. Atlas climatológico de Colombia. capítulo 2, 78-87.IPCC, (2007). Cambio Climático 2007: informe de síntesis. Contribución de los grupos de trabajo i, ii y iii al cuarto informe de evaluación del Grupo Intergubernamental de Expertos sobre el Cambio Climático. Ginebra, Suiza.Jones, R., Noguer, M., Hassell, D., Hudson, D., Wilson, S., Jenkins, G., & Mitchell, J. (2004). Generating high resolution climate change scenarios using PRECIS. Hadley Center for Climate Prediction and Research, UK.Kain, J. and Fritsch J. (1990). A one-dimensional entraining/detraining plume model and its application in convective parameterization, J. Atmos. Sci., 47, 2784–2802.Kain, J. (2004). The Kain–Fritsch convective parameterization: an update, J. Appl. Meteorol., 43, 170 181.Kanamitsu, M. and DeHaan, L. (2011). The Added Value Index: A new metric to quantify the added value of regional models. Journal of Geophysical Research, 116(D11):D11106.Karmacharya, J., New, M., Jones, R., and Levine, R. (2016). Added value of a high- resolution regional climate model in simulation of intraseasonal variability of the south Asian summer monsoon. International Journal of Climatology.Katzfey, J., Chattopadhyay, M., McGregor, J., Nguyen, K., & Thatcher, M. (2011). The added value of dynamical downscaling. CSIRO Marine and Atmospheric Research.19th International Congress on Modelling and Simulation, Perth, Australia, 12–16, December 2011.Kerkhoff, C., Kuensch, H. R., & Schaer, C. (2014). Assessment of bias assumptions for climate models, J. Clim., 27, 6799–6818.Kiehl, J., Hack, J., Bonan, G., Boville, A., Briegleb, B., Williamson, D., and Rasch, P. (1996). Description of the NCAR Community Climate Model (CCM3), technical note NCAR/tn−420+str, 152, 1996.Laprise, R. (2008). Challenging some tenets of regional climate modelling, Meteorol. Atmos. Phys., 100, 3–22.Laprise, R. (2014). Comment on “The added value to global model projections of climate change by dynamical downscaling: a case study over the continental U.S. using the GISS- MODELE2 and WRF models” by Racherla et al, J. Geophys. Res. Atmos., 119, 3877–3881, doi: 10.1002/2013jd019945.Latif, M. (2011). Uncertainty in climate change projections. J. Geochem. Explor. 110: 1–7.Laurent, B., Bergametti, G., Leon, J. and Mahowald, N. (2008). Modeling mineral dust emissions from the Sahara Desert using new surface properties and soil database. Journal of Geophysical Research, 113. d14218.Lawrence, P. and Chase, T. (2007). Representing a new model consistent land surface in the Community Land Model (CLM3.0). J. Geophys. Res., 112. g01023.Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson, S. C., Lawrence, P. J., Zeng, X., Y., Zong-Liang, L. S., Sekiguchi, K. B., Gordon, B. S., and Andrew, G. (2011). Parameterization improvements and functional and structural advances in version 4 of the Community Land Model, J. Adv. Model. Earth Syst., 3, m03001, https://doi.org/10.1029/2011ms000045.León, G. (2000). Tendencia de la temperatura del aire en Colombia. Meteorología colombiana, n. 2, pp. 57-65.León, G. (2005). Verificación de los modelos meteorológicos. Nota técnica IDEAM, pp.12.Loveland, T., Reed, B., Brown, J., Ohlen, D., Zhu, J., Yang, l. and Merchant, J. (2000). Development of a global land cover characteristics database and IGBP discover from 1-km AVHRR data. Inter. J. Remote Sensing, 21:1303-1330.Lucas-Picher, P., Laprise, R., and Winger, K. (2017). Evidence of added value in North American regional climate model hindcast simulations using ever-increasing horizontal resolutions, Clim. Dynam., 48, 2611–2633.Lupo, A. & Kininmonth., W. (2013). Global climate models and their limitations climate change reconsidered ii: physical science and its summary for policymakers. The heartland institute in September 2013 for the Nongovernmental International Panel on Climate Change (NIPCC).Martin, G., Bellouin, N., Collins, W., Culverwell, I., Halloran, R., Hardiman, S. (2011). The HadGEM2 family of met office unified model climate configurations, Geoscientific Model Development. 4, 723– 757. doi: 10.5194/gmd-4-723-2011.MAVDT-IDEAM-PNUD, (2010). Segunda Comunicación de Colombia ante la Convención Marco de Cambio Climático. Instituto de Hidrología, Meteorología y Estudios Ambientales. Bogotá D.C. 443 p.METED, (2008). Funcionamiento de los modelos de mesoescala. https://www.meted.ucar.edu/education_training/lesson/262.NOAA, (2012). Climate Model. Top Ten: breakthroughs. accessed 6/20/2013. retrieved from http://celebrating200years.noaa.gov/breakthroughs/climate_model/modeling_sch matic.html.Oleson, K., Niu, G., Yang, Z., Lawrence, D., Thornton, P., Lawrence, J., Stöckli, R., Dickinson, R., Bonan, G., Levis, S., Dai, A., and Gian, T. (2008). Improvements to the community land model and their impact on the hydrological cycle, J. Geophys. Res., 113, g01021, https://doi.org/10.1029/2007jg000563, 2008.Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C., Levis, S., Li, F., Riley, W., Subin, Z., Swenson, S., Thornton, E., Bozbiyik, A., Fisher, R., Heald, C., Kluzek, E., Lamarque, J., Lawrence, P., Leung, L., Lipscomb, W., Muszala, S., Ricciuto, D., Sacks, W., Sun, Y., Tang, J., and Yang Z.L. (2013). Technical description of version 4.5 of the Community Land Model (CLM). NCAR technical note NCAR/tn-503+str, National Center for Atmospheric Research, Boulder.Oviedo, B. & Leon, G. (2010). Guía de procedimientos para la generación de escenarios de cambio climático regional y local a partir de los modelos globales. IDEAM, Bogotá, Colombia.Pabón, J.D. & Hurtado, G. (2002). Cambios en los patrones de temperatura media anual del aire y precipitación anual en los páramos de Colombia. en: páramos y ecosistemas alto- andinos de Colombia en condiciones Hot Spot & Global Climatic tensor (Castaño-Uribe, editor), 2002, Bogotá D.C., 387 páginas; pp. 242-251.Pabón, J. (2006). Escenario de cambio climático para Colombia. En: Memorias del IV Encuentro de la Red de Universidades del Pacífico Sur (RUPSUR), 8-10 de noviembre de 2006.Pabón, J. D. (2012). Cambio climático en Colombia: tendencias en la segunda mitad del siglo XX y escenarios posibles para el siglo XXI. Rev. Acad. Colomb. Cienc. 36 (139): 261- 278. ISSN 0370-3908.Pal, J., Small, E. and Eltahir, E. (2000). Simulation of regional-scale water energy budgets: representation of subgrid cloud and precipitation processes within RegCM. J. Geophys. Res. 105: 29579–29594.Pesquero, J. F., Chou, S. C., Nobre, C. A., & Marengo, J. A. (2010). Climate downscaling over South America for 1961-1970 using the ETA Model. Theor. Appl. Climatol. 99, 75-93. DOI 10.1007/S00704-009-0123-Z.Posada, C. (2007). La adaptación al cambio climático en Colombia. Revista virtual Ledesma Rev. Ing. ISSN. 0121-4993.Poveda, G. (2009). Evidences of climate and environmental change on water resources and malaria in Colombia, Conf. Series, Earth Environ. Sci., 6, 292054, doi:10.1088/1755- 1307/6/9/292054.Prein, A. F., Gobietz, A., Truhetzs, H., Keuler, K., Goergens, K., Teichmann, C., Fox Maule, C., Van Meijgaarda, E., Déqué, M., Nikulin, G., Vautard, R., Colette, A., Kjellström, E., & Jacob, D. (2016). Precipitation in the EURO‐CORDEX 0.11° and 0.44° simulations: high resolution, ¿high benefits? Clim Dyn. 46, 383–412. doi 10.1007/s00382-015-2589-y.Prömmel, K., Geyer, B., Jones, J. M., & Widman, M. (2010). Evaluation of the skill and added value of a reanalysis-driven regional simulation for alpine temperature, Int. J. Climatol., 30, 760–773.Ratnam, J., Giorgi, F., Kaginalker, A., and Cozzini, S. (2009). Simulation of the Indian monsoon using the RegCM3‐ROMS regional coupled model. Climate dynamics, 33, 119- 139.Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C. and Wang, W. (2002). An improved in situ and satellite SST analysis for climate. Journal of climate, Tomo 15, no 13, pages. 1609-1625.Roads, J., Chen, S., Cocke, S., Druyan, L., Fulakeza, M., LaRow, T., Lonergan, P., Qian, J., and Zebiak, S. (2003). International Research Institute/Applied Research Centers (IRI/ARCs) regional model intercomparison over South America. Journal of Geophysical Research, 108(D14):4425.Ruiz, F. (2007). Escenarios de cambio climático, algunos modelos y resultados de lluvia para Colombia bajo el escenario A1B. Nota técnica. IDEAM, Bogotá, Colombia.Ruiz, F. & Martínez, M. (2007). Report on activities performed in MRI-JAPAN to simulate climate in Colombia and the A1B scenario with the Japanese model using a resolution of 20 x 20 km. Visualizing future climate in Latin America: results from the application of the earth simulator. Latin America and Caribbean region sustainable development working paper 30, 43-59.Ruiz, F. (2010). Cambio climático en temperatura, precipitación y humedad relativa para Colombia usando modelos meteorológicos de alta resolución (panorama 2011-2100). Nota Técnica de IDEAM, No. IDEAM-METEO/005-2010, Bogotá D. C., 91 páginas.Rummukainen, M. (2016). Added value in regional climate modeling. Clim. Change. 7, 145–159. doi: 10.1002/wcc.378.Schneider, U., Becker, A., Finger, P., Rustemeier, E., Ziese, M. (2020). GPCC full data monthly product version 2020 at 0.25°: monthly land-surface precipitation from rain- gauges built on GTS-based and historical data. doi: 10.5676/DWD_GPCC/fd_m_v2020_025.Simmons, A., Uppala, D., and Kobayashi, S. (2007). Era-Interim: new ECMWF reanalysis products from 1989 onwards, ECMWF news., 110, 29–35.Smith, S. (1988). Coefficients for sea surface wind stress, heat flux, and wind profiles as a function of wind speedand temperature, J. Geophys. Res., 93, 15467–15472.Sundqvist, H., Berge, E. and Kristjansson, J. (1989). The effects of domain choice on summer precipitation simulation and sensitivity in a regional climate model, J. Climate, 11, 2698-2712.Taylor, K. (2001). Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183–7192, doi:10.1029/2000jd900719.Taylor, K. (2005). Taylor diagram primer: a brief 4-page overview which summarizes the important aspects of these useful plots.Tiedtke, M. (1989). A comprehensive mass flux scheme for cumulus parameterization in large-scale. Models. Bul. Am. Meteorol. Soc., 117:1779-1800.Torma, C., Giorgi, F., & Coppola, E. (2015). Added value of regional climate modeling over areas characterized by complex terrain-precipitation over the alps, J. Geophys. Res. Atmos., 120, 3957–3972, doi:10.1002/ 2014jd022781.Tiwari, P. R., et al. (2015). The role of land surface schemes in the regional climate model (RegCM) for seasonal scale simulations over western Himalaya, Atmosphere, volume 28, ISSUE 2, pages 129-142, ISSN 0187-6236, https://doi.org/10.1016/s0187- 6236(15)30005-9.Uppala, S., Dee, D., Kobayashi, S., Berrisford, P. and Simmons, A. (2008). Towards a climate data assimilation system: status update of Era-Interim, ECMWF new., 15, 12–18.Urrea, V., Ochoa, A. & Mesa, O. (2016). Validación de la base de datos de precipitación CHIRPS para Colombia en escala diaria, mensual y anual en el período 1981- 2014. Universidad Nacional de Colombia, sede Medellín, Colombia.USAID, (2014). A review of downscaling methods for climate change projections. African and latin american resilience to climate change (ARCC).Vichot, A., Martínez, D., Centella, A., & Bezanilla, A. (2014). Sensibilidad al cambio de dominio y resolución de tres configuraciones del modelo climático regional RegCM 4.3 para la región de América Central y el Caribe. Revista de Climatología, vol. 14, 45-62.Wang, J. and Kotamarthi, V. R. Downscaling with a nested regional climate model in near-surface fields over the contiguous United States: WRF dynamical downscaling, J. Geophys. Res. Atmos., 119, 8778–8797, 2014.Wang, G., Yu, M., Pal, J. S., Mei, R., Bonan, G. B., Levis, S., and Thornton, P. E. (2016). On the development of a coupled regional climate vegetation model RCM-CLM-CN- DV and its validation its tropical Africa, Clim. Dynam, 46, 515–539.Wilby, R. & Dawson, C. (2004). Using SDSM version 3.1- a decision support tool for the assessment of regional climate change impacts. England, UK.Wilks, D.S. (2006). Statistical methods in the atmospheric sciences. Academic Press, San Diego, pp. 148-151.Winterfeldt, J., & Weisse, R. (2009). Assessment of value added for surface marine wind speed obtained from two regional climate models, Mon. Weather Rev., 137, 2955– 2965.Ynoue, R., Ambrizzi, T., Reboita, M. & Da Silva, G. (2017). Meteorología: conceptos básicos. Sao Paulo: taller de textos.Zeng, X., Zhao, M., & Dickinson, R. E. (1998). Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using toga COARE and TAO data. J. Clim. 11: 2628–2644.Zeng, X. (2005). A prognostic scheme of sea surface skin temperature for modeling and data assimilation. Geophysical Research Letters, 32. l14605.InvestigadoresPúblico generalORIGINAL1067880634.2021.pdf1067880634.2021.pdfMaestría en Ciencias - Meteorologíaapplication/pdf8002402https://repositorio.unal.edu.co/bitstream/unal/81537/3/1067880634.2021.pdf8c143461a8a88d68f9198f7308f5729bMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81537/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL1067880634.2021.pdf.jpg1067880634.2021.pdf.jpgGenerated Thumbnailimage/jpeg5784https://repositorio.unal.edu.co/bitstream/unal/81537/5/1067880634.2021.pdf.jpg0842d0596be96602f79569c1083af481MD55unal/81537oai:repositorio.unal.edu.co:unal/815372023-08-04 23:04:26.383Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Publicaciones similares