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Impact of Intermittent Spectral Nudging on Regional Climate Simulation

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Impact of Intermittent Spectral Nudging on Regional Climate Simulation JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D10103, doi:10.1029/2010JD015069, 2011 Impact of intermittent spectral nudging on regional climate simulation using Weather Research and Forecasting model Dong‐Hyun Cha,1 Chun‐Sil Jin,1 Dong‐Kyou Lee,1 and Ying‐Hwa Kuo2 Received 16 September 2010; revised 17 February 2011; accepted 25 February 2011; published 20 May 2011. [1] This study examines simulated typhoon sensitivities to spectral nudging (SN) to investigate the effects on values added by regional climate models, which are not properly resolved by low‐resolution global models. SN is suitably modified to mitigate its negative effects while maintaining the positive effects, and the effects of the modified SN are investigated through seasonal simulations. In the sensitivity experiments to nudging intervals of SN, the tracks of simulated typhoons are improved as the SN effect increases; however, the intensities of the simulated typhoons decrease due to the suppression of the typhoon developing process by SN. To avoid such suppression, SN is applied at intermittent intervals only when the deviation between the large‐scale driving forcing and the model solution is large. In seasonal simulations, intermittent SN is applied for only 7% of the total time steps; however, this results in not only maintaining the large‐scale features of monsoon circulation and precipitation corresponding to observations but also improving the intensification of mesoscale features by reducing the suppression. Citation: Cha, D.‐H., C.‐S. Jin, D.‐K. Lee, and Y.‐H. Kuo (2011), Impact of intermittent spectral nudging on regional climate simulation using Weather Research and Forecasting model, J. Geophys. Res., 116, D10103, doi:10.1029/2010JD015069. 1. Introduction natural characteristics of specific regions. For example, complex topography and land use, warm local sea surface [2] Since the late 1980s, regional climate models (RCMs), temperature (SST), and strong monsoon and typhoons can which can reproduce regional or local details embedded in cause significant systematic errors in long‐term regional cli- low‐resolution large‐scale driving forcings (e.g., general mate simulations over East Asia [Cha and Lee, 2009; Hong circulation model and global reanalysis data), have been and Juang, 1998; Suh and Lee, 2004; Zhong, 2006]. generally used in a number of climate studies. RCMs can [4] An important issue concerning long‐term RCM simu- improve simulation at the regional scales, because they can lations longer than the seasonal scale is the lateral boundary generate added values beyond the highest resolved wave- condition, which controls the consistency between the model length of the global model through detailed topography solution and the large‐scale driving forcing. Since most information, higher model resolution, and sophisticated RCMs have been developed based on limited area models, physical processes. Therefore, RCMs have been used not they generally employ the traditional relaxation method only to reproduce severe weather and extreme climate events proposed by Davies [1976] as a lateral boundary condition. but also to project regional climate change by dynamically This consists of applying a Newtonian nudging, which drives downscaling low‐resolution global model and reanalysis data the model solution toward the large‐scale driving forcing [e.g., Feser and von Storch, 2008; Giorgi, 1990; Giorgi et al., within lateral buffer zones. There have been several studies 1994; Lee et al., 2004; Leung and Ghan, 1999; Wang et al., on the traditional relaxation method that modified the size 2003]. of the buffer zone and weighting function of Newtonian [3] Although considerable efforts have been devoted to nudging. For example, Giorgi et al. [1993] modified the their development and improvement, most RCMs have relaxation technique by which a wider buffer zone was systematic errors that are associated with uncertainties in adopted in the upper troposphere rather than in the middle dynamics, physical parameterization, boundary condition, and lower troposphere. initialization, domain choice, and model resolution of the [5] Spectral nudging (SN) has recently been applied as an numerical models [Giorgi and Mearns, 1999; Wang et al., addition to the traditional lateral boundary condition (i.e., 2004]. In addition, systematic errors in the RCMs can result relaxation method) to ensure coherence of the large scales from the strong internal forcings generated by the peculiar simulated by the regional climate model with those of the driving data. To provide consistency between large‐scale 1Atmospheric Sciences Program, School of Earth and Environmental driving fields and nested model solutions, Kida et al. [1991] Sciences, Seoul National University, Seoul, South Korea. and Sasaki et al. [1995] introduced an alternative approach, 2National Center for Atmospheric Research, Boulder, Colorado, USA. in which the large‐scale fields of the model solutions throughout the entire model domain, while the regional Copyright 2011 by the American Geophysical Union. model generates the higher frequencies. Von Storch et al. 0148‐0227/11/2010JD015069 D10103 1of11 D10103 CHA ET AL.: IMPACT OF INTERMITTENT SPECTRAL NUDGING D10103 [2000] used different method from these studies in terms of section 3, the effects of SN on typhoon simulations are the nudging coefficients, which were applied above analyzed through sensitivity tests to the nudging intervals. In 850 hPa with increasing strength for higher model levels. section 4, intermittent SN to reduce the negative effects of A number of studies have shown that SN can improve the SN is introduced, and its effects on seasonal simulation are RCM performance in simulating the mean features of the investigated. Finally, the summary and conclusions are forced large‐scale climate. Miguez‐Macho et al. [2005] given in section 5. showed that implementing SN can successfully improve the spatial pattern of simulated precipitation. Lee et al. [2004], Kang et al. [2005], and Tang et al. [2010] showed 2. Model and Experiments that RCMs based on the MM5 model can reproduce extreme [8] The RCM used in this study is based on the WRF summer precipitation events over East Asia by providing model version 2.2, which is developed by the National appropriate large‐scale circulations resulting from the effect Center for Atmospheric Research (NCAR). To improve the of SN. Feser and von Storch [2008] demonstrated that SN performance of the model, the SN method proposed by von has an positive effect on reducing the track distance error of Storch et al. [2000] is used along with the relaxation method RCM‐simulated typhoons, and Knutson et al. [2007] for an alternative boundary condition. SN is applied to the showed that a RCM with SN improved the interannual long‐wave spectral regimes (wavelength > 1000 km) of variability of hurricane occurrences by decreasing the number the horizontal wind components itself (not the tendency) at of simulated hurricanes in inactive seasons. To reduce every integral time step over the entire model domain, which systematic large‐scale errors in a regional spectral model, is expressed as Kanamaru and Kanamitsu [2007] recently proposed a * similar approach, the scale selective bias correction (SSBC) RðÞ¼LG \ LR ½1 À ðÞ R ðÞþLG \ LR ðÞ GðÞLG \ LR ; ð1Þ method, in which the errors in large‐scale horizontal wind components are reduced using spectral damping to the ten- where LG and LR are the long‐wave spectral regimes in the dency, and the area mean biases of mass fields are forced to global and regional models, respectively. aG, aR, and a*R zero. In addition, Kanamitsu et al. [2010] modified the are variables corresponding to large‐scale driving fields, SSBC method where the nudging is applied only to the nudged fields, and simulated fields from the regional mod- rotational wind components itself (not the tendency) and els, respectively. The nudging coefficient h is a function of the area average moisture correction is excluded, and they height and is given by h(s) = 0.05(1 − s)2, where s is the showed new method played a role in reducing the systematic vertical coordinate. Therefore, the nudging weighting is errors in the interannual variabilities of simulated height, smaller at the lower troposphere than the upper troposphere, temperature, and wind fields. indicating the relatively weak impact of SN at low levels. [6] Up to now, most of previous studies on SN focused on For SN implementation, a module is added to the WRF its positive effects in regional climate simulations. In prin- model; the model solution and large‐scale driving forcing ciple, SN should not impede the ability of the RCM to are spectrally decomposed by the Fourier transform and develop regional and small‐scale features superimposed only their long‐wave regimes (wavelength > 1000 km) are on the large‐scale driving conditions [Biner et al., 2000; recomposed on the basis of equation (1) at every integral von Storch et al., 2000]. However, SN may also have neg- time step. ative effects by impeding the development of the intrinsic [9] The model domain has 240 × 200 grid points in each small‐scale features reproduced by RCMs, which are not of the zonal and meridional directions with a horizontal grid included in the large‐scale driving forcing. Alexandru et al. spacing of 30 km, and covers East Asia and the WNP [2009] indicated a noticeable reduction of precipitation ex- (Figure 1). Ten grid points are used for lateral boundary tremes as a side effect of SN, and these effects are
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