A Global Atmospheric Analysis Dataset Downscaled from the NCEP–DOE Reanalysis

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JUNG-EUN KIM Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, and NOAA/Earth System Research Laboratory, Boulder, Colorado, and Department of Atmospheric Sciences, Global Environment Laboratory, Yonsei University, Seoul, South Korea

SONG-YOU HONG Department of Atmospheric Sciences, Global Environment Laboratory, Yonsei University, Seoul, South Korea

(Manuscript received 21 September 2011, in ﬁnal form 17 November 2011)

ABSTRACT

A global atmospheric analysis dataset is constructed via a spectral nudging technique. The 6-hourly Na- tional Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis from January 1979 to February 2011 is utilized to force large-scale information, whereas a higher-resolution structure is resolved by a global model with improved physics. The horizontal resolution of the downscaled data is about 100 km, twice that of the NCEP–DOE reanalysis. A comparison of the 31-yr downscaled data with reanalysis data and observations reveals that the downscaled precipitation climatology is improved by correcting inherent biases in the lower-resolution reanalysis, and large-scale patterns are preserved. In addition, it is found that global downscaling is an efﬁcient way to generate high-quality analysis data due to the use of a higher-resolution model with improved physics. The uniqueness of the obtained data lies in the fact that an undesirable decadal trend in the analysis due to a change in the amount of observations used in reanalysis is avoided. As such, a downscaled dataset may be used to investigate changes in the hydrological cycle and related mechanisms.

1. Introduction Forecasts (ECMWF) (Uppala et al. 2005), and the Japan Meteorological Agency (Onogi et al. 2007). Reanalysis A reanalysis dataset is a continually updating gridded datasets have been extensively used by the weather and dataset that represents the state of the earth’s atmo- climate research communities. sphere; it incorporates observations and numerical Specific model variables such as the radiative flux weather prediction (NWP) model outputs. When all have been utilized to obtain useful information on available observation data and short-range forecasts physical mechanisms in the atmosphere (Walsh et al. are utilized, a frozen and combined data assimilation/ 2009). These long-term data, along with in situ observa- forecast model produces an optimal and uniform quality tions, are particularly useful in expanding our knowledge analysis over a long period of time. Reanalysis projects of changes in atmospheric circulations and precipitation have been conducted in operational centers such as the activities due to global warming (Cai and Kalnay 2005; National Centers for Environmental Prediction (NCEP) Wang and Mehta 2008). However, spurious interdecadal (Kalnay et al. 1996; Kistler et al. 2001; Kanamitsu et al. changes caused by observational discontinuities have 2002b), the European Centre for Medium-Range Weather been found in reanalysis data (Kistler et al. 2001; Screen and Simmonds 2011). For example, the use of satellite data and its continuous refinement hinder the examination Corresponding author address: Song-You Hong, Department of Atmospheric Sciences, Yonsei University, Seoul 120-749, South of climate variability and change. Screen and Simmonds Korea. (2011) explicitly documented a discontinuity in the 40-yr E-mail: [email protected] ECMWF Re-Analysis (ERA-40) that leads to significantly

DOI: 10.1175/JCLI-D-11-00534.1

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TABLE 1. Physics package for the reanalysis and downscaled data.

RA (Kanamitsu et al. 2002b) DA (This study) Deep convection Pan and Wu (1995); Hong and Pan (1998) Park and Hong (2007); Byun and Hong (2007) Shallow convection Tiedtke (1984) Modiﬁed version of Tiedtke (1984) Longwave radiation Fels and Schwarzkopf (1975) Chou et al. (1999) Shortwave radiation Chou and Lee (1992, 1996) Chou and Suarez (1999); Chou and Lee (2005) Clouds Campana et al. (1994) Hong et al. (1998) Vertical diffusion Hong and Pan (1996) Hong et al. (2006) Noh et al. (2003) Stable boundary layer Louis (1979) Hong (2010) Gravity wave drag (orography) Alpert et al. (1988) Kim and Arakawa (1995); Hong et al. (2008) Gravity wave drag (convection) N/A Chun and Baik (1998); Jeon et al. (2010) Land surface model Pan and Mahrt (1987) Yhang and Hong (2008); Ek et al. (2003) Ocean surface model Charnock (1955) Kim and Hong (2010)

exaggerated warming in the Arctic. Such exaggerated year. A summary of the obtained results and conclusions warming isspecifically due to the discontinuity of satellite are given in section 4. data refinement in 1997. Thorne and Vose (2010) raised this critical issue and argued that a substantial re- 2. Model and dynamical downscaling procedure thinking of the current strategy for producing reanalysis a. Global model products is required. An alternative approach for overcoming the afore- The model used in this study is a version of the NCEP mentioned drawbacks in long-term datasets is to use Global Spectral Model (GSM; Kanamitsu et al. 2002a); dynamical global downscaling. Developed by Yoshimura its evolution has followed a path that is rather separate and Kanamitsu (2008, hereafter YK08), dynamical global from the ongoing development of operational NCEP downscaling is a global version of a spectral nudging medium-range forecast models. Since 2000, the model technique used in a regional spectral model. YK08 pro- has been used to modify message passing interface (MPI) posed the global downscaling method and nudging con- computing methods and implement realistic physical figuration and performed downscaling experiments during processes at Yonsei University. The model has great 2001 using a T248 (about 50-km resolution) global model flexibility with multiple platforms in either thread mode with the NCEP–Department of Energy (DOE) reanalysis or message parallel mode (Park et al. 2008). It also has (RA2; Kanamitsu et al. 2002b) as the large-scale forcing. multiple options with regards to physics parameteriza- An evaluation with high-resolution observations showed tion. The physics of the model include longwave and that the monthly averaged global temperature field and shortwave radiation, cloud–radiation interactions, daily variability in the downscaled precipitation were sig- planetary boundary layer (PBL) processes, shallow nificantly improved. convection, gravity wave drag, simple hydrology, and As an extension of the YK08 method, we generate vertical and horizontal diffusions. Detailed descrip- new 31-yr global analysis data on the order of approxi- tions of the physics package used in this study are given mately 100 km. The data are forced by RA2 using dy- in Table 1. namical global downscaling. One of the main objectives b. Dynamical global downscaling of this study is to demonstrate that dynamical global downscaling is capable of reproducing long-term global The dynamical global downscaling method is a mod- reanalysis data without expensive high-resolution data ified global version of the scale-selective bias correction assimilation. Another goal of this work is to investigate (SSBC) scheme (Kanamaru and Kanamitsu 2007). Prior the sensitivity of the physics in the downscaling. Brief to the downscaling process, the driving reanalysis data descriptions of both the downscaling method and the should be preprocessed. The surface pressure is re- employed model are given in section 2. An evaluation of calculated with the hydrostatic relationship to provide nudging variables and the precipitation against global higher-resolution topography in the high-resolution observations is presented in section 3. For a detailed global model. In addition, the temperature, humidity, examination of how downscaled fields fit to observations, and wind fields are vertically interpolated to the sigma a 1-month analysis (July 2001) is conducted. Although the levels of the new model. The following are the equa- results from other months are not shown, the capability tions for nudging when a fully implicit time scheme is of the downscaling method is similar throughout the used:

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m5M observations in the analysis data are interpolated to the iml f(l,f) 5 å A(m,f)e , with location of the radiosonde stations. The analysis is m52M performed over two representative regions: East Asia A f (208–508N, 1008;1508E) and North America (208– (m,8) 508N, 1208;608W). > 2pR cosf <>A jmj . E f (m,f) L As shown in Fig. 1, the bias in the temperature mag- 5 , nitude for the downscaled dataset (designated by dashed > 2pR cosf :> 1 E lines) is quite similar to that in the reanalysis over both [Af (m,f) 1 aAa(m,f)] jmj # a 1 1 L regions. In addition, the wind field over 900 hPa is significantly improved. On the other hand, the humidity where f is a physical variable (full field), A is the Fourier profiles exhibit severe dry biases with a maximum around coefficient, and the subscripts f and a denote forecast 850 hPa, and the ranges for the downscaled data are and analysis (driving data), respectively. Here l, u, RE, greater than those for the reanalysis. Drying within the m, and M denote the longitude, latitude, radius of the entire troposphere is related to the warm bias of DA126. earth, wavenumber, and the truncation wavenumber, However, the temperature bias is relatively small, which respectively; also, a is a nudging coefficient and L is the may be due to the fact that the model physics have a more critical nudging scale where waves longer than L will be profound effect on the moisture fields than the temper- nudged. The nudging configuration in this study is the ature fields (Hong and Pan 1996). Considering that the same as that employed in YK08. Global nudging is ap- humidity is biased wet for the model (Wang et al. 2002), plied only to waves whose physical wavelengths are DA126 can lessen the systematic wet bias in the rean- 2000 km or longer with a nudging coefficient a of 0.9 for alysis data. This result is confirmed because the large- wind (U and V), temperature, and surface pressure. This scale variables in the reanalysis were successfully retained provides a nudging effect of about 50% for specified in the global downscaling. wavelengths. The quality of the downscaled precipitation is also examined. The Climate Prediction Center (CPC) Merged c. Integration Analysis of Precipitation (CMAP) data (Xie and Arkin The downscaling experiment is performed with the 1997), with a 2.08 resolution, and the Global Precipitation T126 (about 100 km) and 28 sigma levels as the down- Climatology Project (GPCP), which provides 18 daily scaling model, along with 6-hourly snapshots of prog- precipitation estimates over both land and ocean (Huffman nostic variables from RA2 (resolution of T62, about et al. 2001), are used to verify the global distribution and 200 km) and 28 levels as the lateral forcing. The simu- zonal mean amounts of precipitation. For a quantitative lations were forced by the sea surface temperatures evaluation of the land precipitation, the CPC unified gauge (SSTs) obtained from observations with a resolution of analysis (Chen et al. 2008), which covers the entire globe at 18 (Reynolds and Smith 1994) and National Center for a daily interval, is used. Atmospheric Research (NCAR) ice data. The annual variations in the analyzed and observed Downscaling is formed in three streams: 1979–89, precipitation over the lands of East Asia and North 1987–99, and 1998–2011. To avoid a spinup of soil mois- America are shown in Fig. 2. In contrast to RA62, which ture, the three streams overlap over 2-yr periods, 1987–88 overestimated the precipitation over all of the years, the and 1997–98. Therefore, the downscaled data cover 31 precipitation amounts of DA126 closely represent the years from January 1980 to February 2011. For conve- ranges of the observed precipitation. The domain- nience, RA2 and the downscaled datasets in this study are averaged amount of precipitation and the root-mean- referred to as RA62 and DA126, respectively. square error (RMSE) also reveal an overall improvement (not shown). The temporal correlations for RA62 and DA126 against the CPC are 0.56 and 0.43 over East Asia, 3. Results which are slightly lower than the corresponding values for To examine the accuracy of primitive variables such as the reanalysis. The temporal correlations for RA62 and the wind, temperature, and relative humidity in the DA126 over North America are 0.37 and 0.46, re- downscaled data, each downscaled variable and the spectively. It should be noted that the variabilities in the corresponding radiosonde observation data (RAOBs) reanalysis and the downscaled precipitation are highly are compared for July 2001. The RAOBs from the correlated even though the two datasets were generated University of Wyoming Web site (UWYO; http://weather. by different physics. Correlation coefficients between uwyo.edu/) are processed via simple quality control. RA62 and DA126 are 0.87 and 0.84 for East Asia and For better verification, the four grid points nearest to the North America, respectively. This suggests that the annual

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21 21 FIG. 1. Vertical profiles of the temperature (K), wind magnitude (m s ), and specific humidity (g kg ) for RA62 (solid line) and DA126 (dashed line) relative to radiosonde observations over (a) East Asia and (b) North America. variability in the modeled precipitation may be mostly due is exaggerated in the reanalysis precipitation, is reduced to large-scale fields. in all downscaled datasets. On the other hand, the Another experiment is conducted to investigate the DA126 results are similar to those of the additional cause of the improvement in the downscaled precipitation. experiment (DA62). It should be noted that the physics This experiment, referred to as DA62, is downscaled using in RA62 are different from those of the downscaled data a physics package and nudging configuration that are (see Table 1). This demonstrates that the precipitation is identical to those used for DA126, but with different more sensitive to the model physics than the model resolutions (T62, resolution of about 200 km). The ex- resolution. periment is integrated for 2 months from June 2001 to The observed global distribution of the analyzed pre- give a 1-month spinup time, and the analysis is conducted cipitation in July 2001 and the corresponding observa- only for July 2001. The 1-month averaged zonal mean tions are shown in Fig. 4. The reanalysis precipitation is precipitation data from all downscaled datasets are characterized by wetness in most continental regions, obtained and compared with the observed precipitation with the exception of some regions of central Africa and (Fig. 3). The two downscaled datasets are found to be the northern part of East Asia (Fig. 4b). However, the closer to the observations than the reanalysis data. The biases are obviously reduced in Eurasia, India, and North intertropical convergence zone (ITCZ) intensity, which America by dynamical downscaling (Figs. 4d,f). Such an

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FIG. 2. Interannual variability of RA62 and DA126 for East Asia (208–508N, 1008;1508E) and North America (208–508N, 1208;608W).

improvement is apparent regardless of the resolution. resolutions with physics. This indicates that the down- Furthermore, the quantities of precipitation for the scaled dataset can overcome both the systematic biases main rainband, including the ITCZ over the ocean, are in reproducing prognostic components in a model and realistic. The correlation coefficients between each da- inhomogeneities introduced by the increasing amount taset (RA62, DA126, and DA62) and the GPCP are all of observations. Inhomogeneous differences in the sea greater than 0.7, which indicates that the overall spatial level pressure between RA2 and DA126 over the data- pattern is reproduced well by all datasets. The results sparse regions, which showed up when new data became confirm that the downscaled data may not only retain available (shown in Figs. 1 and 2 of Kistler et al. 2001), the accuracy of large-scale fields but also correct erro- confirm that the artificial trends of the conventional re- neous precipitation amounts that existed in the original analysis data are significantly alleviated in the downscaled reanalysis.

4. Summary and conclusions A 31-yr downscaled dataset is created using a T126L28 (about 100-km resolution) global model forced by RA2 with T62L28 as the large-scale forcing. The ﬁt of the dynamical global downscaled dataset to observations is also investigated. A comparison of the downscaled dataset to radiosonde observations reveals improvements with respect to temperature and wind. The improvement in the downscaled precipitation is more prominent. The spatial distribution of the downscaled precipitation adequately reproduces that from observations. In addition, regional details over land and tropical oceans are better matched with observations because of the improved physics. The datasets from the dynamical global downscaling are supposed to describe large-scale ﬂow consistent with near-perfect circulation in the reanalysis, yet improvements on synoptic or smaller scales including precipitation are achieved because of the model at higher FIG. 3. Zonally averaged precipitation for July 2001.

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21 FIG. 4. Global distribution of the monthly precipitation (mm day ) in July 2001. (left) (a) GPCP, (c) DA126, and (e) DA62; (right) the difference between (b) RA2 and GPCP, (d) DA126 and RA2, and (f) DA62 and RA2.

dataset (not shown). Therefore, the dataset employed in like to acknowledge the support from the KISTI super- this work can be utilized for long-term climate research computing center through the strategic support program related to recent climate shifts. While the reanalyzed for the supercomputing application research (KSC-2010- precipitation may have some bias due to both inade- G2-0001). quacies in the physical parameterizations of the forecast model used in the reanalysis and the observational errors of large-scale variables, it can provide some important REFERENCES information on global climate change without repeated Alpert, J. C., M. Kanamitus, P. M. Calpan, J. G. Sela, G. H. White, high-cost data assimilation. and E. Kalnay, 1988: Mountain induced gravity wave drag parameterization in the NMC medium-range forecast model. Preprints, Eighth Conf. on Numerical Weather Prediction, Acknowledgments. This work was funded by the Basic Baltimore, MD, Amer. Meteor. Soc., 726–733. Byun, Y.-H., and S.-Y. Hong, 2007: Improvements in the subgrid- Science Research Program through the National Research scale representation of moist convection in a cumulus pa- Foundation of Korea (NRF) funded by the Ministry of rameterization scheme: The single-column test and impact Education, Science, and Technology. The authors would on seasonal prediction. Mon. Wea. Rev., 135, 2135–2154.

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