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

dc.contributor.advisorBaquero Bernal, Astridspa
dc.contributor.authorEspitia Rodríguez, Javier Fernandospa
dc.contributor.researchgroupGrupo de Simulación del Sistema Climático Terrestrespa
dc.coverage.countryColombiaspa
dc.date.accessioned2022-06-08T18:43:37Z
dc.date.available2022-06-08T18:43:37Z
dc.date.issued2021-09
dc.descriptionilustraciones, gráficas, mapas, tablasspa
dc.description.abstractPrimero, 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).spa
dc.description.abstractFirst, 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.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ciencias - Meteorologíaspa
dc.format.extentxxi, 186 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/81537
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Geocienciasspa
dc.publisher.facultyFacultad de Cienciasspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ciencias - Maestría en Ciencias - Meteorologíaspa
dc.relation.referencesAldrian, 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.spa
dc.relation.referencesAlfaro, 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.spa
dc.relation.referencesAlmazroui, 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.spa
dc.relation.referencesAmador, 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.spa
dc.relation.referencesAmbrizzi, 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.spa
dc.relation.referencesAnthes, 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.spa
dc.relation.referencesArakawa, A. & Schubert, W. (1974). Interaction of a cumulus cloud ensemble with the large- scale environment. Part I. J. Atmos. Sci., 31:674-701.spa
dc.relation.referencesArtale, 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.spa
dc.relation.referencesBliss, 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.spa
dc.relation.referencesBonilla, 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.spa
dc.relation.referencesBretherton, 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.spa
dc.relation.referencesBrutsaert, W. (1982). Evaporation into the atmosphere: theory, history and applications, USA, Reidel Himgham mass, 299 pp.spa
dc.relation.referencesCalderón, 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.spa
dc.relation.referencesCastro, 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.spa
dc.relation.referencesCeballos, J. (2009). Manifestación del cambio climático “los glaciares en Colombia”. calentamiento global, más ciencia, mejores políticas. Revista la Tadeo 63 ISSN 0120-5250.spa
dc.relation.referencesCollins, 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.spa
dc.relation.referencesCollins, W. J., et al. (2011), Development and evaluation of an Earth-System Model- HadGEM2, Geosci. Model Devel., 4, 1051–1075.spa
dc.relation.referencesCurry, 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.spa
dc.relation.referencesDe 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.spa
dc.relation.referencesDe 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.spa
dc.relation.referencesDickinson, 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.spa
dc.relation.referencesDickinson, 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.spa
dc.relation.referencesDi 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.spa
dc.relation.referencesDi 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.spa
dc.relation.referencesDi 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.spa
dc.relation.referencesDi 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.spa
dc.relation.referencesElguindi, 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.spa
dc.relation.referencesEmanuel, K. (1991). A scheme for representing cumulus convection in large scale models. J. Atmos. Sci., 48:2313-2335.spa
dc.relation.referencesFalco, 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.spa
dc.relation.referencesFeser, F. (2006). Enhanced detectability of added value in Limited-Area model results separated into different spatial scales, Mon. Weather Rev., 134, 2180–2197.spa
dc.relation.referencesFeser, 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.spa
dc.relation.referencesFoley, A.M. (2010). Uncertainty in regional climate modelling: a review. Prog. Phys. Geogr. 34: 647–670.spa
dc.relation.referencesFritsch, J., & Chappell, C. (1980). Numerical prediction of convection driven mesoscale pressure systems. Part I: convective parameterization. J. Atmos. Sci. 37: 1722–1733.spa
dc.relation.referencesFunk, 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.spa
dc.relation.referencesGao, 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.spa
dc.relation.referencesGiorgetta, 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.spa
dc.relation.referencesGiorgetta, 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.spa
dc.relation.referencesGiorgi, 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.spa
dc.relation.referencesGiorgi, F., & Mearns, l. (1991). Approaches to regional climate change simulation: a review. Rev. Geophys., 29: 191-216.spa
dc.relation.referencesGiorgi, F., & Mearns, l. (1999). Introduction to special section: regional climate modeling revisited. Journal of Geophysical Research, Vol. 104, no: d6: 6335–6352.spa
dc.relation.referencesGiorgi, 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.spa
dc.relation.referencesGiorgi, F. (2005). Climate Change Prediction. Climatic Change, 73, 239–265.spa
dc.relation.referencesGiorgi, F. (2006). Regional climate modeling: status and perspectives, J. Phys. IV, 139, 101– 118.spa
dc.relation.referencesGiorgi, 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.spa
dc.relation.referencesGiorgi, F. (2010). Uncertainties in climate change predictions, from the global to the regional scale. Epi web conf. 9: 115–129.spa
dc.relation.referencesGiorgi, F., et al. (2012), RegCM4: model description and preliminary tests over multiple CORDEX domains, Clim. res., 52, 7–29.spa
dc.relation.referencesGiorgi, F. and Gutowski., W. (2015). Regional dynamical downscaling and the CORDEX initiative. Annual review of environment and resources, 40(1):467–490.spa
dc.relation.referencesGiorgi, F. and Gutowski., W. (2016). Coordinated experiments for projections of regional climate change. Curr. Clim. Change rep. 2: 202–210.spa
dc.relation.referencesGiorgi, F. and Gao, X. (2018). Regional earth system modeling: review and future directions, Atmos. Ocean. Sci. Lett., 11, 189–197.spa
dc.relation.referencesGiorgi, 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.spa
dc.relation.referencesGrell, G. (1993). Prognostic evaluation of assumptions used by cumulus parameterizations. mon. wea. rev., 121:764-787.spa
dc.relation.referencesGrell, A., Dudhia, J. and Stauffer, D. (1994). A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR technical note.spa
dc.relation.referencesGutowski, J., et al. (2016). WCRP Coordinated Regional Downscaling Experiment (CORDEX): un MPI de diagnóstico para CMPI6, Geosci. Model Dev., 9, 4087–4095.spa
dc.relation.referencesGuyennon, 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.spa
dc.relation.referencesHackenbruth, 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.spa
dc.relation.referencesHostetler, 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.spa
dc.relation.referencesHostetler, 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.spa
dc.relation.referencesHostetler, 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.spa
dc.relation.referencesHoltslag, 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.spa
dc.relation.referencesHurtado, A. (2009). Estimación de los campos mensuales históricos de precipitación en el territorio colombiano. Tésis de Maestría, Universidad Nacional de Colombia Sede Medellin.spa
dc.relation.referencesIDEAM, (2005). Clasificaciones climáticas y confort térmico anual. Atlas climatológico de Colombia. capítulo 2, 78-87.spa
dc.relation.referencesIPCC, (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.spa
dc.relation.referencesJones, 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.spa
dc.relation.referencesKain, J. and Fritsch J. (1990). A one-dimensional entraining/detraining plume model and its application in convective parameterization, J. Atmos. Sci., 47, 2784–2802.spa
dc.relation.referencesKain, J. (2004). The Kain–Fritsch convective parameterization: an update, J. Appl. Meteorol., 43, 170 181.spa
dc.relation.referencesKanamitsu, 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.spa
dc.relation.referencesKarmacharya, 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.spa
dc.relation.referencesKatzfey, 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.spa
dc.relation.referencesKerkhoff, C., Kuensch, H. R., & Schaer, C. (2014). Assessment of bias assumptions for climate models, J. Clim., 27, 6799–6818.spa
dc.relation.referencesKiehl, 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.spa
dc.relation.referencesLaprise, R. (2008). Challenging some tenets of regional climate modelling, Meteorol. Atmos. Phys., 100, 3–22.spa
dc.relation.referencesLaprise, 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.spa
dc.relation.referencesLatif, M. (2011). Uncertainty in climate change projections. J. Geochem. Explor. 110: 1–7.spa
dc.relation.referencesLaurent, 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.spa
dc.relation.referencesLawrence, P. and Chase, T. (2007). Representing a new model consistent land surface in the Community Land Model (CLM3.0). J. Geophys. Res., 112. g01023.spa
dc.relation.referencesLawrence, 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.spa
dc.relation.referencesLeón, G. (2000). Tendencia de la temperatura del aire en Colombia. Meteorología colombiana, n. 2, pp. 57-65.spa
dc.relation.referencesLeón, G. (2005). Verificación de los modelos meteorológicos. Nota técnica IDEAM, pp.12.spa
dc.relation.referencesLoveland, 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.spa
dc.relation.referencesLucas-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.spa
dc.relation.referencesLupo, 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).spa
dc.relation.referencesMartin, 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.spa
dc.relation.referencesMAVDT-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.spa
dc.relation.referencesMETED, (2008). Funcionamiento de los modelos de mesoescala. https://www.meted.ucar.edu/education_training/lesson/262.spa
dc.relation.referencesNOAA, (2012). Climate Model. Top Ten: breakthroughs. accessed 6/20/2013. retrieved from http://celebrating200years.noaa.gov/breakthroughs/climate_model/modeling_sch matic.html.spa
dc.relation.referencesOleson, 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.spa
dc.relation.referencesOleson, 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.spa
dc.relation.referencesOviedo, 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.spa
dc.relation.referencesPabón, 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.spa
dc.relation.referencesPabón, 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.spa
dc.relation.referencesPabón, 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.spa
dc.relation.referencesPal, 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.spa
dc.relation.referencesPesquero, 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.spa
dc.relation.referencesPosada, C. (2007). La adaptación al cambio climático en Colombia. Revista virtual Ledesma Rev. Ing. ISSN. 0121-4993.spa
dc.relation.referencesPoveda, 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.spa
dc.relation.referencesPrein, 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.spa
dc.relation.referencesPrömmel, 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.spa
dc.relation.referencesRatnam, 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.spa
dc.relation.referencesReynolds, 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.spa
dc.relation.referencesRoads, 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.spa
dc.relation.referencesRuiz, F. (2007). Escenarios de cambio climático, algunos modelos y resultados de lluvia para Colombia bajo el escenario A1B. Nota técnica. IDEAM, Bogotá, Colombia.spa
dc.relation.referencesRuiz, 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.spa
dc.relation.referencesRuiz, 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.spa
dc.relation.referencesRummukainen, M. (2016). Added value in regional climate modeling. Clim. Change. 7, 145–159. doi: 10.1002/wcc.378.spa
dc.relation.referencesSchneider, 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.spa
dc.relation.referencesSimmons, A., Uppala, D., and Kobayashi, S. (2007). Era-Interim: new ECMWF reanalysis products from 1989 onwards, ECMWF news., 110, 29–35.spa
dc.relation.referencesSmith, 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.spa
dc.relation.referencesSundqvist, 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.spa
dc.relation.referencesTaylor, K. (2001). Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183–7192, doi:10.1029/2000jd900719.spa
dc.relation.referencesTaylor, K. (2005). Taylor diagram primer: a brief 4-page overview which summarizes the important aspects of these useful plots.spa
dc.relation.referencesTiedtke, M. (1989). A comprehensive mass flux scheme for cumulus parameterization in large-scale. Models. Bul. Am. Meteorol. Soc., 117:1779-1800.spa
dc.relation.referencesTorma, 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.spa
dc.relation.referencesTiwari, 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.spa
dc.relation.referencesUppala, 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.spa
dc.relation.referencesUrrea, 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.spa
dc.relation.referencesUSAID, (2014). A review of downscaling methods for climate change projections. African and latin american resilience to climate change (ARCC).spa
dc.relation.referencesVichot, 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.spa
dc.relation.referencesWang, 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.spa
dc.relation.referencesWang, 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.spa
dc.relation.referencesWilby, R. & Dawson, C. (2004). Using SDSM version 3.1- a decision support tool for the assessment of regional climate change impacts. England, UK.spa
dc.relation.referencesWilks, D.S. (2006). Statistical methods in the atmospheric sciences. Academic Press, San Diego, pp. 148-151.spa
dc.relation.referencesWinterfeldt, 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.spa
dc.relation.referencesYnoue, R., Ambrizzi, T., Reboita, M. & Da Silva, G. (2017). Meteorología: conceptos básicos. Sao Paulo: taller de textos.spa
dc.relation.referencesZeng, 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.spa
dc.relation.referencesZeng, X. (2005). A prognostic scheme of sea surface skin temperature for modeling and data assimilation. Geophysical Research Letters, 32. l14605.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc550 - Ciencias de la tierra::551 - Geología, hidrología, meteorologíaspa
dc.subject.lembDepth-area-duration (hydrometeorology)eng
dc.subject.lembPrecipitación pluvialspa
dc.subject.lembHydrometeorologyeng
dc.subject.lembHidrometeorologíaspa
dc.subject.proposalCORDEXeng
dc.subject.proposalValor agregadospa
dc.subject.proposalModelo regional del climaspa
dc.subject.proposalParametrizacionesspa
dc.subject.proposalEstudio de sensibilidadspa
dc.subject.proposalDominiospa
dc.subject.proposalResoluciónspa
dc.subject.proposalAdded valueeng
dc.subject.proposalRegional climate modeleng
dc.subject.proposalParameterizationseng
dc.subject.proposalSensitivity studyeng
dc.subject.proposalDomaineng
dc.subject.proposalResolutioneng
dc.subject.unescoPronóstico meteorológicospa
dc.subject.unescoWeather forecastingeng
dc.titleValor agregado del modelamiento regional del clima sobre áreas caracterizadas por terreno complejo: precipitación sobre los Andes Colombianosspa
dc.title.translatedAdded value of modeling regional climate over areas characterized by complex terrain: precipitation over the Andes Colombianseng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
1067880634.2021.pdf
Tamaño:
7.63 MB
Formato:
Adobe Portable Document Format
Descripción:
Maestría en Ciencias - Meteorología

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
3.98 KB
Formato:
Item-specific license agreed upon to submission
Descripción: