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Climate Downscaling Over Nordeste, Brazil, Using the NCEP RSM97

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Climate Downscaling Over Nordeste, Brazil, Using the NCEP RSM97 15 FEBRUARY 2005 S U N E T A L . 551 Climate Downscaling over Nordeste, Brazil, Using the NCEP RSM97 LIQIANG SUN International Research Institute for Climate Prediction, Columbia University, Palisades, New York DAVID FERRAN MONCUNILL FUNCEME, Aldeota, Fortaleza, Brazil HUILAN LI International Research Institute for Climate Prediction, Columbia University, Palisades, New York ANTONIO DIVINO MOURA Instituto Nacional de Meteorologia—INMET, Brasilia, Brazil FRANCISCO DE ASSIS DE SOUZA FILHO FUNCEME, Aldeota, Fortaleza, Brazil (Manuscript received 17 November 2003, in final form 12 July 2004) ABSTRACT The NCEP Regional Spectral Model (RSM), with horizontal resolution of 60 km, was used to downscale the ECHAM4.5 AGCM (T42) simulations forced with observed SSTs over northeast Brazil. An ensemble of 10 runs for the period January–June 1971–2000 was used in this study. The RSM can resolve the spatial patterns of observed seasonal precipitation and capture the interannual variability of observed seasonal precipitation as well. The AGCM bias in displacement of the Atlantic ITCZ is partially corrected in the RSM. The RSM probability distribution function of seasonal precipitation anomalies is in better agreement with observations than that of the driving AGCM. Good potential prediction skills are demonstrated by the RSM in predicting the interannual variability of regional seasonal precipitation. The RSM can also capture the interannual variability of observed precipitation at intraseasonal time scales, such as precipitation in- tensity distribution and dry spells. A drought index and a flooding index were adopted to indicate the severity of drought and flooding conditions, and their interannual variability was reproduced by the RSM. The overall RSM performance in the downscaled climate of the ECHAM4.5 AGCM is satisfactory over Nordeste. The primary deficiency is a systematic dry bias for precipitation simulation. 1. Introduction northward in May, and moves out of Nordeste in June. Therefore, most of Nordeste experiences one rain sea- Nordeste, Brazil, extending from 2° to 12°S, 36° to son during the year, that is, from February to May. 43°W is a semiarid region with recurrent drought con- Previous investigations have firmly established that ditions. The climatological evolution of precipitation in sea surface temperature (SST) anomaly forcing is the Nordeste is directly affected by the migratory nature of primary factor responsible for the interannual cli- the Atlantic intertropical convergence zone (ITCZ). mate variability in Nordeste (Harzallah et al. 1996; The ITCZ starts to shift southward in late boreal sum- Hastenrath and Heller 1977; Mechoso et al. 1990; mer, reaches the north coast of Nordeste in February, Moron et al. 1996; Moura and Shukla 1981; Pezzi and continues to move southward in March, and reaches its Cavalcanti 2001; Ropelewski and Halpert 1996; Roucou southernmost position in April, then starts to retreat et al. 1996; Uvo et al. 1998; Ward and Folland 1991). The current atmospheric general circulation models (AGCMs) forced by observed SSTs can capture the Corresponding author address: Dr. Liqiang Sun, International Research Institute for Climate Prediction, Columbia University, large-scale circulation in northeast South America 61 Route 9W, Monell Bldg., Palisades, NY 10964. (Moron et al. 1998; Sperber and Palmer 1996). How- E-mail: [email protected] ever, they are unable to resolve the local precipitation © 2005 American Meteorological Society JCLI3266 552 JOURNAL OF CLIMATE VOLUME 18 patterns in Nordeste due to the coarse resolution (Chen et al. 1999; Hong and Leetmaa 1999; Mo et al. (Nobre et al. 2001). 2000; Sun et al. 1999a). Dynamical downscaling is one of the approaches Although dynamical downscaling provides enhanced used to investigate the precipitation variability at local details of climate simulations, there is a need for more scales. A high-resolution limited-area model is run for research to examine the regional model ability in simu- the region of interest, forced by the large-scale circula- lating observed local climate conditions and evaluate tion prescribed from a lower-resolution AGCM. The the statistical structures of climate signals at various strategy underlying this technique is that AGCMs can spatial and temporal scales to answer the question of provide the response of global circulation to large-scale whether predictability of climate systems is improved forcings, and nested regional models can account for with regional, over global, modeling. A logical ap- the effects of local, sub-AGCM grid-scale forcings proach is to use multiple AGCMs with multiple en- (Giorgi and Mearns 1999). Regional models have been sembles and force multiple regional models (Leung et tested for climate downscaling over North America, al. 2003). This task greatly exceeds our current compu- South America, Africa, Asia, and Europe (Fennessy tational limits. In this study, we use one AGCM and and Shukla 2000; Giorgi and Marinucci 1991; Hong et one regional model with 30-yr multiple ensembles to al. 1999; Kanamtisu and Juang 1994; Nobre et al. 2001; study the climate dynamical downscaling of precipita- Roads 2000; Seth and Rojas 2003; Sun et al. 1999a,b; tion in Nordeste. Our primary objectives are to find out Takle et al. 1999; Ward and Sun 2002). The simulation (i) is the regional model capable of producing large- of both finer spatial-scale details and overall monthly or scale information? seasonal mean precipitation were improved in the re- (ii) is probabilistic information improved in the re- gional models. Examination of other atmospheric vari- gional model compared to that in the AGCM? ables indicated that the regional model simulations (iii) is the finer spatial-scale information produced in were generally as good or better than those from the regional model skillful, or is it just “noise” on AGCMs alone. Finer spatial-scale features developed the top of the large-scale signal? in regional models are attributed to four types of (iv) is temporal character of variability skillful in the sources: (i) the surface forcing such as detailed topog- regional model? raphy, (ii) the nonlinearities presented in the atmo- This paper is organized as follows. Section 2 de- spheric dynamical equations, (iii) hydrodynamic insta- scribes the experimental design and observed data bilities (Denis et al. 2002), and (iv) noises generated at available for this study. Results from dynamical down- the lateral boundaries. scaling are discussed in section 3, and the summary and Studies that concern analysis of daily precipitation in concluding remarks are given in section 4. climate models are relatively few. Mearns et al. (1990) examined several versions of the AGCMs and found errors of too high and too low daily variability of pre- 2. Experimental design and data cipitation depending on the version investigated. A study by Gershunov and Barnett (1998) indicated that a. Experimental design the AGCM missed important aspects of the ENSO sig- The AGCM used in this study is the European Com- nal in seasonal statistics of daily precipitation, although munity–Hamburg (ECHAM) AGCM version 4.5, de- it is capable of capturing the ENSO signal in seasonal- veloped at the Max Planck Institute for Meteorology averaged precipitation. The analysis of daily precipita- (MPI) in Germany. The model was configured at tri- tion in AGCMs probably is of limited value, given the angular 42 (T42) spectral truncation, giving a spatial crude horizontal resolution (i.e., the model can neither resolution of about 2.8° lat ϫ lon, with 19 vertical layers resolve important topographic influences on precipita- (Roeckner et al. 1996). The ECHAM4.5 AGCM is one tion, nor synoptic-scale precipitation processes) and the of the forecast AGCMs used operationally at the Inter- crude parameterizations of precipitation (Mearns et al. national Research Institute for Climate Prediction 1995). However, the model parameterization schemes (IRI). It produces well the large-scale climate variabil- are steadily improving, and regional models have rela- ity over northeast South America (e.g., see http://iri. tively fine horizontal resolution. This mitigates some of columbia.edu/forecast/skill/SkillMap.html). the limitations of AGCMs, and examination of daily The regional model used in this study is the Regional precipitation may prove more fruitful. Giorgi and Mari- Spectral Model (RSM) version 97, developed at the nucci (1996) investigated the sensitivity of simulated National Centers for Environmental Prediction precipitation to model resolution and concluded that (NCEP) (Juang and Kanamitsu 1994). It is based on the the effect of model resolution on daily precipitation primitive equations in terrain-following sigma coordi- statistics is evident. Several studies indicate that high- nates. The NCEP RSM is one of the regional models resolution regional models appear to be capable of im- involved in the IRI/Applied Research Centers (ARCs) proving the statistics of high-frequency precipitation to- Regional Model Intercomparison over Brazil, and the ward more realism on finer spatial and time scales overall performance of the RSM is good among all the 15 FEBRUARY 2005 S U N E T A L . 553 regional models (Roads et al. 2003). A case study with and Giorgi 1998). The test simulations show that the the RSM on downscaling of ECHAM AGCM seasonal Atlantic ITCZ is very critical for the precipitation simu- prediction over Nordeste also produced encouraging lation in Nordeste. The entire tropical Atlantic Ocean results (Nobre et al. 2001). has to be included in the domain. The displacement of The NCEP RSM97 is used to downscale ECHAM4.5 the ITCZ occurs when only a portion of tropical At- AGCM simulations. The one-way nesting of the NCEP lantic Ocean is included in the domain. With the reso- RSM97 into the ECHAM4.5 AGCM is done in a way lution of 60 km, the main topographical features are that is different from convectional methods, which use resolved by the RSM. The São Francisco Valley runs global model results along the lateral boundary zone roughly in a south–north direction, bounded on the only.
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