Sensitivity of CWRF simulations of the China 1998 summer flood to cumulus parameterizations

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1 Sensitivity of CWRF simulations of the China 1998 summer flood to cumulus parameterizations Shuyan Liu a,b,c, Wei Gao *b,d, Xin-Zhong Liang e, Hua Zhang c, and James Slusser d a State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, China; b Sino-US Cooperative Center for Remote Sensing-Nanjing, Nanjing University of Science and Technology, Nanjing, China; c Laboratory for Climate Studies, China Meteorological Administration, Beijing, China; d USDA UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University, Colorado, U.S.A; e Illinois State Water Survey, Illinois Department of Natural Resources and University of Illinois at Urbana-Champaign, U.S.A ABSTRACT Better understanding the dynamics of the East Asian monsoon system is essential to address its climate variability and predictability. Regional climate models are useful tools for this endeavor, but require a rigorous evaluation to first establish a suite of physical parameterizations that best simulate observations. To this end, the present study focuses on the CWRF (Climate extension of WRF) simulation of the 1998 summer flood over east China and its sensitivity to cumulus parameterizations on CWRF performance. The CWRF using the Kain-Fritsch and Grell-Devenyi cumulus schemes both capture the observed major characteristics of geographic distributions and daily variations of precipitation, indicating a high credibility in downscaling the monsoon. Important regional differences, however, are simulated by the two schemes. The Kain-Fritsch scheme produces the better precipitation patterns with smaller root-mean-square errors and higher temporal correlation coefficients, while overestimating the magnitude and coverage. In contrast, the Grell-Devenyi ensemble scheme, using equal weights on all closure members, overall underestimates rainfall amount, suggesting for future improvement with varying weights depending on climate regimes. Keywords: Regional climate model, CWRF, cumulus parameterization, precipitation, flood 1. INTRODUCTION Regional Climate Models (RCMs) derive benefits over global climate models through a more accurate representation of regional climate forcing, achieved through higher resolution orography, land-water contrasts and land surface characteristics. Regional forcing can produce statistically significant climate signals, particularly for processes forced directly by topography including orographic rainfall and monsoon circulations. Such high resolution climate scenarios are important for resource management and impact assessment. Better resolved small-scale processes can have improved large-scale impacts and, in addition to downscaling climate information, RCMs can be used to study the upscale impact of regional forcing on the large-scale climate. Thus RCMs downscaling skill is the most important issue. Many of RCMs deficiencies are sensitive to the representation of physical processes, especially cumulus, radiation and surface parameterizations [1],[2],[3], and in principle the model deficiencies can be substantially reduced or eventually eliminated through better process understanding and resultant model improvement. Liang et al. [4] showed that for both the present and future climate simulations, the CMM5 results are sensitive to the cumulus parameterization, with strong regional dependence. The paper indicated that the deficiency in representing convection is likely the major reason for the PCM s unrealistic simulation of U.S. precipitation patterns and perhaps also for its large warming in the central U.S. Thus the adequate representation of convective processes is particularly important in RCMs. East Asia has distinct cumulus characteristics critical to precipitation due to the complex topography and a carefully selected cumulus convection scheme will improve model performance. There are several different convective * Address correspondence to: wgao@uvb.nrel.colostate.edu, phone (970) , fax (970) Remote Sensing and Modeling of Ecosystems for Sustainability III, edited by Wei Gao, Susan L. Ustin, Proc. of SPIE Vol. 6298, 62981I, (2006) X/06/$15 doi: / Proc. of SPIE Vol I-1

2 parameterization schemes widely used. This study compares the CWRF performance using the Kain-Fritsch (KF), and Grell-Devenyi (GD) cumulus schemes, focusing on simulations of precipitation during the 1998 summer flood over east China. 2. MODEL SIMULATIONS AND OBSERVATIONS The CWRF is the Climate extension of the Weather Research and Forecasting (WRF) model [5], incorporating inclusively all WRF functionalities for numerical weather prediction (NWP) while enhancing the capability for climate applications. As such, the CWRF can be applied to both weather forecasts and climate predictions. An introductory overview of the CWRF can be found in Liang et al. [7],[8],[9], while the model configuration specifically used in this study was described in Liu [6]. The CWRF has demonstrated greater capability and better performance in simulating regional climate of the U.S. and China than the CMM5 and RegCM3 [2],[6],[10]. The actual RCM performance, however, is region-dependent and is sensitive to cumulus parameterization schemes (CU), whose skills are highly climate-regime selective [4]. The Grell scheme [11] realistically simulates the nocturnal precipitation maxima over the central U.S. and the associated eastward propagation of convective systems, whereas the Kain and Fritsch [12] schemes are more accurate for the late afternoon peaks in the Southeast [2]. Summer rainfall amounts in the North American monsoon region are very poorly simulated by the Grell scheme but realistically reproduced by the Kain-Fritsch scheme, whereas rainfall amounts from moist convection in the Southeast are underestimated by the former and overestimated by the latter [3]. Such drastic contrasts motivate an explicit comparison of CWRF simulations of China east China monsoon rainfall using different cumulus schemes. Hereafter, the CWRF simulations using Kain-Fritsh and Grell-Devenyi ensemble schemes, with all other dynamic and physical configurations being identical, are referred to as KF and GD, respectively. The simulations cover the period of May 1 through August 31, 1998 with two nested computational domains (Fig. 1). The outer and inner meshes contain 7260 and 7391 points and have a horizontal grid spacing of 90 and 30km, respectively; both meshes incorporate 31 identical vertical layers where the model top is located at 50hPa. The initial and lateral boundary conditions used in this simulation are constructed from the NCEP/NCAR reanalysis data. Liang et al. [10] and Liu [6] showed that RCM simulations are less sensitive to domain choice when the lateral boundary conditions are based on the NCEP/NCAR Reanalysis data rather than the ECMWF reanalysis. The NCEP/NCAR reanalysis data are the 2.5 longitude 2.5 latitude grid and pressure-level product at a 6-hourly interval and were provided to CWRF via a relaxation method with a 13 grid points. The 1998 summer was characterized by extreme flood conditions, which were identified with physical mechanisms at both the planetary and local scales and thus is an ideal case for evaluation of CWRF performance. The CWRF results are compared with both the NCEP/NCAR reanalysis (driving fields) and rain gauge station data available over the model domain to demonstrate the downscaling capability and model biases. Figure 1. CWRF domain design nested with the outer and inner meshes at a grid spacing of 90 and 30 km, respectively. Outlined are also three key diagnostic regions over east China, ºE: North China (NC, 32-40ºN), Yangtze River Basin (YRB, 26-32ºN), South China (SC, 20-26ºN). The KF scheme used in this study is based on Kain and Fritsch (1990) [13] and Kain and Fritsch (1993) [12] with recent modifications based on the Eta model tests. As with the original KF scheme, it utilizes a simple cloud model with moist Proc. of SPIE Vol I-2

3 updrafts and downdrafts, including the effects of detrainment, entrainment, and relatively simple microphysics. A minimum entrainment rate is imposed to suppress widespread convection in marginally unstable, relatively dry environments, and the entrainment rate is allowed to vary as a function of low-level convergence. Shallow (no precipitating) convection is allowed for any updraft that does not reach minimum cloud depth for precipitating clouds; this minimum depth varies as a function of cloud-base temperature. Grell and Devenyi [14] introduced an ensemble cumulus scheme in which effectively multiple cumulus schemes and variants are run with each grid box and then the results are averaged to give the feedback to the model. The schemes are all mass-flux type schemes, but with different updraft and downdraft entrainment and detrainment parameters, and precipitation efficiencies. These differences in static control are combined with differences in dynamic control, which is the method of determining cloud mass flux. The dynamic control closures are based on convective available potential energy (CAPE or cloud work function), low-level vertical velocity, or moisture convergence. Those based on CAPE either balance the rate of change of CAPE or relax the CAPE to a climatological value, or remove the CAPE in a convective time scale. The moisture convergence closure balances the cloud rainfall with the integrated vertical advection of moisture. Another is a control trigger, where the minimum cap strength permits the convection to be varied. 3. SIMULATION RESULTS 3.1 General evaluation Table 1 compares observed (China station data) with CWRF simulated rainfall averaged over the three regions for the two sensitivity experiments. For the three regions (namely SC, YRB and NC) and the three statistics, KF results are reasonably realistic, with basically smaller percentage bias (Pbias), RMS error and higher correlation coefficient (CC) than GD. For Pbias, KF and GD showed quite distinct characteristics; for South and North China, KF overestimates precipitation while GD has a more prominent dry bias than the observation. The results indicated that cumulus precipitation mechanisms are different in various regions. Table 1. The comparison of observed and the two CU schemes simulated rainfall (percentage biases (Pbias,%), Correlation Coefficient (CC,%) and Root Mean Square error (RMS, mm/day) over the three regions during 1 May 31 August, Statistics Region KF GD Pbias (%) CC (%) RMS (mm/day) NC YRB SC NC YRB SC NC YRB SC Spatial distribution of monthly mean precipitation Figure 2 illustrates the observed (OBS), CWRF-KF and CWRF-GD simulated monthly mean precipitation from May to August, The CWRF using both KF and GD schemes capture the major characteristics of this flood. The north progress of the monsoonal rainbelt is well reproduced, that is rain located at South China and south of Yangtze River Basin in May and June, along Yangtze River Basin in July and then the rainbelt was between Yangtze River Basin and Yellow River. However, there exist important regional differences. The KF scheme produces more extensive and heavier precipitation than observations for all of the four months. In contrast, the GD scheme underestimates the precipitation over North and South China. Both schemes overestimate rainfall over the Yangtze-River basin. In terms of root-mean-square errors and temporal correlation coefficients from daily precipitation data, the KF is overall more realistic than GD. While for May, GD captured the South China rain distribution more realistic than KF. And in August, the extend and magnitude of rainbelt pattern are also better resolved by GD. Note that, for both cumulus schemes, there exist large model overestimations in the magnitude and extent of precipitation over Yunnan-Guangxi province. This region is identified with steep mountains, where the lateral treatment in the nested domain may be problematic. An Proc. of SPIE Vol I-3

4 independent CWRF simulation using 30 km grid spacing over the entire China without nesting does not exhibit this problem (Liang 2006, personal communication). a1) OBS PR (mm/day) May 1998 a2) CWRF-KF PR (mm/day) May 1998 a3) CWRF-GD PR (mm/day) May 1998 b1) OBS PR (mm/day) Jun 1998 b2) CWRF-KF PR (mm/day) Jun 1998 b3) CWRF-GD PR (mm/day) Jun 1998 c1) OBS PR (mm/day) Jul 1998 c2) CWRF-KF PR (mm/day) Jul 1998 c3) CWRF-GD PR (mm/day) Jul 1998 d1) OBS PR (mm/day) Aug 1998 d2) CWRF-KF PR (mm/day) Aug 1998 d3) CWRF-GD PR (mm/day) Aug 1998 Figure 2. Observed (left), CWRF-KF (middle) and CWRF-GD (right) rainfall (mm/day) in 1998 May-August (top down). 3.3 Daily variation of region averaged rainfall Figure 3 shows the observed, CWRF-KF and CWRF-GD simulated daily rainfall time series averaged over South China, the Yangtze River Basin and North China. The primary variations are reproduced by CWRF-KF and CWRF-GD, including different climate regimes during the four month period. Over North China, KF faithfully simulates the observed total rainfall amount while GD has a somewhat overestimation, yet both schemes capture the observed temporal characteristics very well with a correlation coefficient over 68%. In the Yangtze River basin, both schemes Proc. of SPIE Vol I-4

5 overestimate the total precipitation amount, although KF produces a higher temporal correlation coefficient with observations than GD (71.6 versus 48.1%). This latter contrast is mainly caused by the success of KF and failure of GD in simulating the intermittency between two Meiyu phases during June 29-Jul 19. Over South China, KF has a somewhat overestimation while GD gives a relatively large underestimation throughout the integration period May-August. Correspondingly KF produces a higher correlation coefficient than GD (63.4 versus 49.2%). Therefore, KF has an overall better performance than GD. Figure 3. Observed (thick solid) and CWRF-KF (thin solid), -GD (dashed) simulated daily rainfall (mm/day) variations during 1998 May-August as averaged over north China (top), Yangtze River Basin (middle) and south China (bottom). Proc. of SPIE Vol I-5

6 4. CONCLUSIONS A continuous CWRF integration during May 1-August 31, 1998, driven by the NCEP/NCAR reanalysis data was conducted to study the model downscaling skill and its sensitivity to different cumulus parameterization schemes, focusing on monsoonal rainfall over east China. The two cumulus schemes, Kain-Fritshch and Grell-Devenyi ensemble, both capture the major characteristics of geographic distributions and daily variations of the observed precipitation, including north progress of the monsoonal rain. These results are more promising than using other regional climate models [6], indicating a great perspective of the CWRF for applications in China monsoon simulations. Our study has also demonstrated the more realistic performance of the Kain-Fritshch than Grell-Devenyi ensemble scheme. Yet the latter scheme has its own compelling advantage. First, as discussed above, the Kain-Fritsh scheme generates too excessive and extensive rainfall in terms of monthly mean distributions, while the Grell-Devenyi scheme seems more confined on both accounts. Second, the existing Grell-Devenyi ensemble scheme uses an equal weight for all its closure members. Different closures may work under different climate regimes with distinct precipitation mechanisms. As such this equal weighting is likely unrealistic where the effects of certain closures that actually function are underestimated while others that do not work are overestimated. We anticipate that the Grell-Devenyi ensemble scheme, when optimized with regime-dependent weights on individual closures, can significantly improve the CWRF precipitation predictive skill. This will be one of the future focuses. ACKNOWLEDGMENTS This work was supported by USDA UV-B Monitoring and Research Program under a grant from USDA CSREES( ),National 973 Key Project of China (2002CB412507), NSFC of China under Grant and Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT), and Outstanding Overseas Chinese Scholars Fund of Chinese Academy of Sciences ( ). The views expressed are those of the authors and do not necessarily reflect those of the sponsoring agencies or the Illinois State Water Survey. REFERENCES 1. Vidale, P. L., D. Lthi, C. Frei, S. I. Seneviratne, and C. Schr, Predictability and uncertainty in a regional climate model, J. Geophys. Res., 108(D18), 4586, doi: /2002JD002810, Liang X.-Z., L. Li, A. Dai, and K. E. Kunkel, Regional climate model simulation of summer precipitation diurnal cycle over the United States, Geophys. Res. Lett, 31, L2408, doi: /2004GL021054, 2004a. 3. Liang X.-Z., L. Li, K. E. Kunkel, M. Ting, and J. X. L. Wang, Regional climate model simulation of U. S. precipitation during Part I: Annual cycle, J. Climate, 17, , 2004b. 4. Liang X.-Z., J. Pan, J. Zhu, K. E. Kunkel, Regional climate model downscaling of the U.S. summer climate and future change, J. Geophys. Res.,2006, in press. 5. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, J. G. Powers, A description of the advanced research WRF version 2, NCAR Technical Note, NCAR/TN-468+STR, Liu, S.Y., CWRF application in East China monsoon area, Doctor dissertation, Nanjing University of Information Science & Technology, Liang X.-Z., H. Choi, K. E. Kunkel,, Y. Dai, E. Joseph, J. X. L. Wang, and P. Kumar, Development of the regional climate-weather research and forecasting model (CWRF): Surface boundary conditions, Illinois State Water Survey Scientific Research, ISWS SR , 32pp. ( asp?call Number=ISWS+SR+2005%2D01), 2005a. 8. Liang X.-Z., H. Choi, K. E. Kunkel, Y. Dai, E. Joseph, J. X. L. Wang, and P. Kumar, Surface boundary conditions for mesoscale regional climate models, Earth Interactions, 9, 1-28, 2005b. 9. Liang X.-Z., M. Xu, W. Gao, K. E. Kunkel, J. Slusser, Y. Dai, Q. Min, P. R. Houser, M. Rodell, C. B. Schaaf, and F. Gao, Development of land surface albedo parameterization bases on MODIS data, J. Geophys. Res.,D11107, doi: /2004JD00579, 2005c. 10. Liang X.-Z., K. E. Kunkel, and A. N. Samel, Development of a regional climate model for U. S. Midwest Proc. of SPIE Vol I-6

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